From 7bf371a48780ae795274393264293901ba551be9 Mon Sep 17 00:00:00 2001 From: wangjunang Date: Fri, 15 Mar 2024 10:52:56 +0800 Subject: [PATCH 01/16] remove Generative_model_v1 --- Modeling eMNS/Generative_model_v1.ipynb | 13356 ---------------------- 1 file changed, 13356 deletions(-) delete mode 100644 Modeling eMNS/Generative_model_v1.ipynb diff --git a/Modeling eMNS/Generative_model_v1.ipynb b/Modeling eMNS/Generative_model_v1.ipynb deleted file mode 100644 index 41401a4..0000000 --- a/Modeling eMNS/Generative_model_v1.ipynb +++ /dev/null @@ -1,13356 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### This jupyter notebook employs a fully connective neural network(FC) or its alias artificial neural network (ANN) to learn the mapping between input current configuration between output magnetic field " - ] - }, - { - "cell_type": "code", - "execution_count": 28, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Good to go\n" - ] - } - ], - "source": [ - "%reload_ext autoreload\n", - "%autoreload 2\n", - "import numpy as np\n", - "import torch\n", - "if torch.cuda.device_count():\n", - " device = 'cuda'\n", - " print('Good to go')\n", - "else:\n", - " device = 'cpu'\n", - " print('Using cpu')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "0\n", - "1\n", - "2\n", - "3\n", - "4\n", - "5\n", - "6\n", - "7\n", - "8\n", - "9\n", - "10\n", - "11\n", - "12\n", - "13\n", - "14\n", - "15\n", - "16\n", - "17\n", - "18\n", - "19\n", - "20\n", - "21\n", - "22\n", - "23\n", - "24\n", - "25\n", - "26\n", - "27\n", - "28\n", - "29\n", - "30\n", - "31\n", - "32\n", - "33\n", - "34\n", - "35\n", - "36\n", - "37\n", - "38\n", - "39\n", - "40\n", - "41\n", - "42\n", - "43\n", - "44\n", - "45\n", - "46\n", - "47\n", - 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"1214\n", - "1215\n", - "1216\n", - "1217\n", - "1218\n", - "1219\n", - "1220\n", - "1221\n", - "1222\n", - "1223\n", - "1224\n", - "1225\n", - "1226\n", - "1227\n", - "1228\n", - "1229\n", - "1230\n", - "1231\n", - "1232\n", - "1233\n", - "1234\n", - "1235\n", - "1236\n", - "1237\n", - "1238\n", - "1239\n", - "1240\n", - "1241\n", - "1242\n", - "1243\n", - "1244\n", - "1245\n", - "1246\n", - "1247\n", - "1248\n", - "1249\n", - "1250\n", - "1251\n", - "1252\n", - "1253\n", - "1254\n", - "1255\n", - "1256\n", - "1257\n", - "1258\n", - "1259\n", - "1260\n", - "1261\n", - "1262\n", - "1263\n", - "1264\n", - "1265\n", - "1266\n", - "1267\n", - "1268\n", - "1269\n", - "1270\n", - "1271\n", - "1272\n", - "1273\n", - "1274\n", - "1275\n", - "1276\n", - "1277\n", - "1278\n", - "1279\n", - "1280\n", - "1281\n", - "1282\n", - "1283\n", - "1284\n", - "1285\n", - "1286\n", - "1287\n", - "1288\n", - "1289\n", - "1290\n", - "1291\n", - "1292\n", - "1293\n", - "1294\n", - "1295\n", - "1296\n", - "1297\n", - "1298\n", - "1299\n", - "torch.Size([1300, 6, 21, 21, 21])\n", - "current shape torch.Size([1300, 12])\n", - "Bfield shape torch.Size([1300, 3, 16, 16, 16])\n" - ] - } - ], - "source": [ - "from ReadData import ReadCurrentAndField_CNN\n", - "import glob\n", - "import os \n", - "\n", - "# TODO zhoujing edit this Data loading \n", - "# print(os.getcwd())\n", - "foldername=\"./Data/\"\n", - "filepattern = \"MagneticField[0-9]*.txt\"\n", - "train_file_num= 1300\n", - "#data = ReadFolder(foldername,filepattern)\n", - "current,data = ReadCurrentAndField_CNN (foldername,filepattern,train_file_num)\n", - "\n", - "fileList = glob.glob(foldername+filepattern)\n", - "position = data[:,0:3,2:18,2:18,2:18]\n", - "Bfield = data[:,3:,2:18,2:18,2:18]\n", - "\n", - "# print(fileList)\n", - "print(data.shape)\n", - "print('current shape', current.shape)\n", - "print('Bfield shape', Bfield.shape)\n", - "\n" - ] - }, - { - "cell_type": "code", - - "execution_count": 3, - - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "tensor([[0.0505],\n", - " [0.0564],\n", - " [0.0570]])\n", - "tensor([[-0.0529],\n", - " [-0.0534],\n", - " [-0.0612]])\n", - "torch.Size([1, 12])\n", - "torch.Size([3, 1])\n", - "torch.Size([1, 12])\n", - "torch.Size([1, 12])\n", - "torch.Size([3, 1])\n", - "torch.Size([3, 1])\n" - ] - } - ], - "source": [ - "#data normalization\n", - "#find min and max value of input position and Bfield\n", - "max_current, max_current_index = torch.max(current, dim=0, keepdim=True)\n", - "# print(max_current)\n", - "min_current, min_current_index = torch.min(current, dim=0, keepdim=True)\n", - "# print(min_current)\n", - "\n", - "max_Bfield, max_Bfield_index = torch.max(Bfield.transpose(0,1).reshape(3,-1), dim=1, keepdim=True)\n", - "print(max_Bfield)\n", - "min_Bfield, min_Bfield_index = torch.min(Bfield.transpose(0,1).reshape(3,-1), dim=1, keepdim=True)\n", - "print(min_Bfield)\n", - "\n", - "dimB = Bfield.shape\n", - "dimc = current.shape\n", - "print(min_current.shape)\n", - "print(min_Bfield.shape)\n", - "\n", - "minB=min_Bfield.expand(3,int(Bfield.numel()/3)).reshape(3,dimB[0],dimB[2],dimB[3],dimB[4]).transpose(0,1)\n", - "maxB=max_Bfield.expand(3,int(Bfield.numel()/3)).reshape(3,dimB[0],dimB[2],dimB[3],dimB[4]).transpose(0,1)\n", - "\n", - "ave_current=0.5*(max_current.expand(dimc[0],dimc[1])+min_current.expand(dimc[0],dimc[1]))\n", - "diff_current=0.5*(max_current.expand(dimc[0],dimc[1])-min_current.expand(dimc[0],dimc[1]))\n", - "\n", - "current_norm = (current-ave_current)/diff_current\n", - "Bfield_norm = (Bfield-(minB+maxB)*0.5)/(0.5*(maxB-minB))\n", - "\n", - "print(min_current.shape)\n", - "print(max_current.shape)\n", - "print(min_Bfield.shape)\n", - "print(max_Bfield.shape)\n", - "\n", - "torch.save(min_current, \"./normalize_data/cnn_min_current.pt\")\n", - "torch.save(max_current, \"./normalize_data/cnn_max_current.pt\")\n", - "torch.save(min_Bfield, \"./normalize_data/cnn_min_Bfield.pt\")\n", - "torch.save(max_Bfield, \"./normalize_data/cnn_max_Bfield.pt\")\n" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Generative_net(\n", - " (proj): Linear(in_features=12, out_features=4096, bias=True)\n", - " (conv3d): Conv3d(64, 3, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n", - " (total_net): Sequential(\n", - " (0): Linear(in_features=12, out_features=4096, bias=True)\n", - " (1): Unflatten(dim=1, unflattened_size=(64, 4, 4, 4))\n", - " (2): BigBlock(\n", - " (block): Sequential(\n", - " (0): ResidualEMNSBlock_3d(\n", - " (conv3d): Sequential(\n", - " (0): BatchNorm3d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", - " (1): Conv3d(64, 64, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n", - " (2): LeakyReLU(negative_slope=0.01)\n", - " )\n", - " (block): Sequential(\n", - " (0): Sequential(\n", - " (0): BatchNorm3d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", - " (1): Conv3d(64, 64, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n", - " (2): LeakyReLU(negative_slope=0.01)\n", - " )\n", - " (1): Sequential(\n", - " (0): BatchNorm3d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", - " (1): Conv3d(64, 64, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n", - " (2): LeakyReLU(negative_slope=0.01)\n", - " )\n", - " (2): Sequential(\n", - " (0): BatchNorm3d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", - " (1): Conv3d(64, 64, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n", - " (2): LeakyReLU(negative_slope=0.01)\n", - " )\n", - " (3): Sequential(\n", - " (0): BatchNorm3d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", - " (1): Conv3d(64, 64, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n", - " (2): LeakyReLU(negative_slope=0.01)\n", - " )\n", - " )\n", - " (shortcut): Identity()\n", - " )\n", - " (1): UpsampleBlock(\n", - " (block): Sequential(\n", - " (0): Upsample(scale_factor=2.0, mode='nearest')\n", - " )\n", - " )\n", - " )\n", - " )\n", - " (3): BigBlock(\n", - " (block): Sequential(\n", - " (0): ResidualEMNSBlock_3d(\n", - " (conv3d): Sequential(\n", - " (0): BatchNorm3d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", - " (1): Conv3d(64, 64, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n", - " (2): LeakyReLU(negative_slope=0.01)\n", - " )\n", - " (block): Sequential(\n", - " (0): Sequential(\n", - " (0): BatchNorm3d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", - " (1): Conv3d(64, 64, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n", - " (2): LeakyReLU(negative_slope=0.01)\n", - " )\n", - " (1): Sequential(\n", - " (0): BatchNorm3d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", - " (1): Conv3d(64, 64, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n", - " (2): LeakyReLU(negative_slope=0.01)\n", - " )\n", - " (2): Sequential(\n", - " (0): BatchNorm3d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", - " (1): Conv3d(64, 64, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n", - " (2): LeakyReLU(negative_slope=0.01)\n", - " )\n", - " (3): Sequential(\n", - " (0): BatchNorm3d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", - " (1): Conv3d(64, 64, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n", - " (2): LeakyReLU(negative_slope=0.01)\n", - " )\n", - " )\n", - " (shortcut): Identity()\n", - " )\n", - " (1): UpsampleBlock(\n", - " (block): Sequential(\n", - " (0): Upsample(scale_factor=2.0, mode='nearest')\n", - " )\n", - " )\n", - " )\n", - " )\n", - " (4): BatchNorm3d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", - " (5): Conv3d(64, 3, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n", - " )\n", - ")\n", - "torch.Size([1300, 3, 16, 16, 16])\n", - "torch.Size([3, 16, 16, 16])\n" - ] - } - ], - "source": [ - "from Neural_network import Generative_net, ResidualEMNSBlock_3d, BigBlock, weight_init, eMNS_Dataset\n", - "num_input = 12\n", - "output_shape = (3,16,16,16)\n", - "SB_args = (64,64,4) # (Cin, Cout, num_block)\n", - "BB_args = (2,2) # (scale_factor, num_block)\n", - "SB_block = ResidualEMNSBlock_3d \n", - "BB_block = BigBlock\n", - "\n", - "Generative_network = Generative_net(SB_args, BB_args, SB_block, BB_block, num_input=num_input, output_shape= output_shape)\n", - "print(Generative_network)\n", - "\n", - "print(maxB.shape)\n", - "print(maxB[0,:].shape)" - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "cuda:0\n", - "cuda:0\n" - ] - } - ], - "source": [ - "MaxB=maxB.cuda(0)\n", - "MinB=minB.cuda(0)\n", - "print(MaxB.device)\n", - "print(MinB.device)" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "1.0\n", - "learning_rate 0.001\n", - - "Epoch 0, Iteration 10, loss = 0.3569\n", - - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([2, 3, 16, 16, 16])\n", - - "Got rmse 0.037769243121147156\n", - - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([18, 3, 16, 16, 16])\n", - - "Got rmse 0.07766822725534439\n", - "\n", - "Epoch 1, Iteration 20, loss = 0.3350\n", - - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([2, 3, 16, 16, 16])\n", - - "Got rmse 0.021998286247253418\n", - - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([18, 3, 16, 16, 16])\n", - - "Got rmse 0.021768633276224136\n", - "\n", - "Epoch 2, Iteration 30, loss = 0.2636\n", - - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([2, 3, 16, 16, 16])\n", - - "Got rmse 0.020224398002028465\n", - - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([18, 3, 16, 16, 16])\n", - - "Got rmse 0.020775873214006424\n", - "\n", - "Epoch 3, Iteration 40, loss = 0.2713\n", - - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([2, 3, 16, 16, 16])\n", - - "Got rmse 0.02040044032037258\n", - - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([18, 3, 16, 16, 16])\n", - - "Got rmse 0.020972078666090965\n", - "\n", - "Epoch 4, Iteration 50, loss = 0.2615\n", - - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([2, 3, 16, 16, 16])\n", - - "Got rmse 0.019090937450528145\n", - - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([18, 3, 16, 16, 16])\n", - - "Got rmse 0.019196767359972\n", - "\n", - "Epoch 5, Iteration 60, loss = 0.2573\n", - - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([2, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.01855389028787613\n", -======= - "Got rmse 0.018339654430747032\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([18, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.01888146810233593\n", - "\n", - "Epoch 6, Iteration 70, loss = 0.2765\n", -======= - "Got rmse 0.020080920308828354\n", - "\n", - "Epoch 6, Iteration 70, loss = 0.2578\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([2, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.018776176497340202\n", -======= - "Got rmse 0.018142255023121834\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([18, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.018817700445652008\n", - "\n", - "Epoch 7, Iteration 80, loss = 0.2547\n", -======= - "Got rmse 0.018919706344604492\n", - "\n", - "Epoch 7, Iteration 80, loss = 0.2880\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([2, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.01840158738195896\n", -======= - "Got rmse 0.01779475435614586\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([18, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.018657561391592026\n", - "\n", - "Epoch 8, Iteration 90, loss = 0.2575\n", -======= - "Got rmse 0.01862613670527935\n", - "\n", - "Epoch 8, Iteration 90, loss = 0.2214\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([2, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.01836530677974224\n", -======= - "Got rmse 0.017790650948882103\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([18, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.01872938871383667\n", - "\n", - "Epoch 9, Iteration 100, loss = 0.2540\n", -======= - "Got rmse 0.018616652116179466\n", - "\n", - "Epoch 9, Iteration 100, loss = 0.2304\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([2, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.02783123031258583\n", -======= - "Got rmse 0.017854269593954086\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([18, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.028987396508455276\n", - "\n", - "Epoch 10, Iteration 110, loss = 0.2638\n", -======= - "Got rmse 0.01863327994942665\n", - "\n", - "Epoch 10, Iteration 110, loss = 0.2785\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([2, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.018599485978484154\n", -======= - "Got rmse 0.017803939059376717\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([18, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.019018132239580154\n", - "\n", - "Epoch 11, Iteration 120, loss = 0.2627\n", -======= - "Got rmse 0.01857650838792324\n", - "\n", - "Epoch 11, Iteration 120, loss = 0.2374\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([2, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.018483992666006088\n", -======= - "Got rmse 0.017841296270489693\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([18, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.01877359114587307\n", - "\n", - "Epoch 12, Iteration 130, loss = 0.2831\n", -======= - "Got rmse 0.018685193732380867\n", - "\n", - "Epoch 12, Iteration 130, loss = 0.2188\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([2, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.018454166129231453\n", -======= - "Got rmse 0.01778922788798809\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([18, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.01888842135667801\n", - "\n", - "Epoch 13, Iteration 140, loss = 0.2470\n", -======= - "Got rmse 0.018570227548480034\n", - "\n", - "Epoch 13, Iteration 140, loss = 0.2509\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([2, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.01833556964993477\n", -======= - "Got rmse 0.01778889261186123\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([18, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.018641281872987747\n", - "\n", - "Epoch 14, Iteration 150, loss = 0.2188\n", -======= - "Got rmse 0.018587127327919006\n", - "\n", - "Epoch 14, Iteration 150, loss = 0.2268\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([2, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.018669970333576202\n", -======= - "Got rmse 0.017731882631778717\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([18, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.018738456070423126\n", - "\n", - "Epoch 15, Iteration 160, loss = 0.2358\n", -======= - "Got rmse 0.018547451123595238\n", - "\n", - "Epoch 15, Iteration 160, loss = 0.2325\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([2, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.01844964548945427\n", -======= - "Got rmse 0.017743149772286415\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([18, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.018633557483553886\n", - "\n", - "Epoch 16, Iteration 170, loss = 0.2679\n", -======= - "Got rmse 0.018534421920776367\n", - "\n", - "Epoch 16, Iteration 170, loss = 0.2440\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([2, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.018480941653251648\n", -======= - "Got rmse 0.017914310097694397\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([18, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.01878700777888298\n", - "\n", - "Epoch 17, Iteration 180, loss = 0.2263\n", -======= - "Got rmse 0.019034089520573616\n", - "\n", - "Epoch 17, Iteration 180, loss = 0.2558\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([2, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.018231114372611046\n", -======= - "Got rmse 0.017735525965690613\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([18, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.018544968217611313\n", - "\n", - "Epoch 18, Iteration 190, loss = 0.2546\n", -======= - "Got rmse 0.018529050052165985\n", - "\n", - "Epoch 18, Iteration 190, loss = 0.2350\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([2, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.01846456713974476\n", -======= - "Got rmse 0.017811158671975136\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([18, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.01852579228579998\n", - "\n", - "Epoch 19, Iteration 200, loss = 0.2344\n", -======= - "Got rmse 0.018538465723395348\n", - "\n", - "Epoch 19, Iteration 200, loss = 0.2432\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([2, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.018418017774820328\n", -======= - "Got rmse 0.017760660499334335\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([18, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.018541591241955757\n", - "\n", - "Epoch 20, Iteration 210, loss = 0.2628\n", -======= - "Got rmse 0.01857392117381096\n", - "\n", - "Epoch 20, Iteration 210, loss = 0.2227\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([2, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.01848178543150425\n", -======= - "Got rmse 0.01778734289109707\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([18, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.01856483891606331\n", - "\n", - "Epoch 21, Iteration 220, loss = 0.2540\n", -======= - "Got rmse 0.01852080039680004\n", - "\n", - "Epoch 21, Iteration 220, loss = 0.2205\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([2, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.01841271109879017\n", -======= - "Got rmse 0.017759889364242554\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([18, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.018583128228783607\n", - "\n", - "Epoch 22, Iteration 230, loss = 0.2483\n", -======= - "Got rmse 0.018522903323173523\n", - "\n", - "Epoch 22, Iteration 230, loss = 0.2427\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([2, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.018574517220258713\n", -======= - "Got rmse 0.017930055037140846\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([18, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.01859833486378193\n", - "\n", - "Epoch 23, Iteration 240, loss = 0.2225\n", -======= - "Got rmse 0.018659131601452827\n", - "\n", - "Epoch 23, Iteration 240, loss = 0.2376\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([2, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.018395230174064636\n", -======= - "Got rmse 0.01778995431959629\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([18, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.01850173808634281\n", - "\n", - "Epoch 24, Iteration 250, loss = 0.2518\n", -======= - "Got rmse 0.0185505710542202\n", - "\n", - "Epoch 24, Iteration 250, loss = 0.2185\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([2, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.018291842192411423\n", -======= - "Got rmse 0.017789192497730255\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([18, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.018492387607693672\n", - "\n", - "Epoch 25, Iteration 260, loss = 0.2145\n", -======= - "Got rmse 0.018524648621678352\n", - "\n", - "Epoch 25, Iteration 260, loss = 0.2267\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([2, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.01835891790688038\n", -======= - "Got rmse 0.01775607466697693\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([18, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.018491851165890694\n", - "\n", - "Epoch 26, Iteration 270, loss = 0.2148\n", -======= - "Got rmse 0.018535234034061432\n", - "\n", - "lr decay from 0.001 to 0.0005\n", - "Epoch 26, Iteration 270, loss = 0.1981\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([2, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.018436266109347343\n", -======= - "Got rmse 0.017759915441274643\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([18, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.018521228805184364\n", - "\n", - "Epoch 27, Iteration 280, loss = 0.2470\n", -======= - "Got rmse 0.01848451979458332\n", - "\n", - "Epoch 27, Iteration 280, loss = 0.2183\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([2, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.018356189131736755\n", -======= - "Got rmse 0.01777377538383007\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([18, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.018489183858036995\n", - "\n", - "Epoch 28, Iteration 290, loss = 0.2075\n", -======= - "Got rmse 0.01850641518831253\n", - "\n", - "Epoch 28, Iteration 290, loss = 0.2237\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([2, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.019154075533151627\n", -======= - "Got rmse 0.017807722091674805\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([18, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.019048623740673065\n", - "\n", - "Epoch 29, Iteration 300, loss = 0.2152\n", -======= - "Got rmse 0.018562287092208862\n", - "\n", - "Epoch 29, Iteration 300, loss = 0.2640\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([2, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.01885761320590973\n", -======= - "Got rmse 0.01783810369670391\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([18, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.018821125850081444\n", - "\n", - "Epoch 30, Iteration 310, loss = 0.2225\n", -======= - "Got rmse 0.0185893252491951\n", - "\n", - "Epoch 30, Iteration 310, loss = 0.2370\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([2, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.018840551376342773\n", -======= - "Got rmse 0.017824675887823105\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([18, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.01867644116282463\n", - "\n", - "Epoch 31, Iteration 320, loss = 0.2561\n", -======= - "Got rmse 0.01856755092740059\n", - "\n", - "Epoch 31, Iteration 320, loss = 0.2294\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([2, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.018678229302167892\n", -======= - "Got rmse 0.01778528094291687\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([18, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.018612008541822433\n", - "\n", - "Epoch 32, Iteration 330, loss = 0.2347\n", -======= - "Got rmse 0.018524637445807457\n", - "\n", - "Epoch 32, Iteration 330, loss = 0.2143\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([2, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.019967369735240936\n", -======= - "Got rmse 0.01782926917076111\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([18, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.019491173326969147\n", - "\n", - "Epoch 33, Iteration 340, loss = 0.2342\n", -======= - "Got rmse 0.018542835488915443\n", - "\n", - "Epoch 33, Iteration 340, loss = 0.2065\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([2, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.019907265901565552\n", -======= - "Got rmse 0.017795225605368614\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([18, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.019617024809122086\n", - "\n", - "Epoch 34, Iteration 350, loss = 0.2223\n", -======= - "Got rmse 0.018517978489398956\n", - "\n", - "Epoch 34, Iteration 350, loss = 0.2028\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([2, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.02049531601369381\n", -======= - "Got rmse 0.017804762348532677\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([18, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.020597215741872787\n", - "\n", - "Epoch 35, Iteration 360, loss = 0.2403\n", -======= - "Got rmse 0.018528873100876808\n", - "\n", - "Epoch 35, Iteration 360, loss = 0.1977\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([2, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.019663790240883827\n", -======= - "Got rmse 0.017834331840276718\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([18, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.01936126872897148\n", - "\n", - "Epoch 36, Iteration 370, loss = 0.2306\n", -======= - "Got rmse 0.018564142286777496\n", - "\n", - "Epoch 36, Iteration 370, loss = 0.2163\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([2, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.019728107377886772\n", -======= - "Got rmse 0.017782676964998245\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([18, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.019497711211442947\n", - "\n", - "Epoch 37, Iteration 380, loss = 0.2150\n", -======= - "Got rmse 0.01854165643453598\n", - "\n", - "Epoch 37, Iteration 380, loss = 0.2117\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([2, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.018843460828065872\n", -======= - "Got rmse 0.017981452867388725\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([18, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.0187347624450922\n", - "\n", - "Epoch 38, Iteration 390, loss = 0.2188\n", -======= - "Got rmse 0.018840106204152107\n", - "\n", - "Epoch 38, Iteration 390, loss = 0.2042\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([2, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.01942981220781803\n", -======= - "Got rmse 0.017827708274126053\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([18, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.019219860434532166\n", - "\n", - "Epoch 39, Iteration 400, loss = 0.2332\n", -======= - "Got rmse 0.01854506880044937\n", - "\n", - "Epoch 39, Iteration 400, loss = 0.2126\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([2, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.022034931927919388\n", -======= - "Got rmse 0.017841249704360962\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([18, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.021991778165102005\n", - "\n", - "Epoch 40, Iteration 410, loss = 0.2222\n", -======= - "Got rmse 0.018527096137404442\n", - "\n", - "Epoch 40, Iteration 410, loss = 0.2029\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([2, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.021054385229945183\n", -======= - "Got rmse 0.01782905124127865\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([18, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.020699087530374527\n", - "\n", - "Epoch 41, Iteration 420, loss = 0.1787\n", -======= - "Got rmse 0.01854018121957779\n", - "\n", - "Epoch 41, Iteration 420, loss = 0.1916\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([2, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.020771989598870277\n", -======= - "Got rmse 0.017834139987826347\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([18, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.02071692980825901\n", - "\n", - "Epoch 42, Iteration 430, loss = 0.2509\n", -======= - "Got rmse 0.018550243228673935\n", - "\n", - "Epoch 42, Iteration 430, loss = 0.2026\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([2, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.021175142377614975\n", -======= - "Got rmse 0.017843103036284447\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([18, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.02053970657289028\n", - "\n", - "Epoch 43, Iteration 440, loss = 0.2011\n", -======= - "Got rmse 0.018544426187872887\n", - "\n", - "Epoch 43, Iteration 440, loss = 0.2338\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([2, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.021447118371725082\n", -======= - "Got rmse 0.017874589189887047\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([18, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.020895879715681076\n", - "\n", - "Epoch 44, Iteration 450, loss = 0.2075\n", -======= - "Got rmse 0.018586695194244385\n", - "\n", - "Epoch 44, Iteration 450, loss = 0.1979\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([2, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.019499948248267174\n", -======= - "Got rmse 0.017859768122434616\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([18, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.01909489557147026\n", - "\n", - "Epoch 45, Iteration 460, loss = 0.1929\n", -======= - "Got rmse 0.018557840958237648\n", - "\n", - "Epoch 45, Iteration 460, loss = 0.1844\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([2, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.0194898322224617\n", -======= - "Got rmse 0.017920469865202904\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([18, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.01925498992204666\n", - "\n", - "Epoch 46, Iteration 470, loss = 0.1839\n", -======= - "Got rmse 0.018656739965081215\n", - "\n", - "Epoch 46, Iteration 470, loss = 0.1994\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([2, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.019482841715216637\n", -======= - "Got rmse 0.017852146178483963\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([18, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.019206583499908447\n", - "\n", - "Epoch 47, Iteration 480, loss = 0.1900\n", -======= - "Got rmse 0.018598107621073723\n", - "\n", - "Epoch 47, Iteration 480, loss = 0.2306\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([2, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.020762145519256592\n", -======= - "Got rmse 0.017813483253121376\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([18, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.020638998597860336\n", - "\n", - "Epoch 48, Iteration 490, loss = 0.1766\n", -======= - "Got rmse 0.01853977143764496\n", - "\n", - "Epoch 48, Iteration 490, loss = 0.1772\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([2, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.0206616148352623\n", -======= - "Got rmse 0.017810292541980743\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([18, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.02038687840104103\n", - "\n", - "Epoch 49, Iteration 500, loss = 0.1867\n", -======= - "Got rmse 0.01854671724140644\n", - "\n", - "Epoch 49, Iteration 500, loss = 0.1914\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([2, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.021980203688144684\n", -======= - "Got rmse 0.017974333837628365\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([18, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.021392162889242172\n", - "\n", - "Epoch 50, Iteration 510, loss = 0.1669\n", -======= - "Got rmse 0.018679045140743256\n", - "\n", - "Epoch 50, Iteration 510, loss = 0.1900\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([2, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.01990322954952717\n", -======= - "Got rmse 0.017863644286990166\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([18, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.019588762894272804\n", - "\n", - "Epoch 51, Iteration 520, loss = 0.1771\n", -======= - "Got rmse 0.018561583012342453\n", - "\n", - "lr decay from 0.0005 to 0.00025\n", - "Epoch 51, Iteration 520, loss = 0.1866\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([2, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.019828075543045998\n", -======= - "Got rmse 0.017853369936347008\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([18, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.019321469590067863\n", -======= - "Got rmse 0.018568385392427444\n", ->>>>>>> main - "\n", - "Epoch 52, Iteration 530, loss = 0.1813\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([2, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.01948579028248787\n", -======= - "Got rmse 0.017857611179351807\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([18, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.018989091739058495\n", - "\n", - "Epoch 53, Iteration 540, loss = 0.1623\n", -======= - "Got rmse 0.018590083345770836\n", - "\n", - "Epoch 53, Iteration 540, loss = 0.1741\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([2, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.019174735993146896\n", -======= - "Got rmse 0.017863444983959198\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([18, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.019158348441123962\n", - "\n", - "Epoch 54, Iteration 550, loss = 0.1536\n", -======= - "Got rmse 0.01858380250632763\n", - "\n", - "Epoch 54, Iteration 550, loss = 0.1833\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([2, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.020349984988570213\n", -======= - "Got rmse 0.017815280705690384\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([18, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.019870957359671593\n", - "\n", - "Epoch 55, Iteration 560, loss = 0.1627\n", -======= - "Got rmse 0.018569668754935265\n", - "\n", - "Epoch 55, Iteration 560, loss = 0.1804\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([2, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.020134374499320984\n", -======= - "Got rmse 0.017820773646235466\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([18, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.019900964573025703\n", - "\n", - "Epoch 56, Iteration 570, loss = 0.1415\n", -======= - "Got rmse 0.01856999844312668\n", - "\n", - "Epoch 56, Iteration 570, loss = 0.1754\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([2, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.019827408716082573\n", -======= - "Got rmse 0.017832664772868156\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([18, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.019334109500050545\n", - "\n", - "Epoch 57, Iteration 580, loss = 0.1613\n", -======= - "Got rmse 0.01857839897274971\n", - "\n", - "Epoch 57, Iteration 580, loss = 0.1861\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([2, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.019772477447986603\n", -======= - "Got rmse 0.017900636419653893\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([18, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.019409190863370895\n", - "\n", - "Epoch 58, Iteration 590, loss = 0.1617\n", -======= - "Got rmse 0.01858394779264927\n", - "\n", - "Epoch 58, Iteration 590, loss = 0.1716\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([2, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.01940748281776905\n", -======= - "Got rmse 0.017881445586681366\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([18, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.018729811534285545\n", - "\n", - "Epoch 59, Iteration 600, loss = 0.1485\n", -======= - "Got rmse 0.018614765256643295\n", - "\n", - "Epoch 59, Iteration 600, loss = 0.1749\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([2, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.020266450941562653\n", -======= - "Got rmse 0.017816992476582527\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([18, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.019767677411437035\n", - "\n", - "Epoch 60, Iteration 610, loss = 0.1772\n", -======= - "Got rmse 0.018560059368610382\n", - "\n", - "Epoch 60, Iteration 610, loss = 0.1489\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([2, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.020221957936882973\n", -======= - "Got rmse 0.017832336947321892\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([18, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.019624875858426094\n", - "\n", - "Epoch 61, Iteration 620, loss = 0.1961\n", -======= - "Got rmse 0.018565161153674126\n", - "\n", - "Epoch 61, Iteration 620, loss = 0.2043\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([2, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.01985899731516838\n", -======= - "Got rmse 0.01783805899322033\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([18, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.019271373748779297\n", - "\n", - "Epoch 62, Iteration 630, loss = 0.1852\n", -======= - "Got rmse 0.018579404801130295\n", - "\n", - "Epoch 62, Iteration 630, loss = 0.1830\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([2, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.020135093480348587\n", -======= - "Got rmse 0.017857735976576805\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([18, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.01928279548883438\n", - "\n", - "Epoch 63, Iteration 640, loss = 0.1327\n", -======= - "Got rmse 0.018592823296785355\n", - "\n", - "Epoch 63, Iteration 640, loss = 0.2002\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([2, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.020081842318177223\n", -======= - "Got rmse 0.017844613641500473\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([18, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.019315050914883614\n", - "\n", - "Epoch 64, Iteration 650, loss = 0.1584\n", -======= - "Got rmse 0.01857578754425049\n", - "\n", - "Epoch 64, Iteration 650, loss = 0.1739\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([2, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.02095058187842369\n", -======= - "Got rmse 0.01784055121243\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([18, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.020284336060285568\n", - "\n", - "Epoch 65, Iteration 660, loss = 0.1501\n", -======= - "Got rmse 0.018573157489299774\n", - "\n", - "Epoch 65, Iteration 660, loss = 0.1849\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([2, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.019520550966262817\n", -======= - "Got rmse 0.01786765083670616\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([18, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.018892914056777954\n", - "\n", - "Epoch 66, Iteration 670, loss = 0.1533\n", -======= - "Got rmse 0.018593696877360344\n", - "\n", - "Epoch 66, Iteration 670, loss = 0.1713\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([2, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.020259616896510124\n", -======= - "Got rmse 0.01787627674639225\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([18, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.019774265587329865\n", - "\n", - "Epoch 67, Iteration 680, loss = 0.1560\n", -======= - "Got rmse 0.01860516332089901\n", - "\n", - "Epoch 67, Iteration 680, loss = 0.2008\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([2, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.020531585440039635\n", -======= - "Got rmse 0.017856068909168243\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([18, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.019781555980443954\n", - "\n", - "Epoch 68, Iteration 690, loss = 0.1345\n", -======= - "Got rmse 0.018591878935694695\n", - "\n", - "Epoch 68, Iteration 690, loss = 0.1713\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([2, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.022116150707006454\n", -======= - "Got rmse 0.01783752255141735\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([18, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.021809684112668037\n", - "\n", - "Epoch 69, Iteration 700, loss = 0.1505\n", -======= - "Got rmse 0.01857602968811989\n", - "\n", - "Epoch 69, Iteration 700, loss = 0.2086\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([2, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.020563572645187378\n", -======= - "Got rmse 0.017829157412052155\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([18, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.019613167271018028\n", - "\n", - "Epoch 70, Iteration 710, loss = 0.1133\n", -======= - "Got rmse 0.01855364628136158\n", - "\n", - "Epoch 70, Iteration 710, loss = 0.1694\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([2, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.020582759752869606\n", -======= - "Got rmse 0.017846617847681046\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([18, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.02009524591267109\n", - "\n", - "Epoch 71, Iteration 720, loss = 0.1284\n", -======= - "Got rmse 0.01859281212091446\n", - "\n", - "Epoch 71, Iteration 720, loss = 0.1910\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([2, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.019368808716535568\n", -======= - "Got rmse 0.01785893179476261\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([18, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.018839646130800247\n", - "\n", - "Epoch 72, Iteration 730, loss = 0.1417\n", -======= - "Got rmse 0.018574481830000877\n", - "\n", - "Epoch 72, Iteration 730, loss = 0.2025\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([2, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.01930714026093483\n", -======= - "Got rmse 0.017848549410700798\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([18, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.01871640980243683\n", - "\n", - "Epoch 73, Iteration 740, loss = 0.1397\n", -======= - "Got rmse 0.018588144332170486\n", - "\n", - "Epoch 73, Iteration 740, loss = 0.1751\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([2, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.020203644409775734\n", -======= - "Got rmse 0.017877919599413872\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([18, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.019688570871949196\n", - "\n", - "Epoch 74, Iteration 750, loss = 0.1405\n", -======= - "Got rmse 0.018608061596751213\n", - "\n", - "Epoch 74, Iteration 750, loss = 0.1696\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([2, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.01971210539340973\n", -======= - "Got rmse 0.017851319164037704\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([18, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.019118545576930046\n", - "\n", - "Epoch 75, Iteration 760, loss = 0.1469\n", -======= - "Got rmse 0.018575098365545273\n", - "\n", - "Epoch 75, Iteration 760, loss = 0.1832\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([2, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.02008669823408127\n", -======= - "Got rmse 0.017861349508166313\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([18, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.019352203235030174\n", - "\n", - "Epoch 76, Iteration 770, loss = 0.1219\n", -======= - "Got rmse 0.018574481830000877\n", - "\n", - "lr decay from 0.00025 to 0.000125\n", - "Epoch 76, Iteration 770, loss = 0.1788\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([2, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.02054458111524582\n", -======= - "Got rmse 0.01785035990178585\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([18, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.01997840777039528\n", - "\n", - "Epoch 77, Iteration 780, loss = 0.1254\n", -======= - "Got rmse 0.018593130633234978\n", - "\n", - "Epoch 77, Iteration 780, loss = 0.1718\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([2, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.019832730293273926\n", -======= - "Got rmse 0.01788400113582611\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([18, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.019365528598427773\n", - "\n", - "Epoch 78, Iteration 790, loss = 0.1414\n", -======= - "Got rmse 0.018603667616844177\n", - "\n", - "Epoch 78, Iteration 790, loss = 0.1633\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([2, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.021129412576556206\n", -======= - "Got rmse 0.017859507352113724\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([18, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.020679883658885956\n", - "\n", - "Epoch 79, Iteration 800, loss = 0.1235\n", -======= - "Got rmse 0.018599120900034904\n", - "\n", - "Epoch 79, Iteration 800, loss = 0.1805\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([2, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.019316241145133972\n", -======= - "Got rmse 0.017890652641654015\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([18, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.01884121261537075\n", - "\n", - "Epoch 80, Iteration 810, loss = 0.1067\n", -======= - "Got rmse 0.01860504224896431\n", - "\n", - "Epoch 80, Iteration 810, loss = 0.1701\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([2, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.020389391109347343\n", -======= - "Got rmse 0.017869791015982628\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([18, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.020004605874419212\n", - "\n", - "Epoch 81, Iteration 820, loss = 0.1364\n", -======= - "Got rmse 0.018612349405884743\n", - "\n", - "Epoch 81, Iteration 820, loss = 0.1818\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([2, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.020140357315540314\n", -======= - "Got rmse 0.017869673669338226\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([18, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.019432680681347847\n", - "\n", - "Epoch 82, Iteration 830, loss = 0.1161\n", -======= - "Got rmse 0.01860201172530651\n", - "\n", - "Epoch 82, Iteration 830, loss = 0.1671\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([2, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.02069701813161373\n", -======= - "Got rmse 0.017870403826236725\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([18, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.020045168697834015\n", - "\n", - "Epoch 83, Iteration 840, loss = 0.1260\n", -======= - "Got rmse 0.018602414056658745\n", - "\n", - "Epoch 83, Iteration 840, loss = 0.1597\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([2, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.01921257935464382\n", -======= - "Got rmse 0.017858261242508888\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([18, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.01887502707540989\n", - "\n", - "Epoch 84, Iteration 850, loss = 0.1177\n", -======= - "Got rmse 0.018585219979286194\n", - "\n", - "Epoch 84, Iteration 850, loss = 0.1775\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([2, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.020670758560299873\n", -======= - "Got rmse 0.0178665854036808\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([18, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.020175762474536896\n", - "\n", - "Epoch 85, Iteration 860, loss = 0.1096\n", -======= - "Got rmse 0.01859894208610058\n", - "\n", - "Epoch 85, Iteration 860, loss = 0.1934\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([2, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.020873336121439934\n", -======= - "Got rmse 0.01785973086953163\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([18, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.02040250599384308\n", - "\n", - "Epoch 86, Iteration 870, loss = 0.1138\n", -======= - "Got rmse 0.018591508269309998\n", - "\n", - "Epoch 86, Iteration 870, loss = 0.1847\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([2, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.0197458378970623\n", -======= - "Got rmse 0.0178836602717638\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([18, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.019263816997408867\n", - "\n", - "Epoch 87, Iteration 880, loss = 0.1329\n", -======= - "Got rmse 0.018601542338728905\n", - "\n", - "Epoch 87, Iteration 880, loss = 0.1810\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([2, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.02016846090555191\n", -======= - "Got rmse 0.017880793660879135\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([18, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.01956762745976448\n", - "\n", - "Epoch 88, Iteration 890, loss = 0.1337\n", -======= - "Got rmse 0.018627651035785675\n", - "\n", - "Epoch 88, Iteration 890, loss = 0.1656\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([2, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.01980590634047985\n", -======= - "Got rmse 0.017932049930095673\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([18, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.019335106015205383\n", - "\n", - "Epoch 89, Iteration 900, loss = 0.1485\n", -======= - "Got rmse 0.018638189882040024\n", - "\n", - "Epoch 89, Iteration 900, loss = 0.1688\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([2, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.020014293491840363\n", -======= - "Got rmse 0.017908144742250443\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([18, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.019545843824744225\n", - "\n", - "Epoch 90, Iteration 910, loss = 0.1070\n", -======= - "Got rmse 0.018639972433447838\n", - "\n", - "Epoch 90, Iteration 910, loss = 0.1830\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([2, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.020052799955010414\n", -======= - "Got rmse 0.017856042832136154\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([18, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.01964973658323288\n", - "\n", - "Epoch 91, Iteration 920, loss = 0.1287\n", -======= - "Got rmse 0.018603269010782242\n", - "\n", - "Epoch 91, Iteration 920, loss = 0.1547\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([2, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.022081131115555763\n", -======= - "Got rmse 0.01787763088941574\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([18, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.021542435511946678\n", - "\n", - "Epoch 92, Iteration 930, loss = 0.1114\n", -======= - "Got rmse 0.01860172487795353\n", - "\n", - "Epoch 92, Iteration 930, loss = 0.1562\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([2, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.020320728421211243\n", -======= - "Got rmse 0.01786932907998562\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([18, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.01985071413218975\n", - "\n", - "Epoch 93, Iteration 940, loss = 0.0973\n", -======= - "Got rmse 0.018590591847896576\n", - "\n", - "Epoch 93, Iteration 940, loss = 0.1473\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([2, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.02073676325380802\n", -======= - "Got rmse 0.017915695905685425\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([18, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.02025364153087139\n", - "\n", - "Epoch 94, Iteration 950, loss = 0.1173\n", -======= - "Got rmse 0.01862844079732895\n", - "\n", - "Epoch 94, Iteration 950, loss = 0.1770\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([2, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.020309114828705788\n", -======= - "Got rmse 0.0179038867354393\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([18, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.019738342612981796\n", - "\n", - "Epoch 95, Iteration 960, loss = 0.1442\n", -======= - "Got rmse 0.018622558563947678\n", - "\n", - "Epoch 95, Iteration 960, loss = 0.1694\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([2, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.020690809935331345\n", -======= - "Got rmse 0.01786062866449356\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([18, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.020366592332720757\n", - "\n", - "Epoch 96, Iteration 970, loss = 0.1085\n", -======= - "Got rmse 0.01858057640492916\n", - "\n", - "Epoch 96, Iteration 970, loss = 0.1870\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([2, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.02154714986681938\n", -======= - "Got rmse 0.017883388325572014\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([18, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.02111038565635681\n", - "\n", - "Epoch 97, Iteration 980, loss = 0.0807\n", -======= - "Got rmse 0.01860121823847294\n", - "\n", - "Epoch 97, Iteration 980, loss = 0.1730\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([2, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.02052135579288006\n", -======= - "Got rmse 0.017890695482492447\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([18, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.020146507769823074\n", - "\n", - "Epoch 98, Iteration 990, loss = 0.1069\n", -======= - "Got rmse 0.01862027682363987\n", - "\n", - "Epoch 98, Iteration 990, loss = 0.1968\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([2, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.020195161923766136\n", -======= - "Got rmse 0.017889609560370445\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([18, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.019612230360507965\n", - "\n", - "Epoch 99, Iteration 1000, loss = 0.1039\n", -======= - "Got rmse 0.018621517345309258\n", - "\n", - "Epoch 99, Iteration 1000, loss = 0.1804\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([2, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.02005671337246895\n", -======= - "Got rmse 0.017864996567368507\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([18, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.01972419209778309\n", - "\n", - "Epoch 100, Iteration 1010, loss = 0.1138\n", -======= - "Got rmse 0.018606960773468018\n", - "\n", - "Epoch 100, Iteration 1010, loss = 0.1766\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([2, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.020116647705435753\n", -======= - "Got rmse 0.01788848266005516\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([18, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.019676728174090385\n", - "\n", - "Epoch 101, Iteration 1020, loss = 0.1109\n", -======= - "Got rmse 0.018629373982548714\n", - "\n", - "lr decay from 0.000125 to 6.25e-05\n", - "Epoch 101, Iteration 1020, loss = 0.1963\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([2, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.020995361730456352\n", -======= - "Got rmse 0.01791391894221306\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([18, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.020434485748410225\n", - "\n", - "Epoch 102, Iteration 1030, loss = 0.1097\n", -======= - "Got rmse 0.01863562874495983\n", - "\n", - "Epoch 102, Iteration 1030, loss = 0.1793\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([2, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.020469633862376213\n", -======= - "Got rmse 0.01791352964937687\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([18, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.019928529858589172\n", - "\n", - "Epoch 103, Iteration 1040, loss = 0.1050\n", -======= - "Got rmse 0.018616491928696632\n", - "\n", - "Epoch 103, Iteration 1040, loss = 0.1639\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([2, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.02067747339606285\n", -======= - "Got rmse 0.017895061522722244\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([18, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.020285028964281082\n", - "\n", - "Epoch 104, Iteration 1050, loss = 0.1060\n", -======= - "Got rmse 0.018621530383825302\n", - "\n", - "Epoch 104, Iteration 1050, loss = 0.1762\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([2, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.021107377484440804\n", -======= - "Got rmse 0.017872951924800873\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([18, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.020665928721427917\n", - "\n", - "Epoch 105, Iteration 1060, loss = 0.1335\n", -======= - "Got rmse 0.018611056730151176\n", - "\n", - "Epoch 105, Iteration 1060, loss = 0.1750\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([2, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.02053198032081127\n", -======= - "Got rmse 0.017882268875837326\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([18, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.020109081640839577\n", - "\n", - "Epoch 106, Iteration 1070, loss = 0.1100\n", -======= - "Got rmse 0.018622366711497307\n", - "\n", - "Epoch 106, Iteration 1070, loss = 0.1711\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([2, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.019796613603830338\n", -======= - "Got rmse 0.01788386143743992\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([18, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.019362103193998337\n", - "\n", - "Epoch 107, Iteration 1080, loss = 0.1158\n", -======= - "Got rmse 0.018628543242812157\n", - "\n", - "Epoch 107, Iteration 1080, loss = 0.1867\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([2, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.020625408738851547\n", -======= - "Got rmse 0.01791835017502308\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([18, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.02026851661503315\n", - "\n", - "Epoch 108, Iteration 1090, loss = 0.0830\n", -======= - "Got rmse 0.018629878759384155\n", - "\n", - "Epoch 108, Iteration 1090, loss = 0.1852\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([2, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.020415805280208588\n", -======= - "Got rmse 0.017889373004436493\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([18, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.01998908631503582\n", - "\n", - "Epoch 109, Iteration 1100, loss = 0.1015\n", -======= - "Got rmse 0.018607545644044876\n", - "\n", - "Epoch 109, Iteration 1100, loss = 0.1941\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([2, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.019800035282969475\n", -======= - "Got rmse 0.017885632812976837\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([18, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.01923244632780552\n", - "\n", - "Epoch 110, Iteration 1110, loss = 0.1239\n", -======= - "Got rmse 0.018627207726240158\n", - "\n", - "Epoch 110, Iteration 1110, loss = 0.1688\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([2, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.01983012445271015\n", -======= - "Got rmse 0.017909511923789978\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([18, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.01946323923766613\n", - "\n", - "Epoch 111, Iteration 1120, loss = 0.0940\n", -======= - "Got rmse 0.018621761351823807\n", - "\n", - "Epoch 111, Iteration 1120, loss = 0.2020\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([2, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.020221011713147163\n", -======= - "Got rmse 0.01790483668446541\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([18, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.019825337454676628\n", - "\n", - "Epoch 112, Iteration 1130, loss = 0.1124\n", -======= - "Got rmse 0.018625224009156227\n", - "\n", - "Epoch 112, Iteration 1130, loss = 0.1658\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([2, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.020081495866179466\n", -======= - "Got rmse 0.017911531031131744\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([18, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.019668757915496826\n", - "\n", - "Epoch 113, Iteration 1140, loss = 0.1247\n", -======= - "Got rmse 0.0186348594725132\n", - "\n", - "Epoch 113, Iteration 1140, loss = 0.1579\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([2, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.020131075754761696\n", -======= - "Got rmse 0.017877202481031418\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([18, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.019780388101935387\n", - "\n", - "Epoch 114, Iteration 1150, loss = 0.1067\n", -======= - "Got rmse 0.018616287037730217\n", - "\n", - "Epoch 114, Iteration 1150, loss = 0.1561\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([2, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.02065911330282688\n", -======= - "Got rmse 0.017903828993439674\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([18, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.02013511396944523\n", - "\n", - "Epoch 115, Iteration 1160, loss = 0.0846\n", -======= - "Got rmse 0.01862749643623829\n", - "\n", - "Epoch 115, Iteration 1160, loss = 0.1943\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([2, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.01982874795794487\n", -======= - "Got rmse 0.017882931977510452\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([18, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.01942128874361515\n", - "\n", - "Epoch 116, Iteration 1170, loss = 0.0939\n", -======= - "Got rmse 0.01862781122326851\n", - "\n", - "Epoch 116, Iteration 1170, loss = 0.1863\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([2, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.020909635350108147\n", -======= - "Got rmse 0.01790747232735157\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([18, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.020496105775237083\n", - "\n", - "Epoch 117, Iteration 1180, loss = 0.0873\n", -======= - "Got rmse 0.01862834207713604\n", - "\n", - "Epoch 117, Iteration 1180, loss = 0.1481\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([2, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.019983060657978058\n", -======= - "Got rmse 0.01790614053606987\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([18, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.019664987921714783\n", - "\n", - "Epoch 118, Iteration 1190, loss = 0.1200\n", -======= - "Got rmse 0.018632778897881508\n", - "\n", - "Epoch 118, Iteration 1190, loss = 0.1888\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([2, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.020673803985118866\n", -======= - "Got rmse 0.017888210713863373\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([18, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.02030804008245468\n", - "\n", - "Epoch 119, Iteration 1200, loss = 0.0870\n", -======= - "Got rmse 0.018628764897584915\n", - "\n", - "Epoch 119, Iteration 1200, loss = 0.1690\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([2, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.021427951753139496\n", -======= - "Got rmse 0.01791139505803585\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([18, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.02113126404583454\n", - "\n", - "Epoch 120, Iteration 1210, loss = 0.1034\n", -======= - "Got rmse 0.018649190664291382\n", - "\n", - "Epoch 120, Iteration 1210, loss = 0.2297\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([2, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.021608775481581688\n", -======= - "Got rmse 0.017895538359880447\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([18, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.02137746289372444\n", - "\n", - "Epoch 121, Iteration 1220, loss = 0.0858\n", -======= - "Got rmse 0.018625864759087563\n", - "\n", - "Epoch 121, Iteration 1220, loss = 0.1949\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([2, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.02119048312306404\n", -======= - "Got rmse 0.017888501286506653\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([18, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.020840350538492203\n", - "\n", - "Epoch 122, Iteration 1230, loss = 0.1240\n", -======= - "Got rmse 0.018608273938298225\n", - "\n", - "Epoch 122, Iteration 1230, loss = 0.1772\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([2, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.02027645893394947\n", -======= - "Got rmse 0.01791575737297535\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([18, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.019896117970347404\n", - "\n", - "Epoch 123, Iteration 1240, loss = 0.0983\n", -======= - "Got rmse 0.01863020472228527\n", - "\n", - "Epoch 123, Iteration 1240, loss = 0.1888\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([2, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.020570719614624977\n", -======= - "Got rmse 0.01791076362133026\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([18, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.020061451941728592\n", - "\n", - "Epoch 124, Iteration 1250, loss = 0.0836\n", -======= - "Got rmse 0.018639344722032547\n", - "\n", - "Epoch 124, Iteration 1250, loss = 0.1825\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([2, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.02059348113834858\n", -======= - "Got rmse 0.017883166670799255\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([18, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.020359201356768608\n", - "\n", - "Epoch 125, Iteration 1260, loss = 0.1003\n", -======= - "Got rmse 0.018626974895596504\n", - "\n", - "Epoch 125, Iteration 1260, loss = 0.2020\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([2, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.020895376801490784\n", -======= - "Got rmse 0.017916833981871605\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([18, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.020499330013990402\n", - "\n", - "Epoch 126, Iteration 1270, loss = 0.1067\n", -======= - "Got rmse 0.018645305186510086\n", - "\n", - "lr decay from 6.25e-05 to 3.125e-05\n", - "Epoch 126, Iteration 1270, loss = 0.1663\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([2, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.019881634041666985\n", -======= - "Got rmse 0.01789473183453083\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([18, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.019586792215704918\n", - "\n", - "Epoch 127, Iteration 1280, loss = 0.0968\n", -======= - "Got rmse 0.018622124567627907\n", - "\n", - "Epoch 127, Iteration 1280, loss = 0.2031\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([2, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.020888801664114\n", -======= - "Got rmse 0.01790202036499977\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([18, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.020537402480840683\n", - "\n", - "Epoch 128, Iteration 1290, loss = 0.1060\n", -======= - "Got rmse 0.018634485080838203\n", - "\n", - "Epoch 128, Iteration 1290, loss = 0.1619\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([2, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.01984056644141674\n", -======= - "Got rmse 0.017912324517965317\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([18, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.019555311650037766\n", - "\n", - "Epoch 129, Iteration 1300, loss = 0.1130\n", -======= - "Got rmse 0.01863214373588562\n", - "\n", - "Epoch 129, Iteration 1300, loss = 0.1737\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([2, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.02085401490330696\n", -======= - "Got rmse 0.01791388913989067\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([18, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.020545030012726784\n", - "\n", - "Epoch 130, Iteration 1310, loss = 0.0927\n", -======= - "Got rmse 0.018621377646923065\n", - "\n", - "Epoch 130, Iteration 1310, loss = 0.1669\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([2, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.019923491403460503\n", -======= - "Got rmse 0.017894010990858078\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([18, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.019649287685751915\n", - "\n", - "Epoch 131, Iteration 1320, loss = 0.1013\n", -======= - "Got rmse 0.018626611679792404\n", - "\n", - "Epoch 131, Iteration 1320, loss = 0.1933\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([2, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.0198333952575922\n", -======= - "Got rmse 0.017872432246804237\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([18, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.01961860992014408\n", - "\n", - "Epoch 132, Iteration 1330, loss = 0.0930\n", -======= - "Got rmse 0.018628733232617378\n", - "\n", - "Epoch 132, Iteration 1330, loss = 0.2016\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([2, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.020242927595973015\n", -======= - "Got rmse 0.01792179048061371\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([18, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.02001442387700081\n", - "\n", - "Epoch 133, Iteration 1340, loss = 0.0985\n", -======= - "Got rmse 0.01863817125558853\n", - "\n", - "Epoch 133, Iteration 1340, loss = 0.1888\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([2, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.020179009065032005\n", -======= - "Got rmse 0.017898013815283775\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([18, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.0199076309800148\n", - "\n", - "Epoch 134, Iteration 1350, loss = 0.0965\n", -======= - "Got rmse 0.01863885670900345\n", - "\n", - "Epoch 134, Iteration 1350, loss = 0.1743\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([2, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.020698005333542824\n", -======= - "Got rmse 0.017908969894051552\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([18, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.020295940339565277\n", - "\n", - "Epoch 135, Iteration 1360, loss = 0.1056\n", -======= - "Got rmse 0.018640518188476562\n", - "\n", - "Epoch 135, Iteration 1360, loss = 0.1835\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([2, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.020664742216467857\n", -======= - "Got rmse 0.017886720597743988\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([18, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.02037283405661583\n", - "\n", - "Epoch 136, Iteration 1370, loss = 0.0913\n", -======= - "Got rmse 0.018633952364325523\n", - "\n", - "Epoch 136, Iteration 1370, loss = 0.1972\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([2, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.02123345620930195\n", -======= - "Got rmse 0.017900392413139343\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([18, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.020944813266396523\n", - "\n", - "Epoch 137, Iteration 1380, loss = 0.0938\n", -======= - "Got rmse 0.018634341657161713\n", - "\n", - "Epoch 137, Iteration 1380, loss = 0.1795\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([2, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.019863804802298546\n", -======= - "Got rmse 0.017873426899313927\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([18, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.01958632841706276\n", - "\n", - "Epoch 138, Iteration 1390, loss = 0.0781\n", -======= - "Got rmse 0.018611736595630646\n", - "\n", - "Epoch 138, Iteration 1390, loss = 0.1884\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([2, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.020674673840403557\n", -======= - "Got rmse 0.01791570335626602\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([18, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.020332789048552513\n", - "\n", - "Epoch 139, Iteration 1400, loss = 0.1499\n", -======= - "Got rmse 0.018639717251062393\n", - "\n", - "Epoch 139, Iteration 1400, loss = 0.1706\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([2, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.02158583514392376\n", -======= - "Got rmse 0.01789906993508339\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([18, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.02121896855533123\n", - "\n", - "Epoch 140, Iteration 1410, loss = 0.0885\n", -======= - "Got rmse 0.01862945407629013\n", - "\n", - "Epoch 140, Iteration 1410, loss = 0.1856\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([2, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.02048998326063156\n", -======= - "Got rmse 0.01789277419447899\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([18, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.02017129957675934\n", - "\n", - "Epoch 141, Iteration 1420, loss = 0.0786\n", -======= - "Got rmse 0.018629273399710655\n", - "\n", - "Epoch 141, Iteration 1420, loss = 0.1617\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([2, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.020796509459614754\n", -======= - "Got rmse 0.01791495829820633\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([18, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.02040759101510048\n", - "\n", - "Epoch 142, Iteration 1430, loss = 0.0934\n", -======= - "Got rmse 0.018648449331521988\n", - "\n", - "Epoch 142, Iteration 1430, loss = 0.1846\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([2, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.020934129133820534\n", -======= - "Got rmse 0.017916206270456314\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([18, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.020528851076960564\n", - "\n", - "Epoch 143, Iteration 1440, loss = 0.0747\n", -======= - "Got rmse 0.01865244284272194\n", - "\n", - "Epoch 143, Iteration 1440, loss = 0.1869\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([2, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.0202984306961298\n", -======= - "Got rmse 0.01792076788842678\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([18, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.019943982362747192\n", - "\n", - "Epoch 144, Iteration 1450, loss = 0.0911\n", -======= - "Got rmse 0.018641458824276924\n", - "\n", - "Epoch 144, Iteration 1450, loss = 0.1771\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([2, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.019622480496764183\n", -======= - "Got rmse 0.01793556660413742\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([18, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.01942550577223301\n", - "\n", - "Epoch 145, Iteration 1460, loss = 0.0838\n", -======= - "Got rmse 0.01865142583847046\n", - "\n", - "Epoch 145, Iteration 1460, loss = 0.1703\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([2, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.020491566509008408\n", -======= - "Got rmse 0.017878027632832527\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([18, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.020261995494365692\n", - "\n", - "Epoch 146, Iteration 1470, loss = 0.1156\n", -======= - "Got rmse 0.018625928089022636\n", - "\n", - "Epoch 146, Iteration 1470, loss = 0.2020\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([2, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.020555244758725166\n", -======= - "Got rmse 0.01789715699851513\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([18, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.020373763516545296\n", - "\n", - "Epoch 147, Iteration 1480, loss = 0.1002\n", -======= - "Got rmse 0.01863696426153183\n", - "\n", - "Epoch 147, Iteration 1480, loss = 0.1829\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([2, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.02074986882507801\n", -======= - "Got rmse 0.017916643992066383\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([18, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.02049894817173481\n", - "\n", - "Epoch 148, Iteration 1490, loss = 0.0813\n", -======= - "Got rmse 0.018639804795384407\n", - "\n", - "Epoch 148, Iteration 1490, loss = 0.1555\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([2, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.02022937871515751\n", -======= - "Got rmse 0.01792399026453495\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([18, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.019794706255197525\n", - "\n", - "Epoch 149, Iteration 1500, loss = 0.0633\n", -======= - "Got rmse 0.018644893541932106\n", - "\n", - "Epoch 149, Iteration 1500, loss = 0.1878\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([2, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.020754149183630943\n", -======= - "Got rmse 0.017904046922922134\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([18, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.020455846562981606\n", - "\n", - "Epoch 150, Iteration 1510, loss = 0.1111\n", -======= - "Got rmse 0.018645184114575386\n", - "\n", - "Epoch 150, Iteration 1510, loss = 0.1758\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([2, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.020582959055900574\n", -======= - "Got rmse 0.01790696009993553\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([18, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.020260192453861237\n", - "\n", - "Epoch 151, Iteration 1520, loss = 0.0857\n", -======= - "Got rmse 0.018643023446202278\n", - "\n", - "lr decay from 3.125e-05 to 1.5625e-05\n", - "Epoch 151, Iteration 1520, loss = 0.1833\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([2, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.021712910383939743\n", -======= - "Got rmse 0.017926467582583427\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([18, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.02152247354388237\n", - "\n", - "Epoch 152, Iteration 1530, loss = 0.0947\n", -======= - "Got rmse 0.018648451194167137\n", - "\n", - "Epoch 152, Iteration 1530, loss = 0.1865\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([2, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.02018391340970993\n", -======= - "Got rmse 0.01791555806994438\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([18, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.01992092840373516\n", - "\n", - "Epoch 153, Iteration 1540, loss = 0.0880\n", -======= - "Got rmse 0.0186543557792902\n", - "\n", - "Epoch 153, Iteration 1540, loss = 0.1678\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([2, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.02098269946873188\n", -======= - "Got rmse 0.017922228202223778\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([18, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.020711151883006096\n", - "\n", - "Epoch 154, Iteration 1550, loss = 0.0879\n", -======= - "Got rmse 0.018634144216775894\n", - "\n", - "Epoch 154, Iteration 1550, loss = 0.1681\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([2, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.02063870243728161\n", -======= - "Got rmse 0.01792423240840435\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([18, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.020348431542515755\n", - "\n", - "Epoch 155, Iteration 1560, loss = 0.0840\n", -======= - "Got rmse 0.018654054030776024\n", - "\n", - "Epoch 155, Iteration 1560, loss = 0.1714\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([2, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.02103978767991066\n", -======= - "Got rmse 0.017875680699944496\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([18, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.020835047587752342\n", - "\n", - "Epoch 156, Iteration 1570, loss = 0.0917\n", -======= - "Got rmse 0.018630411475896835\n", - "\n", - "Epoch 156, Iteration 1570, loss = 0.1885\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([2, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.020666202530264854\n", -======= - "Got rmse 0.017889846116304398\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([18, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.02034708671271801\n", - "\n", - "Epoch 157, Iteration 1580, loss = 0.0928\n", -======= - "Got rmse 0.018639814108610153\n", - "\n", - "Epoch 157, Iteration 1580, loss = 0.1559\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([2, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.020867420360445976\n", -======= - "Got rmse 0.01791134662926197\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([18, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.020591966807842255\n", - "\n", - "Epoch 158, Iteration 1590, loss = 0.0761\n", -======= - "Got rmse 0.018641654402017593\n", - "\n", - "Epoch 158, Iteration 1590, loss = 0.2112\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([2, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.020853890106081963\n", -======= - "Got rmse 0.01790313795208931\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([18, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.02049914374947548\n", - "\n", - "Epoch 159, Iteration 1600, loss = 0.1065\n", -======= - "Got rmse 0.018642941489815712\n", - "\n", - "Epoch 159, Iteration 1600, loss = 0.1950\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([2, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.021354850381612778\n", -======= - "Got rmse 0.01789749041199684\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([18, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.021015260368585587\n", - "\n", - "Epoch 160, Iteration 1610, loss = 0.0984\n", -======= - "Got rmse 0.018636737018823624\n", - "\n", - "Epoch 160, Iteration 1610, loss = 0.1785\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([2, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.02127922885119915\n", -======= - "Got rmse 0.017888110131025314\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([18, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.02092158794403076\n", - "\n", - "Epoch 161, Iteration 1620, loss = 0.0837\n", -======= - "Got rmse 0.018629252910614014\n", - "\n", - "Epoch 161, Iteration 1620, loss = 0.1708\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([2, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.021250557154417038\n", -======= - "Got rmse 0.017931388691067696\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([18, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.0211231280118227\n", - "\n", - "Epoch 162, Iteration 1630, loss = 0.1214\n", -======= - "Got rmse 0.018643662333488464\n", - "\n", - "Epoch 162, Iteration 1630, loss = 0.1797\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([2, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.02060236781835556\n", -======= - "Got rmse 0.017883306369185448\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([18, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.0203230157494545\n", - "\n", - "Epoch 163, Iteration 1640, loss = 0.0899\n", -======= - "Got rmse 0.018636982887983322\n", - "\n", - "Epoch 163, Iteration 1640, loss = 0.1889\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([2, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.02035173773765564\n", -======= - "Got rmse 0.017892900854349136\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([18, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.020107204094529152\n", - "\n", - "Epoch 164, Iteration 1650, loss = 0.0766\n", -======= - "Got rmse 0.01863226294517517\n", - "\n", - "Epoch 164, Iteration 1650, loss = 0.1816\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([2, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.020339272916316986\n", -======= - "Got rmse 0.017900794744491577\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([18, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.02019602619111538\n", - "\n", - "Epoch 165, Iteration 1660, loss = 0.1028\n", -======= - "Got rmse 0.018629789352416992\n", - "\n", - "Epoch 165, Iteration 1660, loss = 0.1641\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([2, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.020183071494102478\n", -======= - "Got rmse 0.017897088080644608\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([18, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.019919978454709053\n", - "\n", - "Epoch 166, Iteration 1670, loss = 0.0838\n", -======= - "Got rmse 0.01864580251276493\n", - "\n", - "Epoch 166, Iteration 1670, loss = 0.1601\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([2, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.020176876336336136\n", -======= - "Got rmse 0.017956214025616646\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([18, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.019963327795267105\n", - "\n", - "Epoch 167, Iteration 1680, loss = 0.0821\n", -======= - "Got rmse 0.018665092065930367\n", - "\n", - "Epoch 167, Iteration 1680, loss = 0.1820\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([2, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.02060777135193348\n", -======= - "Got rmse 0.01791330799460411\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([18, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.020414084196090698\n", - "\n", - "Epoch 168, Iteration 1690, loss = 0.0862\n", -======= - "Got rmse 0.018650511279702187\n", - "\n", - "Epoch 168, Iteration 1690, loss = 0.1754\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([2, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.02061922661960125\n", -======= - "Got rmse 0.01791699416935444\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([18, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.02044736035168171\n", - "\n", - "Epoch 169, Iteration 1700, loss = 0.0823\n", -======= - "Got rmse 0.018649695441126823\n", - "\n", - "Epoch 169, Iteration 1700, loss = 0.1734\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([2, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.020206432789564133\n", -======= - "Got rmse 0.017922157421708107\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([18, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.019805504009127617\n", - "\n", - "Epoch 170, Iteration 1710, loss = 0.1080\n", -======= - "Got rmse 0.018652543425559998\n", - "\n", - "Epoch 170, Iteration 1710, loss = 0.1704\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([2, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.020784707739949226\n", -======= - "Got rmse 0.01788565330207348\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([18, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.020486382767558098\n", - "\n", - "Epoch 171, Iteration 1720, loss = 0.0945\n", -======= - "Got rmse 0.018632039427757263\n", - "\n", - "Epoch 171, Iteration 1720, loss = 0.1788\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([2, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.020391365513205528\n", -======= - "Got rmse 0.01790183037519455\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([18, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.020107947289943695\n", - "\n", - "Epoch 172, Iteration 1730, loss = 0.0884\n", -======= - "Got rmse 0.018637046217918396\n", - "\n", - "Epoch 172, Iteration 1730, loss = 0.2071\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([2, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.02073354460299015\n", -======= - "Got rmse 0.017921438440680504\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([18, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.020406607538461685\n", - "\n", - "Epoch 173, Iteration 1740, loss = 0.0935\n", -======= - "Got rmse 0.01863316260278225\n", - "\n", - "Epoch 173, Iteration 1740, loss = 0.2156\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([2, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.02121773175895214\n", -======= - "Got rmse 0.017934566363692284\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([18, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.021057549864053726\n", - "\n", - "Epoch 174, Iteration 1750, loss = 0.0786\n", -======= - "Got rmse 0.01865658350288868\n", - "\n", - "Epoch 174, Iteration 1750, loss = 0.1798\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([2, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.020312123000621796\n", -======= - "Got rmse 0.017903048545122147\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([18, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.01995673030614853\n", - "\n", - "Epoch 175, Iteration 1760, loss = 0.0874\n", -======= - "Got rmse 0.01864413544535637\n", - "\n", - "Epoch 175, Iteration 1760, loss = 0.1488\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([2, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.02069094032049179\n", -======= - "Got rmse 0.017912695184350014\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([18, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.02044496312737465\n", - "\n", - "Epoch 176, Iteration 1770, loss = 0.0963\n", -======= - "Got rmse 0.018650667741894722\n", - "\n", - "lr decay from 1.5625e-05 to 7.8125e-06\n", - "Epoch 176, Iteration 1770, loss = 0.1693\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([2, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.02078820951282978\n", -======= - "Got rmse 0.017914026975631714\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([18, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.020498916506767273\n", - "\n", - "Epoch 177, Iteration 1780, loss = 0.0679\n", -======= - "Got rmse 0.018644876778125763\n", - "\n", - "Epoch 177, Iteration 1780, loss = 0.1645\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([2, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.02068321779370308\n", -======= - "Got rmse 0.017916861921548843\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([18, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.02040109783411026\n", - "\n", - "Epoch 178, Iteration 1790, loss = 0.0935\n", -======= - "Got rmse 0.018644290044903755\n", - "\n", - "Epoch 178, Iteration 1790, loss = 0.1864\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([2, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.020587915554642677\n", -======= - "Got rmse 0.01790347509086132\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([18, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.02028277888894081\n", - "\n", - "Epoch 179, Iteration 1800, loss = 0.1280\n", -======= - "Got rmse 0.018641501665115356\n", - "\n", - "Epoch 179, Iteration 1800, loss = 0.1872\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([2, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.021253857761621475\n", -======= - "Got rmse 0.017905933782458305\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([18, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.020969431847333908\n", - "\n", - "Epoch 180, Iteration 1810, loss = 0.1114\n", -======= - "Got rmse 0.018637847155332565\n", - "\n", - "Epoch 180, Iteration 1810, loss = 0.1974\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([2, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.02091359905898571\n", -======= - "Got rmse 0.017881298437714577\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([18, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.020637718960642815\n", - "\n", - "Epoch 181, Iteration 1820, loss = 0.0770\n", -======= - "Got rmse 0.01861139014363289\n", - "\n", - "Epoch 181, Iteration 1820, loss = 0.1398\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([2, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.020626753568649292\n", -======= - "Got rmse 0.01791970431804657\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([18, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.020311525091528893\n", - "\n", - "Epoch 182, Iteration 1830, loss = 0.0855\n", -======= - "Got rmse 0.018650230020284653\n", - "\n", - "Epoch 182, Iteration 1830, loss = 0.2002\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([2, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.02051820047199726\n", -======= - "Got rmse 0.017914200201630592\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([18, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.020301513373851776\n", - "\n", - "Epoch 183, Iteration 1840, loss = 0.0709\n", -======= - "Got rmse 0.01864548958837986\n", - "\n", - "Epoch 183, Iteration 1840, loss = 0.1775\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([2, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.020433459430933\n", -======= - "Got rmse 0.017904838547110558\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([18, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.020196720957756042\n", - "\n", - "Epoch 184, Iteration 1850, loss = 0.0778\n", -======= - "Got rmse 0.018642600625753403\n", - "\n", - "Epoch 184, Iteration 1850, loss = 0.1841\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([2, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.021142056211829185\n", -======= - "Got rmse 0.0178990475833416\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([18, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.02087358757853508\n", - "\n", - "Epoch 185, Iteration 1860, loss = 0.0816\n", -======= - "Got rmse 0.018635690212249756\n", - "\n", - "Epoch 185, Iteration 1860, loss = 0.1931\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([2, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.021065140143036842\n", -======= - "Got rmse 0.01788465306162834\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([18, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.020799024030566216\n", - "\n", - "Epoch 186, Iteration 1870, loss = 0.1124\n", -======= - "Got rmse 0.018638165667653084\n", - "\n", - "Epoch 186, Iteration 1870, loss = 0.1818\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([2, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.02091996930539608\n", -======= - "Got rmse 0.01790640689432621\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([18, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.02062329649925232\n", - "\n", - "Epoch 187, Iteration 1880, loss = 0.0681\n", -======= - "Got rmse 0.018641291186213493\n", - "\n", - "Epoch 187, Iteration 1880, loss = 0.1724\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([2, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.021121235564351082\n", -======= - "Got rmse 0.017922470346093178\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([18, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.02082829177379608\n", - "\n", - "Epoch 188, Iteration 1890, loss = 0.0632\n", -======= - "Got rmse 0.018648719415068626\n", - "\n", - "Epoch 188, Iteration 1890, loss = 0.1693\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([2, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.02058183215558529\n", -======= - "Got rmse 0.01790992170572281\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([18, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.020370375365018845\n", - "\n", - "Epoch 189, Iteration 1900, loss = 0.0685\n", -======= - "Got rmse 0.018638746812939644\n", - "\n", - "Epoch 189, Iteration 1900, loss = 0.1988\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([2, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.020912596955895424\n", -======= - "Got rmse 0.017933716997504234\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([18, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.02070021629333496\n", - "\n", - "Epoch 190, Iteration 1910, loss = 0.0760\n", -======= - "Got rmse 0.01865478605031967\n", - "\n", - "Epoch 190, Iteration 1910, loss = 0.2018\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([2, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.021174587309360504\n", -======= - "Got rmse 0.01790831796824932\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([18, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.020903080701828003\n", - "\n", - "Epoch 191, Iteration 1920, loss = 0.1236\n", -======= - "Got rmse 0.01864238642156124\n", - "\n", - "Epoch 191, Iteration 1920, loss = 0.1861\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([2, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.020762138068675995\n", -======= - "Got rmse 0.017933674156665802\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([18, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.02056574635207653\n", - "\n", - "Epoch 192, Iteration 1930, loss = 0.1204\n", -======= - "Got rmse 0.018656091764569283\n", - "\n", - "Epoch 192, Iteration 1930, loss = 0.1894\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([2, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.02060437574982643\n", -======= - "Got rmse 0.01789284311234951\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([18, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.020311914384365082\n", - "\n", - "Epoch 193, Iteration 1940, loss = 0.0724\n", -======= - "Got rmse 0.018643133342266083\n", - "\n", - "Epoch 193, Iteration 1940, loss = 0.1837\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([2, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.020597202703356743\n", -======= - "Got rmse 0.017921660095453262\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([18, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.020320074632763863\n", - "\n", - "Epoch 194, Iteration 1950, loss = 0.0778\n", -======= - "Got rmse 0.018645672127604485\n", - "\n", - "Epoch 194, Iteration 1950, loss = 0.2184\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([2, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.020984338596463203\n", -======= - "Got rmse 0.017942069098353386\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([18, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.02078905515372753\n", - "\n", - "Epoch 195, Iteration 1960, loss = 0.0740\n", -======= - "Got rmse 0.018651368096470833\n", - "\n", - "Epoch 195, Iteration 1960, loss = 0.2016\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([2, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.020281394943594933\n", -======= - "Got rmse 0.01794605515897274\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([18, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.019979994744062424\n", - "\n", - "Epoch 196, Iteration 1970, loss = 0.0628\n", -======= - "Got rmse 0.018665598705410957\n", - "\n", - "Epoch 196, Iteration 1970, loss = 0.1941\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([2, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.02058774046599865\n", -======= - "Got rmse 0.017903337255120277\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([18, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.020297648385167122\n", - "\n", - "Epoch 197, Iteration 1980, loss = 0.0787\n", -======= - "Got rmse 0.018631726503372192\n", - "\n", - "Epoch 197, Iteration 1980, loss = 0.1568\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([2, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.020941246300935745\n", -======= - "Got rmse 0.017930757254362106\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([18, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.02068301849067211\n", - "\n", - "Epoch 198, Iteration 1990, loss = 0.0786\n", -======= - "Got rmse 0.018644997850060463\n", - "\n", - "Epoch 198, Iteration 1990, loss = 0.1730\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([2, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.021232906728982925\n", -======= - "Got rmse 0.017889173701405525\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([18, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.0210273414850235\n", - "\n", - "Epoch 199, Iteration 2000, loss = 0.0979\n", -======= - "Got rmse 0.018634861335158348\n", - "\n", - "Epoch 199, Iteration 2000, loss = 0.1962\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([2, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.020955562591552734\n", -======= - "Got rmse 0.017931727692484856\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([18, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.02065693959593773\n", - "\n", - "Epoch 200, Iteration 2010, loss = 0.0973\n", -======= - "Got rmse 0.018643831834197044\n", - "\n", - "Epoch 200, Iteration 2010, loss = 0.1585\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([2, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.02073424868285656\n", -======= - "Got rmse 0.01793457195162773\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([18, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.02047947607934475\n", - "\n", - "Epoch 201, Iteration 2020, loss = 0.0789\n", -======= - "Got rmse 0.01864797994494438\n", - "\n", - "lr decay from 7.8125e-06 to 3.90625e-06\n", - "Epoch 201, Iteration 2020, loss = 0.1760\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([2, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.021170657128095627\n", -======= - "Got rmse 0.017882423475384712\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([18, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.020903367549180984\n", - "\n", - "Epoch 202, Iteration 2030, loss = 0.0978\n", -======= - "Got rmse 0.018630238249897957\n", - "\n", - "Epoch 202, Iteration 2030, loss = 0.1854\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([2, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.021054252982139587\n", -======= - "Got rmse 0.0179106704890728\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([18, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.020809918642044067\n", - "\n", - "Epoch 203, Iteration 2040, loss = 0.0815\n", -======= - "Got rmse 0.01863718405365944\n", - "\n", - "Epoch 203, Iteration 2040, loss = 0.1662\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([2, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.02050674520432949\n", -======= - "Got rmse 0.01790771447122097\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([18, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.020282136276364326\n", - "\n", - "Epoch 204, Iteration 2050, loss = 0.0857\n", -======= - "Got rmse 0.018642768263816833\n", - "\n", - "Epoch 204, Iteration 2050, loss = 0.1906\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([2, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.021242910996079445\n", -======= - "Got rmse 0.017932306975126266\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([18, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.021006330847740173\n", - "\n", - "Epoch 205, Iteration 2060, loss = 0.0982\n", -======= - "Got rmse 0.018645068630576134\n", - "\n", - "Epoch 205, Iteration 2060, loss = 0.1677\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([2, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.020722877234220505\n", -======= - "Got rmse 0.017895696684718132\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([18, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.02047005295753479\n", - "\n", - "Epoch 206, Iteration 2070, loss = 0.0999\n", -======= - "Got rmse 0.01863880828022957\n", - "\n", - "Epoch 206, Iteration 2070, loss = 0.1863\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([2, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.020647717639803886\n", -======= - "Got rmse 0.017906175926327705\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([18, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.02038734406232834\n", - "\n", - "Epoch 207, Iteration 2080, loss = 0.0702\n", -======= - "Got rmse 0.018638266250491142\n", - "\n", - "Epoch 207, Iteration 2080, loss = 0.1844\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([2, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.020410768687725067\n", -======= - "Got rmse 0.0179122481495142\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([18, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.020215531811118126\n", - "\n", - "Epoch 208, Iteration 2090, loss = 0.0989\n", -======= - "Got rmse 0.018644731491804123\n", - "\n", - "Epoch 208, Iteration 2090, loss = 0.1942\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([2, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.0212820153683424\n", -======= - "Got rmse 0.017905741930007935\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([18, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.021079381927847862\n", - "\n", - "Epoch 209, Iteration 2100, loss = 0.1066\n", -======= - "Got rmse 0.01863606832921505\n", - "\n", - "Epoch 209, Iteration 2100, loss = 0.1953\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([2, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.020922666415572166\n", -======= - "Got rmse 0.017921339720487595\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([18, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.020726755261421204\n", - "\n", - "Epoch 210, Iteration 2110, loss = 0.0659\n", -======= - "Got rmse 0.018634434789419174\n", - "\n", - "Epoch 210, Iteration 2110, loss = 0.1618\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([2, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.021095292642712593\n", -======= - "Got rmse 0.01790483482182026\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([18, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.020853547379374504\n", - "\n", - "Epoch 211, Iteration 2120, loss = 0.0866\n", -======= - "Got rmse 0.01864222250878811\n", - "\n", - "Epoch 211, Iteration 2120, loss = 0.2119\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([2, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.020706621930003166\n", -======= - "Got rmse 0.017920995131134987\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([18, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.020383112132549286\n", - "\n", - "Epoch 212, Iteration 2130, loss = 0.0609\n", -======= - "Got rmse 0.018646348267793655\n", - "\n", - "Epoch 212, Iteration 2130, loss = 0.1762\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([2, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.021094679832458496\n", -======= - "Got rmse 0.017926715314388275\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([18, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.0208793506026268\n", - "\n", - "Epoch 213, Iteration 2140, loss = 0.0989\n", -======= - "Got rmse 0.0186446700245142\n", - "\n", - "Epoch 213, Iteration 2140, loss = 0.1788\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([2, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.020354384556412697\n", -======= - "Got rmse 0.017896084114909172\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([18, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.020097393542528152\n", - "\n", - "Epoch 214, Iteration 2150, loss = 0.0753\n", -======= - "Got rmse 0.01863314025104046\n", - "\n", - "Epoch 214, Iteration 2150, loss = 0.2233\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([2, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.020500604063272476\n", -======= - "Got rmse 0.017927207052707672\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([18, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.020325087010860443\n", - "\n", - "Epoch 215, Iteration 2160, loss = 0.0788\n", -======= - "Got rmse 0.01865380071103573\n", - "\n", - "Epoch 215, Iteration 2160, loss = 0.1986\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([2, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.020845593884587288\n", -======= - "Got rmse 0.017881451174616814\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([18, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.020654039457440376\n", - "\n", - "Epoch 216, Iteration 2170, loss = 0.0917\n", -======= - "Got rmse 0.018636614084243774\n", - "\n", - "Epoch 216, Iteration 2170, loss = 0.1652\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([2, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.020497243851423264\n", -======= - "Got rmse 0.017908349633216858\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([18, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.020281042903661728\n", - "\n", - "Epoch 217, Iteration 2180, loss = 0.0571\n", -======= - "Got rmse 0.018638795241713524\n", - "\n", - "Epoch 217, Iteration 2180, loss = 0.1706\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([2, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.02062189020216465\n", -======= - "Got rmse 0.017895521596074104\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([18, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.02038547769188881\n", - "\n", - "Epoch 218, Iteration 2190, loss = 0.0684\n", -======= - "Got rmse 0.018642153590917587\n", - "\n", - "Epoch 218, Iteration 2190, loss = 0.1775\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([2, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.020478831604123116\n", -======= - "Got rmse 0.017896005883812904\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([18, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.020234890282154083\n", - "\n", - "Epoch 219, Iteration 2200, loss = 0.0888\n", -======= - "Got rmse 0.018639301881194115\n", - "\n", - "Epoch 219, Iteration 2200, loss = 0.1544\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([2, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.020580250769853592\n", -======= - "Got rmse 0.017906561493873596\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([18, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.020325062796473503\n", - "\n", - "Epoch 220, Iteration 2210, loss = 0.0599\n", -======= - "Got rmse 0.01864408701658249\n", - "\n", - "Epoch 220, Iteration 2210, loss = 0.1611\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([2, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.02093607373535633\n", -======= - "Got rmse 0.017906801775097847\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([18, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.02069704793393612\n", - "\n", - "Epoch 221, Iteration 2220, loss = 0.1138\n", -======= - "Got rmse 0.01863635703921318\n", - "\n", - "Epoch 221, Iteration 2220, loss = 0.1566\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([2, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.02103922702372074\n", -======= - "Got rmse 0.017886942252516747\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([18, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.020817240700125694\n", - "\n", - "Epoch 222, Iteration 2230, loss = 0.0813\n", -======= - "Got rmse 0.01863967254757881\n", - "\n", - "Epoch 222, Iteration 2230, loss = 0.1799\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([2, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.020985132083296776\n", -======= - "Got rmse 0.017874542623758316\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([18, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.0207638181746006\n", - "\n", - "Epoch 223, Iteration 2240, loss = 0.0740\n", -======= - "Got rmse 0.018630841746926308\n", - "\n", - "Epoch 223, Iteration 2240, loss = 0.2043\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([2, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.020919764414429665\n", -======= - "Got rmse 0.01788480207324028\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([18, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.02072146348655224\n", - "\n", - "Epoch 224, Iteration 2250, loss = 0.1252\n", -======= - "Got rmse 0.018632646650075912\n", - "\n", - "Epoch 224, Iteration 2250, loss = 0.1719\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([2, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.020795373246073723\n", -======= - "Got rmse 0.017906349152326584\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([18, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.020587394014000893\n", - "\n", - "Epoch 225, Iteration 2260, loss = 0.0746\n", -======= - "Got rmse 0.01865062303841114\n", - "\n", - "Epoch 225, Iteration 2260, loss = 0.1804\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([2, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.021165167912840843\n", -======= - "Got rmse 0.01792685128748417\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([18, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.02098250202834606\n", - "\n", - "Epoch 226, Iteration 2270, loss = 0.0829\n", -======= - "Got rmse 0.018651705235242844\n", - "\n", - "lr decay from 3.90625e-06 to 1.953125e-06\n", - "Epoch 226, Iteration 2270, loss = 0.1738\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([2, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.02075747773051262\n", -======= - "Got rmse 0.017918208613991737\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([18, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.020522870123386383\n", - "\n", - "Epoch 227, Iteration 2280, loss = 0.0817\n", -======= - "Got rmse 0.01864345744252205\n", - "\n", - "Epoch 227, Iteration 2280, loss = 0.1547\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([2, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.021094784140586853\n", -======= - "Got rmse 0.017920738086104393\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([18, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.020817507058382034\n", - "\n", - "Epoch 228, Iteration 2290, loss = 0.0776\n", -======= - "Got rmse 0.01865781843662262\n", - "\n", - "Epoch 228, Iteration 2290, loss = 0.1972\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([2, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.020756520330905914\n", -======= - "Got rmse 0.01792423240840435\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([18, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.020487403497099876\n", - "\n", - "Epoch 229, Iteration 2300, loss = 0.0659\n", -======= - "Got rmse 0.01863516867160797\n", - "\n", - "Epoch 229, Iteration 2300, loss = 0.2135\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([2, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.021128669381141663\n", -======= - "Got rmse 0.017922207713127136\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([18, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.02089671790599823\n", - "\n", - "Epoch 230, Iteration 2310, loss = 0.0732\n", -======= - "Got rmse 0.018640778958797455\n", - "\n", - "Epoch 230, Iteration 2310, loss = 0.2240\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([2, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.02128133550286293\n", -======= - "Got rmse 0.017892315983772278\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([18, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.021019060164690018\n", - "\n", - "Epoch 231, Iteration 2320, loss = 0.0913\n", -======= - "Got rmse 0.018645502626895905\n", - "\n", - "Epoch 231, Iteration 2320, loss = 0.1992\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([2, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.02107204496860504\n", -======= - "Got rmse 0.017940562218427658\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([18, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.02086508460342884\n", - "\n", - "Epoch 232, Iteration 2330, loss = 0.1083\n", -======= - "Got rmse 0.018663723021745682\n", - "\n", - "Epoch 232, Iteration 2330, loss = 0.2071\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([2, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.020585883408784866\n", -======= - "Got rmse 0.01793811097741127\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([18, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.020363610237836838\n", - "\n", - "Epoch 233, Iteration 2340, loss = 0.0855\n", -======= - "Got rmse 0.018658699467778206\n", - "\n", - "Epoch 233, Iteration 2340, loss = 0.1782\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([2, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.020955875515937805\n", -======= - "Got rmse 0.017900962382555008\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([18, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.02075614035129547\n", - "\n", - "Epoch 234, Iteration 2350, loss = 0.0807\n", -======= - "Got rmse 0.0186355821788311\n", - "\n", - "Epoch 234, Iteration 2350, loss = 0.1761\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([2, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.021136188879609108\n", -======= - "Got rmse 0.017956363037228584\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([18, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.020911946892738342\n", - "\n", - "Epoch 235, Iteration 2360, loss = 0.0697\n", -======= - "Got rmse 0.018658315762877464\n", - "\n", - "Epoch 235, Iteration 2360, loss = 0.1839\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([2, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.02097223699092865\n", -======= - "Got rmse 0.017917415127158165\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([18, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.02077510394155979\n", - "\n", - "Epoch 236, Iteration 2370, loss = 0.0953\n", -======= - "Got rmse 0.018644582480192184\n", - "\n", - "Epoch 236, Iteration 2370, loss = 0.1683\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([2, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.021119749173521996\n", -======= - "Got rmse 0.017944511026144028\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([18, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.020892862230539322\n", - "\n", - "Epoch 237, Iteration 2380, loss = 0.0982\n", -======= - "Got rmse 0.018653322011232376\n", - "\n", - "Epoch 237, Iteration 2380, loss = 0.2038\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([2, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.020915210247039795\n", -======= - "Got rmse 0.01790054328739643\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([18, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.020666904747486115\n", - "\n", - "Epoch 238, Iteration 2390, loss = 0.1076\n", -======= - "Got rmse 0.018631519749760628\n", - "\n", - "Epoch 238, Iteration 2390, loss = 0.1714\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([2, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.02095477469265461\n", -======= - "Got rmse 0.017920907586812973\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([18, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.020760927349328995\n", - "\n", - "Epoch 239, Iteration 2400, loss = 0.0879\n", -======= - "Got rmse 0.018644623458385468\n", - "\n", - "Epoch 239, Iteration 2400, loss = 0.1897\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([2, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.02105209417641163\n", -======= - "Got rmse 0.017889060080051422\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([18, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.02086338773369789\n", - "\n", - "Epoch 240, Iteration 2410, loss = 0.0678\n", -======= - "Got rmse 0.01863142102956772\n", - "\n", - "Epoch 240, Iteration 2410, loss = 0.1781\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([2, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.02120218798518181\n", -======= - "Got rmse 0.017913460731506348\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([18, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.020963424816727638\n", - "\n", - "Epoch 241, Iteration 2420, loss = 0.0792\n", -======= - "Got rmse 0.018647311255335808\n", - "\n", - "Epoch 241, Iteration 2420, loss = 0.1723\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([2, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.021071434020996094\n", -======= - "Got rmse 0.017914840951561928\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([18, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.020871520042419434\n", - "\n", - "Epoch 242, Iteration 2430, loss = 0.0831\n", -======= - "Got rmse 0.018643392249941826\n", - "\n", - "Epoch 242, Iteration 2430, loss = 0.1638\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([2, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.021204469725489616\n", -======= - "Got rmse 0.017923790961503983\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([18, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.02097455784678459\n", - "\n", - "Epoch 243, Iteration 2440, loss = 0.0550\n", -======= - "Got rmse 0.01865977793931961\n", - "\n", - "Epoch 243, Iteration 2440, loss = 0.1792\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([2, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.020893460139632225\n", -======= - "Got rmse 0.017906034365296364\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([18, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.02066824771463871\n", - "\n", - "Epoch 244, Iteration 2450, loss = 0.0805\n", -======= - "Got rmse 0.018639402464032173\n", - "\n", - "Epoch 244, Iteration 2450, loss = 0.1617\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([2, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.020771559327840805\n", -======= - "Got rmse 0.017905613407492638\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([18, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.020581193268299103\n", - "\n", - "Epoch 245, Iteration 2460, loss = 0.0824\n", -======= - "Got rmse 0.018635543063282967\n", - "\n", - "Epoch 245, Iteration 2460, loss = 0.1625\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([2, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.020994961261749268\n", -======= - "Got rmse 0.01790984533727169\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([18, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.020763851702213287\n", - "\n", - "Epoch 246, Iteration 2470, loss = 0.0865\n", -======= - "Got rmse 0.018653737381100655\n", - "\n", - "Epoch 246, Iteration 2470, loss = 0.2031\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([2, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.020722059532999992\n", -======= - "Got rmse 0.017937002703547478\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([18, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.02048446051776409\n", - "\n", - "Epoch 247, Iteration 2480, loss = 0.0552\n", -======= - "Got rmse 0.018652889877557755\n", - "\n", - "Epoch 247, Iteration 2480, loss = 0.2045\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([2, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.02106744609773159\n", -======= - "Got rmse 0.017901550978422165\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([18, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.020882761105895042\n", - "\n", - "Epoch 248, Iteration 2490, loss = 0.1124\n", -======= - "Got rmse 0.01865473948419094\n", - "\n", - "Epoch 248, Iteration 2490, loss = 0.1508\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([2, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.02095465175807476\n", -======= - "Got rmse 0.0178908109664917\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([18, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.020726677030324936\n", - "\n", - "Epoch 249, Iteration 2500, loss = 0.0881\n", -======= - "Got rmse 0.018644653260707855\n", - "\n", - "Epoch 249, Iteration 2500, loss = 0.1892\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([2, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.02118087187409401\n", -======= - "Got rmse 0.01790762133896351\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([18, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.02095751464366913\n", - "\n", - "Epoch 250, Iteration 2510, loss = 0.0679\n", -======= - "Got rmse 0.01864984631538391\n", - "\n", - "Epoch 250, Iteration 2510, loss = 0.1739\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([2, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.02055649645626545\n", -======= - "Got rmse 0.01793668605387211\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([18, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.020355932414531708\n", - "\n", - "Epoch 251, Iteration 2520, loss = 0.0611\n", -======= - "Got rmse 0.018648533150553703\n", - "\n", - "lr decay from 1.953125e-06 to 9.765625e-07\n", - "Epoch 251, Iteration 2520, loss = 0.1653\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([2, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.021142881363630295\n", -======= - "Got rmse 0.017936687916517258\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([18, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.02099830098450184\n", - "\n", - "Epoch 252, Iteration 2530, loss = 0.0930\n", -======= - "Got rmse 0.018653593957424164\n", - "\n", - "Epoch 252, Iteration 2530, loss = 0.1951\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([2, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.020636778324842453\n", -======= - "Got rmse 0.017903877422213554\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([18, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.02042718045413494\n", - "\n", - "Epoch 253, Iteration 2540, loss = 0.0834\n", -======= - "Got rmse 0.01864013634622097\n", - "\n", - "Epoch 253, Iteration 2540, loss = 0.1892\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([2, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.020936544984579086\n", -======= - "Got rmse 0.017906464636325836\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([18, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.020729364827275276\n", - "\n", - "Epoch 254, Iteration 2550, loss = 0.0862\n", -======= - "Got rmse 0.01863039843738079\n", - "\n", - "Epoch 254, Iteration 2550, loss = 0.1731\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([2, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.021218599751591682\n", -======= - "Got rmse 0.01791882887482643\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([18, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.021011441946029663\n", - "\n", - "Epoch 255, Iteration 2560, loss = 0.0881\n", -======= - "Got rmse 0.018652746453881264\n", - "\n", - "Epoch 255, Iteration 2560, loss = 0.1697\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([2, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.021023239940404892\n", -======= - "Got rmse 0.01790045015513897\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([18, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.020803872495889664\n", - "\n", - "Epoch 256, Iteration 2570, loss = 0.0928\n", -======= - "Got rmse 0.018633650615811348\n", - "\n", - "Epoch 256, Iteration 2570, loss = 0.1995\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([2, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.020957697182893753\n", -======= - "Got rmse 0.017918016761541367\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([18, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.02076796069741249\n", - "\n", - "Epoch 257, Iteration 2580, loss = 0.0716\n", -======= - "Got rmse 0.018643563613295555\n", - "\n", - "Epoch 257, Iteration 2580, loss = 0.1877\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([2, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.020784106105566025\n", -======= - "Got rmse 0.017927706241607666\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([18, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.02058819681406021\n", - "\n", - "Epoch 258, Iteration 2590, loss = 0.1066\n", -======= - "Got rmse 0.018647581338882446\n", - "\n", - "Epoch 258, Iteration 2590, loss = 0.1890\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([2, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.020947672426700592\n", -======= - "Got rmse 0.01789235696196556\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([18, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.020734336227178574\n", - "\n", - "Epoch 259, Iteration 2600, loss = 0.0646\n", -======= - "Got rmse 0.01863253116607666\n", - "\n", - "Epoch 259, Iteration 2600, loss = 0.1791\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([2, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.020893409848213196\n", -======= - "Got rmse 0.01793610118329525\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([18, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.02070501632988453\n", - "\n", - "Epoch 260, Iteration 2610, loss = 0.0872\n", -======= - "Got rmse 0.018650464713573456\n", - "\n", - "Epoch 260, Iteration 2610, loss = 0.1914\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([2, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.020368188619613647\n", -======= - "Got rmse 0.01791919209063053\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([18, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.020185157656669617\n", - "\n", - "Epoch 261, Iteration 2620, loss = 0.0827\n", -======= - "Got rmse 0.018637871369719505\n", - "\n", - "Epoch 261, Iteration 2620, loss = 0.1587\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([2, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.020727530121803284\n", -======= - "Got rmse 0.017917653545737267\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([18, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.02048356831073761\n", - "\n", - "Epoch 262, Iteration 2630, loss = 0.0594\n", -======= - "Got rmse 0.018652746453881264\n", - "\n", - "Epoch 262, Iteration 2630, loss = 0.1743\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([2, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.021152060478925705\n", -======= - "Got rmse 0.017926311120390892\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([18, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.020945856347680092\n", - "\n", - "Epoch 263, Iteration 2640, loss = 0.0772\n", -======= - "Got rmse 0.018658781424164772\n", - "\n", - "Epoch 263, Iteration 2640, loss = 0.1859\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([2, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.020924855023622513\n", -======= - "Got rmse 0.017917044460773468\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([18, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.020737573504447937\n", - "\n", - "Epoch 264, Iteration 2650, loss = 0.0568\n", -======= - "Got rmse 0.01864154078066349\n", - "\n", - "Epoch 264, Iteration 2650, loss = 0.2105\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([2, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.02084336429834366\n", -======= - "Got rmse 0.017933834344148636\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([18, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.02064260095357895\n", - "\n", - "Epoch 265, Iteration 2660, loss = 0.0917\n", -======= - "Got rmse 0.01865706779062748\n", - "\n", - "Epoch 265, Iteration 2660, loss = 0.1813\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([2, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.020784450694918633\n", -======= - "Got rmse 0.017889846116304398\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([18, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.020591894164681435\n", - "\n", - "Epoch 266, Iteration 2670, loss = 0.0715\n", -======= - "Got rmse 0.018645383417606354\n", - "\n", - "Epoch 266, Iteration 2670, loss = 0.1590\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([2, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.02121538668870926\n", -======= - "Got rmse 0.01790018007159233\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([18, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.021065430715680122\n", - "\n", - "Epoch 267, Iteration 2680, loss = 0.0784\n", -======= - "Got rmse 0.01863849349319935\n", - "\n", - "Epoch 267, Iteration 2680, loss = 0.1889\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([2, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.020588897168636322\n", -======= - "Got rmse 0.017906704917550087\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([18, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.020400041714310646\n", - "\n", - "Epoch 268, Iteration 2690, loss = 0.1007\n", -======= - "Got rmse 0.018635308369994164\n", - "\n", - "Epoch 268, Iteration 2690, loss = 0.1623\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([2, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.021220384165644646\n", -======= - "Got rmse 0.017897767946124077\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([18, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.021015139296650887\n", - "\n", - "Epoch 269, Iteration 2700, loss = 0.0767\n", -======= - "Got rmse 0.018627433106303215\n", - "\n", - "Epoch 269, Iteration 2700, loss = 0.1705\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([2, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.02101726084947586\n", -======= - "Got rmse 0.017925651744008064\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([18, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.020811259746551514\n", - "\n", - "Epoch 270, Iteration 2710, loss = 0.0886\n", -======= - "Got rmse 0.01865975745022297\n", - "\n", - "Epoch 270, Iteration 2710, loss = 0.1720\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([2, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.020661229267716408\n", -======= - "Got rmse 0.01793918013572693\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([18, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.020476099103689194\n", - "\n", - "Epoch 271, Iteration 2720, loss = 0.0830\n", -======= - "Got rmse 0.018663810566067696\n", - "\n", - "Epoch 271, Iteration 2720, loss = 0.1663\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([2, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.02095891535282135\n", -======= - "Got rmse 0.017917228862643242\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([18, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.020768815651535988\n", - "\n", - "Epoch 272, Iteration 2730, loss = 0.0982\n", -======= - "Got rmse 0.018640080466866493\n", - "\n", - "Epoch 272, Iteration 2730, loss = 0.1417\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([2, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.021099228411912918\n", -======= - "Got rmse 0.017940178513526917\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([18, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.020914148539304733\n", - "\n", - "Epoch 273, Iteration 2740, loss = 0.0679\n", -======= - "Got rmse 0.01865801401436329\n", - "\n", - "Epoch 273, Iteration 2740, loss = 0.1840\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([2, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.020825475454330444\n", -======= - "Got rmse 0.01791556552052498\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([18, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.02062998339533806\n", - "\n", - "Epoch 274, Iteration 2750, loss = 0.0736\n", -======= - "Got rmse 0.01864752173423767\n", - "\n", - "Epoch 274, Iteration 2750, loss = 0.1749\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([2, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.02113938331604004\n", -======= - "Got rmse 0.017916597425937653\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([18, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.020988553762435913\n", - "\n", - "Epoch 275, Iteration 2760, loss = 0.0745\n", -======= - "Got rmse 0.018649397417902946\n", - "\n", - "Epoch 275, Iteration 2760, loss = 0.1669\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([2, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.020793674513697624\n", -======= - "Got rmse 0.01789693534374237\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([18, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.020543338730931282\n", - "\n", - "Epoch 276, Iteration 2770, loss = 0.0755\n", -======= - "Got rmse 0.018621550872921944\n", - "\n", - "lr decay from 9.765625e-07 to 4.8828125e-07\n", - "Epoch 276, Iteration 2770, loss = 0.1581\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([2, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.020815545693039894\n", -======= - "Got rmse 0.017896803095936775\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([18, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.02058369666337967\n", - "\n", - "Epoch 277, Iteration 2780, loss = 0.0726\n", -======= - "Got rmse 0.018633101135492325\n", - "\n", - "Epoch 277, Iteration 2780, loss = 0.1797\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([2, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.020942404866218567\n", -======= - "Got rmse 0.01793692074716091\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([18, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.02076108381152153\n", - "\n", - "Epoch 278, Iteration 2790, loss = 0.0741\n", -======= - "Got rmse 0.018666820600628853\n", - "\n", - "Epoch 278, Iteration 2790, loss = 0.1631\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([2, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.020892225205898285\n", -======= - "Got rmse 0.017897602170705795\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([18, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.02066633477807045\n", - "\n", - "Epoch 279, Iteration 2800, loss = 0.0596\n", -======= - "Got rmse 0.018641654402017593\n", - "\n", - "Epoch 279, Iteration 2800, loss = 0.1987\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([2, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.020931703969836235\n", -======= - "Got rmse 0.01788647659122944\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([18, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.020779630169272423\n", - "\n", - "Epoch 280, Iteration 2810, loss = 0.0629\n", -======= - "Got rmse 0.018637819215655327\n", - "\n", - "Epoch 280, Iteration 2810, loss = 0.1699\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([2, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.02074124664068222\n", -======= - "Got rmse 0.017903830856084824\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([18, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.020525868982076645\n", - "\n", - "Epoch 281, Iteration 2820, loss = 0.0561\n", -======= - "Got rmse 0.018635496497154236\n", - "\n", - "Epoch 281, Iteration 2820, loss = 0.1756\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([2, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.020677143707871437\n", -======= - "Got rmse 0.01792171783745289\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([18, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.020465832203626633\n", - "\n", - "Epoch 282, Iteration 2830, loss = 0.0816\n", -======= - "Got rmse 0.01865874044597149\n", - "\n", - "Epoch 282, Iteration 2830, loss = 0.2116\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([2, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.021058175712823868\n", -======= - "Got rmse 0.01793527789413929\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([18, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.020884599536657333\n", - "\n", - "Epoch 283, Iteration 2840, loss = 0.1086\n", -======= - "Got rmse 0.018652193248271942\n", - "\n", - "Epoch 283, Iteration 2840, loss = 0.2016\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([2, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.021002784371376038\n", -======= - "Got rmse 0.01791108213365078\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([18, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.02080482989549637\n", - "\n", - "Epoch 284, Iteration 2850, loss = 0.0677\n", -======= - "Got rmse 0.018652044236660004\n", - "\n", - "Epoch 284, Iteration 2850, loss = 0.1598\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([2, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.0208596121519804\n", -======= - "Got rmse 0.017925675958395004\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([18, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.020644081756472588\n", - "\n", - "Epoch 285, Iteration 2860, loss = 0.0844\n", -======= - "Got rmse 0.018646201118826866\n", - "\n", - "Epoch 285, Iteration 2860, loss = 0.1750\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([2, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.020798711106181145\n", -======= - "Got rmse 0.017910541966557503\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([18, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.02059725858271122\n", - "\n", - "Epoch 286, Iteration 2870, loss = 0.0597\n", -======= - "Got rmse 0.01864559017121792\n", - "\n", - "Epoch 286, Iteration 2870, loss = 0.1849\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([2, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.02098514512181282\n", -======= - "Got rmse 0.017899256199598312\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([18, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.02078663371503353\n", - "\n", - "Epoch 287, Iteration 2880, loss = 0.0864\n", -======= - "Got rmse 0.018641775473952293\n", - "\n", - "Epoch 287, Iteration 2880, loss = 0.1624\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([2, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.02124418504536152\n", -======= - "Got rmse 0.017924409359693527\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([18, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.021045437082648277\n", - "\n", - "Epoch 288, Iteration 2890, loss = 0.0590\n", -======= - "Got rmse 0.01865164376795292\n", - "\n", - "Epoch 288, Iteration 2890, loss = 0.2019\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([2, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.020914724096655846\n", -======= - "Got rmse 0.017922746017575264\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([18, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.02072186954319477\n", - "\n", - "Epoch 289, Iteration 2900, loss = 0.0770\n", -======= - "Got rmse 0.01863585226237774\n", - "\n", - "Epoch 289, Iteration 2900, loss = 0.1681\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([2, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.02057029865682125\n", -======= - "Got rmse 0.017917891964316368\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([18, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.020368004217743874\n", - "\n", - "Epoch 290, Iteration 2910, loss = 0.0546\n", -======= - "Got rmse 0.01863810606300831\n", - "\n", - "Epoch 290, Iteration 2910, loss = 0.1847\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([2, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.020997555926442146\n", -======= - "Got rmse 0.017937950789928436\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([18, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.020824309438467026\n", - "\n", - "Epoch 291, Iteration 2920, loss = 0.0893\n", -======= - "Got rmse 0.01864773780107498\n", - "\n", - "Epoch 291, Iteration 2920, loss = 0.1790\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([2, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.020948108285665512\n", -======= - "Got rmse 0.01793268509209156\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([18, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.020715508610010147\n", - "\n", - "Epoch 292, Iteration 2930, loss = 0.0651\n", -======= - "Got rmse 0.018662890419363976\n", - "\n", - "Epoch 292, Iteration 2930, loss = 0.1837\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([2, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.020873527973890305\n", -======= - "Got rmse 0.017893997952342033\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([18, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.020642083138227463\n", - "\n", - "Epoch 293, Iteration 2940, loss = 0.1003\n", -======= - "Got rmse 0.018636085093021393\n", - "\n", - "Epoch 293, Iteration 2940, loss = 0.1943\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([2, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.0207979679107666\n", -======= - "Got rmse 0.01790769025683403\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([18, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.020581506192684174\n", - "\n", - "Epoch 294, Iteration 2950, loss = 0.0906\n", -======= - "Got rmse 0.018647121265530586\n", - "\n", - "Epoch 294, Iteration 2950, loss = 0.1569\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([2, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.020561346784234047\n", -======= - "Got rmse 0.017910409718751907\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([18, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.020392630249261856\n", - "\n", - "Epoch 295, Iteration 2960, loss = 0.0477\n", -======= - "Got rmse 0.018643535673618317\n", - "\n", - "Epoch 295, Iteration 2960, loss = 0.1758\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([2, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.02086225338280201\n", -======= - "Got rmse 0.017919454723596573\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([18, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.02065644972026348\n", - "\n", - "Epoch 296, Iteration 2970, loss = 0.0835\n", -======= - "Got rmse 0.0186562892049551\n", - "\n", - "Epoch 296, Iteration 2970, loss = 0.2063\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([2, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.020962592214345932\n", -======= - "Got rmse 0.017911924049258232\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([18, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.0207548588514328\n", - "\n", - "Epoch 297, Iteration 2980, loss = 0.0670\n", -======= - "Got rmse 0.018643252551555634\n", - "\n", - "Epoch 297, Iteration 2980, loss = 0.1840\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([2, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.021161973476409912\n", -======= - "Got rmse 0.017904464155435562\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([18, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.021027324721217155\n", - "\n", - "Epoch 298, Iteration 2990, loss = 0.0753\n", -======= - "Got rmse 0.018641799688339233\n", - "\n", - "Epoch 298, Iteration 2990, loss = 0.1690\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([2, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.02053670771420002\n", -======= - "Got rmse 0.01791589893400669\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([18, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.020333625376224518\n", - "\n", - "Epoch 299, Iteration 3000, loss = 0.1104\n", -======= - "Got rmse 0.0186419989913702\n", - "\n", - "Epoch 299, Iteration 3000, loss = 0.1852\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([2, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.02078142948448658\n", -======= - "Got rmse 0.01789853908121586\n", ->>>>>>> main - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([128, 3, 16, 16, 16])\n", - "maxB SHAPE torch.Size([3, 16, 16, 16])\n", - "y SHAPE torch.Size([18, 3, 16, 16, 16])\n", -<<<<<<< HEAD - "Got rmse 0.020630627870559692\n", - "\n", - "training stop at epoch: 299\n", - "training stop at epoch: tensor(0.9899, device='cuda:0')\n" -======= - "Got rmse 0.018638979643583298\n", - "\n", - "training stop at epoch: 299\n", - "training stop at epoch: tensor(0.9921, device='cuda:0')\n" ->>>>>>> main - ] - } - ], - "source": [ - "from Neural_network import Generative_net, ResidualEMNSBlock_3d, BigBlock, weight_init, eMNS_Dataset\n", - "from Training_loop import train_part_GM,get_mean_of_dataloader\n", - "\n", - "batch_size = 128\n", - "# construct dataset\n", - "dataset = eMNS_Dataset(\n", - " train_x=current_norm,\n", - " train_y=Bfield_norm\n", - ")\n", - "###############################################\n", - "# Config the neural network\n", - "###############################################\n", - "num_input = 12\n", - "output_shape = (3,16,16,16)\n", - "SB_args = (64,64,4) # (Cin, Cout, num_block)\n", - "BB_args = (2,2) # (scale_factor, num_block)\n", - "SB_block = ResidualEMNSBlock_3d \n", - "BB_block = BigBlock\n", - "DF = False # whether using divergence free model\n", - "\n", - "Generative_network = Generative_net(SB_args, BB_args, SB_block, BB_block, num_input=num_input, output_shape= output_shape)\n", - "epochs = 300\n", - "learning_rate_decay = 0.5\n", - "lr_max = 1e-3\n", - "lr_min = 1e-6\n", - "learning_rates = [lr_max]\n", - "# learning_rates = np.arange(1e-8,1e-5,1e-6)\n", -<<<<<<< HEAD - "schedule = [25 50 75 100 125 150 175 200 225 250 275]\n", -======= - "schedule = [25, 50, 75, 100, 125, 150, 175, 200 ,225, 250, 275]\n", ->>>>>>> main - "weight_decays = [0]\n", - "\n", - "train_percents = np.arange(1.0,1.01,0.1)\n", - "RMSE_history_end = np.zeros(len(learning_rates))\n", - "RMSE_val_history_end = np.zeros(len(learning_rates))\n", - "loss_history_end = np.zeros(len(learning_rates))\n", - "iter_history_end = np.zeros(len(learning_rates))\n", - "mse_history_end = np.zeros(len(learning_rates))\n", - "mse_val_history_end = np.zeros(len(learning_rates))\n", - "train_stop_epoch = np.zeros(len(learning_rates))\n", - "\n", - "################################################\n", - "# Train the neural network\n", - "################################################\n", - "index=0\n", - "for train_percent in train_percents:\n", - " print(train_percent)\n", - " epoch_stop = 0\n", - " \n", - " for learning_rate in learning_rates:\n", - " print('learning_rate',learning_rate)\n", - " for weight_decay in weight_decays:\n", - "\n", - " # split the dataset to train, validation, test\n", - " train_set, valid_set = torch.utils.data.random_split(dataset, [0.9,0.1])\n", - "\n", - " #Using Dataloader for batch train\n", - " train_loader = torch.utils.data.DataLoader(dataset=train_set,batch_size=batch_size,shuffle=True)\n", - " valid_loader = torch.utils.data.DataLoader(dataset=valid_set,batch_size=batch_size,shuffle=True)\n", - "\n", - " get_mean_of_dataloader(valid_loader,model=Generative_network,device=device)\n", - " # test_loader = torch.utils.data.DataLoader(dataset=test_set,batch_size=batch_size,shuffle=True)\n", - " Generative_network.apply(weight_init)\n", - " optimizer = torch.optim.Adam([{'params':Generative_network.parameters()}], lr=learning_rate, weight_decay= weight_decay, betas=(0.5,0.99))\n", - " RMSE_history, RMSE_val_history, loss_history, iter_history, mse_history, mse_val_history,epoch_stop,Rsquare= train_part_GM(\n", - " model=Generative_network, optimizer=optimizer, train_loader=train_loader, valid_loader=valid_loader, epochs=epochs, \n", - " learning_rate_decay=learning_rate_decay, schedule=schedule, weight_decay=weight_decay, DF=DF,verbose=False, device=device, maxB=MaxB[0,:], minB=MinB[0,:],\n", - " lr_max=lr_max, lr_min=lr_min,max_epoch=epochs)\n", - " \n", - " #save RMSE and loss after early stopping\n", - " RMSE_history_end[index] = RMSE_history[epoch_stop]\n", - " RMSE_val_history_end[index]= RMSE_val_history[epoch_stop]\n", - " loss_history_end[index] = loss_history[epoch_stop]\n", - " iter_history_end[index] = iter_history[epoch_stop]\n", - " mse_history_end[index] = mse_history[epoch_stop]\n", - " mse_val_history_end[index] = mse_val_history[epoch_stop]\n", - " index=index+1\n", - " print('training stop at epoch:',epoch_stop)\n", - " print('training stop at epoch:',Rsquare)" - ] - }, - { - "cell_type": "code", -<<<<<<< HEAD - "execution_count": 15, -======= - "execution_count": 24, ->>>>>>> main - "metadata": {}, - "outputs": [], - "source": [ - "torch.save(Generative_network, 'EMS_CNN.pt')\t# 这里会存储迄今最优模型的参数" - ] - }, - { - "cell_type": "code", -<<<<<<< HEAD - "execution_count": 16, -======= - "execution_count": 25, ->>>>>>> main - "metadata": {}, - "outputs": [ - { - "data": { -<<<<<<< HEAD - "image/png": 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", -======= - "image/png": 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", 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", -======= - "image/png": 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", ->>>>>>> main - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "import matplotlib.pyplot as plt\n", - "import numpy as np\n", - "ave_site = 5\n", - "ave_kernel = 1/ave_site*np.ones(ave_site)\n", - "loss_history_conv = np.convolve(loss_history.numpy(),ave_kernel,'same')\n", - "\n", - "\n", - "plt.title('loss')\n", - "plt.plot(iter_history,loss_history,'-o')\n", - "plt.plot(iter_history,loss_history_conv,'-*')\n", - "plt.legend(['loss','loss_conv'])\n", - "plt.xlabel('iterations')\n", - "plt.ylabel('loss')\n", - "plt.show()\n", - "\n", - "plt.title('Train and Val RMSE(sample_num=1000)')\n", - "plt.plot(iter_history[0:epoch_stop],RMSE_history[0:epoch_stop]*1000,'-o')\n", - "plt.plot(iter_history[0:epoch_stop],RMSE_val_history[0:epoch_stop]*1000,'-*')\n", - "plt.legend(['train CNN','val CNN'])\n", - "plt.xlabel('iterations')\n", - "plt.ylabel('RMSE(mT)')\n", - "plt.grid()\n", - "plt.show()\n", - "\n", - "plt.title('Train and Val loss(sample_num=1000)')\n", - "plt.plot(iter_history[0:epoch_stop],mse_history[0:epoch_stop]*1e6,'-o')\n", - "plt.plot(iter_history[0:epoch_stop],mse_val_history[0:epoch_stop]*1e6,'-*')\n", - "plt.legend(['train CNN','val CNN'])\n", - "plt.xlabel('iterations')\n", - "plt.ylabel('mse(mT^2)')\n", - "plt.grid()\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plt.plot(learning_rates,RMSE_history_end*1000,'-o')\n", - "plt.xlabel('learning_rates')\n", - "plt.ylabel('RMSE(mT)')\n", - "# plt.ylim([0,25])\n", - "plt.grid()\n", - "plt.legend(['CNN'])\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "299\n" - ] - } - ], - "source": [ - "print(epoch_stop)" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "torch.Size([1, 3, 16, 16, 16])\n" - ] - } - ], - "source": [ - "print(position[0:1,:].shape)" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "3\n", - "tensor([[[ 7.4531e-01, -2.8595e-01, -1.6134e-01, 9.9453e-01],\n", - " [-3.9411e-01, 7.2109e-02, 6.4595e-01, 7.5357e-01],\n", - " [-8.7148e-01, -2.8280e-01, 6.4141e-01, 9.7694e-01],\n", - " [-4.9431e-01, -1.1629e-01, -4.4661e-02, -3.5106e-01]],\n", - "\n", - " [[-1.4154e-02, 2.8972e-01, -3.6363e-01, -1.3209e+00],\n", - " [ 5.5361e-01, -1.9701e-01, -1.4015e+00, -1.8554e+00],\n", - " [ 8.2961e-01, 4.6889e-02, 3.8124e-01, 1.4983e+00],\n", - " [-2.0719e+00, -7.9564e-02, 8.4467e-01, -2.2339e-01]],\n", - "\n", - " [[-8.2462e-01, -6.9545e-01, -2.1640e-01, 1.3348e-01],\n", - " [-1.3291e+00, -5.5713e-01, -8.0198e-01, -1.8188e+00],\n", - " [-4.0643e-01, -4.9686e-01, -2.9385e-01, -4.1198e-04],\n", - " [ 1.6279e+00, 1.2113e+00, 5.3727e-01, 2.7987e-01]]])\n", - "(tensor([[ 0.5828, 0.0783, 0.8595, -1.0928],\n", - " [ 0.2756, 0.4847, 0.4742, 0.4073],\n", - " [-1.0861, 0.3923, 0.6823, 1.7316],\n", - " [-2.1407, -0.1064, 1.2755, 1.5558]]), tensor([[-8.2462e-01, -6.9545e-01, -2.1640e-01, 1.3348e-01],\n", - " [-1.3291e+00, -5.5713e-01, -8.0198e-01, -1.8188e+00],\n", - " [-4.0643e-01, -4.9686e-01, -2.9385e-01, -4.1198e-04],\n", - " [ 1.6279e+00, 1.2113e+00, 5.3727e-01, 2.7987e-01]]))\n", - "tensor(-4.7684e-07)\n", - "torch.Size([4, 4])\n" - ] - } - ], - "source": [ - "import torch \n", - "# a = torch.range(1,48).reshape(3,4,4)\n", - "a = torch.randn(3,4,4)\n", - "b = a+1\n", - "grad_a = torch.gradient(a)\n", - "grad_a_x = torch.gradient(a[0])\n", - "grad_a_y = torch.gradient(a[1])\n", - "grad_a_z = torch.gradient(a[2])\n", - "print(len(a))\n", - "print(grad_a[2])\n", - "print(grad_a_z)\n", - "error = torch.sum(grad_a[1] + grad_a[2]) - torch.sum(sum(grad_a_x)+sum(grad_a_y) +sum(grad_a_z))\n", - "print(error)\n", - "print(a[1].shape)" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n" - ] - } - ], - "source": [ - "print(Generative_net)" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [ - { - "ename": "ModuleNotFoundError", - "evalue": "No module named 'torchviz'", - "output_type": "error", - "traceback": [ - "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[1;31mModuleNotFoundError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[1;32mIn[14], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mtorchviz\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m make_dot\n\u001b[0;32m 2\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mtorch\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mnn\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mfunctional\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mF\u001b[39;00m\n\u001b[0;32m 3\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mTraining_loop\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m grad_loss\n", - "\u001b[1;31mModuleNotFoundError\u001b[0m: No module named 'torchviz'" - ] - } - ], - "source": [ - "from torchviz import make_dot\n", - "import torch.nn.functional as F\n", - "from Training_loop import grad_loss\n", - "x = torch.randn(2,12)\n", - "y = Bfield[0:2]\n", - "preds = Generative_network(x)\n", - "print(preds.shape)\n", - "loss = grad_loss(preds,y)\n", - " # optimizer.zero_grad() #zero out all of gradient\n", - "loss.backward()\n", - "\n", - "make_dot(loss, params=dict(Generative_network.named_parameters()))" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.12.1" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} From 3b6b4177bbb6eafa18dddcb0b220332771af4d7b Mon Sep 17 00:00:00 2001 From: wangjunang Date: Fri, 15 Mar 2024 14:47:30 +0800 Subject: [PATCH 02/16] update --- Modeling eMNS/Generative_model_ETH_v0.ipynb | 76 +++++-- Modeling eMNS/Generative_model_v0.ipynb | 233 +------------------- Modeling eMNS/Training_loop.py | 2 +- 3 files changed, 61 insertions(+), 250 deletions(-) diff --git a/Modeling eMNS/Generative_model_ETH_v0.ipynb b/Modeling eMNS/Generative_model_ETH_v0.ipynb index de8ee9c..bb7ae79 100644 --- a/Modeling eMNS/Generative_model_ETH_v0.ipynb +++ b/Modeling eMNS/Generative_model_ETH_v0.ipynb @@ -34,7 +34,7 @@ "from ReadData import ReadETHFolder, ReadETHFile\n", "foldername=\"./ETH_Data/v/\"\n", "currentname = \"./ETH_Data/\"+\"currents_3787.h5\"\n", - "file_num = 3407\n", + "file_num = 1000\n", "data_shape = (16,16,16,3)\n", "Bfield = torch.tensor(ReadETHFolder(foldername,file_num, data_shape)).permute(0,4,1,2,3)\n", "current = torch.tensor(ReadETHFile(currentname))\n", @@ -86,6 +86,10 @@ "print(min_Bfield.shape)\n", "print(max_Bfield.shape)\n", "\n", + "print(minB.shape)\n", + "print(maxB.shape)\n", + "current_norm_max, index = torch.max(Bfield_norm.transpose(0,1).reshape(3,-1), dim=1, keepdim=True)\n", + "print(current_norm_max)\n", "# torch.save(min_current, \"./normalize_data/cnn_min_current_ETH.pt\")\n", "# torch.save(max_current, \"./normalize_data/cnn_max_current_ETH.pt\")\n", "# torch.save(min_Bfield, \"./normalize_data/cnn_min_Bfield_ETH.pt\")\n", @@ -110,23 +114,33 @@ "metadata": {}, "outputs": [], "source": [ - "from Neural_network import Generative_net, Generative_net_test, ResidualEMNSBlock_3d, BigBlock, weight_init, eMNS_Dataset\n", - "from Training_loop import train_part_GM,get_mean_of_dataloader\n", - "from tqdm import tqdm\n", - "\n", + "from Neural_network import Generative_net,Generative_net_test ,ResidualEMNSBlock_3d, BigBlock, weight_init, eMNS_Dataset\n", "###############################################\n", "# Config the neural network\n", "###############################################\n", "num_input = 8\n", "output_shape = (3,16,16,16)\n", - "SB_args = (64,64,4,1) # (Cin, Cout, num_repeat, num_block)\n", + "SB_args = (64,64,1,4) # (Cin, Cout, num_repeat, num_block)\n", "BB_args = (2,3) # (scale_factor, num_block)\n", "SB_block = ResidualEMNSBlock_3d \n", "BB_block = BigBlock\n", "DF = False # whether using divergence free model\n", "\n", - "Generative_network = Generative_net(SB_args, BB_args, SB_block, BB_block, num_input=num_input, output_shape= output_shape)\n", - "print(Generative_network)" + "Generative_network = Generative_net_test(SB_args, BB_args, SB_block, BB_block, num_input=num_input, output_shape= output_shape)\n", + "print(Generative_network)\n", + "\n", + "from torchviz import make_dot\n", + "import torch.nn.functional as F\n", + "from Training_loop import grad_loss_Jacobain\n", + "x = torch.randn(2,8)\n", + "y = Bfield[0:2]\n", + "preds = Generative_network(x)\n", + "print(preds.shape)\n", + "loss = F.l1_loss(preds,y)+grad_loss_Jacobain(preds,y)\n", + " # optimizer.zero_grad() #zero out all of gradient\n", + "loss.backward()\n", + "\n", + "make_dot(loss, params=dict(Generative_network.named_parameters()))\n" ] }, { @@ -135,11 +149,11 @@ "metadata": {}, "outputs": [], "source": [ - "from Neural_network import Generative_net, Generative_net_test, ResidualEMNSBlock_3d, BigBlock, weight_init, eMNS_Dataset\n", + "from Neural_network import Generative_net_test, ResidualEMNSBlock_3d, BigBlock, weight_init, eMNS_Dataset\n", "from Training_loop import train_part_GM,get_mean_of_dataloader\n", "from tqdm import tqdm\n", "\n", - "batch_size = 16\n", + "batch_size = 8\n", "# construct dataset\n", "dataset = eMNS_Dataset(\n", " train_x=current_norm,\n", @@ -150,16 +164,16 @@ "###############################################\n", "num_input = 8\n", "output_shape = (3,16,16,16)\n", - "SB_args = (64,64,4,1) # (Cin, Cout, num_repeat, num_block)\n", - "BB_args = (2,2) # (scale_factor, num_block)\n", + "SB_args = (64,64,1,4) # (Cin, Cout, num_repeat, num_block)\n", + "BB_args = (2,3) # (scale_factor, num_block)\n", "SB_block = ResidualEMNSBlock_3d \n", "BB_block = BigBlock\n", "DF = False # whether using divergence free model\n", "\n", - "Generative_network = Generative_net(SB_args, BB_args, SB_block, BB_block, num_input=num_input, output_shape= output_shape)\n", + "Generative_network = Generative_net_test(SB_args, BB_args, SB_block, BB_block, num_input=num_input, output_shape= output_shape)\n", "epochs = 500\n", "learning_rate_decay = .5\n", - "learning_rates = [1e-4]\n", + "learning_rates = [1e-3]\n", "RMSE_lr = []\n", "schedule = []\n", "linear_lr = False\n", @@ -198,11 +212,11 @@ " # test_loader = torch.utils.data.DataLoader(dataset=test_set,batch_size=batch_size,shuffle=True)\n", "\n", " Generative_network.apply(weight_init)\n", - " optimizer = torch.optim.Adam([{'params':Generative_network.parameters()}], lr=learning_rate, weight_decay= weight_decay, betas=(0.9,0.99))\n", + " optimizer = torch.optim.Adam([{'params':Generative_network.parameters()}], lr=learning_rate, weight_decay= weight_decay, betas=(0.5,0.99))\n", " RMSE_history, RMSE_val_history, loss_history, iter_history, mse_history, mse_val_history,epoch_stop,Rsquare= train_part_GM(\n", " model=Generative_network, optimizer=optimizer, train_loader=train_loader, valid_loader=valid_loader, epochs=epochs, \n", " learning_rate_decay=learning_rate_decay, schedule=schedule, weight_decay=weight_decay, DF=DF,verbose=False, device=device, maxB=MaxB[0,:], minB=MinB[0,:],\n", - " lr_max=learning_rate, lr_min=2.5e-7,max_epoch=epochs, linear_lr=linear_lr, grid_space=dimB[2]*dimB[3]*dimB[4])\n", + " lr_max=learning_rate, lr_min=0.0000025,max_epoch=epochs, linear_lr=linear_lr)\n", " \n", " RMSE_lr.append(RMSE_val_history[epoch_stop].item())\n", " \n", @@ -215,11 +229,7 @@ " mse_val_history_end[index] = mse_val_history[epoch_stop]\n", " index=index+1\n", " print('training stop at epoch:',epoch_stop)\n", - " print('training stop at epoch:',Rsquare)\n", - "torch.save(Generative_network, 'EMS_CNN.pt')\t# 这里会存储迄今最优模型的参数\n", - "print(RMSE_lr)\n", - "print(learning_rates)\n", - "print(RMSE_lr[0],learning_rates[0])\n" + " print('training stop at epoch:',Rsquare)\n" ] }, { @@ -228,6 +238,21 @@ "metadata": {}, "outputs": [], "source": [ + "torch.save(Generative_network, 'EMS_CNN_ETH.pt')\t# 这里会存储迄今最优模型的参数" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "\n", + "print(RMSE_lr)\n", + "print(learning_rates)\n", + "print(RMSE_lr[0],learning_rates[0])\n", + "import matplotlib.pyplot as plt \n", + "plt.plot(learning_rates,RMSE_lr)\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", "ave_site = 5\n", @@ -241,6 +266,7 @@ "plt.legend(['loss','loss_conv'])\n", "plt.xlabel('iterations')\n", "plt.ylabel('loss')\n", + "plt.ylim([0,1])\n", "plt.show()\n", "\n", "plt.title('Train and Val RMSE(sample_num=1000)')\n", @@ -248,10 +274,11 @@ "plt.plot(iter_history[0:epoch_stop],RMSE_val_history[0:epoch_stop],'-*')\n", "# plt.plot(2e-5*np.arange(epoch_stop),RMSE_history[0:epoch_stop]*1000,'-o')\n", "# plt.plot(2e-5*np.arange(epoch_stop),RMSE_val_history[0:epoch_stop]*1000,'-*')\n", - "plt.ylim([3,80])\n", + "# plt.ylim([15,20])\n", "plt.legend(['train CNN','val CNN'])\n", "plt.xlabel('iterations')\n", "plt.ylabel('RMSE(mT)')\n", + "plt.ylim([0,100])\n", "plt.grid()\n", "plt.show()\n", "\n", @@ -263,7 +290,8 @@ "plt.ylabel('mse(mT^2)')\n", "plt.grid()\n", "plt.show()\n", - "print(epoch_stop)\n" + "print(epoch_stop)\n", + "\n" ] } ], @@ -283,7 +311,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.7" + "version": "3.10.13" } }, "nbformat": 4, diff --git a/Modeling eMNS/Generative_model_v0.ipynb b/Modeling eMNS/Generative_model_v0.ipynb index 97df892..635581d 100644 --- a/Modeling eMNS/Generative_model_v0.ipynb +++ b/Modeling eMNS/Generative_model_v0.ipynb @@ -9,25 +9,9 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Using cpu\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/qubot/.pyenv/versions/3.10.13/lib/python3.10/site-packages/torch/cuda/__init__.py:628: UserWarning: Can't initialize NVML\n", - " warnings.warn(\"Can't initialize NVML\")\n" - ] - } - ], + "outputs": [], "source": [ "%reload_ext autoreload\n", "%autoreload 2\n", @@ -43,19 +27,9 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "torch.Size([1000, 6, 21, 21, 21])\n", - "current shape torch.Size([1000, 12])\n", - "Bfield shape torch.Size([1000, 3, 20, 20, 20])\n" - ] - } - ], + "outputs": [], "source": [ "from ReadData import ReadCurrentAndField_CNN\n", "import glob\n", @@ -80,108 +54,9 @@ }, { "cell_type": "code", - "execution_count": 47, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Generative_net(\n", - " (proj): Linear(in_features=12, out_features=8000, bias=True)\n", - " (conv3d): Conv3d(64, 3, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n", - " (total_net): Sequential(\n", - " (0): Linear(in_features=12, out_features=8000, bias=True)\n", - " (1): Unflatten(dim=0, unflattened_size=(64, 5, 5, 5))\n", - " (2): BigBlock(\n", - " (block): Sequential(\n", - " (0): ResidualEMNSBlock_3d(\n", - " (block): Sequential(\n", - " (0): BatchNorm3d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", - " (1): Conv3d(64, 64, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n", - " (2): LeakyReLU(negative_slope=0.01)\n", - " )\n", - " (shortcut): Identity()\n", - " )\n", - " (1): ResidualEMNSBlock_3d(\n", - " (block): Sequential(\n", - " (0): BatchNorm3d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", - " (1): Conv3d(64, 64, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n", - " (2): LeakyReLU(negative_slope=0.01)\n", - " )\n", - " (shortcut): Identity()\n", - " )\n", - " (2): ResidualEMNSBlock_3d(\n", - " (block): Sequential(\n", - " (0): BatchNorm3d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", - " (1): Conv3d(64, 64, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n", - " (2): LeakyReLU(negative_slope=0.01)\n", - " )\n", - " (shortcut): Identity()\n", - " )\n", - " (3): ResidualEMNSBlock_3d(\n", - " (block): Sequential(\n", - " (0): BatchNorm3d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", - " (1): Conv3d(64, 64, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n", - " (2): LeakyReLU(negative_slope=0.01)\n", - " )\n", - " (shortcut): Identity()\n", - " )\n", - " (4): UpsampleBlock(\n", - " (block): Sequential(\n", - " (0): Upsample(scale_factor=2.0, mode='nearest')\n", - " )\n", - " )\n", - " )\n", - " )\n", - " (3): BigBlock(\n", - " (block): Sequential(\n", - " (0): ResidualEMNSBlock_3d(\n", - " (block): Sequential(\n", - " (0): BatchNorm3d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", - " (1): Conv3d(64, 64, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n", - " (2): LeakyReLU(negative_slope=0.01)\n", - " )\n", - " (shortcut): Identity()\n", - " )\n", - " (1): ResidualEMNSBlock_3d(\n", - " (block): Sequential(\n", - " (0): BatchNorm3d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", - " (1): Conv3d(64, 64, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n", - " (2): LeakyReLU(negative_slope=0.01)\n", - " )\n", - " (shortcut): Identity()\n", - " )\n", - " (2): ResidualEMNSBlock_3d(\n", - " (block): Sequential(\n", - " (0): BatchNorm3d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", - " (1): Conv3d(64, 64, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n", - " (2): LeakyReLU(negative_slope=0.01)\n", - " )\n", - " (shortcut): Identity()\n", - " )\n", - " (3): ResidualEMNSBlock_3d(\n", - " (block): Sequential(\n", - " (0): BatchNorm3d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", - " (1): Conv3d(64, 64, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n", - " (2): LeakyReLU(negative_slope=0.01)\n", - " )\n", - " (shortcut): Identity()\n", - " )\n", - " (4): UpsampleBlock(\n", - " (block): Sequential(\n", - " (0): Upsample(scale_factor=2.0, mode='nearest')\n", - " )\n", - " )\n", - " )\n", - " )\n", - " (4): BatchNorm3d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", - " (5): Conv3d(64, 3, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n", - " )\n", - ")\n" - ] - } - ], + "outputs": [], "source": [ "from Neural_network import Generative_net, ResidualEMNSBlock_3d, BigBlock, weight_init, eMNS_Dataset\n", "num_input = 12\n", @@ -197,101 +72,9 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "train_percent 0.9\n", - "Epoch 0, Iteration 7, loss = 0.3510\n", - "Got RMSE 0.32754695415496826\n", - "Got RMSE 0.32418933510780334\n", - "\n", - "Validation loss decreased (inf --> 0.350983). Saving model ...\n", - "Epoch 1, Iteration 14, loss = 0.2121\n", - "Got RMSE 0.172689288854599\n", - "Got RMSE 0.17061683535575867\n", - "\n", - "Validation loss decreased (0.350983 --> 0.212098). Saving model ...\n", - "Epoch 2, Iteration 21, loss = 0.1391\n", - "Got RMSE 0.11217285692691803\n", - "Got RMSE 0.11062062531709671\n", - "\n", - "Validation loss decreased (0.212098 --> 0.139108). Saving model ...\n", - "Epoch 3, Iteration 28, loss = 0.1029\n", - "Got RMSE 0.08286809921264648\n", - "Got RMSE 0.08175507187843323\n", - "\n", - "Validation loss decreased (0.139108 --> 0.102948). Saving model ...\n", - "Epoch 4, Iteration 35, loss = 0.0791\n", - "Got RMSE 0.06362708657979965\n", - "Got RMSE 0.06284409761428833\n", - "\n", - "Validation loss decreased (0.102948 --> 0.079143). Saving model ...\n", - "Epoch 5, Iteration 42, loss = 0.0631\n", - "Got RMSE 0.05140509828925133\n", - "Got RMSE 0.050583772361278534\n", - "\n", - "Validation loss decreased (0.079143 --> 0.063142). Saving model ...\n", - "Epoch 6, Iteration 49, loss = 0.0528\n", - "Got RMSE 0.04265155643224716\n", - "Got RMSE 0.04214133322238922\n", - "\n", - "Validation loss decreased (0.063142 --> 0.052829). Saving model ...\n", - "Epoch 7, Iteration 56, loss = 0.0461\n", - "Got RMSE 0.037536486983299255\n", - "Got RMSE 0.03704578056931496\n", - "\n", - "Validation loss decreased (0.052829 --> 0.046067). Saving model ...\n", - "Epoch 8, Iteration 63, loss = 0.0451\n", - "Got RMSE 0.03338546305894852\n", - "Got RMSE 0.03275797888636589\n", - "\n", - "Validation loss decreased (0.046067 --> 0.045055). Saving model ...\n", - "Epoch 9, Iteration 70, loss = 0.0400\n", - "Got RMSE 0.03064017929136753\n", - "Got RMSE 0.029906103387475014\n", - "\n", - "Validation loss decreased (0.045055 --> 0.039980). Saving model ...\n", - "Epoch 10, Iteration 77, loss = 0.0368\n", - "Got RMSE 0.02855002135038376\n", - "Got RMSE 0.028041211888194084\n", - "\n", - "Validation loss decreased (0.039980 --> 0.036841). Saving model ...\n", - "Epoch 11, Iteration 84, loss = 0.0329\n", - "Got RMSE 0.026944860816001892\n", - "Got RMSE 0.026317913085222244\n", - "\n", - "Validation loss decreased (0.036841 --> 0.032892). Saving model ...\n", - "Epoch 12, Iteration 91, loss = 0.0331\n", - "Got RMSE 0.024812057614326477\n", - "Got RMSE 0.024366017431020737\n", - "\n", - "EarlyStopping counter: 1 out of 5\n", - "Epoch 13, Iteration 98, loss = 0.0290\n", - "Got RMSE 0.024349229410290718\n", - "Got RMSE 0.02376655861735344\n", - "\n", - "Validation loss decreased (0.032892 --> 0.029036). Saving model ...\n" - ] - }, - { - "ename": "KeyboardInterrupt", - "evalue": "", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[9], line 58\u001b[0m\n\u001b[1;32m 56\u001b[0m Generative_network\u001b[38;5;241m.\u001b[39mapply(weight_init)\n\u001b[1;32m 57\u001b[0m optimizer \u001b[38;5;241m=\u001b[39m torch\u001b[38;5;241m.\u001b[39moptim\u001b[38;5;241m.\u001b[39mAdam([{\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mparams\u001b[39m\u001b[38;5;124m'\u001b[39m:Generative_network\u001b[38;5;241m.\u001b[39mparameters()}], lr\u001b[38;5;241m=\u001b[39mlearning_rate, weight_decay\u001b[38;5;241m=\u001b[39m weight_decay)\n\u001b[0;32m---> 58\u001b[0m RMSE_history, RMSE_val_history, loss_history, iter_history, loss_val_history,epoch_stop \u001b[38;5;241m=\u001b[39m \u001b[43mtrain_part_GM\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmodel\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mGenerative_network\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43moptimizer\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43moptimizer\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtrain_loader\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtrain_loader\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mvalid_loader\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mvalid_loader\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mepochs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mepochs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mlearning_rate_decay\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mlearning_rate_decay\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mschedule\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mschedule\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mweight_decay\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mweight_decay\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mverbose\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdevice\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdevice\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 60\u001b[0m \u001b[38;5;66;03m#save RMSE and loss after early stopping\u001b[39;00m\n\u001b[1;32m 61\u001b[0m RMSE_history_end[index] \u001b[38;5;241m=\u001b[39m RMSE_history[epoch_stop]\n", - "File \u001b[0;32m/home/rslsync/Qubot/Codes/Qubot_Elastica/Qubot_Elastica/Modeling eMNS/Training_loop.py:286\u001b[0m, in \u001b[0;36mtrain_part_GM\u001b[0;34m(model, optimizer, train_loader, valid_loader, epochs, learning_rate_decay, weight_decay, schedule, verbose, device)\u001b[0m\n\u001b[1;32m 284\u001b[0m loss \u001b[38;5;241m=\u001b[39m F\u001b[38;5;241m.\u001b[39ml1_loss(preds, y) \u001b[38;5;241m+\u001b[39m grad_loss(preds,y)\n\u001b[1;32m 285\u001b[0m optimizer\u001b[38;5;241m.\u001b[39mzero_grad() \u001b[38;5;66;03m#zero out all of gradient\u001b[39;00m\n\u001b[0;32m--> 286\u001b[0m \u001b[43mloss\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mbackward\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m \u001b[38;5;66;03m# compute gradient of loss\u001b[39;00m\n\u001b[1;32m 287\u001b[0m optimizer\u001b[38;5;241m.\u001b[39mstep() \u001b[38;5;66;03m#update parameters\u001b[39;00m\n\u001b[1;32m 289\u001b[0m tt \u001b[38;5;241m=\u001b[39m t \u001b[38;5;241m+\u001b[39m epoch\u001b[38;5;241m*\u001b[39m\u001b[38;5;28mlen\u001b[39m(train_loader) \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m1\u001b[39m\n", - "File \u001b[0;32m~/.pyenv/versions/3.10.13/lib/python3.10/site-packages/torch/_tensor.py:522\u001b[0m, in \u001b[0;36mTensor.backward\u001b[0;34m(self, gradient, retain_graph, create_graph, inputs)\u001b[0m\n\u001b[1;32m 512\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m has_torch_function_unary(\u001b[38;5;28mself\u001b[39m):\n\u001b[1;32m 513\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m handle_torch_function(\n\u001b[1;32m 514\u001b[0m Tensor\u001b[38;5;241m.\u001b[39mbackward,\n\u001b[1;32m 515\u001b[0m (\u001b[38;5;28mself\u001b[39m,),\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 520\u001b[0m inputs\u001b[38;5;241m=\u001b[39minputs,\n\u001b[1;32m 521\u001b[0m )\n\u001b[0;32m--> 522\u001b[0m \u001b[43mtorch\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mautograd\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mbackward\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 523\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mgradient\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mretain_graph\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcreate_graph\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43minputs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43minputs\u001b[49m\n\u001b[1;32m 524\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[0;32m~/.pyenv/versions/3.10.13/lib/python3.10/site-packages/torch/autograd/__init__.py:266\u001b[0m, in \u001b[0;36mbackward\u001b[0;34m(tensors, grad_tensors, retain_graph, create_graph, grad_variables, inputs)\u001b[0m\n\u001b[1;32m 261\u001b[0m retain_graph \u001b[38;5;241m=\u001b[39m create_graph\n\u001b[1;32m 263\u001b[0m \u001b[38;5;66;03m# The reason we repeat the same comment below is that\u001b[39;00m\n\u001b[1;32m 264\u001b[0m \u001b[38;5;66;03m# some Python versions print out the first line of a multi-line function\u001b[39;00m\n\u001b[1;32m 265\u001b[0m \u001b[38;5;66;03m# calls in the traceback and some print out the last line\u001b[39;00m\n\u001b[0;32m--> 266\u001b[0m \u001b[43mVariable\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_execution_engine\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrun_backward\u001b[49m\u001b[43m(\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;66;43;03m# Calls into the C++ engine to run the backward pass\u001b[39;49;00m\n\u001b[1;32m 267\u001b[0m \u001b[43m \u001b[49m\u001b[43mtensors\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 268\u001b[0m \u001b[43m \u001b[49m\u001b[43mgrad_tensors_\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 269\u001b[0m \u001b[43m \u001b[49m\u001b[43mretain_graph\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 270\u001b[0m \u001b[43m \u001b[49m\u001b[43mcreate_graph\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 271\u001b[0m \u001b[43m \u001b[49m\u001b[43minputs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 272\u001b[0m \u001b[43m \u001b[49m\u001b[43mallow_unreachable\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[1;32m 273\u001b[0m \u001b[43m \u001b[49m\u001b[43maccumulate_grad\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[1;32m 274\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n", - "\u001b[0;31mKeyboardInterrupt\u001b[0m: " - ] - } - ], + "outputs": [], "source": [ "from Neural_network import Generative_net, ResidualEMNSBlock_3d, BigBlock, weight_init, eMNS_Dataset\n", "from Training_loop import train_part_GM\n", diff --git a/Modeling eMNS/Training_loop.py b/Modeling eMNS/Training_loop.py index 7717943..c793bb9 100644 --- a/Modeling eMNS/Training_loop.py +++ b/Modeling eMNS/Training_loop.py @@ -323,8 +323,8 @@ def train_part_GM(model,optimizer,train_loader,valid_loader, epochs = 1, learnin preds = model(x) # loss function in the paper "Modeling Electromagnetic Navigation Systems" # loss= lamda_b*|y-preds| + lamda_g*| nabla(y) - nabla(preds)| - loss = F.l1_loss(preds, y) + grad_loss_Jacobain(preds,y) optimizer.zero_grad() #zero out all of gradient + loss = F.l1_loss(preds, y) + grad_loss_Jacobain(preds,y) loss.backward() # compute gradient of loss optimizer.step() #update parameters From 085ae628fd245aa7635c54c0126c48d9ee3ecbeb Mon Sep 17 00:00:00 2001 From: root Date: Fri, 15 Mar 2024 10:36:16 +0000 Subject: [PATCH 03/16] test ETH v1 --- Modeling eMNS/Generative_model_ETH_v0.ipynb | 1533 ++++++++++++++++++- Modeling eMNS/Neural_network.py | 7 +- Modeling eMNS/Training_loop.py | 4 +- 3 files changed, 1519 insertions(+), 25 deletions(-) diff --git a/Modeling eMNS/Generative_model_ETH_v0.ipynb b/Modeling eMNS/Generative_model_ETH_v0.ipynb index bb7ae79..28fa865 100644 --- a/Modeling eMNS/Generative_model_ETH_v0.ipynb +++ b/Modeling eMNS/Generative_model_ETH_v0.ipynb @@ -9,9 +9,17 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Good to go\n" + ] + } + ], "source": [ "%reload_ext autoreload\n", "%autoreload 2\n", @@ -27,7 +35,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -43,9 +51,18 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "torch.Size([1000, 3, 16, 16, 16])\n", + "torch.Size([1000, 8])\n" + ] + } + ], "source": [ "print(Bfield.shape)\n", "print(current.shape)" @@ -53,9 +70,31 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "tensor([[0.4591],\n", + " [0.4241],\n", + " [0.3447]], dtype=torch.float64)\n", + "tensor([[-0.4902],\n", + " [-0.4390],\n", + " [-0.3529]], dtype=torch.float64)\n", + "torch.Size([1, 8])\n", + "torch.Size([1, 8])\n", + "torch.Size([3, 1])\n", + "torch.Size([3, 1])\n", + "torch.Size([1000, 3, 16, 16, 16])\n", + "torch.Size([1000, 3, 16, 16, 16])\n", + "tensor([[1.],\n", + " [1.],\n", + " [1.]], dtype=torch.float64)\n" + ] + } + ], "source": [ "#data normalization\n", "#find min and max value of input position and Bfield\n", @@ -98,9 +137,18 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "cuda:0\n", + "cuda:0\n" + ] + } + ], "source": [ "MaxB=maxB.cuda(0)\n", "MinB=minB.cuda(0)\n", @@ -145,15 +193,1460 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 26, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "train_percent 1.0\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + " 0%| | 0/1 [00:00 Date: Sun, 17 Mar 2024 14:30:24 +0000 Subject: [PATCH 04/16] ETH test v2 --- Modeling eMNS/Generative_model_ETH_v1.ipynb | 334 ++++++++++++++++++++ Modeling eMNS/Neural_network.py | 44 ++- Modeling eMNS/ReadData.py | 3 +- Modeling eMNS/Training_loop.py | 20 +- Modeling eMNS/utils.py | 17 +- 5 files changed, 400 insertions(+), 18 deletions(-) create mode 100644 Modeling eMNS/Generative_model_ETH_v1.ipynb diff --git a/Modeling eMNS/Generative_model_ETH_v1.ipynb b/Modeling eMNS/Generative_model_ETH_v1.ipynb new file mode 100644 index 0000000..efa25e7 --- /dev/null +++ b/Modeling eMNS/Generative_model_ETH_v1.ipynb @@ -0,0 +1,334 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Train ETH data to CNN generative network" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "%reload_ext autoreload\n", + "%autoreload 2\n", + "import numpy as np\n", + "import torch\n", + "if torch.cuda.device_count():\n", + " device = 'cuda'\n", + " print('Good to go')\n", + "else:\n", + " device = 'cpu'\n", + " print('Using cpu')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from ReadData import ReadETHFolder, ReadETHFile\n", + "foldername=\"./ETH_Data/v/\"\n", + "currentname = \"./ETH_Data/\"+\"currents_3787.h5\"\n", + "file_num = 1000\n", + "data_shape = (16,16,16,3)\n", + "Bfield = torch.tensor(ReadETHFolder(foldername,file_num, data_shape)).permute(0,4,1,2,3)\n", + "current = torch.tensor(ReadETHFile(currentname))\n", + "current = current[0:Bfield.shape[0],:]\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "print(Bfield.shape)\n", + "print(current.shape)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#data normalization\n", + "#find min and max value of input position and Bfield\n", + "max_current, max_current_index = torch.max(current, dim=0, keepdim=True)\n", + "# print(max_current)\n", + "min_current, min_current_index = torch.min(current, dim=0, keepdim=True)\n", + "# print(min_current)\n", + "\n", + "max_Bfield, max_Bfield_index = torch.max(Bfield.transpose(0,1).reshape(3,-1), dim=1, keepdim=True)\n", + "print(max_Bfield)\n", + "min_Bfield, min_Bfield_index = torch.min(Bfield.transpose(0,1).reshape(3,-1), dim=1, keepdim=True)\n", + "print(min_Bfield)\n", + "\n", + "dimB = Bfield.shape\n", + "dimc = current.shape\n", + "\n", + "minB=min_Bfield.expand(3,int(Bfield.numel()/3)).reshape(3,dimB[0],dimB[2],dimB[3],dimB[4]).transpose(0,1)\n", + "maxB=max_Bfield.expand(3,int(Bfield.numel()/3)).reshape(3,dimB[0],dimB[2],dimB[3],dimB[4]).transpose(0,1)\n", + "\n", + "ave_current=0.5*(max_current.expand(dimc[0],dimc[1])+min_current.expand(dimc[0],dimc[1]))\n", + "diff_current=0.5*(max_current.expand(dimc[0],dimc[1])-min_current.expand(dimc[0],dimc[1]))\n", + "\n", + "current_norm = (current-ave_current)/diff_current\n", + "Bfield_norm = (Bfield-(minB+maxB)*0.5)/(0.5*(maxB-minB))\n", + "\n", + "print(min_current.shape)\n", + "print(max_current.shape)\n", + "print(min_Bfield.shape)\n", + "print(max_Bfield.shape)\n", + "\n", + "print(minB.shape)\n", + "print(maxB.shape)\n", + "current_norm_max, index = torch.max(Bfield_norm.transpose(0,1).reshape(3,-1), dim=1, keepdim=True)\n", + "print(current_norm_max)\n", + "# torch.save(min_current, \"./normalize_data/cnn_min_current_ETH.pt\")\n", + "# torch.save(max_current, \"./normalize_data/cnn_max_current_ETH.pt\")\n", + "# torch.save(min_Bfield, \"./normalize_data/cnn_min_Bfield_ETH.pt\")\n", + "# torch.save(max_Bfield, \"./normalize_data/cnn_max_Bfield_ETH.pt\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "MaxB=maxB.cuda(0)\n", + "MinB=minB.cuda(0)\n", + "print(MaxB.device)\n", + "print(MinB.device)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from Neural_network import Generative_net,Generative_net_test ,ResidualEMNSBlock_3d, BigBlock, weight_init, eMNS_Dataset\n", + "###############################################\n", + "# Config the neural network\n", + "###############################################\n", + "num_input = 8\n", + "output_shape = (3,16,16,16)\n", + "SB_args = (64,64,1,4) # (Cin, Cout, num_repeat, num_block)\n", + "BB_args = (2,3) # (scale_factor, num_block)\n", + "SB_block = ResidualEMNSBlock_3d \n", + "BB_block = BigBlock\n", + "DF = False # whether using divergence free model\n", + "\n", + "Generative_network = Generative_net_test(SB_args, BB_args, SB_block, BB_block, num_input=num_input, output_shape= output_shape)\n", + "print(Generative_network)\n", + "\n", + "from torchviz import make_dot\n", + "import torch.nn.functional as F\n", + "from Training_loop import grad_loss_Jacobain\n", + "x = torch.randn(2,8)\n", + "y = Bfield[0:2]\n", + "preds = Generative_network(x)\n", + "print(preds.shape)\n", + "loss = F.l1_loss(preds,y)+grad_loss_Jacobain(preds,y)\n", + " # optimizer.zero_grad() #zero out all of gradient\n", + "loss.backward()\n", + "\n", + "make_dot(loss, params=dict(Generative_network.named_parameters()))\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from Neural_network import Generative_net, Generative_net_test, ResidualEMNSBlock_3d, BigBlock, weight_init, eMNS_Dataset\n", + "from Training_loop import train_part_GM,get_mean_of_dataloader\n", + "from tqdm import tqdm\n", + "\n", + "# current = torch.randn(100,8)\n", + "# Bfield = torch.cat((0.04+0.01*torch.randn(50,3,16,16,16),-0.04+0.01*torch.randn(50,3,16,16,16)),dim=0)\n", + "batch_size = 8\n", + "# construct dataset\n", + "dataset = eMNS_Dataset(\n", + " train_x=current,\n", + " train_y=Bfield\n", + ")\n", + "# print(dataset.x[0])\n", + "# print(dataset.y[0,0,0])\n", + "###############################################\n", + "# Config the neural network\n", + "###############################################\n", + "num_input = 8\n", + "output_shape = (3,16,16,16)\n", + "SB_args = (64,64,1,4) # (Cin, Cout, num_repeat, num_block)\n", + "BB_args = (2,2) # (scale_factor, num_block)\n", + "SB_block = ResidualEMNSBlock_3d \n", + "BB_block = BigBlock\n", + "DF = False # whether using divergence free model\n", + "\n", + "Generative_network = Generative_net(SB_args, BB_args, SB_block, BB_block, num_input=num_input, output_shape= output_shape)\n", + "epochs = 350\n", + "learning_rate_decay = .5\n", + "learning_rates = [1e-3]\n", + "RMSE_lr = []\n", + "schedule = []\n", + "linear_lr = False\n", + "weight_decays = [0]\n", + "\n", + "train_percents = np.arange(1.0,1.01,0.1)\n", + "RMSE_history_end = np.zeros(len(train_percents))\n", + "RMSE_val_history_end = np.zeros(len(train_percents))\n", + "loss_history_end = np.zeros(len(train_percents))\n", + "iter_history_end = np.zeros(len(train_percents))\n", + "mse_history_end = np.zeros(len(train_percents))\n", + "mse_val_history_end = np.zeros(len(train_percents))\n", + "train_stop_epoch = np.zeros(len(train_percents))\n", + "\n", + "################################################\n", + "# Train the neural network\n", + "################################################\n", + "index=0\n", + "for train_percent in train_percents:\n", + " epoch_stop = 0\n", + " print('train_percent',train_percent)\n", + " for learning_rate in tqdm(learning_rates):\n", + " for weight_decay in weight_decays:\n", + "\n", + " # split the dataset to train, validation, test\n", + " train_set, valid_set = torch.utils.data.random_split(dataset, [0.9,0.1])\n", + "\n", + " # normailzation\n", + " extremes = dataset.train_norm(train_indices = train_set.indices)\n", + "\n", + "\n", + " #Using Dataloader for batch train\n", + " train_loader = torch.utils.data.DataLoader(dataset=train_set,batch_size=batch_size,shuffle=True)\n", + " valid_loader = torch.utils.data.DataLoader(dataset=valid_set,batch_size=batch_size,shuffle=True)\n", + "\n", + " # get_mean_of_dataloader(valid_loader,model=Generative_network,device=device)\n", + " print(\"----------------------------\")\n", + " \n", + " print(\"----------------------------\")\n", + " # test_loader = torch.utils.data.DataLoader(dataset=test_set,batch_size=batch_size,shuffle=True)\n", + "\n", + " Generative_network.apply(weight_init)\n", + " optimizer = torch.optim.Adam([{'params':Generative_network.parameters()}], lr=learning_rate, weight_decay= weight_decay, betas=(0.5,0.99))\n", + " RMSE_history, RMSE_val_history, loss_history, iter_history, mse_history, mse_val_history,epoch_stop,Rsquare= train_part_GM(\n", + " model=Generative_network, optimizer=optimizer, train_loader=train_loader, valid_loader=valid_loader, epochs=epochs, \n", + " learning_rate_decay=learning_rate_decay, schedule=schedule, weight_decay=weight_decay, DF=DF,verbose=False, device=device, maxB=extremes[2], minB=extremes[3],\n", + " lr_max=learning_rate, lr_min=2.5e-6,max_epoch=epochs, linear_lr=linear_lr)\n", + " \n", + " RMSE_lr.append(RMSE_val_history[epoch_stop].item())\n", + " \n", + " #save RMSE and loss after early stopping\n", + " RMSE_history_end[index] = RMSE_history[epoch_stop]\n", + " RMSE_val_history_end[index]= RMSE_val_history[epoch_stop]\n", + " loss_history_end[index] = loss_history[epoch_stop]\n", + " iter_history_end[index] = iter_history[epoch_stop]\n", + " mse_history_end[index] = mse_history[epoch_stop]\n", + " mse_val_history_end[index] = mse_val_history[epoch_stop]\n", + " index=index+1\n", + " print('training stop at epoch:',epoch_stop)\n", + " print('training stop at epoch:',Rsquare)\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "torch.save(Generative_network, 'EMS_CNN_ETH.pt')\t# 这里会存储迄今最优模型的参数" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "\n", + "print(RMSE_lr)\n", + "print(learning_rates)\n", + "print(RMSE_lr[0],learning_rates[0])\n", + "import matplotlib.pyplot as plt \n", + "plt.plot(learning_rates,RMSE_lr)\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "ave_site = 5\n", + "ave_kernel = 1/ave_site*np.ones(ave_site)\n", + "loss_history_conv = np.convolve(loss_history.numpy(),ave_kernel,'same')\n", + "\n", + "\n", + "plt.title('loss')\n", + "plt.plot(iter_history,loss_history,'-o')\n", + "plt.plot(iter_history,loss_history_conv,'-*')\n", + "plt.legend(['loss','loss_conv'])\n", + "plt.xlabel('iterations')\n", + "plt.ylabel('loss')\n", + "plt.ylim([0,10])\n", + "plt.show()\n", + "\n", + "plt.title('Train and Val RMSE(sample_num=1000)')\n", + "plt.plot(iter_history[0:epoch_stop],RMSE_history[0:epoch_stop],'-o')\n", + "plt.plot(iter_history[0:epoch_stop],RMSE_val_history[0:epoch_stop],'-*')\n", + "# plt.plot(2e-5*np.arange(epoch_stop),RMSE_history[0:epoch_stop]*1000,'-o')\n", + "# plt.plot(2e-5*np.arange(epoch_stop),RMSE_val_history[0:epoch_stop]*1000,'-*')\n", + "# plt.ylim([15,20])\n", + "plt.legend(['train CNN','val CNN'])\n", + "plt.xlabel('iterations')\n", + "plt.ylabel('RMSE(mT)')\n", + "plt.ylim([0,100])\n", + "plt.grid()\n", + "plt.show()\n", + "\n", + "plt.title('Train and Val loss(sample_num=1000)')\n", + "plt.plot(iter_history[0:epoch_stop],mse_history[0:epoch_stop]*1e6,'-o')\n", + "plt.plot(iter_history[0:epoch_stop],mse_val_history[0:epoch_stop]*1e6,'-*')\n", + "plt.legend(['train CNN','val CNN'])\n", + "plt.xlabel('iterations')\n", + "plt.ylabel('mse(mT^2)')\n", + "plt.grid()\n", + "plt.show()\n", + "print(epoch_stop)\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.7" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/Modeling eMNS/Neural_network.py b/Modeling eMNS/Neural_network.py index f324fc3..7b42429 100644 --- a/Modeling eMNS/Neural_network.py +++ b/Modeling eMNS/Neural_network.py @@ -17,6 +17,39 @@ def __getitem__(self,index): def __len__(self): return self.n_samples + + def total_norm(self): + """ + Apply min-max normalization to the given tensor. + + :param tensor: A PyTorch tensor to be normalized. + :return: the max value and the min value. + """ + min_x = self.x.min() + max_x = self.x.max() + min_y = self.y.min() + max_y = self.y.max() + self.x = 2*(self.x - min_x) / (max_x - min_x) - 1 + self.y = 2*(self.y - min_y) / (max_y - min_y) - 1 + return (max_x, min_x, max_y, min_y) + + def train_norm(self, train_indices): + """ + Apply min-max normalization to the given train tensor. + + :param tensor: A PyTorch tensor to be normalized to range [-1,1]. + :return: max and min value. + """ + min_x = self.x[train_indices].min() + max_x = self.x[train_indices].max() + min_y = self.y[train_indices].min() + max_y = self.y[train_indices].max() + + self.x = 2*(self.x - min_x) / (max_x - min_x) - 1 + self.y = 2*(self.y - min_y) / (max_y - min_y) - 1 + return (max_x, min_x, max_y, min_y) + + ############################################################################### # plain 1D Conv network block @@ -159,14 +192,13 @@ def __init__(self,SB_args,BB_args,SB_block,BB_block, num_input, output_shape): D, grid_x, grid_y, grid_z = output_shape # d_max = max(output_shape[1:]) # q = np.log2(d_max) - 3 - q = BB_num_block-1 + q = BB_num_block Nout = int(grid_x * grid_y * grid_z * Cout / (2**(3*q))) # projection layer self.proj = nn.Linear(num_input, Nout,bias=True) # Unflatten layer self.unflatten_shape = (Cin, int(grid_x/2**q), int(grid_y/2**q),int( grid_z/2**q)) - # conv in hidden layer self.conv1 = nn.Conv3d(Cin, Cout, 3, padding='same') self.conv2 = nn.Conv3d(Cin, Cout, 3, padding='same') @@ -238,7 +270,7 @@ def __init__(self,Cin,Cout, num_repeat): for _ in range(num_repeat): NNstages.append( nn.Sequential( - nn.BatchNorm3d(Cin), + # nn.BatchNorm3d(Cin), nn.Conv3d(Cin,Cout,3,padding=1,bias=True), nn.LeakyReLU(), # nn.Dropout3d(p=0.1) @@ -373,8 +405,6 @@ def __init__(self,SB_args,BB_args,SB_block,BB_block, num_input, output_shape): Nout = int(grid_x * grid_y * grid_z * Cout / (2**(3*q))) # projection layer self.proj = nn.Linear(num_input, Nout,bias=True) - nn.init.kaiming_normal_(self.proj.weight) - nn.init.zeros_(self.proj.bias) # Output Conv3d layer self.conv3d = nn.Conv3d(Cout,D, 3, padding=1) @@ -386,8 +416,8 @@ def __init__(self,SB_args,BB_args,SB_block,BB_block, num_input, output_shape): nn.Unflatten(1,(Cout, int(grid_x/2**q), int(grid_y/2**q),int( grid_z/2**q))), # nn.Dropout3d(p=0.1), *NNstages, - SB_block(Cout, Cout, SB_num_repeat), - nn.BatchNorm3d(Cout), + # SB_block(Cout, Cout, SB_num_repeat), + # nn.BatchNorm3d(Cout), self.conv3d, ) def forward(self,x): diff --git a/Modeling eMNS/ReadData.py b/Modeling eMNS/ReadData.py index 0732ce6..7092467 100644 --- a/Modeling eMNS/ReadData.py +++ b/Modeling eMNS/ReadData.py @@ -88,7 +88,6 @@ def ReadETHFolder(foldername, filenum, data_shape): data = np.zeros((filenum, *data_shape)) for i in range(filenum): - f_num += 1 filename = foldername + str(f_num).zfill(4) + ".h5" with h5py.File(filename, "r") as f: # get first object name/key; may or may NOT be a group @@ -99,6 +98,8 @@ def ReadETHFolder(foldername, filenum, data_shape): # this gets the dataset values and returns as a list data[i] = np.array(f[a_group_key]) + f_num += 1 + return data def ReadETHFile(filename): diff --git a/Modeling eMNS/Training_loop.py b/Modeling eMNS/Training_loop.py index 2e645e6..e58b031 100644 --- a/Modeling eMNS/Training_loop.py +++ b/Modeling eMNS/Training_loop.py @@ -4,7 +4,7 @@ import torch import torch.nn.functional as F from early_stopping import EarlyStopping, EarlyDecay -from utils import compute_discrete_curl, denorm +from utils import compute_discrete_curl, denorm, max_min_norm import numpy as np def adjust_learning_rate_sch(optimizer, lrd, epoch, schedule): @@ -317,6 +317,8 @@ def train_part_GM(model,optimizer,train_loader,valid_loader, epochs = 1, learnin x = x.to(device=device,dtype=torch.float) y = y.to(device=device,dtype=torch.float) + # x,_,_ = max_min_norm(x,device) + # y,_,_ = max_min_norm(y,device) optimizer.zero_grad() #zero out all of gradient if DF: preds = compute_discrete_curl(model(x),device=device) @@ -324,17 +326,19 @@ def train_part_GM(model,optimizer,train_loader,valid_loader, epochs = 1, learnin preds = model(x) # loss function in the paper "Modeling Electromagnetic Navigation Systems" # loss= lamda_b*|y-preds| + lamda_g*| nabla(y) - nabla(preds)| - loss = F.l1_loss(preds, y) + grad_loss_Jacobain(preds,y) + F.mse_loss(preds, y) + l1_loss = F.l1_loss(preds,y) + Grad_loss = grad_loss_Jacobain(preds,y) + loss = l1_loss + Grad_loss loss.backward() # compute gradient of loss optimizer.step() #update parameters tt = t + epoch*len(train_loader) +1 - adjust_learning_rate_cosine_v2(optimizer, lr_max, lr_min,max_epoch,tt,len(train_loader)) + adjust_learning_rate_cosine(optimizer, lr_max, lr_min,max_epoch,tt,len(train_loader)) # early_decay(loss, optimizer, learning_rate_decay) ########################################################### # print loss during training if verbose and (tt % print_every == 1 or (epoch == epochs -1 and t == len(train_loader) -1) ) : - print(f'Epoch {epoch:d}, Iteration {tt:d}, loss = {loss.item():.4f}') + print(f'Epoch {epoch:d}, Iteration {tt:d}, loss = {loss.item():.4f}, l1 loss={l1_loss.item():.4f}, grad loss={Grad_loss.item():.4f}') rmse_val,mse_val,Rsquare = check_rmse_CNN(valid_loader,model,grid_space, device, DF,maxB=maxB,minB=minB) rmse,mse_train,R_TEMP = check_rmse_CNN(train_loader,model, grid_space, device, DF,maxB=maxB,minB=minB) rmse_val_history[tt//print_every] = rmse_val @@ -347,7 +351,7 @@ def train_part_GM(model,optimizer,train_loader,valid_loader, epochs = 1, learnin # return rmse_history, rmse_val_history,loss_history, iter_history elif not verbose and (t == len(train_loader)-1): - print(f'Epoch {epoch:d}, Iteration {tt:d}, loss = {loss.item():.4f}') + print(f'Epoch {epoch:d}, Iteration {tt:d}, loss = {loss.item():.4f}, l1 loss={l1_loss.item():.4f}, grad loss={Grad_loss.item():.4f}') rmse_val,mse_val,Rsquare= check_rmse_CNN(valid_loader,model, grid_space, device,DF,maxB=maxB,minB=minB) rmse,mse_train,R_TEMP = check_rmse_CNN(train_loader,model, grid_space, device,DF,maxB=maxB,minB=minB) rmse_val_history[epoch] = rmse_val @@ -434,6 +438,8 @@ def check_rmse_CNN(dataloader,model, grid_space, device, DF, verbose=False, maxB for x,y in dataloader: x = x.to(device=device,dtype=torch.float) y = y.to(device=device,dtype=torch.float) + # x,_,_ = max_min_norm(x,device) + # y,maxB,minB = max_min_norm(y,device) num_samples += x.shape[0] if DF: scores = compute_discrete_curl(model(x)) @@ -441,8 +447,8 @@ def check_rmse_CNN(dataloader,model, grid_space, device, DF, verbose=False, maxB scores = model(x) # compute mse and R2 by de-normalize data - mse_temp += F.mse_loss(1e3*denorm(scores,maxB,minB), 1e3*denorm(y,maxB,minB) ,reduction='sum') - R_temp += F.mse_loss(1e3*denorm(Bfield_mean.expand_as(y),maxB,minB), 1e3*denorm(y,maxB,minB), reduction='sum') + mse_temp += F.mse_loss(1e3*denorm(scores,maxB,minB,device), 1e3*denorm(y,maxB,minB, device) ,reduction='sum') + R_temp += F.mse_loss(1e3*denorm(Bfield_mean.expand_as(y),maxB,minB,device), 1e3*denorm(y,maxB,minB,device), reduction='sum') rmse = torch.sqrt(mse_temp/num_samples/grid_space/3) diff --git a/Modeling eMNS/utils.py b/Modeling eMNS/utils.py index 34cdf45..eea502c 100644 --- a/Modeling eMNS/utils.py +++ b/Modeling eMNS/utils.py @@ -53,12 +53,23 @@ def plot_3D_vector_field(position, vectorField, figsize=(5,5), length=1): ax.quiver(p[:,0], p[:,1], p[:,2], vector[:,0], vector[:,1], vector[:,2], length=length) plt.show() -def denorm(x, Bmax, Bmin): +def denorm(x_norm, Bmax, Bmin, device): ''' This function de-normalize the max-min normalization x = 0.5*(x_norm+1)*(Bmax-Bmin) - Bmin ''' - x_norm = 0.5*(x+1)*(Bmax.expand_as(x)-Bmin.expand_as(x)) + Bmin.expand_as(x) - return x_norm + x = 0.5*(x_norm+1)*(Bmax.expand_as(x_norm).to(device)-Bmin.expand_as(x_norm).to(device)) + Bmin.expand_as(x_norm).to(device) + return x +def max_min_norm(x,device): + """ + Apply min-max normalization to the given tensor. + + :param tensor: A PyTorch tensor to be normalized. + :return: A tensor with values scaled to the range [-1, 1], the max value and the min value. + """ + min_val,_ = torch.min(x, dim=1, keepdim=True) + max_val,_ = torch.max(x, dim=1 ,keepdim=True) + normalized_x = 2*(x - min_val) / (max_val - min_val) - 1 + return normalized_x, max_val, min_val \ No newline at end of file From 726252bc241cc29e43c24f37b3b9f35e1a94f9a8 Mon Sep 17 00:00:00 2001 From: wangjunang Date: Mon, 18 Mar 2024 18:38:02 +0800 Subject: [PATCH 05/16] update tune hyperparameter code --- Modeling eMNS/Generative_model_ETH_v1.ipynb | 84 +--- Modeling eMNS/Generative_model_ETH_v2.ipynb | 482 ++++++++++++++++++++ Modeling eMNS/Training_loop_v2.py | 350 ++++++++++++++ 3 files changed, 846 insertions(+), 70 deletions(-) create mode 100644 Modeling eMNS/Generative_model_ETH_v2.ipynb create mode 100644 Modeling eMNS/Training_loop_v2.py diff --git a/Modeling eMNS/Generative_model_ETH_v1.ipynb b/Modeling eMNS/Generative_model_ETH_v1.ipynb index efa25e7..f141eeb 100644 --- a/Modeling eMNS/Generative_model_ETH_v1.ipynb +++ b/Modeling eMNS/Generative_model_ETH_v1.ipynb @@ -9,9 +9,17 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Using cpu\n" + ] + } + ], "source": [ "%reload_ext autoreload\n", "%autoreload 2\n", @@ -38,76 +46,12 @@ "data_shape = (16,16,16,3)\n", "Bfield = torch.tensor(ReadETHFolder(foldername,file_num, data_shape)).permute(0,4,1,2,3)\n", "current = torch.tensor(ReadETHFile(currentname))\n", - "current = current[0:Bfield.shape[0],:]\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ + "current = current[0:Bfield.shape[0],:]\n", + "\n", "print(Bfield.shape)\n", "print(current.shape)" ] }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "#data normalization\n", - "#find min and max value of input position and Bfield\n", - "max_current, max_current_index = torch.max(current, dim=0, keepdim=True)\n", - "# print(max_current)\n", - "min_current, min_current_index = torch.min(current, dim=0, keepdim=True)\n", - "# print(min_current)\n", - "\n", - "max_Bfield, max_Bfield_index = torch.max(Bfield.transpose(0,1).reshape(3,-1), dim=1, keepdim=True)\n", - "print(max_Bfield)\n", - "min_Bfield, min_Bfield_index = torch.min(Bfield.transpose(0,1).reshape(3,-1), dim=1, keepdim=True)\n", - "print(min_Bfield)\n", - "\n", - "dimB = Bfield.shape\n", - "dimc = current.shape\n", - "\n", - "minB=min_Bfield.expand(3,int(Bfield.numel()/3)).reshape(3,dimB[0],dimB[2],dimB[3],dimB[4]).transpose(0,1)\n", - "maxB=max_Bfield.expand(3,int(Bfield.numel()/3)).reshape(3,dimB[0],dimB[2],dimB[3],dimB[4]).transpose(0,1)\n", - "\n", - "ave_current=0.5*(max_current.expand(dimc[0],dimc[1])+min_current.expand(dimc[0],dimc[1]))\n", - "diff_current=0.5*(max_current.expand(dimc[0],dimc[1])-min_current.expand(dimc[0],dimc[1]))\n", - "\n", - "current_norm = (current-ave_current)/diff_current\n", - "Bfield_norm = (Bfield-(minB+maxB)*0.5)/(0.5*(maxB-minB))\n", - "\n", - "print(min_current.shape)\n", - "print(max_current.shape)\n", - "print(min_Bfield.shape)\n", - "print(max_Bfield.shape)\n", - "\n", - "print(minB.shape)\n", - "print(maxB.shape)\n", - "current_norm_max, index = torch.max(Bfield_norm.transpose(0,1).reshape(3,-1), dim=1, keepdim=True)\n", - "print(current_norm_max)\n", - "# torch.save(min_current, \"./normalize_data/cnn_min_current_ETH.pt\")\n", - "# torch.save(max_current, \"./normalize_data/cnn_max_current_ETH.pt\")\n", - "# torch.save(min_Bfield, \"./normalize_data/cnn_min_Bfield_ETH.pt\")\n", - "# torch.save(max_Bfield, \"./normalize_data/cnn_max_Bfield_ETH.pt\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "MaxB=maxB.cuda(0)\n", - "MinB=minB.cuda(0)\n", - "print(MaxB.device)\n", - "print(MinB.device)" - ] - }, { "cell_type": "code", "execution_count": null, @@ -224,7 +168,7 @@ " RMSE_history, RMSE_val_history, loss_history, iter_history, mse_history, mse_val_history,epoch_stop,Rsquare= train_part_GM(\n", " model=Generative_network, optimizer=optimizer, train_loader=train_loader, valid_loader=valid_loader, epochs=epochs, \n", " learning_rate_decay=learning_rate_decay, schedule=schedule, weight_decay=weight_decay, DF=DF,verbose=False, device=device, maxB=extremes[2], minB=extremes[3],\n", - " lr_max=learning_rate, lr_min=2.5e-6,max_epoch=epochs, linear_lr=linear_lr)\n", + " lr_max=learning_rate, lr_min=2.5e-6, linear_lr=linear_lr)\n", " \n", " RMSE_lr.append(RMSE_val_history[epoch_stop].item())\n", " \n", @@ -326,7 +270,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.7" + "version": "3.10.13" } }, "nbformat": 4, diff --git a/Modeling eMNS/Generative_model_ETH_v2.ipynb b/Modeling eMNS/Generative_model_ETH_v2.ipynb new file mode 100644 index 0000000..e42ca73 --- /dev/null +++ b/Modeling eMNS/Generative_model_ETH_v2.ipynb @@ -0,0 +1,482 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Train ETH data to CNN generative network" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "%reload_ext autoreload\n", + "%autoreload 2\n", + "import numpy as np\n", + "import torch\n", + "if torch.cuda.device_count():\n", + " device = 'cuda'\n", + " use_gpu = True\n", + " print('Good to go')\n", + "else:\n", + " device = 'cpu'\n", + " use_gpu = False\n", + " print('Using cpu')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from ReadData import ReadETHFolder, ReadETHFile\n", + "foldername=\"./ETH_Data/v/\"\n", + "currentname = \"./ETH_Data/\"+\"currents_3787.h5\"\n", + "file_num = 100\n", + "data_shape = (16,16,16,3)\n", + "Bfield = torch.tensor(ReadETHFolder(foldername,file_num, data_shape)).permute(0,4,1,2,3)\n", + "current = torch.tensor(ReadETHFile(currentname))\n", + "current = current[0:Bfield.shape[0],:]\n", + "\n", + "print(Bfield.shape)\n", + "print(current.shape)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from Neural_network import Generative_net,Generative_net_test ,ResidualEMNSBlock_3d, BigBlock, weight_init, eMNS_Dataset\n", + "###############################################\n", + "# Config the neural network\n", + "###############################################\n", + "num_input = 8\n", + "output_shape = (3,16,16,16)\n", + "SB_args = (64,64,1,4) # (Cin, Cout, num_repeat, num_block)\n", + "BB_args = (2,3) # (scale_factor, num_block)\n", + "SB_block = ResidualEMNSBlock_3d \n", + "BB_block = BigBlock\n", + "DF = False # whether using divergence free model\n", + "\n", + "Generative_network = Generative_net_test(SB_args, BB_args, SB_block, BB_block, num_input=num_input, output_shape= output_shape)\n", + "print(Generative_network)\n", + "\n", + "from torchviz import make_dot\n", + "import torch.nn.functional as F\n", + "from Training_loop import grad_loss_Jacobain\n", + "x = torch.randn(2,8)\n", + "y = Bfield[0:2]\n", + "preds = Generative_network(x)\n", + "print(preds.shape)\n", + "loss = F.l1_loss(preds,y)+grad_loss_Jacobain(preds,y)\n", + " # optimizer.zero_grad() #zero out all of gradient\n", + "loss.backward()\n", + "\n", + "make_dot(loss, params=dict(Generative_network.named_parameters()))\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Tune hyperparameters" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from Neural_network import Generative_net, Generative_net_test, ResidualEMNSBlock_3d, BigBlock, weight_init, eMNS_Dataset\n", + "from Training_loop_v2 import train_GM\n", + "from functools import partial\n", + "from ray.train import RunConfig, ScalingConfig\n", + "from ray.tune.tuner import Tuner\n", + "from ray import tune\n", + "from ray.tune.schedulers import ASHAScheduler\n", + "\n", + "# construct dataset\n", + "dataset = eMNS_Dataset(\n", + " train_x=current,\n", + " train_y=Bfield\n", + ")\n", + "# split the dataset to train, validation, test\n", + "train_set, valid_set = torch.utils.data.random_split(dataset, [0.9,0.1])\n", + "\n", + "# normailzation\n", + "extremes = dataset.train_norm(train_indices = train_set.indices)\n", + "\n", + "tune_schedule = ASHAScheduler(\n", + " metric=\"loss\", # metric to optimize. This metric should be reported with tune.report()\n", + " mode=\"min\",\n", + " max_t=10,\n", + " grace_period=1, # minimum stop epoch\n", + " reduction_factor=2,\n", + " )\n", + "param_space = {\n", + " \"scaling_config\": ScalingConfig(\n", + " num_workers = 1,\n", + " use_gpu = False,\n", + " #resource_per_worker = {\"CPU\":1, \"GPU\":1}\n", + " ),\n", + " # You can even grid search various datasets in Tune.\n", + " # \"datasets\": {\n", + " # \"train\": tune.grid_search(\n", + " # [ds1, ds2]\n", + " # ),\n", + " # },\n", + " \"config\": {\n", + " 'epochs': tune.choice([10]),\n", + " 'lr_max': tune.loguniform(1e-4,1e-2),\n", + " 'lr_min': tune.loguniform(1e-5,1e-7),\n", + " 'batch_size': tune.choice([4,8,16]),\n", + " 'L2_norm' : tune.choice([0]),\n", + " 'verbose': False,\n", + " 'DF' : False,\n", + " 'schedule': [],\n", + " 'grid_space': 16**3,\n", + " 'learning_rate_decay': 0.5,\n", + " 'skip_spacing': 1,\n", + " 'num_repeat' : 4,\n", + " 'num_block' : 2,\n", + " 'maxB' : extremes[2],\n", + " 'minB' : extremes[3],\n", + "}\n", + "\n", + "}\n", + "param_space = {\n", + " 'epochs': tune.choice([10]),\n", + " 'lr_max': tune.loguniform(1e-4,1e-2),\n", + " 'lr_min': tune.loguniform(1e-5,1e-7),\n", + " 'batch_size': tune.choice([4,8,16]),\n", + " 'L2_norm' : tune.choice([0]),\n", + " 'verbose': False,\n", + " 'DF' : False,\n", + " 'schedule': [],\n", + " 'grid_space': 16**3,\n", + " 'learning_rate_decay': 0.5,\n", + " 'skip_spacing': 1,\n", + " 'num_repeat' : 4,\n", + " 'num_block' : 2,\n", + " 'maxB' : extremes[2],\n", + " 'minB' : extremes[3],\n", + "}\n", + "\n", + "train_percents = np.arange(1.0,1.01,0.1)\n", + "RMSE_history_end = np.zeros(len(train_percents))\n", + "RMSE_val_history_end = np.zeros(len(train_percents))\n", + "loss_history_end = np.zeros(len(train_percents))\n", + "iter_history_end = np.zeros(len(train_percents))\n", + "mse_history_end = np.zeros(len(train_percents))\n", + "mse_val_history_end = np.zeros(len(train_percents))\n", + "train_stop_epoch = np.zeros(len(train_percents))\n", + "\n", + "################################################\n", + "# Train the neural network\n", + "################################################\n", + "\n", + "def trainer(config):\n", + " train_GM(train_set=train_set, valid_set=valid_set, device=device, config=config)\n", + "\n", + "tuner = tune.Tuner(\n", + " trainer,\n", + " param_space = param_space,\n", + " tune_config =tune.TuneConfig(\n", + " scheduler=tune_schedule,\n", + " num_samples=10, # number of samples of hyperparameter space\n", + " ),\n", + " # run_config = RunConfig(storage_path=\"./results\", name=\"test_experiment\")\n", + ")\n", + " \n", + "tuner.fit()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "trainer(config = {\n", + "'epochs': 10,\n", + "'lr_max': 1e-3,\n", + "'lr_min': 2.5e-6,\n", + "'batch_size': 8,\n", + "'L2_norm' : 0,\n", + "'verbose': False,\n", + "'DF' : False,\n", + "'schedule': [],\n", + "'grid_space': 16**3,\n", + "'learning_rate_decay': 0.5,\n", + "'skip_spacing': 1,\n", + "'num_repeat' : 4,\n", + "'num_block' : 2,\n", + "'maxB' : extremes[2],\n", + "'minB' : extremes[3],\n", + "})" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from Neural_network import Generative_net, Generative_net_test, ResidualEMNSBlock_3d, BigBlock, weight_init, eMNS_Dataset\n", + "from Training_loop_v2 import train_GM\n", + "from tqdm import tqdm\n", + "\n", + "# construct dataset\n", + "dataset = eMNS_Dataset(\n", + " train_x=current,\n", + " train_y=Bfield\n", + ")\n", + "\n", + "Config = {\n", + "'epochs': 10,\n", + "'lr_max': 1e-3,\n", + "'lr_min': 2.5e-6,\n", + "'batch_size': 8,\n", + "'L2_norm' : 0,\n", + "'verbose': False,\n", + "'DF' : False,\n", + "'schedule': [],\n", + "'grid_space': 16**3,\n", + "'learning_rate_decay': 0.5,\n", + "'skip_spacing': 1,\n", + "'num_repeat' : 4,\n", + "'num_block' : 2,\n", + "}\n", + "train_percents = np.arange(1.0,1.01,0.1)\n", + "RMSE_history_end = np.zeros(len(train_percents))\n", + "RMSE_val_history_end = np.zeros(len(train_percents))\n", + "loss_history_end = np.zeros(len(train_percents))\n", + "iter_history_end = np.zeros(len(train_percents))\n", + "mse_history_end = np.zeros(len(train_percents))\n", + "mse_val_history_end = np.zeros(len(train_percents))\n", + "train_stop_epoch = np.zeros(len(train_percents))\n", + "\n", + "################################################\n", + "# Train the neural network\n", + "################################################\n", + "index=0\n", + "for train_percent in train_percents:\n", + " epoch_stop = 0\n", + " print('train_percent',train_percent)\n", + "\n", + " # split the dataset to train, validation, test\n", + " train_set, valid_set = torch.utils.data.random_split(dataset, [0.9,0.1])\n", + "\n", + " # normailzation\n", + " extremes = dataset.train_norm(train_indices = train_set.indices)\n", + "\n", + " Config['maxB'] = extremes[2]\n", + " Config['minB'] = extremes[3]\n", + "\n", + " #Using Dataloader for batch train\n", + " train_loader = torch.utils.data.DataLoader(dataset=train_set,batch_size=Config['batch_size'],shuffle=True)\n", + " valid_loader = torch.utils.data.DataLoader(dataset=valid_set,batch_size=Config['batch_size'],shuffle=True)\n", + "\n", + "\n", + "\n", + " print(\"----------------------------\")\n", + " \n", + " print(\"----------------------------\")\n", + " # test_loader = torch.utils.data.DataLoader(dataset=test_set,batch_size=batch_size,shuffle=True)\n", + "\n", + "\n", + " \n", + " RMSE_history, RMSE_val_history, loss_history, iter_history, mse_history, mse_val_history,epoch_stop,Rsquare = train_GM( \n", + " train_loader=train_loader,\n", + " valid_loader=valid_loader, \n", + " Config=Config, \n", + " device=device)\n", + " \n", + " \n", + " #save RMSE and loss after early stopping\n", + " RMSE_history_end[index] = RMSE_history[epoch_stop]\n", + " RMSE_val_history_end[index]= RMSE_val_history[epoch_stop]\n", + " loss_history_end[index] = loss_history[epoch_stop]\n", + " iter_history_end[index] = iter_history[epoch_stop]\n", + " mse_history_end[index] = mse_history[epoch_stop]\n", + " mse_val_history_end[index] = mse_val_history[epoch_stop]\n", + " index=index+1\n", + " print('training stop at epoch:',epoch_stop)\n", + " print('training stop at epoch:',Rsquare)\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "torch.save(Generative_network, 'EMS_CNN_ETH.pt')\t# 这里会存储迄今最优模型的参数" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "ave_site = 5\n", + "ave_kernel = 1/ave_site*np.ones(ave_site)\n", + "loss_history_conv = np.convolve(loss_history.numpy(),ave_kernel,'same')\n", + "\n", + "\n", + "plt.title('loss')\n", + "plt.plot(iter_history,loss_history,'-o')\n", + "plt.plot(iter_history,loss_history_conv,'-*')\n", + "plt.legend(['loss','loss_conv'])\n", + "plt.xlabel('iterations')\n", + "plt.ylabel('loss')\n", + "plt.ylim([0,10])\n", + "plt.show()\n", + "\n", + "plt.title('Train and Val RMSE(sample_num=1000)')\n", + "plt.plot(iter_history[0:epoch_stop],RMSE_history[0:epoch_stop],'-o')\n", + "plt.plot(iter_history[0:epoch_stop],RMSE_val_history[0:epoch_stop],'-*')\n", + "# plt.plot(2e-5*np.arange(epoch_stop),RMSE_history[0:epoch_stop]*1000,'-o')\n", + "# plt.plot(2e-5*np.arange(epoch_stop),RMSE_val_history[0:epoch_stop]*1000,'-*')\n", + "# plt.ylim([15,20])\n", + "plt.legend(['train CNN','val CNN'])\n", + "plt.xlabel('iterations')\n", + "plt.ylabel('RMSE(mT)')\n", + "plt.ylim([0,100])\n", + "plt.grid()\n", + "plt.show()\n", + "\n", + "plt.title('Train and Val loss(sample_num=1000)')\n", + "plt.plot(iter_history[0:epoch_stop],mse_history[0:epoch_stop]*1e6,'-o')\n", + "plt.plot(iter_history[0:epoch_stop],mse_val_history[0:epoch_stop]*1e6,'-*')\n", + "plt.legend(['train CNN','val CNN'])\n", + "plt.xlabel('iterations')\n", + "plt.ylabel('mse(mT^2)')\n", + "plt.grid()\n", + "plt.show()\n", + "print(epoch_stop)\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import torch\n", + "import torch.optim as optim\n", + "import torch.nn as nn\n", + "from torchvision import datasets, transforms\n", + "from torch.utils.data import DataLoader\n", + "import torch.nn.functional as F\n", + "\n", + "from ray import train, tune\n", + "from ray.tune.schedulers import ASHAScheduler\n", + "\n", + "class ConvNet(nn.Module):\n", + " def __init__(self):\n", + " super(ConvNet, self).__init__()\n", + " # In this example, we don't change the model architecture\n", + " # due to simplicity.\n", + " self.conv1 = nn.Conv2d(1, 3, kernel_size=3)\n", + " self.fc = nn.Linear(192, 10)\n", + "\n", + " def forward(self, x):\n", + " x = F.relu(F.max_pool2d(self.conv1(x), 3))\n", + " x = x.view(-1, 192)\n", + " x = self.fc(x)\n", + " return F.log_softmax(x, dim=1)\n", + "def train_mnist(config):\n", + " # Data Setup\n", + " mnist_transforms = transforms.Compose(\n", + " [transforms.ToTensor(),\n", + " transforms.Normalize((0.1307, ), (0.3081, ))])\n", + "\n", + " train_loader = DataLoader(\n", + " datasets.MNIST(\"~/data\", train=True, download=True, transform=mnist_transforms),\n", + " batch_size=64,\n", + " shuffle=True)\n", + " test_loader = DataLoader(\n", + " datasets.MNIST(\"~/data\", train=False, transform=mnist_transforms),\n", + " batch_size=64,\n", + " shuffle=True)\n", + "\n", + " device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n", + "\n", + " model = ConvNet()\n", + " model.to(device)\n", + "\n", + " optimizer = optim.SGD(\n", + " model.parameters(), lr=config[\"lr\"], momentum=config[\"momentum\"])\n", + " for i in range(10):\n", + " train_func(model, optimizer, train_loader)\n", + " acc = test_func(model, test_loader)\n", + "\n", + " # Send the current training result back to Tune\n", + " train.report({\"mean_accuracy\": acc})\n", + "\n", + " if i % 5 == 0:\n", + " # This saves the model to the trial directory\n", + " torch.save(model.state_dict(), \"./model.pth\")\n", + "\n", + "search_space = {\n", + " \"lr\": tune.sample_from(lambda spec: 10 ** (-10 * np.random.rand())),\n", + " \"momentum\": tune.uniform(0.1, 0.9),\n", + "}\n", + "\n", + "# Uncomment this to enable distributed execution\n", + "# `ray.init(address=\"auto\")`\n", + "\n", + "# Download the dataset first\n", + "datasets.MNIST(\"~/data\", train=True, download=True)\n", + "\n", + "tuner = tune.Tuner(\n", + " train_mnist,\n", + " param_space=search_space,\n", + ")\n", + "results = tuner.fit()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.13" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/Modeling eMNS/Training_loop_v2.py b/Modeling eMNS/Training_loop_v2.py new file mode 100644 index 0000000..0787712 --- /dev/null +++ b/Modeling eMNS/Training_loop_v2.py @@ -0,0 +1,350 @@ +######################################################################### +# Training loop +############################################################################# +import torch +import torch.nn.functional as F +from early_stopping import EarlyStopping, EarlyDecay +from utils import compute_discrete_curl, denorm, max_min_norm +from Neural_network import ResidualEMNSBlock_3d, BigBlock, Generative_net +import numpy as np +from ray import train, tune +from ray.train import Checkpoint +def adjust_learning_rate_sch(optimizer, lrd, epoch, schedule): + """ + Multiply lrd to the learning rate if epoch in schedule + + Return: None, but learning rate (lr) might be updated + """ + if epoch in schedule: + for param_group in optimizer.param_groups: + print(f'lr decay from { param_group["lr"] } to {param_group["lr"]*lrd}') + param_group['lr'] *= lrd + +def adjust_learning_rate_cosine(optimizer, lr_max, lr_min,max_epoch,tt,len_dataloader): + """ + Cosine decay to the learning rate every iternation + + Return: None, but learning rate (lr) might be updated + """ + + for param_group in optimizer.param_groups: + new_lr = lr_min+0.5*(lr_max-lr_min)*(1+np.cos(tt/(max_epoch*len_dataloader)*np.pi)) + # print(f'lr decay from { param_group["lr"] } to {new_lr}') + param_group['lr'] = new_lr + +def adjust_learning_rate_cosine_v2(optimizer, lr_max, lr_min,max_epoch,tt,len_dataloader): + """ + Cosine decay to the learning rate every iternation + + Return: None, but learning rate (lr) might be updated + """ + phi = (5*tt)/(max_epoch*len_dataloader) + decay_lr_max = 0.5**int(phi) + for param_group in optimizer.param_groups: + new_lr = lr_min+0.5*(decay_lr_max*lr_max-lr_min)*(1+np.cos(phi*np.pi)) + # print(f'lr decay from { param_group["lr"] } to {new_lr}') + param_group['lr'] = new_lr + +def adjust_learning_rate(optimizer, lrd): + """ + Multiply lrd to the learning rate + + Return: None, but learning rate (lr) might be updated + """ + for param_group in optimizer.param_groups: + print(f'lr decay from { param_group["lr"] } to {param_group["lr"]*lrd}') + param_group['lr'] *= lrd + +def adjust_learning_rate_linear(optimizer, linear_increment): + """ + add linear_increment to the learning rate + + Return: None, but learning rate (lr) might be updated + """ + for param_group in optimizer.param_groups: + print(f'lr decay from { param_group["lr"] } to {param_group["lr"]+linear_increment}') + param_group['lr'] += linear_increment + + +###################################################################################################################################### +# def train_part_GM(model,optimizer,train_loader,valid_loader, epochs = 1, learning_rate_decay =.1,weight_decay=1e-4, schedule=[], grid_space= 20*20*20, DF= False, verbose=True, device= 'cuda',maxB=[],minB=[], lr_max=1e-4, lr_min=2.5e-6,max_epoch=200, linear_lr=False): +def train_GM(train_set,valid_set, device, config): + """ + Train a model using torch API + + Inputs: + - model: A Pytorch Module giving the model to train + - optimizer: An optimizer object we will use to train the model + - epochs: A Python integer giving the number of epochs to train for + + Returns: model accuracies, prints model loss during training + """ + #---------------unpack config--------------------- + # print(config) + epochs = config["epochs"] + verbose = config['verbose'] + lr_max = config['lr_max'] + lr_min = config['lr_min'] + DF = config['DF'] # whether using divergence free model + grid_space = config['grid_space'] + schedule = config['schedule'] + learning_rate_decay = config['learning_rate_decay'] + maxB = config['maxB'] + minB = config['minB'] + skip_spacing = config['skip_spacing'] + num_repeat = config['num_repeat'] + num_block = config['num_block'] + + #################################################### + #--------------model construction------------------ + #################################################### + num_input = 8 + output_shape = (3,16,16,16) + SB_args = (64,64,skip_spacing,num_repeat) # (Cin, Cout, skip_spacing, num_repeat) + BB_args = (2,num_block) # (scale_factor, num_block) + SB_block = ResidualEMNSBlock_3d + BB_block = BigBlock + + + model = Generative_net(SB_args, BB_args, SB_block, BB_block, num_input=num_input, output_shape= output_shape) + model = model.to(device=device) + + ##################################################### + #-------------------data loader---------------------- + ##################################################### + #Using Dataloader for batch train + train_loader = torch.utils.data.DataLoader(dataset=train_set,batch_size=config['batch_size'],shuffle=True) + valid_loader = torch.utils.data.DataLoader(dataset=valid_set,batch_size=config['batch_size'],shuffle=True) + + ##################################################### + #-------------------optimizer-------------------------- + ##################################################### + + optimizer = torch.optim.Adam( + [{'params': model.parameters()}], + lr= config['lr_max'], + weight_decay= config['L2_norm'], + betas=(0.5,0.99)) + + #------------------------------------------------------ + num_iters = epochs*len(train_loader) + print_every = 100 + adjust_epoch_count = 0 + if verbose: + num_prints = num_iters // print_every + 1 + else: + num_prints = epochs + + # initial loss history and iter history + rmse_history = torch.zeros(num_prints,dtype = torch.float) + rmse_val_history = torch.zeros(num_prints,dtype = torch.float) + iter_history = torch.zeros(num_prints,dtype = torch.float) + loss_history = torch.zeros(num_prints,dtype = torch.float) + mse_history= torch.zeros(num_prints,dtype = torch.float) + mse_val_history= torch.zeros(num_prints,dtype = torch.float) + + patience = 20 # 当验证集损失在连5次训练周期中都没有得到降低时，停止模型训练，以防止模型过拟合 + early_stopping = EarlyStopping(patience, verbose=True) + early_decay = EarlyDecay(patience, delta=0.005, lr_min=lr_min) + epoch_stop = 0 + + ########################################################### + # train loop: + # step 1: update learning rate + # step 2: put model to train model, move data to gpu + # step 3: compute scores, calculate loss function + # step 4: Zero out all of gradients for the variables which the optimizer will update + # step 5: compute gradient of loss, update parameters + ########################################################### + for epoch in range(epochs): + for t, (x,y) in enumerate(train_loader): + model.train() + x = x.to(device=device,dtype=torch.float) + y = y.to(device=device,dtype=torch.float) + + # x,_,_ = max_min_norm(x,device) + # y,_,_ = max_min_norm(y,device) + optimizer.zero_grad() #zero out all of gradient + if DF: + preds = compute_discrete_curl(model(x),device=device) + else: + preds = model(x) + # loss function in the paper "Modeling Electromagnetic Navigation Systems" + # loss= lamda_b*|y-preds| + lamda_g*| nabla(y) - nabla(preds)| + l1_loss = F.l1_loss(preds,y) + Grad_loss = grad_loss_Jacobain(preds,y) + loss = l1_loss + Grad_loss + loss.backward() # compute gradient of loss + optimizer.step() #update parameters + + tt = t + epoch*len(train_loader) +1 + adjust_learning_rate_cosine(optimizer, lr_max, lr_min, epochs, tt, len(train_loader)) + # early_decay(loss, optimizer, learning_rate_decay) + ########################################################### + # print loss during training + if verbose and (tt % print_every == 1 or (epoch == epochs -1 and t == len(train_loader) -1) ) : + print(f'Epoch {epoch:d}, Iteration {tt:d}, loss = {loss.item():.4f}, l1 loss={l1_loss.item():.4f}, grad loss={Grad_loss.item():.4f}') + rmse_val,mse_val,Rsquare = check_rmse_CNN(valid_loader,model,grid_space, device, DF,maxB=maxB,minB=minB) + rmse,mse_train,R_TEMP = check_rmse_CNN(train_loader,model, grid_space, device, DF,maxB=maxB,minB=minB) + rmse_val_history[tt//print_every] = rmse_val + rmse_history[tt // print_every] = rmse + iter_history[tt // print_every] = tt + loss_history[tt // print_every] = loss.item() + print() + + elif not verbose and (t == len(train_loader)-1): + print(f'Epoch {epoch:d}, Iteration {tt:d}, loss = {loss.item():.4f}, l1 loss={l1_loss.item():.4f}, grad loss={Grad_loss.item():.4f}') + rmse_val,mse_val,Rsquare= check_rmse_CNN(valid_loader,model, grid_space, device,DF,maxB=maxB,minB=minB) + rmse,mse_train,R_TEMP = check_rmse_CNN(train_loader,model, grid_space, device,DF,maxB=maxB,minB=minB) + rmse_val_history[epoch] = rmse_val + rmse_history[epoch] = rmse + iter_history[epoch] = tt + loss_history[epoch] = loss.item() + mse_history[epoch] = mse_train + mse_val_history[epoch] = mse_val + + print() + adjust_epoch_count += 1 + + # load back training state + checkpoint_data = { + "epoch": epoch, + "net_state_dict": model.state_dict(), + "optimizer_state_dict": optimizer.state_dict(), + } + checkpoint = Checkpoint.from_directory(checkpoint_data) + #Send the current training result back to Tune + train.report({'rmse_val':rmse_val.item(), 'rmse_train': rmse.item(), 'loss':loss.item()}) + + + + adjust_learning_rate_sch(optimizer, learning_rate_decay, epoch, schedule) + epoch_stop = epoch + + + + return rmse_history, rmse_val_history,loss_history, iter_history,mse_history, mse_val_history,epoch_stop,Rsquare + + +def get_mean_of_dataloader(dataloader,model,device): + num_samples = 0 + b = torch.zeros(1,device=device) + model.eval() + for x,y in dataloader: + y = y.to(device=device,dtype=torch.float) + # use sum instead of mean, what do you think? + y_sum = y.sum(dim=0,keepdim=True) + num_samples += y.shape[0] + # print(y.shape[0]) + b =b+y_sum + return b/num_samples + + +def check_rmse_CNN(dataloader,model, grid_space, device, DF, maxB=[],minB=[]): + ''' + Check RMSE of CNN + ''' + mse_temp = 0 + R_temp=0 + Rsquare=0 + num_samples = 0 + # print(Bfield_mean) + + data = next(iter(dataloader)) + mean = data[0].mean() + + Bfield_mean=get_mean_of_dataloader(dataloader,model,device) + + model.eval() # set model to evaluation model + + with torch.no_grad(): + for x,y in dataloader: + x = x.to(device=device,dtype=torch.float) + y = y.to(device=device,dtype=torch.float) + num_samples += x.shape[0] + if DF: + scores = compute_discrete_curl(model(x)) + else: + scores = model(x) + + # compute mse and R2 by de-normalize data + mse_temp += F.mse_loss(1e3*denorm(scores,maxB,minB,device), 1e3*denorm(y,maxB,minB, device) ,reduction='sum') + R_temp += F.mse_loss(1e3*denorm(Bfield_mean.expand_as(y),maxB,minB,device), 1e3*denorm(y,maxB,minB,device), reduction='sum') + + + rmse = torch.sqrt(mse_temp/num_samples/grid_space/3) + + Rsquare=1-mse_temp/R_temp/num_samples + print(f'Got rmse {rmse}') + + + + + return rmse, mse_temp/num_samples/grid_space/3, Rsquare + + +def grad_loss(preds, y): + ''' + preds, y shape: (batch, dimension, grid_x, grid_y, grid_z) + This function computes lamda_g*| nabla(y) - nabla(preds)| + ''' + grad_preds = torch.gradient(preds,spacing=1.0) + grad_y = torch.gradient(y, spacing=1) + grad_loss = 0 + for i in range(2,5): + # accumulate grad loss for grad_x,y,z + grad_loss += torch.mean(torch.abs(grad_y[i]-grad_preds[i]))/3 + return grad_loss + +def grad_loss_Jacobain(preds,y): + ''' + preds, y shape: (batch, dimension, grid_x, grid_y, grid_z) + This function computes lamda_g*| nabla(y) - nabla(preds)| by Jacobian + ''' + Jaco_preds,_ = Jacobian3(preds) + Jaco_y ,_ = Jacobian3(y) + + grad_loss = torch.mean(torch.abs(Jaco_preds - Jaco_y)) + + return grad_loss + + +def Jacobian3(x): + ''' + Jacobian for 3D vector field + -------input---------- + x shape: (batch, dimension,grid_x, grid_y, grid_z) + ''' + + dudx = x[:, 0, 1:, :, :] - x[:, 0, :-1, :, :] + dvdx = x[:, 1, 1:, :, :] - x[:, 1, :-1, :, :] + dwdx = x[:, 2, 1:, :, :] - x[:, 2, :-1, :, :] + + dudy = x[:, 0, :, 1:, :] - x[:, 0, :, :-1, :] + dvdy = x[:, 1, :, 1:, :] - x[:, 1, :, :-1, :] + dwdy = x[:, 2, :, 1:, :] - x[:, 2, :, :-1, :] + + dudz = x[:, 0, :, :, 1:] - x[:, 0, :, :, :-1] + dvdz = x[:, 1, :, :, 1:] - x[:, 1, :, :, :-1] + dwdz = x[:, 2, :, :, 1:] - x[:, 2, :, :, :-1] + + dudx = torch.cat((dudx, torch.unsqueeze(dudx[:,-1],dim=1)), dim=1) + dvdx = torch.cat((dvdx, torch.unsqueeze(dvdx[:,-1],dim=1)), dim=1) + dwdx = torch.cat((dwdx, torch.unsqueeze(dwdx[:,-1],dim=1)), dim=1) + + dudy = torch.cat((dudy, torch.unsqueeze(dudy[:,:,-1],dim=2)), dim=2) + dvdy = torch.cat((dvdy, torch.unsqueeze(dvdy[:,:,-1],dim=2)), dim=2) + dwdy = torch.cat((dwdy, torch.unsqueeze(dwdy[:,:,-1],dim=2)), dim=2) + + dudz = torch.cat((dudz, torch.unsqueeze(dudz[:,:,:,-1],dim=3)), dim=3) + dvdz = torch.cat((dvdz, torch.unsqueeze(dvdz[:,:,:,-1],dim=3)), dim=3) + dwdz = torch.cat((dwdz, torch.unsqueeze(dwdz[:,:,:,-1],dim=3)), dim=3) + + u = dwdy - dvdz + v = dudz - dwdx + w = dvdx - dudy + + j = torch.stack([dudx,dudy,dudz,dvdx,dvdy,dvdz,dwdx,dwdy,dwdz],axis=-1) + c = torch.stack([u,v,w],axis=-1) #vorticity + + return j,c \ No newline at end of file From 595c64cf23e21a443a5d1f79136ee912dab2f065 Mon Sep 17 00:00:00 2001 From: wangjunang Date: Tue, 19 Mar 2024 14:00:10 +0800 Subject: [PATCH 06/16] update tune files --- Modeling eMNS/Generative_model_ETH_v0.ipynb | 1517 +------------------ Modeling eMNS/Generative_model_ETH_v1.ipynb | 12 +- Modeling eMNS/Generative_model_ETH_v2.ipynb | 260 ++-- Modeling eMNS/Training_loop_v2.py | 31 +- Modeling eMNS/utils.py | 19 +- requirements.txt | 7 + 6 files changed, 156 insertions(+), 1690 deletions(-) create mode 100644 requirements.txt diff --git a/Modeling eMNS/Generative_model_ETH_v0.ipynb b/Modeling eMNS/Generative_model_ETH_v0.ipynb index 28fa865..89b6c5b 100644 --- a/Modeling eMNS/Generative_model_ETH_v0.ipynb +++ b/Modeling eMNS/Generative_model_ETH_v0.ipynb @@ -9,17 +9,9 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Good to go\n" - ] - } - ], + "outputs": [], "source": [ "%reload_ext autoreload\n", "%autoreload 2\n", @@ -35,7 +27,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -51,18 +43,9 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "torch.Size([1000, 3, 16, 16, 16])\n", - "torch.Size([1000, 8])\n" - ] - } - ], + "outputs": [], "source": [ "print(Bfield.shape)\n", "print(current.shape)" @@ -70,31 +53,9 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "tensor([[0.4591],\n", - " [0.4241],\n", - " [0.3447]], dtype=torch.float64)\n", - "tensor([[-0.4902],\n", - " [-0.4390],\n", - " [-0.3529]], dtype=torch.float64)\n", - "torch.Size([1, 8])\n", - "torch.Size([1, 8])\n", - "torch.Size([3, 1])\n", - "torch.Size([3, 1])\n", - "torch.Size([1000, 3, 16, 16, 16])\n", - "torch.Size([1000, 3, 16, 16, 16])\n", - "tensor([[1.],\n", - " [1.],\n", - " [1.]], dtype=torch.float64)\n" - ] - } - ], + "outputs": [], "source": [ "#data normalization\n", "#find min and max value of input position and Bfield\n", @@ -137,18 +98,9 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "cuda:0\n", - "cuda:0\n" - ] - } - ], + "outputs": [], "source": [ "MaxB=maxB.cuda(0)\n", "MinB=minB.cuda(0)\n", @@ -193,1454 +145,9 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "train_percent 1.0\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - " 0%| | 0/1 [00:00= 2.0 +numpy +pandas +ray >= 2.9.3 +matplotlib +h5py From dea099ff35e362f840cd25eefc9e6322ee566d14 Mon Sep 17 00:00:00 2001 From: wangjunang Date: Tue, 19 Mar 2024 14:39:58 +0800 Subject: [PATCH 07/16] delete ETH v1 --- Modeling eMNS/Generative_model_ETH_v1.ipynb | 270 -------------------- 1 file changed, 270 deletions(-) delete mode 100644 Modeling eMNS/Generative_model_ETH_v1.ipynb diff --git a/Modeling eMNS/Generative_model_ETH_v1.ipynb b/Modeling eMNS/Generative_model_ETH_v1.ipynb deleted file mode 100644 index b6f79ca..0000000 --- a/Modeling eMNS/Generative_model_ETH_v1.ipynb +++ /dev/null @@ -1,270 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Train ETH data to CNN generative network" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "%reload_ext autoreload\n", - "%autoreload 2\n", - "import numpy as np\n", - "import torch\n", - "if torch.cuda.device_count():\n", - " device = 'cuda'\n", - " print('Good to go')\n", - "else:\n", - " device = 'cpu'\n", - " print('Using cpu')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from ReadData import ReadETHFolder, ReadETHFile\n", - "foldername=\"./ETH_Data/v/\"\n", - "currentname = \"./ETH_Data/\"+\"currents_3787.h5\"\n", - "file_num = 1000\n", - "data_shape = (16,16,16,3)\n", - "Bfield = torch.tensor(ReadETHFolder(foldername,file_num, data_shape)).permute(0,4,1,2,3)\n", - "current = torch.tensor(ReadETHFile(currentname))\n", - "current = current[0:Bfield.shape[0],:]\n", - "\n", - "print(Bfield.shape)\n", - "print(current.shape)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from Neural_network import Generative_net,Generative_net_test ,ResidualEMNSBlock_3d, BigBlock, weight_init, eMNS_Dataset\n", - "###############################################\n", - "# Config the neural network\n", - "###############################################\n", - "num_input = 8\n", - "output_shape = (3,16,16,16)\n", - "SB_args = (64,64,1,4) # (Cin, Cout, num_repeat, num_block)\n", - "BB_args = (2,3) # (scale_factor, num_block)\n", - "SB_block = ResidualEMNSBlock_3d \n", - "BB_block = BigBlock\n", - "DF = False # whether using divergence free model\n", - "\n", - "Generative_network = Generative_net_test(SB_args, BB_args, SB_block, BB_block, num_input=num_input, output_shape= output_shape)\n", - "print(Generative_network)\n", - "\n", - "from torchviz import make_dot\n", - "import torch.nn.functional as F\n", - "from Training_loop import grad_loss_Jacobain\n", - "x = torch.randn(2,8)\n", - "y = Bfield[0:2]\n", - "preds = Generative_network(x)\n", - "print(preds.shape)\n", - "loss = F.l1_loss(preds,y)+grad_loss_Jacobain(preds,y)\n", - " # optimizer.zero_grad() #zero out all of gradient\n", - "loss.backward()\n", - "\n", - "make_dot(loss, params=dict(Generative_network.named_parameters()))\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from Neural_network import Generative_net, Generative_net_test, ResidualEMNSBlock_3d, BigBlock, weight_init, eMNS_Dataset\n", - "from Training_loop import train_part_GM,get_mean_of_dataloader\n", - "from tqdm import tqdm\n", - "\n", - "# current = torch.randn(100,8)\n", - "# Bfield = torch.cat((0.04+0.01*torch.randn(50,3,16,16,16),-0.04+0.01*torch.randn(50,3,16,16,16)),dim=0)\n", - "batch_size = 8\n", - "# construct dataset\n", - "dataset = eMNS_Dataset(\n", - " train_x=current,\n", - " train_y=Bfield\n", - ")\n", - "# print(dataset.x[0])\n", - "# print(dataset.y[0,0,0])\n", - "###############################################\n", - "# Config the neural network\n", - "###############################################\n", - "num_input = 8\n", - "output_shape = (3,16,16,16)\n", - "SB_args = (64,64,1,4) # (Cin, Cout, num_repeat, num_block)\n", - "BB_args = (2,2) # (scale_factor, num_block)\n", - "SB_block = ResidualEMNSBlock_3d \n", - "BB_block = BigBlock\n", - "DF = False # whether using divergence free model\n", - "\n", - "Generative_network = Generative_net(SB_args, BB_args, SB_block, BB_block, num_input=num_input, output_shape= output_shape)\n", - "epochs = 350\n", - "learning_rate_decay = .5\n", - "learning_rates = [1e-3]\n", - "RMSE_lr = []\n", - "schedule = []\n", - "linear_lr = False\n", - "weight_decays = [0]\n", - "\n", - "train_percents = np.arange(1.0,1.01,0.1)\n", - "RMSE_history_end = np.zeros(len(train_percents))\n", - "RMSE_val_history_end = np.zeros(len(train_percents))\n", - "loss_history_end = np.zeros(len(train_percents))\n", - "iter_history_end = np.zeros(len(train_percents))\n", - "mse_history_end = np.zeros(len(train_percents))\n", - "mse_val_history_end = np.zeros(len(train_percents))\n", - "train_stop_epoch = np.zeros(len(train_percents))\n", - "\n", - "################################################\n", - "# Train the neural network\n", - "################################################\n", - "index=0\n", - "for train_percent in train_percents:\n", - " epoch_stop = 0\n", - " print('train_percent',train_percent)\n", - " for learning_rate in tqdm(learning_rates):\n", - " for weight_decay in weight_decays:\n", - "\n", - " # split the dataset to train, validation, test\n", - " train_set, valid_set = torch.utils.data.random_split(dataset, [0.9,0.1])\n", - "\n", - " # normailzation\n", - " extremes = dataset.train_norm(train_indices = train_set.indices)\n", - "\n", - "\n", - " #Using Dataloader for batch train\n", - " train_loader = torch.utils.data.DataLoader(dataset=train_set,batch_size=batch_size,shuffle=True)\n", - " valid_loader = torch.utils.data.DataLoader(dataset=valid_set,batch_size=batch_size,shuffle=True)\n", - "\n", - " # get_mean_of_dataloader(valid_loader,model=Generative_network,device=device)\n", - " print(\"----------------------------\")\n", - " \n", - " print(\"----------------------------\")\n", - " # test_loader = torch.utils.data.DataLoader(dataset=test_set,batch_size=batch_size,shuffle=True)\n", - "\n", - " Generative_network.apply(weight_init)\n", - " optimizer = torch.optim.Adam([{'params':Generative_network.parameters()}], lr=learning_rate, weight_decay= weight_decay, betas=(0.5,0.99))\n", - " RMSE_history, RMSE_val_history, loss_history, iter_history, mse_history, mse_val_history,epoch_stop,Rsquare= train_part_GM(\n", - " model=Generative_network, optimizer=optimizer, train_loader=train_loader, valid_loader=valid_loader, epochs=epochs, \n", - " learning_rate_decay=learning_rate_decay, schedule=schedule, weight_decay=weight_decay, DF=DF,verbose=False, device=device, maxB=extremes[2], minB=extremes[3],\n", - " lr_max=learning_rate, lr_min=2.5e-6, linear_lr=linear_lr)\n", - " \n", - " RMSE_lr.append(RMSE_val_history[epoch_stop].item())\n", - " \n", - " #save RMSE and loss after early stopping\n", - " RMSE_history_end[index] = RMSE_history[epoch_stop]\n", - " RMSE_val_history_end[index]= RMSE_val_history[epoch_stop]\n", - " loss_history_end[index] = loss_history[epoch_stop]\n", - " iter_history_end[index] = iter_history[epoch_stop]\n", - " mse_history_end[index] = mse_history[epoch_stop]\n", - " mse_val_history_end[index] = mse_val_history[epoch_stop]\n", - " index=index+1\n", - " print('training stop at epoch:',epoch_stop)\n", - " print('training stop at epoch:',Rsquare)\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "torch.save(Generative_network, 'EMS_CNN_ETH.pt')\t# 这里会存储迄今最优模型的参数" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "\n", - "print(RMSE_lr)\n", - "print(learning_rates)\n", - "print(RMSE_lr[0],learning_rates[0])\n", - "import matplotlib.pyplot as plt \n", - "plt.plot(learning_rates,RMSE_lr)\n", - "import matplotlib.pyplot as plt\n", - "import numpy as np\n", - "ave_site = 5\n", - "ave_kernel = 1/ave_site*np.ones(ave_site)\n", - "loss_history_conv = np.convolve(loss_history.numpy(),ave_kernel,'same')\n", - "\n", - "\n", - "plt.title('loss')\n", - "plt.plot(iter_history,loss_history,'-o')\n", - "plt.plot(iter_history,loss_history_conv,'-*')\n", - "plt.legend(['loss','loss_conv'])\n", - "plt.xlabel('iterations')\n", - "plt.ylabel('loss')\n", - "plt.ylim([0,10])\n", - "plt.show()\n", - "\n", - "plt.title('Train and Val RMSE(sample_num=1000)')\n", - "plt.plot(iter_history[0:epoch_stop],RMSE_history[0:epoch_stop],'-o')\n", - "plt.plot(iter_history[0:epoch_stop],RMSE_val_history[0:epoch_stop],'-*')\n", - "# plt.plot(2e-5*np.arange(epoch_stop),RMSE_history[0:epoch_stop]*1000,'-o')\n", - "# plt.plot(2e-5*np.arange(epoch_stop),RMSE_val_history[0:epoch_stop]*1000,'-*')\n", - "# plt.ylim([15,20])\n", - "plt.legend(['train CNN','val CNN'])\n", - "plt.xlabel('iterations')\n", - "plt.ylabel('RMSE(mT)')\n", - "plt.ylim([0,100])\n", - "plt.grid()\n", - "plt.show()\n", - "\n", - "plt.title('Train and Val loss(sample_num=1000)')\n", - "plt.plot(iter_history[0:epoch_stop],mse_history[0:epoch_stop]*1e6,'-o')\n", - "plt.plot(iter_history[0:epoch_stop],mse_val_history[0:epoch_stop]*1e6,'-*')\n", - "plt.legend(['train CNN','val CNN'])\n", - "plt.xlabel('iterations')\n", - "plt.ylabel('mse(mT^2)')\n", - "plt.grid()\n", - "plt.show()\n", - "print(epoch_stop)\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.13" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} From 6822a7a50fb78238b3d8bdd59348fb96accf05d0 Mon Sep 17 00:00:00 2001 From: root Date: Tue, 19 Mar 2024 09:00:05 +0000 Subject: [PATCH 08/16] update tune v3 --- Modeling eMNS/Generative_model_ETH_v2.ipynb | 62 +++++++++++++-------- Modeling eMNS/Training_loop_v2.py | 26 ++++----- Modeling eMNS/utils.py | 8 +-- 3 files changed, 57 insertions(+), 39 deletions(-) diff --git a/Modeling eMNS/Generative_model_ETH_v2.ipynb b/Modeling eMNS/Generative_model_ETH_v2.ipynb index d0aade2..bc9c12e 100644 --- a/Modeling eMNS/Generative_model_ETH_v2.ipynb +++ b/Modeling eMNS/Generative_model_ETH_v2.ipynb @@ -36,7 +36,7 @@ "from ReadData import ReadETHFolder, ReadETHFile\n", "foldername=\"./ETH_Data/v/\"\n", "currentname = \"./ETH_Data/\"+\"currents_3787.h5\"\n", - "file_num = 100\n", + "file_num = 300\n", "data_shape = (16,16,16,3)\n", "Bfield = torch.tensor(ReadETHFolder(foldername,file_num, data_shape)).permute(0,4,1,2,3)\n", "current = torch.tensor(ReadETHFile(currentname))\n", @@ -116,17 +116,17 @@ "extremes = dataset.train_norm(train_indices = train_set.indices)\n", "\n", "tune_schedule = ASHAScheduler(\n", - " metric=\"loss\", # metric to optimize. This metric should be reported with tune.report()\n", + " metric=\"rmse_val\", # metric to optimize. This metric should be reported with tune.report()\n", " mode=\"min\",\n", - " max_t=10,\n", - " grace_period=1, # minimum stop epoch\n", + " max_t=120,\n", + " grace_period=10, # minimum stop epoch\n", " reduction_factor=2,\n", " )\n", "param_space = {\n", " \"scaling_config\": ScalingConfig(\n", " num_workers = 1,\n", - " use_gpu = False,\n", - " #resource_per_worker = {\"CPU\":1, \"GPU\":1}\n", + " use_gpu = use_gpu,\n", + " resources_per_worker = {\"CPU\":10, \"GPU\":2}\n", " ),\n", " # You can even grid search various datasets in Tune.\n", " # \"datasets\": {\n", @@ -135,7 +135,7 @@ " # ),\n", " # },\n", " \"train_loop_config\": {\n", - " 'epochs': tune.choice([10]),\n", + " 'epochs': tune.choice([350]),\n", " 'lr_max': tune.loguniform(1e-4,1e-2),\n", " 'lr_min': tune.loguniform(1e-5,1e-7),\n", " 'batch_size': tune.choice([4,8,16]),\n", @@ -195,9 +195,9 @@ "}\n", "\n", "scaling_config = ScalingConfig(\n", - " num_workers = 1,\n", + " num_workers = 2,\n", " use_gpu = use_gpu,\n", - " #resource_per_worker = {\"CPU\":1, \"GPU\":1}\n", + " resources_per_worker = {\"CPU\":4, \"GPU\":1}\n", ")\n", "\n", "run_config = RunConfig(checkpoint_config=CheckpointConfig(num_to_keep=1))\n", @@ -212,18 +212,27 @@ " run_config = run_config,\n", "\n", ")\n", - "result = trainer.fit()\n", - "# tuner = tune.Tuner(\n", - "# trainer,\n", - "# param_space = param_space,\n", - "# tune_config =tune.TuneConfig(\n", - "# scheduler=tune_schedule,\n", - "# num_samples=10, # number of samples of hyperparameter space\n", - "# ),\n", - "# # run_config = RunConfig(storage_path=\"./results\", name=\"test_experiment\")\n", - "# )\n", + "# result = trainer.fit()\n", + "tuner = tune.Tuner(\n", + " trainer,\n", + " param_space = param_space,\n", + " tune_config =tune.TuneConfig(\n", + " scheduler=tune_schedule,\n", + " num_samples=30, # number of samples of hyperparameter space\n", + " ),\n", + " # run_config = RunConfig(storage_path=\"./results\", name=\"test_experiment\")\n", + ")\n", " \n", - "# tuner.fit()" + "results = tuner.fit()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "best_result = results.get_best_result(metric='rmse_val',mode='min')" ] }, { @@ -233,7 +242,16 @@ "outputs": [], "source": [ "from utils import plot_ray_results\n", - "plot_ray_results(result, metrics_names=['rmse_train','rmse_val'])" + "plot_ray_results(best_result, metrics_names=['rmse_train','rmse_val'])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "plot_ray_results(results, metrics_names=['rmse_train','rmse_val'],ylim=[20,50])" ] }, { @@ -404,7 +422,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.13" + "version": "3.9.7" } }, "nbformat": 4, diff --git a/Modeling eMNS/Training_loop_v2.py b/Modeling eMNS/Training_loop_v2.py index 5588e9c..f27233d 100644 --- a/Modeling eMNS/Training_loop_v2.py +++ b/Modeling eMNS/Training_loop_v2.py @@ -214,20 +214,20 @@ def train_GM(config): print() adjust_epoch_count += 1 - # create checkpoint - base_model = (model.module - if isinstance(model, DistributedDataParallel) else model) - checkpoint_dir = tempfile.mkdtemp() - # load back training state - checkpoint_data = { - "epoch": epoch, - "net_state_dict": base_model.state_dict(), - "optimizer_state_dict": optimizer.state_dict(), - } - torch.save(checkpoint_data, os.path.join(checkpoint_dir, "model.pt")) - checkpoint = Checkpoint.from_directory(checkpoint_dir) + # # create checkpoint + # base_model = (model.module + # if isinstance(model, DistributedDataParallel) else model) + # checkpoint_dir = tempfile.mkdtemp() + # # load back training state + # checkpoint_data = { + # "epoch": epoch, + # "net_state_dict": base_model.state_dict(), + # "optimizer_state_dict": optimizer.state_dict(), + # } + # torch.save(checkpoint_data, os.path.join(checkpoint_dir, "model.pt")) + # checkpoint = Checkpoint.from_directory(checkpoint_dir) #Send the current training result back to Tune - train.report({'rmse_val':rmse_val.item(), 'rmse_train': rmse.item(), 'loss':loss.item()}, checkpoint=checkpoint) + train.report({'rmse_val':rmse_val.item(), 'rmse_train': rmse.item(), 'loss':loss.item()}) diff --git a/Modeling eMNS/utils.py b/Modeling eMNS/utils.py index b7b2a21..ba8ba36 100644 --- a/Modeling eMNS/utils.py +++ b/Modeling eMNS/utils.py @@ -1,6 +1,6 @@ import torch import matplotlib.pyplot as plt -from collections.abc import Iterable +import ray def compute_discrete_curl(A_field, device): ''' A_field: (batch, Dimensions, grid_x, grid_y, grid_z) @@ -75,12 +75,12 @@ def max_min_norm(x,device): normalized_x = 2*(x - min_val) / (max_val - min_val) - 1 return normalized_x, max_val, min_val -def plot_ray_results(results, metrics_names): +def plot_ray_results(results, metrics_names,legend=False, ylim=None, xlim=None): # result_metrics = results.metrics # num_plot = 0 # check if multi-result or a single result - if isinstance(results, Iterable): + if type(results)==ray.tune.result_grid.ResultGrid: dfs = {result.path: result.metrics_dataframe for result in results} else: dfs = {results.path: results.metrics_dataframe} @@ -89,4 +89,4 @@ def plot_ray_results(results, metrics_names): ax = None for data in dfs.values(): - ax = data[metrics_name].plot(ax=ax) \ No newline at end of file + ax = data[metrics_name].plot(ax=ax, legend=legend, ylim=ylim, xlim=xlim) \ No newline at end of file From cece0f95f13c1549d1e55d72fd9970391d2640e8 Mon Sep 17 00:00:00 2001 From: root Date: Tue, 19 Mar 2024 10:56:04 +0000 Subject: [PATCH 09/16] tune v3 --- Modeling eMNS/Generative_model_ETH_v2.ipynb | 61 +++-- Modeling eMNS/Training_loop_v2.py | 249 +++++++++++++++++++- Modeling eMNS/utils.py | 8 + 3 files changed, 298 insertions(+), 20 deletions(-) diff --git a/Modeling eMNS/Generative_model_ETH_v2.ipynb b/Modeling eMNS/Generative_model_ETH_v2.ipynb index bc9c12e..d90b528 100644 --- a/Modeling eMNS/Generative_model_ETH_v2.ipynb +++ b/Modeling eMNS/Generative_model_ETH_v2.ipynb @@ -7,6 +7,15 @@ "### Train ETH data to CNN generative network" ] }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "!pip install -U \"ray[data,train,tune,serve]\"" + ] + }, { "cell_type": "code", "execution_count": null, @@ -90,12 +99,36 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "metadata": {}, - "outputs": [], + "outputs": [ + { + "ename": "KeyboardInterrupt", + "evalue": "", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m/tmp/ipykernel_44369/275140010.py\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 128\u001b[0m )\n\u001b[1;32m 129\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 130\u001b[0;31m \u001b[0mresults\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtuner\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[0;32m/home/vipuser/anaconda3/lib/python3.9/site-packages/ray/tune/tuner.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 379\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_is_ray_client\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 380\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 381\u001b[0;31m \u001b[0;32mreturn\u001b[0m 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"\u001b[0;32m/home/vipuser/anaconda3/lib/python3.9/site-packages/ray/tune/impl/tuner_internal.py\u001b[0m in \u001b[0;36m_fit_internal\u001b[0;34m(self, trainable, param_space)\u001b[0m\n\u001b[1;32m 626\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_tuner_kwargs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 627\u001b[0m }\n\u001b[0;32m--> 628\u001b[0;31m analysis = run(\n\u001b[0m\u001b[1;32m 629\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 630\u001b[0m )\n", + "\u001b[0;32m/home/vipuser/anaconda3/lib/python3.9/site-packages/ray/tune/tune.py\u001b[0m in \u001b[0;36mrun\u001b[0;34m(run_or_experiment, name, metric, mode, stop, time_budget_s, config, resources_per_trial, num_samples, storage_path, storage_filesystem, search_alg, scheduler, checkpoint_config, verbose, progress_reporter, log_to_file, trial_name_creator, trial_dirname_creator, sync_config, export_formats, max_failures, fail_fast, restore, resume, reuse_actors, raise_on_failed_trial, callbacks, max_concurrent_trials, keep_checkpoints_num, checkpoint_score_attr, checkpoint_freq, checkpoint_at_end, chdir_to_trial_dir, local_dir, _remote, _remote_string_queue, _entrypoint)\u001b[0m\n\u001b[1;32m 1013\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1014\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1015\u001b[0;31m \u001b[0mrunner\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcheckpoint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mforce\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mwait\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1016\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mException\u001b[0m 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478\u001b[0;31m self._checkpoint_manager.checkpoint(\n\u001b[0m\u001b[1;32m 479\u001b[0m \u001b[0msave_fn\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msave_to_dir\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mforce\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mforce\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mwait\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mwait\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 480\u001b[0m )\n", + "\u001b[0;32m/home/vipuser/anaconda3/lib/python3.9/site-packages/ray/tune/execution/experiment_state.py\u001b[0m in \u001b[0;36mcheckpoint\u001b[0;34m(self, save_fn, force, wait)\u001b[0m\n\u001b[1;32m 222\u001b[0m \u001b[0;31m# This context will serialize the dataset execution plan instead, if available.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 223\u001b[0m \u001b[0;32mwith\u001b[0m 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at\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 178\u001b[0m \u001b[0;31m# a debuggability cost\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 179\u001b[0;31m \u001b[0;32mfor\u001b[0m \u001b[0mchunk\u001b[0m \u001b[0;32min\u001b[0m \u001b[0miterable\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 180\u001b[0m \u001b[0mfp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwrite\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mchunk\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 181\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/home/vipuser/anaconda3/lib/python3.9/json/encoder.py\u001b[0m in \u001b[0;36m_iterencode\u001b[0;34m(o, _current_indent_level)\u001b[0m\n\u001b[1;32m 429\u001b[0m \u001b[0;32myield\u001b[0m \u001b[0;32mfrom\u001b[0m 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+ "from Training_loop_v2 import train_GM, train_GM_ray\n", "from functools import partial\n", "from ray.train import RunConfig, ScalingConfig, CheckpointConfig\n", "from ray.train.torch import TorchTrainer\n", @@ -124,9 +157,9 @@ " )\n", "param_space = {\n", " \"scaling_config\": ScalingConfig(\n", - " num_workers = 1,\n", + " num_workers = 3,\n", " use_gpu = use_gpu,\n", - " resources_per_worker = {\"CPU\":10, \"GPU\":2}\n", + " # resources_per_worker = {\"CPU\":4, \"GPU\":1}\n", " ),\n", " # You can even grid search various datasets in Tune.\n", " # \"datasets\": {\n", @@ -135,13 +168,13 @@ " # ),\n", " # },\n", " \"train_loop_config\": {\n", - " 'epochs': tune.choice([350]),\n", - " 'lr_max': tune.loguniform(1e-4,1e-2),\n", - " 'lr_min': tune.loguniform(1e-5,1e-7),\n", - " 'batch_size': tune.choice([4,8,16]),\n", - " 'L2_norm' : tune.choice([0]),\n", + " 'epochs': 350,\n", + " 'lr_max': 1e-4,\n", + " 'lr_min': 2.5e-6,\n", + " 'batch_size': 8,\n", + " 'L2_norm' : 0,\n", " 'verbose': False,\n", - " 'DF' : tune.choice([True,False]),\n", + " 'DF' : False,\n", " 'schedule': [],\n", " 'grid_space': 16**3,\n", " 'learning_rate_decay': 0.5,\n", @@ -251,7 +284,7 @@ "metadata": {}, "outputs": [], "source": [ - "plot_ray_results(results, metrics_names=['rmse_train','rmse_val'],ylim=[20,50])" + "plot_ray_results(results, metrics_names=['rmse_train','rmse_val'],ylim=[3,50])" ] }, { @@ -280,13 +313,13 @@ ")\n", "\n", "config = {\n", - " 'epochs': 10,\n", + " 'epochs': 350,\n", " 'lr_max': 1e-4,\n", " 'lr_min': 2.5e-6,\n", " 'batch_size': 8,\n", " 'L2_norm' : 0,\n", " 'verbose': False,\n", - " 'DF' : True,\n", + " 'DF' : False,\n", " 'schedule': [],\n", " 'grid_space': 16**3,\n", " 'learning_rate_decay': 0.5,\n", diff --git a/Modeling eMNS/Training_loop_v2.py b/Modeling eMNS/Training_loop_v2.py index f27233d..de81176 100644 --- a/Modeling eMNS/Training_loop_v2.py +++ b/Modeling eMNS/Training_loop_v2.py @@ -5,7 +5,7 @@ from torch.nn.parallel import DistributedDataParallel import torch.nn.functional as F from early_stopping import EarlyStopping, EarlyDecay -from utils import compute_discrete_curl, denorm, max_min_norm +from utils import compute_discrete_curl, denorm, max_min_norm, denorm_ray from Neural_network import ResidualEMNSBlock_3d, BigBlock, Generative_net import numpy as np from ray import train, tune @@ -69,7 +69,7 @@ def adjust_learning_rate_linear(optimizer, linear_increment): ###################################################################################################################################### -# def train_part_GM(model,optimizer,train_loader,valid_loader, epochs = 1, learning_rate_decay =.1,weight_decay=1e-4, schedule=[], grid_space= 20*20*20, DF= False, verbose=True, device= 'cuda',maxB=[],minB=[], lr_max=1e-4, lr_min=2.5e-6,max_epoch=200, linear_lr=False): + def train_GM(config): """ Train a model using torch API @@ -100,7 +100,7 @@ def train_GM(config): device = config['device'] train_set = config['train_set'] valid_set = config['valid_set'] - + #################################################### #--------------model construction------------------ #################################################### @@ -113,10 +113,20 @@ def train_GM(config): model = Generative_net(SB_args, BB_args, SB_block, BB_block, num_input=num_input, output_shape= output_shape) - model = model.to(device=device) - # prepare model for training - # model = train.torch.prepare_model(model) + + + # #################################################### + # #---------------GPU parallel----------------------- + # #################################################### + if torch.cuda.device_count() > 1: + model = torch.nn.DataParallel(model) + if device == 'cuda': + device = 'cuda:'+str(torch.cuda.current_device()) + model.to(device) + print(device) + # # prepare model for training + # model = train.torch.prepare_model(model) ##################################################### #-------------------data loader---------------------- ##################################################### @@ -236,6 +246,179 @@ def train_GM(config): + return rmse_history, rmse_val_history,loss_history, iter_history,mse_history, mse_val_history,epoch_stop,Rsquare +#------------------------------------------------------------------------------------------------------- +def train_GM_ray(config): + """ + Train a model using torch API + + Inputs: + - model: A Pytorch Module giving the model to train + - optimizer: An optimizer object we will use to train the model + - epochs: A Python integer giving the number of epochs to train for + + Returns: model accuracies, prints model loss during training + """ + #---------------unpack config--------------------- + # print(config) + batch_size = config['batch_size'] + epochs = config["epochs"] + verbose = config['verbose'] + lr_max = config['lr_max'] + lr_min = config['lr_min'] + DF = config['DF'] # whether using divergence free model + grid_space = config['grid_space'] + schedule = config['schedule'] + learning_rate_decay = config['learning_rate_decay'] + maxB = config['maxB'] + minB = config['minB'] + skip_spacing = config['skip_spacing'] + num_repeat = config['num_repeat'] + num_block = config['num_block'] + device = config['device'] + train_set = config['train_set'] + valid_set = config['valid_set'] + + #################################################### + #--------------model construction------------------ + #################################################### + num_input = 8 + output_shape = (3,16,16,16) + SB_args = (64,64,skip_spacing,num_repeat) # (Cin, Cout, skip_spacing, num_repeat) + BB_args = (2,num_block) # (scale_factor, num_block) + SB_block = ResidualEMNSBlock_3d + BB_block = BigBlock + + + model = Generative_net(SB_args, BB_args, SB_block, BB_block, num_input=num_input, output_shape= output_shape) + + + + # #################################################### + # #---------------GPU parallel----------------------- + # #################################################### + # if torch.cuda.device_count() > 1: + # model = torch.nn.DataParallel(model) + # device = torch.cuda.current_device + # prepare model for training + model = train.torch.prepare_model(model) + ##################################################### + #-------------------data loader---------------------- + ##################################################### + + train_loader = torch.utils.data.DataLoader(dataset=train_set,batch_size=config['batch_size'],shuffle=True) + valid_loader = torch.utils.data.DataLoader(dataset=valid_set,batch_size=config['batch_size'],shuffle=True) + + ##################################################### + #-------------------optimizer-------------------------- + ##################################################### + + optimizer = torch.optim.Adam( + [{'params': model.parameters()}], + lr= config['lr_max'], + weight_decay= config['L2_norm'], + betas=(0.5,0.99)) + + #------------------------------------------------------ + num_iters = epochs*len(train_loader) + print_every = 100 + adjust_epoch_count = 0 + if verbose: + num_prints = num_iters // print_every + 1 + else: + num_prints = epochs + + # initial loss history and iter history + rmse_history = torch.zeros(num_prints,dtype = torch.float) + rmse_val_history = torch.zeros(num_prints,dtype = torch.float) + iter_history = torch.zeros(num_prints,dtype = torch.float) + loss_history = torch.zeros(num_prints,dtype = torch.float) + mse_history= torch.zeros(num_prints,dtype = torch.float) + mse_val_history= torch.zeros(num_prints,dtype = torch.float) + + patience = 20 # 当验证集损失在连5次训练周期中都没有得到降低时，停止模型训练，以防止模型过拟合 + early_stopping = EarlyStopping(patience, verbose=True) + early_decay = EarlyDecay(patience, delta=0.005, lr_min=lr_min) + epoch_stop = 0 + + ########################################################### + # train loop: + # step 1: update learning rate + # step 2: put model to train model, move data to gpu + # step 3: compute scores, calculate loss function + # step 4: Zero out all of gradients for the variables which the optimizer will update + # step 5: compute gradient of loss, update parameters + ########################################################### + for epoch in range(epochs): + for t, (x,y) in enumerate(train_loader): + model.train() + + # x,_,_ = max_min_norm(x,device) + # y,_,_ = max_min_norm(y,device) + optimizer.zero_grad() #zero out all of gradient + if DF: + _, preds = Jacobian3(model(x)) + else: + preds = model(x) + # loss function in the paper "Modeling Electromagnetic Navigation Systems" + # loss= lamda_b*|y-preds| + lamda_g*| nabla(y) - nabla(preds)| + l1_loss = F.l1_loss(preds,y) + Grad_loss = grad_loss_Jacobain(preds,y) + loss = l1_loss + Grad_loss + loss.backward() # compute gradient of loss + optimizer.step() #update parameters + + tt = t + epoch*len(train_loader) +1 + adjust_learning_rate_cosine(optimizer, lr_max, lr_min, epochs, tt, len(train_loader)) + # early_decay(loss, optimizer, learning_rate_decay) + ########################################################### + # print loss during training + if verbose and (tt % print_every == 1 or (epoch == epochs -1 and t == len(train_loader) -1) ) : + print(f'Epoch {epoch:d}, Iteration {tt:d}, loss = {loss.item():.4f}, l1 loss={l1_loss.item():.4f}, grad loss={Grad_loss.item():.4f}') + rmse_val,mse_val,Rsquare = check_rmse_CNN_ray(valid_loader,model,grid_space, DF,maxB=maxB,minB=minB) + rmse,mse_train,R_TEMP = check_rmse_CNN_ray(train_loader,model, grid_space, DF,maxB=maxB,minB=minB) + rmse_val_history[tt//print_every] = rmse_val + rmse_history[tt // print_every] = rmse + iter_history[tt // print_every] = tt + loss_history[tt // print_every] = loss.item() + print() + + elif not verbose and (t == len(train_loader)-1): + print(f'Epoch {epoch:d}, Iteration {tt:d}, loss = {loss.item():.4f}, l1 loss={l1_loss.item():.4f}, grad loss={Grad_loss.item():.4f}') + rmse_val,mse_val,Rsquare= check_rmse_CNN_ray(valid_loader,model, grid_space,DF,maxB=maxB,minB=minB) + rmse,mse_train,R_TEMP = check_rmse_CNN_ray(train_loader,model, grid_space,DF,maxB=maxB,minB=minB) + rmse_val_history[epoch] = rmse_val + rmse_history[epoch] = rmse + iter_history[epoch] = tt + loss_history[epoch] = loss.item() + mse_history[epoch] = mse_train + mse_val_history[epoch] = mse_val + + print() + adjust_epoch_count += 1 + + # # create checkpoint + # base_model = (model.module + # if isinstance(model, DistributedDataParallel) else model) + # checkpoint_dir = tempfile.mkdtemp() + # # load back training state + # checkpoint_data = { + # "epoch": epoch, + # "net_state_dict": base_model.state_dict(), + # "optimizer_state_dict": optimizer.state_dict(), + # } + # torch.save(checkpoint_data, os.path.join(checkpoint_dir, "model.pt")) + # checkpoint = Checkpoint.from_directory(checkpoint_dir) + #Send the current training result back to Tune + train.report({'rmse_val':rmse_val.item(), 'rmse_train': rmse.item(), 'loss':loss.item()}) + + + + adjust_learning_rate_sch(optimizer, learning_rate_decay, epoch, schedule) + epoch_stop = epoch + + + return rmse_history, rmse_val_history,loss_history, iter_history,mse_history, mse_val_history,epoch_stop,Rsquare @@ -295,7 +478,61 @@ def check_rmse_CNN(dataloader,model, grid_space, device, DF, maxB=[],minB=[]): return rmse, mse_temp/num_samples/grid_space/3, Rsquare +#----------------------------------------------------------------- + +def get_mean_of_dataloader_ray(dataloader,model): + num_samples = 0 + b = torch.zeros(1) + model.eval() + for x,y in dataloader: + # use sum instead of mean, what do you think? + y_sum = y.sum(dim=0,keepdim=True) + num_samples += y.shape[0] + # print(y.shape[0]) + b =b+y_sum + return b/num_samples + +def check_rmse_CNN_ray(dataloader,model, grid_space, DF, maxB=[],minB=[]): + ''' + Check RMSE of CNN + ''' + mse_temp = 0 + R_temp=0 + Rsquare=0 + num_samples = 0 + # print(Bfield_mean) + + data = next(iter(dataloader)) + mean = data[0].mean() + + Bfield_mean=get_mean_of_dataloader_ray(dataloader,model) + + model.eval() # set model to evaluation model + + with torch.no_grad(): + for x,y in dataloader: + num_samples += x.shape[0] + if DF: + _, scores = Jacobian3(model(x)) + else: + scores = model(x) + + # compute mse and R2 by de-normalize data + mse_temp += F.mse_loss(1e3*denorm_ray(scores,maxB,minB), 1e3*denorm_ray(y,maxB,minB) ,reduction='sum') + R_temp += F.mse_loss(1e3*denorm_ray(Bfield_mean.expand_as(y),maxB,minB), 1e3*denorm_ray(y,maxB,minB), reduction='sum') + + + rmse = torch.sqrt(mse_temp/num_samples/grid_space/3) + + Rsquare=1-mse_temp/R_temp/num_samples + print(f'Got rmse {rmse}') + + + + + return rmse, mse_temp/num_samples/grid_space/3, Rsquare +#---------------------------------------------------------------- def grad_loss(preds, y): ''' preds, y shape: (batch, dimension, grid_x, grid_y, grid_z) diff --git a/Modeling eMNS/utils.py b/Modeling eMNS/utils.py index ba8ba36..0812754 100644 --- a/Modeling eMNS/utils.py +++ b/Modeling eMNS/utils.py @@ -62,6 +62,14 @@ def denorm(x_norm, Bmax, Bmin, device): x = 0.5*(x_norm+1)*(Bmax.expand_as(x_norm).to(device)-Bmin.expand_as(x_norm).to(device)) + Bmin.expand_as(x_norm).to(device) return x +def denorm_ray(x_norm, Bmax, Bmin): + ''' + This function de-normalize the max-min normalization + x = 0.5*(x_norm+1)*(Bmax-Bmin) - Bmin + ''' + x = 0.5*(x_norm+1)*(Bmax.expand_as(x_norm)-Bmin.expand_as(x_norm)) + Bmin.expand_as(x_norm) + return x + def max_min_norm(x,device): """ From f07d21a294b4b85f1dc2ec6f4381dd884e71ae8a Mon Sep 17 00:00:00 2001 From: root Date: Wed, 20 Mar 2024 06:30:48 +0000 Subject: [PATCH 10/16] tune v4 --- Modeling eMNS/Generative_model_ETH_v2.ipynb | 93 +++++++++------------ Modeling eMNS/Neural_network.py | 1 + Modeling eMNS/Training_loop_v2.py | 10 ++- 3 files changed, 48 insertions(+), 56 deletions(-) diff --git a/Modeling eMNS/Generative_model_ETH_v2.ipynb b/Modeling eMNS/Generative_model_ETH_v2.ipynb index d90b528..b2f69d6 100644 --- a/Modeling eMNS/Generative_model_ETH_v2.ipynb +++ b/Modeling eMNS/Generative_model_ETH_v2.ipynb @@ -45,7 +45,7 @@ "from ReadData import ReadETHFolder, ReadETHFile\n", "foldername=\"./ETH_Data/v/\"\n", "currentname = \"./ETH_Data/\"+\"currents_3787.h5\"\n", - "file_num = 300\n", + "file_num = 1000\n", "data_shape = (16,16,16,3)\n", "Bfield = torch.tensor(ReadETHFolder(foldername,file_num, data_shape)).permute(0,4,1,2,3)\n", "current = torch.tensor(ReadETHFile(currentname))\n", @@ -99,33 +99,9 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "ename": "KeyboardInterrupt", - "evalue": "", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)", - 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trial_name_creator, trial_dirname_creator, sync_config, export_formats, max_failures, fail_fast, restore, resume, reuse_actors, raise_on_failed_trial, callbacks, max_concurrent_trials, keep_checkpoints_num, checkpoint_score_attr, checkpoint_freq, checkpoint_at_end, chdir_to_trial_dir, local_dir, _remote, _remote_string_queue, _entrypoint)\u001b[0m\n\u001b[1;32m 1013\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1014\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1015\u001b[0;31m \u001b[0mrunner\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcheckpoint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mforce\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mwait\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1016\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mException\u001b[0m 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import train_GM, train_GM_ray\n", @@ -151,15 +127,15 @@ "tune_schedule = ASHAScheduler(\n", " metric=\"rmse_val\", # metric to optimize. This metric should be reported with tune.report()\n", " mode=\"min\",\n", - " max_t=120,\n", + " max_t=350,\n", " grace_period=10, # minimum stop epoch\n", " reduction_factor=2,\n", " )\n", "param_space = {\n", " \"scaling_config\": ScalingConfig(\n", - " num_workers = 3,\n", + " num_workers = 1,\n", " use_gpu = use_gpu,\n", - " # resources_per_worker = {\"CPU\":4, \"GPU\":1}\n", + " resources_per_worker = {\"CPU\":4, \"GPU\":1}\n", " ),\n", " # You can even grid search various datasets in Tune.\n", " # \"datasets\": {\n", @@ -178,9 +154,9 @@ " 'schedule': [],\n", " 'grid_space': 16**3,\n", " 'learning_rate_decay': 0.5,\n", - " 'skip_spacing': tune.choice([1,2,4]),\n", - " 'num_repeat' : tune.choice([1,2,4]),\n", - " 'num_block' : tune.choice([1,2,3]),\n", + " 'skip_spacing': tune.grid_search([1,2,4]),\n", + " 'num_repeat' : tune.grid_search([1,2,4]),\n", + " 'num_block' : tune.grid_search([1,2,3]),\n", " 'maxB' : extremes[2],\n", " 'minB' : extremes[3],\n", " 'train_set' : train_set,\n", @@ -203,7 +179,7 @@ "################################################\n", "\n", "train_loop_config = {\n", - " 'epochs': 10,\n", + " 'epochs': 350,\n", " 'lr_max': 1e-4,\n", " 'lr_min': 2.5e-6,\n", " 'batch_size': 8,\n", @@ -228,9 +204,9 @@ "}\n", "\n", "scaling_config = ScalingConfig(\n", - " num_workers = 2,\n", + " num_workers = 1,\n", " use_gpu = use_gpu,\n", - " resources_per_worker = {\"CPU\":4, \"GPU\":1}\n", + " resources_per_worker = {\"CPU\":8, \"GPU\":2}\n", ")\n", "\n", "run_config = RunConfig(checkpoint_config=CheckpointConfig(num_to_keep=1))\n", @@ -245,18 +221,30 @@ " run_config = run_config,\n", "\n", ")\n", - "# result = trainer.fit()\n", - "tuner = tune.Tuner(\n", - " trainer,\n", - " param_space = param_space,\n", - " tune_config =tune.TuneConfig(\n", - " scheduler=tune_schedule,\n", - " num_samples=30, # number of samples of hyperparameter space\n", - " ),\n", - " # run_config = RunConfig(storage_path=\"./results\", name=\"test_experiment\")\n", - ")\n", + "\n", + "result = trainer.fit()\n", + "# tuner = tune.Tuner(\n", + "# trainer,\n", + "# param_space = param_space,\n", + "# tune_config =tune.TuneConfig(\n", + "# scheduler=tune_schedule,\n", + "# num_samples=1, # number of samples of hyperparameter space\n", + "# ),\n", + "# # run_config = RunConfig(storage_path=\"./results\", name=\"test_experiment\")\n", + "# )\n", " \n", - "results = tuner.fit()" + "# results = tuner.fit()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "print(torch.device(type='cuda', index=0))\n", + "print(ray.train.torch.get_device())\n", + "print(torch.device('cuda:0'))" ] }, { @@ -265,7 +253,8 @@ "metadata": {}, "outputs": [], "source": [ - "best_result = results.get_best_result(metric='rmse_val',mode='min')" + "best_result = results.get_best_result(metric='rmse_val',mode='min')\n", + "print(best_result)" ] }, { @@ -284,7 +273,7 @@ "metadata": {}, "outputs": [], "source": [ - "plot_ray_results(results, metrics_names=['rmse_train','rmse_val'],ylim=[3,50])" + "plot_ray_results(result, metrics_names=['rmse_train','rmse_val'],ylim=[0,25])" ] }, { @@ -323,9 +312,9 @@ " 'schedule': [],\n", " 'grid_space': 16**3,\n", " 'learning_rate_decay': 0.5,\n", - " 'skip_spacing': 1,\n", - " 'num_repeat' : 4,\n", - " 'num_block' : 2,\n", + " 'skip_spacing': 2,\n", + " 'num_repeat' : 2,\n", + " 'num_block' : 3,\n", " 'device' : device,\n", "}\n", "train_percents = np.arange(1.0,1.01,0.1)\n", diff --git a/Modeling eMNS/Neural_network.py b/Modeling eMNS/Neural_network.py index 7b42429..efd6e06 100644 --- a/Modeling eMNS/Neural_network.py +++ b/Modeling eMNS/Neural_network.py @@ -421,6 +421,7 @@ def __init__(self,SB_args,BB_args,SB_block,BB_block, num_input, output_shape): self.conv3d, ) def forward(self,x): + # print('In model') return self.total_net(x) class Two_Branches_NN_net(nn.Module): diff --git a/Modeling eMNS/Training_loop_v2.py b/Modeling eMNS/Training_loop_v2.py index de81176..d5f46e5 100644 --- a/Modeling eMNS/Training_loop_v2.py +++ b/Modeling eMNS/Training_loop_v2.py @@ -8,6 +8,7 @@ from utils import compute_discrete_curl, denorm, max_min_norm, denorm_ray from Neural_network import ResidualEMNSBlock_3d, BigBlock, Generative_net import numpy as np +import ray from ray import train, tune from ray.train import Checkpoint import tempfile, os @@ -121,10 +122,11 @@ def train_GM(config): # #################################################### if torch.cuda.device_count() > 1: model = torch.nn.DataParallel(model) - if device == 'cuda': - device = 'cuda:'+str(torch.cuda.current_device()) + print(f'we are using {torch.cuda.device_count()} GPU') + # if device == 'cuda': + # # device = 'cuda:'+str(torch.cuda.current_device()) + # device = ray.train.torch.get_device() model.to(device) - print(device) # # prepare model for training # model = train.torch.prepare_model(model) ##################################################### @@ -179,7 +181,7 @@ def train_GM(config): model.train() x = x.to(device=device,dtype=torch.float) y = y.to(device=device,dtype=torch.float) - + # print(f"Outside: input size {x.size()}") # x,_,_ = max_min_norm(x,device) # y,_,_ = max_min_norm(y,device) optimizer.zero_grad() #zero out all of gradient From 3a8fcc0ad7707beea6dbb4590477b06f436b3fc7 Mon Sep 17 00:00:00 2001 From: wangjunang Date: Wed, 20 Mar 2024 16:33:18 +0800 Subject: [PATCH 11/16] fix SOFA bug --- .../Qubot/MagneticCatheterSim/mcr_controller.py | 2 +- .../mcr_radiologyInstrumentDOFs.py | 13 ++++++++++--- SOFA_playground/Qubot/Qubot_flat.py.log | 5 +++++ ros2_ws/src/qubot/qubot/sofa_sim_v2.py | 6 +++--- 4 files changed, 19 insertions(+), 7 deletions(-) diff --git a/SOFA_playground/Qubot/MagneticCatheterSim/mcr_controller.py b/SOFA_playground/Qubot/MagneticCatheterSim/mcr_controller.py index 70ade11..0e8ed0d 100644 --- a/SOFA_playground/Qubot/MagneticCatheterSim/mcr_controller.py +++ b/SOFA_playground/Qubot/MagneticCatheterSim/mcr_controller.py @@ -88,5 +88,5 @@ def onAnimateBeginEvent(self, event): # apply magnetic field visualization self.PhysicsModel.MagneticFieldVisual.forces = magnetic_field_visu.tolist() # print(magnetic_field_visu.tolist()) - # print(self.PhysicsModel.CollectorMagneticForceField.forces[:]) + # print('forces:',self.PhysicsModel.CollectorMagneticForceField.forces[:]) self.PhysicsModel.VisualCatheter.VisuOgl.OglLabel.label =f'{self.magnetic_field_spherical[0]:.0f} mT' \ No newline at end of file diff --git a/SOFA_playground/Qubot/MagneticCatheterSim/mcr_radiologyInstrumentDOFs.py b/SOFA_playground/Qubot/MagneticCatheterSim/mcr_radiologyInstrumentDOFs.py index f32b75a..bf3de87 100644 --- a/SOFA_playground/Qubot/MagneticCatheterSim/mcr_radiologyInstrumentDOFs.py +++ b/SOFA_playground/Qubot/MagneticCatheterSim/mcr_radiologyInstrumentDOFs.py @@ -74,11 +74,18 @@ def Instrument_DOFs( 'ConstantForceField', name='CollectorMagneticForceField', indices=np.arange(topo_instruments[0].nbsections[-1]), - forces=np.tile(np.zeros(6), (topo_instruments[0].nbsections[-1],1)), - indexFromEnd=False, + forces=np.tile(np.zeros(6)+100, (topo_instruments[0].nbsections[-1],1)), + indexFromEnd=True, showArrowSize=1e-2, showColor=[1,0,0,1]) - + # PhysicsModel.addObject( + # 'ConstantForceField', + # name='CollectorMagneticForceField_test', + # indices=np.arange(topo_instruments[0].nbsections[-1]), + # forces=np.tile(np.array([100,1000,0,0,0,100]), (topo_instruments[0].nbsections[-1],1)), + # indexFromEnd=True, + # showArrowSize=1e-2, + # showColor=[1,0,0,1]) PhysicsModel.addObject( 'ConstantForceField', name='MagneticFieldVisual', diff --git a/SOFA_playground/Qubot/Qubot_flat.py.log b/SOFA_playground/Qubot/Qubot_flat.py.log index f8ce5af..e1fcb6e 100644 --- a/SOFA_playground/Qubot/Qubot_flat.py.log +++ b/SOFA_playground/Qubot/Qubot_flat.py.log @@ -11,3 +11,8 @@ //Camera.minBBox = -297.071 -25.5 -16 //Camera.zFar = 550 //Camera.maxBBox = 640 259.5 0.951057 + +--- NEW SESSION: 20/03/2024 15:13:57 --- +/PhysicsModel/CollectorMagneticForceField.forces = 0 0 0 0 -3.79641e-09 6.2e+07 0 0 0 0 -3.79641e-09 6.2e+07 0 0 0 0 -3.79641e-09 6.2e+07 0 0 0 0 -3.79641e-09 6.2e+07 0 0 0 0 -3.79641e-09 6.2e+07 0 0 0 0 -3.79641e-09 6.2e+07 0 0 0 0 -3.79641e-09 6.2e+07 0 0 0 0 -3.79641e-09 6.2e+07 0 0 0 0 -3.79641e-09 6.2e+07 0 0 0 0 -3.79641e-09 6.2e+07 +/PhysicsModel/CollectorMagneticForceField.totalForce = 0 0 0 0 -3.79641e-08 6.2e+08 +/PhysicsModel/CollectorMagneticForceField.showColor = 1 0 0 1 diff --git a/ros2_ws/src/qubot/qubot/sofa_sim_v2.py b/ros2_ws/src/qubot/qubot/sofa_sim_v2.py index 19c0244..ecf197f 100644 --- a/ros2_ws/src/qubot/qubot/sofa_sim_v2.py +++ b/ros2_ws/src/qubot/qubot/sofa_sim_v2.py @@ -39,9 +39,9 @@ def __init__(self): def mag_callback(self,msg:MagneticSpherical): self.get_logger().info('subscribe mag msg') self.get_logger().info(str(msg.magnetic_field_spherical)) - self.magnetic_field_spherical[0] = msg.magnetic_field_spherical[0] - self.magnetic_field_spherical[1] = msg.magnetic_field_spherical[1] - self.magnetic_field_spherical[2] = msg.magnetic_field_spherical[2] + self.magnetic_field_spherical[0] = msg.magnetic_field_spherical[0] # amplitude + self.magnetic_field_spherical[1] = msg.magnetic_field_spherical[1] # theta + self.magnetic_field_spherical[2] = msg.magnetic_field_spherical[2] # phi From 9ebd61a1ec5874acbc109f255b66c5e1073c0a0c Mon Sep 17 00:00:00 2001 From: wangjunang Date: Wed, 20 Mar 2024 09:47:36 +0000 Subject: [PATCH 12/16] tune v5 --- Modeling eMNS/Generative_model_v1.ipynb | 2 +- Modeling eMNS/Generative_model_v2.ipynb | 461 ++++++++++++++++++++++++ Modeling eMNS/ReadData.py | 1 - Modeling eMNS/Training_loop_v2.py | 2 +- Modeling eMNS/utils.py | 33 +- output/b3s2r1.png | Bin 0 -> 50389 bytes output/b3s2r1best.png | Bin 0 -> 13770 bytes 7 files changed, 493 insertions(+), 6 deletions(-) create mode 100644 Modeling eMNS/Generative_model_v2.ipynb create mode 100644 output/b3s2r1.png create mode 100644 output/b3s2r1best.png diff --git a/Modeling eMNS/Generative_model_v1.ipynb b/Modeling eMNS/Generative_model_v1.ipynb index 1eeb3e3..962509a 100644 --- a/Modeling eMNS/Generative_model_v1.ipynb +++ b/Modeling eMNS/Generative_model_v1.ipynb @@ -424,7 +424,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.12.1" + "version": "3.9.7" } }, "nbformat": 4, diff --git a/Modeling eMNS/Generative_model_v2.ipynb b/Modeling eMNS/Generative_model_v2.ipynb new file mode 100644 index 0000000..5b4e4e5 --- /dev/null +++ b/Modeling eMNS/Generative_model_v2.ipynb @@ -0,0 +1,461 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Train ETH data to CNN generative network" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "!pip install -U \"ray[data,train,tune,serve]\"" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "%reload_ext autoreload\n", + "%autoreload 2\n", + "import numpy as np\n", + "import torch\n", + "if torch.cuda.device_count():\n", + " device = 'cuda'\n", + " use_gpu = True\n", + " print('Good to go')\n", + "else:\n", + " device = 'cpu'\n", + " use_gpu = False\n", + " print('Using cpu')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from ReadData import ReadCurrentAndField_CNN\n", + "import glob\n", + "import os \n", + "\n", + "# TODO zhoujing edit this Data loading \n", + "# print(os.getcwd())\n", + "foldername=\"./Data/\"\n", + "filepattern = \"MagneticField[0-9]*.txt\"\n", + "train_file_num= 1400\n", + "#data = ReadFolder(foldername,filepattern)\n", + "current,data = ReadCurrentAndField_CNN (foldername,filepattern,train_file_num)\n", + "\n", + "fileList = glob.glob(foldername+filepattern)\n", + "position = data[:,0:3,2:18,2:18,2:18]\n", + "Bfield = data[:,3:,2:18,2:18,2:18]\n", + "\n", + "# print(fileList)\n", + "print(data.shape)\n", + "print('current shape', current.shape)\n", + "print('Bfield shape', Bfield.shape)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from Neural_network import Generative_net,Generative_net_test ,ResidualEMNSBlock_3d, BigBlock, weight_init, eMNS_Dataset\n", + "###############################################\n", + "# Config the neural network\n", + "###############################################\n", + "num_input = 8\n", + "output_shape = (3,16,16,16)\n", + "SB_args = (64,64,1,4) # (Cin, Cout, num_repeat, num_block)\n", + "BB_args = (2,3) # (scale_factor, num_block)\n", + "SB_block = ResidualEMNSBlock_3d \n", + "BB_block = BigBlock\n", + "DF = False # whether using divergence free model\n", + "\n", + "Generative_network = Generative_net_test(SB_args, BB_args, SB_block, BB_block, num_input=num_input, output_shape= output_shape)\n", + "print(Generative_network)\n", + "\n", + "from torchviz import make_dot\n", + "import torch.nn.functional as F\n", + "from Training_loop import grad_loss_Jacobain\n", + "x = torch.randn(2,8)\n", + "y = Bfield[0:2]\n", + "preds = Generative_network(x)\n", + "print(preds.shape)\n", + "loss = F.l1_loss(preds,y)+grad_loss_Jacobain(preds,y)\n", + " # optimizer.zero_grad() #zero out all of gradient\n", + "loss.backward()\n", + "\n", + "make_dot(loss, params=dict(Generative_network.named_parameters()))\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Tune hyperparameters" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from Neural_network import Generative_net, Generative_net_test, ResidualEMNSBlock_3d, BigBlock, weight_init, eMNS_Dataset\n", + "from Training_loop_v2 import train_GM, train_GM_ray\n", + "from functools import partial\n", + "from ray.train import RunConfig, ScalingConfig, CheckpointConfig\n", + "from ray.train.torch import TorchTrainer\n", + "from ray.tune.tuner import Tuner\n", + "from ray import tune\n", + "from ray.tune.schedulers import ASHAScheduler\n", + "import ray\n", + "\n", + "# construct dataset\n", + "dataset = eMNS_Dataset(\n", + " train_x=current,\n", + " train_y=Bfield\n", + ")\n", + "# split the dataset to train, validation, test\n", + "train_set, valid_set = torch.utils.data.random_split(dataset, [0.9,0.1])\n", + "\n", + "# normailzation\n", + "extremes = dataset.train_norm(train_indices = train_set.indices)\n", + "\n", + "tune_schedule = ASHAScheduler(\n", + " metric=\"rmse_val\", # metric to optimize. This metric should be reported with tune.report()\n", + " mode=\"min\",\n", + " max_t=350,\n", + " grace_period=350, # minimum stop epoch\n", + " reduction_factor=2,\n", + " )\n", + "param_space = {\n", + " \"scaling_config\": ScalingConfig(\n", + " num_workers = 1,\n", + " use_gpu = use_gpu,\n", + " resources_per_worker = {\"CPU\":4, \"GPU\":1}\n", + " ),\n", + " # You can even grid search various datasets in Tune.\n", + " # \"datasets\": {\n", + " # \"train\": tune.grid_search(\n", + " # [ds1, ds2]\n", + " # ),\n", + " # },\n", + " \"train_loop_config\": {\n", + " 'epochs': 350,\n", + " 'lr_max': tune.grid_search([1e-3,1e-4,5e-4]),\n", + " 'lr_min': tune.grid_search([1e-5,2.5e-6,2.5e-7]),\n", + " 'batch_size': 8,\n", + " 'L2_norm' : 0,\n", + " 'verbose': False,\n", + " 'DF' : False,\n", + " 'schedule': [],\n", + " 'grid_space': 16**3,\n", + " 'learning_rate_decay': 0.5,\n", + " 'skip_spacing': 2,\n", + " 'num_repeat' : 1,\n", + " 'num_block' : 3,\n", + " 'maxB' : extremes[2],\n", + " 'minB' : extremes[3],\n", + " 'train_set' : train_set,\n", + " 'valid_set' : valid_set,\n", + " }\n", + "\n", + "}\n", + "\n", + "train_percents = np.arange(1.0,1.01,0.1)\n", + "RMSE_history_end = np.zeros(len(train_percents))\n", + "RMSE_val_history_end = np.zeros(len(train_percents))\n", + "loss_history_end = np.zeros(len(train_percents))\n", + "iter_history_end = np.zeros(len(train_percents))\n", + "mse_history_end = np.zeros(len(train_percents))\n", + "mse_val_history_end = np.zeros(len(train_percents))\n", + "train_stop_epoch = np.zeros(len(train_percents))\n", + "\n", + "################################################\n", + "# Train the neural network\n", + "################################################\n", + "\n", + "train_loop_config = {\n", + " 'epochs': 350,\n", + " 'lr_max': 1e-4,\n", + " 'lr_min': 2.5e-6,\n", + " 'batch_size': 8,\n", + " 'L2_norm' : 0,\n", + " 'verbose': False,\n", + " 'DF' : False,\n", + " 'schedule': [],\n", + " 'grid_space': 16**3,\n", + " 'learning_rate_decay': 0.5,\n", + " 'skip_spacing': 1,\n", + " 'num_repeat' : 4,\n", + " 'num_block' : 2,\n", + " 'maxB' : extremes[2],\n", + " 'minB' : extremes[3],\n", + " 'device' : device,\n", + " 'train_set' : train_set,\n", + " 'valid_set' : valid_set\n", + " # You can even grid search various datasets in Tune.\n", + " # \"datasets\": tune.grid_search(\n", + " # [ds1, ds2]\n", + " # ),\n", + "}\n", + "\n", + "scaling_config = ScalingConfig(\n", + " num_workers = 1,\n", + " use_gpu = use_gpu,\n", + " # resources_per_worker = {\"CPU\":4, \"GPU\":1}\n", + ")\n", + "\n", + "run_config = RunConfig(checkpoint_config=CheckpointConfig(num_to_keep=1))\n", + "\n", + "# def train_loop_per_worker(params):\n", + "# train_GM(train_set=train_set, valid_set=valid_set, device=device, config=params)\n", + "\n", + "trainer = TorchTrainer(\n", + " train_loop_per_worker = train_GM,\n", + " train_loop_config = train_loop_config,\n", + " scaling_config = scaling_config,\n", + " run_config = run_config,\n", + "\n", + ")\n", + "\n", + "# result = trainer.fit()\n", + "tuner = tune.Tuner(\n", + " trainer,\n", + " param_space = param_space,\n", + " tune_config =tune.TuneConfig(\n", + " scheduler=tune_schedule,\n", + " num_samples=1, # number of samples of hyperparameter space\n", + " ),\n", + " # run_config = RunConfig(storage_path=\"./results\", name=\"test_experiment\")\n", + ")\n", + " \n", + "results = tuner.fit()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "print(torch.device(type='cuda', index=0))\n", + "print(ray.train.torch.get_device())\n", + "print(torch.device('cuda:0'))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "best_result = results.get_best_result(metric='rmse_val',mode='min')\n", + "print(best_result)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from utils import plot_ray_results\n", + "plot_ray_results(best_result, metrics_names=['rmse_train','rmse_val'])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "plot_ray_results(results, metrics_names=['rmse_train','rmse_val'],ylim=[0,1])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "!tensorboard --logdir=~/ray_results" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from Neural_network import Generative_net, Generative_net_test, ResidualEMNSBlock_3d, BigBlock, weight_init, eMNS_Dataset\n", + "from Training_loop_v2 import train_GM\n", + "from tqdm import tqdm\n", + "\n", + "# construct dataset\n", + "dataset = eMNS_Dataset(\n", + " train_x=current,\n", + " train_y=Bfield\n", + ")\n", + "\n", + "config = {\n", + " 'epochs': 350,\n", + " 'lr_max': 1e-4,\n", + " 'lr_min': 2.5e-6,\n", + " 'batch_size': 8,\n", + " 'L2_norm' : 0,\n", + " 'verbose': False,\n", + " 'DF' : False,\n", + " 'schedule': [],\n", + " 'grid_space': 16**3,\n", + " 'learning_rate_decay': 0.5,\n", + " 'skip_spacing': 2,\n", + " 'num_repeat' : 2,\n", + " 'num_block' : 3,\n", + " 'device' : device,\n", + "}\n", + "train_percents = np.arange(1.0,1.01,0.1)\n", + "RMSE_history_end = np.zeros(len(train_percents))\n", + "RMSE_val_history_end = np.zeros(len(train_percents))\n", + "loss_history_end = np.zeros(len(train_percents))\n", + "iter_history_end = np.zeros(len(train_percents))\n", + "mse_history_end = np.zeros(len(train_percents))\n", + "mse_val_history_end = np.zeros(len(train_percents))\n", + "train_stop_epoch = np.zeros(len(train_percents))\n", + "\n", + "################################################\n", + "# Train the neural network\n", + "################################################\n", + "index=0\n", + "for train_percent in train_percents:\n", + " epoch_stop = 0\n", + " print('train_percent',train_percent)\n", + "\n", + " # split the dataset to train, validation, test\n", + " train_set, valid_set = torch.utils.data.random_split(dataset, [0.9,0.1])\n", + "\n", + " # normailzation\n", + " extremes = dataset.train_norm(train_indices = train_set.indices)\n", + "\n", + " config['maxB'] = extremes[2]\n", + " config['minB'] = extremes[3]\n", + " config['train_set'] = train_set \n", + " config['valid_set'] = valid_set\n", + "\n", + "\n", + "\n", + " print(\"----------------------------\")\n", + " \n", + " print(\"----------------------------\")\n", + " # test_loader = torch.utils.data.DataLoader(dataset=test_set,batch_size=batch_size,shuffle=True)\n", + "\n", + "\n", + " \n", + " RMSE_history, RMSE_val_history, loss_history, iter_history, mse_history, mse_val_history,epoch_stop,Rsquare = train_GM(\n", + " config=config)\n", + " \n", + " \n", + " #save RMSE and loss after early stopping\n", + " RMSE_history_end[index] = RMSE_history[epoch_stop]\n", + " RMSE_val_history_end[index]= RMSE_val_history[epoch_stop]\n", + " loss_history_end[index] = loss_history[epoch_stop]\n", + " iter_history_end[index] = iter_history[epoch_stop]\n", + " mse_history_end[index] = mse_history[epoch_stop]\n", + " mse_val_history_end[index] = mse_val_history[epoch_stop]\n", + " index=index+1\n", + " print('training stop at epoch:',epoch_stop)\n", + " print('training stop at epoch:',Rsquare)\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "torch.save(Generative_network, 'EMS_CNN_ETH.pt')\t# 这里会存储迄今最优模型的参数" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "ave_site = 5\n", + "ave_kernel = 1/ave_site*np.ones(ave_site)\n", + "loss_history_conv = np.convolve(loss_history.numpy(),ave_kernel,'same')\n", + "\n", + "\n", + "plt.title('loss')\n", + "plt.plot(iter_history,loss_history,'-o')\n", + "plt.plot(iter_history,loss_history_conv,'-*')\n", + "plt.legend(['loss','loss_conv'])\n", + "plt.xlabel('iterations')\n", + "plt.ylabel('loss')\n", + "plt.ylim([0,10])\n", + "plt.show()\n", + "\n", + "plt.title('Train and Val RMSE(sample_num=1000)')\n", + "plt.plot(iter_history[0:epoch_stop],RMSE_history[0:epoch_stop],'-o')\n", + "plt.plot(iter_history[0:epoch_stop],RMSE_val_history[0:epoch_stop],'-*')\n", + "# plt.plot(2e-5*np.arange(epoch_stop),RMSE_history[0:epoch_stop]*1000,'-o')\n", + "# plt.plot(2e-5*np.arange(epoch_stop),RMSE_val_history[0:epoch_stop]*1000,'-*')\n", + "# plt.ylim([15,20])\n", + "plt.legend(['train CNN','val CNN'])\n", + "plt.xlabel('iterations')\n", + "plt.ylabel('RMSE(mT)')\n", + "plt.ylim([0,100])\n", + "plt.grid()\n", + "plt.show()\n", + "\n", + "plt.title('Train and Val loss(sample_num=1000)')\n", + "plt.plot(iter_history[0:epoch_stop],mse_history[0:epoch_stop]*1e6,'-o')\n", + "plt.plot(iter_history[0:epoch_stop],mse_val_history[0:epoch_stop]*1e6,'-*')\n", + "plt.legend(['train CNN','val CNN'])\n", + "plt.xlabel('iterations')\n", + "plt.ylabel('mse(mT^2)')\n", + "plt.grid()\n", + "plt.show()\n", + "print(epoch_stop)\n", + "\n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.7" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/Modeling eMNS/ReadData.py b/Modeling eMNS/ReadData.py index 4f6e719..46a7e76 100644 --- a/Modeling eMNS/ReadData.py +++ b/Modeling eMNS/ReadData.py @@ -79,7 +79,6 @@ def ReadCurrentAndField_CNN(foldername, filepattern, filenum): fileCounter = len(fileList) for i in range(filenum): - print(i) if i == 0: #read position + field data data_temp = ReadData(filename=fileList[i]) diff --git a/Modeling eMNS/Training_loop_v2.py b/Modeling eMNS/Training_loop_v2.py index d095eec..1460a3c 100644 --- a/Modeling eMNS/Training_loop_v2.py +++ b/Modeling eMNS/Training_loop_v2.py @@ -105,7 +105,7 @@ def train_GM(config): #################################################### #--------------model construction------------------ #################################################### - num_input = 8 + num_input = 12 output_shape = (3,16,16,16) SB_args = (64,64,skip_spacing,num_repeat) # (Cin, Cout, skip_spacing, num_repeat) BB_args = (2,num_block) # (scale_factor, num_block) diff --git a/Modeling eMNS/utils.py b/Modeling eMNS/utils.py index eb53a10..dd13c56 100644 --- a/Modeling eMNS/utils.py +++ b/Modeling eMNS/utils.py @@ -1,4 +1,4 @@ -import torch +import torch,ray import matplotlib.pyplot as plt def compute_discrete_curl(A_field, device): ''' @@ -58,8 +58,8 @@ def denorm(x_norm, Bmax, Bmin, device): This function de-normalize the max-min normalization x = 0.5*(x_norm+1)*(Bmax-Bmin) - Bmin ''' - x_norm = 0.5*(x+1)*(Bmax.expand_as(x)-Bmin.expand_as(x)) + Bmin.expand_as(x) - return x_norm + x = 0.5*(x_norm+1)*(Bmax.expand_as(x_norm).to(device)-Bmin.expand_as(x_norm).to(device)) + Bmin.expand_as(x_norm).to(device) + return x def denorm_ray(x_norm, Bmax, Bmin): ''' @@ -70,3 +70,30 @@ def denorm_ray(x_norm, Bmax, Bmin): return x +def max_min_norm(x,device): + """ + Apply min-max normalization to the given tensor. + + :param tensor: A PyTorch tensor to be normalized. + :return: A tensor with values scaled to the range [-1, 1], the max value and the min value. + """ + min_val,_ = torch.min(x, dim=1, keepdim=True) + max_val,_ = torch.max(x, dim=1 ,keepdim=True) + normalized_x = 2*(x - min_val) / (max_val - min_val) - 1 + return normalized_x, max_val, min_val + +def plot_ray_results(results, metrics_names,legend=False, ylim=None, xlim=None): + + # result_metrics = results.metrics + # num_plot = 0 + # check if multi-result or a single result + if type(results)==ray.tune.result_grid.ResultGrid: + dfs = {result.path: result.metrics_dataframe for result in results} + else: + dfs = {results.path: results.metrics_dataframe} + + for metrics_name in metrics_names: + + ax = None + for data in dfs.values(): + ax = data[metrics_name].plot(ax=ax, legend=legend, ylim=ylim, xlim=xlim) \ No newline at end of file diff --git a/output/b3s2r1.png b/output/b3s2r1.png new file mode 100644 index 0000000000000000000000000000000000000000..497522859f82bf030c0c52576c7c5cb9d30f4a23 GIT binary patch literal 50389 zcmX_HWl)>X*To9KEm$e;?p`doJ1rD<*A$o1CdD;qu?Ckyi#K@jqCr}mq9u4K6qh%@ z|A+TOGS6&w^33ksz4zR6&c+++YY-DWCBVSIAlA}UHO9ce1f!qt0J!LH*GpUU0EP@$ z?Iqa6*99DGAK;9kXAk!C@&$XjJ1_@12L!tN`iKdN3ySeGyMe)efwDqE-v2*A&^N$U zXuL>u3jHVeewr467#M_<|9vnqJ`~VmU<`n?RFzFb3J$x1I|W`?Uq0G+PK^6h7Ki(s zNFnyRC}!}9M=Ys%n^#hM18`WDR$~C*4O6GF7%>ALL 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-250,178 +250,7 @@ def train_GM(config): return rmse_history, rmse_val_history,loss_history, iter_history,mse_history, mse_val_history,epoch_stop,Rsquare #------------------------------------------------------------------------------------------------------- -def train_GM_ray(config): - """ - Train a model using torch API - - Inputs: - - model: A Pytorch Module giving the model to train - - optimizer: An optimizer object we will use to train the model - - epochs: A Python integer giving the number of epochs to train for - - Returns: model accuracies, prints model loss during training - """ - #---------------unpack config--------------------- - # print(config) - batch_size = config['batch_size'] - epochs = config["epochs"] - verbose = config['verbose'] - lr_max = config['lr_max'] - lr_min = config['lr_min'] - DF = config['DF'] # whether using divergence free model - grid_space = config['grid_space'] - schedule = config['schedule'] - learning_rate_decay = config['learning_rate_decay'] - maxB = config['maxB'] - minB = config['minB'] - skip_spacing = config['skip_spacing'] - num_repeat = config['num_repeat'] - num_block = config['num_block'] - device = config['device'] - train_set = config['train_set'] - valid_set = config['valid_set'] - - #################################################### - #--------------model construction------------------ - #################################################### - num_input = 8 - output_shape = (3,16,16,16) - SB_args = (64,64,skip_spacing,num_repeat) # (Cin, Cout, skip_spacing, num_repeat) - BB_args = (2,num_block) # (scale_factor, num_block) - SB_block = ResidualEMNSBlock_3d - BB_block = BigBlock - - model = Generative_net(SB_args, BB_args, SB_block, BB_block, num_input=num_input, output_shape= output_shape) - - - - # #################################################### - # #---------------GPU parallel----------------------- - # #################################################### - # if torch.cuda.device_count() > 1: - # model = torch.nn.DataParallel(model) - # device = torch.cuda.current_device - # prepare model for training - model = train.torch.prepare_model(model) - ##################################################### - #-------------------data loader---------------------- - ##################################################### - - train_loader = torch.utils.data.DataLoader(dataset=train_set,batch_size=config['batch_size'],shuffle=True) - valid_loader = torch.utils.data.DataLoader(dataset=valid_set,batch_size=config['batch_size'],shuffle=True) - - ##################################################### - #-------------------optimizer-------------------------- - ##################################################### - - optimizer = torch.optim.Adam( - [{'params': model.parameters()}], - lr= config['lr_max'], - weight_decay= config['L2_norm'], - betas=(0.5,0.99)) - - #------------------------------------------------------ - num_iters = epochs*len(train_loader) - print_every = 100 - adjust_epoch_count = 0 - if verbose: - num_prints = num_iters // print_every + 1 - else: - num_prints = epochs - - # initial loss history and iter history - rmse_history = torch.zeros(num_prints,dtype = torch.float) - rmse_val_history = torch.zeros(num_prints,dtype = torch.float) - iter_history = torch.zeros(num_prints,dtype = torch.float) - loss_history = torch.zeros(num_prints,dtype = torch.float) - mse_history= torch.zeros(num_prints,dtype = torch.float) - mse_val_history= torch.zeros(num_prints,dtype = torch.float) - - patience = 20 # 当验证集损失在连5次训练周期中都没有得到降低时，停止模型训练，以防止模型过拟合 - early_stopping = EarlyStopping(patience, verbose=True) - early_decay = EarlyDecay(patience, delta=0.005, lr_min=lr_min) - epoch_stop = 0 - - ########################################################### - # train loop: - # step 1: update learning rate - # step 2: put model to train model, move data to gpu - # step 3: compute scores, calculate loss function - # step 4: Zero out all of gradients for the variables which the optimizer will update - # step 5: compute gradient of loss, update parameters - ########################################################### - for epoch in range(epochs): - for t, (x,y) in enumerate(train_loader): - model.train() - - # x,_,_ = max_min_norm(x,device) - # y,_,_ = max_min_norm(y,device) - optimizer.zero_grad() #zero out all of gradient - if DF: - _, preds = Jacobian3(model(x)) - else: - preds = model(x) - # loss function in the paper "Modeling Electromagnetic Navigation Systems" - # loss= lamda_b*|y-preds| + lamda_g*| nabla(y) - nabla(preds)| - l1_loss = F.l1_loss(preds,y) - Grad_loss = grad_loss_Jacobain(preds,y) - loss = l1_loss + Grad_loss - loss.backward() # compute gradient of loss - optimizer.step() #update parameters - - tt = t + epoch*len(train_loader) +1 - adjust_learning_rate_cosine(optimizer, lr_max, lr_min, epochs, tt, len(train_loader)) - # early_decay(loss, optimizer, learning_rate_decay) - ########################################################### - # print loss during training - if verbose and (tt % print_every == 1 or (epoch == epochs -1 and t == len(train_loader) -1) ) : - print(f'Epoch {epoch:d}, Iteration {tt:d}, loss = {loss.item():.4f}, l1 loss={l1_loss.item():.4f}, grad loss={Grad_loss.item():.4f}') - rmse_val,mse_val,Rsquare = check_rmse_CNN_ray(valid_loader,model,grid_space, DF,maxB=maxB,minB=minB) - rmse,mse_train,R_TEMP = check_rmse_CNN_ray(train_loader,model, grid_space, DF,maxB=maxB,minB=minB) - rmse_val_history[tt//print_every] = rmse_val - rmse_history[tt // print_every] = rmse - iter_history[tt // print_every] = tt - loss_history[tt // print_every] = loss.item() - print() - - elif not verbose and (t == len(train_loader)-1): - print(f'Epoch {epoch:d}, Iteration {tt:d}, loss = {loss.item():.4f}, l1 loss={l1_loss.item():.4f}, grad loss={Grad_loss.item():.4f}') - rmse_val,mse_val,Rsquare= check_rmse_CNN_ray(valid_loader,model, grid_space,DF,maxB=maxB,minB=minB) - rmse,mse_train,R_TEMP = check_rmse_CNN_ray(train_loader,model, grid_space,DF,maxB=maxB,minB=minB) - rmse_val_history[epoch] = rmse_val - rmse_history[epoch] = rmse - iter_history[epoch] = tt - loss_history[epoch] = loss.item() - mse_history[epoch] = mse_train - mse_val_history[epoch] = mse_val - - print() - adjust_epoch_count += 1 - - # # create checkpoint - # base_model = (model.module - # if isinstance(model, DistributedDataParallel) else model) - # checkpoint_dir = tempfile.mkdtemp() - # # load back training state - # checkpoint_data = { - # "epoch": epoch, - # "net_state_dict": base_model.state_dict(), - # "optimizer_state_dict": optimizer.state_dict(), - # } - # torch.save(checkpoint_data, os.path.join(checkpoint_dir, "model.pt")) - # checkpoint = Checkpoint.from_directory(checkpoint_dir) - #Send the current training result back to Tune - train.report({'rmse_val':rmse_val.item(), 'rmse_train': rmse.item(), 'loss':loss.item()}) - - - - adjust_learning_rate_sch(optimizer, learning_rate_decay, epoch, schedule) - epoch_stop = epoch - - - - return rmse_history, rmse_val_history,loss_history, iter_history,mse_history, mse_val_history,epoch_stop,Rsquare def get_mean_of_dataloader(dataloader,model,device): @@ -482,57 +311,6 @@ def check_rmse_CNN(dataloader,model, grid_space, device, DF, maxB=[],minB=[]): #----------------------------------------------------------------- -def get_mean_of_dataloader_ray(dataloader,model): - num_samples = 0 - b = torch.zeros(1) - model.eval() - for x,y in dataloader: - # use sum instead of mean, what do you think? - y_sum = y.sum(dim=0,keepdim=True) - num_samples += y.shape[0] - # print(y.shape[0]) - b =b+y_sum - return b/num_samples - -def check_rmse_CNN_ray(dataloader,model, grid_space, DF, maxB=[],minB=[]): - ''' - Check RMSE of CNN - ''' - mse_temp = 0 - R_temp=0 - Rsquare=0 - num_samples = 0 - # print(Bfield_mean) - - data = next(iter(dataloader)) - mean = data[0].mean() - - Bfield_mean=get_mean_of_dataloader_ray(dataloader,model) - - model.eval() # set model to evaluation model - - with torch.no_grad(): - for x,y in dataloader: - num_samples += x.shape[0] - if DF: - _, scores = Jacobian3(model(x)) - else: - scores = model(x) - - # compute mse and R2 by de-normalize data - mse_temp += F.mse_loss(1e3*denorm_ray(scores,maxB,minB), 1e3*denorm_ray(y,maxB,minB) ,reduction='sum') - R_temp += F.mse_loss(1e3*denorm_ray(Bfield_mean.expand_as(y),maxB,minB), 1e3*denorm_ray(y,maxB,minB), reduction='sum') - - - rmse = torch.sqrt(mse_temp/num_samples/grid_space/3) - - Rsquare=1-mse_temp/R_temp/num_samples - print(f'Got rmse {rmse}') - - - - - return rmse, mse_temp/num_samples/grid_space/3, Rsquare #---------------------------------------------------------------- def grad_loss(preds, y): From 2cc91783df8fffaf949e97bd880dc01e72cb1490 Mon Sep 17 00:00:00 2001 From: wangjunang Date: Thu, 21 Mar 2024 14:53:59 +0800 Subject: [PATCH 14/16] tune v5 and plot v1 --- .vscode/settings.json | 5 + Modeling eMNS/Generative_model_v2.ipynb | 406 ++++++++++++++++-- Modeling eMNS/Neural_network.py | 6 +- Modeling eMNS/Training_loop_v2.py | 182 ++------ Modeling eMNS/utils.py | 203 ++++++++- .../mcr_radiologyInstrumentDOFs.py | 8 - 6 files changed, 599 insertions(+), 211 deletions(-) create mode 100644 .vscode/settings.json diff --git a/.vscode/settings.json b/.vscode/settings.json new file mode 100644 index 0000000..edbfa7b --- /dev/null +++ b/.vscode/settings.json @@ -0,0 +1,5 @@ +{ + "cSpell.words": [ + "denorm" + ] +} \ No newline at end of file diff --git a/Modeling eMNS/Generative_model_v2.ipynb b/Modeling eMNS/Generative_model_v2.ipynb index 5b4e4e5..8fa372b 100644 --- a/Modeling eMNS/Generative_model_v2.ipynb +++ b/Modeling eMNS/Generative_model_v2.ipynb @@ -18,9 +18,25 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Using cpu\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/qubot/.pyenv/versions/3.10.13/lib/python3.10/site-packages/torch/cuda/__init__.py:628: UserWarning: Can't initialize NVML\n", + " warnings.warn(\"Can't initialize NVML\")\n" + ] + } + ], "source": [ "%reload_ext autoreload\n", "%autoreload 2\n", @@ -38,9 +54,19 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "torch.Size([146, 6, 21, 21, 21])\n", + "current shape torch.Size([146, 12])\n", + "Bfield shape torch.Size([146, 3, 16, 16, 16])\n" + ] + } + ], "source": [ "from ReadData import ReadCurrentAndField_CNN\n", "import glob\n", @@ -50,7 +76,7 @@ "# print(os.getcwd())\n", "foldername=\"./Data/\"\n", "filepattern = \"MagneticField[0-9]*.txt\"\n", - "train_file_num= 1400\n", + "train_file_num= 146\n", "#data = ReadFolder(foldername,filepattern)\n", "current,data = ReadCurrentAndField_CNN (foldername,filepattern,train_file_num)\n", "\n", @@ -108,13 +134,188 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 38, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
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Tune Status

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Current time:2024-03-21 11:57:19
Running for: 00:00:43.56
Memory: 10.8/31.0 GiB
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System Info

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Logical resource usage: 2.0/16 CPUs, 0/0 GPUs\n", + "
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Trial Status

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Trial name status loc iter total time (s) rmse_val rmse_train loss
TorchTrainer_0385f_00000TERMINATED192.168.8.117:20581 10 38.5563 3.16977 2.374340.0456446
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\n", + "\n" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[36m(TorchTrainer pid=20581)\u001b[0m Started distributed worker processes: \n", + "\u001b[36m(TorchTrainer pid=20581)\u001b[0m - (ip=192.168.8.117, pid=20651) world_rank=0, local_rank=0, node_rank=0\n", + "\u001b[36m(RayTrainWorker pid=20651)\u001b[0m Setting up process group for: env:// [rank=0, world_size=1]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[36m(RayTrainWorker pid=20651)\u001b[0m Epoch 0, Iteration 16, loss = 0.1583, l1 loss=0.1200, grad loss=0.0383\n", + "\u001b[36m(RayTrainWorker pid=20651)\u001b[0m Got rmse 9.797602653503418\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[36m(RayTrainWorker pid=20651)\u001b[0m Checkpoint successfully created at: Checkpoint(filesystem=local, path=/home/qubot/Trained_model/EMS_CNN_s_2r_1b_3/TorchTrainer_0385f_00000_0_2024-03-21_11-56-36/checkpoint_000000)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[36m(RayTrainWorker pid=20651)\u001b[0m Got rmse 8.676706314086914\n", + "\u001b[36m(RayTrainWorker pid=20651)\u001b[0m \n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2024-03-21 11:56:48,192\tWARNING experiment_state.py:323 -- Experiment checkpoint syncing has been triggered multiple times in the last 30.0 seconds. A sync will be triggered whenever a trial has checkpointed more than `num_to_keep` times since last sync or if 300 seconds have passed since last sync. If you have set `num_to_keep` in your `CheckpointConfig`, consider increasing the checkpoint frequency or keeping more checkpoints. You can supress this warning by changing the `TUNE_WARN_EXCESSIVE_EXPERIMENT_CHECKPOINT_SYNC_THRESHOLD_S` environment variable.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[36m(RayTrainWorker pid=20651)\u001b[0m Epoch 1, Iteration 32, loss = 0.1101, l1 loss=0.0793, grad loss=0.0308\n", + "\u001b[36m(RayTrainWorker pid=20651)\u001b[0m Got rmse 6.467669486999512\n", + "\u001b[36m(RayTrainWorker pid=20651)\u001b[0m Got rmse 5.52919864654541\n", + "\u001b[36m(RayTrainWorker pid=20651)\u001b[0m \n", + "\u001b[36m(RayTrainWorker pid=20651)\u001b[0m Epoch 2, Iteration 48, loss = 0.0885, l1 loss=0.0615, grad loss=0.0270\n", + "\u001b[36m(RayTrainWorker pid=20651)\u001b[0m Got rmse 5.83610725402832\n", + "\u001b[36m(RayTrainWorker pid=20651)\u001b[0m Got rmse 4.7611260414123535\n", + "\u001b[36m(RayTrainWorker pid=20651)\u001b[0m \n", + "\u001b[36m(RayTrainWorker pid=20651)\u001b[0m Epoch 3, Iteration 64, loss = 0.0745, l1 loss=0.0504, grad loss=0.0241\n", + "\u001b[36m(RayTrainWorker pid=20651)\u001b[0m Got rmse 4.861945152282715\n", + "\u001b[36m(RayTrainWorker pid=20651)\u001b[0m Got rmse 4.036675930023193\n", + "\u001b[36m(RayTrainWorker pid=20651)\u001b[0m \n", + "\u001b[36m(RayTrainWorker pid=20651)\u001b[0m Epoch 4, Iteration 80, loss = 0.0710, l1 loss=0.0473, grad loss=0.0238\n", + "\u001b[36m(RayTrainWorker pid=20651)\u001b[0m Got rmse 4.330577373504639\n", + "\u001b[36m(RayTrainWorker pid=20651)\u001b[0m Got rmse 3.654207944869995\n", + "\u001b[36m(RayTrainWorker pid=20651)\u001b[0m \n", + "\u001b[36m(RayTrainWorker pid=20651)\u001b[0m Epoch 5, Iteration 96, loss = 0.0633, l1 loss=0.0413, grad loss=0.0219\n", + "\u001b[36m(RayTrainWorker pid=20651)\u001b[0m Got rmse 3.7916672229766846\n", + "\u001b[36m(RayTrainWorker pid=20651)\u001b[0m Got rmse 3.040168285369873\n", + "\u001b[36m(RayTrainWorker pid=20651)\u001b[0m \n", + "\u001b[36m(RayTrainWorker pid=20651)\u001b[0m Epoch 6, Iteration 112, loss = 0.0529, l1 loss=0.0326, grad loss=0.0203\n", + "\u001b[36m(RayTrainWorker pid=20651)\u001b[0m Got rmse 3.4171268939971924\n", + "\u001b[36m(RayTrainWorker pid=20651)\u001b[0m Got rmse 2.6344687938690186\n", + "\u001b[36m(RayTrainWorker pid=20651)\u001b[0m \n", + "\u001b[36m(RayTrainWorker pid=20651)\u001b[0m Epoch 7, Iteration 128, loss = 0.0482, l1 loss=0.0292, grad loss=0.0191\n", + "\u001b[36m(RayTrainWorker pid=20651)\u001b[0m Got rmse 3.147188186645508\n", + "\u001b[36m(RayTrainWorker pid=20651)\u001b[0m Got rmse 2.4714787006378174\n", + "\u001b[36m(RayTrainWorker pid=20651)\u001b[0m \n", + "\u001b[36m(RayTrainWorker pid=20651)\u001b[0m Epoch 8, Iteration 144, loss = 0.0521, l1 loss=0.0321, grad loss=0.0200\n", + "\u001b[36m(RayTrainWorker pid=20651)\u001b[0m Got rmse 3.2194416522979736\n", + "\u001b[36m(RayTrainWorker pid=20651)\u001b[0m Got rmse 2.39068865776062\n", + "\u001b[36m(RayTrainWorker pid=20651)\u001b[0m \n", + "\u001b[36m(RayTrainWorker pid=20651)\u001b[0m Epoch 9, Iteration 160, loss = 0.0456, l1 loss=0.0271, grad loss=0.0186\n", + "\u001b[36m(RayTrainWorker pid=20651)\u001b[0m Got rmse 3.169771194458008\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[36m(RayTrainWorker pid=20651)\u001b[0m Checkpoint successfully created at: Checkpoint(filesystem=local, path=/home/qubot/Trained_model/EMS_CNN_s_2r_1b_3/TorchTrainer_0385f_00000_0_2024-03-21_11-56-36/checkpoint_000001)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[36m(RayTrainWorker pid=20651)\u001b[0m Got rmse 2.3743371963500977\n", + "\u001b[36m(RayTrainWorker pid=20651)\u001b[0m \n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2024-03-21 11:57:19,905\tWARNING experiment_state.py:323 -- Experiment checkpoint syncing has been triggered multiple times in the last 30.0 seconds. A sync will be triggered whenever a trial has checkpointed more than `num_to_keep` times since last sync or if 300 seconds have passed since last sync. If you have set `num_to_keep` in your `CheckpointConfig`, consider increasing the checkpoint frequency or keeping more checkpoints. You can supress this warning by changing the `TUNE_WARN_EXCESSIVE_EXPERIMENT_CHECKPOINT_SYNC_THRESHOLD_S` environment variable.\n", + "2024-03-21 11:57:19,911\tINFO tune.py:1042 -- Total run time: 43.58 seconds (42.73 seconds for the tuning loop).\n" + ] + } + ], "source": [ - "from Neural_network import Generative_net, Generative_net_test, ResidualEMNSBlock_3d, BigBlock, weight_init, eMNS_Dataset\n", - "from Training_loop_v2 import train_GM, train_GM_ray\n", - "from functools import partial\n", + "from Neural_network import eMNS_Dataset\n", + "from Training_loop_v2 import train_GM\n", "from ray.train import RunConfig, ScalingConfig, CheckpointConfig\n", "from ray.train.torch import TorchTrainer\n", "from ray.tune.tuner import Tuner\n", @@ -124,11 +325,11 @@ "\n", "# construct dataset\n", "dataset = eMNS_Dataset(\n", - " train_x=current,\n", - " train_y=Bfield\n", + " x=current,\n", + " y=Bfield\n", ")\n", "# split the dataset to train, validation, test\n", - "train_set, valid_set = torch.utils.data.random_split(dataset, [0.9,0.1])\n", + "train_set, valid_set, test_set = torch.utils.data.random_split(dataset, [0.85,0.1,0.05])\n", "\n", "# normailzation\n", "extremes = dataset.train_norm(train_indices = train_set.indices)\n", @@ -144,7 +345,7 @@ " \"scaling_config\": ScalingConfig(\n", " num_workers = 1,\n", " use_gpu = use_gpu,\n", - " resources_per_worker = {\"CPU\":4, \"GPU\":1}\n", + " resources_per_worker = {\"CPU\":4, \"GPU\":0}\n", " ),\n", " # You can even grid search various datasets in Tune.\n", " # \"datasets\": {\n", @@ -170,6 +371,7 @@ " 'minB' : extremes[3],\n", " 'train_set' : train_set,\n", " 'valid_set' : valid_set,\n", + " 'num_input' : 12,\n", " }\n", "\n", "}\n", @@ -188,8 +390,8 @@ "################################################\n", "\n", "train_loop_config = {\n", - " 'epochs': 350,\n", - " 'lr_max': 1e-4,\n", + " 'epochs': 10,\n", + " 'lr_max': 5e-4,\n", " 'lr_min': 2.5e-6,\n", " 'batch_size': 8,\n", " 'L2_norm' : 0,\n", @@ -198,14 +400,15 @@ " 'schedule': [],\n", " 'grid_space': 16**3,\n", " 'learning_rate_decay': 0.5,\n", - " 'skip_spacing': 1,\n", - " 'num_repeat' : 4,\n", - " 'num_block' : 2,\n", + " 'skip_spacing': 2,\n", + " 'num_repeat' : 1,\n", + " 'num_block' : 3,\n", " 'maxB' : extremes[2],\n", " 'minB' : extremes[3],\n", " 'device' : device,\n", " 'train_set' : train_set,\n", - " 'valid_set' : valid_set\n", + " 'valid_set' : valid_set,\n", + " 'num_input' : 12,\n", " # You can even grid search various datasets in Tune.\n", " # \"datasets\": tune.grid_search(\n", " # [ds1, ds2]\n", @@ -218,7 +421,8 @@ " # resources_per_worker = {\"CPU\":4, \"GPU\":1}\n", ")\n", "\n", - "run_config = RunConfig(checkpoint_config=CheckpointConfig(num_to_keep=1))\n", + "run_config = RunConfig(checkpoint_config=CheckpointConfig(num_to_keep=1),storage_path='~/Trained_model', \n", + " name='EMS_CNN_'+'s_'+str(train_loop_config['skip_spacing'])+'r_'+str(train_loop_config['num_repeat'])+'b_'+str(train_loop_config['num_block']) )\n", "\n", "# def train_loop_per_worker(params):\n", "# train_GM(train_set=train_set, valid_set=valid_set, device=device, config=params)\n", @@ -230,37 +434,74 @@ " run_config = run_config,\n", "\n", ")\n", - "\n", - "# result = trainer.fit()\n", - "tuner = tune.Tuner(\n", - " trainer,\n", - " param_space = param_space,\n", - " tune_config =tune.TuneConfig(\n", - " scheduler=tune_schedule,\n", - " num_samples=1, # number of samples of hyperparameter space\n", - " ),\n", - " # run_config = RunConfig(storage_path=\"./results\", name=\"test_experiment\")\n", - ")\n", - " \n", - "results = tuner.fit()" + "# train the model\n", + "result = trainer.fit()\n", + "#----------------------------------------------\n", + "# tuner = tune.Tuner(\n", + "# trainer,\n", + "# param_space = param_space,\n", + "# tune_config =tune.TuneConfig(\n", + "# scheduler=tune_schedule,\n", + "# num_samples=1, # number of samples of hyperparameter space\n", + "# ),\n", + "# # run_config = RunConfig(checkpoint_config=CheckpointConfig(num_to_keep=2),storage_path=\"/home/qubot/ray_results\", name=\"test_experiment\"),\n", + " # checkpoint_score_attribute='rmse_val', checkpoint_score_order='min\n", + "# )\n", + "# # tune the model \n", + "# results = tuner.fit()" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 44, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Result(\n", + " metrics={'rmse_val': 3.169771194458008, 'rmse_train': 2.3743371963500977, 'loss': 0.045644618570804596},\n", + " path='/home/qubot/Trained_model/EMS_CNN_s_2r_1b_3/TorchTrainer_0385f_00000_0_2024-03-21_11-56-36',\n", + " filesystem='local',\n", + " checkpoint=Checkpoint(filesystem=local, path=/home/qubot/Trained_model/EMS_CNN_s_2r_1b_3/TorchTrainer_0385f_00000_0_2024-03-21_11-56-36/checkpoint_000001)\n", + ")\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ - "print(torch.device(type='cuda', index=0))\n", - "print(ray.train.torch.get_device())\n", - "print(torch.device('cuda:0'))" + "from utils import plot_ray_results\n", + "print(result)\n", + "plot_ray_results(result, metrics_names=['rmse_train','rmse_val'],ylim=[1,5])" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, "metadata": {}, - "outputs": [], + "outputs": [ + { + "ename": "AttributeError", + "evalue": "'Result' object has no attribute 'get_best_result'", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[7], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m best_result \u001b[38;5;241m=\u001b[39m \u001b[43mresults\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_best_result\u001b[49m(metric\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mrmse_val\u001b[39m\u001b[38;5;124m'\u001b[39m,mode\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mmin\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[1;32m 2\u001b[0m \u001b[38;5;28mprint\u001b[39m(best_result)\n", + "\u001b[0;31mAttributeError\u001b[0m: 'Result' object has no attribute 'get_best_result'" + ] + } + ], "source": [ "best_result = results.get_best_result(metric='rmse_val',mode='min')\n", "print(best_result)" @@ -282,16 +523,88 @@ "metadata": {}, "outputs": [], "source": [ - "plot_ray_results(results, metrics_names=['rmse_train','rmse_val'],ylim=[0,1])" + "!tensorboard --logdir=~/ray_results" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 92, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Got rmse 2.705864906311035\n", + "rmse for test set: 2.7059mT\n", + " mse for test set: 7.3217mT\n", + " R2 for test set: 0.9840\n", + "plot sample rmse: 2.2255mT\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ - "!tensorboard --logdir=~/ray_results" + "\n", + "from utils import estimate_test_set \n", + "test_estimator = estimate_test_set(result.checkpoint, test_set, train_loop_config)\n", + "test_estimator.fit()\n", + "test_estimator.peek_z(2)" + ] + }, + { + "cell_type": "code", + "execution_count": 114, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "torch.Size([16])\n", + "torch.Size([3, 16, 16, 16])\n", + "torch.Size([3, 16, 16, 16])\n" + ] + }, + { + "data": { + "image/png": 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", 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3khAmE0NF15j/iYBpHmGHb32svMMrtkiKDCI5MghREJifIZNC96gdq8NN04hE+7CXuIJNBmZ4UpJmTz7ym5rA/VWiILBsSozmd9/WygSnENLUqVNZuHAhpx6zksLkUPW4yq4xnv+8mOrqarq6unA4HIiiyLTEcOI897unZZjmfrn+NTo6ysjIiIbETpudrJ7vnV3NAa9RwRxP2nF43MkNb+/V1Bsnw99yjA4d/wropKUjIHx7r0xGb2Ty5Z5DZGZmkpWVpTl+Uba8QAdNIlUPMcuk5ZBEgjxChoqOwPZFikovOtj7vO+VtKk/X7AghYfPyGPl1FhuPlp2em/xNBBPVB1OjZfJoGfUrkZmX1UGbvrNiQvxi44+OKCNpJQU4akzExGAul4re5sH+NxH4KFgIml9uM//GACj0cjJs7XR0LpW+fVqampi69at7Ny5k9raWp4+LZMlWbJ441efVnP3mlqMRqM61bm9vZ39+/eTFoZ6L5/ub6dn2DppOvGW4/LUn7fU9HDwe8QbvaM2znhmG/Mf+CqgIlOHjn81dNLSoYGSDrTZbKoVk91uV4UOyclJqrO61j5JXiDbBi387J39HPnEFhxO76IW6hFP2FwSkR53iboebXQkSRLt7e2EC3KKcXjc+/gIHwJbkh3DqbOSyIwNIT9ZjqTKOkZY9MhWljy6FYfLS1xRISa1yVhJK+5uDrwoC4LAPI+aT0F6pNZ26oIFqbx1xVx+fVIuc9LlYy9/8yB3r6lh0KIVPEwkrYPtI6pn4kQcNyNe8+/CtCjy8vJYvHgxK1asICcnR7af6m1lhnnAc8+jfFHeQ3Fdn9oD5nK5GB8fl+tq0+UamNMt8ca2OkZHR7FaraqDvfL+LcmJJdyTPo0INjErVfsa+OKN4kZKWwcZsjq46rXdOF2BWxN06PhXQSctHSp8rZhATmP19fWxbds2Qk0ya427DerfNKTl2dUPWZ1squqlY8imkZcfnZfA2XNTiA42UJQhE43VZ6c+Pj7Onj17aGpqwjnUBUDLwBjhnufd1TAY8Jqz40IJEiVcblkY4ZLg3T3tmmOUFGFhipza6xy20Tdm9zsXwIJMOUWo1Og2HtIS3JT4UOakRRJkFDki16v8c0tw35dawUNcmJn8ZPk5FcXjugo5yptoP5UYEaQ+N8D6yl41kjGbzSQmJpKXl8eSJUu49tQigr2CTH7xYTlrN+/k0KFDjIzI0asoihyZl6DaT723t4NhyzhWq1UVdigk5nI5Oc0T6Q1aHXxxsCPgawNww9G56gamuK6Pu1eX/dUZY4e6hzUbGB06/hHopKUDkMfbT7RiqqmpYd++fUyfPp2YcLnO1Onjru67WMV4SMvmdKs1q/UV3erfbz52Gg+fNZOsKCNHTpEX8g6PU0V1kzwQMjg4mMWLF3P88oUAjDkgIczj+dfcR0lJCfX19QwMDOByyYugKAikTfClXTch/Xd6YRK/OCqH61ZlM91DYOvKu/ntF/6qOqWupdxbTa+NkQD9YgBH5mrl/ftahrHYtYvzck+0paQpX93ezPlvVAUcw3K8T7Q17nSzoTJwvSwiNJgz53rTiTaXgD0kDofDQXd3NwMDA+zZs4dkqZ8PL5tOTIiB7hE7K57aRV3fuGYgpkJiR0/zEuYzm2pxTkIyZqPIsQXeellp66DfPfvi2jf2cOwTWzjruWLceh1Mxz8BOmn9j0OSJMYs45zw1BYWPvwtjX0WrFYrO3fupLe3l6KiItLT09X6SP/Y95PWoNVBuCeVFxHk7xImCALpUfKx3SM2Cu7byOmvlBMUn0lhYSFGo5GwIKOq8MtLlRf9QbtAXGIyVquV8vJytmzZwr59+2hqaiI9TL4OpenXOMES6ZRZifx4WQa5iWEsyY4G4Hdf1fPh/k6+qtISXF5SOKFmAy7PrUnA5kP9AV+7qfGhmr4ySZLUiMr3ue8/dTr3nzodAeizOOkcdfBmlf9CPzFF+F5Ju98xCs7xIS0JyMtIJj8/n5ycHKKjo0lLS8PpsNNQW8PsKIfn+uCKN0vpGh73m+q8IDNKdeeo7Rnji31NAQdiAly1PFv9OT06VKPMnAilPlbWPsyvPj6gE5eOfxg6af0Pw+1209fXR2NDHYd6xhi1ufhLcR3FxcVERUWxdOlSwsPlqOjCRbKEOiJYJhxBEDQLmWpUa3GQ7lnIa7r9FX2CIBBmElQSlMlB4LUDw5qU2aLMaJZPieGM2UlEBhtxSzAkRjBz5kyWL1/OokWLSEhIYHR0lAwPaRkF+f/lHSO43IFrLUs9pKXg/i9rVcd4kAlvrqdWlRrmSRFWBY54BEHgiGneFOGA1cn+Vu0MrWkJYZw1J5ncxHBWTvXWuLZ3SnwzQS6fEBHEfE9NTQAOtI+oY04mYkZyOJkxXsJ8ZUcLIBOn0WgkJSWFgoICli1bxh2nz1OPG7O7ueLVXWzbVUJDQwODg4NIkoTJaOCM2clMiZMjwue3NNHUIzc3T5zqPDctgoRwWSzzVWUX5e2TCzceOHOW+vP7e1q557PvTyc6XW6e+6b2r6YcdfzvQiet/0EoVkx2ux2LxUJnZ6c6GfhAUy+FhYUUFBRorJiyPYtZp6e36mCnRUtaHhIasDpUW6dAjcaCIDA2NkasUd79K9Lx1gnjQ548t4AXLipkxdRYdSFfc7CLS17bh93pJiwsjPT0dAoLC9VIy2MMj80p8fbarZSVldHW1obFYlEXwQWZUZpIzO5y+6W3js2L56w5SRyfJUcQ39T0suiRrdR2+fsSHjEhRfjR/snrQacWJk34jf/CfGKB7Jeo1KIufb2UJzbVBTzfxQvT1J/Xl/eo740v+QuCQEZClErEAMHBISQmJDA2NqaOYdm/fz/nThN56oxsRKCq28I5r5Qy7hIwetSj3d3dFBcXY7GMceosb1T4+PpqzWfBF0flJaqfDUAVfARCQ88o+fes49H1Ndz2YalOXDoCQiet/zEoYgu73Y4kSRgMBpxOJ56RVyTFx5GUNHFxheRIeVffMThO3r0buf7jOvZ3exf76BBP75TVycLMaABGbE6Nj5/b7WZ4eJiOjg7yUuVjlLSi1UMcgcbTz/F4/r1b0kFp2wi/26BdxJPDBIIMAg6fdbPHlEhYWBhdXV3s3LmT4uJiKioqGO7vYWaytwhmtbv9nC/Om5/C/afmsSrdSFKYnCocd7r52fvlfv1Ji7KiNCnB9RW9k9Z4jpwep5I0QNugP6kfNyOe8+encO1Kb0vBqzva2BOgefrUwkR1s+EG3tzVNulC/4ujctSf+ywOsjPTmTVrljqGJS4ujpGREVpqK4gJks8x7nDz47f2YbG71DqYMuH55IIE9XxfV/ewrapNk05UrkMUBS5b6tseIQR8jwEyYkNRXt4PS9q459NyPZ2oww86af0PQbFiUsQWgiDQ2yv73SVEyKTTNokkOz7cLE8C9vnduibv4pwUGcSOO1ax766juGhxhnq+mm45OrFarezatQubzUZ6ejozM+VFT6mHdA7bsNpdARfdOelaCfanB7s04giDIDtfZMYEc/kSOfrY3WolKzub+fPns2rVKvLz8wkKCqKtrY0U0cdbEPhyErWcIAgcMdUrUOgctvGqJw2nwGQQWeHxDwwPMjDudLO6tEMlYV+EmAwc41O3emNnmx9hxoeb+c1JuVy6OI2IIO/X8+cfVKiRlIKoEBOLfdKd75e0s6N5NCAp+NasBq0uPj3Qpd5jeHg4GRkZFBYWsnLlSh49Y7r6uMouK1e+XMz+0lJ6enrUiGpGSiQnzUxglkeRecfqasZtcuTuq04cHx/nnHmpqjjnz1vraR0IPN/MaBC5dGmm+u83dzRx1+qDAY9VsKOuj7s/+f5jdPx3QSet/wFIkkTPkIU/bz6Ey+VCFEWcTif79++nra0Nk8nEtGSPG0QAGyaQF+d4Tx0jzJO6svm4XxhEgZhQs9rAOyNJbsJ9aUsjR/z+O9Z9s42IiAgSExMxGo1M8ajp7J4+HwnY65kXNREzUyLwCVAQBQGjQZsCe/H8PL742WIuXyLX3so6Rpj/uy28W9KGwWAgNjZWdaA4d1Wh5vx/2dHIrl27qK2tpbe3F6fTS4grsiM0xyo1H18cmRvHjKQwFnok6w9vqOeS1/cFvJczfFKEHcM2ttUFHv4oCAKXLPZaMcWEGNXX3xc/LspQfx53SjzwXS9dY4FTdWfO8ar+XtzSpOln833exbkpmibr5XnJxERHMzIygtVqVcew3LQwgjuOkp+/fdjGxa+XIhiMmqnOFouFSKOTxR4LKodL4tF1VZNGhD87chq+lLvoe+aG/em7Oi58aQdv7Wzm4bWVejrxfwQ6af2XQ5IkttR0sejhb3hoXS2tg+MMDg6ybds23G43c+bMQRAEjs2XaylWh3vSL79iUKvYLI1+j01dfrK82G+q7qVzxMGX3bKIQqmTKTO2ekZs6rTiHfWBVXqhZgPTk7xj7G1Ot1+TrjLJOCEiSO2NcrnhiU0Nfs4N8zNjNCm91jGB4LhUXC4XtbW1bNmyhT179jA+Pk56iEOzgH9T6+81eFphIh9cvYC7Tpym/q6m28KH+/wjuCU5MWrPG8Bfdrf5HaPgR4vS1AW8eXCc3lH/3rLF2dEsyY4mL9FLpvduGQq4+bhuZaZ6vs4RuxptBcJPVngjnv0dFrKyssjKyiIyMlIzhmWwqVzdUNR0W7j5g4M4XG6/qc5nzPaS9WelHeyobg841TkxMlj9LAJ+0aUvTpzlQ8Lf1fPrT8r0Zuf/Aeik9V8Mpfdqdoq3hvPM+jL27NlDtid1FhQUhNvt5njPQmGxuzQ+f75QSCvRs+MfsjHpIpETI6cHlUVye/Mo4w6XWhdJjw7mlcvmsf6m5dxw5BQAdjUOYnVMkiJM06YI11d4peoT5fcrfcZ9WB1uXt/ZqnmsySAyPyMKEdm6CeDrJjuhSVkUFRVRVFREWloakiTR0d7OrEgvAXxZ1s3ohFEfSjouOTKYGYne1/qhdYf8/BWNosAxPj1R2+oHeOa7Rr/7BTn9V+BJv0kSvL6j1e8YURB4+UezefNyr0JwyCbx8w8r/F7HULORE31qUY9+VceQJXCT9fnzU1TByp7mYUqah5AkKeAYlssXegnp20ODXP9aMeXl5bS3tzM+Po4gCByXn8BJBQlqNHr9B2XYbDbGxsZUdeLIyAjj4+NcssRLmH/cVEvnJB6VmXGy0bKCd3Y1c/P7pQGPVfDkV9X8bm3l9x6j4z8bOmn9F8JXbOF2uwk2m9QFaGB4lMWLF5Odna3WtSRJIthkUCOoq9/cx6KHv8U2IUJJ8ogxIkLkaMkNmjEfCjo7O7G0axt3gwwiwSaDusAbDSLLp8aREhXM0ikyyexvHWLRI1t5bqu/wauiflOEB1+Wd/sdo2DlNG1KyZdIFNx14jS2/XIZlyyWa2B/3t7KZa/vxy1JBAcHk5KSQkhICNOnT+fCFTPUx9ldEk9/WkxpaSnNzc2MjIxoyOHypd6UXkK4SX1NfXHSjBgEUCOuF7c2s6dpMOC9+EY87+9tn9TJI8RsYHaqN5V53Iy4gLWtXx0/FUUTanW4Oe3FPQGFIyaDyLF5XkL43YZDuN1uv3OKoshPj5qmSd+etyiTkJAQOjo62LFjB8XFxdTXVnPz0mjmeTrB+8ec3PxxlWaq8+bNm+np6WFWookMj5zf6nDz0Bflk0b/t53gfW8E4CSf6GsibvhLCX/YdIgXvqvnqY01ejrxMIVOWv9lcLvdOBwOtS4jiiK9vb1EmuQvqCMoiqgo705fFEW1uK7I2ss7Rhged/LytibNuVfmxvHTVdmcPjtVTUf59hG53W4qKiooKyvjqIUzCTaJqqh71O6iY2jcLyoCSIsOIT0mRP33Gzva/Ixv56TLC7Ly0Po+66TuHIWpEUT6+Bx9VOpvVJsRE0J4kJHjZ3gjj74xB69u9wotFFIvyk0iPMh7vn3DocTExDAwMMDevXvZsmULBw8epLW1lWWZoeoGoWPYHtAUeEZCME8cEcTNx0xRf3fdu2U09PkLFI7KjSPYk8q0uyTe2jV5OvG246aqP7+7pyPgoMjYMDOnzU5Sv/gDFic/e/dgQOL68TIvYVZ1jbGlMbDII8Rk4DSfWt3n1aNMmTKFBQsWsGrVKvLy8lQRTLrN+/p+d2iAuz6rwiWhEpfRaMRoMHDuXC/5fH6gk6/LWgJOdZ6TEU1hmsfFBGjsC+z2D3DhIu/9PLWxllvfL8Wm20sddtBJ678Evr1XLpdLXVyqq6vZv3+/SkiN/dphicqCL0mSeoyyLH2wV7tAHpEbz83HTOOYGQlqo+z+liHaB62MjY2xY8cOBgcHWbZsGWmpKeQmhmse/011T0DSAtkEV8G40823E0Z+pEUF84ujcnjholnM8Tit/2lLIwc8jgu+5zSIgqroA9hyaGDSOVHRoSbmpnkjlBe3NvvZNhlFgRPyZXITBajqtvL07lHmzJnDypUrmTNnjlrjObCvhLxo77W8vLXR7zkFQSDSLHDB/FQ1chx3unlwbW3AY69b6V1sX93e4mc0rGBueiQRZoUwbWyYxM3+7pNyefRMb4RS0jLsN9EZ5Abmq4q8keOjmzsD2k+BNsL8traPQ55rNBgMxMXFqSKY049byaJ0b+S7pryXn7+xjepquddLEQqdNTeFEwsSOMIzmPO2TyoZtVgDTnW+arlXUv/UVzU0ByB/gBXTE8hL9r7XH+9r44pXdn+vrP6+z8r481b/10bHvw86af0X4O0djSx88CuKHpZH2CtThXfs2EF/fz/Lli1j4RS5ZtU3Ztd8SRVy8yUtRaQQyIZJgVJj+rS0g6Oe3Mbzn20nJiaGpUuXEhoqn0cRYyiKwg0V3ZOTVo7WET15QlpNEAR+vCyDZVNiOc4THX2wv4sb3q8I0KKrTRE63dKkCzjA+Qu8lkhxYSaNo7yCixel8cKFs1T14PqqXjZV9/rVeFauXMl1q7w9URtr+vls0zaqqqro6upS++OUezomzyuBn+hlqOCC+amqRZVLggtf3ceQNXDd8eRc70bhiU0NARWCQUaR4woSNGKUc+cFTqv94qgcEsNlIYrdBf+33RIwgpuWEEahT3ryjtWBFYJGo5E7TszT/O7cBenq56KkpISdO3fS09rA7Svi1Q1Ev8XJnZ/VYjAY/KY6L88MJUG9Rom7PjkwabPzr07ykrUowImzkhBF/+gR4NI/7+S14iYeWFPJs7pLx38MdNI6zOF2uyltGaRvzEH3qINBq5OOjg6Ki4tVEgkLC+Omo6dgNoo4XBItA95oSxTlj4AkSWR7ZOiKEq+xzzLpLlSZK6X89dNWE/n5+er5ABZkRbNiWhwXepwbStuGcboJ+OVfOoG03v8e372jfWotAxYHm9v9z7l8irautbp0cqXcMXkJak2mddBGebs3paecd3piGMunxnLtCq/E/LZPKtWIQoHBYGBVQbrqnSghUDIahcFgUGdj1dTUYLPZ6Onp4edHeM/30raWgCQTFmTk+YsKSfL0vo073Fz9lwMBo8cTp4WqkXLHsG1ShaAoCFy2xOuo8fCGwK4bgiDwc5/G5NpBF3esrgp4nRctTFV/rukeY2N14KnOeUnharQM8Pr+QaZNk5WXixcvVsew1NfXMyt4UD1uU00fv1lTgxtBM9U5yGTkvHnyxkMAthzq472d9QGnOh85PYFpCTKxuyUCKjIVnDnXez+Pra/h+rf3aprldfx7oJPWYQpl7pXdbtfskl/fuJfKykrmzJmjIRGTQWS6J11X1j7E9jrvFF2QyS/HE0VY7PIX0+6S1ObgiTDjJDHYSxR9Fie9o1qZ9ZlzUvjzpfO4+6Q84sLMWOwu7v+mi44R/yghKTJYjfQANlX3TZqKyogJIcPHqLa405+0YkJNnDM3mSuWyLLxfa3DXPvOgYDnCzUbmJfhrfO9uctfpafghIJEleAcLomPAsjaBUHgmuXelN7HZf2MhyayePFiVq5cqTqO1NXVUXdgN3GeWxmwOvgsQP0NYGFmNA+d7o1QqrrGeH+v/3NHBYncs8q7AXhoXS1We+CF9jyfpt/9rcOUtg0HPO7UwiSWZHtfn3UVPQGVjMfnJ/DrE6bimffJXZ9W+dUmFfimE0uahynxOH4EBQVpxrDcdOYyEsO89cTPDnZz0xvFHDp0iL6+PjWdeN78VFZNi+WUWXJG4aG1tXQNjKhTnZVmZ4fDwTWrstXzPfvNIcomGXp59vx0Mn1qrV8e7OSs57ZNGuUCvPhtHV+UTl531PGPQyetwxC+c68kSaIg1bugjI7bWb58OYmJiX6PU/qXbvmwnCve2IfV7tKkB1OjgjEZZPGBIjzYHqB3qr29nZKSErIjvEThckuYDYE/TqIosMqT+trbMc7vdwReJHxThGN2F7saByd9DU6d5b0/Q+DsDvedMp1bj52qjqgvrh9ka13gXjDfKGFdRQ89kzRZC4KgifSmB1AmKtd39mzvNV751kEGLQ5MJhNRUVEEBwezdOlSli9fznlzvCnCJzbWsmuP18zWN821OCuaSJ/U5VHTA6cT5yQHq1GZ0w1XvFEaMKWXFBmkSaPe+WnglJ4oCPzq+Gma3/mmNRUEGUUuWphGeJDHmsvp5pYPywNe41HT49UWCoDbV1cjSf42XkFBQdx2XK7md8umxeNwOKiurlZ76oa6WnjguFRVGDRqd3PHZ96pzt3d3ezcuZOxsTGOyolQB4K6Jbj1/f1+SlmQr+XuUwt8XgcoSInUvAe++MW7+3h4XRXXv7Ofl7fU6+nEfxF00jrM4Ha7NXOvBEGgrbWFcKP8BWlxRBAc7C+zBshP0bo7vLytURNpGQ0im36xnAN3H83PjpCVbTsbvY4NLpeLsrIyKisrmTVrloa0ADZVT143WpXrXeSahlzs9jmvgqU5MQQbRaZ4eqfWfo+s/dh8r+qvaVRW6k2GK31cI279yN8OCeR6kqLSc0kEjGIU+KbLnv620c+PEOQBkL89bQbpnsZpm9PNte/IKj3fxSwoKIgrVk5XPRiHHQKlw6F+ZrZNTU2MjIxw1VJvSu/uz6r9nlc59z0neRf6iq4x7lgdeIbX+fNTVDFIy8A4aysCv4fTEsIoSPBOcb7r86qAxwFc6yPT31w3wH1f1PiRplEUuHCBd6PQO+bgqzZBk15WcGx+gobgXt83SN6MGSxbtoylS5eSlpaGzWajsrKSnGBv6ntX0xC3fVKB2+N3KIoiBoOBIJOBCxfI2QlRgOquUZ5cX4HValXbRJTX8dj8JNUA2i2B2SRO6p0402fz+OAXlfzkzRIGJ+mD0/HDoZPWYYLP9rdS3jqAzWbD7XYjiiIOh4N9+/bR0NBAtmehrwwgsVaQn6wlrZ0NA5pIC+Q0nSgKao2puK6XC1/axcjICNu3b5cL38uXk5CQQHa4diH6JECqTMHyqdoaU2Wn/3Uek5fAJ5dO5eq5ckT42cEufv1p4MUxNyFUjaAAPisPXD8BNIuj1eGmqc/qd4zZKHJyQSKZnrTjC1ubaR8JnFbLivUW/nvHHN8r8rjvFK+PX1XXKFUBnOLDgow8eY53R//czh5y8/I1ZrZDQ0Ps27ePZGuTmtI70D5C6YTRJYoQZ1VunCYSW1fRw64AG4XlU2P58meLUGwO71lT7ecgouCMGd7Pz8H2Uc2GxhcXLkxVXeoBPtrfGdD545y5ydx+3BSyPY3oX7SIAVNvRlHgCp90YuewjZeLZel8SEiIZgzLQ+ctxOepWV/Zx1V/Lqarq0slI1EUuWBBGrmJYZzmidhf3NrM/sZeVZ3oO9X5+qO8rQTv725lU2XgOuHVK3M089W+qujilKe3UtUZOO2q44dBJ63DADN+s5ab3i3lVx/LxqCiKDIwMMC2bdsAWLZsGT/y7Bx7Ru2TyrvzksLx3SQOepwvfHu11OdMjiDEJGBzyvWgxz7ZQWKiXJcJDg5GEARSQtEo0Pa1DE5aqI4KMTHbx9WifYLDucPhoKLsAE0N9aQH2xCQd7afl3Wzo7yesbExTYQiCF4ZOsCa8r6A4gCQCanAkxqVQB3wOBG/OTmXl3/k9SV8YKcNiz3wOc/y8fF7YUtTwPQbyDZLYZ4ij1tCrcVN3K3Pz4hUDW2dbrjqrQNIoJrZzp49m5UrV7Jy8VwWp3vrLL/8oJSysjLa29uxWq2ac1+/KkvzHNMS/FOZRlEgLTpE7dq2OSVu+ySwY8TClCDiQ7zv92QKQVEQuPXYKZrf5Sb4ezZGh5q4ZFGaT41T4KfvBk4nnjUnWZOWe35zk59ziyAIxESG87MjcjS/jwwNVt03lMh1oKuNV86byj7P/DMJuO3TGjUq853qvCo7XENGt394wK9+qzz//Wd454cZBHC43CRGBM586Phh0EnrPxhK75XifVfeMYLd6aauro6SkhKmTJnCvHnzMJvNHDU9jrhg+cs3cfetICzIqBE71PWOYfHUtSYuPgZRYFmONzr6vNlAZs5UNX0jCAKiAP93eh6fXLuYjJgQ7C6J72oDD0wEOMankffj/e1qWm14eJjt27fjcrlYsmQJswsLWZrjTbXc+1UbxTt3q+NFOjo6sNlsnDRT3iULwMC4a9Lx9ADXLPemCJ/f3BjwGKMokBIdopKH1QkPfN0eMK122ZJ0tW5U32fl6+rJB0Veutib0rv3i5pJj/vomgXqvw+0j/DA2loNGYqiSGRkJFesmKo6RnRa4GA/qvtEZ2cnvb29dHZ2kh1t0rhk3L568pTeNT6NxN/W9vPYV3V+nwlRkNWJSjqxb8yhRjwTcdqsJGJCvCTzm89rA6ZRBUFgUVa0+u+KzlHu/9LfrSLUbOACn9YEp1viF5PUyy5coI30tjaN4QyNIzY2VjOG5cCBA5jd3qi7dXCca94+iBtBM9XZZBC5YokcrRtFgb4xO798by9Wq9VvqvPRMxKZmSpvzlwS5MSFqalfHf8c6KT1HwpfdeBxnohCkuC77bvp6OhgyZIlZGVlqbtqURSZ6glkbv+ojIe+9K93gDZF6JZkoUWgSGtkZIQkYUhzrMOljXQAjsqNpSA1kmM9Izdu+bCMXQ2BxQ5H+NS1hqxOttf30dLSws6dO0lLS2PBggWYzTIRXO+j8HIKJoqKlqnjRVpbW9m2bRuWlnLuXhnDco8Zw52fVU06y+qYvHhMHsXG/rYRyr8njeo7e6pp0BawSB8VYtJYLN356eSDEM+Z511st9QN0DYUWOSRGBGkuqGDnNIbDOADuWJqLO//eL5qx/THXYPMmTeflStXEhUly+tbWlrYtm0bS2K8jbYlzUOUtQdOVV21LIMws3c5eGNXG1VdWjm/JEmcMDWM9TcsUSPGZ79r9PNiBDm6vXSJT0pvxKZxG/HFg6flaZzdP9jXGZAML16YxsULUzmlQP4c7W0Z5rsa/7RwqNnAZT7P7ZLgsc1diKLoN4bl6fNnaxbBvS3DnPlsMXWNTYyOyqlcudk5lcQIMwsyoxCQif0vOxr9pjo7nU5+cay3nrizsZ9XtjUGvG8dPww6af0HQpl75XDIi8GphfKiJwFjkpmioiIiI7UGsqIoMsXzq94xB+/saQ2YLluVG8cps5LURtBva3o1kZYkSbS0tLBjxw5W5Hojo3GnW+Mc7utbCLIUXH483PT+wYBGujOSw4n02QH/+ZtKamtrmT9/PlOnTtWkzGanRRJslD+ePaN27G7U8SKLFi1i5cqV5E6bxtwkM1Fml/rcl/55lyqF9oUgCCz22dG/FkCyreD02UlqQ3S/xaX2rU3EqbOSSPUIBMadbu6YJJJJjgxSjWIB/rBt8p6xn/mQtcXm0qRffRFmNqizzUZsspuG0WgkKCiIuLg49TU6d2kuUT5zuW56t1SVi/uOYDEZRI1MH/BLeUqSRHiQgRCTSIsnveuS4GfvlQW8xvPnp6jiFoBnNzcFFCbEhZk5c5ZWCRmorhYfbuZnq7LY4NP/ddvqyoCftYsWpqppWYDafgffNmufWxAEMhJjNPJ7gIFxiYH+fkpKStQxLD2d7bxzSQHRIUa1N/GxTQ20DzsQBIHGxkZKSkoYHR1lYUoQM31ET79bW8mB1kH/F0jHD4JOWv9B8I2ulP4TSZJgsFXdiVZawtXx574QBIGpPmo+h0vi3d3+C/NZc1N54rxCfnGMLGH+pqYHlyQTpdPp5MCBAyqRHL2wQDOW46MJtk6KYzvAnPRoPBzDgMXBS1u1voXK8RfNjeO4TPn6d7bamFa4gLg4f+m2IAis8FhFSfir+UwmEwkJCeTl5XFGbrD6+tT0O3h8XQVbtmxh3759NDU1MTw8jCRJ3HNyrvqBX1fRo2my1pzbIHK0R8TgkuCVYn8DX5B39I+f6xVQrKvs5ZVJoombjsxmdqpcVytps9A8Ejgqm58RSVasXLNyA/evPRTwOEEQOGuO1+/vo/2dvLmrVZNWM5lMpCYncvHidFWa32OV2HRoWJWLl5SUUF9fz8DAAOfMTdaQzEQJvCLyiAoxaUyI97WO8GwAp/qoEBNn+9T+nG5p0hTlpQu9U5gBXtneGvD9iQoxaWpbVoebK9884JdOjAoxcaGnjUE576sHxgJOMPjxsgzN5mDY7qbEEsvKlSs1Y1gqDuylpN6bBrY5Ja5++yBOCXUKuDLh+drlaepzO1wS1/+lhN7BUc1UZx0/DDpp/ZtR2zXC2c9to+jhTTgcDvbv3093dzeiKKp+fmMjw6RFyzv6nY2BU2+iKJIQ7CbcZ1dtn6SxE2BxdgwmA/SM2Lltm5uRkRGKi4ux2+U+r7g42SV82VQvoXxa2uFnAeU7Vn3ZFO+x0SGBe1lOyDJwSuo4EWYBF3Dxa5OPkrh6mbcO9dautknvRxRFFmZ4F9FZ03NYtGgRCQkJDA0NsX//frZs2UJfcy1XzI9R02CT1URA2/z6yvYWrJMo6malRJDuU6QPZJALMC8jit/4yNBfLg08bkMQBH60yFsDW1PWTd8krg23HjtV06P2Z4+jxkSRx3Urs/jomgWEehbmVw5aWLRkKUuXLiUlJQWr1Up5eTn7dxVzRJo3Omnos2qcRBTSAnj4DK0V0wtbm6kIoAi9siidv1wxl2WeCcvbGwbZFcDRPiHMxLJk73U73RK/WRPYif33Z+dr/r2/bZgH1/mT+6WL05idFqHWFB1uuH21v8gkKsSkSScCPLe5iUO9Vr8xLH88d7rmuM5hGyf8oZiGzgHVBd9gMHDk9ARmpoRjNojEhBhpGRjnrtVljIyMMDQ0pEkn6iT290EnrX8zNlV1sbd5kI6hcb6p6mR8fBy73U5bWxvbt28nPj6eJUuW8OrlcpH+UI+F4QCyYFEUEZBYkOlt0P2mZnJhgtkoqnOxrC54c3MVqampLFy4kKAgb0/M0pwYws0GTKKcdlScC5Tn1IzlKPKml9Yc1KbAlB6v9vZ2wsPDCDHLEVzPqJ3V+wNL5WemRhLnifSGxp2sm6SHCODaJd6oY3VpF2FhYaSnpzN79mxWrFihmtoemexUU0813RZe31yNzeZfY5qdGkFUkLyIjjsl3tw5ucuBb1+S7+szEb4RQssYvLkzcIry1FmJamoU4I4AC61yvrN93NAHLA4GxwNPI/6qsgeLQ/6bxe7msY31hISEkJqaysyZM1m+fDmLFi3iogXJmvrSQ+tqONQg13d8jZinJYarpskKytr9SSs5Mpj+MTvFPo3it3xY7lf/c7vdnJRt1ERbJc1DfBGgV29hZjSLfGp/AAfahv3SmXFhZv543kw+P+g9x46GQTZW+X+OLl2cphFvuCX41epKTYpdFEVmZSdz/vwUzWN7rW5qe0bp6elRxUKdnZ385vhs1t2wmKfPn4VBgPVVfXxaJn8nlanOvjWxiQMxdQSGTlr/RkiSxCWLvDu8h9fLu8XW1lZqamqYO3cueXl5iKJIZmwoUxPCcLklNlb5f5EVVd98HzuifS2DkwoTAE2h/IMGA2mZOX679DPmpLD7ziM5fY6canl1uzftN1F1WDQlFpM6OHCQtkE5vaNEjKOjo+Tl5WE0Grn/dO9u+a5PK2jpt6jn9MWVRRlqT9SLW5oCfqEFQWBqXLAa3VV2jWmc0Cea2l7royR8fEsXb67dxs6dO6mpqaGnp0dt3H5oRShRnhEnL25pnFTOf0JBgppW6x1z8umBwFZMqdEhmtrWYxvr2RbAoSMi2Mhtx03hqFxZvbmzaYiaAP1dAFcszVBJxg28Uhp4fMixM+JVP0SAd/a082WZ93MkCAJhYWEsmJHDcfnxZHrUiTYXPLm5nT179tDd3U13dzdtbW1YLBZ+NkFS//imhoBefkU5Meq4FoChcRePfKV1TpckiaQwUaMIBbh/bW3A3q1bjtZK6qu6xnhnj79fZbDJ4Pf4O1ZX+dXMokJM6vdBudbaHktA8cjPVmVp0qgAH9aLxCenasawdNeWUnOghKCRNq5YIItHHtlQR0P/uEadqDjc+A7EDDTVWYcMnbT+TVA+qAZBUi1lBElicHAQh8PBsmXLSEhI0Dym0FMTuXN1JdVd2l2tQlrzMrwCDacbvq0JHJ0MDQ0Rb/ESUJBRIMSncK3AbBQRRYECz/TjTVW97KqXC+G+NS2QZfJzfUjz3d1tdHZ2sn37duLi4li8eDFBQUFIksSR0+PxGJcjCAQclghwyeI0fnGkrOZrHhznk/2BCQFQTVNBJoTJcNlS79h5CThm2ULVpLWurk61BjK5bSxM8biHu+H5Lf51OpDnSfn2bT22cXILn18d721UlYCtAWyyAM6fn8otx3hVjL+cpHcqMzaEI3zc4fd2OantC9xDdOcEK6b/W38oYH/ZA6fmsea6RUR7IsPNrQ6yZy0kKioKs9lMV1cXO3fuZKjhILMTvTVPi93Fo1/5p+mCTAZuOVrbO/X2nnZNJkBJrf1keSZ3HDdVdayw2t3c/6X/yJZZqRF+rvhPbqr3q4OFmg3c4fOag+ypec1fDvq9R5csSiMqxMiirCh1YXx2c5PfoNO4MDNXLc3Q/K5j1MUbB8c0Y1hWrlxJbm4uRqORophRZkS5GXe6uend/TS3d2K329V0otFoxGg0qiRmt9t1EpsEOmn9G+ByuTRWTIrFUVO/FUOQnLIJZMV0XL7Xy+7n7x3U/E3ZXRckh/PIWQVc5IngvizTpukkSaKxsZFdu3Yxc2omiZ5eo8FxN1UBahIKTvGZCPvTdw5M2t916RLvl/nd3c3sO1BGYWEhM2bMkFOYnscIgsBRnr4th0uaNJVpEAWm+hT9H1h3yG83r5zz3PkpqkvGtvoB6nsDz1UKCzJygs/Y+Vd2dqgmrUuXLqWoqIi0tDTcbjdhPn08r+9sY199V8BF4xyfNN2Q1cmbkwxrzEsKJz/B+96uq+iZ1Bj49xsb1J8b+qysm8TWytetHeCxrb0Br/HEggRSfOyQhsad7A5QXwo1G/jFh+UM+swV+8VHVZhMJuLi4pg/fz6rVq0iPz+f82cpmxT5+dZW9LJ+fwN2u/Y9unBhqro5U7DGJ22nOFVMiQ8lJsTIOyXezcmGql52Nfm7b0xsnra5JO7+vNqPiM+fn0pymHapK+sYpmVAW1eMCDay5qcLiQ8zq8pMtwS3fFzh9x5dtiRNI1ICWH9ojG99JPhGo5H4+Hhyc3NZsngxz1yymNgQAy3DLh7ZUMfWrVsDRviBSEzxTtRJTCet/y+oaB9SBy06HA51ppIoitjtdnLNcj+UG4F6S9CkH8IV07x9Tg19FnUAIngjLZMoj1RQxoF8XdVDuydNpwg9GhsbWbhwIVOmTNEQ4dsB1IYKYsKC1PTbmN3Fy1sb/WpaAEfPSOCps2cQGywwbHPTGZKtupqDNqV4/RHeFM9jG2rVv0/EzJQIEsMVA1iJG94v83texfD3sbO8acfnvmtgMvxokdfa6bMDXaqzPUBwcDApKSmEhYXx0yOnavqXfvpBFV9skicVK2kySZLISwrn02sXMs3j/PDMt4FnWQFcMNtbC+oddfDunsAE97sz8jQ1njs/q6Y2gOv+wswozpqTxIkeIm4fcQYcSWIQBa5bqV3o7197KOB13jwh/VbXa6G4dVx9fwwGA7GxsZy+NJ+56ZGEmrxR+oMbW/h281Z27dpFbW0tvb29CJKbG4/M1pxzQ1UvG6vkzYoSaQHETiA3gDs+8VcdzkgO5zhPf6AiSNnbMsx7JdoaqUEUuHRmiOZ3Tresjpx47zanxOdl2s1BY5/VryE9LMio9umZfNQw93xRTd9YYOFMUnQoj5xVgABsbnNhiZ/hF+H7vmZKDdFgMKjqXuU7Z7PZNDWx/yUS00nrXwiXy82se9dz8tNbeeqrGux2u9obIwgC/f39FBcXk58YrDqw72i1TdqkGmwyqHJogJhQ75db6ZtSHpuXFI4oyJLty1/fy+DgINu2bcPtdrNs2TJiYuSF87z53sX78wMdWL+nBnaFj9AiJTIoYKQ10NeLubcai0P+/RPfaOtQvo+ZkRxOlCcF1dhvZU+AXb+CG47wLrYut1Yh5/vzwswodQe8vqpv0mhrTlqkWrdxQ0D1GYBBFLnGZzKu1SmQX1BARESEmiYrLi6msrKSEMcQMZ4amNUp8XYAvz2AJRlhxId4r/kP3zYGVCeGB5u4wOf9cbol/rLbv24jCAL3n5rHhfO90d7D6wOT0amFiaqTB0BzvzWgICQ7LpQT87VO7i/st2Kf4IElCAIPnpbHpp8vJSNGjuIGbVBGJllZWbhcLmpra9myZQupthaSw7Up6AfW1jBocaibOJBrYEo9T0HvmIMXA7RR/GxVFgsyo7jMx3Xk9xvr/NKEhfEihUna7MWB9hFe2qZtZ0iKDOJcn6hZwZ+3tzIyoYn6vHkppEUF4XJLhHvKhQMWJ/euqZ6UOJbmxKj9cA+ub8BmilAj/OXLl2tes82bN7N7924OHTrE0NAQoiiqpr9KPcyXxHzTiYFmif23QCetfyEMBlFdOF7cXI/V5lAX2NraWvbu3cu0adNYsmAu954qT1Td02Gb1LAU4JRC7xfq7V3aIrGvs4UgCGpzZXO/lXc27iYrK4v58+errhMgewwuSTYQGSRisbv5snzyxtfLlnpJ64/fNmgIyO12U11dTWlpKfn5+aTGyBGH3SXxm8+89RjfxwiCwH2neifJ/vGbOvX3E3HizES1QF7VZdE0OgOac/o2lV7/XlnAuo0iLVcW8C/Luv0WJQXnz0/B7NlNS8DbpQNkZ2erabIZM2ZgMplobW3VOE489U0DXYNjfucziALHZXnfg3GHmye+DlyD+8mKTI2IYW1596Q7+Wt9fPssDjefB4i2TAbRr5n2mc1NdA7718FuPXaK2mQNcnTycYW/o0ZWbAg/fquUlgHvOV7b2Y7NFMGMGTMoKiqiqKiIzIx0zslTNl3ye9JvcfLgl1UaZSLAXSfmau4bZPHIRFHFtIQwHjo1l3dKOtSo1O6SuPPTKs37LkkSVy/S1ogBXtzSzP5W7T3ddtxUjWgF5NE7E01/zUaRB07L45OfLOSWRSFqn+J3hwb46Htqr9etymJ+RiRjdhe3feJVKAYFBZGUlKS+ZsuWLSMjIwOHw0FnZydDQ0Ps2bOHuro6+vv7VeNs34GYE6c6d3Z2MnfuXAYGApsbH47QSetfCEmSVKftcaebbfX9jI+Ps2vXLrq7u1m6dCkZGRkIgsCctCiSIoOwOiXeOxjYOxDgVB/S+mhvu8bPbWK6ztcZe21XKDk5/upAQRD4+eJIziuUd7Z/2Bh4gi3IKZEcz3TjrhEbdUNu3G434+Pj7N69m56eHoqKikhNTeW3PmT0wd52Gvu86kDfazy5MFmN9nY0DExaVwsxGTQztO7zKc5PvKcHTvP20rQOjk8qyrh4URrv/Xg+IEekD60LfO8RwUbO9BFafLivQ523ZTAYiIuLY9q0aSxatIiHTvfet9MNF7y8h+27dqsNvIrT+KoME3/+0WwKPQKX90s66A9ARvHhZk0TscXh5ulvAqc9bztGK3Z4cP2hgA2658xNITs2hKWe3imHS+L+tf6eiMmRwX5Rx9v7+zXKTAWLPOdSIAFry70iICXlevXx88iODQEfYf366gFW7zrEyMgIzc3NjIyMkBhh9iPXvjEHD6475Bc5vL6rjXGnG9+yU2nbCO/5qAndbjczk0M51jMHTEnpuZGl7b7K0FCzgTuO04o3AF7c2uI3KHNRVjRT4kPJjoAbl2kj3ab+wM3rRlHgkTNnEBVipLxjlCe/Dvx+BgcHk5ycTH5+PllZWcTFxZGamqqOYdm8ebOmOTwQiY2NjXHgwAFNG8vhDp20/kVQrJguWez94vUPyCm6iIgIioqKiIjwWr2IoqD2X62uHpt0zP2U+FA1pTY07mRbnbfw65seHBgYYLrkTfvY3ZNMSkQmu10tcq2ka9TGN9WTz7G6dLFXaPFZnZPh4WGKi4sJDQ2lqKiI8HA5zbkoO0bTl6Q4DgRKKUb4HPdQgMVTwTk+E5q3HOrXuBv4nnN+RpSm4bd1ksUDYF+zd4OwvrLbL4JT4Dsk0i3BQ+sDpxOPzU/UPHefTUAMj1cbeDdv3kxLSwsGl430MIkhq+uvnvNyH1k7wMelXZQH6Ik6b0EqscHeIx0uiQfW+ivvQs0GPv3pQn6yIhPFoWrLoYGAAzKvXp6pqdk4JbhnTY2f+e3NR00hPkwrTHh2cxNNfdr0rFEUuPOEqTx3wSyOnu5NAb5bb8ApmBgYGGDv3r1s3bqVpVHDhJu1S9S6ih6/3q1fHT9NbYvwxWaf+1EW9JuOysYgwOzUSDUyaxuy8chX2g3LKbMSmZUSrrlup1vito8rA0rw3W43pxXEqBsMu0vijtWVk6bmkiODeeBUuUH7zV1tfFc7+WgdkMVbJpOJ1NRUdQzLkiVLSE5OVj9bW7ZsYe/eveoAUZfLhcViUe29/h5s3ryZ0047jdTUVARBYPXq1Zq/S5LEPffcQ0pKCiEhIRx77LHU1vp/1ibi2WefJTs7m+DgYJYsWcKuXbv+rusCnbT+6VBC85GREVwuF/MyYwjy5A1e397CrFmzmDlzJgaDv7x8brqsxHJJ8PK2wPJqQRD48sYilQw/3OvdTYqiiMvlor6+nj179jB9ag6ZnhpYQ6+FjqHJXRjOyPdK1X/5UXlAk1iACxelM8czYqSsz83OigamT59OYWGh5p4EQeAyHyXh/V9Uq7+f+EU+wyeK2dM8xDcBTFBBrkP96vip3h6eL2vUc068nx/7uGl8e6iftsHA954R6yUYpxvu8hms6HveaQlhLM2JVhfmTdV9tA7418sEQeCGI7I1v3uzbExt4F24cCFhYWE4nU42bD9As891fVvTF3BiclZsiGpIrODeL/zdIkRB4OJ87eK0vWGQvS3+kfugxcFVbx3At+z1QgBJf3JkEOfO0zbTHmgf4d0SbW3NIAoaxw+Qo637JzjVAyybEsvnZV18XdOvpgCHbBIfN8CcOXNU+6Tk2ChOmyK/3r7v8INra2n3+SyLgsCjZ2pdMgC21g2ofXBKzSwnLpQ1P1tEVIiB1Cjva/VJaZcqClHOebtPtOV0SxhFgY5hW0CnDrfbjdFo5K4Tc1XLLIvdhe17XGmOmh7HJR73k7s+rw6YolXgcrn8vl+hoaGkpaVpmsMTExMZGxtj79695OTkcMkll2AwGNi9e7fGa/KvYWxsjDlz5vDss88G/Pujjz7K008/zQsvvMDOnTsJCwvjhBNOYHw88PcM4L333uOWW27h3nvvZe/evcyZM4cTTjiB7u7JN8mBoJPWPwCr3clP3tjD6n1yRKOoA1taWjh48CCiKGK1jDHdwwf1I2COiJ30fL4Nt69tD9xICxAfHsS5npTahopuNa0kCALV1dW0tLSwePFisrOzNcTxpy2B0xCiKFKUEUKQp3Fq1OZiQ2XgD5JBFHjhwplMi5aP3T0SSXp6esBjz/YREWyq6sFidwYkrRnJkUyN945MuTvATl65vx8tSuOo3GgAvqnuU+XvE8958sxEQn1qW7/bEDj1l5cUwQKf3rYdjYO8X+IvdgB4/sJCPvnJAnUB/dWngZ30j8+PV9WOAN/V9rOrcRBBEAgPDyc2NpaIiAguO3Uls5O99213Sfzo5Z2UexwVfGXjP1meyV0nTGVemhydV3eP8dlB/5rVgiQDGVHaqOO3X9b6WWDFhpnJ82klACjrGAmY+vtxUQZmg0CcTxT35Nf1fhuBo6bHMTtNO2h0V9MQbwWQ/+9rkdNsvlLykg47VodL0wx+6xmLSI40I+ElrjG7mxvf2k15ZSVdXV3Y7XYWZkWrSkJAbXL/9WfVdI94B6cCvLenna9r+mkdtGmVf2uqNZH2vIwoVZEZZJAjLQH4pqbPTxCjeIUGGUWeOX8mMSFGGvqsAdOZvrj56Bzyk8MZsjq5Y3XlpO0PE0lrIpTm8PT0dGbNmsWRRx7JJ598wvz5cvr75JNPJjY2llNOOYW1a9dOeh4FJ510Eg8++CBnnXWW398kSeKpp57i7rvv5owzzmD27Nm88cYbtLe3+0VkvnjiiSe45ppruPLKKykoKOCFF14gNDSUV1555a9ejy900voHsOihjWyo6OKu1WXYHU5VcmowGHC5XLS2trJjxw5uWpFMXmIYbgnWHJy8QJsZG4pHfMbI+Pfvij4okRcCCfjduhr6++V6mSAILFu2jKgomSnPnpdKuMee5qN97QFdHRQiuXChl3wCTdgF6O/vZ/3m7RwalL9c62tH6Z0kpZYWHcICjwOEfJ21AUkL4OfHeBtfR22uScUGkiRR3CBHDi7guc2NAYUboWaDphb0bW2f6tAxEbdPqF/4zsbyvVajKPC7r+pUl++qztGADhCBxA4PrvXOk/L6NYo8cEaB5rgOC1T0uWlublb7eGpra4k3jnNSfpzqrg7w8Po6v/dTAC6br43K6nstAY18nzxHG5243LJwZeKGISkyiE9+spBb5xuIDZE/SzanxH0T5l4JguDnVAHwh28aODSBDF+7dDYT37URB2yYYNUVZBS5flU2q6bFcrFPm0LNoMSXh8ZpampSX6ezpnjJyuGWCDaJ9Fsc3LG6CqeHVACuKMpQU4O+43YsdhfNE1LJNx+dg9kgYHNJBBtF9b3//cY6jW2VLymmRAXz6Fn5iAJ8eqCLD/YGtikDWczx+7PyCTMb2NsyHDDahb9OWhMhiiILFy7kzDPPJDMzk97eXjZv3syxxx6rEWL9EDQ0NNDZ2cmxxx6r/i4qKoolS5awffv2gI+x2+2UlJRoHiOKIscee+ykj5kMOmn9AziuQF4Ux2wuXtp8SPPBHRsb49ChQ7LCbH4B53sI4eN9gXfxClbmyLt+u0tiy6HJ89w3He1d5D890MnGbSWYzWZycnIwmbw77bAgI0+eV0hiRBA2pxTQ508RcFzsU6+aqEyUJIn6+npKSkpYPnu6qq6SgFs+1DY6++LFH82jKEeW168u7cDmdAckrWPy4jUmuw99dmDSESM5cd4I4YN9nbQMB5b1XrAglauK0tUP+W/WBK6XFaREMMcnQtjWMMieSTwE2we9BG1zSTy4LnAe/5y5yUR4xngANPRb+bTUf8MyJT6UY/K0zg6vl1mZM38hK1asICcnR5VA//T1HfSOeesp4w4X3QFIc2V2OFN8IleAF7c20TChvpQRG6ppiga5vvNGAAn8kNXOXcVO+q0udcHf0TDIhiptU/iCzChWTdNmExQ1n68EPy06RCMUUvDoxjo6h7UR3Bmzk/jpikzeK+nwiDhk/OXgCJGZBerrlBhq4NgM+eIEZFWmQZBTzmsavN/NuDCznzsIyGn5zw5om8dTo4K54/ipvHLJbB4/p0C9d5cku5Qom0u3260hlaU5Mepctoc31PkpFH2RGRvCPSfLasmJqkUFvmvL34OxsTHCwsIQRZG5c+dy8803c8wxx/zd5/FFZ6f8Ofbtv1T+rfxtIpS+s7/nMZNBJ61/ADcf483hry2XndmHhoaorq5W+6GUsRsrPV/kys5RKr9nAOGP5nl3yU9/M7mSLzrUxPRE78LUZEwjODg44OK9KjeeZVNk4nhpa4OfyEOJfrLjQslV8/FulWDtdjt79+5V046ZmZn8/ChvdLKzcZC2ScZ8RAQb+c0p0zGKAjanmw9LuwNeo9Nh54g078fx20Yre8uq2Lx5M3v37qWxsVEdMTLRYfwPewM/d05cKDcfPYVjZ8jvwd7mIQYCzHMCuNpnci/Aoxv8J/cCPHVugSZC2FTdx+cBouewICPv/Xg+H149Xz3+sY31amTkGx36ztAC2Rnlle0tmM1mEhMTVQn00xcv0Li6uyS49s29NDQ2MTIyojawGw0iP12RyXEz4pmRJL+fTjfc90WNX33p+lVZmI3aZeCP3zX6WRdlxIR4PQ59TmEW/aPcnx+VgwCaxuiqrjGe36yNIn66MkvjKhFihOFxlxwZTfiMXvFmKU63RGO/VW0/cEnw8IZD6uuUl5fHPectJTbUpKYTlUBqbbPEun2yQMHtdnP+ghQKkr1CC2Vz8cmBLj7cp30/z5+fyqKsaFZNi+WXx3ojybbBce79oga32x2QVK5Yms5xM+JxuiVu/agiYFSu4OSZiXx+3UIuXRw41f73RloKRkdHVXHUfwt00voBkCQJl8tFYriRlEg51O4atlFfX8/u3btJSkrCbDZrFDvPb/bWkx7dMLlCLic+lFlx8ttS1jbip75S0NfXx5Hx3sW6pHU04ARiALvTxWrPLr9j2M66Cm0txPdx16zIVtOJT39dx+DgIMXFxX5px3Pmp+LrGTrZuHmAn71zUF2EXipuZdypXZD6+vooLi7mhNwIdaFzA5uH41iyZAmJiYmMjIyoI0YsnfXkJ3hf226LRONA4BTl6tIONlTJEatLglcnGf64KjeWzBjvLr6ya4w9nf5ilNgwM+fO00Ynr25vDViDS44M4v61tWpKadTuosRHrahgemKYX7T10tYmv1RVQnQEP53gaNEx5ubdvZ2q4s5isTAwMMCRUyL4/VkzsLvcav/Q3pZh9XOgnjMiSNOYC3LK7K7PqjWRUXSomfOn+y+ad3xa5bcYT08M49cnTOOdK+ayMNNbL3y5uIX9rd77DzUbNFOirU45vbe3ZZjnfBwoBEHgNh+y8G1wTp/gWRkeZOTnR2UDUOgTPUsI/GHXEFtLDrBlyxYOlJbyk3nhKhFbHW4yPM3m/7f+kJ+0XcEli9I00elXVb0c8Bw7kbQEQeCBU6czJT6U7lE7v/ykclKXFID06JBJ//ZDSctisRAaGvrXD/w7kJws339Xl3Yd6erqUv82EfHx8RgMhr/rMZNBJ62/gkB2Qb5WTOfMl7/wPaN2vjrQzOLFi0lNTfV73F0neaOD4voBmvsDk5Eoitw838yq3Dgk4C8B0nRKY/JZS3IxezTL+1qHsTgJSFpmo0Eztfe3a6omnYt1xpwU7j11BgLQMWzj/U1yU/K8efM0acdgk4EjMr3E8ex39QEXboBbjvFGZWN2N6sbvPdSX1/P3r17yc3N5eglc1n908Us8ix0nx/sxm0wk56eTmFhoTpiJDw8nFOytDv8l3d1qZOefbF8ijZV9dr21oAbAVEQ+PGydLWvDuCNcmvAwvhPV2Zpmm5reywB6xYut8TuJi1Jvb+3I+B79BOPS4Jaa3HDg+tq/T5HVyxNJ2aC593HtQ6yZi6ksLAQURQZHBxkx44dHPfUVup7rfhqMCo7/WuVVyxN17QdCEBl16ifW8QRqZAZrVUnWh1ufv1ZlV8Ed/rsRG78oIK9LcOqU76EbJ3kO3ng9NlJajQo37d8npe3tbC9wdsQe8GCVGanakUeAJ+VdbN2ggT+jNnJvHn5HGalRKh1TQGJIZvE6s5o5s6bT1xcHAnGcVakSOo9twyMkxhuwumWuOXDwJGRIAjcdeI0FntGo8SFmUjxNKgHIpWwICNPnVtAmNlASfMQT0zSk/XX8J8UaeXk5JCcnMymTZvU3w0PD7Nz506KiooCPsZsNrNgwQLNY9xuN5s2bZr0MZNBJ61JsKa0ndn3refil3aov1N6rxRjS1EUWZ4ehDKGZ99YFFFRUar03BcRwSaNsmp1aeDirCiKOJwuVe313p4WRj0pJaWJt7Ozk6VLlzIlO5PTZntzxO9UTm4BddfJXtIctDrZ6tPfNTFCW5oZgbKGbRuKDNiUDHDCFO/OcMDi4PODge/p+IJEjZpuW5dA/4iFffv20dwsE73SZJ2bGM7Vy+QUiUvSRnCKqiwnJ4fLjl9Elo9cfXe7nb+s3cLu3bs1jgEJEUGc7GOOKwE3fVgR8DrPnpvC0+fNZFlOtPw62STWVPlHRokRQZw9R5ub/8O3DX7NwcEmAzceoY2MvjvUz7pa/118QUoED52Wx59/NFtNAW5vGGR9pTaCDTYZ/Ixix51u7lt7iEiPC/vUqVNZuXIlly3SStUBvqrooqm9SyN/jgoxcVVROqFmA8mRQWpk+OKWZsp9UtkCcMsR2qjMKApsbxjktQkR7MbKXrpH7bgleRSJoB4vamTgBlHwE8KIgpfgFOIQBIEHT8/TbBYUA957v6ihxiedKQrw6Ff1vL2nnTVl3WTHBiN5rmBn4yDvlPaTkZHB7Nmz+b8LiogONqjpxO5RB2FG6B61c+O7+xkZ9VdTmgwiT5xTQGZMMH1jDm75pAqH2z/SUpATF8pDp8vfv7d2tfHlJMbH34d/pKb1Q0hrdHSU/fv3s3//fkAWX+zfv5/m5mYEQeAXv/gFDz74IJ999hkHDx7ksssuIzU1lTPPPFM9xzHHHMMzzzyj/vuWW27hpZde4vXXX6eyspLrrruOsbExrrzyyr/r2nTSmgS/31DN8LiT7fX9lLUO4nQ6VS8vRbhQXV1NX2MlL5yVDcgLUsuAVTW4nIifrvSmQtaX+cuVQf7gO10udnh2meNOeHd3K729vRQXFxMcHKxpTP7Jyhzmefq7dnXYcTgD91fNSI7QFOc/8unv8m1KHh4e5iev7cDqWdN2NI1ojHl9kR1jZkm6Vyr/+FeB5b2CIHDT0b4jOQR+9/EOte6npBwBBix2rn3XSypPf9sY0NZK7gPT5v839UeTkZGBzWajoqKCzZs3s3//fi7ID9LUV+p7LZpdvC/u/LSS4oZB9d9do4FVnFcWZWjqS6M2F09/2+h33OVLM1TneQWv7R1gzOH/Op1WmMibu9o0G4THN9b5RbBnz00mzaeBWUAemPiuxwFCEASMRiOXr8xVPS0V9Fnd/PbLarZs2UJJSYnaiHrRghTW/mwRpxcmqgbBbuS+NYVkJEliYUYER/r4AipX9sdvGznok1I7fU4y89Ij/Y5r6rdSM8H0d1FWNItTzByZHcrirGjckkxmfWMO7vJxbc+JC9WIN3pG7WTHhWB1uLn5w3KGx731QuUz43BJNPaPYxA8ik0BjdQ/OszMLcfKn01F/j7mlI2ny7rGufP93aq3ZGdnpzowNCrExDMXzCIi2MiB9lHerf/+pfSYvHiu8fQO3rumJmBrwffhH0kPhoWF/fUDJ2DPnj3MmzePefPmATLhzJs3j3vuuQeA22+/nRtvvJGf/OQnLFq0iNHRUdatW6eZTlFXV0dvr3fTdcEFF/D73/+ee+65h7lz57J//37WrVvnJ874a9BJaxI8cs5s9ecfv7GHUeu42qBotVrZuXMnvb29skdY4TRWTI1FkmTV3WS1pVXT49XhcYd6Lar7ui9EUcQoCqpwA+Dprw+xu2Sf2sRrNHpTOdlxoTx+7iwiggxYnfBt/eQijx8v8+7Q11d0q42syvW2tLSwc+dO7j5WOyvoHh/vQF8IgsA9RyWp/oE9I3a/BUnB6bOTCQ/yXveGZonpM+f4yW9jQs1k+CzIXSN2Xi7WpqkUnFaYxCNn5JETJb+m+9tGaR4PpqCgQG22jIuLw2AbYVGCduH/3fpDAVN/E3/1acWQZhevICMmhJNmJpLmU1P5aH+nZuEGeVd+6zHeeowADNvc/KXCvwnz6+o+vq7pU/uBAOICOJ6bDCI/89S2BLyE8OTX9XSPac2EHzh1ut/jd3fBcHQuKSkpjI2NcfDgQfbsKOb6t3bzp20t+Hri1vVaWFchRwbKSJlbjpmiNgW73BJmTw/T7aur1KwAwGNn5auiCQUScPsnVX6NtDcuCCUjyoQkSeTEheDyvAbF9QMauf5PlmeS7DNepanPSkyIkeaBcX7t4zf4+mVzCfUpurokj22TBGsrejSbqzNmJ3HcjHgePC2P61fJaVolGPyuQ6RZSMJkMtHS0sK2bd6BoeHuMR49PVdWKPaIvFzs31bgi+uPyGbZlBhOmplAeszk9atA+KGkpagH/14ceeSRqrDH97/XXnsN8Jg0338/nZ3ytPWNGzcyfbr2s9bY2Mh9992n+d0NN9xAU1MTNpuNnTt3smTJkr/72nTSmgSLc2KJDpUX2UGLA4tdDs87OzspLi4mKiqKpUuXEh4eTm3XiFqMfW+PLDSQJMmPuEwGkTNme1M2j27wl0srPV6/PtGbzrO5wBaXS3p6esA0XVXXCFbPKPV3SvsntYA6aVaSOsZdwju6Q/IMn6ytrWX+/PksnJWnGSxY2TnKwQDRlhKhmURBXTzf3BH4i2sU4YQp3sXG6pR4fXtgMnrwNK2zwsvFrQH7tkJMBk6elcSCZG8ks8XjgKA0W2ZkZDBnzhx+feZCjeqvvs/KYx8VU11drc4yAnkAYojRe6QbuOWj8oCmu785KZe11y9ilc/Y+QfX+Q9WPHlmItlx8iKl/GVzi53iem20d8yMeJWwlePKO0b5c4DF8JRZiRw1PY7rV2WpX+Jxp8RrFQ7N8+clhXOmTwpZSWU/sqkJwmKZNWsWK1asYMGCBQza5fsed3g/tyYR8uJk4lRIKycuVB05H2ISsbskTAaB1sFxHljrrcMlRQZp3EFEAdKigui3OPjlx1pRwrbWcd4sHWJ38xB9Yw5CTN6eqPdL2tXIKdRs4Pbjpqjnk5AFLkZRznT8aav8mYoINmp8IEHCIAoYBJm0Xtzq/eyJgsAT5xRw0sxErl2RxbnzkpHwjjt5fHMHzvBkFi1axMqVK5kyRX7+uro67C0HudAjUHn620bWf4/htEEUePq8mfz2lOmqS87fin+kpuVrF/ffAJ20AkCSJKxWK2cXyrUQm9NNefsQZWVlVFRUUFhYSEFBgfohstjdampizO7mizK5QTJQtHX3yXmcWigvIvtahnBOUBMpRBCBhVQf0c9HByZX572zq1WNGlqGnWysCjytOCzIyENnFqhpwg/2ttE7MExTUxMul0sj0f/FMdo6w/MBZlMp9bfbPq5QF5gP97ZzaEK0ZbFY2LlzJ8uT3ISYRATP0S9tawzo4zY3PYqZPr5vLrfkJ5dW8NTX9XxY7d21f1XVGzCdOCUhzM8K6fNGiVGHdlpxa3Mj1yzVqpma+scDCi2CTSK//rSKzXUDKOOkLA6X33gXgyhw/QRZO8g2VJYJxz55boHfcc9tbqRqgoBCWQCNBoE8HyFD9aDEZxVaMrzhiGx1kbS5INQkMmJzceenVXJE43HqeOvKBUxcSx1uuOHdg3y7ZRsul4v+/n7sdjvXrcziupWZvHrpHKJCjDhccmT0ZXkPn/sMd7xkcRo5HsKW61tOQk0ipW3D/N7HyHhOglF97uFxp7oJAzi1MIlgn3ldx+bJLRxuCbJjQ3C4JFWQ9NzmJrYckjcux86I52hVWCMguSUuXCA3KD+7uYl1Ff7fE1lokcuRubG4JLlmN+50q9ZiJpOJhIQEpk+fro4TOW9eMkd4BB13flbFR1/v0pjY+iLIKAbceH4flA3wD6lp/dD04H8y/mdJq2PQSveQf3rO7XbjcDjo7u6mIMj75X94zQHGxsZYtmyZXw52TkaUZlz8M5sbkaTJlHwi95+WT3iQSOewjfUV/kVZt9vN/v37udSnkL6raXDSPPjj587SRBEPr5t8ns+phcn8/pxZgJzv/+TrnURGRhIVFaXJRxekRDIr1VuT2FTd6+fALggCBlHQeAdKwP1feAf2dXd3s337dqKjoznpiCK23LqckzLk18XmlHi1ODAZ3TjBv++DvR0BZ2OdOUEQ0TY47qd6U/DjZRn8aFGqOr13xO5mQ5tRnVacmpqK1WplKh1EmrSv31PfNPiZ6QrAV9XyYqbwZGOflaou//fp+Px4pieGaWprbUM2/vhdo+a4vKRwTpnpdbMPMgq4JHkxnOhj96etTTz1TSOVXWNEehR6ogAJE2poSZFBXO6p/wmC7BRv9EjLfVNvceFBGkcLQZCjja5xgS+7IpAkiY6ODrZu3UpN2T6SxRF+9Oo+lmVHI+KNDh9cV6s6nJsMIr86Xut2EuKx13p7T7sqSggzCdx5ZGDp80vbWtjk08QsCAK/PmEaT56TzwsXFTIzJRyLw02YWRZU3PFplepu/+sTpqlpQjfw4f5OjvfMCbv782qNq4UCoyjw6Fn5zE6NwOmWiA4xasyafREUFER0dDQX5wexfEoMDrfAH0sdtPWPqQbJ+/bto6mpSe0z/HuhrCP/P9OD/8n4nyStG97eS9HvvuaM54rVD5HSe6WILQwGA9FmiVNnRCEgUT8ExuTphIQEzkUrxVyQ1Xm1w4KfglA99sODjNrkD+ILWxrVdJ7VauXAgQMALF26lItWzCDEZ4f5/HeBR2xEhZo5doZXIdc+ZGPzJG4aDqdLnVsF8N1ABMnJyQG/TD9erm24/eM32udXpPK/Pkk7YXdn4yC7G/upqalR52sVFBQgiiIhJgODNq+023dH7YtlU2LUKcAgLziPb/K//+y4MJamahfpl4tbAo6FmJkSwa+On6bpiXp/bzv9Y3aCg4NJTU1l5syZHLVqJdeu8L13iVGbi7s+2qcpxguCoJmUrOD2Tyr9IihRELj/lOms+elCFqZ6Nwdv7WpTU8sKfn5UtioKsDllO6JDPRae/lYb7Z40M0HdrAx7FHpuCbY1+i/EVxWlExtmIidOfk2VyPy5zU2aOtxFi9LUSFySUOeNra0eZFePyOzZs1mxYgWZmZk8sGUAlwRrK3vxKTMxLz1SVfaB/F4qvoBmgyywUP5+3xc11HaP4Xa7WZUTqRn+GGQUVNHEnZ9VUe1jLZYeHczvN9Zz0nO7KMqJITUqiDG7ixCTyMi4k1s+qsDllkiKDOK6ZXJ0ZRDkrMnWugHmpkdic7q56YNyugIY1YaYDDxzwSyyYkMYtDr52btlmnqdL1wuFyajgcfOymdKfCh9FhfPlDqYv3gpixYtIiEhgaGhIbXP8MCBA7S0tDA6Ovo3kZiyjvxQ0tLTg/8F6PPIaDuGxnl5SwOSJKnqQCVvDzKJnJo8yqkz5S/cnwJMTlVw4swkzfDB7zoCizEATcRU0zXKt7W9dHd3U1xcrMpTQ0JCCDEb2PiLZczxqLACNaYquPFobTrvuW/rA34hRkbH+NbHRX13yyj72i0Br/W4/ETOnpui2ittrOrRLBwKaUWFmDh/gVYKfd8npao0PzXV6xn35u5WirsNquDhrZ3NASPIiU7tAJsP9auqSl9cWqiNYNyS3CAa6P5veL+Mt3a3q/WKuDCzJvWkPPdFS7KIU6cLy//f0WZnzd5GtRhfW1tLYazkZxLbPWoPONRxZmoE39T0Udo5rk66BfyGC6ZEBXOJT8OvUmN6Y2cbOxu9958RE6qOfAdvpPNl1QCfTzDTDQsy8vYV83jy7BlcU5Su3pXTLXGHT++UURS46wRvZNQ+bFfTe+/XC9T1WjGbzSQnJ3Pn8T4bNZ91f2/TAFt2l2oW5tuOnUJEkDwTLTLYQM+onZhQE1aHm1s+qsDikNNfd5+Uq36PbE4Ji93FgsxIrA43N75frtY2v6ntpW3IhiTJmxSbU049Wx3y/69d4e2lO31mDFMj5RaK6BAjFruLul4LGTHB9IzaufGD8oCTo2NCTTx/4Sxiw0xUd4/xiw8rAjYHK6m7iGAjz5w/k+gQIxWdozyxqUE1sZ09ezYrV65k7ty5REVF0dfXx549e9i6dStlZWW0tbVhsVgCfmYV0vpHbJz+m/A/SVpPXTBX/fnhtZWUNPSqjalKc2ZZWRkAy5cv56dHysKAb2t6J7VgMhlELljoXWjKBgRaJmkgfuLcQs2/H1pTxv79pRQUFDBz5kzAmxKIDw/it6fIooz2ofFJnz8vKZy8eO/udn/rMLsatQt8Z2cne/fs4uRcbYf8M8VdAUnLZBB5+KyZ3HOKXNAWQFOv8pXK/2RltoY4Dg26GI3J9dvlXbIoA5Po/WI63fDbL6oCfllPyE/g7SvmcoaPkODRr/wl4PGhRo6f5q2BKaqzib544LUdUhRyXSN2/hBgqKLJIHJmbjDpkSZCfWY6fVAvsHDJMtUTsKamhhPjB/0e/15Jhx/Bdg7beGxTAw63LKtWcNT0eCbix0UZmnlkilrv7s9r1PopwLUrtGo6RUPywNpav3TqkNXB2S/t5dWdbcxJi0BCjnjbBsbZ1ei9h8XZ0WoKTQAa+qykRgXhcAv8ak2dGnGcNz+VRVlRTMS4W+AP+2y0dvayZ88etm3bRn9rHa+fm0lMsMhjZ+ZjMggMWByEmg009lv580GZ9RIjgrj56Bz1uVsGxxkZd5ERHUzHsI2bP6zA7nRzbF4CR/hEZX1jDqwONwJy9L6tvt/7mZIkLs03EhVi5KqiDApTwxkZdzJqcxEVbKSyc5S7PqsOKLbJiAnhuQtmEWIS2dk4yG/W+Fth+dabMmJCePLcAuamR2o2FCB/XyIjI8nKymLu3LmsWrWKwsJCwsLC6OrqYufOnQHl9Yqv4Q+phVksFj3S+m9AUlQw8zLkL5tbAiS590oQBOrq6tizZw9paWkIgkB5p4XTn9+pPvbFLY2TnveChenqLB2Qc/aBMDs9St29ArQOuzCnzyQlJUX98CtkUNY+xEWv7FWPfXJT4EGBAOfMjNb8+xlPOsntdlNZWUlZWRmFhYXcf95iTcG9ccDG7o7Ao+Z7R2w8tqFWVWpt8hF5+E5KTo0K5ogcbU/QU1/X++1MjQaRM7K0v9vZMBBQPGIyiBSmRTJkdaitAoqH4UScVxChLuzKkvLoBn8X9IfPzFd95hS8vac9YBR7RIaZF89M5xYfq6H2IRsv7WhXPQGXLVvGuccs5Uife1c47q5PK+keGFFfo+TIIJZmR2muEeC3X9b4zTqLCjFxwxHZXFWUwdz0SHWeU+ewjd9t8H4GTAaR35zojYycEmTFBGF1uPnlJ5UaUcpH+ztwSXJ0Vdo2QrhZjninJYax3EcBCfDLY6ZoXM3bh2yEGCSaB8a574taNSPx0Gl5GiVcTlwICWEmWoYcvN1gYsXKlcycOZPg4GB+9WUTf97ZwY3vl3Gkx03FYpcNeBuH3QyOy+/refNTmJseiYTcO1XTPUZkiJFws8i+1mFVofjIGTM0gyeNooCyrn+4r1OVoLvdbtIjDGy4YQlXFmXw/IWFTE8MY8DiwGQQMIqygOfZ7wJnUmamRPDEOQUYRYEvyrr5wzeNmr9PVPYtzIzmjcvmkBjx/YMXRVEkOjqanJwc2Vh71Sry8/M18vodO3ZQXy9H7YEcX/4a9EjrvwSSJHHjUd6C83t72rHZbOzevZu2tjaWLFlCRkYGbrebaQmhmp392vIu6nsDCyKy40L55KdLSPJ8WH279yfi8vkTnL13y55wgiBoIphgo0GTuviuto89TYMBz1mUGcbdy731hF2NA+xr7GHnzp0MDAyoIpLwICMXLdKm3j6qdfgpGQFe295Mx7BNTed9UdbFnqYB9VqV1GppaSlFMRYyY4LU1FtTv5X3S7SpL0EQWJYo+TXcPryuxk91B3Dxq3v5trZfTZHV9lg0AwCVcyaEGTnLRxAiuxvYeW7CqIcgo6jxsVNw7xc1fikiUYDnd/Tw4Po6tR9LFCA3QbsIhISEcPtJ+Spp2t1y7aZ7zMldH5aou+euri4eOnUaEziTUZuLX33qbxJ70cJUzp6TRHJkEPFhJvXvnx/sZkOll+RXTotl+RQv6bQN2YkINlDbPaaZyHv3ibmqvx7IUnGAmu4xHv1Km85MiQrm1mOm8NhZ+fx0hfxZsbnk+19f2aNuyFKigrnxyGz1cQ19VpbmxGA2CHxb28/zW1qIiYlh6tSpWNwm9fX5qmEcZQajW4JTMsHsHpeb9wWBe06SXc8dLokQk0h5xyg58WEIwOoDXbyxq42wICOP+Ax/dLol8pPD1ff36W8b+fxglxoJKfPVokJMvHhRIVmxIfSOOYgJNXteh9FJrchWTI3l3pPljMsr21s0Kd1Ayr6/NyoCuWYVGxvLtGnTVHn91KlT1efYsmULu3btora2lt7e3r9pqOMPdcT4T8Z/JWk9+EU5XQGUgeC1YlqcFaUu7h/t72D1xq0EBwezbNkyIiMj1SgiLMjIiQWJmnM8N4kgAuRI4vgCObWy+mCvn6u42+2moqKCWGsr0SHe3VlxfT8HPGaivs3J0xLDWZKj3QU/st5/cirIHoOzEkz8xNN4ajYI1JWXEhkZyZIlSzTGmVcvz9Kk8zotEp8EGFtyy7HTCAvS1nx+u8Yrlbbb7Wzfvh273c6FJyxnw89XcMORXlJ4ZVuTX9+Y0SDwM5/UiSh4VH9bGwHtF36i67XTLXHPmmq/xUWSJK5ensHPVmWxIDNKjRDe2tnm1xx81twU0n0amENNIk39Vp6bIKuXgPV18mPbhsYxGwTcktwLNvH1z4gJUXuXDILX1HVHt8hgSBomk4mmpibKSnZyhjdwwygKBBtlJd/EmmlV5winv7iHdRU9hAYZ1M1AdmwIs9O8yk7BM2VXIU2nW0K5vA/3darefIIgy+QD7aXeLWnnkwlmuhcuTGV1aQddI3aOnxGHG0EViPx+Y71a3/zRojSNf+DnZd1qe8GftjWz3kOw71w1n2CfHrghnzrYO4cktlQ0q67+xrFubliewgsXzuT5CwsJMoocbB+hwNMK8cSmejYf6mdxdjQXLPCqbMs7RtndNKSaAN+zpoY9LSN+pBIfbualiwtJjgyiZ9RORnQw95+a970bzTPnJKuq1kc21PFVlbe15YeIJP4aFHl9SkoKoaGhrFixgqysLHVUjeJqosjrJwq/XC4XVqtVJ63/dCx44Cte3tLI6c9u0yxsSkRgs9lwuVwYDQYeOiOfGfEm3BJsHohk9uzZqtuE8iF0u93ccYK20/vLg120TzK+/bLX9vDmzjYEZGnxSz4L0djYGDt27GBwcJCVy5dxRVG2ZjSD4gQ/0VHj3lO0ozgOtA0HnLUliiIfVozy0NpadeHcPBjFzJkz/b5UyVHBnFaolfE+/U2dX4+TKArceaL2/mu6x/hobzsjIyP09fWRlJTEokWLCAoKomt4nFe3NxPhkWDPy4hGnLAQCILAKTPj1IhUeZte2tZEy4BV7b4HOGlmomZBBDjYPsp7JVobKpBTlNetzGJWcjihJo8DAvj1WBlFgV8c7WUOiyp0aNUo+URB4DdHeDcsChGtq+hhTZl/q8JPVmTy5uVzuPcU+fVS7vqJLZ0kpGWxePFiVqxYwRkFMSR6ssNOt4RnUAAvbm1mt08UnR4djOi5t+b+cfV1bBmw+s2cmhIfyoULUzGLkBRuYtTmUo117/vSK0GflhCmsUIyCKimsg+uraXcRwJ+12dVbKsf5JPSLio7R0kOkbA55cjnvPkpTPVEnEZRjox83+Uvy3tY4Uk5/ubzaqo6R4kONfF/moZfecNSkByOwy3wUpWRjBlzSUpKYmxsjGxXO7d/VMbXe6u5Y2U8BlEmpanxobgluGN1JQMWB7ccPUW1tRIFeeBn6+A4J+TLY0F+u6md1lH/TV5KVDAvXVxIbJiJlsFxfv5BuZ/qcyKuWZ7B+fNTkIBfra6ipHlItXb7V0EhRbPZTFJSkjqqRhE5Wa1WysvL2bJlC/v27aOxsZF9+/YxMCBnRP7RmlZ2draaAfL97/rrrw94/GuvveZ3rG87zT+K/zrSUr5IXcM27v1MFlMozuy+YguLxYKpt5YLc+XFddOhYU0fkrLIu1wuUqODWTHVW/R1SfDnbY0Bnz8lUuto8OaOZrqGbXR0dLB9+3ZiYmJYunQpoaGhXLsym4+uXaJa3YyOO3G5JT/SmpoQTtEUbbT15KY6vwhGFEUq+pzqNQKsrxma1DvwmpXZ/PJYb7qqe8TOX3b5j+44a06KSjAKHttQTUNbF5GRkUyfPl0ljnP/tJvhcScj4/KX//ODneyeIAgRBAGTQeQnK7PV6ADA7nTzu/X+LiG/PcXfiugP3zaqdSBfl/pz/rSH13e1IQjyTYmCfy8XwHEz4jW+fEoUdc+aGtWsWBAEFqaHsjQ72u/xD607ROsEG664MDM2p5vHN9ZzZG6s6qrQPWpXvQ7NZjMxUZFcPse7kHRbJJLCRNwS3PL+AXbsPUBbWxsGyckjZ3oXeWXKrkuCWz+q9HMhv25lFncvgKfPmkZ8mJkBi4Moj1ruto8r1VrgtSuySPHk5lySPD5+cVY0dpfELz6qUBV6lyzyCotaBm10WgVVoVfVqU2lFaZFcuFCeUq2QlY7GgbJTw7H6nDz8w/L6R+zc1x+Aifke4UnbklOg6aHwYDVya2f1REem8isWbP4v4Nmhh0Cb5SP86edPSxPlK+/rtdCSoSJ24/JIibURKjZoNpVuSXZfeXrmj6cLon5GZFYHBKP7/YnepBT+n+6qJCIYCP7Woe5+aMK9f0PBKVH7MjcOOwuids+qcTq+NeS1mSkGBISQkpKCjNnzlRtyxISEhgeHubCCy9URV2vvPIKpaWlk6qZ/xp2795NR0eH+t9XX30FwHnnnTfpYyIjIzWPaWqaXHn99+K/jrSevnCe+vNbO5r5uKQFm82mcWbv6OiguLiY2NhYCmbOVOsVT27y5v+VRVgJuX+6yienA7xf0kbvqH9/x/2n5WsXYpfEg5/sUZ008vPz1Q+gKAqkRAWx0DM2pGPYppLWxFDfN+UGUNExwvpK7W5/bGyMy6fa/d7Uh74M3GycmxjONSuzuXC+Z3yDgEYpp8BoELnJp+lURPbP2z4c7ecd+Pg5M/0e/5vPtAuBQjLnzU/l65uXa1SXX1f38uW+Jux274JckBLh42wAkcHyQvzAWn9Zu/KvMbsLoygTka/pq3oPgsDPj8wmKzaElKgg2YpIFKjrtfDChDTdb07K1ZjjZsYEM2Z38etPqzV1qJFxJ1f/5SBD4042H+onIdyMS4L85HCO8+mjkySJ+Ukmijxu8gBdY25iQowM2uGVMnmTs2PHDiIGDzEnyfsaC8jjMLpH7dy+ulLz/BFBBl6tkrj4L5VcvjQNs0FgyOok2CjSOmhVR96Hmg382qfhd9DqpHXQSkZMMJ3DNtViKT8lgpt86lUgyQo9QR6Fc88a7efqpiOzef2yOTx1bgEnz0zA6ZZo6LWQFGGmfcjGrZ7z/vqEacT4TOi974saVqZAQriJ+l6LShw/8vlcdFokNneKxHms1TpGHJRWHmL79u1UV1eTFWLngvnJGEWBc+elYDIIbKrpIzrERGaUkQGbxHXvlmmUlwryksJVhWBx/UDAIZS+MIgCj541g5VTY3n0rBmYBOlfkh5U8LdYOCm2ZYq8vqKigmeeeQaDwcA333zDihUrSEpK4qmnnvq7nz8hIYHk5GT1vzVr1jB16lSOOOKI770e38f8vaa434f/OtJKjg7hyOneBcKEUzW6dblcHDhwgMrKSubOnUuFNYLzXy6hzbNj/7amVyMy8HVrX5gVzWVLMojw1HfsLilgVBJkMnD5Uq3UdWO9lYz8eX5vXN+ojXkPfUNx/QBBRoGWAStv7GgOaLi7IDOawtRIDSH+4es6nC55fP2hQ4doamoiJjyYS5dqRRb7W4f5MoCrvNstcfvHZby5p4tQg9xMeiiAOSzA6bNTSAyX003KlX1eNUzLkFbRtGRKHIt8ZneBXJz/k6deBV7SCjIZCDaJ7GwYUM8dYYKhLlk5pYwZGRgY4IYjsnwaaZ2IglxbWlvRo4m0Xrq4UK3ZKAtPXa+FPwZwYF82JYZPr13I706fQYhJVGc5vVLcQoVPa0FmbAgX+0Qd3SM2Qkyyku3PPma+EcFGlYjckuxCLgqyd+PzEwQh8mDDqSyfEqO6VQx57mt7i5V6MZWVK1eSm5vLTxdEqmpPCZgSJRJsFNjdNKRpOC6uG6BhRMDlhsc3NTDDE0mOO91cuzKTmSne6O7I6XHq7LAgo0j7kA2zQSTUJLLHZ+7TVUUZPpZaAkFGgYsWpmIQ5BTgCz4efgZR4NLXS1n5xHaOzYtjWU404043Fk/vVMewjZ5RO7FhZn59Yq7njHK0936dxKUL5F7H3U1D3PtFDVcWpatNycqxfRanqiT9qEGkkUQEQaChoYEFxlbuWWLmgmkC9x+fgVEU+Lqmj6RwI9FBcKjHwi8+LA8YSc1Nj+QP583EZBDYWN0rTyT+nsbfEJOB5y6cxcLM6H95evCH+A4aDAamT59OZGQkX3zxBf39/Xz22WesWrXqH7oWu93OW2+9xVVXXfW9YpPR0VGysrLIyMjgjDPOoLy8/B96Xl/815EWwM3HeQ1Xn9xYgyTJIzeKi4sZHx9n+fLlJCQkcGJBEhNf9sc3enfvinkteDzJTs7jl55zBxtFzp2XSiBcszJL3ZmLyIv8S9v95e+xYWa1nmNzKg4FDYw6BL8IQhAEnr1oNi9cPEf9XUOvhQ3lHezZs4f29nZyc3MxmUz87Igpqhebcn+PfVXrV69ySxJrPKPiLZ4//WV3q59TuSRJtDQ1cEyyncXp3vqSS4K3Kqx+13rvqd6UlkIgL2xuoMGjulRIxuWWWP7YFhr7LPRb5B3wiAMyc2WXdmXMSHl5Oe2VJSxP89b/lPv63YY6hm3eRSguPGiCm4WMN3a2aupFynU8vL6Wy98sVYlWQB6VkTJhIu61KzLVvqlxpyzQAXh+c5OmDvbEOQUaSb3y/r64tZltHjNfBbmJYTxzwSzSo4M4dVYibglVPv7oV/U0D9qJj49nxbwCrluZrT5ud7uNghj5xK9ub+XdrZWMjo6ybGoM0yO978WBthE19fzU1w1+03jvPH4qR+bG8odzC4gJNVHXayHL45jx1q42Pj/YhUGUZe3KZsnmlNjTOMitHoXec5ub+MJT33t8Yx0Wu0tuGP64ilG7iylxoYyMOwk1G/jjeQWq3dkJ+fEcmxePhBw5uySBP25t48dF6RgEWFPWzXObm3jg1OlqH5oEhJlFrliSxo8Wyd+9R75po1OQJ1wfvWo5RQVZOBwOYqxtXDndiUGA3W3jZEYaCTWJjI67JnW2KMqJ4fdn5WMQ4LMDXTyyoe5vcqz4ob6Afyv+EbNcRXxlMpkoKipi/vz5/9C1rF69msHBQa644opJj8nLy+OVV17h008/5a233lJHELW2Bp4a/vfisCKtPY19HPP4t4w7An/oFCumvIQQCj1TTusHnLz6TRm7du0iLS2NRYsWqUXB0CAjF/mkIAD2Ng/xXa0scggU8VR3jpAaFcS4082rAVzKXS4XbfU1LEuSP+xqVHKwk5ourempIAh+Iosxm4vV9c6AFlBJkcHMTotksafGkhkdhLmnCpPJxLJlywgPD8ftdhMdauIyT7QlITt1dwzZeGWCz5/RIHLXidrnlyS49/NKtV7hcDjYu3cvra2t3HLGEt68pohz5nnVWmU9LjXtpCA3MZyTZ8lRpVuC6BATDpfEvWuq1P4eSZJdt5dky/UPp1tSCe63aw9hc4skJyerY0YWLFjAFYsSubQgiPxoyWNmKg+ffHW/1tPtyiLtDKsZSWFIyF5zvguWzeHig30yaW8+1E9ShBkJaOy3qqSonDcqxMR1HlVmkFGgd9ROYoSc/vOdxhseZORun74pAdS5Und+Vq2O5BAEgVGbk+P/uIOH1tfRNjjO9IRQrB4PvfPmJ2vk6VcsTSc71tvbt7cHlmbJ5318Ww9fbi1h27ZtXJnnUkUoIGcE5NEh8PMPyjV1nZSoYH55zBRK24b543kFBBtFKjtHVeuk335ZS8uAlakJYVy1JEW9n5oeC+srerl4oUwcv1lTzb6WIe48IZd0n8nGB9pGqO+zEGoS6RtzcPvqKtUgWZkAPDs1gifOnsHsWDcOl8SftrWobiAvbG1mQ1Uvj545Q/1sjNndfLCvk7PnJnPqrERcEtzycSUlzUMEBQWRnJxMfn4+y5Yt4+qTlnD3UckYBDjQ4yQrzMXN80TG+rsYGxsLSEhH58XzwGnyd+LtPe08M0nvli/+VerBf/T8itz9h8jvJ8Of//xnTjrpJI3LzUQUFRVx2WWXMXfuXI444gg+/vhjEhISePHFF/8p13DYkFbrgIVzX9hBXc8Y5z6/3U+EoIgtFCumh88s4PxZMnH9aWc3+bPnMXXqVL838BfHTvNGRZ7/P7HxEG63pIm0AE57djtv72lTvcre3t2qsSAaHR1l+/btjI6Ocv7sWKbEeBdOSZLTeRNx1txUEiO0daHvWpwcCmAOu6mym6WPbmZX4yBBBmgetFHtTGDOnDkYjUYNyV65LFONthR7vz9taaRrQjH6okXpJEVoe6bKO0Z4e1cLQ0NDFBcXA6itAPd9XskXB7tUUliWaiA30V9Se9NRU9TXc9DqwCgK7GwYYHVph0paDoeDK6a7VRcH5S3tGrHzmI8DuOJCvmTmVG49YzFHFmYSG2pUZx7tarNS3dShphKDDILqqgBQ1TVGXJiJ9iEbj2zwvgdBJgO/PsFrRdQ1YlcbeH+zxr+t4IIFKTx1bgGv/Gg2JoNA94id8CADzQPjmvOeVpjEXA9RScjpxNwEuZn19k+8IzkEYMATYe5rHaZ92EZYkIExu4tuz7UoMBtF7vRca7JHbrinZYTchFDGnfBOcxhTpucTaoTL8308ooBjcyPJTQilb8zBje97FXJtg+Oc9VIJz29p5oG1h/j5UbKrSXX3GNmxIVy/KkttDbh0QRJpYRISslqwtG2Yul4Lq6bF4nBJ/PzDCtqHxnnpR7M1M7QEz2sQH2biUI+FG98vVyP++HAzdxw/BYvDxeW5blZMkdOJ7+/t4LRZsmrz/i9riQk1qZZeoiC7X1z9l4NcvDCVI3NjsTnd3PB+GZWdWreW0NBQzi2azq1FMRgEqBwUeLfGRU9vL7t372bbtm2Ul5fT0dHB+Lj3e3FaYZJqZfWnbc28Nsm4HQX/P9KD/wkWTk1NTWzcuJGrr77673qcyWRi3rx5HDo0uTHC34PDhrTSY0JJ9ijzytqH+e3n3hyp0nvlK7aINdpZFjVCQjAM2eG9A4MBzxsVYuLCRXJNwS3JKrLqrlG+KOvyI61j8uT8ustznMst8bv1NQC0trayfft2EhMTWbx4MekxITx5YpJGZBAVbPRr4BVFgduPz/U5Rnaqfra4K8BOUFIXdiXT99refvrHvLtXX/unJ86bxU1HeWcPWR1untioJU5RFPiVz7jzcE/N7omNh9iwZRcZGRnMnz8fk8lEacsQ7+xpY9zpZtQmX0Bxu4u9zf6vbU58GGfOTSHCk1JT7uR362sZdchfqOLiYkJNAjf7DElU1unVB7rYXKuV9Q9Y7Kx6spjnt7RoXPWvnBtJenykmkrcvHkzme5OpsR4NwNKc/LqA118Xe21dzp/fqpGRagMYPy2to91DVqhjckgkhMXwl2f1/DzI+Soa8zzOmyt72fQ4hNFnDBNvZe2IRtmo0CYx9HhzVJZzRkWZOSP53uFK6M2F2M22fh2Q2Wv3xytZVNieflHhay9fjHHe+TcncM2okNkQvhDcTeSBFceO5cjp3nVpl9WDTE3fIRIs0zgt39chtMlO1EoEXW1p8F4jqf/q7HfSlSIUd3kGUS4ZLo8jyo2zKTaGkmSxAyPu8QN75cTGWzi7hO9n2cJmJMWyfM+Cr3bPpEFJF3DNi5/4wA3fVDJi1UiVy1NoygnGqvDzdc1fSyfEsMvj51Cdlwo163MoiA5HLcEYWYDAxYH175bxuVL0lmQGcWozcVP3z0Y0Ch5capZJa6v68f4uDVU49TR1tbG9u3b2bFjB9XV1XR3d3POnAR+7hGhPL6pQdMKMBH/qenBf3Zj8auvvkpiYiKnnHLK3/U4l8vFwYMHSUlJ+esH/w04bEgL4OmL5qo/v769iXd2NatGt8puRJIkamtr2bt3Lx+3BBHraeB9dXsTzZN4AV6zwuubp8iK//B1HW60Tu03Hj2NSE8tQ+nZ2Vzbx+sbdlNTU8PcuXOZPn06oihiMBhwOJ2M213EeBRP2fFhGA3+L/nJs5LVHe3QuLyY7Gu38E2N1jtvYWow2Z5auhtZ7Tc87uSxr2SZuK+tEsiGt+fOS2VOmvxlFwXIig3xI8Oj8+JJC5V/N2pzERUkYnG4+WYwhilTpqgL15yMKGYkyV8Cm9ONQZAXpTtXVwScYXXH8dPZeusKjpweL0+4NYoMWhy8X+ukoqKCtLQ05s+fz2VF2WoNyS3JfTsg9xf5ztsKCzKqrvBlHaPEefqQ3jw4ikUyq6nEhQsXEhcby7m5RhKCJRKCZTVhjOez8Nsva1VZtyAIPHhanqa2qfjuvV1upbbPS1xDVgdn/amExn4rf9zczLKcaCTk5uRnzp9JtE/P3YzkcM7zNBuLgtxfVOARQnxSOcKeDvm8K6bGcvJMr3AI5AgEZEeHicRttbs44skdXLoojTlpkYzYXJgMAiJwsH2EMSce49np6gYE4JMmIxfNiZOHJdYNcedfttLTVMOdq7x9aBJyxKc0EN//ZS2bPbOpJEkiK0JuTv78p4t49oJZBBlFttQNkBIdTEKYrPy79eMKTpmVwJE+Q0R3NA7y7p52/nBuAUFGkW9r+7n/yxoqO0ZUsUzNkMhVb5cjCAIzksIYs7s40D7CAo/dmskg8vAZMwg2iljsLqYlyLWyG94v58dFGeQnhdM/5uAnbx/wm4rsdrtZnhXGI5561acHurh/7SGioqOZOnUqCxcuVN0nFFHHli1bmGPu4pz8cK4rSmZGktavc+L5/93qwUAYGxvTGAr8I3C73bz66qtcfvnlmsnpAJdddhl33nmn+u/777+fDRs2UF9fz969e7nkkktoamr6uyO0yXBYkdbinDjNjKfoYAMOh0NVB46Pj7N79246Ozv5oCOGzc02qgdcTImXB8UF6gECObf/0BkFLMmJUdMfLQNWdnS4NTUtgyhwp88uUunuf3X/CIuWLCUhwbv4ONxw0UedfHqgkzCz/CY/9109bYP+O0GDKHDdETmqzYyygD6yvha7U1YHNjU1sWvXLu461qsMVLjnk/0d7G4c8KvBfXmwkyOf3ErboB2Tpw8pOSrYL0VqMBg4NdP7uCGbGxHYVDvI5lotcT5z4Wz1Z6UXrLHPwtMTxpYARIea+KS0A4fLTUyoEbvTjUmE7DAXU6dOZdq0aQiCgNkocruPBLuic5SEcDM9o3Z+55N2MxtEHj/ba9vTZ3FgEGHULvHH3UOagYaZmZlcetxC1t60nCfOnEqEWWTA6iLUKNFvcXD7B/vp7+/H7XYzPTGMCxZ4c/Tf1vaxOCsKlwSPbOlR62BRISamesZ22JxuihsGiQ01YXG4uWeNvw3UjUdkMz0xjCuXpiMCu5uG1PrWcyWjtHka1O88fprGHDc5Kohz5iSrs6Ea+uTN1oDFwY0fVDA87uTSN0rJTQwlNUp2dMiMDeHVS2bjEWGSFBnEzR7PRIMgR5Bv7B/gak+abV2LQOmAkbwwK4sTvJsYUZDFICfmx+OS4JcfV1DePqLWIu0uN2e8uIeYEKPqx/dNTR+z0yMJNgq0DY7Tb3Fy3ym5msb5j/Z3yuIGT33qk9IuSttHuH5VluY1K64foKprjMRwMyPjTq55+wC1HkXrlPhQ7j0ll1cvncNfrpjH4qwoxuwubv24gquXZ5AdG0L7kI1r3znIgMW72VEioRPyE/idz/Pf/2WtqhA0Go3qcMclS5aoQqDzZwQzJ6hH07g7cS7WvzrS+kdrWv8MbNy4kebmZq666iq/vzU3N9PR4W3gHxgY4JprriE/P5+TTz5ZFcEVFPgPOP0hOKxIC+DXJ3sXrd+tr8bhkglLGe0RFhZGUVER95zqPa6+14ooyGav2+oCz5k6e14qD58pK7+cbokz5iSzKivYTxBx+pwUVc007pQINQp0jLlZfVC7uIeYjYR5CuKtg+PqGIaH1tYEfP4z5qSw5daVLM6OVkUGjX0WvjjQTmlpKfX19SxcuJBVc/M4ZZZXOj/Ns4jet6YKl6QdPPnGzmbcEvSO2YkOkReQh9fV+A8zFATyoyUWJcK8ZPnegjzjOn77RbUmisqIDdWIVxQ3h1eLm9jfom1ifnNnM/etqWZbXb+aznO4ITUyiMjISM2xJxYkaTYkY3Y5TbamrJtNPum8o6bHa5p9XW75tSrrcfDqdv/aw91rarnivTquXpEpu5Q4BcJMAkWpJioqKti8eTOlpaWcNc2oDlK0OWVj2NhggY4RJ/f7jJB/6UezNd6B/RYHoiCn3XxHzYNMch9ePZ/TCpP4ladGsr91mJRwI2MOiV9+LAteokNN/MpnzMfBthEsThfz0iMYtbm46YNyRsadxISaWOLTTvDhvk7GPXLyxn4rj3xVh1vy9hieOz+FxVlRXLE0gyVZUVgdbt4p6VAdMP6wox9z0jSevGQpSUo7gwRup5MlkUPMSTJjdbj52XtlMhnZJG75qJLOYRtnvbSXdRXd3HacXLvcVN3Hqtw43rp8LsmRQcSFmbnnZK+sXcSbmv3NSfJr8XJxC2FmA0UeE2GQCVYU4K4TpzErJYJBq0xcCnGfOiuJBZlRhJoNPHvBLJZPicHqcHPnp1X8eFkGSRFmRsedDFr9SUv+nCXyf6fLxPXR/k4eWhd4hI0i6igoKGDZsmUsXryYhIQERkZGNHOxWltbcTgc/1Sxw0T8IzWtfxZpHX/88UiSxPTp/o3+3377La+99pr67yeffJKmpiZsNhudnZ188cUXzJs3z+9xPxSHFWlJksTCzEhmpkQgCtDUZ+XlbY1UVFRw8OBBCgoKmDVrFkajkfTYMI6e5v0yKB/L/1tbE3Amjt3p4vp3Sjl7rmxt9F1tHzaX4N9FLrk5a5q3VmLwpPv++G29xmfQYDBw21JvX8yAxYGATJxfVwd2NDeIcFRuPGaDPLLjzMIEYkfqcDgcLF++nJgYuU7xi2OmqunMQ70WIoINHOoZ45292hEjL18yT+3v6Rm1E2QUGR538ts13lEgbrebQ4cOIQjw8Ol5vPrjIqYmhGF1uAgyirQOWP16vG46eqoq6x62Q2FqBG4J7lxdjs2H4M6YnaKKXMo7RokM8kSmldpFBeTa2l0nTeexs2cyKyUci91FnCdNdv/aWs3O+Z6TczUecUdkyaq6Z75r1MjPD3WPsq6iB5cET37dyGLPGA2nJHDE3OlqKjEmJgb76CAnpsrPYRLlMSIRZgEBWFveo/ryxYWZufMErTpwfkYUBkE2sn23xLvjlCSJG94v48w/lVDZOaLa//RZXUQHC1xZlK7ex6mzEtUeL+U5pyeFkxRhprHPyp2fVuGWJJ4+fyaxPsrIfos8kkMU4KuqPtY0e7/SoiDwp4tnc868ZJ4+fxaFqREMWZ0U1w+wJCtKHYIoGgz83xneTZ7FJfD0ATcXFYSSES4/x00f1yC6JXJivbXEzw9289hX9SzMlF/XDZW9vOdz/8fkxXPG7CSmxIdy01E5spT8YDclzcPc4KkJPrqxniOmRhFl8rp9CIDD5eaFi2YxIylMFl68dUCdRqwg2GTg6fNmctR02Z3it1/WcmVRBm9cPlcddgn+kdApsxLVlPD7ezt4aJLZa+p77BF1pKenU1hYqM7FioyMpKenB4fDQVlZGRUVFXR0dKgjRf5Z+EfSg/9tDu9wGJGW2+3G4XDgcrl45sJC/u8MOdR87rsGqttlB/OJhb5bjvI2pApAuLK47/bvFyh6dDOVnaO8v7eDnLgQBi0O3qmwaCItJcxdkixw1PRYEsLNjNhcRIUYGbI6edan0VMURXKiDKyY5rV/CvXUGB78strP4+xg2zCLfvcdj3x1iAUp8mK9rqKboJgkFi5cqHGeyIwN5RyfHjHBc5cvbvt/7J11dFSH9v0/Y5GJuxJ3DxYSrFSpUSqU4gXqrq/62r66uxvQIqXU3WmRBAlxJ+7uOnZ/f9yZm0wECPLa72+9sxbr9cG1uXbu2WefvWtoGxzW7bO1UnHHmcMvWJMqxG9FLfyY38zQ0BDp6em0tIhJtEuwZtYzu5ju5yBZgKyc6cvFCebn1dnGgmtGzA0VNvbgaK2kvLVfskMBcdj2uqTh4dDuIZHm3jkEz+6sHfOimOrnSJy3HVeliF5Srb0aHKyUtPdp+W5E4pziZM3qpOFq76/KAWLdVegFuOerIgnOC3G35fSw4fO/v6oLR2slQzrReLBPo5egxISEBO65bA7/Os2HZ89yxUoBVd0G/I3fHU/+XEpJk9iMvzTRizjjSIUA1HYOcI3R5fnZX8vINFacfUN69pSKw+pfZjeRWt5BrLcdGr2AHJnEMATxxfjvc0OxVMqlntL2Qw2cE+mGpVLOX6XtvP5XJWoLhRlECuBma8EjRqmr3+vlfJkzrJTy5M+HOe/Ng9z/dRGPXxhOkKuaph4NDT0aQt1tuOm0AGwtlcwMcJRmn5RyGR0Del442M/jF8fhZW9BU7/AO4UCV4UMYjOipaEzCByo6pJcpt/cXcWm/cPP1/3nhLB1bSLrU6bw7MWiWsx3ec2UtfRzhVHo9vmdNSwKGCbh6AW4+8sifi1q5Z1lsYS4qWnu1bB+c84YhX8LpQgZL4wSFTie+7VszDzaePDdhbEeUuLafqiBp45xJst0rezt7QkICCAxMRG5XE5wcDCWlpbU1dVJliLFxcVSUjuR+F/SMo9/fNIyzV6ZyBYymQwvB2umu+qJdBTQGeDLGkusra3HrOthb8Vc4/vWIECw8evrtZ3ltPeZ67ZdYBSP1eoFWoyabr9XDpLTILqJVldXs3//fry9vZk1cwZvr5jKi0tEM8euAZ042W8YThgmBY77Fw43+bU6A+52FtR1DvL2rgqz/TtaKyUSSFqtBmcLgUG9jI154wvz3nhaEB+sSiTARU33oA5ntYpBrYEvKszJGKtn+eExwiTQpKH46HeF/PLnXiwtLUlOTkYmk7H641wGdQa2H6qXpKW+zG6Qei8j48pkf5zVSmyUYlVoYSzp3t9bSU5dFzqdjpycHKIt2wkY8XVuqZQjB3aVd/N5pvnAdWppK+e+sY/bPy9k2XTxwnUN6lg108dMBw/gmtl+Eu1eJ4ieZB52FtR2DvLET8PU2qcuisR2hKN054AOlVxGZfsAD42itVuolJwW5c2vtTKeuzgKGVDZAz62coZ0Aq//kEFWVha1NTXcfZqPVEU2dmv4q7SdM8JFRt8dnxfS0jOErZWSJy8anoOr7Rwkr74HG5WM9kGxyhnZB5viZM3TF0Xw3fUzuHVBAAAfH6iT4Lz39tbwc2EL0/0cpRkpEKvo7kEd180W+1VP/VLO3rJ2hrR6PjPOof1e0sYVH2ZwWqgznnYWVLcPoJLLOHuE4sStCwLxd7ZGZxCtY1p6Ndz9VREPnxeGnaWCyh4Zv3a68OyiEDPiiloJq6MsWDtN3Nbzv5XzWaZYcaVXdZLy/F5e/qNcGt5VymX8WNBCR7+WcyJdWT3dgyQPGdfNEasvpVyGgEiY+SK7kXeXxRLgbE1D9xDrN+dIIyemUCnkPH1RBIviPNALopDtSMX6iXpOi+I8+M8FYciAben1fJHVOGaZo4UgCBgMBsl+ZTSpo6ysjN27d5Oenk5ZWRnt7e3jzmAeKf4p7MF/Svyjk9bo2SuZTCZJMR0+fJhHF0WJMkCVnePaaigUCs72MUhfrtl1Pfg6WtE9qBszM/XgeeES26p3SC/BX28eaOdQZhZlZWVMmzZNIg90D2jYkFrFFcb+joe9JXefHTpMETZKQAW72bDI6POk0QvYGtmHH6ZWmc14TXFWs3TqcK+qXSNDLoOdxa18P44Ek5eDFdP9HHhgoQiVtfdrUcjEHtPQiOFrlULOvecMk0cOVnXgbaeiY0DH9402krK9TCbj0fOG+yr7KjpwVqvoG9Jz75f5Y6xA1BYKPloRzcMzRHPD5h4NPo5WGAS494s8dqemMTg4yJzZs3licYy0Xr9Gzxwf8Rw88WMJVW3DjM7K9kEMgli9vLOnhgRfsZr5KruR2lGJ09ZSyS2nBTDFyQoXazkdg+JQtakPZrKbV1so+M8F5jj8qiQflHIZvxa1snmEL1JVWz8XvnWQH/JbeHFnBReEiMm+sV9g5QxvnlwyDRcXFzo6OuisKmCBMY/KZCJ5RKPTE+xqTWufRtLZOz/Gw0w3UQD6tQJqpQiZjnbMnRvsyCPfl3BpvAfLp3sjIPZeTMnl62xxFOLWBYFm9irP/1aOi42SGW4GUVD3i0Iq2wd45bLh5veA1sCHabXojbTxgsZebv2sQKrArVUKHr8wnHvPDmbHVVMJdlXT3KPhkR8Oc8ccT5RyUVx3V2Uf188dVh3p18Gnhf1Ms+vmDG9xW4/+cJjt+8q45bN8UWA6rZazXttPfkMPj5wnemX9XNiKIMCa6R4oFHKumePHdD8HdAZBMnd8ZWclm/bX8d7yWHwdrajtHOSqLTljxIIVchmPXRDGkkQRgn3ouxI+MXp+mYha48XF8Z48cn4op4W6cGHs5PXxRqrnmMJkKRIWFsasWbOYPXs2Pj4+DA0NUVhYeERSx3hxvESP/v7+/yWt/2YYDAZpWBfEyqW7u5u9e/ei0WiYPXs2ccE+3GwUkn3m58NjqieFQoGdysDLS2Il8kCfEZb79FCdmXW8SiHngXOHv4oNAtgooaZbxzfFfaSkpODsLFYp7X1DJD2ziz+KW8mq7cLL3pLG7iEzV+GRTL6bTgtCbaFApZBR3tpPkKsarV7gkRG9pfr6epIsG1GPgF5Mw6WP/1A85rdVtfWT/NxubvgkhxXGKkRtoeRsH4OZuCuIJAcT3XpIJ6DTif21neU9Eq1eLpczP8TJzLvLRDI4WNXJhlFqGgCuNkreL4AHzg1DIReZY2qVHFf5AA6OzpJdyXR/RxZGD1OrDzXpiHSzMqqP50mza8tn+pI0gmSRVduDnaWSniE9d39ZOEYz7qI4T766ZhqPneGBtVJGcVMfMUbY7omfSqUeyFkRrqSM+F1fZjdylbEqefH3CgnO87C3QGXsUVa0DfBzxRBhLhboDQLf5TXTqVMwZcoU4uPjmTdvHncujMTDRsEsTzlKBHaXdeBjA2qVjMzabp43Dkg/cn4YDiPYge42Cu5KdpASp8kxV6PTc8arB0it6OTM1w4Q4qbmzHAXtHqB1AqR+ffKkiixx2Kh4NFRyfjJXyoItRf7vn0aPTduzyPKy86sSpUhVmYpQU6oLRTsr+zk3q+KpI+SBF97dHoDdpZK3l8RR6CLNY3dQ7y9v5ll4eJHwb7KTi6f5s3UKSK8KZdBXrOGd0sseeCSGVwQIfqZPflHHTNch69Zn0bPe6k1PPZTKSlBTijl8EtRKw/9XIlBEBPPUxdFEOKm5olFEdxlnN/btL+W13dV8e7yWLzsLalsH+CqLTljngm5TMa/zw2Rfu8TP5eyaX/tUV/6lyR48eqSKAktmEyYqqYjbd/S0hIvLy+J1DFSjd1E6sjNzaW2tnZcpY4TkXH6Hzz4XwiT75VGo8FgMNDZ2Yler6eiooIDB8Rh1+nTp2NpKX4FX5nsR6SnLZ0DWp76yZyZZ7rQp4U6c+/CMAJc1HT0awl2VfPERVEEuZpf0IvivfA3SuUM6QxEOYoP3NelGpr7hx8+ZxtLye20qLEXB9O80P4aDhkHbUcqtfs5q9lz11z+c6HYi6huH8BCKeNAZQe/FDSRn59PYWEhM6bGm7EjHa1VhBiVFJ74sdjsWL/NaaBfo0erF/g8s4EQNxt6hnRsKxurEC+Xy7hxjg8z3GXYqqB5ACKNSezhb4voHtBKKhVPXBSFYgQTakGY+IX/8h9lZtYtgiBw9ttZlHTBXZ/ns3Sa+KLQ6vXcsCCEqXHRZg/yXWeFSgodfToY1AvYWirIru2WfMQAXrgsxsy+vWdIFEjNb+jlhd/NafUKuYx1m3N4encL1ySI1zK3vgd/Zyvife2xNjIgZTIZ9y8MkSrujn4dvxW1cma4CzqDyOJr69NgpVLy/sphSr9GDyVtoupF54COW3YUSL1IuVyOr4cb39+UzBtrUnhykQgz7aoaIMFYWG1Nr2fjnwWoDENm0k5NfXp+KR/kofOGVRe+y2vCQqmQSBYavcCjP5bS2K0h0tOW3iE93+Q0SdA1iBqJy6Z742VvyelhLgjAJ2VyFsd5EuBiTVOPhpu253PdXD/CjNJMAqKF/eJ4D15dEiUJxJqYj/d9Vcjzv1cw98U0fi1s4a2lMfg7W9PUq+WnSh33nxPCx2sScLGx4KmLIrCzVBg1E2Xk1Pdw/adF3HFOhKSlmNGmYLqPOXQ/pDOwv7KD/ywMRqWQ8VdZF+8VCGj1BjztLfn86mmkBDmxZpYvj10QJs1WPfdbOW9dEYO7rQX1XYPjDhKLhphBkoLG87+V822Z9qiVyvGy/0wfpsdaCY1WYzeROuzs7GhpaeHgwYOkpqZKpI7BwcEToryfqJfWPzH+UUlrpO+VIAioVOIDnJmZSXV1NTNmzDAbdgVRP+/RRZHIZfBNTiN7RpgjjvTEslIpeHJxFDKZqPrtYmsxxpxQIZdx+wgLjqx2OcGOCgZ1Bh77vsjsC+i9lQnSfxc19uJqa4EgwP3GQduRCvEAMgQMej1nRIgNYwcrFXcu8Mems4zu7m5SUlJwd3cXXXUdxN/d3KvB18kauQy+y20yYx3etCBY0qbr0+ip7RB7FEVdcjODRBCrOKGhkIfOmsJLl4vHXdDQg5udBc09Q3ywt0qqDKc4WbM2ZRj6ya3rZm6I+LV/9+d5EjtQJpMR4zU8aLw9vRZvG9AaZDyxs2HMsPEUJ2vuPzeMl5bEYKuSUdE+RJSXcWbprwoyazoBUcnjNmM/B0Q6+11GSaat6fVmNvNP/nyY7Loeqrt0fJzXx+VTRRi2oWuIm+b7S0O6IA5Vr0ueYtymjNKWfvo0BgKcrWju1XCPsdqI97E36xcBOKtVOKuVHG7u4/5viszgvA1pNcx4di9KpVISkU1rFDg91JEpDirclYOkp6dj017MDK/h49lXN0R2bQ9rjYaMD39XQlZtN1vXJkpOAgB5DT0UNfbirBbtSK7blispbwDctiCQL66ZxkuXRXFhjBsGZDzyw2HWzvLFWa2isKmXf39XwrOLI6SPAa1e4F9fFmGhkPPs4kiJ/v3qn5VkGUkMgzoDT/5SxpWbc7g43hMvWyUtAwIfH6iVKl5vByseOk+s9gJd1DirlRQ397F+cw43nxbAGeEiqy+vaZBAF/PEleAqx66zhOtj5KjkkNUqcLtRgV0+4vleHO/Ji5dGYaEQ58Ge+LmUVy+P5u1lsSROcWC8kBltZ0wzYF+V63l//3gKMyceBoNBUuE5nhhN6pg7dy6RkZFYWlpSW1sryahVVlbS0tKCTje+7up40d/f/79K61SGwWAY43tlct5UKBSkpKTg6Og47rpxPg6sTBJfSA9/V8SAZvjFauqDgWjvYbIN+fc3hXSPol13dnZi3VbMvCkWRHraojWIs1hKuUiB/2WEf1W0tz3nRA1DXp39WhzVSirb+nn9z3IzeLCwoZuZz+ziwW+LifK0xcVGHJzNKqnCxdmZpKQkiUiiVMi5esbwkPKfJa3MCxE/3U1VkSneXBYv/fegzoDe+FC+tLOK8tY+DAYDBQUFFBYWEh8fz92/t/PmrgrWJovnoH9Iz4qZvtwwP9DM3uO6eYGS621zr4YBjR5ntYqS5j5eHtELfOHiCKzkw1Tl+j6wVskpae7jyXHm0ZbN8CXcw5abpqmRy+BAZQdxPvYYBHj2l+EZpyum+xBqZKPpDPDRgTopkTz8fYkE+92+IBAr44u4fVDgy+wmQtzUaPQCt382bGZoiqtSpnDz/ADeXxGLlVJOWkUH0V52kiTRm7tEmO7WBYF4jSCvKOUynl4cKXo0FbfxlnG5jn4tb+yqQm8QuOOLQnLrulkc54EA7K3o5qHzIzhvzlTmzZtHTEwM1890NoN/P89qZHBwkAWhzmj0Ard+lk/XgI5Xlph7kgnATfMD8LCzoKJtgJt3DOv3aXR6znp1Px/tq+X+swJIdDGgMwg8+XMZ1831w0mt4pJ4T4LdbCS4TYb4oXPttlwc1UoeOlfseb6fWsMl8V5mXleN3UO8vLMCnQDOllDdIfaUWoyzfguj3Hjp0ii2rk1k02pxRquibYB1m3O49bRAkgMdGdQJNPcMmSXj/Y16Mg1+LE4K444ZalRygb9KO1j3YSrFh0UNSdPzc3q4K28vi5VsSx794bCZePB4IZPJuG6uP3cYP3g+zmjhud/KT3riOtm6gwqFAmdnZ4KDg5kxYwazZs2S/m00qaOjo2NCUocgCP+rtE5VmODAoaEhCXsWBIGSkhIyMzORy+WS5caR4tbTg/FysKS2Y4A3/hJhJJMn1sgLe9vpwSLc0T0kKWQIgkBFRQUHDx7E39+fd9fP5Z0VidhbKqjrNRDnI37RFY0Q5AS4f2GY1HfSGQTWzBK/7D7YW0Vxy4C0XwdrFTojM/C1PyuIcRUf3j/qoMfac8xNPzvAjhsSrbnBaDx5oKoTH0crmnuGePaXYVWPMA87M+q7p70lYQ5iArv7s1xS0/bR2dlJcnIyj/3RQFFjL5k1XeTWdRPlZUefRk9xUy8KucwsydpZKbn19GCUchlKuYz06k6p17UhrZp9FaK0T1d7K8tDzPtMrrbiy377obox811P/FjMea/v44eyIVYliom4sLGHsyLdeOOKeKmCVspFd1hTVHcMUtrSR4KPOGx7p9GF19pCycsjiAZavUBpSz82FgqaejSSmaEprFQKzo50RaMz8NRFon3K9/ktnG00aPxofy3NPUOoLRQShRygvG2AD9NqpGN6e081Pxe24KRWsSTRU1ru58JWdpa0EeFhw5DOwL++KqK6fQC5XI6TkxMzYsJ45IIIbp1pz7oYseraltWKeqgVf3s57X1abvgkhwgPGzNKP0DPoJa3jfp9WbXd/MtoVHjBW+n0avS88EcFyz/KJclNkERkX/qjgucujuQ0IxFk6TQv5gaLjspqC9GB+IZP8vB1suI2o3rGq39VsmKmj5lYr0ohQyWXcW+SerintDVXIkNk1XZR2NBDgIuaTavjmeJkRV3nIFdtzeG2BYEk+tpzQawHT49wYQbYsK+Ol9LaWBA9hVsSLbFUysluMfD4X81k5eaxe/dusrOzqampIdJFyQcrYnFSqyho7GXNR9mSc/WRYm3yFJYY+UUfH6jjnRH+XycjTrUahumZCA8PZ9asWSQnJ0ukjvz8fInUUVVVNYbUcTIo74888oj08W/6ExERccR1duzYQUREBFZWVsTGxvLDDz+c0DGMjr81aY2EA0HEhQcGBti/fz/Nzc0kJyejUqmOiSJqa6nkofPFk/lharXUfxmdtKwthmHCzzPr+aOwkYyMDKqqqpgxYwaBgWLV4WFvyZ3zxYSQVdvFY4siuPX0YLN9ejpYsS5lWIZm64Eazo50wyDAoz9XDMMojtbcOH9YdfyvqgGCXKwRgHu/zKd3lJuqQqFghoeclUlTmOHvSL9GL0E7zT1DZi/iO88MkeSf6ruG8LUVPYfyGnr5tU5OUlISarWaxy+Kkgga6dWd9A3psLaQk17VyZt/VZiJ7QIsmerNTzcn8/TF4lf/j/nNzA1xQRDg3i/yOZiVR1lZGXHOAilBwyQHa5Wcq1JM1WyBpPcoCAKfZ4iwZU6Ljr8qepgdJCqE59Wbz9UATPNz4MJYd7wdLLFQyDhQ1YWfsxpHayWFjb0SyWF2sDOnBQw/mDLEisrGQkF6dZdZH6y4qYfF76ZzzbY8ylr7JEHUb/OauDDWnU2r43G3E5NuSpAT86ZYStvcV9HJwapOVhpnmf79bTFFjb08sDCUYLfhQdauQR1FTX142lvS0a/lhu15ZoPR50a7Y2Oh4LwIJ6kK+L5azpmhDjhayihvG+TVr/dxlscg/o7DcOJLOytJLe/g9cujJajsyZ9KuWUElFrRNsjbRQqGdAZive0Y0Bq4/fMC6WNLJpPx6AVhRHjY8OziSElN4sbt+UR62nClEap8a1cVl41Ixlq9gKVSdEb+YGUcHnYWlLf2c/XWHN7aJbL7VmzK5qotOdR1DrJxZZw4W9Wj4fpP8rjzjEAeOCeEeSEuXG0kwFgY9RK/zW3m/p9qCHFS8sbSaKyUcrKatHxS50h0fCJOTk60t7eTnp5OR3kOD6XY4G6jpLJ9gFWbsigfxw1hdMzzNHDfmaJi/eL4k+egC/893UFT8rKyspJIHbNnz2bGjBm4urrS1dUlkTruu+8+XnjhBXp6ek4KPBgdHU1DQ4P0Z8+ePRMum5qayrJly1i/fj2ZmZksXryYxYsXk5eXd8LHYYq/LWkJgsDQ0JA0eyWXy2lqaiI1NRV7e3uSk5OxtbVFqVQe81zD6eFunBPljt4g8OA3okTO6KQFMN3fiVVGOPH+L/MY0AnMnj17DPx4RpgzyV5yDAK8u7tqXPO4q+f446RWYWepoKVXQ++QKJBb2tLPzzXDc1uXRdvhNIw4Ud42gLONOLM1mmShMcAdf/Yx94Xd3LwgCAfj4O6FcZ68syJBYrgBuNhacMOIhPhHnUCyq3icOwr7KGgUafUO1ioePG+YHVnVPoBCJm7nrV0VHO4UzL7SlAo5Byo7USpkrJwpvsyyarvwtrfAoNNQ3dzJjBkzkMngwXOHK86S5j6aeoZInGJP75Ce23fkodGJuP/WddOk7Vd2aMip68bdzoKGriHu+CwPvUGgsbGR3bt3c+jQIZaFKdmyPIxnF0cgA77JbWJhlFgVfXKonp8KxP7WtTNdJMksAdhb3sGj54uQ15aD9XyTI1Z81e2DmPL9639V8VdpO2dFuGAQ4I/iNrPzCrAqVo2DpZwYbzvJqVcmk5FifNnfsiOfzgEtL18aJZE8QFTKf395LN4OllS1D3DrjnyJVn7bZ/k8uaeD239pZX6oi9Rn25DRwdqUAO4+I5Drz4rGTm3FlREy5LLha/Lcb+UUNvTwzOJIZMCOzAba+7Rm1R4IpFV0UtTUS4yXLT2DOq7dliu93F1tLdi8JoH5oS68siSaeSFiVXbzp/nM9HfgIuOsU1ZNt3SuZUBFh5bH0/qwUMj5cGU87rYWlLb0s+XgcP90f2WnCAt+Vsjqmb5EeNjQ3q/l+u355BhV0m+cF8BMfwdsLJXcuiAQa5WcjPp+nto/SKCLmjevGLa9v/+HSty8fCS2ZnR0NIGuNvxrmhIPa4GmHg0rNxxid0HNEXs9BoOBi+Pc+fKaaZJTxMmKU21LcqSkaCJ1TJkyhbi4OObMmUN8fDz29vZ8++23aLVazj77bNauXcvmzZvN9AEnE0qlEk9PT+mPq6vrhMu+8sorLFy4kLvvvpvIyEgee+wxpk6dyuuvv35c+x4v/rakNbLcNBgM5OXlkZ+fT0xMDNHR0dKFGi/pHCkePC+cQFc161L8kcvMWXymEASBi4MUuFoJdAzBT43qceFHuVzO0lA5Po5W1HQMjNunsbdW8duts9m8bjoWSjmp5e3MDxUv6i91cooauigpKSEvJ5v7zhgmOCjlcP/CUGQy+CKrgd9G9Mv+Ku+hY1AcVl7/caZUzX2b08juEUQTU6xOmsJMfweiXRSAjANtSuaHuaA3CPzrizyp/3HFdF/Jgh1AZzCwMFpkeb2bqzWrCF7bWcr9Xxdw26e5hLrbEO9rT8+gDkGn4akzXFl8RrKkIO3vbE7e+Da3iVmBzjhaq8ir7+aF38RRgAgve6mfBiIzsLlHg6VCRlp5O//ecYD8/HxCQ0Px9vZGP9THso25VJYUcnmk+LLZkdHAudGiSoSpknW0VrIqerjHkV7dxW/FbVxr/Kp/9MfD5DeIMOTSqcMv+KzabnaXdeDvbEWfRs9Nn+aZzf/YWch5+TxPtlyZIBkDfnygjkRfe/yNw653fF6Aj6MVd4wg8PQO6fn4YB1vXB6NnaWCzNpuHvimGK3eQGq52Ket6dZz8bvp6A0GFsWK1+D1XZWEe9pKPY3LzkiSfkOgo9hnevrXcrKKy7lupjh+8cauKiI8bSV2IIiQ7oJQF95dHkeUpy3t/Vqu3iLKIA3p9Mx+MY1L30unpmOAly6NkmSQbv2sgDPCXbkqZQrvrojjkfNCRVcARGmrul4DqzZlYRAE3l8Zh6uNBV2DOhyszF+qeQ09PPR9CQEuahKN983VW3I4WNWJQi7jmcWRfLo+kXUpU9iwMh5HKwXVPQZWbszCxcZCSlz7KjrJqBbHEUwQa3BwMOfMncnW9TMId7OiRyNw+zcVvP+t+KFTUVFBV1eXhBqMZPdZqU5+RfTfsCU51u3L5XIcHBy47777+OqrrwB47bXX8PDw4JVXXjFTYp9MHD58GG9vb4KCglixYgXV1RNDrGlpaZx55plmf3fOOedIo0snI/5WeFAul5sZJ6akpODp6Wm2zGSTlrudJT/cmMx5MR7j9rQ0Gg2HDh2ipbGOJxZFIpPBjox6M9bhyH1byQWeuThaghNHJhdT2FopifC0k4Z4v89tIjnAkbN9BNoqCyWo86KkMOYaSRU6A7y/t4orjQ7DD35TSGuv2Ny+INqVQGP/VKsXePWPcpKNENy9XxZITXBTDPb3sj6oj3tSHPCzk9E9ZKCtV4OrrWgXYVJgl8tlPHFRlKRmMKg1kBzkTKCrmo4hgaf/GJZW8ncehhUe/q4YdysDaoVAQz/8WKcygywEQeD6eYFcFO8p2cK/s7uSlUlihbYxrVpiPt5+RjCu1sO3nbeDFf+5QDxvnxf00O8chqenJ15eXty+s5/GAXg5F6wtLZnlKUcvwF/FLdw9y4HZPsNV+GwfpTTjJQN+LGhBaxCYb6wkbjMSM+45O0QaazCdg/Y+Lb6OVtR3DXHrZ8MkB5lMRl7TIEvez2BBmIsE572xq4pLEzyxtVTgrLZAqxdYPsObaX7DbLbthxr4JreZl4xK6D8XtvDaX5XsuGqa9NAZBNi0v47U8g6iPW3R6gVu2VFA3gjvpmvmBvLWFTF8fUOyxDTclDeIZmCAc6aI1+qJn0o5L9QGayOErDMI7Clr53BLn5kM0tVbclj7UTZDOgMlzf1c/O4hHvi2mBvm+nFmuCtavcAdnxcQ622Hk1qFjaWS5y+OxEIhQ2sAR0sZDd1DrPkom36Nng9WxuFso6JrcHgY33T+rVRyLorz4O1lscwyemRd/0keu0rbcbW1kCqeaG87XjzPG08bOQ3dQ6zalIVcJuPtZbE8sSic2cHDMlwjw91BzaY1U0kKcGRID+8WKykbsqOvr4+cnBwzQVvTtTwV8U+2JQG44IILePrppzl48CAffvjhpLeTlJTExo0b+emnn3jrrbeoqKhg7ty59PSM7y/W2NiIh4c5BOvh4UFj4+TVRiaKvzVpVVdXs2/fPjw8PJg5c+a4UkxKpXJSNE/AjMo+Mmm1t7ezd+9eiY14WrQvK2eakkbBuL0lvV7PjAAn1hurnQe/KRyTNEyxfIYvZ0W6oTUI1HT0s8DbgI21FbNmzZIm0+9bGCb1looae+ka0BHhaUtHv5YHvy6Upvevj1VIYrd6QSCtvAM3OwvxxftlPgajZFR1dTUHDhzgj1ZbHt8/xHWxKmwtFeTV9xDv68DsYGdWJw3bmcR427Nk2nCT//EfirlhXiAqOaRW9fDxflEpfVG8F2dHDrMYfy3txdNRvD47Mur5IrNeehEYDAZsLJU8viiKmxYEctlUb/GFvK+GxUY1kPu+KqBnUIelSsH104Y19+q7BtmVVcp5wSJ2+siPZZKi90Vxwzf/xqwuDJb2hLur6dfBxuxusvKL2L17N7W1tQwODnLnfG8ujvfgPqNi+odptcwMcCTAWRyQveuLQgCevziSkdMO0V52vHVFDPZWSnLqeiR5p/IODS+ktlPc3McZr+7H19FK0ud79c9K7j4ziBcujURtoUAuExUZRr68N+yrJbO2WxoC3pBWy/6KDm6cYU7Vbu3TorZUMNPfgX6Nnus/GYbzlHIZuw63UdsxwO2nB7LKaEX/Qe4AyTGhnBfhhI1KhlrbyaUBw5Vyv9bAtdtyKWzq5b3lcfg5WVHXNURHv1ZiXAL8VNDC5R9kIpfD7CAndAaBO78o5Lciceg8wtOW+84J4ZrpTjx1mj2Rxspt/WZRleKDFXGSg4FKPgzRDmoNHKzqxEol5/XLYzgt1IUhnYFbd+Tza5G5YLSnrZJH5tgR521Ht7Eqa+/THlWhwsZSyZtLYzgrQky4T/zZSLHWhTlz5jB16lQcHBxobRV/x8GDByksLKSxsRGNRnPE7U4m/huuxcebtKysrMy8r47nOM8991yWLFlCXFwc55xzDj/88AOdnZ18+umnk97WyYq/NWnpdDqmTp1KaGjohCd0spXWeOvrdDrKyso4dOgQwcHBJCQkSHDgHWeGMMXJmoauIf4c5R01Elq89fRgKbk88HXBuNRZmUzGYxdG4qZWUNul4bNyOWFhYWY3TrCbDc9eGsPLS2IlaPCcSHdUChk7S1r5LKMeuVyOnUrg9jNGqInL4IGFYVip5Owta+f9PRXk5uZSWlpKnYU/23M7KWnu4/VsLbfOFhPF70WtLJnqjaeDOY5/+xnB2FspsVLJ0eoFnv+tlMWhYtJ49pfDFDSIxIgHzg5gBPuZ8rZBphpnYx75rogSo8+RIAiimsPLe5j93G5unB9E4hQHegZ1ZNd2kxToxOOLIiUX43hPSzPyxA9VBmZFBTLD35E+jZ7bPxOHeG9dECjNgoHIomzo1uBgraS2R89n9fZMmz4DW1tbtFotzWX5zFI3EafuZM1UsaJ94fdyVszwRm0kZmzaX0uEpy1Xzx6GKfdVdvJNThMvXxYl6eK9tbsKfwclDpbifdmv0XPHF4XUdQ4yP9QZnUHgmV/LzRilU5ysueP0IBbFunOzUcX8jV1VdPRrpZmhJ34uxc5Sxuwpw9dEBswLcebVJdGSHcc1W3No6BrkwW+L2HaogfPfSueF38u5do6fRP//z4+lzAp25dOrp7PsrFlcc3YCM4zeWCq5mDhu+CSXP7LLeWVxCF72ltR2DeFma2GmmiIgKrQn+NpzXrSbceC6QOoZnh/txnmhNjhZK/lwZZzkY3XdJ7lUtg3w3vJYHKyVJAU6YjFiwx+m1XL3l+KH2IuXRnJulJs0zG3qM4L40eNgpeD9lXGcFipClXd8XsDWERJbE4WFUs5zF0dyaYInBkE0Dv34QB12dnb4+/sTHS0SiSIiIlCpVFRXV7Nnzx4OHDhAaWkpbW1tJ/R++Se7FtvY2Jz0CtPR0ZGwsDBKS0vH/XdPT0+amsxZw01NTWMQtBOJvzVpBQcH4+LicsRlTjRpgVjR1dXVkZSUhJ+fn9mFVFsoeOaSaDaumSqJ5o7ctyCIFY2FUs5zl8RgoZTz1+E2Pkkf+0BpNBpKC7JZGynqBh5slfNt7lg48YJYT86N8eCWBWIf5O3dlawwVnxfZNVLfb7Vs/wkKEsQ4LOMeu5fKH61v/xHOXkNIqS6YnYwagvxUtb3Cbyzr4lLjMrs939dSHlrn9n+nW0s+GBVIr/fOltU/e4eIrtFx6wporzU7TvyqKxtIC/jIDfOHIZnZMB18wKYZ/xqvmV7Lv06MWk9+0spzT0aOgd0nP9GGmtmTcHD3pKKtn6sVQpODzd35704UMBaCY5GN+FHvy9mZdIU3O0sKWvt5+Hvxf7hS5dFmylkdA/qWDHdGyulnD1lHby9rxFnZ2fs7OxwC0vkvn1w+2+dxNn0kORmwCCIqghrprlydqQry41yXtfO8SPUfThxvpdaQ0Fjr+QB9dbuatLqNLxwjjsj1X3+PNzOwaouQlzVYlW0Pc/MMmPpNC/+c0E418zxlxLVc7+V42Kj4uJ4DwwCvLy/i5WxdtIsmAC89EcFfx5u580rYiQl9mu25dLZr5OW2bS/jvPePIi3gyWXJYgmkQ9/f5isWvEjQ6lUckWIDD8nK2K97UkOsEdngCf+aubr1Fyui9DgaCmjpnMQd7thZiKAq/H4nlwUwYWx7kbF/EK+z2tm2YYsLv+0lt8rh1Ap5Lx5RSxnGOWl7vxChDO3rU3k9ctjePDcER5aMjEZrt+cQ9eAjqcuiuCSeDG5PPBtsTQEb0IXrFUKXrosSrJweeqXMl78o9xsmHu8UMhlPHxeKOtTpmChkBE5om9rGv51cXEhJCSEmTNnMmfOHPz9/dFqtRQVFU1aC3BknGp48Hi339vbe9Jci0dvt6ysbIyjhimSk5P5/fffzf7u119/JTk5+aQdw98+p3W0MFVKxxNtbW20tbUhl8tJSUkZYzxoiml+jiQHjcXORypqAIR52HLnGSL09MzPJVSMSAYdHR3s3SsqI6w5bw43GTURn/6twmy5kXHd3EApAfxR3MI9Z4eyac00SU3DQinn4QuGZyL2lLVTWNPKVFdxmPeDAgNalKiUCt5bOWyy1tqv44e8JoKNL9Zbt+eMsUIJcrXhYFUHbyyLx9ZSQUm7HgcrJZ72ltR29PNNah7R0dFcdXai1FcQgLs+z+Pm04LwcbSiumOArWXisd6/MAw3owJFv0ZkDc4JdsFCIePPklZeNQ4lazQaWltbUekH2LAyjl13zuP8GA90BoF/f1PI3WeFSGKqHx2ow9Pekn+dFWR27B52ljy+SCRGfLS/jp9LxBfNg9+WYBCgoVfPv/fpuGBGKFO9rRnSC2xJbyLFpoXSonxqa2vRaYYkiSCTw66JRm9i9L2V0UvbgIHHLgg32/+ARs+95wQT7m5De5+W67blSoPM3+Y2Mf3p3Xy8v5arZ09hbbLYh3rsx1Km+TlwRrgLt850wMNWyVMXRUgwpUGA+78pYk9ZO+8sM2rstQ3Q3DNEvM/wS7h7UMfzv1eQWtHBaaHizNUD3xTzU4H4cWStkvHByjg+XBXPm8viJTmlTcUy+m28efR0N2xV0NCtwXPEO621T8uN2/Np79Pw2AXhLI4TE+y9XxdR1tqPRg/vZfVy1mv72bivhgcXhkoJ6JEfDvNzYQtymShAu9SYdNztLLGzVJBT38PKjZlUtvXz8PmhEsT6+E+lbEirkRILiHDogwtDuMU4jrAhrZb7vi4a1wNvZMhkMm5bEMiX10xnxgijzPEqIQsLCzw8PIiMjJzQ4DE3N5e6ujoGBsZKRY2M/wY8eCIGkCdaad1111389ddfVFZWkpqaysUXX4xCoWDZsmUArF692ozgceutt/LTTz/xwgsvUFRUxCOPPEJ6ejo33XTTCR3HyPjHJ63JUN5NIQgChw8fJiMjA0dHR1xcXMwgumON0UkLRKuP5CBnBrQG/vVFPhqdqIuYnp5OUFAQCQkJKJVKrpsXSJijjAGtgTs+yxsj9gpi7+3ZS6LxcrCkun2ArNouVIrhQV9BEJgd7MI5Ue7MMg73bstq5awYH3yMitePfCfCL9P9ncx8rwZ1BgJcbXC1taCkuU9aDqCuo5/Zz+/ith15/JDbxPOXxiADfj7czQw3uHeakrXnzsLLywuZTMbD50dIuoHdg3ru/iKfxxeJNO/cdhkb99Uhl8vYtGaqRPIQEIkrvk5ipfj27ko+O1BBamoqcrkcb29v1NZWvJ9axVOLo0ic4iCp75sqlD+KW9EbBC5L9GKG/3Af6D8/HMZKKZeUxl9Na6a4Xcem1fFSv6Zfa+DfP5RhZ2NNsIs13VrYUGaFzNKG5uZm9u3bR3dlHvfNdmTrFUGsMr5IH/m+hFhvW84Id0FngCd3tRLva8c5kcM0XwFo7dXy1hUx+DhYUt0xyI3b8+js1/DgdyXoBdHc8IoPMzkz3JWl08SX+MPflXBhjAfTva2QyWRM83OQNPJsjRp+D3xTzP7KDt5dHmuUYepDIZdLHwQguvu29mq4/5wQqXq796si/jjcgUwmY0dGAx+m1SCXwROLwiXl82d21lGvt+ODVYkkBzrwyuJgAhzE50IuEyhu7uOKD9IpqGnlkfNDuTRhLKTT0a/l9b+qOPeNA6gUMikBvbKzkud/F6uie84OZuk0Lz5ek8DWtYlSP23Vpiz2V3Zyz1nB0rzWi39UkN88aPZilslkXD3bj8cvFEcpfshv4fpP8ugZPPrHq98opYyRCXG8GG3waKKN29ra0tTUxL59+0hNTaWoqIjm5uYx3lj/VHjwZEk41dbWsmzZMsLDw7n88stxcXFh3759uLmJyEl1dbUZlT4lJYWtW7fy7rvvEh8fz2effcZXX31FTEzMRLuYdMiEUyHIdYyh1+uPWkWVlZXR19dHXFzcEZczxeDgIDk5OQwNDZGQkEB9fT06nU7CticTgiDw888/M3/+fDOSSGPXIIve2sd50e4s9BxgsK+HhISEMXNeX//yF08cMtA1qGfNrCncf24440V2bRcrPkxHqxe4b2EYy6Z6sHPnTs4++2zkcjl9/QPk5eawNb+fn6pEptYjF0Rw/9fiLNqTi6O4NNGbrgEtC17YRZ9WvKSL4jy5NNGLtR9lYhDgsQsjuXy6DyVNPVz45n5p/2dHumOj6+LLw0MoZLBxdQIzg8xnMd78q5zK1n72V3TQ2DNEUqAT50S58+j3xchlsHHNVJICnXl9ZxmvjTCCvGyqNw7WKj7YW4VKLvDiuT742kJ5h5a7f23BIIj7v/ecUNZsyqCmY4B4HzsWx7lzUbyXNDtV3T7Ape8dYtCY/K2Uct5dHsvHB+r4tagVOwsZO66eQW3nAFdtyTU7die1CpUcmnu1TPNz4N1lscgRxZh/z2+gs7uLUBstn1VbsrtO9Nt6+bIonv+liKouPY9fGM68EGcufveQJFirkMEzF0cS7m7D6o+y6ejXMjvIiel+DrzyZ6XZ/hfFujOoM/BLYStKuYx7ZzsyzduakJAQtHoDL++sYHWSL+/tqWZ7RgMy4D/nhxHuYcO6zTn0afRM93Mgq7YbnVGN3UmtYtPqePydrfn3dyV8k9MkOl97w29GD0ZfRytuOz2QM8NdePGPCj7aL0Lat54WwFXGnl5NxwBLP8igZ0iPjUpGn1ZArRS4IUbOVH8nthbr+a6oy+z3yBATt4O1kl9uSmJHRgPPG4e4F8V58Mh5oWYzbx39Wm77LJ+Mmm6pkro00YsPUmvo0+g410c3oZV7ank7t39eSL9GT5i7DW8ujTHzhztadHV1kZuby5w5c455nZGh0+no7Oyko6OD9vZ2SRrJ2dkZZ2dnWlrE2b3Q0NCjb+w44vDhwxOemyPFpk2b+Pzzz9m5c+cpOa6/M/7xldZkelqtra2kpqZiZWVFcnIydnZ2J9QTG48yD6ISxqdrYlhg34JKzoS6iK42Su47Q/yi3LSvhp3FLWOWAYj3deDec8Sb8rlfRBFYEL/i2tvb2b8vDWtra55bNZfZwWKV99qfFVwzxx9Pe0v8jNWMg7WKNXHDX1ff5DSSV98jEToe+7GY/PpuwjzsuOusYXWPXwqb2VszSIyrAr0Ad3xeMMZo7/p5gTx7aQzvrkoULS0qOiio7yHJXYS2bt+RR1P3ENfMDTTrFaWWtZHsPEiUkyim+9Sedro1AmqVDNPn0i+FzazaeEgiiGTX9bCvskuyogfxC/qm+QHMD3Fmpr8jgzoDN32az7rkKYS4WNKjEbh5Rz6x3vbS1z+IL9j5Ic68tSwOW0sFh6q7ePDbYuQKBRV9Sp7Y08HrOQb2Dnhx23xfpnuq0BoE7vg8nwv8BO6cYcM54U44WKt4bIQViMlssKy1n9cvj8ZaJWdveQflbf1mVRmIFvMHKjs5PUxUlX92bwf5zeL5VSnkrJ01BQ87Sx5YGMIV00QPrYe+L6GwsZfXjAoYdZ2DXDtnmDzS0a9lzUfZFDX28uj5YZwf447eMJywQDSdvOuLQi59L4NoT1uuMVY3r/xZyas7KxAEgSlO1jyxKAJ7KyUPnBtGrLcd/ToZr+ZCbhtc4qdhnqc5SmD6yp0T5IS1Ss6aWb48fqEItX6T08QdnxeYCSY7qVW8tzxOInk88sNhXt5ZwdpkX245LfCIflcpQc5sMM6ElTT3sXJTlpkP3dHiRCshpVKJq6sroaGhJCUlMXv2bKZMmSLJKNXV1dHS0kJ1dTU9PT0nXdvwRBTeT0VP658Q//ikdSyUd4PBIGkVhoWFERsbK8GBJ0rkGD2cLAgCVVVVHM7LxM/Pj6lTp2JhYTHhusl+tqwxzmLd+1UBTd3j66WtmOnLecbezl1fFNKrhYqKCg4dOkRISAixsbFYqJS8eFksvk7W1HYMkFPXzZfXJTEjYFhGab6fFbEeI80BSwlyVbMgXNTcu+XTXLoGtFw1O0DyngJoHpRxuNNAoIs1Lb0abt6ebQZp/pTfzMyn/sRaKeOlJbHIZfBZZj2eaghxtSYp0AkbSwUWSjlPXhQl9Wrqu4Z47M9mXrxiGgEuauq7BnlmTzsu1gqeu3S4+q3rHOTuz/M4PdwVhUzU8Xt9VMWyKsmH1y6P5rXLoyV69G2f5XNLijv2FjION/dx79dF3HpagCSoKgC/FrWg1RnM2IGv7KykuWdIegF/mtXMTd81sHpuGDP9HRjSw4ZCA2qhX2KbeQjtXBzjzBQnK4keftcXhXT0a3n+ElEm69vcZtztLPFzGr4GSrmMMyNcef6SSOYGO6MxwBO728mt6+ZwUy+nv7KPC946yOGWfu4/J1hKuo/8cJiKtn5euzyaj9YkcO0cP84ymkGqFDI6+rWs25xDRk0Xj18YzukhIoQ6yryAstZ+7vm6mN1lHdxulH16L7WGZ38VBWQXhLnw440zuTDWg/dXxDE/RBTwfWZvOxUKH15alcK5geI9PnLT3+e38MBXBWh0ei6K8+RlI2nmz8PtXLstl+4RcJ6FUnQXNkG6H6TWcPeXhQxq9ZLB60QR5WXH5isTCDB6e636KJtD1V0TLj8yTjZ8Z2lpiaenpySj5OLiglqtpqOjg4yMDPbs2UN+fj719fUMDh5dG/FocaI9rf8f429NWsfSJDxa0hkcHOTgwYM0Nzcza9YsfH19zbY72iJksjFyfZ1OR3Z2NuXl5UybNk3SKZwoTAnvrrNCifKyo7NfyzM/Hx53WZlMxmOLIgl0VdPUM8Smw3Kqa2qYOXOmGePRUa3ijSviJOr7B3vNzRkVCgU3JDnzwaoErpjugyDA3V/kc9Vsf7EP1jHAvV/mMzA4yKpgnRn1OcBBxbsrE8Vqp7ZbMqls6xvith25dA3qWPj6PgY0Oqky/KYSVk7z4MXLYiRX5jhfB5YmijM2SjlU9Qg8/mslr14ei62lgoKWIT7I7OLCOC8WjlDK1wuiS/Ey40v7vdQaM9v01LJ2Ul5IpbKtjzeWxhBo9Ix6cU8TV8dZYGup4IxwF6wtlDy5KFz6bX0aA9d+kouz2kKamfowrYbeIT3rjUQJgIbuIW7ZkY+dlZIwdzW9WngjX05w3Az8/f3RaDSc5tjBbREDXBOhZ36ADTqDwO2fFyCXIYnsfnygjnMi3SRpJ51B4PeiVspb+3nx0khi3FQM6ASu+ySPG3fkIyDKal363iEe/LaYVTN9pHmsx34spbp9EE97S2QyGf85PwxfRyt0eoEgF2uRer4tl92l7Tx45hSmuskwCGMTl4VC7KGtS/HjAaPw7+aDdfznh8PUtPfz5E+H6R3SobZQ8PKSaIlC/tiPpbyTWs+VCfbcmuTEUxeZQ9zfFrSx9K29pGXkEm6n4bVLRQfwjJpu1n2cbaYwIpPJuGFeAE9cGI5SLpOYhe39uqO+C3wcrfh4dQLxPqLChmnc4mhxKntOJvk5Z2dn4uPjmTt3LrGxsVhbW9PQ0EBaWhr79u2jpKRk0rYipjiRntb/ktbfFEdKWi0tLezduxe1Wi3BgeOtf7zsw5H77+npITU1Fa1Wa+ZifLR1TSzAl5bEcl6Mh5n+3+iwtVTy9AXBWMihpEvOoUEPHBzGegZFeNrx5EWiuvn7e6vM1NTlcjm+dkrmhLjy4HnhzAp0Eh2CP8/n0QtFe40/ilt585tUgj3suXWE/FBxm4ZD1Z28aKykPs+sZ+vBWlxsLCVRXL1B4LYdeZS29HKZscH/9O/VFI4wiKyrq2O6ZSO3JLuy+cppWKvk7CltY2Na9TDpo6yfbQdreXRRJC42w5Wqi42Km+cHcrWRoPDoD4fZX9lB75COG7bn0zukZ+mHWWxNF2WS3G0tqOzQ8E2plq+vmcZFcSJ5INbHnvVGaSlLpZyuAR1Xb80hzseem4wzVE/+XEq8rz0Jvuas0t+L2zg32h0fOwWt/Xpu+qwIla0TUVFRTIlMZFutHbb2jqyJkJHgIohKFp/mYWUY5KZ5fnjaW3J+jDv3jIBgOwZ0rNucQ1lLP/fMdiTcRUX3oI5+jWhyaYpvcpu58O10BEHgimkiseaJn0vZYpxZsrNS8sIlkbyzPJbt66dKc023f5bPbyUdXBUtJm6DYF4VafQCZS199Gv0XDHdm8cvDJN8tBa9k873+S3MezGN534ro7lniIfPC+WGueJ5endvNW9l9DIvQM35MR5cY+yHKeUyrFVySrvhwV3d7C+qYaA6lzsS5DhaySlu7mP1piyzkQAQ+17vLo8Vh7nre3jgzw5qe46OhjiqVby/IpYnF4WzbJTn2URxqokSI7cvl8txdHQkKCiIadOmMXfuXIKDxXvAZCty6NAhysvL6ezsPKaP6f+5Fo+N/xNJa3TSMRgMFBcXk5WVRUREBLGxsRNe2JMxnGxim3l7e5u5Jh8tRkKLAS5qXloSi7PN+FAiiC/7ltIcbkkWIaAP9zewa9TAsynOj/Vk/WzxpXL/V/mSqv1IixGVQs4rl8dJsNzrf5ZzzQwXLg4QuDw5lJiYGNbNDjDTI3zw60KUchl3nil+jT/5YwkHKzt4a3kCLuphfcZPD9WTUdNFkL3IVLxuazaNXQMUFhZSVFTErOmJzIsNJNHfSYITv8hqIKeum9UJjgA8/XMJOr3AU4uH7UXa+sSm/dUpvtIw6u2fF9Lco+GMiOGZvrd2V3Pb54XcfVYwNhZyDncaePznMomoAHDdXD/WJPmyY30i4e42tPVpuWpLDhfEuEt07Xu+KuLq2X7YWw2zS20tFVwQ7c4Dsx1wVSuobBvguk/yqGrvZ8kHmaTX9LLq81qaLH14d20yswPs0BrggZ+qobWCf89QIO9tZmGIDWcYbUEsFDK6B3VctTWHyg4t/57vSoSHDV0DIrFmpFavVi+w+WA99lZKiX7/9C9lfHxAbFj5OFqR6GuPlXGuaZFxrurJP+rYVS+IdiShzsT52EkwpUwGaRWdXPlxNi09Q1wU58mzF0ciR5QUA9AaBD7aX8d5bxzgX18VMSfYiYfPC0Uugz+rBnlyVxv9Gj03zvdnQZgLCb72vLMslilOVjT36Xk6XQveMcyPC+ax+Y64WkFN5yDLPkgnvaDMrOczw9+RLVcm4OdkRUu/gX/93EhaRcf4D8aIsFIpjqqUMTL+Tm1ApVKJm5sbYWFhkq2Il5cXAwMD5Obmmlmv9PX1jdsPO9Hh4v8f4x8PD46mvA8MDHDgwAFaWlokb5kjxYnAgzqdjoGBAerr60lMTCQkJGRScw8jE8iRQq/Xk5eXR1FREYmJiVx9dgJzvcR+zN1f5FPfOT42fueZIRIx48ZPcujs146xGHFUq3hreTz2Vkoya7o4VNHGPYunS5CjSiHn8UVRqFUyAh2V6AwCN32SzZwQF2l+6pZPc0Sq8wjTSYDy1n5SvBT4O1nS1D3ElR+k0dDSRnJyMhszO7js3YNc8vZ+pvo58p8LIgHRpdjeSsE5Qda8uyIBF1sL5oe5Sr5gMsSX6wPflfDI+WEkGAVXb9iex71nhxDgPNwrKmnu496vCknxt0Uhg50lbTz+47CZpEoh55o5Uwh0teGd5bFST+SarblcO9dPsuZ4ZWeFGcmid0jP1VtzkcvgPwtcRep5Yy8Pf1csGRn2aw3c93UxV2/L59p5QVKv6v0SFQMWTvT29pKdnc1C5zZ8bOVcO9OVRB9beof0PL6ni4oOLe8siyXIVU1Hvw57S/ORDAdrJZdP9ea2BQFS1fnsr+VsTKvh0vcOMev5VDbtq0VvEHjswnBWzhCfg09K9Ly3t5rnL47k3eVxvHZ5DGoLBYIgVpyFjb2s2JhFaUsf50S68drl0ahGvQX0gijvtHxjFh39Wl5dEo2FAg41DLJus8iUfPqiCN5dLroHb70ykRlGlYzbPi/ix3IN86dFs/2aJEJcrZnmY4NS22vW82loaMDLVsHmKxMJd1bSpxW44ZM8Ps88PiXyieJIJI+TEZNJilZWVnh7exMdHS1JTTk5OdHW1sbBgwfZu3cvBQUFNDY2MjQ0JG3/f/CgefyfqLRMSau5uZnU1FRsbW0l65JjWf944MHe3l727duHIAgEBwcfUY7/SPs+WtLq7+9n//799PT0kJKSIu1naaiCCHc1fUM6curGbzor5DIzYsadn+UiIBuzT3crgauMKh37mgR25JlvL9bHno0X+/LkAmem+zvSO6Tn2i1Z3LIgWNSa69Ny4yfZRHnZceUIlXYZ4KqW8/ACD2xUUNFl4Mt6O6ysrPnUqBiS39DD2a+k4u1oJVmovLG/jUQPFUmBwxDrfQvD8HKwJNJLtAH5taiNZ38t4+VLI/F1FE0F7/y8kJcujZJmxkB8wVop5VwXp5LgLpMDcWFjD3NeSGP5hkzkMhnvLY8zm6v698IQFka58eqSaE4Pd2XpVBGOk8ugsn2A/+zuwlop560rYrCxUHCopocoTzvUI97y2XU9rP4oG3trJTMDRFbjQ783onfyZ86cOSRPj+e1872Yat/HCt8uopxlDOkFHv2rlbz6bt5dFouvoxUdAzp8HYcTcteAjts/L6BzQMfNpwVIzMEX/qigqUeD3iDw/O/lnPP6frYcrOPm0/xZZ3S8fmt3NS/+UYGVSk6Qq5onjQr1rrYW+DlZ0dA9xOpNWeyv7GBeqAtvLYvFUmn+MaYyGoCeFurC/FAX/p1ij72lnPyGXlZtyqK1VyPR2h3VKt5ZFstliaJKx/O/l/PQdyU4Wqv4aE0iz1+eQMKonk9dXR179+6lJDeDm2MF5kyxMmMWHk0F41jjnzpHJZPJsLOzw8/Pj4SEBObOnUt0dDSWlpbU1NSwd+9e9u/fz8DAAL29vZNGi/5/dS2G/0NJq7CwkOzsbKKiooiJiTnmG+V4Kq36+nrS0tJwc3PD0dHxuG/68WxRRkZLSwtpaWk4OjqSlJRkNgtmqVLw+LkBbF03nYXRE8MhJmKGtUrOnrJ2Ps7pMoMZTAOSp0V68uC5YjXxwm+l/DpKrd7WSolSDm9cEU+gq5rG7iFu3ZHDs5dE46hW0dg1RE3HALefEUyAi0ilFYA3s7Xkl1bynzO9Ucll/FLYwit/lPHNDbMkyKtzQMv6jzPp7NewKFYchn3pQLdZMrazUrJt/Qy+uHYmz10SJSWgTQfqeNMoZptd183be2q4f6G5GadGJ5DgJueBhcPOwtsP1XOrkeSQW9/DwjcOkFbewTvLYyUvqDu+KOShc0PxMSaLO88MIshVjUImw1mtpKFXz4N/tOBiYyFRz1MrOkic4sDoeru4qZdXLosiKcBREr0taOzD3t6e+/7spMfOn9Pnz+GFi0KJcxVV02/Zkc+3qTk8usCFM0Kd+GhVHJEe4oeYQiYe9+qPsmjoHuKm+QFSj2lktPVpefbXcs598yBKuYzLw8TnYmt6vWSHckaEK09fFMGO9VPZcmUiU6fY0zOk57pteXyd00hSgBPvr4gzE/vVGgT0BoF9lR3ih5uTgucXehltegZZtSnLTI1epZDz0Lmh3Ht2MHIZfJXTxNVbc9DqDVJyG9nzmT59OnPnziUwMBAFAlf4D7LQV3xOP0it4fZPcxnQHH8v2hT/zZ7WiYRCoZCsV2bMmCGdG4PBQF1dHbt27SIjI0OyXjkatf5/lPdTFMcCtZkUmdvb20lJSZlQ82qimExPS6/Xk5+fT2FhIfHx8YSHh6NUKo8bXpwIHjQpdmRlZREZGUlUVNSYG18ul+NppyLOdywRY3REeNrx5GKRPv55fjd7qvsRBIGSkhJycnKIiYkhPDycFUl+kqHj3Z/nScK4I4/VUa3ivZWJuNhYUNTYyzO/lPL6FbF8ce1MQt1tsVIZnZ+N62kM8G6Rkqhgfx67SIQA395dyb6Kdp69xHwKfuvBOnLqe0jwtGJID9duyaKqbdh59pvsBn4raubsSDf+fa6YgDak1fJHcZtEVy9t6eP0UBfOCB/ub/18uIutRVqWJHpJlOonfirlmjl+EoOvX6Pnoe9L+Pe3JTywMESybb9xe54kcWWtUvDcxZFsXZvI1rVTcVPLqe/RsX5LDn5O1hKtfW95B/GjyBvOahUWCjmvLolmmp8DvUN6rtmWy+J30ilu6uOWzwq5bnsBfQpb7p3jQsoUa/SCjNcyBjhU2c5i9zZKcg9xQ4IFthaiBYuNhdhPW7Uxi5LmPq6fN6xnODJkQHuflg8PNjPDQ8lTF4nMye/ymrn9M3Fm6uxIV+yslDiqVby7PE7qFz74bQlv7aoi3seejavizQSKBURI8sHvShjSGfB1EA0kIz1Epfd1m7PZXdo+fBwyGStm+PDm0hjsjAzC5RsyOTwB00+lUuHu7o5arSY0NIT/LJnJnXPcUMjgj9JOnvwsVYISTXDZZONoihgnGqcqKZrOjVwuJz4+nlmzZuHh4SHBziapqdraWvr7+8cksZNRaT311FPMmDEDOzs73N3dWbx4McXFxUdcZ+PGjWZeiTKZDCurk2u8+Y+utBobGzl48CAAiYmJx9VYNCWto32ZmGC67u5uUlJScHd3N1v/eGK8Kk+j0ZCenk5jYyOzZs3C23t8FtSx9sNMcV6MB1cZiRlvHerhiz/209TURHJyspnC8n0Lw5hj7INdvzVbslmRyWTSOZriZM3bK+KxMrL+vs5qNFMhiPGwZnGoBWujVATYQY/GwLqPMpgV6My1Rj+tB78pNLLohqtEGeLMzcNnehPooKC9T8vVmzNp79Pwwd5KXvy9jJs+yeXOzWkE0MJV08XE9PLOCirbBnj98mg+XpOAk40FD58XhusIUsvOWgPP/lrOdXP8JNmiJ38u45b5AWbnKbO2mzs+L2B9si92Vkoya7u57bNhZ2EXtQpnGxU+jlb8Z54T7jYKqtoHWLc5m0hPWx416hDKEEgOdJS2e6Cqi5t35APw5tIYyfywbkQ/8kBVF0vez+DNQz2sjLPnQqP541tZA3Q4hhMZKUKhayPlhDsI3D1NiZ+DkuZeDVd+lMWh6i6um+sv6fKZwnRnJ3rb4Gwl44IYD15ZIs5M/VXazvINGUx7eg/3fFlIfdcglko5Ty+OkCSk3txdxYPflRDqbsO2dVNZnST2x5RyGXLEgeHHU3tpHzDgamvBhlVxJBs9sm7+NI+vss29kmYHO7P5ykSmGOWbVm7K4q/DY/3qpOMXRIdxtVrNlfMjeW9FHAsjXbn1HHMocf/+/Rw+fHhSyuz/VHjwWMPU07K2tsbHx4fY2Fjmzp1LQkICdnZ2tLS0sH//ftLS0igsLCQtLY36+vqTQsT466+/uPHGG9m3bx+//vqr5IRs8uqaKOzt7WloaJD+VFVVHXH5ycbfnrTG+woyGAwUFBSQl5dHTEzMCX0pmW6oIyWApqYmUlNTcXJyGgPTHQ3iO1KMXrezs5PU1FSUSuWEFP2R6062wrvjzBASPC0JtBNws1WN2/dTKkT6vQkCvPGTbAa1+jEEjjgfB168TGT97cio553dlWa/YVmsA3dfNoe7k2zwc7SgsXuI9R9nsmaWH2dHuqHVC9z4SQ5XzwnAw25YzTytvI22fj13J9ni42hFVfsA123NQq0aJiL8XKnh/l19eCj6OMtHPKbHfjxMQ1sXNkY1eye1iscuDCPCw0bq5Ww+WMfLOyu5/5xgSYX8nb3VXBJvDq9aqhQsjvfkLaNDblpFJ3d9UUjfkJZF76Rz5qv7+Wh/Lc7Wch4/3dUoDiySEGYGOPLKZVG8szyOpy+KkNTSFTJILe/gum256A0Cb14RQ5y3HUN6AcsRw3ACsKdmiJt/aMLX0Uqah3ro+8P8VjFISEgI11yQwuarkkiJ9OPfKbYE20PPkJ6rt2TzWVoJq6a5c7vRkHKkasjB2j4+KtSh1RuYH+rC28tiRGZlywAC8ENBC+e+cYC7vigkt76H2xYE8tC5oZKSxfWf5NE9qOOO04OYHyLar6QEO2FvpaSsU88t39WSVduNjaWSN5bGcEGMyFr893clvLun2uzDMMhVzdYrEyWPsJs/zWdDWs24H4+jE8sMf0eeuyQKVxenMVCiXq+nuLhYgssqKyuPqETxfwUeHC8EQRh3+zKZDHt7ewICAkhMTGTevHmEh4ejUql4/fXXiYqKory8nC1btvDrr78eVfR3ovjpp5+48soriY6OJj4+no0bN1JdXc2hQ4eOuJ5MJsPT01P6M9oU8kTjb09ao6Ovr499+/bR2dkpORmfaLUDjLu+wWCgqKiI3NxcYmJiiIyMHHODnAj70LSuSUXj4MGDBAQESKK6R4rjSVoN9XUs8+vntkQL5s6cOuE+7K1VvLM8AQdrcYj4ga8LzCotU5wR4caDRr3El34vY+Of+Rw8eJDAwEDi4uJQKBTYWyp4+twpeNpbUtbSx/XbsvjPBZFEe9sxK9CJQFc1z1wyrHzR0a/jnp9qGdQZeH9VIo7WKrJru/k5t5bTvYf339Cr48n9gzh7eLMwzAEBePy3Gt7+ehfZ2dnU1tYy1cuKT9ZNZc10D1aGi9d5w75a3tpdzTOLI5k6xZ7eIT27StuJHgF79Wv0fJfXTLyPPa9fHmNUcWjj2m3iS1tAtBO587d2Gnp0bFgZJ/Vy1m3OJsrLFmuVAmcbC567OBKFDKK8bLG1kJNZ2836zTlodAbeWhZLtJctQ3oB5ahpX70gqtU/fN6w6vljP5Xy8YFaPjlUz79/rMDR1Z1ZU+PYem0KswPt0Rrg0T8aeeGrfUTJ6rhzliOPneVrNkj8V63ItOwe1DHdz5ENqxKkRA+i5NbPhS2s3JjFio2Z2FkpeXVJtCjNVdnJmo+yaOoZ4pnFEdx1ZhCvXx7DJ+sS8bWV0z6gZ93mbL7MbkSlkPPkonCpWsuo6UI/Km84qlW8vSxWqnxf/KOCB78rGSMefTRFDBiGyyIiIkhOTiYpKQl3d3e6u7slVmJeXt4YJYr/RtI6VZWW6Z11tO0rFArJemXLli3k5uaiUqno6+tj3bp1ODk5ce21157w8XR1iT3oo82o9vb24u/vz5QpU7jooovIz88/4X2PjL9VMBdEuMx0CA0NDeTn5+Pj40N4eLh0s/3555/Ex8fj5OR0pE2NGxOJ3g4MDJCdnY1erychIWHCUvrw4cMMDQ0dl0pxVVUVLS0tqFQq2tvbSUhIOObfkJ6ejoeHB1OmTDnqsqbKtKmpCV9fX7q6upg5c+ZR19tX0c76jzLRGQTWTXdlgYdm3PWe/qmYDWk1KGTw8uIQzk4IGHOcQ5ZOLP8wna4BHXNDXHj24igc1RaSi/TjPxTz8f4a7KyU9AzqcLWW8dn1s6nr6GftRxlo9LAo2pnqLp3kD2WKK2f50tg9xE8FLVgqZTx+hgceij4OVXWyvULBw3PsYKiPSpU/z/wmivXeOM+f5TN8WPNRFhEetlw/148rNmSZKYXfeUYgV86awq7Sdm7dkY/OIBDobEVFu/mIwWmhLqxJ8uHf35VQ2zmIr6MVG1bFSZbxWbVdxPnYU9LUx7Xbcmnv1xLoYs27y0Vyw1Vbcihq6kMuE5OGKW47LYB1xpf+yzsr+TCtxmy/apWc6+b6s3SaNxZKOY/+UMKX2eIg+drpbpznL6O9vZ0fKzR8VSlHhqhAojWIlc7rl0czxcmayrZ+Vm3MonPEbzeJ3sb52LHlykSKjP295l4NrjYWvL40mmivYSQg9cAhPj4sY0+laHq5fLo3d50ZhEoh54f8Zk4LdUFtMf7LVRAEtqbX8+yvZRgESPS156XLoqSh8n379hEaGnpUb72JwmAw0N3dTXt7O+3t7fT09GBtbY2zs7Okdn4qBG0NBgN//vkns2fPPubZzcnE0NAQe/fu5bTTTptU4hUEATc3N7KzswkPD6ekpITm5mbmzp173MdiMBhYtGgRnZ2d7NmzZ8Ll0tLSOHz4MHFxcXR1dfH888+za9cu8vPz8fX1nXC9ycTfXmnJZDKJAFFQUEBsbOyYiudki962tLRI1PlZs2YdEfs9EXhQq9XS0dHB0NAQKSkpk0q6x7rfgYEBs16cvb39MVdoswKd+bdRoePD9Faqu8aytYaGhpjn0M50D1FM9/4fKyltHnbrNVVoIe62vLMiASuVnN2lbTw5Sq7qrrNCeGpxFN/fOAsfexWtAwJXbjxEZ+1hro5RIQO+yW9nur+T2aCvpVLOpQmePLkonNlBTgzpBB79s4UhR39ezVfQ2A83/9bDXzU6vPpKWRYhvjze2FXFjox6Nq6K54lF4fg5q3nsfHOl7Bd+r+DdPdXMC3HmmcWit1VF+yAeowwS/zzcxk2f5vP60mh8jZYwaz8WnYVNxyiXyYjwtGXj6ng87CyoaBtgzUdZdA3oeHd5HKHuNswKcDJTv3j5z0qe/kV8kd+2IIAbRpEs+rUGXvyjgrNf288HqdXcdUaQNLO1Ib2FzyuVzEyaxb2LZ3BmsC3nBSq4NVqHo4U4Q7fswwwOlLcR4KJmx9XTJN8wEBOWraWChZEitBrhacuWtQmEutvQ2qdh7cfZZn0oSwU8dIa3RATZml7Pddty6ejXcl60+4QJy3SPrJjhw1tXiASNzFqRoFHcJN5HJzpLNZqVOGfOHIKCgjAYDHR1dVFTUyNBiZM1eTxSmJ6zU1XJ6fV6SSpqMqHRaNBoNNjZ2SGTyQgPDz+hhAVw4403kpeXxyeffHLE5ZKTk1m9ejUJCQnMnz+fL774Ajc3N955550T2v/I+NuTlmkeyvTSHQ//PBmqFnq9XhLWNSlpHAt1/njhwYaGBsrLy1GpVJNS0TCFXC4/6sPV1tZGamoqdnZ2Ui9usrDiFTN8WT/bn3sW+OBjaw7RmPpXtjY2vLd+NolTHOgZ1HHNliwzAodpf4lTHHl1aRxKuYxvcxp5+ucS6TdYqRRckuiNh70Vz1/gj4uVjMr2AZ7Z38/ys2ZK8lbv763iuhFq5kM6A7d+VkDXgI6XLosSCQ5Deu75qggfR/Gc6gzwSbmc9yvtmRPqyuXGxPXyzkre/TWH1pYWtFotZ0S4cuUsX/5zfig3GFmGr/1VyRt/VXJWhKukS9jUo8FmxEtYIYMFYS4Eu9qwYVUcU5yMiWtzDi/9Uc7lH2QaWYK9BLqo+WiNqPRQ3zXEmo9E/b0NK+N444oYHh2VOLem13PXF4UM6QxcP9df6lWNjK5BHa//VcXCNw6Q4GvPvWcHIwM+OVTPPV8XobK05oWlidy6IIgYHwc+uCKCICcVXYN6rvkkj9e+SaOvpZYNlwebJa7eIT2v/FnJt7li9eZpb8VHq+MlksUtO/Ild2GDwYBCLlZ+L18WhdpCwYGqLpZ9mCEln6NFSpBI0DCdm9UfZZNR03XSIbyRUKKLiwt+fn4SlJiZmTkhlDjZMN33pwoePN7zYiJKnCxFjJtuuonvvvuOnTt3TrpaUqlUJCYmUlpaelKOBf4BSauwsBAXF5cxBIiRcTL0AwcGBkhPT5cYdUdT0hi57mQSpsFgoLCwkPz8fPz9/bG0tDyuG+9IyUcQBMrLy8nIyCAsLMws+Y4mVBxL/OvsUM6PdDZLkjU1NVL/KjY2FrWlBW8uiyfARS3aZGzJom9IN6YXNj/UlSeNskyb9tXw3p6xzCFrwwDXR2hxslZQ3S0Kvi6O9+LauQE8dH44q2dNMRv0LW/tZ/2WHPqG9Ly+NEZ0DO7XoTcIOFkPV2V5DX3c/F0dFvaurJ8lXt+Pcvv4YFcpe/bs4dChQ1wSJOPMIBuuG5Eg3t5Tzcs7K1kU6yGJyfZp9JLgrl6A34pa2FPWjqe9FR+ujBeZcZ2DbEgTpZXKWvu57P0M7vlK9DjbtHpE1bI5m+r2AZRyGedGu3N+iJXR5dcDlULGb8WtXLNVVN9flzyF+842n0MDUdV9QGsgzMOGFTN8eGZxhCQ6e8P2PJ7/rYzOfi1yuZywKe5su2omp4e5oBdkvJurZXNWO7VlRdwbqyHOTTxnKrmMIZ2B+78p5qmfS9HqDdgaSRYmc8nHfyrlhd/L0Y+gjp8R7sqWKxMkduCqTVn8XDi+7c7oCHJVs3VtIkkBjrjbWRDiZnNMPa3jDUEQsLS0xNfXl7i4OObOnUtcXBxqtVqaxzSJ2ra2tk7qPWN6L5yqYz8R3UE48aQlCAI33XQTX375JX/88QeBgWM/qI4Wer2e3NzcSY8qHSn+9qQ1ffp0IiIijvhiPx734pEhCAJ5eXmSz9Zk5E0mAw8ODg5y4MABaabMycnppDEPTaHT6cjKyqK6upqZM2eO6XkdS4U20f4MBgMGg4H8/HwOHz7M1KlTCQgIkB5KZxsL3luZgJNaRX5DD3d8lodBYMz+Lor34r5zxB7CC7+VsuOQqI5hmk+rqanB30XNx+tm4KhWkVvXzTVbsrhuXiArZoq/547TAwhxU+NiY4GTWiklLo3OwNvLYo3KDhpsLJVmJAeDADsyGrgk0Utyx91arKPBJhgvLy+u/aaOR77MZNfu3STZd3N9kgiPfZhWw7O/lbN0mhd3nB7INbP9uCpxuKczqBO4+dN8fsxvxtPekg9XxuPnZMXoM/1DfguL3k7n7d1VPLc4gjhvO7oGdFy1NZeDVZ0ALI9S8/w5Hjx6QTjvLIuVILNVm7Ko7xpk+QwfHjnPvAej1QtYKuVUtYtMsHOj3XnzihiJQPHxgXqWflLJ+zkDlLf2o7YQdQnXzhK/jD8rGuD7djdmTEvg36d74W8vR2sQsDHm/K3p9azfnCMpXfzn/DBuNo4LbNxXy9s5WjQjmBYhbjZsW5soVWV3fVHIa39WHpOShYO1ireuiOGDFXHYWylP6SzV6GpFLpfj4OBgBiUGBwdLc427d+8+ZijRtO1/WtLq7+9HrVafcAV44403snnzZrZu3YqdnR2NjY00NjaasRFXr17NfffdJ/3/Rx99lF9++UX6qF65ciVVVVVcddVVJ3QsI+NvT1rHUoUcLzwoCAJlZWUMDg7i5eVl5rN1rHGs8KAJqrOxsWHWrFnSTXMyB5N7e3tJS0tDp9ORkpIyrgL88bAOYbi3eODAAbq6ukhOTh63Me7nrObt5QlYKuUcqu6ksV8Y98G+MsWfq+eI/Y+Hvi3kl/xGMjMzaWhoIDQ0FKVSSai7LR+uSsTOSsmh6k5u+kT08JLJZFipFLxyWTRfXjONzWsScbezkBIXwLvL43C3taC2cxA3W5XZvn0drXCzs+Tm+QHSS/upXyu5/tt6GnoN/For45lcC8p7ZMxw6GVJoHhvbT5Qx8PfFLBqpjc3nxbAmYHWLAgQq38bCwU6g8A9XxXxSXq9WeIaHTqDwPaMBp77rZx3l8dKKhnXbcvlr8NtaPQCAY7iMc/wd2TT6gSpD7ZiQxZFjb1cmujFk4vCCXQR9y8Dow1JHp9miJBdcqATH66MkypCAdhVq+Oid9K5/pNc9lV0cPvpgTx8XqhkW3/7NxW4e/mw4coZeDtYcuVMD+5JccRKIc6wXfz2fn5KL6a3t5erZ0/hqYtEG5FDzQbe22+uouJgreLNK2JZkySe43f3VnPrjnx6h45eragUctxNoxCnUB/waBCbSqXCzc2N8PBwUlJSSEpKwsPDg56enqNCiaeSOXgi2zcpvJ9oMn3rrbfo6uritNNOw8vLS/qzfft2aZnq6moaGob1Ijs6Orj66quJjIzkvPPOo7u7m9TUVKKiosbbxXHF384e1Ol0R01Iubm5WFlZTYoBpNFoyMnJob+/H4VCQUBAwDFDgiOjpaWF4uLiCe26TVBdeXm5OBw6AvPt6OggOzub0047bdL7LS4uRq/XSxe7sbGR3Nxc/Pz8CA0NnfBB7OzsJDMzkwULFkxqf5WVlRQVFUmCnkd7WHYWt+DrZM1gU8WE10YQBB74upDPM+tRyeGeJBuWnj6Njo4OysrKSElJASCjupP1H2fSr9FzRoQbL1wSiUwwf9lUtw+wdnM2zT0agl3VvL8ijq4BLWs+zqZnUEeYIxSNEAmf7ufA65eLVO5nfy1ns9HaY3ScHenKTXN8+bOwgZf2NCEAKZ5wwwxH9DodCgtrPimTcd1cPz4+UMf2Q+IDesM8f66b40dzj4b1W3KkCsgU1io5z10cyfxQF4Z0Bu76ooA/D7ejlMtQK6FPK3Dv2SFcmuiJSiGnsXuIG7bncbi5DxsLBS9eGkVKkBM6vYHbPxfXtVTKpSHo5dO9ufusYJRyGRVt/Vz23iGzSsgUIW5qXrs8mtqOQe74vICeIT0+jla8cXk03o5WWKvE61zR1s8tn+ZR2T6IUg5LggTmeoteUbVDVryxt45XLo0g0Nt9zD4Avs1t4pHvS9DoBYJc1by6JBp/5/Hh/tGxc+dOkpKSTons0KFDh/Dx8TEbsD/WMBgM9PT0SKzE7u5uiZXo7OyMXC6nsLCQ2bNnn/TjBnF+tKamhunTp09qvb/++oubbrqJioqKU6oG8nfF315pHUtMFh7s6OggNTUVhUJBcnIyVlZWJx2mA5EdmJGRQW1tLTNnzhzTpDzRwWQTXFdcXExeXh5xcXFmowDjxfH0tKqrqykpKUGpVB7R5mVkLAh3I9Tddtz5rpHHcnOyK7HOIg37lYwhytuHxhzjVD9H3lwWj4VSzu9FLdz/dRF6g/k2/Zyt2bAyHnc7C8pa+7lqSw4O1ireviKWZy8I4rpoGUGu4ktPIYP06i6u2Sq65/7rrCCpRzY6fils5bIPs+kyiAaRchmkNsIHeRp6+/poa25gsWcXmrY6rp/hzLVGyPHNXVU882sZbnYWfLAibswLekBr4MmfSylr6cNSKefFS6Mku/lujYBeEH2yznn9ANvS63FSq9i0Kp6ZRrX0G7fn8W1uE0qF6Pgb6m6DQi5jcZxIVNqaXs+NxnmsQBc1310/AyuF+QtKKZfRO6TH096KWYFObL4yURIfXrUpy2y0INBFzSfrpnJmuCs6A2wrlfFjqxNKCyuc9e3cGqWjsaqM0tJS2tvbx9xjF8Z6sGl1glQRL9uQyZ6ydo4WgiCc8krreKshE5QYGBjItGnTzKDEw4cPk52djVarPemsRFOcqC3J/48JC/4BSetkuBebQhAEKioqSE9Pl4Z4VSrVCSWPiSA+U9kLTAjVnSg8qNVqSU9Pl1yZj2WyfDLwoMFgIC8vj9LSUmnMYLI3+kQ9NNO1yMvJ5rmLI5jm50jPkI6rPs6koUczZvnkIGdeWxqHSiHjh/xmHvupdEx/xM/Zmg9HJS4vB0tSAh2wkMMLl0RipZSzIMwVeysFOfU9RmdcLfcvDOGS+PG/tjV6gZy6bi6K8+CZxRGizUlFP1srLOixcOOh/QZqOgcpKioihipWRYuQ4JaD9dz/TTHONio+XBnHv84MYlaAIyAmjHojSeFAZScqhZynLorg8lHJs6VXw5M/l7Lw9QN8kd3Ii5dESbqA939TzPt7q1FbKHj98mg+Wh3PYxeG89KlUVir5KSWd7BiYybV7QN4OVjx9kW+WI54x+kMAg5WSjr6xPNtIkGYGJjXb8tlR8YwtGNjqeTFSyO59bQAZMC3Be08uqebKeFxWFpa4OHhgUajoaCggF27dkleUCbtuxhvO7avmyrZydy4PW9CFYyR9wmcOjLDyeyXjYQSk5OTCQsLQ6VS0dPTQ1ZWFrt37z4prERTnEjS+v/VlgT+AUnrWOJYkpZWqyUzM5OqqipmzJhhRiA4UUWN0evW1tayf/9+fH19mTp1KiqVatx1j7e/BOJ8lGkweTLkkWMlYgwODkqWKMnJyTg4OBzXl+J4lZZerycnJ4fKykpmzpxJoJ8vby+PJ8zDFhtLJfIJqsHTwlx5/tIY5DL4MruJZ38tG7Nt/1GJy5SUQCQHfH3ddF66LIoNqxJwsVFR3NzH2o+zaenR8PD5oSyKHR/ecrezRAAWRrnz/CWiOO/emiEe/KuD+h4tt/3Swe9dbgRFxnPFVE+ui7dCLhP4Pq+ZqzYeQNPbxfLpXjx/iagfqDMIuNio6BnSc+22XL7NbUIuk/HgwhAuCB47/tDap+H538rZkl7H04sjpF7cK39W8sRPpXjYWRJuVIA/M8JV6oNVtg2wbEMmByo7cbdV8vRp9hJdXy6D4uY+Lv8gk0PVopqBk1rF+yviON8owfToj4d5/rdyqbKVyWRcNdtPUtbPqe9h6YcZHO4UcHFxISoqitmzZzN9+nScnJxobW3lwIEDpKWlUVRUhKG/k3eWRknyVBv31UrXZ7wwXd+/q6d1IqFSqbC0tCQ2NpY5c+YQHx+PjY0NDQ0NJ8RKNMX/XIvHj/8zSetIF72rq4vU1FQEQSAlJQVHR0ezfz8R9uHIKs1E3ywpKWHq1KkEBwcf8SvOlEAmm7hqamqoqalBrVYfk+TT6H2apKMmChN8amtry8yZM49rvssUo5OWadh5YGDArAK1t1bxwapEtq6bjrfDxKrPC6M9eGKRqBa/5WA9r/1ZOWaZ0Ynr5i9K6R4Sj8G07TB3GzauisfT3lIc9P04m/quQR69IJxzo91QyTGbWfo+r5n7vi5CqzdwZoSrUVV+eJ8CsCOzkUs35vNVuZ5VZ07l5UujsFTKyGjUcNPnRfz0xy7Ki/K4b44zZ4U58dlVUzk70lWqmt7eLdL/V8TYsCJubGXuYK3kskQv5DIZd5wRJM1jbc9oYPoze3j4u2K6BsQEEOlpy7a1icR529E9qOPabbn8UNyNp62SzVcmYGepwGBUim/t07B+czYfH6hFEAQslHKeWhQuDQpv2l/LbZ8VSGr3AHOCnflkXSJh7ja092l5OUfgs5xWiZ5ua2uLn58fiYmJzJ07l/DwcBQKBeXl5exP28sFHt1cN92Rxxb6m53n0WG65/5b7MGTGSNdi0dDiXPnzjWDEifDSjzRY///2bUY/gFJ63jci00hCALV1dUcOHCAKVOmMHXqVCwsxtrZnyg8KAiCpInY19dHSkrKMUnOHItY78gwORgfPnwYf39/rKysjguug7E0dNPfVVdXk56eTkhIyJj5ruOttEy/r6Ojg7S0NOzt7Zk5c+aYgWp3O0tcbC2O2ndbFOfB/WcHAfBeag3v7a0es4yUuGwtqGgf5JVcgbY+c9gxwEXNptXDM1VXfpRNdccATy6KYMvaqbx1RQwWxj6QXCbS1W/6NJ++IR3zQ124d7bTGFffQZ2B9/bWcO4bB6jv1vDu8jjsLBWUdcH75bYobJxwoI9Fbm0UZ6ezLgIujxO12t7YVcXD35eg1RtYHu/EraPU2rsGdNz/TTGdxspkxQwfXrhUTOA6g8AX2U0seHkfj/90mMq2ftzsLPlgZRznGntlr+9vY2NOHwEual5ZEo1KIUMvCMwOdkIviDYj//qqiH6NqLRw3Vx/nl0cgYVCxp+H20Tdwe5hC5ApTtZ8vCaBc6PdMAjw8q46Hvi2mEGt+bNk0r4LDQ2VbOW9vb2Z5y2DllIz2Gy0xcj/5UrrSP0ypVJpBiWa4P3xoMSJBG1PhPL+P3jwb47xIDqdTkd2djZlZWVMmzaNoKCgCV/wJ0NwNy0tDWdnZ2bOnHnM/jCmh2UyckwmuM7Ozu64Kx8YmyhNCbG0tJTp06fj5+dndr6Ot9IyVZM1NTVSMoyOjj4qWeRocflUL+48QxxmfPXPyjH2F2BMXKvicbNR0dAv46otOWMSl7eDFRtXxRPkqqapR8OVH2dT1tJHpKctUV52/PvcUCwUMlbO8JH6ROu35NDepyHR05IH57lgNcrVVy6D7kEdbb0apk5xYMOqeFxtLChtHeCeX5v5pcWGGutgIiIisFCpONu9jyVBBmSIsOczaT30Dem5arYfT1wYjq2xEaWQy9hf2cmyER5UZ0W48a8zg6R9aw0C2w81sOjtdG7ankdOXQ9PLwqXZqp+rhjixu15hHvY8uziSD5cEcdbS2O492yRafhTQQvLNmRS3ir6mJ0b7c4HK+NxVqvoHtSZqcYDqC0UPHNRBJcECihk8G1uM6s3ZZtZrowOk638aNisvr6e1NRUM4sRrVZM0P8XK63JbHuktchoKHHfvn3jQon/62mNH/9nktZIeLCnp4fU1FS0Wi0pKSlHVR0+3qRlMBgoKysDIDw8fFwV+COFadmjJYPW1lZSU1NxcHA4bjmm0fscWTWZhp57e3sn1EA0vTSOp9rq6OigpKSEadOmjUmG48WxVnVXzprCDXP9CXe3YW7I+NfY39maNy4Lw8FCoLSlf9zE5W5nyYaVcUR4iFDXus05kuvu4nhPvrt+BnefFcwHK+KMg9O9rP4om+Y+HbHuFry9LM5MM9AgiHNTLsb5sHAPWz5aEy/pEn60v55nf6tgzafl1Anih86/Lp7Ff870wlIB+W16bvuuml3pOcx0F3hmkWgPojcIOFgrqe0cZOWmLHaWiNp/q5J8eeLCcLPfJAB/lbazfksO3+e3cM0cP+6d64qlAomgEeZuQ6yPvaT99+HKONxsh9l9vxaJKhYJvvZsWZvAm0tjcLUdi1TIZDIWeAu8emkYzmoVhU29XPFhBmkVHWOWHR0jYTPTMG9gYCA6nY6ioiIOHDgAiJB4b2/vSWfgnUpm4kh4cDJxLFDioUOH6OrqQqvVTvqc/K+ndYpjsuzB2tpa9u3bh5eX1zFr+h1P0hoaGiI9PZ2WFvHBdnV1ndT6gCR2eTQ5pszMTMLDw80qlBNNWqZ129vbzfQJJ6oSjzXBjoyhoSHq6uokQeCjfTyYYjJQ5HVz/fj4ygRJEXy88He25pYYAXdbiwkTl7ONBR+sjCfOR+wBXbUlRyIneBn7YLE+9mxaHY+3gyVV7QPc/0c7lZ1apvk58N7yONQWcsmxWQCe/qWcl3dWYBAEpjhZ89HqeLwdho+zqn2Am3fkc/kHGeyu7OGimSFsWpOIo6WM+n4Z/97Vzd6CKnS1eayMVDHTx4r3l4Qww+hBdeuOfN7bK/pULYrz4LELzHULlXIZjtZKzggX782UKdb8Z57jGIKGKRKnOPDp+qlM9xO3f8fnhbzwezk6g4CvozXBbhO/6ARBYIafA9vXJxLtZUvngI7rtuUelR04Oky6gJGRkaSkpBAbG4tMJqOjo4P09HRSU1MpLCykqalJqsKONybyozpZcbK2PR6U6OnpiVarpbGxUXIprqurOyZvLJOy/f+v8bcnrWMJpVKJTqeTSBCJiYmEhoYeM6Qw2aRlIipYWlqSnJx8QvDiscoxjTfjdSLwoF6vp6qqikOHDk0KsjvWF1BXVxdpaWmoVCpcXFwm1I2caF8T7Uev11NQUEB2djZVVVX09fVhpTz6bepmJfDByrgjJi57KyXvLY+TZqGu25ZLarn5LFGgi5qP14i6gR2DBh7c2cbBqk4SfO357eZZ3GWE6kx33gepNdz9ZSGDWj1udpZ8un4awa7m56KoqY87Pi/kkncP0dyj4ZE5dvg5qGgfMPDcIR1ynxhWJgdy+3Q1LZXFrPLt4qwAkc346p+V/OurIga0ehbHe5rpEuoMAp0DOt7dW43OyP4LclKNIWiYFDQAXG0teG9FHFca2Ykb99VyzVZRvmmiMM1SyWQyPO2t2LQ6gcVxojbhi39U8PD3JUe9PuOFyYpdoVAQHx/P3LlziYyMRKVSUVlZye7du0lPT6e8vJyurq5JPw+nul92qlyLTVCira0tISEhxMfHY2trS2NjowQlFhcX09LSMi5B7X/w4D8ghoaG0Gq19Pf3k5KSMumqZzJzXpWVlaSnpxMUFERcXBxKpfKkz3mdajkmgKKiIsrLy8ftX40Xk6m06uvrOXDgAH5+fsclhDlR0jLR8Pv6+nB1daWzs9Ps67u5uXnch9T02wJc1GaJ67EfD49ZVm2h4I2lMcwNdmZQZ+CmT/P5o7jVbBl3O0s2roon0lVFv1bgum25/FbUip2VklUzfVgc54GVSs7KGT6SaO16Y5J0sFaxbd1Uwt3Nv3TlMlFUt75rEDe1nOfP9ZKS522fF7Etv4+o6GjmzJnDtKkJ3JzizpXRFshlAj8VtHDFewcoqm7kimleXD/Xz2zbH6TWcO3WHDr6xXMzmqDx2I+lPP1LqZTYlHIZd54RxAuXRKK2UHCwqoulH2SQVds17vUaPUtlqZTz6AVhPLgwBKVcxpzgY6uwx4uRc1QKhQJnZ2dCQkJISkpi9uzZ+Pj40N/fT05ODnv27JlUxXGqrUNOtcGkXq9HqVSOCyUClJaWSlBiRUUFdXV16HQ6+vr6juiKPpl44403CAgIwMrKiqSkJAnOnSh27NhBREQEVlZWZlyXiAABAABJREFUxMbG8sMPP5yU4xgZf3vSOtrLtKGhgezsbEAU1z1WEsTIOJakZSJ2VFZWMmPGDPz9/c0eppOlIdjY2EhaWhoeHh5Mnz59XLbjeOsda5ge5oGBAZKTk4/Zw+tYKi1BECguLqagoICEhASCgoIkduVkYryk1dnZSVpaGnZ2dkybNg0fHx+zr2+lUkl5eblEHa6qqjLrgZj+15S4Zvg7cN/ZIePu30ql4JUlUZwV4YpWL3DH5wX8kG+uq2dvpeSheS7M9LFEoxe484sCPs2oRyaT8e9zQ/lk3VTuOTuYd5fHYmelJKeuhxUbsyhv7cdapWDbukTCPYYTl0EAtUpBqBGCs7NU8vayWC6MFeelPtxXx7Sn97AtvR6VtQ2BgYHcuXgW71wRg4OVgvIOLes/KWHjd7tIse/kgnB7QCRvWCrlHKjq4rafmihq00q/8ZmLIiSCxpaD9dxkVNAwxdmRbmxbm0iQq5rmXg1rP85hy8G6MddmvAFgmUzG0mne/HDDDM42enIdTxyp52RpaYmXlxcxMTHMmTOHhIQESbh1NHlhImdy+L+dtEZXchNBib29vTz00EP4+flx8OBBDh48SGVl5Qntf/v27dxxxx08/PDDZGRkEB8fzznnnENzc/O4y6emprJs2TLWr19PZmYmixcvZvHixeTl5Z3QcYyOvz1pTRQmN96CggKio0W79uNt0h4tafX09JCWliYRO0bPeZ1opWXy8ioqKpLkmMLCwo464zXZfba1tZGWloZcLic6OnpSCX4i1qEptFothw4dktQ53NzcpPVONGnV19dLNiijPc5MX98mOvWsWbNwd3c3q8JMZBlTFRbgoubDlfF42E/c71Qp5Dx7caSUNO79qogvs8wZipZKGXcnO3FZojgo+9iPpby1uwqVYlgyaoa/I5vXJEjySCs3ZrK/sgOVQs72dYkEG5eTAf1aPdd9ksuf1UPSMTxxYTiuNiKhQ2cQeOqXMs54ZR+v/llBS88Qs4Jc2L5+GmHuNnRr4fUCBentKi4LMhDvLOBhLXD/bAcCnCzpGDTwyK5ONu6rkeC8a+b48eKlkVir5Owt7+BfXxaa/cYgVzVbr0yQ5sme/qWMe78uMpvZOpJqhdcRZu6OJY5VsUImk2Fvb09AQMAY8kJJSQm7du2SxAV6enrM5iP/aSrsxxrHIkE1kpX4zjvvsG3bNiwsLDh48CChoaGEhYXxr3/967j2/+KLL3L11Vezdu1aoqKiePvtt1Gr1Xz44YfjLv/KK6+wcOFC7r77biIjI3nssceYOnUqr7/++nHtf6L4RySt0TdVf38/+/btk9TGTWKXJ1uKCcQX5r59+/D09Jyw8jnRSstE6mhtbSU5OfmkyzGZYM2MjAxCQ0MnVOg4UshksgkTkAnOlMlkzJo1ywwvPx6tQ9N+RlZuiYmJZiomE4W1tTW+vr5jqjBgwipsolDKZTx+YTiXT/VCAB76voQto4R1FXIZD50byrVGY8o3d1XxxE+lZtqIQa5qtlyZIMoXDYlK7F9mN6KQy/l0fSIpQU4IgIVChlYv8F72AG/ub0WrF1/Yv9ycZOaW3DOk5729NZz9+gEe+LaYfo2ej9ckSJXhK2lt/NbmwBtrZvHOknCi3Sy4I1bHNFcDBkF0ZL5pe440iHxWhBubVicQ5Koe12TSxlLJ8xdHcveZQSiM82orNmZS2SbS4k+l1NLxsvtGVxxJSUm4ubnR1dVFRkYGe/fupaRE7LWdKKFjovg7Kq0jhVKpZN68eTg4OPD888/T3t7OCy+8QEBAwKT3rdFoOHToEGeeeab0d3K5nDPPPJO0tLRx10lLSzNbHuCcc86ZcPnjjX9E0hoZTU1NpKam4ujoKCk/mzTxTiRpje6FmCq5wsJC4uPjj0jsOBEihknw1sLCglmzZh0zq+dYE6VJpaOiooLp06czZcqUE2Iejl6vublZSurjSVadSKWVkZEhmXIeDzvTVIWZzOmSkpJwd3enq6vrmHphgCSttDpJdAB4e3eVNNwLSBXLTfMDuP+cEEmh4u4vCyXFdRDZie+viGOhUTfwoe9KeHVnBUqFnBcviSTc3Qa9QeCcSPF3flvUzbXbcmnvE/2rvr1+xhibE51B4JucJvaWt6O2UPD8JZHcMFdUsdh8oI67vyrGydmZ8PBwFsydzd1z3FgTbYlSDrvKurj4zTR+TMulubmZEBdLvrh6miQFNd41WZ3ky/sr48SZsxaRFv97cespTVonQxtQJpOhVqvNjB6jo6Ole3XPnj0cPHiQsrIyOjo6jvsDdHScamuS46XU9/b2Ymtri52dHRdeeCE33HDDpLdhglxHf2B7eHjQ2Dh2ZhLE1sdklj/emJy51CkMg8FASUkJtbW1xMTEjLESMDEIjydMCcD0AhoYGCArKwtBEEhOTj6qJcLxJC3TwG1vby+enp7Ex8dP6uE0De0eydV1YGCAzMxM5HK5pGYPx1f9mNYb2SMyWa7ExMRMSLg4HtNJU9/NYDCQnJw8biKc7HGDONTq6+uLr68vBoOBzs5O2traKC8vJz8/HwcHB1xcXHBxcTFTwZbJZNx1RhBO1ipSgpxwnEB2aNl0b5xtVNz3dRG/FrXSOZDLK5dFY2clPkaWSjnPLI5gipMV7+2t4b3UGmo6B3n8wnBeXxpDRVs/yYFORP2czjvZAxys6mLZhkxeuSyaCE9btq5NZNmHmdSMGNx1s7VgQaioviKXybh+nj8h7moe+KaYtIpOlm/I5LXLYwhyVWNhYcFF0c6cO9OT2z8voKFb1E5c3tDDVKch7O3tcXFxwdnZGTs7u3HP83Q/Rz5dn8hdXxaSUdPNbZ8VsGamF/Hyf1aldaSQy+U4OTmhUChobW0lKSmJ9vZ22trayMvLw2Aw4OTkJFmMHK8lyvEmlcls/0RU3v9/jX9EpWUafm1razODA0fGiapamBKAaZDXNLd0LDfsZCuXkeoTDg4OODs7H7cc00T7NZlOOjg4jFHpONFKy0RKqampISkp6YgMwclWWm1tbWRkZACQmJh4XFDmsYRcLh+3FzZRFWYSio3yOjLr6pxIN966IgYbI+tOFOMdliaSy2Tcclogj14QJilQXLUlBwuFjORAkRQzzVPFS+f74udkJSnB/1TQjIO1io2r4yWoUIaoAr9uSw4ZNcPMvrMi3Ph4TQLeDpZUdwyyfEMmuw63SR840d527LhqKnODndHoYWOhjt+6PXB2Mzc3zM/Pp7GxEY3GnO7uZmfJ+yviWDlTrD43HWigsFN2ypLWqVbDsLCwwNPTk2gjO3Pq1Kk4ODjQ3NzM/v37SUtLOyKF/GjbP1XHLgjCpJOWIAj09/efMHvQ1dUVhUJBU1OT2d83NTVN6E3m6ek5qeWPN/72pCUIAllZWdja2h4RPjsZUkylpaXSIO/ohv/R1j/Wfff397N//35JfeJ4vbwmSloj+1ejB5JHrnu8OoKm49doNKSkpGBvb3/UdY4lQQqCQFVVFRkZGYSEhEjrHmm7kzlu0z4mClMvLC4ujnnz5h0TI3GiSApwYsOqeElFftVH2VSPMoG8ON6Tt5fFYGelJKu2W2IWmo7T39GCrWsTmR3kxKDOwN1fFvHKzgpcjDCjs1qFAFgp5TT3aFj3cbZEsABRhWPb2kSm+Ym0+Zs+zeeLwuFjd7BW8frSaG6eH4BcBt/ktXL3z004+IQwd+5cYmNjsbKyoqamRoLPysvL6ezsxGAwoFLIueesYJ67OIIrEj2IdT518OB/k90nk8mws7PD39+fqVOnMnfuXKk1YKKQH6uw7amEB03P1GS3r9Fo0Ol0J5y0LCwsmDZtGr///rvZMf3+++8kJyePu05ycrLZ8gC//vrrhMsfb/zt8KBMJmP69OlHvThHU3o/UpiSRn19PUlJSUd9EY+OY2Xytba2kp2djaenpyT5dLwkjvGSlqmCa29vZ8aMGWNYjiPXPZ59CoJAfn4+3t7eREREHNPL5FgSpMFgkFQOpk+fjq2tLUVFRSddsudYw1SFmSqxgYEB2traaG9vp6KiApVKhbOzM1qtdsKRhEhPWz5ek8C123Kp6RikoLEHv1FGkEkBTmxek8AN2/MkaaZXLovCVFs6WKt4Y2kMr+ysYMO+Wt5PraG4qY9nFkfw5hUxrN+cQ59GT5SnLQWNvbzwewUZNd08dkEYDtYqnG0seG95LE/9XMaOzAa25PVS06PnhQA9VioFciN7MM7Hjnu+KqK4uY+lH2bw+IXhnBHuiqOjI8HBwWg0Ggk+y83NRRAEnJyccHFx4bQgZ+YF2HLgQMPYk3AS4mT6XY237aPdw0qlEldXV6mnOjAwIDkVV1VVmd0rzs7OZgo8pxIeNL1vJrv93t5egJMCD95xxx2sWbOG6dOnM3PmTF5++WX6+vpYu3YtAKtXr8bHx4ennnoKgFtvvZX58+fzwgsvcP755/PJJ5+Qnp7Ou+++e8LHMjL+9qQFYlY/WlI4XnuRrq4uMjMzARGOmmzCgqNXWiP7P1FRUfj4+Ej/dqJDwqZ1+/v7yczMRKlUkpKSckT5qsnu06T+rtFoCAgIICIiYlLHeaTko9FoyMzMRKfTkZycjLW1tfTxcaxJ66eCZt7ZXY2TWsmHqxLGPYbJbG90mKqw0b0wE1zU398/bi9MlG5KYF9lBwujxvfpMjELb9mRT05dD9dszWVdrCWXuhtnAOWiDUm4hy0Pf1/C7rJ2lm/I5JUl0by6JJrdZe3ctiCAzzIbeebXMnaWtLH0w0xeuCSSaC87VAo5D50XSriHDU/9XMrBukEauocIdBmGvWcFOvHp+qnc/WUhmbVin+rKWb7ccloAKsUwfObp6YkgCPT09NDW1kZDQwPFxcVYWVkhCALt7e04Ojqe1Bf1qdQGPJ5tmyjkPj4+GAwGuru7aW9vp7a2lsLCQmxtbaUEdqqTlkkGbjLR19cnEVNONJYuXUpLSwsPPfQQjY2NJCQk8NNPP0lki+rqarPjS0lJYevWrTz44IPcf//9hIaG8tVXXxETE3PCxzIy/hFJ61hisvCgiQhRXFxMSEgIZWVlx/1Fd6RqSavVkpubS09Pz7hV3PHOeI3ULTRVcF5eXsdUAU2GiGEwGMjPz6elpQVra+tjslwZva+JkkVPTw8ZGRnY29szbdo0iZo+2SSzu7Sd0tZ+jnb1TkblNvLLWiaTodFosLe3H1OFmQgNrrYWXBBz5BEGFxsLPlgRxwPfFvNLYSvvZg/SSxv3enlJ5+L8GHcCXKy57bMCKtsHWLExk2cuiuDOM0TZqKXTvInxtuPOLwqp6xxk1aYs7jkrmMunekmDvoq+FgxypVnCMoWHvaiS8fLOCj7aX8fGfbXk1HXz3MWRuNsNfwCZ5qHs7e0JDAxEq9XS0NBAWVkZhYWFaLVaqQo7ERKDKf4bPa3jDblcjqOjI46OjgQFBaHRaOjo6KCtrY2CggK0Wi2lpaW4u7tL5+Jk/ZYTJWGcrGR60003cdNNN437b3/++eeYv1uyZAlLliw5KfueKP72ntaxxmSSlokGXlpaytSpUwkMDDxpRpAjwzSUbGLBjVfFnciMl0wmo6amRurDRUVFnTTIDoZlk0z9N6VSeVLULUBswO7btw8fH58xRpaTTVoO1qZkN/ExnKpQKBQnpRdmpVLw3MWRXJUyBYCt2R3c83URmhG0+WgvOz5Zl8jUKfb0Dol9KpNgrunft69LZEGYC1q9wOM/lXLP10X0DYmVa7SbBTN8Jk4iKoWcu88M5sVLI7GxUJBR083lH2RwsKpz4nVUKpycnKQKf/r06Tg6OtLS0iKRGI6kSnG0+L8gaGsKCwsLPDw8JPdmmUyGg4MDra2tHDx4UHJvPtKIxbHG8fbLTArvp/KZ+LvjH1FpHasR5LHcCH19fWRlZUkPmYlVd6JEjtGJp6Ghgby8PAICAggJCZnwN8jl8uMabtTpdAiCQH19PTNnzhxXn3CiOBZ4sLOzk8zMTFxcXIiOjkahUBwXlDm6qhsJlcbGxo7LHJps0vJxFHtFiqPcJ2u35FLY2MesAEfeuCL2WH/CMcex9sJMVchox2m5TMatCwIRelrYmDdI94AO+Sj/KhMR45lfytie0cCrf1ZS3NTHoxeEobZQ4GCt4pXLovhofx0v/VHOj/ktFDb28uIlUcd8Ps+KcCPM3ZbbPy/gcHMfV23J4ebTAlifPGXc+9hUDZkci21tbfH390en00lQaklJCUNDQzg6Okq//1henn93T+tEQhAEfH19JbJVZ2cn7e3t0oiFvb29dL/Y29tP6nceL/TY19d3UqDBf3L8I5LWscSxJJ2mpiZyc3Px9fUlLCzM7KKfaNIyrWsaFq6rqyM+Ph539/F7GaY4HnjQ1L8SBIGYmJhJJSzTPo+UfOrq6igoKCA0NNRMY/F45rtGVnUmokhHR8cRCS+TTVqWRnfhiY7MtL2q9kE0eoEco1fWqY6JemFHmwubP0VF1BRXUqL8UMrHvshUCjkPnhtKuIctT/5cys+FLVS29fPKkmh8HEU36zWzfInzseOuLwupbBtg+YZM1sZZc17ksb2w/J2t2XJlAo//VMo3OU2UtfRPuOxEEN5IEoMgCGZJvLy83CyJOzk5jTvecKrhwVO5bRhm95ncm00OzoODgxKho6amBkBKYC4uLke1VDpRePB/ldY/IBQKxYQVi8Fg4PDhw9TU1Iw7mGxa/0ThwaGhIbKystBqtSQnJx8TQ2ey8KCpf+Xt7Y1OpzuuG3ei5GNKuPX19SQmJo5RoTgeqrwJHhwcHCQjI0MadD7aQzkerPhdbiO9QzrmBDvhaj38uy1V4n8f7djcbS3oHNAdtfcF0NWvwdpCgYXy5FCWJ1OFGQwGYr2tsbU88uO3ZKoXwW5qbv+8gOLmPq74MIMXL41ihr8jIPpj7Vg/lfu+KSa1vIO3Mvoo65LxhM8UrFRH/13WKgWPXxDGnCAn5oe6TPiiO5aXv6n5r1armTJlCnq9nq6uLtra2qioqJAqj9HDzf+X4MHR24aJ2X0m92Zvb28EQZAIHfX19RQXF6NWq6X7xdHRccxz/j/X4onjH5G0jtUIcnBwrMX34OAg2dnZR00kJ1ppDQ0NkZqaipOTkxmp4GhxrJCbIAhUVFRQVlYmMRDb2tqOe8Zr9D41Gg3Z2dkMDQ1NqAJyvDqCer2etLQ0XF1dj+rbNXK90Unozs9ENejVSVO483R/6e9NfloT5SzT/RPkYk1JS/9R76dZz+2hT2Ng5Qxv7plACf5E40hVWH9/P1qtFo1GM6YKGx1TpziwfV0it+4ooKCxl6u35PDvc0O5NFEc+Ha2seDNpTG8u7eat3ZV8UtZL5Ubs3jx0ij8nY/ucSaTyTg3+showfFUQyaJLZMx6ODgoJTER1LJdTrdhGMFJxqnMmlNhpJu6n2ZLEa0Wq1E6CgqKkKr1eLo6CidLxsbmxPqaf0PHvyHxHhzWu3t7WRnZ+Pi4nLURHK8SUsQBDo6Oujp6SEiIsIMTjuWOBZ4UKfTkZeXR2dnp1n/6kSULUYmhJEsvsTExAnP0/FUWi0tLej1+jFQ49HiSKxDAYPZv1maktZRtunjKFZ3vUNH7n1q9OKWvs9v4V9nBU94zCcLYhldhR08eBAbGxu6urqOqRcmGi/G858fDvNjfjO+ozQK/x977x0eR3W9j7+zVVr13nuxrN6rZYtmegk99E4IkISWBMgnJJAQIAQIhAAB0zsEAphimous3pvVrd7brrS9zPz+mJ07M7srWZJNfk6+Oc/jx5J26t2Ze+455z3vK5VQuKU8Br6WBfy9RY++WR0u2dWM35+VjFOPQjaEs2ORwnNzc3OCki8sLGBychIWiwVarZak17y8vI6Js/l3RHGbGRdOvTk4OJgwWHCpRC6tqlQqwTAMLBbLhlhj9Hr9/yKt48WE6D+OFWJgYABbtmxBVJTrArLQNuO0bDYburq6MDs7C3d3902xJR8pPajX69Hc3AyFQoHS0lLRqvNoGpO5/aanp9HR0YG4uDgkJKw+QTvudyTjJCHGxsZAUdSGx8aV05JKABsNmDWLqKwcI9BqTxnrjCQUsKS34Nn9QxhaMOClyzMJQAAAYv3ZydxsY2CxsawOrizC1w3DCwZIKJaUVi5dfUy+HtQjNt4KpfzYvSpcFBIaGrruWpibXIqHz9mCKwsjVqWaSg+S45mzIvBknQbNY8u4+6NuNOdrcPfJ8auOxXrsWNedhFBybiHKtRW0t7eDYZgN1X9Ws/+E1CNFUfDw8ICHh4corTo0NAS9Xo/KykonQMda5/1fevA4Ms7pWCwWdHZ2QqPRbAhVt1Gnpdfr0draSrSp+vudVXDXY2s5grm5ObS3tyM8PBxbtmxxSce0WadlNpvR39+P4eFhZGZmrksOZb08ghw3oU6nQ3Z2NpqamjZ8jS6dFkXBBgYUY0V2djaWl5cxOzuLsTkNACkYBhicmMV7zSxr9MCcDknBnuR4Ub785Da9bEKUn+v0WGqoJ4YXDDBZV3dsVhuNH707AQaAGkP4vzOSNnyPq5nwvl3Vwjh2iuHhYchkMlEUthY3IsMwCFDJsOuKLDyzbxgv14zh7cZJdEyu4PHztyJ8k9pXPzRYghN7DAsLc9ncrFKpyP1vpLn5h6RZ+qEai7kFzdLSEjw8WDFQLgoTspVwz4y7u/gZ5xje/5vtuHBa661pmUwm1NTUQKVSOUUlR7KNoPg4Z8I18y4vLx/THi9h/SotLQ3h4eGr7rtZOqb5+XlIJBIUFxevm4dsPTUtnU6H5uZmuLu7o6SkhEDzNzqxOTotjUYD2v57eGQUPD094e3tjejoaMRqjUBjPRgAmslBss8XLSP4SUUiWYkHePBplPElw6pOKz/aB190zUFrskFjsMDH3Tn9IpNKIJMAFhr4qG0at2yPQaDnD1N7EZojI8N6EYmcURQFmYTCHSfGISfSG/d/1ouOyRVc9FIz/nFZBtKOQAjsyn5Ip+V4bFfNzcKGXpvNRibtgIAAp0lbaDRN/2CEzD80nJ5zuK4c+uLiIqanp9HX1wd3d3dCL+Xn5wedTrcp1p+1bHh4GA899BC+//57TE9PIzw8HFdccQXuv//+NefgiooK7N+/X/S3m2++Gc8///xRXc9x4bTWY2q1GgaDAYmJiUdMc7kymUwGk8m05jar0TEdyx4vq9WKjo4OaDSaI/IgbsZp6XQ6TEywQoabcexrRVoLCwtobW1FREQEaSkQpmzX852c9bcaDM7rEOUBvJ/LnovreZNQEgAMaLvqLIdaUwiioZKSUuBgNQDAZlhGdXU1PDw8WEZsqwHhPkpMakxoHtPg5ZpxTGqM+PynhaJr2BrKT9ydkysoS/B3ea3xfnL0LlhAUYDBsvr3b7ba8Nvdffj5iXEI8z46JV+hbTQKc/wOKpID8P71ubjro0MwWWmXTBnrsR/aaa01+TvWf3Q6HaHY6u/vh5ubG7l/To5kvcc+GvuhVYttNptLyR7OocfGxsJqtWJpaQmLi4t45ZVX8PTTT8Pb2xvZ2dloa2tDZmbmMfneenp6QNM0XnjhBSQmJqKzsxM33ngjdDodHn/88TX3vfHGG/Hggw+S348FSOS4d1oc2erU1BRkMhlhCN+oHSnSslgsaG9vh1ardXImm414HPfV6XRoaWlxWb860r7rsbm5ObS1tcHLywsKhWLDqKzVIi2Om7Cvrw9bt25FZGSk6Bq5bdZj8zoTaAbQWNjvdmBgAENDQ8jKyoKkthMAA5uNXSETxyXo0DKZrVBIKZhtDDyDIrCtMIyswocG+hHvDkxqgI7RRdSO6gAAh6a1SA3lUyZCYtvGUc2qTqsgwh29CxZYbMyq8HSGYVDwWBVoBuiZ0eHjm/LWNVFsZjI5UhQmlUoJUo2LwiJ83fD6VdlY1FugUmxukj1eeqmO1NxsNptFkegPyQ3471AtFsoNuTJOvTkoKAgPPPAALrroIlx33XWYmppCWVkZvLy8sHPnTuzatWvdaGdXdtppp+G0004jv8fHx6O3txfPPffcEZ2WSqX675MmAVZ/gfV6PWpra7G8vIzs7Oyj4pZbK1ri6Jg4UUjH6EcoIrnZ887NzRFYeH5+/rocykbh8q2trdi6dSshPt2ouYq0OIXnwcFB5OfnixwWsPFGYYl9eylYqZjx8XEUFxez+j32RlupVAKZTAaFQgGlUgkPd/7lNVtpgiYcX9RDIpEgODgYUqkUubm5SI9joeAmI98s+9x3PVhZWSHX6O0mIxN40yivU+Vo2aF8jaxyYMHlNhRFIdieNhyc1+OfrUdWaT2WHIlCvTCFQgGj0YimpiaRXpgENEK9Nwdm4K73/69Iay3jmpu3bNmCkpISFBYWIjAwEEtLS2hoaCAR2bGgVXK0f4fT2kgkR1EUKTX89Kc/xeLiIt555x0nCrVjZRqNhrQzrGVvvfUWAgMDkZ6ejnvvvRd6/epN7Ou14ybScqxxONaVzGYzbDbbpl+g1ZzWeuiYuIfTZrNt+AHg+phaW1vXrF+5svU4LQ7huLCwQIApY2Njm2aWF+7niqHd1T7A+ifiYC8lFnQWeCsYmM1mMtnabDZE+7uje1qLlBAvlP/lIBZ0FuRF++CNa3Ihl1Kw2BhAKoOnUoYVkw1zWvaZ4BYUNpsNccHeAKZAK70gwTJoAL4yGs3NzSLWgvMygvF20xR6ZpZxya5mjC4acODOEhEwI9ZXAQos1H7PoXmck+l6xXhmRgh2VbOsB3Nas8ttAMBG03hgdx88zBZcF7Wu4Vq3ubu7Q6lUIjQ0FCEhIS5rYVwq0dPTc0Pv0PESaa1lrpqbuWZ3x+bmzYyBo/070oObOT4HeVcoFKioqEBFRcUxv7aBgQE888wzR4yyLrvsMsTExCA8PBzt7e341a9+hd7eXnz00UdHdf7jxmlxxjAMBgYGMDw8LJrkuS9wM46D21/otDZCx8Sde6OOwGq1Et2ojfIHAkd2WgaDAS0tLZBIJCK5ks00CXPn48ZoNYZ2R3OUUDmSWe3HpygKSUlJolYGjtGIAWCyE8lOL7N1SLlUAovNBhvDRkpTyyYsGdj0T1dXFz9pe7HHmlAb4aOSYUlvxbLEA+Xl+SKGhny5Hh9IpDBY2PQhALxRP4HrSnhvIpdSCPOUYlJrQ/2IelUY/RlpwcRpeayRgtv5t3rMrpghoYAzsi1YB6BzQ8YtHNaqhY2MjDjVwo70Ph1v8iHrMalUCqlUitDQUISFhblsbubu39/ff8Op9H8HEGOz3IPrRQ/++te/xqOPPrrmNt3d3SKpoomJCZx22mm46KKLcOONN66570033UR+zsjIQFhYGE466SQMDg4iISFhXdfoyo4rp8WxNhgMBifU29E6LeHkuFE6JmGktV7jUHbcy7AZGOpadbilpSW0tLQgODjYif19s8rFHLnvzMwM2tvb193bBYgjrX+1TuLr7jlsDfXE7SfwD+f09DR0Oj0AChJ7ZC1s0iTnYRh4KKTQmmwEAMH1UllsNAK9lOid1UFrtKCpqQkSuRIJaVlwd3dHdAC73eyKGcnBKizprRic04Gmafj4+MDPzw+JiYkwGAxIGGxHz5wJrJuk8GLlMM6Id0NQoD953lKD5JjU2mC00mge06AgxpekODlLDFJBJZdAb6HxQfMUrioSp1A5OyUlEG81TIJmgPv2jOP9qFB4HIHKaSO2WkS0HkTiWlHYf0KktdqxuedztebmsbExHDp0CF5eXhtqbv4h4fTA5iItDqiy3rnmrrvuwjXXXLPmNvHx8eTnyclJnHDCCSgtLd2UsGNRUREANlL7r3BaHOu4j48PkckQGjexWa3WTTUbcg5gaWkJra2t8Pf3XzcdE6dttV6nNTs7i/b2dkRFRSEuLg7ff//9ph7y1Rjix8bG0NPTs2pj9dEAR5aXlzE1NbUqQ/tqJnRaL1ePondGi6rBBdx+QoIIlenm5g7ojaAoYGRkBGazGUFBQVAoFIQzkGaASD83zKyY4WuHo3MRjsXG4LqSKFQNLsJsMuKuSgoakwlbWw7hnzcVINjbHW4yCYxWGhG+7uid1WN6xQyGYUhdg6IoVk48JgA9c5NQSiUw2RgEqiQYHOhDT7eFqEIXhsowYfBE97QW933SiwWdGe9clyNCIEooCoWxvtjXv4jhRQMG5nRIDHJeCF1dFIm3GiYBAFMrFgwvGJAW7hqC/nnHDN5qnMSb12Q7Ocm17EgOYDNR2PFa0zqSrRatCJubOeVmLgpbb3PzDwny4I6/WRqn9TotDsSxHpuYmMAJJ5yAvLw8vPLKK5u699bWVgBAWFjYhvcV2nHhtBiGQXd3N2JjY9ekAjpaTSyz2YzGxsYNUw4B62OnYBgGg4ODGBoaQnp6OsLCwsg+riCs67lm4Tk5JOX09DTy8vJWLYRuxmnZbDbMzMzAYDAcEYovNC5CEjotq/3cFJyZ32V9XQAAlcoDvr6+ZKXr4+MDs4l10DTDwN9DTMn0q52JsNoYhHgrsbgwDwBYsUqgUsgAkxkjiwZyPeG+bjg8r0ewHXxgstJYNgN+7jIS3dlsNqSGsvBbuVQCk82GeSPbJsCR3Y6NjSEERlQEuaF7Gpi116vu+bgHn/0kX/T8bEvwx77+RQDApx0zuPNEfoXKWZiPG4HkyyRAcojrCP+fzVP43ZdsM/sDu/vw4FnJ63pWN+NcVovCuDqQj4+PCIh0rJ3X8SBNolAoNtzcfLwBMTj7IWicJiYmUFFRgZiYGDz++OOYm5sjn3EL24mJCZx00kl4/fXXUVhYiMHBQbz99ts444wzEBAQgPb2dtxxxx3Yvn07MjMzj+p6jgunRVEUiouLj7jdZvulbDYbhoaGYLVaUVRUBD8/vw0f40jntlqtaG9vx8rKiii1yUWIR0vHxKU0rVYrSktL12yq3Oj5OIZ2m81GqGI2Yo7n4+DhSpkEDQ0NYBiGAC6i/NzQN6tDeoQPegye+GxQizBvb9yY6gO67jAAoLOrC5SJdTgGMzvmZ6aHkIhtcWIUAKA100iPUGFWa4bBYoPebINKIUWE3WkppBICpOid1SE93BNTGjNSQti+rqxItsaos59Da7JhZEGHKD92IrdYLGwacUsA/tHZBztlIUYWDXjrYC8uLIglsGSOeR0AdrfP4LbtsZBJKaco6cy0YLxYPQaDhcE3PfM4wwVZ7VkZwfj9l/1gAPyrfQYx/u64oSx6Q9/JZkwYhQEgUdjY2BgMBgOqqqpICm09tbD12P8fkdZatt7mZqvV+oMS024mM7PR9OB67ZtvvsHAwAAGBgac0MPcYtVisaC3t5egAxUKBb799ls89dRT0Ol0iIqKwgUXXIDf/OY3R309x4XTAtYXHbgizT2ScdpU3GpuMw6Lu77VnJZWq0VLSwthiXAs6m5GU0u43/LyMpqbm+Hr64v8/PwjPswbibS4tGxgYCA8PDygVqs3dZ3CSMtfxd6/lLHC3d0daWlpBEXp7cZGm8FeSrxSM4buaS2UMgl+d3YqPD2mgaVlREfHQDPGRlMGsxVNTU3w9/eHWq2GVqvF9uJ8KBuaYbLSiA1QoXZIDRkFAmOP8GUdibtcgtNSg/DloTnc/2kP5rVmeLvJUPvLcgBAXJAX/FVyLOr5FGzdsBrh3uz1c1FAVFgwCmNnUTPEj423bRk1NTXw8PAgk/jFuaHY3TGLOZ0FxX+uQkm8H569JF00VidtCcSLdtDGG3XjLp2WUi5FSZwvqu3ne71uAhflhrlk7rDRNG55txNnpgcj/BhHQlwUZjabodfrERYW5hSFbRaRyNnxEGmtZas1N4+NjUGtVmNpaWnV5uajsc2kHw0GA2iaXjcDznrtmmuuOWLtKzY2VjQHREVFObFhHCs7bpzWeh7cjaYHuWbbiIgIxMTE4MCBA5t+kFdLD3L1q+joaCQlJbm8j6MhvjUYDKirq0NCQgLi4uLWNU7rBWJMTk6iq6uLpEvHxsY2BeBwTA8a7BIycpkMGRkZJB1HURRhaqcoVlYDYMEV3HEAwMPTA2lxSqCzFzaGgp+fH4aHh2G1WuHm5obJyUkEqGSYXDYj1IuNyCw0m0r0VMqQGuqF4jg/RPi6Q6WQ4ctDc0TXZNloxYTagAhfd1AUhcwIb+zr53uwqoc0uCQ/EsvLy5iZmUFoaCisVivK4nxETssjNB7bEnxIPaizsxM7PBioI5X4esgEC83gwMAiuiZXRHWrrWGe8FYAy2agc0qLtollZEU4R7ZXFUUSp1WR7O/SYQHAiX+tw6LegrphNe4rUCB2nd/ZRoyLhlxFYVwtTNhOsJEo7D8JmShsbtZqtXB3d4e3t/eqzc0qlWrTjpxhmA07QC7KWY/O33+yHTdOaz223vSgsLbEweY5QMPROC3huYXQ/COBFjZTY2IYBrOzs9DpdMjNzV13wXQ95xMytGdnZ5Njb+Q6r3+9GdWHFxHq7Yb/y6YI/+DQ0BA0yysAKCgErBZcmpTzbRRFIcKHg+hD9D/DsDpSAJva+6ZjBAemZfDw8MUfdkZgfn4e7mDrSwtzfDPv4JwOWZE+uDA3HBfmsq0SpClYMHd82j6DW7bHAgBxWkqZBCYrjYZRDWbmF/DPyi6ckRON2NhY0DSN7YkBePz7EXKMj1onUZHkh6CgIISEhJBaiMF9El8PzZLtfvZ+O966IhUhAb4soIeikB0kxYEJ9ll6q34Cqed4OkHpi+L84KlkEZSfd87iFyfEEScvtDPTg/BGPYtIfLzJjKhIIzbwqKzLXNWy1lMLW08UdrxHWmsdWy6XH1G5WQirX68j5+aZjTotrVYLiUSyZungv8H+65wWR8ek0+lEtSXuAbBarZvKxQtTfKudYz37rscsFgva2tqwsrICDw+PDTks7nyrOR8hQ7sj3H+9LO8AMKkxgmYAtd5CUJ2dnZ2Yn5+Ht48PoF4GRTnrDnGkuBIKiAtU2X9mPxOiB2MDVISuqXbeHS0zBgBqBAVlIzg4GIldNAaX5yCXuwFgQRifV7fDIzcMgYGB8PHxAUVRSAlh8/vzWj4FmBnhJfiZjXICPNierhWjFSe/0AmAwpVnRpN+n8RQH8QHqnB4nl3N1gxroDNa4CaXkDH39PTEzvwtiGlc4YEhYNDS1gFPBUVW4AUhEkChwoGhFXx5aA7f9Mxj78+L4avioymZhMLpqUH4oGUaZhuD1+sn8IsT4py+h1vKY/Fm/SQYAEYb8Nuvx/FlcoTLfjKDxYbb3uvEkxekwnuVyM2VHQmAcTRR2PFW09rssV01NzsqN2/EkQMbd1o6nW5NQdH/FjsuaJyA9TO9rzX5c3RMAFBSUiJyJkcDiODOTdM0tFqtiPJpPfnjjaQHueNTFIWUlJRNPYCr3adOpxNdu2MaYSORlo3mGlnZ37u7uwkIRaVkJ0RPpdRJKI+kBwEEebKRltWOcHjonBR8dFMByhL8MD4+DpWUvZbcBL7uMzDL8gkG2dOCEncvQus0Z1ORhuv9+/ejs7MTVu0i/O3OgGte3tfHpwPT7Q5sdsWCLQHcRM5u+JdveUZ5AKhICiA/W2wM6se0kMlkJB1rtVphsVhwbga/yDDaKJxSUYaMjAwolUoMDw8jWmnAVQlmRPuwk7eVZnDT2+2w0uIFg1BR+O2GCaj1zu0PXm4y7EjiUaQXZ/m7dFhmqw3Ff65C/YgG57/YBI3B+Vir2UZRg1wUlpmZifLycqSmpkImk2FoaAiVlZVobm7G8PAwVlZWSNr4WBsX+f+QhLlrHZuTGElKSkJRURFKSkoQEhJCmvarqqpw6NAhTE9Pw2wWs6hwx97ouGi12v85rePNZDLZqkCMyclJ1NbWIjw8HLm5uS7h5UfL1q5Wq1FbW4vQ0FDk5eWtG8K+XmcwOztLjs/dw7FQLgZYhvba2loEBQWtOj4bibQ4mQ6VnG0lkMvlKCwshEKhQLw9gsqP8cXvP+9F+kN7UfzYAQAC1gaKQrAXewwGbF0rxl+FLSEemBo5jMHBQQT7sMcJ8eL7ZEYW2UiH23dWayLOZNEIfDbuhqd6VMjIzCROIljBTgoe9qioYURNjuftJsd71+filbP8keghnsj/1TqFeQEtU0VyoOjzb3rmUT+2AkrK8iTK5XJIpVKckcpvpzHaUD24CG9vbyQkJKCoqAju7u7w9fXFBUl8uq97RoeHd3eJnu/cKB/Ca2iw0Hi9btzl93N5QQT5+ateNYlmhaaQSQlAZWbFjGveaMOCbnXKKaEdDdRdyJFYVFSE4uJihISEEHARTdMYGho65vyA3HvzQ0ZaG4mEuObmjIwMlJeXIz09HUqlEmNjYzh48CAaGhpw+PBhaDQaWK3WTV23Xq//r69nAf9hTsuV0+F6l7q7u5Gdnb0qfyC3/2ZeDIZhoNVqMTU1hfT0dCQnr69vhrMjpQe5GlxbWxvS0tLI8Y9GBJJbaTIMg5GRETQ3N2PLli1ISUlZ9YXYyPnc5OwLy1gtkMlkiItjU1c0TZOIQUpRUBusoBlgxcTeP01qWny0BAArRiusVitaW1sJj2KQl5v9uvix5n4Otu87t2LGj+2T9uiSEW82jKN/To/maSuSkpJYItUktr7lJmVPPjinQ2fXIczNzcFoNMI42QeryYCLtmeI7jHYSynS0MqO8oaXkp+ovuiaxY1vtuG3n/VCIuFJfiMDvJAWxsOOP2ydhtVqhdlshsViIRP51afkQaXgv4uGkSV8v78SLS0tGB0dhclowAU5oSiJYxGvL1WP4cevtDh9F4WxvgixO/H+eRO+OjTntA0A3H8ar5AwMKfHdW+2Q292/VxabTQe+3oANnrjWmlrmWMUBmDVKOxoiIW55/h4rJdxzc0JCQkoKCjAtm3bEBkZCYPBgPb2drS0tICmaUxOTh5RTklo/0sP/pttM+lBo9GIhoYGLC4uoqSk5Ii1n82g+CwWC5qbm2E0GhEREbEpmv21zsvVmMbGxlBUVCTqFj8ap8Udey2GdkdzjLT2983irbpRtI+LmdAZhoFOz9Zs3NxYJnah/hV3CKmEQpi9wZf7mxCIIUTETS6uoKGhATRNo6CgACqVCn524IHGYCU0ToemVgAAycGeuCQvHKdsDUJ8ILu6nBFERa/UjpGf0yPZSV9ub0WwMcDosg3d3d2orKyEXq9HaGgo4gPcSCoRYHkPueZmAJBJJNhuj+o8Bc5rd+cMJtQG8rtEIsFlhfxYHxhYgtrIrsz1ej2ZiGxWC36UyRMQ5ieEYFtJEQIDA7G4uIj6+nrkyKdwZ54CcgkbkXZNafFWw4To+5BQFM7P5p/Lp/cOwWx1fm5K4/xEUWtBjI9LuZJFnRmFf67CGw2TuP6tdphtPwxYgjtmfHy8yyisqqqKMNVvdLH5Q0dax5IRg2tuTktLw7Zt2xAfHw+pVIqpqSlUV1ejrq4OAwMDWFpaWnM++H9BtRg4jpzWekwYKS0tLaGmpgbu7u5sHWUdjX4bTQ8Ka2RhYWGb7sFYLdLS6/Woq6uDyWRCaWmpU1Pv0TqtpqYmqNVqlJSUrKs/zfF8d37YhQe/6MNd/+wkf6NpGl1dXdAb2ElaIZNBKpViYGAAIyMj0Gq1fKQloRDtz0ZLFFhnJwRi+LrLEWlPWX14oB0/+c6Iq79YhkTK1no4B7KkM5OG5YFZNj2YEuqJB87cgkvzIxDgIYePO/u5uz0FOLSgJw54ix2MsaCzEImO+jngiRYbek2+iImJYaVjqquR6CVAiAKoGlwUjdHZGaG4vjRalCqkGeB3u3tFDv+01GDY/SxsDPBZ5xw6xuZJC4afnx8oisINJTzr/7/apmGkJYiIiEB2djbKy8vZNgoAZwh6ix/9ehB7OidF13WugIF+QmPCe83izwHWSVxXwjvTsUW9y2jGTyWH3B7RNo1q8EiVGibb5qOe1cwxGtpILexIUdi/I9L6IbgHKYqCm5sb3NzckJeXh23btpEm566uLlRWVqK9vR3j4+MwGAyifblI61hbbGwsYb7h/j3yyCNr7mM0GnHrrbcS0MkFF1yAmZmZY3I9/1HoQa6mNTIygr6+PiQnJyM6OnrdD+ZGnNb09DQ6OjqIZEl/f79TwXS95sr5cCrAnPTKahxpm3FaOh0LVpDL5cjPz183WtIx0qIFERPAS5XYbDZ4eHoB6hVIKAoZGRlYWFggirrjE+y9mAwGhNl7lBhyfPu57GAHu69BUKA/LHYH0TGhQXaUL/zsTmtRb0GAhwJLegvG1OIXlbvuhEAPNI9p4K+SY0Jjgo+bjDwXcYHuUEhZSPtpqcF4tXYMH7TOgQEFdZ8Zt50Vi9jYWFgsFozKBlE/M0WO/X5NHzJ8rQgMDIRCocD2pABsTwpA+8QydnfwL2FyiBgN5i6XoiI5EN/1sk3ST+0dAgUG714ah+RkHgWoUCiQFKRC/5weVhp4v3kK1xVHkPSwv78/AgMD8fukJHz6p4Ng7GP5yFf98FwZRUhQIAICAhDm44M0f8AicUPfvBF//uYwwDC4skisgXJ2Rgie/H4IRiuN6iEN9vcvoiI5QLQNRVG4++QEPGinkmqfteC3389jV1TMqv1ijSNq5EX7bFjyBHAdDR1tX5gjavVY2w+JTBRGcUdSbnZ3d4dCoWCZYhYXf7Ca1oMPPihidT8SAO2OO+7A559/jg8++AA+Pj647bbbcP7556Oqquqor+W4ibTWq/aq0Whw+PBh5Ofnb4o/8EhOi+th6uzsRGZmJmkY3myDMHdebl9hjSk5OdmJoV1om3FaMzMzqK+vBwCyUl2vOXEd2icVGUVBq9WitrYWCoUCBQUFiPRjI9vkEA+sWIFerRJL7hGoqKiAjy8b1S0uzGO4p50cT6PV47Hzt6Lh1+U4PycUQ0NDkFhZJxQRyiPlvu5mazJcX9KS3oIwe0/X7IrrHH9CEHs93KQ6tWwik6JMIkFSsAe83WRQWlmHzsHsZ7Vm9M/yTv7U7BjRcVtnrRgaHsH+/ftRX1+Pw4cPY3l5GelhniT1CQDdU1qna7q6WOwwGFB4pGpRtDCQSCS4vow/55sNk7Ax7LXZbDZSC2NoGidt4Z3LJQXRSEqIF63Ab9xiw59O9CfUVY99O4TOyRXRNXi5yXBWBj/Wj34zSGRghHZeVgi83fhnZ0Znhdbk/O5YbDQu2dWEa99sx88+6CKo0vXYRqKhI0VhTU1Noijs38EN+EM6LVdRHNfcHBMTg5ycHJSXlyMxMRFjY2O499578fDDD6O2thZPPPEEDh06dEzERjnz8vJCaGgo+beWc9RoNNi1axeeeOIJnHjiiYRkt7q6GrW1tUd9LceN0zqS6XQ6HD58GDabDaWlpT8If6DFwkpdTE9Pk/z6evddy7j0IJda42pMUVFrKwEKARVHMg7M0d7ejoyMjA0hATlz3IeLdHyUIKjGrKwsUBRFAAlRfu746buduP39TtzxYRckEgnkCnYyj4uLQUVxDjneN/ur0dxQj8mRIXS1t2F0dBRRwexKesVoI5B0bnIkkZbOjJ/Ze5S0JhvO/nsdMv6wD09+x0PSuboWFxVabAzplQKAFy/PxMtnBSDNfQlyKQVhtuu9Jr5GFOHrjig/N3Isg4VBhzUUny0EIyIiAisrK2hsbERlZSXyQ/iJpX5kyQlGnhPljWAPKdwF80/L2DLerBfXpM5ID4bMft0aow2fH1oALZFBqVSKIPW3b+efl9caJuHj54+tW7eirKwMubm5kEklWFpcQLof74Suf7MNfTNih3pJLp+SHFcb8aqg/seZXCoRpRJDPKQI93FmO5dJKAwtsOO8r38Rd310yKUTdGVrRVprmSMisaSkBKGhoaJa2NDQEAAcc8Vi7rp/6EhrPalHTrn53HPPRW9vLy6++GIkJCTg22+/RV5eHmJiYjA7O3vE46zHHnnkEQQEBCAnJwd//vOf1xzXpqYmWCwWnHzyyeRvKSkpiI6OJuWWo7H/CKc1OzuLmpoaeHt7w83NbVPSJMDajoerX1EUhZKSEqeC5mb5A7l9zWYz6uvrsby8vG6ny70U61Evbm9vJ2CO0NDQTUVpjvtw0YhJr0VqaiqSkpJ4lnT7pC+TUEQ+xGqnY7KRmpYEIf688GVSRh6ioqIwMTGBhYUFFpVmYSe8JZ2R1K24yYzUtPQWbAnxhExCwWSlMbpogI1mSEQGAAl2mP2SoJepzQ4gsVqtGOrtglqtRllxEdLDxKmNQgHZLQAUx7GONMaPZRZ4et8wvu1bxLTNA1lZWaioqEBGRga2xfB1VJoB3q7sIalZmqbR1dmJX+ZQePGyNNHxl41i5yaTSHBdKe+Qfv9FH859rh4URUEul0OpVEKhUCAuyAvRdoeqNdnwYfMkUfT28PCARCJBamoqnrmigBxLb6Fx3evNqG/pwPT0NCwWC1JCPZEd6Y0AD3Z8n90/gr32NKbQLswJg5u9B653wYLPu5wnQIqi8MAZyeT373oXcNPb7Wv2gRktNvIcccc4GuPg5MIojHtfXUVhR2ubbf7dyPE3I2OkUqlQXFyML774AouLi3jllVc2TEzgyn72s5/h3Xffxd69e3HzzTfj4Ycfxi9/+ctVt5+enoZCoSDyPpyFhIRgenra9U4bsOPaaTEMg/7+frS1tSE1NZVQ6mzWVnNa09PT6+rx2uy5LRYLpqenoVKpUFRURJjBj2TrEZ80Go2oq6uDwWBASUkJAXOs12nt/GsVTnzyIFpGl0SRFk3TMJvZicfP1w9hYWGw2WwkLWITgC0C7Gk8LjNkI2k5Cm4yCVHzXdYb0TswiMMGFSoqKpCVlUXSeYcGRuAmYe9zeIGd+KP9VbhtRyxuLo+FTCIhfUa+Kta5jS8ZCG9hgl2/akpjJPfWMKKG0WhEY2MjQSW6u7sjL9oXAE8bJXR+AFBsh5hzApSc3f9Jj4iH76ySdAQKqJW+6llETU0NDh48iIMHD2JlZQUnlhYgNy6YOBsA8HIh/PjzE+JJfxvAgimEERAHqb9SkHL8+8Fx2Bj2PTGZTGy7gdUKXw835EfzoJ7cKB8EeKswOjqKgwcPoqmpCXcWeuFf16ZCSrGpxF/88xAm1PzYAWyq9UcCVOJj3xx22eB8elqQKFXaPLaM5ypHnLYDgK6pZWx7ogaXvNwMi+3YRyvcdxMeHk4IrB2jsM0iEjn7dyATN+MQhQzv7u7uOOmkk1ZdEPz61792Alc4/uvp6QEA3HnnnaioqEBmZiZ+8pOf4C9/+QueeeaZDcHxj6UdN07LcXDNZjOampowNTWF4uJihIeHH1WKDnDNH9jb24vOzk5kZWUdscdrM+eemJjA1NQUPDw8kJGRsaGH8UiRllqtRk1NDby8vFBYWCiKQNfrtEYWDZhQG9E/qyP7cLpjVvv9KhUykt4kYpw075ii7REJR8RA2DLsD3+kfcL+sr4Htx+g8XiDDloLDT8/P8RFsPUVT/9gwpAxOreMqqoqLEwM4dIMH/woi03TxgawkzqHAGQAzNlh7qHeSqgUUtgYPv1VMzCPE56qwV0HTMjJySGLkdxoNvqT2b/ruuEl0ZgUx/nip9tj8fuztpC0HQCMLhnw1SE+2qAoCmcK6kP9ahoT7nFommEndpvNhurqarS2tuKcLXx090adMzExRVG4xqEG9vi3g6h3uLYz0oJJGnXZaMWnXfOgKAqHDh2Ch4cHVCoVbDYbbirlU3u1oyuIjYtHYWEhSktLERYWBplVj7bWViTZA2GaAS59uRmTGrHjuqIggtBrLekt+Mt3h+FoEorC7RWx5HeVQoJbtsU4bQcA933SC5OVRve0Drf/sxcm2w8LlHAVhcnl8lVrYes9NnD8wek3gh686667SH/rav+EqsVCKyoqgtVqxfDwsMvPQ0NDYTabnRQjOALqo7XjxmkBvONaXl4WpeqE/IFHk6MWOh5uYp6dnUVxcTGCg50lIoS20fQg1/Tc09ODiIiITTE+r+W0Jicn0dDQgLi4OKSnpzs95ButacllvJhjbW0tZDIZ6WuS2P8uRGMJI61wewQkRBsqpBLSW6Wg2OvfEsXXCPf2skhBTqpEa6YRH8LOoEZGhqSkJNhsNnR0dGD//v1ob29HgJI9DidRTzN8CpFFELJOLckurjiltUJjBpaMbEqRs2w7Ga/FfsELOgvGl/jP/VQK3FYRh22JASiM9SV/lwAojBGndc9IC7GPEfv7o9+N4tUeoKikFOXl5SgoKIC3tzfSPXXgOO4nNCZ82+HsuM7KCMGl+Xy9iWGA1xzqTX4qBa4piSLQ/j98OYCfvlZDkKJubm4sWCaGbzg2WGg88d0ALBa2ETw0NJQwM/zp3C3k2GqDFZfvakBj92HodDowDINof3fkhvCR4b/aZ/Cv1ik42mmpQQi1n09vpvG6Q92Os2cu5lOlzeNaPNVBYWb52K/YXRHxclFYYmLiqrUwLgpzpRjOGUc9dbxFWhthxAgKCkJKSsqa/xwlljhrbW2FRCJZdc7k2IK+++478rfe3l6Mjo6ipKRkw/flaMeV0wLYyKSurg6RkZFOqTqZTEZo+zdjnNPi6ldSqdRl/Wq1fdebHuSixIWFBeJ0jwXyEOCjw0OHDiE7O5v0UDjaeiItWoD0UkikZGUUHBzMsovYEXnbEv3w2DcDuOAfjfjr9+xKW1i38ndIDz72o1S03r8Dl+SFo6+vDzCzDsHd25ecr2aIdVpcf5XGYEVWJJvS0pltCAwKQnR8ElJyipCXlwcPDw942FhAweIS3+zcKwAZJAR5INRbCZNWA0+Z+Bn5+4Fh8rOvuxxJweKX+6BDPxZnJwj7sQB0TC6LPk8P90KErxuEoDmTDdhVM8YCVry8EB8fj+2lReT+AODlykHCj8jVmtzkUvz2jC3E+QLi/ivO7j45EfeckkB+r5lmoPOKglTKcj1KpVIolUpcVsBHW283TkNvthFEosViAU3TiA8PQlk874iXzcDMghoNDQ2oqalBb28vzo2n8OJ5UYi199z99vN+kZMHWODGtSV8pPhS1ShpBBdatL8Kp27lx3RcB1z+aotThMdZ/fASLnixad3gDs7WA5RYKwrj0qiuorAfGpm42R6wH0IAsqamBk899RTa2tpw+PBhvPXWW7jjjjtwxRVXkLr8xMQEUlJSCGrZx8cH119/Pe68807s3bsXTU1NuPbaa1FSUrIusd8j2XHjtDhkXU9PD7Kzs5GQkOA0GXNf5NHwB+p0OtTW1iIiIgI5OTnrhoSvN9ISOkSu6floQRyc87FarWhubsbMzMwRGUDW47SEw2tYWUBHRwcAIDEx0d4IzH6mlMnwcesMema02N3BFlKF6cFAT35hwTEx2Gw2tLW1YW5uDtGhLFR7xcQzW3DMuRyIQ2O04OK8cEgpCjQDbP9LFQofO4ib3m4nvH07clMBADqa/84+2NeM7u5uzM3N4YHTE/HCmUGIxSwy7arE3C0eGFgUwbE56RNOHuXt+nGc8GQVXjw4LBojR77BT9vFhWSKolAa5Y4AJQPhNPP8gREcnteJtr2pLJb83L4ArHjFwEaxsO39+/ejoaEBQ0NDuKGYZ0V5fpXa0ElxKlGT5c8+6MLAnPh852WFkgjQSjP4w9dDUCgU5D3iamDXF/PRndnGICc9FeXl5diyZQsoioIvZYRhdhghSjbLwQC48KVmTC+LHc2PskMJ4pMG8JvdfaTmKLQ7ToyHIOuKCF83QssltM7JZVz/Vgf6ZnU4/x+N6+ZK5O5tI45lI1GY2Wz+wUAYwNGlB4+101IqlXj33XexY8cOpKWl4Y9//CPuuOMO/OMf/yDbOKoWA8CTTz6Js846CxdccAG2b9+O0NBQfPTRR8fkmo4bp0VRFBQKxZqTsVBeZKNG0zTm5uawvLx8xPrVauc+khPgAB2ODvFoQByc8zkSQ/tq+61lwvufnppCbm4uAPYhtNls4OYbqQREgkNvcUQIUiIwgsZoIfRaVqsVhYWF8PNka14rRitxUhb7tQkjLZlEgnBfscaWMK0XY69pzWithH5omvYiReMD+/dhdHQU0dHRyLI7LY4BPsLXjcDhARAwBsehODCvx8yKGbuqxWm7CF83bAnhx3pf3wJxzJx+WKnXEj6+PhNnZPDpTwkFmK3iaK9iSyDiAtixYAD86vNhPFbHSsSUlZUhNDQUarUaHov9sA83ema0aBgWR4Fzc3PoaGvFDQV835bRQuPWd9tF9E1BXkqcnBJE0oRfHZrD3/YPQ6FQEBSuTCZDdqQ3EgXR3S8+7ALAqnwnJyfD09MTCQkJ+GVFOFkE6Mw2XPJiI7qGp8lz5i6X4vIC1gFKJRT6Z3V4dv8wHC3C1w1nC8bKaHH9nKaFecHbjf1+RpeMuPTlFifHvJodbTS0VhTW2toKi8VyTBGJQttMepBrPD7WqsW5ubmora2FWq2GwWDAoUOHcO+994rq55xqcUVFBfmbm5sbnn32WSwuLkKn0+Gjjz46JvUs4DhzWsnJyWvSMUkkkk1FLVy6TqvVwsvL64j1K1e2FhCDQzl2dHQgMzPTySFultmC25djlw8MDFwV3ehoXH3qF++3Y+vvvkXZnw84bSPM26elphKIak9PD+bm5mDjoL0SijgJblIUUjUFeCoINLp9aBo/e70Gf++wkWv1sjeprhitZCU+qWHrGKHebvjVzkT8aidL5hrpy07qHBrNZKVJQ3GYjxIKqQRWmiEs72MaC1swlsrh6emJ2NhYLC4uAovDAHj4/OCcTsR+zoExhub18HXnY5ZloxWVA2InIUwRGq00qgYXQdM0enp6MDY2htKifAQGBuIcQSrPbGNE/IScOTJUtIwv4+DAAtzd3REVFYWcnByccEIFdiTwrQKPfNqKlpYWjI2NYWhoCO3t7UhLS8MtO9NFxLxnpAdDIRO/0k9emIbdtxaR3184yKftJBIJ5HI53NzccEURn0rsntFhb+8c39jMMFAqlUiOj8WPBTW3RSONB7/oI9RCk5OTOD8jEFcURuCOE2MBALtqxrGvzxlOf1NZNFmYHJrW4qWqUadtKIrCw+ekkN+nl0248rVWVB92ncoFgJEFPfpntcc0hecYhW3duhVyuXzDtbD12r+jpvWfbMeN01qvbRTFx4E6ZDIZtmzZcuQd1jivq3qa1WpFS0sLJicnnRqSOTua9KDNZkNfXx+2bNmCrVu3rvtF5BzllF2s0bFvhkuTcqZUKsAwDCnmHz58GItqtna0OL+AUHsKkGMltwnSgzKJhAgYflQ7iIPTQOOUGZyb5hqRl41WkgZasKP+vNxkuLo4ikz4kXYkolCld07LOi0JRSHKXle5sYwl4xtbMiL/0Src/K0BCalZhDn7slPLAIBw5pltDD6vasXMzAwsFgvCfdxwzykJ2HVlNkrieT0qAHjiu0HR98ylCLllyBt1ozj7mUrMzC+goKCArG6L4/xELBL/bHHm/zsrIwRuMnGE/8uPD4lSX1KpFD89IYn83q2mMGdTYWRkBAMDA1AoFNBoNFjRqHH3STzC69P2GZeoRA+FTASBv/q1FiLxwl9XqIjB/ndfHoZEKoVGo4Fer4dMJoPZbMZVBaGi1N621Cjk5ubC29sbU1NT6Gyuxwk+C5Do1WSbn394CH2z4gbnaH93nJzIO+bnK0fQNelcA9ue6I/UUN4xa002vHBw1GV081nHDM5+vhEXvdSMvjnDD1Z3ksnYxu+N1sLWa8dTTet4tP9qpzU5OUlAHdnZ2ZDL5UdVWwLE9TRu4rfZbGsKQm4mPcjV+CwWCxITE4/I0O7qelkn6/yZUFuLm3+U9lqTr68vkpOTUVpaCpUH+wKsLGtA2ychs8UGtVoNhZSCh0IKhYxla1BK2PvLS+TrMV120IKXHSG4YrQQglz1Ks2nUX7OPWwKgahhjD8bietMNvjYU0dcFu75Kh5pF+DljrgAcdTevUBjcJAFQDQ1NeHEcAbpwQqUJ4qdVm6UtyhSTg/3QqCngghY1g5rMKSh8eWcr0jaXCqhRGmvf7ZMOdEaeSpluP/0LThXwO6uNlidopEtIZ6iY/3ii0m822tBfn4+kpOTYTabWfXs4XZ4ytlrndKYsNdFVAMAL1yeRX7WmW246a02shgAAJVCih9lh5Gao8Zoxa1vt6KrqwspKSnw9/eHVCpFsJcSO1P4tOSumjEYGDliY2MJwWtMTAxygyXws2eQaAb48cstaBsTw/cvy/Ynz5+NAe79tAdGh944lgeRd8wUgJ+Wu6ZvG1k0gLEf6+49M2iYOvqox5UJa07HCpG42vHXa1wJ4X9O699s66kxrSUEyRmXuuEQdhyoQyaTHRWIgzs2AMzPz5OUXV5e3qrwUGDj6UGz2YyGhgao1Wp4enqui8F+1XM6DOnY2BjR1hJGniFechQ9dhA5D+9HJ4eQs38fKVuSkBzHalaZbQxaWlpwYegiXj07ADkBrHOV0uwLGR7MT2j7+zlYOxt9aE02nLCFjVpMFhpTGgOeOzCEvb18cy8XaQmdWvMojxaMC1Ah1FuJ5ZUVhLmxzwG38v+ya1a0ss2MYKMLbggOqSmUlpaitLQUQUFBWFhYQE1NDeQLYoXihhGxFIuEoojQpDDj93nXnBMY4YIcPn02r7M4scSz24ThzpN59B8FZ8AHRVF46JwU+Kn4yG3vBDBtkiEkJATp6enYsWMH8vJycaagB+yBT1iasOXlZdFYuMtlKBAwfyzpLVgxit+jG7fFYP8dZUR4snpUC4t/HCIiIohWmEKhwDUlPOW8jQbu/rhbpBUWFBSEtLQ0PPyjdLKd2cbgujfb8d63dRgaGsLy8jIivGQoDpfitK1B8HOXYWjBgDv/2eU0XgUxviiytx4wAP709aCTcwOAW7fHINSbl5/5S90yXqxyHZUdja2VetwsIlFom0kP6vUsY///nNZxaEeKtLj+q/n5eSdQx9Gi+AA7JdDQEFpaWkg/w5FWRRuJDjn0oUKhQFFREYH5b+Z6aZpGcjD7EHsopOju7kZfXx/y8vIQEREBq9VKogeFXA6d2QazjSE1DwK2oCj4e9h7sUBhx44dyMrKglQqRVdXF6ampkj9RqMzECfB1ZC4mtay0YqyBH/IJBRoAGc8W49n9g3j0a95pxFlr2lNqE2kF6l5jHcid5wYh13nhiFFMo28uCByb9zxpwQ9P5zT4uDtLWMaFD9WidOfb0F0dDRyc3NRUVGBgvRkxPjwzmFwXo+uwVERq39hpDs85UC0D19PpBngsa8HROOeEuopQlO+3ziOlw46IwCDPJXIEDDgv9/knEqUMDR2RvOTFwPg1nc7SKqXoij4+Pjg2opUss2CkcF3vQtobGzEgQMH0NXVRdgffncmv0gxWWyI8BFHtQEeCviq5LgklZv4KNy9W4yClEgkyIz0RV40n9prGV/BvgE2ihKS/BZFexHgCQDYGAopMWHQ6XRoaWlBX18frkikcc82fqFTOajGKzXO9a3bdvDNyoPzejzuosmZoig8fLa4BPD0vmH8cc+A07YAYLLacPqz9fjNZz0uP1/N1pu+22wUtlmnBeB/Tut4tLUcgEajQXV1NeRyOYqLi52KkkfDqMFRm3R3d2N4eBiFhYWIiIg48o5Yf6Q1MzND0IfZ2dmELHU9+1qtNtG9cUAMLni1Wm3Ekfv6+trRgUKeQf5YY/YeHB49SBEAhY1mm4yVSiUWFhbg7++PoqIi+HuyE2B7zwCJRpaW2dWftwCIIaH4ZmQOACGMVjj2jAWdGYF2hoyeadaJCsEP+fn5KEgMIdcHADIKIjohzmlNaYzwdZfBQrOObU5rxpB9IpbJZAgODsZJqXxaEwC+aB3DgQMHUF9fj46ODigWBvDupXF48DyxurHURRP3lYU82OL7/kU88f1hfNHlrCV0czk/Eb9RNwazAB5uMpnQ2NiIE2OUcJcLUJ7LJvz+8z7RcaL93XF2RghJ7T3TpEOvNBYZGRmQyWTo7+/Hvn37sDjSjaQANhKx0MAfvuoXHYdTOEh1V5O6G80AV7zS4tSXxTHYc1f228/7YYWUQOq550/IzGFjgO9HLUhPT0d5eTkbwUmlGBoaQlEAv0B44vthp9aC7EgfUT/Ze01T2Nu34DSmBbF+KIzhHaqEAomSHe2SXc0YVxvxSfssfru71yU835VtFuThGIWlpaW5jMI2kx7U6XSk1vbfbseV09qMejFnk5OTqK+vR3R0NJnwXe272eZko9FI/i8tLYWPj88R9uDtSI7HkaFdiD5cj9Pa8sC3SHtoL6oP8zUDbj/hWHG8hzYbS1gqbNSiKIpMQNN2ZJ8Q1s7VfaQSCn/ZcwinPF2Hp9qB7OxseHt7E2Lc0MhYEll1j81j//790EwO4ZbiEPysgp2kuboWJzditjGYsyMEfdzlxMlxyLjpZRMsFgte39MAjUaDwsJCeHt7IzWUjVSWDWyay0IDh+d5gEFyiAeUMglWTDakh4sFNp9wWKmXJYjrWofNnigvL4dKpcL09DSkFDA+Ngr58gSCPQRoQ5PV6bk9N0sMVgCA337WC51JnI47ITmQtBKoDVZ8ZSek1el0aGhogKenJ8oKclB51zYSdQLARbliBwsAj/4oFT+rYGs/Ngb42/4RqGkltmzZgrKyMpISPTGKX8F/1DKFwfEZojp96NAhzMzMID8/H7/cyQNB1AYLHtjd63TtPz8hDvedzqI+dWYat7/fAalUKoLUn5oaTHrhAOCV2gl0TagJItHDwwNFRUV44KIieAic8/2f9eHZL1swPz9PnuHbdsQCAALtRL/3fXLIpVTNnQJwCs1g1f6uPwiiso/bZnDLu51rEv1ydixkSSQSCfz8/FxGYQDQ3Ny8oVqYVqslpMn/7fYfd4eOVE4cXVJ3dzeys7MRHx+/Jn8gsPHm5KWlJVRXV0MikSAlJWXDqxmpVCpitRaaK4Z2oW2kHiZcKUokEuh0OszZ1UKlUqnI4VMUBUZQ8JJQIHBpElUxvNPiGNhNVhrfdE5hycwyf3NjzZHA6iw0Ar1Yp2SRs6zoAV5uyPNUw2uhG83NzfCRsd+fv4qvA84LJhYu2tq5leXZWzHZkPWng3isQQ+jXzwhHI7wdYO3mwzC0akd4h23XCrBr3Ym4plL0lGWIKZf2ts3L5rw8qJ9Rai+yv5FnPNcPZ6smkNBQQFOOOEEpKWlQSKRIMufP2P14CLmNWJkXLCXkpDucqY32/D4t+LaGUVRKE/go4A36sYxPDWHhoYGBAcHk/OpFDKcl8U/F984EPxydmVRJDjEOwPghjfbSG1QpVIhOjoaN51eQNKpDIBffXwI+/btQ2VlJebn55GZmQkPDw+clxVKetwAiOD8APtM3Fwei/OyeAdaN6wRISYlEgnclAqRXhgD4O5/9cJotpCFoNVqhcpNiTtP4ut8ALCrdQWNnX04cOAAWltb4WNT46VLUvDpzXlwk0mgNTO44rUWUSsDwPZ3FYXzz9YfvxoQLWY4Sw/3xikpfC2xbliNy15pcbktZ12Ty5hePvbNxVwUtnXrVgCslMdGamE/lGrx8Wj/cU5LCKbg6lccXdKRaPi56GsjTmtsbAyNjY1ISEhYE2yxlq3GIbgaQ7vjvuu9XrNAJEqv12Nqagrx4eykGOqjJMfhOARFYoQUBXd7o63Bat+OYtNfEoqvGwHAlnB2QrbSvNaXJ5cCNFkRYoe1L+otpEGVA0AEBATAA6yzWFriQQrCKYDr1ZpdMYnY0QHglTqe046iKKTZZUY4oo3KAXHK6NL8CJy0JQjbE8VABz+VHIGe/PepkElQGMvel4/dEY4tW1E/C7h5eEEikSAgIAApKSm47pRssp+NAZ7fXYeamhoMDAxArWajiOtLo/G7M5MhbJv6smvWaSV/18kJpMeta2oFZ7zYAa17KJKTk0WLr9sqeLXjD5onsegielDIJLhdsN30sgl3fNApWsy4y6U4OzOEnPPQEvDNpIzIvDc0NKC2thbjI0M4P4N3qI99M+BysvRQyHCCQPn4t5/1onlMLdrmvKxQIoMCAONqE575bgATExMICwsDwzCwWq04M9VftB1AITcvD0VFRQgICMDCwgL0Y11oaWog/YJTGjNueLPd6dou3OJGol2DhcY9H3e7pIL69c4EB1JkI/66d8hpOwB4q2ECl77Silu/mMGkdvNqE2sZ944GBARsqBb2Qzitffv2rcoA39DQsOp+FRUVTtv/5Cc/OWbXdVw5rY2kB7n6lUKhIHRJ6z3+epwAly7p6+tDbm4uYmJiNo0+dBXhrcXQLrQNRVpWmnATqtVqBAUFISmSbaQO8lTg6tdbkf6H/Sj/y0H2HgXvOUXxrBcae7rti1uL0fF/FUgP88Shrk5Sq0qL4lNpHGccF2lpjTacn82uvhmGwXP7h3DxS42o7J+HSqVCTEwMCtPYlJJNyjuk975vRGdnJ2ZmZhDpq4S3mwxLyysIkLC1FG7V3z6+LIKRc06LI9HtmGBXogazOBUXG+BOnCnAkuRyaUXOyuxRjxT892RjgJcdgAGZEd4iFpA+iy9iY2NhMBjQ0tKC/fv3w0s/iR2RMpwsWMmnhXk5ydVH+7vjorxwwV8o/HH/nJMsip9KgWQ7oMTmgkiXs6uLo0mkDAATaqNTe8EvToxH5d1lJM7+5LANvvGZKCoqwvbt2xETEwODwYAtUj6iW9Jb8NfvXQMahAAPBsANb7ShY4LnaHSTS3FFYSR2JAWQ1PFbbQvwiU5BWFgYFAoF2+SskOP6Er5ObKEZ/PLjbiiVSkRFRSE7Oxvl5eXYuiUZV6TzE3TDqAY3vN4ErY6PkMI8KJycwD4bMgmFvlkd/uwQ6QJsVHxNMV93U0op/OKEWJf3yfFcWmjg7m/mVuWrPBrjUo/CuXCtWth3332HHTt24KWXXjpqXTJHKy0txdTUlOjfDTfcgLi4OOTn56+574033ija77HHHjtm13VcOa31mNTe8FhfX4+YmBhkZWWtmz+Qoqh1gTFMJhMaGhqwtLREogNg8+hD7mHinM+RGNqFtpEeL3c5RbgJw8PDoVQqwTC80B5Hl8MpA9OrRFrLAig0BwgwmUzwsafzhGAHq42LtNh9V0xW5NiRZQs6C/52YBidkyt4rW6c7MPpYi0YGVKrWZL5QaFQYHBwEBkYwRPbpEiTTCIrhh17T3tTs9nGYFBA5ZNqR+CF+yghk1BYMliQ9cf9yH+kUhRhUBSFojhf0Xg5TjqFUR6QUYC/m/j7eOngqEhHiqIokWR94+gy3u/R4zAdSJCVSqUSg4OD2CLhARi1w0uY1zpHSNsdSlQaoxWPukC8CQlpX68dg9bk3PqhkEnw6I+2kt/lEopIvnDm7SaHxGZBmeC8177RhkmNEQqFAmFhYcjIyMCFp+1AcQwPp3+pegz7q1jIularJdFNkJdSlGaz0IwTM8dN22Lw90szkBbAvasUfvHpENHU4iD1F+dFiqKtprFlvNMwTiD1FEUhICAAd5ydiwCBc64f1+OaVxpQWV2LgYEBmEwmXJHthx9lheB3Z7D1ufeaprC70xkQc11JFKmjmmwMHv3msFPKEQAeOCMZ/vYWBLMNuPW9TrxWN35MIfVHQiY61sIKCgpw3nnnYWBgAAMDA4iIiMB1112HDz74AFqtdtXjrMcUCgVCQ0PJv4CAAHzyySe49tprj+ggVSqVaF9XWaTN2n+U06JpGouLi1Cr1cjJyUFcXNyGVxdHclocg4ZSqURRUZG4eXST6ENOxsBms62Lod1x35pRLV6pHkHHhGaVbdj/u7p7QNM0iouLoVAosLy8DKOBR+ZxLBPcZC581yQUr1PFZUtWVlZQX18PlUqF3Nxcskr2UPCLBC7dRSItkxX+KjncZBIwAFR2RyhkY+ec1rTGRFJ0I2oLkpOTUVJSgsjwMBgMBnh6ekKpZ1f7Fis/7jECGDUHxhic1yPN7sCsNAMGwAfNYnmMolhxnembbl4bS6vVYmagA6+eE4R//rRMFK3IpBR0DpHbGeliSZIXKkfx4Bd9YEDBz88PSUlJKC0txZWnlcJbyb5mDAM8+Uktent7sbi4SNhOLIsTKIz2ggBrgfebJ7G/X5zqPGVrMGGHMNkYbP9LFWpcrPbL4v0Ju8XQogG1Q+JttFotGhoa8IsyvnnZYKFx81ttIlVliUSCq0tiye80Q+HFbhpqtRp1dXU4ePAgISu+XqC8bKMZIuQptP7+fuwM45/HRZ0Fv3i/Q7SNSikX9YEBwF/2jmJcwzp7DlJPWy34eYVYs0sLNyQlxMFsNkOn00EzNYyLYy2Idedrl/d90uvEvOHlJsPN2/hzVh1ewq5q50hWKqHw8Lk8rRTNAI9/exj/t7uP0JodrW0U5BEQEIA77rgDN998M0488US89dZbCAwMxIMPPriq3tVm7dNPP8XCwgKuvfbaI27LXUd6ejruvfdeEZnu0dpx5bTWmsC56MdgMMDf3x+BgYGrbruWreV4pqamUFdXh6ioKJcR3NES33Z1da2Lod1xv8eqlvDInn48uqff5TaE1ULlidzcXEilUgQFBUGpVJIHV6vVwlfBXjv3fglXkxRFocJem/BTKVD+eCVKnmpE47In0tPTIZVK4W13TFaah3D0zrCTU3ygB35SHoNL8iJAURTC7D1AAfaepUW9BVb72AV6KiCXUrAxDLbZU3JzKyb0zyzjzjdrsLS0hOLiYhQXF+PCk1n9nWUzf62fHWzHzMwMrFYrovxYMIbFxoiUfwHgpSrxxFPo4LSqBhdhtS+EGhoaWKLjzDTIZVIR36DebCMOm7O0MC9EOkiSGCw03m4YF22nUqnwI0HDcfUMBbPVio6ODuzduxfj4+OIiYnB4+en4pHz+H4rbzeZqBcKYFkrdl2ZTZyb0Urj9g86nRwqRVH4cQGfZnvwiz4SDWg0GjQ2NiI8PBxbt27FBQJl4sF5Pf70lTjC25YYgPtOS8JZ6Wxk2TxpxEG1NyoqKrB161ZCVjzX24Qt/nyE8JtPe2GyLzQYhkF3dzdmZmZw9o5C5Ebxq+69/Yv42z4xkvPSvHB4u8lIFG6lGdzzcQ8kMjmB1EskEpyWGihCJk5oTGhboJCamgoPDw/ExcXBw8MDluU5+MjZ+2cAXPlaKwbntE7nDBcc62/7h9EwooajlcX7Y2uAcyQkPUaZuc3yDmq1Wnh7e+OEE07AY489ho6ODqSnpx95xw3Yrl27cOqppx6Rneeyyy7Dm2++ib179+Lee+/FG2+8gSuuuOKYXcdx5bRWM41Gg5qaGri5uSEhIeHIO6xhrpwWVwfq6upCVlaWS1kUYPPpQb1eT5R/18PQ7ni93KW4KiRPTEyQCSkkNJygFD09PZGVlYWtKdzKkIKbmY3UJGBTlCYTn6qSULwg4+zSChb1FtAM0LfM59c5sIXWbCP1L67xNNrfHT87IR4X5LA5J24C4KI3CoBazzFYUAi3O7VtiawjmddZcO4LTdgzZII0dAsZozA/DwR4iAEwnfNWDA4OYt++fWhpacElGT6475Q4lNk5BLnoZ3rZhLZxPjqN8HVDlJ1xQymTQG+hcfKTVTjjhVYkJyeLvveTtgh0tBjg+14xPRJFUTgt1Zl4+cnvDztBrK8TRA4zWitmJIEwUG5wd3dHZGQkZmdn0VpfDT/dMDi8y7LRiuEF59Wpj7tcpJOlN9tw/yc9TikqITPH8IIBlYOLWFhYQFNTE+Lj40lbxX2nJ4sm3BvKxIS+UgmFKwoj8dDZyeRvz1WO4tC0DoGBgUhJScG2bdtQVFSECwlwg4HObMPd77AgqY6ODiwtLaGgoAAeHh74zeniBuC/HxgRReIeShleuCwT++8sQ5Q9Ku+b0+MfB0cIpF6pVELlpsRPtomjsv/b3YfRebau6e7uTuovf72E76+z0AwueqkZnxxsI1yUCpkEt+2IhZtMgpRQT9AMcPdHh1ymcy/fqhQRzcQFblzgdTXbrNPaCIXTr3/961UBFty/nh5xw/X4+Dj27NmD66+//ojHv+mmm3DqqaciIyMDl19+OV5//XV8/PHHGBx0riluxo57pzU+Pk7qV5mZmVAoFEetXiyMliwWC5qamjAzM3NEBePNRFocVZBEIkFSUtK6GNqFJpFIyCQslJ3gGkG7u7tJk26wpwwWiwVjSzxZKGXf2cPDA+V2PSoawOjoKKqqa8jxDHo9PO1prIUVAwE+DC/wTaU82MJKgA/jatfifVwDsbfSrq/EAB4CDiQuRajWW4niLWev1YvZIbbaU2Kco+xblojQiOVBZoTqBuC2NAgJJVBQptgIUGjF9roW51RndVYsWyiMW9xF25XE+4kg8J85NLsCwNmZIbilPEbU9Gq00Piic1a0XZCXEiUCCPzPPjiEX1fqkV9QiOTkZBQVFaG8vBxRkZEoDOPH4q9fdbJs+w4LJcf02dfdc3i3UTxmUX7uyI70Jgue+z/uxMvftCIlJQXR0fz+HJqQsz9/43piUcrlKBHUBK96rYUsWCiKgqenJy4tT7V/r+xJvxvS44uqFszMzMDDwwOLi4swm81ICfUUUUp5KKVOUXJWpA88lTJIBM7g7wdGRIsQiUSCc3PCyUIEYKPdX/6rBzq9AVKpFGazGVarFdmRPigRqFBbaOChg8voGhgmkPI0Tz3euyoVr1yeAS+FFIt6K659o9WJPzLCA9iZxEeLT38/JGJtORr7dzC833XXXaRNaLV/8fHxon1eeeUVBAQE4JxzztnwtRUVsSoDAwOugTwbtePKaQlXKxx6r7e3F7m5uaR+dTSsFoC4z0ur1aKmpgYURa1LwXij5x4dHSU8f+7u7psq2EokErKq42pRHLP89PQ0y/xhdwxdUyvIfqQKpz9bD6uL7v4wAW1Pbn4hdlaU4cPL4vHcTm/U1dZh4BBbX7BKFATlJlxpCmHtl9vTTwaLFRf9owHb/1Iluj/uXMI7Hpzl6xyc0xqf1yBQzp6DY3RoGtWIjpUSwn4v3DX1z2pBMwxmDEBMTAzy8/OxY8cOpCbFI1ZAx2RjgOb+cdEih6traQ3iptQ/fCFOvbrJpSgXwOTrhtUiMAYAJAV74vYT4kWRDwCcuMU5dX1VkXgbsw0iSXqlUomIiAj8/oI88reqcRNq2npIRDk2Ngaj0YgQbyV+VhErWu0fHFxwer52XZmN23awEPgFA41X+yVQeDtf2xUCBo8DA4su61EA8ORFfLrJZKVx45ttIjYTLiorjvUl0duTHVLk5OTAy8sLo6Oj2L9/P+rr63FOIu+cdSYbnvremZYJAF66Mkv0+7VvtGJFUHeTSSS4ZTtb2+IWd10zBjQbAuDn5weJhCV0ttls+PkOcRRJUUBGZjaBlGtXVjDW14H6+lpozex7PrxoxC3vdjgpF19XEEwWdjSAez7q3pBI5Wq2WYZ3rrl4PRYUFEQo6Fb7J2zvYRgGr7zyCq666qoNL7oBoLW1FQAQFubcFL8ZO66cFmdC9F5JSQlB7wFHR8XE7U/TNGZnZ1FbW4vQ0NB1a1StNz3IMbQPDAwgPz8fkZGRm9bUkkgk8FWyb2OwlxIGgwF1dXWwWq0oKiqyKyOzn0f68atVTiyPEkxtIQLU36LeDJW7O1ITY1CYlQp3dzcEeLErVo3eDA+KfQFlFC+6yUVaK0YrEoLYF2R62YSuaS3mdWbC6g6ApP+WBXDrVsEq+c6TEvDFtclIpSaQHsk6Eo6/UGe2YUIQwW2xR1ohdt0unZlGxkP7cOazddDb6zlyuRyhoaEo3yJugv28ZQT79u1Dc3MzRkdHkRbEvoxWGy1irRic16N5VC3a9yQ7Ik5qj96e+G4QP3tPDBwAgLJEf1Ef279anXkEM0MU8HFo83t637BI5BJgnX24YHHRy4ShqKgIfn5+mJ6exsGDB1FTU4OTw23Ij+KRfYWxvk4pKjeZBCeG28hLzjDADW+2iuiiACA1zIssDAA2LebKvN3kIkb8eZ2ZsKdwdlVRJF74cToKQtizGm3Ab7+ZQEJCAoqLi7F9+3ZEREQgwcOCSA/eEbxaM4a+aedoJcLXXZSqNVpoXPZys4gw96yMEFQkBeD6whBwy6TXWpZwaEYPpVJJiH5TwrxFDPVGK4N7Pu6GVCZHWFgYgZRnp6fhkjTeAdQMqXHzG41YXtGS9HuItxJXFrILN4WUwqzWjF/9q8cpKtuoHY1q8bEWgOTs+++/ZxW1b7jB6bOJiQmkpKSgvr4eADA4OIiHHnqINEN/+umnuOqqq7B9+3ZkZmYek+s57pwW13/l5ubmsv/KkRFjoyaRSDA7O4u2tjakpaU5NXGuZetxmEKG9pKSEvj5+ZHzrrbvOw1juOvDDrzjUMTn9uMiEIPZgpqaGvj4+CAvL4/0jUnt1y9EvHVNOWsT+bjLybGW7FGDWq1GfX09AgICUJKfDQAw0hQyotiX22y1kUnfZmTrDismK5lYuToVAHzQMkV+5j6fXjERBGGrvXeHYRgsTI1haKAPWVlZyLfLmcg4uQeKTy8CfKTVP6dHhp1PkJsaXnZAeeXZ007cV9qvdydpxJmZGfS1N+L3+TReOicU6aHilaljP9P2pABIKQpcz/aHLVP4tndelKICWOmUM9P5tPIHLVMikMvi4iJam5twarKPqH5koxn85rNuJ3j1TwSchK/VjmHncy0Ij4pGQUEBduzYQXrCTg7kv+Nn9w1Bb+QdCCdMOj0xjpvL+PpW36wOD3/lDOi5WNArdmhKi7ohZ1QiAPz8BD5tZLExiPEXp1W5hv/rsgRqz/2LeL+ZdeRcRJmdnY3bTuah+QyAG19vQkNjI0ZGRqDT8dHeXSeL69izKyZRW4ZMIsHvTw5DlnwaAQJBz5+93wGtyQqJREJqYT87MUG0WGkaW8ZLVaOwWq2wWCxgGAa+vr741Tm5BN4OADVjBtzwehP2H6yG0WiEVqvF1YXhuDAnFM9cnAZ3uQR1w2o8V+lMjrwROx4FIHft2oXS0lKkpKQ4fWaxWNDb20vQgQqFAt9++y127tyJlJQU3HXXXbjgggvw2WefHbPrOa6cls1mQ0tLC+Li4pCZmenyyzsaeRGr1Yrl5WWo1WoUFRVtOFw9Uk3LkaHdES6/2r5PfjeI3R0zeOGAcye+RCIhE92SZgXx8fFITU0V0UJxhLHCRV6/g+gewzBQSCXEmTz+zQDSHtyLE59tRlxcHFJSUuCr4iDxDBJCWeSamQaZ9AMkBuQH0fAxL8C4yE5Ci3oLYRSoH1aT83FOZ0pjwunpLFJyckmPZ/cfxs2v1mB8fBz5+azib7LdKXHweZqBqBcrNkAFN5kEBovNqfbhyI7OIe44P9A3q4OFkiMoKAhmsxmBgYEoy94Kq9WKWKU4DSZ3eBt83eXIF5Cvcvbrf3U7peJ+lM0/S7MrZjIWMzMzaGlpQXJyMn5xWgb23lEKleBE6WFejuoxuDA3HOV22imaYXkJf/MpWxiXy+Wkj+ryM3cg0IMDx9B4/EOe7qe9nUVYFhQU4OYdSSJmjvebJp0Iac9MD8EDZyQT/r9r32jDAQfIPcBGZakCteQ7PuSlRAwGAxobG+Hp6YnSglxsi/cln/1ud68TmOX09BCEeiuJE5kzUvh+SkbqwFVVVejt7YUXDKI+MK3JJupRm5mZIWrOJ23lFw9zWgvucZA6iQ/0wFkCnTIAeO7gGNon2WdBCKm/56Q40XY9agqyALv46NgYWhtqcF6EAdEKPe49ORYAqwztqHy9ETsaIMYP5bTefvttVFVVufwsNjYWDMOgoqICABAVFYX9+/djYWEBRqMR/f39eOyxx/57+7SkUim2bdu2Zv/SZklv9Xo96urqQNM0wsPDNzWIa0VLXLpRyNDuuO9qTosTOXSFDqQoChI7u57CzR0xMTGE4JTr/+I0EmmaIZMTF904DiNXF9Jo9WAAmGwUKcyrFFIStXE1KRsDWCVyxMTE4PozirHrhu24siwBUqsRCu5c9lsV6jMFeykgpShYaQYnJrNOq2dGh2f3j+DgmAlZufkknREb4A65lBLRUAnhxlIJhfNzwnBdSRRhbuccOZuW5CMOP5UCifbUJSfceOozNSj+Sx18/fyRnZ2N8PBwZGRk4JqdfP0IAHZ9246mpiaMjo7CYGDTdielBJGx4Wxk0YB9DgzjmRHe8BOs8j9qncLe1n50dXUhMzMTERER8FXJEeipFPH49c/pXD7rVxeLwRa7O2bQMCwWUZRIJLiiiK/TfDujhJ9/AIaHhzE7OwuKojA2Ngbtshp/EvQXqeQSEaQfYHuVLsmPwC93JpK/3fZeh5NeGCBmsa8fUaN9QgOdTofGxkb4+fmRhvkHztoq2u/ODzvRJ0AJyqUS/Pn8VOy5vRh+7uz4vtayhICYFFRUVCApKQk2mw1dXV3IdePZORgAt77TDitNY2pqioxxSEgI7js9WQSg2T+wiGf3ixeDt2yPhZSiCICJAfDLT3qht1JQKBREXeGUlAAkBLqLztsywzY4Z2dno6CgAH5+fpidnYWvph8VEewL8et/dWNiaXN9SUejWvxDpQePNzuunBaAI/L7bYb0llu5+fn5HVUx0FW0xDG0t7W1OTG0C20th8cVdC0O+XCbzYaRkRHEeLDnTIvwxci8Fv9qncKCzkzOw0VaVpqBm91Zac2uHaSPHUzhaa9ZMYBIn4nrRxLKxk+q+bQTt9LPyspClF1F2N/d3jxrNaO1tRUTExOwWSykhqa0RxZGK39/3/bzE7BMIiE1Mi5qqz4sXq3+5vRk3H1KIk5KCQQFkJSdhAKCvMT1SC46CrI3Li8bbbAyQIPGQ/TdbAnxIpB8ADikkcHPPwBzc3OoqqpCTU0N4hUrKI/3cZK3eMFBI4uiKFyUy6fYdnfM4NZPx6HxinPqybuuVNDIOrjkJPsBsOjFuEBx6u2ejw85cReenx1OxmxeZ8Hje0exQiuxbds2JCcnw2azoaOjA6r5HvjYa6N6C80LfTrYhbkRhK7LSjO48c02pz6w09KC4CNw0L94vxONjY0IDQ0lvVsAC7YRIisDPBVIDBZHA3nRvojwdSdpXwC48MVGMBSF4OBgpKamory8HOefUICcMD5lPLJkxG/fq8WhQ4eQmZlJxlghleDnJ4rTiS9UjhCNOIBVv379mhx894tSpASzz/Cc1oz7P+1h0/FyOZRKJdyUSvy8Qhxt/aN6HF0LNIHUc7ps5eXl+NWpiUjwlWHZZMPNrzegpa3d3lrizES/mh1NTet/hLnHqXFOaz11LYZhMDIygubmZiQnJyM1NfWo1YuF+x6Jod1x39UiLQ7KLUT8cWS6VquV1KooAKf+rQ73f9aL95r4+hEXHdEMQ6iYOCRTWYI/3rgmB7/cmcjCfw3sZJUczTvvFgFcl3NaKrkE6fY00CNf96Ho0QO44c1W0XVzKcBwf3bC0Vol8Pb2xsTEBCorK+ElYa9haHIOAWLeW7wvuH4A2GIXq+ScSO+0DgazFZUD4pSSt5uciDqy98ymIIXGwak1OvHfHVVsKYrCDoEz0pppLEp8kZeXhx07diAuLg5eUisujdCg1EMMY9+51RmFd0FuuNPfHt077qTTFOnnLiICfs+FACRFUbixLEYkoji7YnZy5oGeCuz+aSFJse0bt+GJNhpubm5k0t++fTvy8vJwfiqf6rzvo3YMDg46sYZTFIXfn8VHZYPzevzyo0MigIFSJsWFOeHke5heMePVQYXLBdvN5bHk5ymNScRJKLS/XpRB0qQ6sw1XvtJCzklRFLy8vPDzU8Q1lU/6jZjUU+js7ERnZyemp6dhsVhwSV44iaIAdlEX5VB7y4nyYd8VShyVvSGgG5NIJDhpazCyIsRZmdf7pdCYQcQuOXqp8JBgPHt5LrzdZBjRUni/34rJyUlUV1ejvr4eg4OD0Gg0a2aJNpMeZBgGer3+f5HW8WoSiWRdKD4OwTc4OIj8/HxERbEpjaNBHwrPux6Gdsd9V3NasQHsy88h0Lhmai8vL6SkpEBqhx2YrTR5sQcFarJcpGVjGHIMjgU8wEOBvGhfhKmA+vp6Emkp5PxLPS+Y3LkVtNZkQ4S9/6V3RosVk03k3AAets71X1loBt7BkSgsLER5eTl+sT0SD5a6wVc7inB7KUphz+t1T2sJQwbAal8BIKm9CY0ReY9U4idvdzgBFXKixHUmx1Rdrv3zqRULOR/Agk8c6w2c0+LSqruqRnD+C/VgKAlCQ0ORkZGBHTt24ISibKQE8BHdu7XDojQiAIR7K5DsL55wlo1WvFnnDLC5NJ+HwH/QPOFSPv68rDC8clU2+V0qAcoTnQUNg9wpFIbw9zmzYhaR/FIUBW9vb9x4UjpxbjN6BgcGWCaQyspKQsdks9lwTlYYSS8DrNyLY6PzHSfF45WLE+EtY7+bunEjPmoVL0QAtsa4RbDI+PkHnS4nbaVcissELB7tkyu4/1MxSKUgxhel8X7YHssejwGFF/sUSEvPhFKpxNDQEPbv34+O1mZcmMq/jyYrjWf3uWZuf1rQdAwAj3w9IHKsFEXhFyeKe5Z0VuD+zwcBiVSU+bFarQj2kOKhM9kU6+5eLeZV0SgrK0N0dDSMRiPa29tRWVmJrq4uTE9Pi9SxueMcbzWt482OO6d1NEKQnBmNRtTX12N5eRmlpaUEwbeefY90Xpqm183Qztlfvx/A/d8v4K021wVaXr8KmJ6eRn19PWJjYwmbM2g2qlSvaEkqSJiy4xowbTSDVDvrOZdyNFttWFhYQENDA0JDQ5EQyUaDOrON1IUMglQiB2tfNlmJHDtXBzNYaFHUwIE6hGnNPjsARKlUIs5PDn+JAblZ6ciKtadvKHZbbyUwL5BW4CKt4UU9YgPEdYTvesT6UZzTItFFHx+N0TSN+bFB3J4pwde35DhRNw3Mi8EXRXF+UMok4MqJ+wcW0TOjwxPf8U22HEnpBQWx5G/jWhqdwzMkjdjb24u6ujoUhzpPOE/vG3KqDV1eGA4Pe1Fw2WjD9ieq8LvdzrLvwV5uPLs7DfyzRewYOB7By7PFzuzJ7w47oRx9VXJRZPl2jxXbt29HWloaKIpCb28v6Qm7rYTfLkAlJ+lbzhbm59Ha2ooHT+Wd7/991ut0ToqiRHW32RUz/uGQWuXspztiRejKLzpnMSDo7aMoCveWeOHSaD3SQthV0LzOgqerppGUlISSkhKUlZUhNDQUJcE2eMj45/L1unE0Dju/f5G+7jg9TUwocO0bLTCY+TmiIMYHBWFynBwlJcTNzWPLeKFqnMDpOXopANgW74tri9io+4HP+zCqNhF9tG3btiErKwvu7u4YGxtDVVUVGhsbMTQ0hJWVlX8LI8Z/uh13Tms9tpbj4RyKSqUiSr3r3Xc95+V6yNbD0M7ZW/Xj6Fmw4JtB102bXNOuycJy0mVlZRFUjru7O7Ymsys3vckMKcXOrtMaHZaWlsAwDB9p0SDig1qTFakP7kX2wwdQ3dCKLVu2IDExET52B6k2WOBuj8oGBEi9uEAVUkI94S6XIsKuayVcdbePu+7FyrQrA+/umMY1r7Xg4Y8bcfjwYeTm5iIsLAzZdqflbqeKUhsZDAwexv79+9HU1ASVRQ0AGF00IMNBZfgfB8XSIBxCkPOV/XM66ExWmC0WtLa2Ynl5GdeeVoSIIF8nReJgT3HN1F0uRZGdKUEumDHfa5oi3Hmc7UwNEiH9OFb3yMhITExMQK/XI9PHjN+U+aAkmp9A3OVSOPZ6y6VSXCtgttCabPiwZcqlTtZPBCm2XdUjZOHA8QhGRETgxIJ0UVMzzQB3ftjlVAO7aVsMTk9jv4sJjRG5jxwE3DyRkpKCsrIy0hO2xV0HhYQd4HGNCV+0jJAIaXp6mqD1duYli6DhN73VhpFFcVR2VkawqB3j2f3DLhWC/VQKnJ/Dp63d5Hytk2EYDAwMYGxsDHl5ebhUAAb5Z9sM9hxi07fu7u6IiopCSUEurt8mrkfd+k4rqhuaMTY2JoqQ7zlFDIPXm2m8Xse2UtA0jY6ODvwkXYrHLy/Goz9KI9v94+AIKgcWRJB6TrH5too45Ed7Q2+mcffHPVjRs+rbNE3Dy8sL8fHxKCgoQGlpKcLDw6HVatHc3AyNRoOpqSnMzs6uu7WHpmno9fr/Oa3j2WQymcsvdGJigjiUjIwMlyuWzTothmEwPs5KJKyXoZ0zrs4kBCIIzcP+ucXGoLi4GEFBQbDZbKQo6+HORnLuHl4I8WEdiUICtLW1Yf/+/TDqWadjtdngZ5d1MFr48dF4xiA8nF35cek/jcFKIqgxARDgN6cn46ObCnByShCpWZkEM66Qrob0Yi2bUWZvOt3dMYP6ETU+6VlBYWEhfH19AbDsEQCIhhXNAAEJGSgrK0NQUBCs2kWEqRik+EngL2WjEs6HHJpawZLeLDpvsJ36KcBDDpoBdjxRhbxHDsJG08jPzyeLlW0OTuuzDmdpih1J7GQvZK83WWm80yBmiQ/yVCIjnK8bfNo+jd7JJbT2DiA0NBQVFRUoyc9GfpQn8v34yEpjsEBCOzujC3LCRS8gzQD3fdLttN2pqUEkcp7XWvB195yIR5DjTLz75AQkCSKihCAVWZhwlhXpgz+es1UEtrjCXj/i6JhiY2NRUlSIi/P4dN1Dew5j3759qK+vR1dXF9LT0xESwkLHn72UbxpdMdlw01vtou9LKZPiwbO34MXLM8k5K56scin/cU2x2JE/sqef9JxNTEwgP59FnV6QE45AgYTJXf/sQs+0uDfx8sIoEaBoxULhzW4LZmbYCLm6uhr9/f1Q2gy4KEcM0IrwVYKmabS3t8NgMCA/Px8KhQInbAkUpTHv+eiQUxQtkUjgrlTgLxemI9BTgcF5A/707TBh5uBqYVarFXK5nKBZy8vLoVQqiVZWZWUlmpubSd/aarUwrqftf07rODZHx0PTNLq7u9HT04OcnJwjQuY36rSsViuam5uxtLQEmUy2boZ2zrg6k8Xm/NAZjUaodGwhPthLid5FC/62dxCHJjVEDE4u5VJ9NFH1lcoVRLtJav+8s+sQ5saHAQDzy7wjap3hXyofe6SjMViI01nSOa96Ad4pza/wE5C3Oz8Jck5tZtmEhCA2XcM5Gq0FkCv5KDc2wB0yCQWTYAxaRjUEgZWfn4+vfl6Ov124Ben+4r4zuZSCWVDzoSiKpAg5aL7eQsPGAIcRKmI3iQ9UIUzA3l03tCTicATYJuJATwVSQsUv/fOVI07RFtePRVEs7+LFr3VhV68CW7duhUwmI0rNN55dBpU9lcQA+NvuBjJJcurGId5KnJgiBnQcGFjEN93idChFUSJ14Ge/78NTn7di69atIh7B2AAVHjybJ6PtndERkI7Q3ORS3FbBI+xGFg0uaZQuEdTdNGagQ++N5eVlKBQKdHR0oKmpCSMjI0j0kyFEwB8Z5Kkg3JScnbQlCGUJAdhqr12arAxueLPNaSKOC1SJ7vXthgnsbegkPWfCifnBs8UyIde+3kr4EAEWVPSHc1Lwr58UIMoOfNk3rMeSRzR27NiB+Ph4mEwmFvkrmxKlJv/vsz58X9sCk8mEvLw80TN1zyn84mDZaMVdH3aJ6rP8OCjxxAVpkFIUPu+aw2eHlqBUKgmknlNs5sAc3FhERkaiqKiIcKGq1Wo0NDSQNPT8/LxoDjtWTuuPf/wjSktLoVKpyGLT0UZHR3HmmWdCpVIhODgY99xzzxEjwsXFRVx++eXw9vaGr68vrr/++qPS+jrunNZGa1pmsxlNTU1YWFhASUnJESVLNuq09Ho9amtrQdM0srOzN0XFxLGnO/qs5eVl1NbWItCHnfBNNgZXvNyMv+0fxgtVY2QsFPa+E7ONQZBdfVdrsoGiWO2mq7Yl4u6T43FGSSZCfdmXSWOiIRNEKtwLIYy0brYzL6gNFpQ8Vomch/eJJpFwX/ZcKyYb6VNqHePTg0KJERXFPricb2HAOgjO5FIJaQzmVH+bxzTomNAQTSiOium00mx4KqWE9cJsY1BdXY2WlhaMj4/DZDIRsIXZAY79gkMqkaIoUbRltjGod+h3ivB1w/47SvHMxemQC/JE2VHeUMrEkcrO1CBIKUqkRda3YMakA4JRJpHgrHS+ibVFo0JcXBxMJhNaW1uxf/9+dHZ2Yme8ykFiHnh6n7MI4cPnbiWIw2G1Ff8clqBD7ZxJyIr0Iceb05rxtYMD5OzKokgoBf1Mu6pH8a1D7TAhyAN50T4EzLKraRnLPgkoLy8nETLXTnJ2FP9O9c2siCTshfbmdbnk59qhJZf1rauL+dQfA+ChA/PIy8tzYsfZkRSASAFzit5ic6JROjklCMnBnrgin4+O7viwC0tGGqGhoUhPT8eOHTtwUkkudibyk77JSuOJumUkJCY69VwqZVL85cI0Ev12T2sxNO/ctgAA+TG++MWJbJryj1/1oXtaRyD1nGIzlxEymfgUotVqhVKpRGRkJLKyslBeXo4tW7aAoij09fWhsrISra2tePnll9HW1kbqakdjZrMZF110EW655RaXn9tsNpx55pkwm82orq7Ga6+9hldffRW//e1v1zzu5Zdfjq6uLnzzzTfYvXs3Dhw4gJtuumnT13ncOa31GEflxDFQSKVSl5RPq+27XqfFvZABAQHIy8uDUqncVGNzjH2yFrISzMzMoK6uDtHR0chMZVfHBrMNcvtEMr7ER0dc87HZRhNIuF5QKD4tNRjXlcYgzl8J/RKb2zfZKMLavqwzoLq6Gn19faAs7MulMVgIO/aC1gyN0QqTlRHRP3koZKQW4UfIavmVrISicOdJCfj1iZFYHu+DXAIIXbojaIBLEXJOZE/3LC7Z1Yx7PhZz3UklFGki5swakABfX19MTU2hsrIScg1bcxhd1IvqTNPLzrBqTrOLq1ntqh7Fmc/WiohXKYqCh1KGbQJ0XtOIxmkF7adSICdcDJ8GgD9+2ef0N2GD8PCSEZMWd6Snp2P79u3IysqCQqGAr2ka/5dphLAl6/ysMBG7OcACa87bIgZD3PuvbpFoI2fC9NVf9x52+bwqZBL8TEDJBAB//mbACZ7/0uVZuL+crZNaAPzmy2EMzetEPUoVFRW4uCwZKjubhtZM4zfv1WJyctIJHecul4n4C/+6d8gJdVgQ44vUME+EebLP74weeLnOGZlIURRuE/RRWWyMSykRALisKIpEUjQDXPxSI2w0r+rt4+ODu87IEDnb4RXg8d1tOHDgALq6ukR1psQgD9x3GquIbKEZl+hPzq4tjcYJyYGw2Bjc8WEnqecJFZs5vT1fX194e3s7RWEASBRfUlJCGpvfe+89/PjHP4bVasWdd96Jb775ZkN9YUL7/e9/jzvuuAMZGRkuP//6669x6NAhvPnmm8jOzsbpp5+Ohx56CM8++6zT98xZd3c3vvrqK7z00ksoKirCtm3b8Mwzz+Ddd9/F5KRzq8d67D/SaclkMqjVatTW1iI8PBw5OTlOq6HVbL1OS8jQvnXrVlJsBeAUbf3qo06c9bcavF3vrHYKgKwGaZpvRm5vb0dmZibi4uLgbvdmBouNgB6EL59CkB7k0mLcPKQ2mGG02jAzM4PGxkakJMSQlzMmgHUSXh4qJCUlwWKxYHSArZnozDYweg0kFCAstX3QLH6QOFg7VytzlCI5JVqKCNMo8tKSkRAsTk8ICXoBkL4end3hcvPjot7iJB2fFSmGtdeO6RAXF4eCggJs374dKSGe2BlB4+otDKId2lOqHHqZiuP8IKUokp6tG1ZjaMGAB79wdjQ7t/KpX53ZhgYBNRXDMDh8+DBSVCzQQBhM7OtfQJMD4W5coAqxgv6gX3zQiROeqiYRcnJyMraVlWFbWSkuzuSd5WtVg+jr6yNpRE6GJkG2CG+BvIvBQuPBz53v4dL8COLIR12wd3B2ZVEkUUIGgEvyw0kqmrvfgb4ehElWECVg+73y1RYR+4lMJkNkWCguK+AjpM/6jeg7PIIDBw6gvr4ehw8fJj1hfzhH3G/1u929oroqwzD4VZ4cDxXLEWAHefy9ctQJmQgAp6cFi56zOz7sckr/AuxCSAhdn10x45rXWkURbYinHOWRMgS5U2SB89kIBXlIPGQyGfr7+7Fv3z6SFj092Rs7twbBRjO4+6ND0Jlcp8kkFKt4HOnrhnG1Efd9IobyW61WtLa2QqlUIjs7G25uboSZwxFSbzabYbPZ4O7OsuPs2bMH77zzDnx9faHT6XDttdciICDgmHL9cVZTU4OMjAxSywSAU089FcvLy+jq6lp1H19fX+Tn55O/nXzyyZBIJKirq9vUdRx3TutI6UGGYaDVajE9PY3MzEwkJSVtSICNc1qrRUuuGNqF+wLObByfd86gf07nUnMJ4GXubQzQ0dFBmpGDg4Nhs9mI8B/NsLBkAKJJXMExZtho5NqRc2YbjfSH9qL0z1V4+st2UhyPj4sjHIJENVhnJpDb007cQSa0zt5++CrYceAcnZA/EODrWll2RN+y0Yo7PujE2X+vxeDgIHp7e5GdnY2IiAgCzea49Xod+A+5z0ccYO0A8EmbeCWdE2mnayK8hmxKz0bTGB4ehnppEb+7IA+3nV+B7UniGqO7YR5TU1NkherlJkOW/XjCvq2vD805rZArkgNEzujzTha4wTAMenp6MDY2hmtOzsZ1JVFOHHbTy84r3OvLePLbBZ0FM8sm/LNFvDBQqVS48aQ0Qsc1awCax7UkjVhVVYXp6WkU5eeK4OMA8EXXLL7sEoNL/FQK3LSNP+8dH3bi3Ofrna5NJpHgnlP42taug6Mk0uJQcxqNBvn5+fjbZbxEyKLegrs/6nJKxV2cF06eLRsD/HPCE+Xl5YiIiMDKygrpCZsfO4xiAboyJcSTRP02mw1tbW2wmY3Iz8/H7YJI6opXWzC3Ih5juVSCawTpxGWjFQ/s7nW6VwC4sihKxMbfNKbBn78eIOdtbW3FJckK7Pl5GR62O1YGwG/3jCEqLhFlZWUoLS0ladHa2lqcEaRGoEqKsSUDHnKxCOLMx12Opy5Kh0Iqwd6+BbxSzaaxuXq5TCZDZmYmQSNzzByOkHqKokj6kANzUBSFwMBAvPjiixgbG0N1dTWKi4tXvZbN2vT0tMhhASC/T0+7nvump6edNAplMhn8/f1X3edIdtw5rbWM05EyGAwIDQ11GsD12GrRErA6Qztn3APl6LS4SGg1PZ0gT3YlaGMY6HQ6FBcXw9PTk6gZc3pYABBqr1kJ00N8pMXAQyEjaERuyqgfViM/P588HP72qIirHenMvJOWSSX47hclaPz1dpxWUYa4YHYy5xbwE0sGHD58GFotK8PARVoyKQV/u0Pd0z2HwXkDqnrGUFBQQKRjuOI0RyXVNyOG+HPpwellE4HIc/aWA1Iv0+5kbDQDCcVO+Nl/3IeiR/ZjdnYWBQUF8PX1hVQqxYlpEaJ966ZZ+qv9+/ejsbERw8PDKLRPkr7u/FhbaMaJcNfHXY5CgUDh191zmFjS4ovKZiwuLqKwsBDhQX64+5REEdJNON5COy01CG4y8Wv2pz0DTpGlUiYVCSMenJOjrKwMKpUKVqsVUqkU9fX1SFXMozyGT4NTgMvI4ucnxpOeLLONQf+sDl90OiMnC2P9SAp4yWDFx61TZALX6/UEiZkU5IkEAVlx5cAi/rpXDN6I9HPHnScnoNRO3fRd7zyuebMDERERyMrKQkVFBekJqwjiI6vOqRV0jy+S81osFuTl5UGhUODi/EjSz2ajGVyyq8kJLn9hTpgIJfhJ+7TLqEwhk+CnO2Kd7p8j6qZpGqWFuXBTyHFmRiiRRJnUmPDwHpYZX6VSidKi2anJ+HmhNygAn3bM4G+76zExMeEyRZca5oX7TktCqLcSedG+ZD6TyWR2QJXr/ixXkHohmOP777/HzMwMGIZFgApprTajUny823+M09LpdKitrYXNZkNkZOSmGvCA1aOltRjaOeMIah335WDFK6ukBxJ8uWGmoPeJw1++H0bPJEvnIpFIIJNKSFE3NoCdGGiBfCIPxGAnp0B7r5G9hIAFq0LEyMHVn7gIj2bYojxnod5uUCmkkEgkiA1kJ/NAL9Y5eSkl0Gg0qKurQ3V1NeRmtj40oTaK6JMAoEnrJ0IscU6Ju/YlvUVUcwn3UeLL24pQfc82ZHMNwvbPTBZaVD/ydpMTRGKcfUzMNgZ6KxCckCHq/mcpefhHuXXGTPLnISEhWFxchOcKv7IV2vOVw04IwdPS2MWQTEJBa7LhlGca8IfqZRQUFIiei5RQTxEd04ctzjl6D6UMp6eLV5oGC41nXDA0COsz3/fO4/kvGjCxYkNZWRm2bduG0tJSJEQE46Z0GTztjbMMAMpqcpk5uN2BN+/+T3swpRGndymKEtXAnvz+MBqammGz2Yjj4Oy5H4trHeNLBifAyPWl0Xj2Ep6SqW1ihUjuSCQSBAQEICUlBVecvo3w/gHALe+0Yt++fdDpdEhISBCl+x87n++Nml424ZZ32kU1XQ+lDD8/IR73nZpI6sbXvdEKs9W5DHBxbjjRbQNYjbSmpiYAQG5urui8D52dQoBLH7VMOQFVZDIZgoODccH2bOIMX2nXo6V/DJWVlaitrcXAwABJ8wLARblh+PSWQmSEe6KlpQUSiWRNh+XKhPyIX375JV599VXce++9LjNOm1EpXs1CQ0MxMyNe+HC/r0ZhFxoaitlZMQWa1WrF4uLimrR3a9lx57RcDfz8/Dxqa2sJIEKhUBwVFRMgdlpHYmgXmisOQW6VJ+yiFx67r6OF/P7T9zrxet043myYICsdgO/lCrJDh00WHvDhqZShMMYX+dG+AEAYsVV2Z7bkoKjL1Z/8VAoC9T3v+XqkP7QXX3SKQ3IukuKio2UTjazsbMKy7W8vF/ROzMNfwq6OufRZw6gY8MA5tWW9lfApNg2p8dyBYbIKjPFXQUIJgBb2Y1lsNNHT4izHXtfyUor//mylGCGokEmQZx8bgJXy6JnRws3NDVFRUcjNzcUVZ+7AyxfF488n+YqAGyEeMtAOz9KJW1hSXqsg/aUxAwPzzoznFwr4Br8+NOcUQQHA+dlhTn97s35cJL8CALlRvtiRxIMUnmsz4ZkOmsCtVSoVYmJikJeXhysFUd7zB4YIGnFmZoY45tQwLxQKpFVMVhr3fHTICVxyQU4Y+U41Biu+HDIhICbFSRg10k+F7QIQBSsr4vy+KuVS3FzOX98fvux3qvdJJBJcJWiunjVQaF2Sw9fXF+3t7aL7KYv1FiEs28aXnQQ7f1wQgSuKogi4xGChce7zDU7X5qGU4UpBinVwXo+vhq3Iyclxchy+KjkeOS+V/P77z3udgCqc/aQ8FnnRPjBYGbwxIENJ2TbExMRAr9ejpaVFdD8yxkocVnZ29qYX4Lt378ZNN92EN998E7/61a9cbrNRleK1rKSkBB0dHSIn9M0338Db2xupqamr7qNWq8nCAGBFJWmaRlFR0QbulrfjzmkJjWEYDA0NoaWlBSkpKSJAxGadFkVRorrWehjaheYq0uKYIxyL2ENDQ2hra0NOVgZJEXHopP45vehc7vYUCMdMzoCFtQNsL9KrV+fgrxenY3FxERIzWysKsZ/XSjOil8nfXtNa0psRba8VaAxW0AzwSZt4pcT1fXEO0sYAk2ojpFIpgoODsT0nBblRPsiO9IGf3WnJ7FRMaoMV4ws82jDUWwkvpQw0QFJst37QiWf2DeFjB4RYcogH3OUS0os1r7M4sUFwYIx5tbg2trdvwYl5nEtJceP8l28GcMbfakndRSGXo3hrDEryslBoZ8AAWLbwygMH0NzcTOTsAz0VyI50Jh994HPnWsnZGfxq0UIz+Kpr1mmb3CgffHBDPs4Q0AUlBKqctMEA4JJsxzqZGd/0OB/zpu1xxNEMrwDSkEQoFAoMDg4SsMDo6Ch+UhYp2q95TOPEhRjq7YavfpoPrgXvg14LfiwgrBWaMHp7o24car0zepHdLp7USRkAt73b4cRmf3paMPzcZeQ+3ulnkLQ1lfQfKpVKDA4O4sCBAzgvnp/YZRI40XNxdn1pNFmUjCwa8KevnOtMVxRGkEUVAHzQZ8L0iuvU/o6kAFxgX3RIKMqlfBDA1l4f/VEqvN1k6JhcwfNVE0QNWXg/hw8fRmVlJVZWVuDn5weDwbBhNDIA7NmzB9deey1efvllnH/++Rve35WNjo6itbUVo6OjJFXb2tpKeqp27tyJ1NRUXHnllWhra8OePXvwm9/8Brfeeiuhsquvr0dKSgomJth0/9atW3HaaafhxhtvRH19PaqqqnDbbbfh0ksvJYQHG7Xj1mlxkgrDw8MoLCxERASfwjgW6sVms3ldDO06kwVXvNyIk5+qwqLO5DLS4iYfLlVC0zQ6OzvJtYeEhBD2dJmdhml8USeikuEirQAPBQELzKyYsKgzQ2tiJ4bJyUm0tLQgKpCdzDnABUWJHSYH5ljSWwi7NYeGOuTAGsBFYksGfjyFMg6xASo8cWYkTg1Uoyglyn4+e7EYQENdHerr6zE0NASdTodEe8pHSNsDQMSeDbAggHSHula9QEMLAOK9aER7AoXRXqL0n5Vm8F2PmP29NN6ffAYA1UNqDC8a8ELlMBztxC08cMNoA9yi0khh+ODBg6iqqkK8nK2JCIUhu6a0TizrId5KEQLvqe8HUfjoAfKdAexCKS3cC1cU8g5kcE6PWYeJUqvVgprtRbin+LX8zae9TtB2pUwqIg5+v2MJycnJKC0tRWlpKQIDAzE3NwftcDuu3cpP+AophfOyxc+6wWDAcHcbLhVIzK+YbHj82wE4Wlq4N0FE2hjgsa+dt+Hu+R6B4rDGaMUt77bDIAS+2Kz4Za4Evy9nFwh6C42CRyqxbLTCz88PSUlJKC0tRVlZGUvia0+AWGjg9jfrsbi46PQuUhSFn58gcKz1E07MJp5yCuXh/BjbaOCn77Sv6jx+fVoiovzcMKc14097nFWfOQv3ccND9obnXdWjqLb3H3LclfHx8VAqlfD29kZCQgJJwx88eFBEWHwk27t3L6688ko8//zzuPjii4+4/Xrtt7/9LXJycvDAAw9Aq9UiJycHOTk5aGxsBMDOu7t374ZUKkVJSQmuuOIKXHXVVXjwwQfJMfR6PXp7ewkICgDeeustpKSk4KSTTsIZZ5yBbdu24R//+Memr/O4c1oURREGdb1ej5KSEvj4iOHPRyMvAoD0RKyHoV0plaBhRI2xJQNqDi+5jPJCvO10R1aagDlWVlZQUlICLy8v2Gw2kkL0VfENuxzZ6uDgIAFCSCgKMfYaznnP12PbX6rw3IER9Pf3E6RebCg7QXN1MBsNEW2Ov8BpcZEWF4Es6i2iFTQXJc5rzYTT7426MZzxbC0+bZvCyMgIOjs7kZ6ejm0ZCaAAwmpBA0jPzUd4eDjUajXq6urgTbOrMq5vg1v19s/pnAADjpIPVQMLLNUNzWBqagqLIz14+dIt+MNF+aL0HwAUx4p/Twr2QICHQpTSA1iiVMfJSMjRBwD7BpcRGxuLgoICpKens89FlDtC3BlEOZAMuFLzvaKId0aLeiu0Jhvu+ciZjikr0pvUIxmIa2Acj2BkZCSuKRPXGFaMVvz5m0E42s9OiCMgne965lH06AEcmloWpRF37NiBK8uTEeDO9foxeHtvO0kj6nQ6NDQ0IDAwED87K0+Uin2tdhwHB9a+30/apzHjAjUJAFcWi9F6xbF+5Dk0Go1obGxEfLA3frQ9Fyl2pgyaAS7Z1UTqtwDLJ5gUH4urSnhEZOWoAV/XsjRm7e3top6w68qiybsBAH/4so+gQC0WC5qbm3Fusgof3pCLK+0Lif45PV6pcd2y4qGQ4eFzt4IC8HHrtJMCs9BO2RqES/LYCOLXn3QTcBaHiqRpGnl5eYiOjkZOTg4qKiqIBllPTw8hLHbkRyT3XVmJSy+9FE8//TSuuOKKDSGnj2SvvvoqabEQ/uNUiQEgJiYGX3zxBfR6Pebm5vD444+LyikVFRVgGAaxsbHkb/7+/nj77bexsrICjUaDl19++ajYO447p2U2m1FTUwNvb28UFhY6Ed4CR0d6q1arYTab4ebmti6GdpmAEaF9XOMyPRhupwmy2hgC5igsLCS1N4ZhSKQVwBG2UhR27NiB2NhY6HQ6WI1sfaN/eAx+buKv5UD3JGZmZlBYWIiAgAAy8QlTgoNzPElpYawfHjgzGZcXRCDaLtQos0daDCOOpAI85CSK4VgmmsaWMbxgwAv7BzA8PIy8vDwEBwfDXS510iXqmNIjMjKSvIDZcWwKbGRmCXIJj3CkGWB/v/hl5xCCQXZo/qcdM0j/wz785PV69PT0EJVhgCcCJmPiIDFCURRK7CnCQAEp7rLRioOD4m0jfN1EoJI93XOYWzGi9tBh0jpwRkUpvrurAr89I0m0bwCtdmqaPSXFGSF4oH/BKR1GURQuEHDcvd80AStNO/EInpcdhghfpaj2Vje05JQSLYjxw/VlfF1oxWTDLe+IpVw4lpGfCmibPujWoX9gAPv27UNNTQ08PDwQHR0NpUyKu05OFJ3jno+dufXOTA8htS0GwFWvNeMfLiJaiqJwqwCtV2l3gAaDAY2NjfD19SWk069fwzNljC0Z8X+f9TgtNi7NjxA1/7466Ia8vDx4eHhgbGyM9ISNDg/jxmJ+nBmwQCLOYSkUClQU5SA13Ad3nZJAUvJPfn8YE2rXzBZ50b64toTNNDywu8clsTFnv9qZiMQgDxjMNvTP6kDTNNra2mC1Wp16SqVSKQIDA5GSkoJt27YRwmIhP+Jzzz2Hr776CpWVlbjooovw2GOP4dprrz2mDus/yY47p6VQKJCTk4O0tLRVGdQ3mx6cnJxEQ0MDFAoFIiMj160Qyr0nA3M6l+nBghh2smQADJq98K8xJSHHBOx6RnanFWqPyijwKsCZmZkIC2LhyQYLDZmZdSrcPDits6GwsJAg5jho9bLRSvqYhGShScEeuCQvAgWxfqT/RS+QH/EXFLUpiiLRlqedL46rRYwvW1FYWCiKdLleK26bqkGeEkkqlWJnZgzuPy0Jvzkvh/DMcbanqZ/QMAF8pDWntUBCsYwGDAM0jLNQaw5KDzg7ra8OOdd5uLqWI4OQo9w6wDoa7j6W9BbseLIGd3wyguzsbJIqlkgkKEiKQIygp+zAhJVMkA0NDRgeHgasJpzkwCHIAHj4K+dUkrCnaE5rwXvV/Xj7ezGPoKdShj23l+AUQaNzZoS3iNCXs8sKxBP5nNbslIoFWIFKbrsZHY0FtwiC5gOA6upqVFdXI9NTJ4q2NAYrPu8Uj7WPuxzPX5aFk+33PLZkxF/3DmFS4wxUuSCHP+/okhGftIyjsbERAQEBSE1NJROvp1JGIOYA8Fn7DF6oFFM8BXsp8aPsMNIaMLpkxEv1s0hISEBRURHpCVteXkaMdVyU2jWZLWhqaoJSqURWVhZ59xVSCZ65OB0AC6m/9V1n/TbObj8hDknBHljQWfC7z3tXTSe6yaV48sI0/POmAhTG+BCH5YhOdDQhYXF+fj7hR2xubsbVV1+N0047DUlJSfD09MTCguuG8f8X7LhzWgBLV7LWKmKj6UGGYdDb24tDhw4hOzsbKpVqQxyCXApmUmN0ivIYhsHK/CQBJzx6cBHvNk2iYYSdzDnSWy87/yDnvMw2MXhCZZ+Q/IJCkJXEpkE47Sm9hUFnZycmJiZgNptJJLGgs+DCHHaC/fuBYWT8YS8e2SMuPHM1K6MgNXfYAbXGMXZwZTGOI9FCAwsO8xAHa+fqZr2zK6gdWsL3vSwcOC5QhcsLI5Ed5YvcGDHDerifByYnJ1FZWYn6+npo5ycR5q2wXyfvGMw0MLoinhBSQj1FEu+NI2pRCglgVZp/fkIc7jxJLLdOgXJCfTmmCAFAYwE0tDiypygK5wrAFi1TRmzJyMW2bdsQFhaGpaUl1NbWIkHqPIns618Q8S8C7IQvVD3+4/cTeL5bAp8AMSxeQlG4rSKW/L7n0KxLiqIADwXOyxLXqJ74btBJtFEhleCEZP68T+89DO/QGOTk5JA0Ynx8PKwWMx7I4cVGKQBnpbnm83zgTJ6clwFw/RutTu0DXm4ynJ3Jg0v++NUAAoOCkZKS4vSOP36BGIH29L4htDtQcv3+rC147eocRPux2Y2Xqsfw0BcsQEapVBIE8OknV+CCTP66Xzg4inmtAf7+/k6UQ5mRPrjQHgH3zerwqkBAU2hKmRSPnLcVMgmFb3vmXSoGcJYQ5IFIXyXa2tpgNps3xNrDGRcl33rrrZBKpbj11ltxxhln4KmnnkJoaCieeOKJDR3vv8WOS6d1JNtIepDrOJ+ZmSGyHxtNL3KpPZpmROlBjj1jeHgYfg6NpXt7F0SRHOeshKilGUHa5bdnJKP67m24JC8cHhK2iMkpAjOgYJZ7YWJiAgcOHMDEIFsvmdeaierxstEKGw3s6xenwtzkUoSQhmX2b3UOrBeRHAfhigHeDujXzx0aUrlIi0tzHp434Lo3WnHfJ84NihwLBXfetnmGqBpHRERArVYjTMFGXXKrOC3jSKQqoSgU2RFjMgkFs43B7e924OrX+HaCIE8lbi6PxdkZIeSe2bGxiIAqALA11BNh3konEuOHXRTahX1WNMNGeW5ubqK06DlFW3Busgq5gvldIaUQoHJ+xYSADIBiSWG/cEYmJgZ5EoZ6G8OmE12ZUJcLYCPWe//V7QRtv//0JGSFseniKT2Fqz4cxXuN7DG5CTI9PR2nnbwDuZHs4oQB8Lv3awgaUVhnCfBQ4OJcPg03smhwGV1eKpA40VmBA3NKl4tSpUyKHwkccGKQh0gKRmjP/Tib/PxO4yT29op7qCQSCW7YkUSePRoUXu5TYGZmBgcPHkRNTY2oh+re05IIeKjZoZVDaFtDvUjK8w9f9jn1vXHGyZqYTCbk5uY6tQ+s1zo7O3H22Wfjl7/8JZ555hk89NBDaGpqwtjY2DEFYfwn2XHptI6Uq+VSdEeCigoZ2ktKSkjxT+i0vuycxuW7GnH7u22rHocDRkglFDm32WxGY2MjNBoNiouLEegpdgwdk2KUHuf4GAbwcmOd0fiSEQs6lkvMVyWHj7sMk+Nj0M2xE4lUxnuQjOQ4MuEnx7AThZVmsDQ5zJ7Xvp2rlyjaXofi5CIcr+3m8hh8fn0q8uRTSAwQRxqORfYtIewYjszriaYVwDpNZ/5A1mlx2IjOyWUwDENWxDk5OdiRxk64DiUhfN87jyWd+NxcipBjtagcXETDiNplw+wOgbzF0ILB6T5sNhvS/e0sIYLHrfrwEvod6Kdi/FWIE6QInz8whJLHKjG7Ytf9kkoRHhqCP11ahPvOyyPbmW0M9lc3iCZ8hmHgaZhBqAPa/dP2GZEYJ2fCCf/thnGn6BJgo9tbtseIwAeu4PQ27SJOChKf4+E9/SIQD8BO+PfsTCa/H5yWQObhh7m5OZEOlVqtxt0niwUUP2ieciJKjvZkcFMqEOHFPn9/3TuEk/9a7fL9/ekOHvk3MKdDv4sx4e45LYwv5v/8gy50ToqdTaC7BPnB/Jj0zJux5BlDaskGg4H0UA32duP/TmTfq+/75p0iZKFdXxaNrAhvaE023P9pj1M6kaPAMhqNTrImG7Hu7m6cddZZuO2225yah8PCwkQUc/8v2XHptI5kXJi9VrTkyNAufHCEdal3GibQOKrGd2sggrhVu8ZohUQigcFgQG1tLeRyOQFzcKs0LpU45lCE55yWwUITZd7r3mxD+V+q8F3fAmiaRk9PD4aHh1GavRWAmH/w8Dyb7lEqlYiPiSapsoRoFqjAkd5abAx6R6dF6U/OaXEIraF5HS55qZEwkxvVczjc04mUlC1EYZh7MIwOiL8oP3e4ySQw2RgkOkiwf+TACBHq7SaKeAwWGgNzOoJe1Gg0CDOP4cWzQ/D6zdtEtRkvOVBffVAkR8I5rSWDeJJ99Gvn1X2FQFYegIjNgFtw5IWwY+jhJk7bvFbrjCK7SNBEPKu1QGO0ukQIpoZ7k4ZtABhECIKCgsiEv2/fPkxNTeGCLHE/FgOWONZxIr+6JJIAZRb1Vlz4QgPu+rDT6by3V8Tj0nz+Gqc0RlGz9tjYGLq7u/Gj8izC6wiwz8uv/9XtdN6MCG+yKLExwOfDVlEakZNYaaytwjlJYnCO0Amq1Wo0NzfjRwXxeFoQHU1qTC41vCJ83UQs8Hf/0zURKwC8djV/PCvN4CdvtxPwi8lkQmNjI85P9cY9J8eTOu59n/SAoSQICwtDRkaGqIfK2ziDshD2eb/vX51YULuOuGQSCf503la4ySSoHVoSQeq5dhe9Xn9UEVZfXx/OOussXH/99fjd7373/yzowpX9RzotroN8NTCGK4Z2x/151gB2tWajmVUjN07sUGtiSSonJiYQGhpKCro2m41QJ3EpvWWj+Nq49OCy0Sqa1ABgd/s0WlpaoFarUVhYiITwQKdjtDpwqeVE+qAwxhdJkcFO2kUfVPVg//796OjowPT0NCLsKSbOsc7rLOiYXMHHbVMYGBhAX18fcnJyEB4ejmR7JMWR9DpyuEklFJFA5zgVOXvXgccP4KOtMDsT90UvNiLrj/swNTOLpqYmZCTHoywnFZ5KuUiORGMGcvJZfkGuDjYz0IFAFStdLxTs+753wYlhuzjOT0SOu+fQLCbVBozNqVFfXw+VSoVLTszDBzfk49Hztor23blVXF8CgNPSguE4bTSOqDGhdo5sfyxwHl91LyIgNBzZ2dnw9/eHVCqFn58fopkZOPIgNI9p0DQqHm+FVCpiJx+Y1+PLQ3OY0jij3G7bEUeusXZYjd4ZNmIcGhrCwMAAcnNzERAQIKKLAlgeQcfoiKIoXF0cRcbwnYYJ/ObTbhhsIGlETmLl2rwACEjg4W7TwmAwYHFxEc3NzUhMTERMTAy2hnqhMJYH9bxYxfcyCU2YPh2Y02OPC9ANAKgUcpFg5KLegherRmEymdDU1AQvLy+cVZaFa0tj8LeLWQoqvZmNjjjjeqi4nrCHLylEoEqKqRUrHviwAVVVVejt7XXqCYsNUOFuO9nwE98dhlpvIQ5Lp9M5UWBtxA4fPoyzzjoLP/7xj/HHP/7xfw7LwY5Lp3WkL2k1DsC1GNqFJoy0iuL4Vd3cKlo8J9mbUWkrjb29c2jSsFIfNE2T4/jbazw+duckbIYF+EhrxcgrBnNzassIW//iuO183GUk1cMxpvfMiFNWf/9xJl69OgcpoV4kkuIsOj4eubm5cHd3x9DQEFYm2R6fsfkV+Kn4qEJvpnF4bBIFBQXw92fHIcXutCz2+xpdMjrVRnKifJAb5YPEIHEKanjB4AQF5hyRyu7MzTYGNAM8900n0tLSEBPD994UCXqvGADtMybExfFp0cjISGz1Z8dDOElaacaJ989NLkWZQPyxcVSDk5+uxW1vtyAoKAgZGRlQymVIC/dCcbw/PAX9RF91ORfYQ73dSB+b8Bof/8a5ufZHAtomo5XG7rZJvPddPaxWK0pKSpCdnY0zT96BZy5IxPlb+WjVV0khVKp1Ils9LyvM6Xm665+HnBZZHkqZyPG/Uj2KgYEBjIyMIC8vj6jRFsf5ke+Zsz/t6Xdit7i2JJooIdsY4KPWaVz3eiv5XDjh334S3xrw95pZHKg8iKamJvj6+sLLy4tc658FPIIA8IsPO52yEmUJ/ojycyOLsd993uuSIg0Qg0EA4Iq8YDQ2NsLb25vA6QEgO8qHgG8+75zFoWnXUVSQrxceOocFhOybksDmEwmr1YqOjg6nnrBL8yNwQU4YXrgsEz7uMnR1dR21wxoZGcGZZ56J8847D48//vi6Ec7/L9l/7Ig4gim4lM9qDO2r7SvUbao77BpGyrE8mBngHz1SvN5lIgztnAPlohiOPFfr8JJ52+tJKyYridy4l1JjBrKyskjak6IoBNvTamfb5S++OTSHrD/sw9N7nVMqHMkuF3DNaEzw8fFBYmIiSkpKcFIRKysxoTEjWC6emOZVMaJGv9hAFWQSCjaaZdqw0QzqDi+JQAD3nZaEN6/NxeWFkeKUnlJKSHo54yItR+TbwTmFE0u/IzWPsLjO1cHOzE9EQqCK1NY4O9A1LuLdA4AdSewkJZzwB5eBqLgE0cJIIZXgZAG8fE/3nEvm9DPsSsTCJdWe7jlR3xvAcj5y8HsA+P2Xg/hDrRFhiWkkXSSRSFCRFoXbT+UlP9QmBvu7xgi6cmhoCFqtFp5KqcgRAkDr+DL2uFAlFrKYf9Yxg87BcaRn5Yoa6CmKwsPnbkWSYNFxemowQYQK7cz0UCI8CgCHprUuI5/zs8MI68qSCWialyAqKgoKhUKk1Ezr1DhlCx8daU023P5eh4gAV0JR+PDGAjx7CRsdaQxW5D1yAHv7nO832EspirbueK8Nvr6+hE1eaI+fv5UsBm97t3PVzMqO5ECcmR4CmgGeODiDpJSt2L59u1NP2P/H3lWGSXFm3VNt4+7u7i7AMEhwi5FkAxEIUWJkky/ZjSe72biRjQciEAMCCTI4M8AM4+7uPe4zPdNS34/qqq7q6h4sJLCZ8zw8u+mprq5qee977z33nLzcXGwINYC/lQBlZWUYGRm5rIDV3t6OZcuWYcmSJfjggw9mApYeXLPvCrvERyu0i8VivQrt2s+lgxZbbqhEi6AAUBP0VaXFYLGtQQIYGZcxdHZAo/cnVP+3UkXZ2NPwsDHGAymeWBfvChcL2hSSWhgVKqBPa5dLLxTm6rLj6JQSchWp0z6dVkGnB1yLte7Dz4laQIenSER4cUtf32U2Mk11kiQhEQqY8h8dGDbtLMGLB2p4tHFDsRCBLAmjkUklj/AQ7GQGkYDA0IQChqx6mHREziutRbqZMz1BADhR1Yvbt+Xju2xNj2llmAN+ezABzyzn0qOdzcWoq6tDeno60wdL8qCubUKuuW4VCezI4bPwFgdr3pcJuYo3kAxQBpEWRiK4spTdBQTQreXxBGgzBCk89QufYelgboBQFkOuaMyCYVfSMj9nz55Fkg2/CvDKwRqeTcdsH2ske2nmBp/NIXH9VyW85wY6muLZZRqyxdEqvrcYQJWD/76Qq9Dxj32VvM/OUCzk+IvtbxPDx8+fU0aktQSXWHVDwlp5ghzNOCQSgKpMzPGz4SjBP/ZzOc9PCwA2JGvYkw3DJPoNHHVWawzFIjy3lLpn6fCkXnNMAHhmsS+sjMWo6R7Dl2dbqFlLtfwSeyZsaGgIOTk56Orqgrm5OYaGhi5J+EAqlWLZsmVITU3Ff//735mANQ2uynfmQmq49KzW+RTaXz9cg9u+zEVzn4aFxA5aBEEwdXtjCbfLMDY2hqysLAgEAjhZckthXx44i4KCArS0tEAmkzGNXrmKZKxEWvonMKFWMXCxNMTDqV64IdIJiiGq/KQgNfeprZZNZ1ra74W2ygIAxlBRrP7hN/ZyZ3TMDEXYmOyOR1NcIBmjgh591p5JSjarsLAQGRkZqKiogIcF9R7S2SENbfNCAJxyFACca+Qu9kZiIUOTtzfm3suPWhRuSk+POp9IQGBoUonC1mF8zBoypd8PH1tjTm8wu1OBWbNmITExEVZWVujo6EB1UQ7cuQkZAOCLs828eaIkbytORvbW0VrMfusMJuSazM3aRILTT8zilKRUJN+hGQAiHQ144wPF7cO8uSMAuJdlFJlW3oVRBcF8n1NTUxEQEAAnUwGCrcBZ7GVyJVr6ud8HkiRxoxc3SPeNyXUalMZ5WDGSXyOTSp7tPY0lIQ5wZDFFJ+QqPLmnnFc2XuptiBArtZiyTIk1n+TgzaN1vL7R3NnJuCVck4kWNEhRX1fLsfCg8eGt4cz/lytJ3LezhPfZBdlK4MriBD2zt1LvgPANUU6M8stnZ5r1HmdtIsE/llAlz48zmnjMTgMDAzg7O0MsFsPQ0BAhISEQiUQcKaa2tjbIZLop8Wx0d3dj+fLliI+PxxdffHHJqu9/FVyVQetCIBAI0NHRcV6F9q8yW1DQMoRfijQ/Wu3SIp3NNLEWe9qZ1MHBAVFRUUwQoSEVOsDW1hbd3d04c+YMOpsoBtvwhJwxOLz1y3zE/Oe0RkFdqURZWRkmBrogEhBg/1x6tXaQdKY1Nqng6LdNKUnezt7Dmi4PaqxKNn5biBd+0+zs14WZwk/VhpQQSpGBfm0LQzHDogoLC4NQKISZklpYh0a4fTRdGUqEKzdopdf2MZplAKUxF2s1iZsDDLBAy6xRqT0kBTCzWOxS1cC4nCevQxAEUnw1ZaG2QRnaBiZgYmLC6Ai6uroyi6iBUPNaI5NK7C7gBmCJUMDJtpr6Zegfl+PF/dxhbZFAgARPK86gs7aC/eDgIIoK8rHQhz9j9LIOJYX5gbYwVgsmK0ng6V8qsH57AQDqu2pnZ4fg4GB8c98cvLhUQ6JQKpQYaatGc3MzxsfHGWVua7Ecc325pdYXD1TzqO0AJY1E47PTTTqtNwQEgf9bzJV3KmkfRgGLNNLe3o6p7ka8vzaM8atq7JvA9qxWXlZmbGyMR5aEMmouraNATssIp4xIl3pdLI04aiNVXaN46UAN8x5OTEwgPz8fq4PMEe5Cvd/943J8ksGd82Pj7RtDYCwRorh9GLsKdAdqAFgWYo9UPxsoVCSe+7WKo9lJkiQqKiowOEgZsDo5OfGkmDo7O3XOhLHR29uLlStXIjQ0FNu3b58JWBeAazJoKZVKyGQy9Pb2TqvQDmjIDmz/He2gRe8229XzPmz2ob+/P1QqFezVRAu6hVPeNQ4PDw/ExsYiJSUFfm5Uz6FvVAYDObc8V9RK6R3m5+djYmICiQkJcLLgMggL27g7cDpI9oxOMeU6GkcquT0Fuqc1OC5naMpZjYPYWyIFSZJoampCeXk5wsLCkBTqw9HJk45MYkqhgkAggLW1NQICArAkkZK1GZ4iOf2bht5x9AxxA1mEC7VrpfcLRyt7EPrqKbx3gnI/zs3NxcpgK7xwcyKWh3E/p0Gt0hYAJKhLW9ozXx+lN/GOTdFBa2/qG4dKpUJFRQV6enqwfl4Ybo52xoJAbllULq3j+U/RQYtNxjxU3s3T/BMKCKwO19zLryVdzPxUX18fCgoK4O3tjbtTuaxEgOoJacsiCQgCD7B6UWcbBpDfMoQMLa1GY4kIK6M8GCffKZJA6YgR+vr6kJmZiVOnTmF0dBS+vr64Z7Yn57kTchVePcQfDbgz0Y35jXSPyrG7oAO/lvAX8uuC7PHUdRqlESdzA0ZOqbW1FdXV1RQD1cGOw3YkAdy3o5gXDE0kIixlbRK+qZjCHK0yIm2x8kiMKed7uLdYip8KOjA+Po68vDzY2dnhwaXR+GFjLEMw+eR0E0dpnw0HcwM8omZQvnO8Xq/jOEEQeH65P0zUAW6n2sySJElUVlZiYGAAMTExHH1UthRTXFyczpmwn376Cdu2bUN9fT1Wr14NHx8f7Nix45Lp8X81XJVBa7ryIK0ATysJT6fQDmhKfk16yoMAsEBNcSZUSnx7LB/nSmoQExMDFxcXhiFIBxGaCs52P5VIJAj0pnoYMiWBAFfuYvrJsQpkZmbCwMAAMTExFKlAXdqiS5PalPaFgbb45s4oPL3Ylzcoqm1jb2MihpmBCCQ0WRdAlVPOFpShubkZsbGxlBqIgGBo7QBVPtImEgQ4UDtW6agSXqzXJgF8sj8b586dQ0MDFZRcLQ1gbSwGSVIbBCVJDVDvKaR0Hp2dnREcHAyBQIAAB1OOLbquvlGosxmMxELI5NxF7kBZF4/WnuBpyaG1v3G0Hss+ysbPJ/MwPEw5DUd62uGlFQHYyCrBAYDAxh0SiYTTB3OXjMPMQAi2ULxCRWK7DvVvtrHjsEyBjNo+nKtoQlGRRkfQx84EN0Q6cUgZEhHBYUlqzufIG7B+Zl8VrwQnFBBYHqrpHR2oGUNISAhMTExgYmLCmCiONBTB14q7az9U3s1z3zUzFOGz2zVkkJcP1eLpvXyHY4GaAk/3ntqHJpFR14+mpiaGTk+Tn9bGOMOaxVJt6BvXOZPFVotvG5ThYHk3p4yYnJwMOzs7DA/2Y6mr5vdqJCaQ7GaM/Px82NvbIyAggFkzPlgbyhh4/n13Be81afwt3gWBjqYYlinw9jG+gj4NR3ND/P06HxiJBRALKXv7qqoq9Pf3IzY2VqegNxu0vih7Jqy3txdvvPEGwsPD0drailmzZlEaljO4IFyVQUsfBgcHkZWVBTMzM9jY2FxQ78teXWZju/tqB63Zamp0SecYXj09gNMjNrCysoJSqYRSqYRAIGD6FrTvVd8YdxdnbihiSBhu9tSPl7668p5JqFQq9Pf3o7KyEt3d3XBWn49mLx6t7EHkv06hUi1862RhiFgPSziaGzKZFn0+gdZcFkEQTF/LRou9t7e8H/Hx8ZzgziZPAEBOYx8OsHTUbE0l+GBtKA48lMDU/2mkxIbC3d0dIyMjyM7ORmZmJnwsBcw10+gbl8PW1Qs+PhqmnlBAMLtzgMoitXt0YqEAsWq3Xbawr1xJ4piWh5ahWMgT0gWAr0rGeQtKoIMpM68GAEdrh+Dv78/pg/V0dSLYnL87/zKzhUd48Hcw5YwaPPJTGTbsaoCTTxCcnDQB7dVVgXh+maYHNqUgeeMLAMU4XBLCZVMOjMuxU0dJ9hGWX1Rd7zi+O5oLI2NjxMfHc0wH14ZawImVpEuEBBx0yEoleVsjUqvMe/9Ovr8UQRAMmxUA3jpchcbGRg6dHqBKqE8v9uc8d1tWK07VcD+/MGczfHRrKJP5P/drFeJfz8C4OrM1NjaGu7s7YmJi8OrtKcwGZUJOYs+pfAiFQlhYWHB+y65WRowWY0ZdP8o7+eQq+hpfWOYPAlTmlqfl5cbGzdHOOPBQAm6JcUZ1dTV6e3t5GdaFgO7trV+/Hs7Ozpg1axb++c9/4vjx40xQm8H5cc0ELVqh3cvLC6GhoRcsmksz4KaUJENj1g5aVhLuj7NCOsZQ2mmGoL2Wfl+fFoVbQBCwNhHD3FDEyAzRGJoikJqaiqioKEgkEtTU1GBygOqpEEpqMZyQqzClJPFVJl+s08eWay9S0sZv5tNSUwYibkArGhDz2JR0CcVDzYJ771QznvylAq0Dmp7ewkA7eNoYI1IraDUPTMHZ2RkREREMScDXitpVK6bYu3MC6e38/ghNa6ffx9fSarDh20LOMffP8cQ3d0ZxMgoAnIyFhi5ae+sIif4JvjngIlY5Kr9liCkL0X2w+Ph43D6XmiNi/zBkchXSilt4Isu0bxL7nt/M4M94uVsbMWokAPCzjiFsALiVdz7g/ZMN6Bnl9jCtTQw42drWYjnW/9rP0MbpxXHdvAjsemAW815PKUlk5RUiOzsb9fX1GBkZYQLTQyz5JIDyP/upgH+dm1llzPr+Kajs/XVWO5aF2nOkrwCgsJVbTSAIAvP87fD4Am/m+kYnlbh/J5/taGggwd/iNJnZ7mYxrKxt0NDQwHFqnpiYwAvL/Znvw+M/89VDaES4WuAmtdLJSwerdUpkAdRv28HMANXV1ejp6UFsbOx5Gcr6MDY2hptvvhmGhoY4fPgwHn/8caSlpaGvrw/vv//+JZ3zr4arPmhpK7R7enqCIIgLtieJVZsHEtAslOyg1d/fj4byAo7CQvfIJEiS5FDaGUkbde1IplBhbErB2Y0efywJ556ag1neGsoxQP04x6ZUsLS0ZHb3ccFUf2BwlMv006V5RmdaCjVxobl/nLcL9rKh5qvEBPeH1zE0yWvAB6kzrUEt1Y73T/AtPKJcuUGLLXdFkwQWRVMsq3EFN2B+d66FUaankehlCUBDGjlZ249zjYOo79FkH1FuFoj1sORJMaXrMGBM8aOyZO1y4udn+Y34RaxZLBLAQz+U4vpPc7jnC7DH88v8cX8Kt5xoMtKCjIwMTh9seYg9zwYlu2mQp7AOAHeyRG1P1vTyAhFA3Tfb5wugNjN7Cvk9pr+nahbwSSVVln1iD78cZmUs4cy/lUzZwc3Njek3njlzBlVVVQiwUCFA67X/c7iOx060MzNAtJMmY/3v2XZs/LYQk1p0eQFB4NcHEjibqCRvruo/jdviXGDDyqrzWoZ4kmAAcFO45vvQO6FC+YQZp4xIS2Xl5WRjUzT1vW0blE2rI/j4Am9YG4tR3zOOb3TIdwHUGlRTU3PZAWtiYgJr164FSZL49ddfGashADA1NUVkZOQlnfevhqsyaNGBQpdCO40LzbTCXS0BUIsUXSKkg1Zrayvy8/MRGBAAZ0uWNYaSxIRCxSk/0pnW6KSS+YHF/+c0Vn2sWfREAgEUCgVqK8s4dGeFimSyJPr+vB2o65ogDOHAohP3jk3hbNY5ZrAUoGy8DUQCJghOyFV4+MdSfMFamO9KcsORe0Mwz7wHVqyBKCOxABZamZ+/gykEBDW0ye4JZehwqfW0MeIw5Urbh3k04SAHY6p0SZKc800oSBTXUot9Xl4eWlpa4GIqgLWxmOcw/FoaX1kixt2SQxo5WMbPYoxJGRyNAVLr8V0FHTyiR5iLOUOoASgGXHXXGApaNIuaRCjArbEuzA6cxpCFHyIjI2FgYMD0waqKchBgybskvKlDJWNJsB0ziK0kgTeP1OOub7gZJkEQeGKBDx5kBUwC4CifA8Dw8DB6GsoR7cQtT52u6+f1JwHgSRaB4kRNP+SGloiIiMDcuXMRFBTEMOFSbLhly0mFCi8d0KjP0/2cewMUCLCnfi+V0jFkNQ7iwR9Kea8rFBBYzbr2d4/X6xzoFQkEzPwUjZcO1jAyVAAwOjqK1uoSxDgbMmXyN4/UYVKh5JQRaW3E2S4ipKirtC/+WoqOTqnOTa6lkZiRY/o4o4nHdCRJErW1tejq6kJMTMwlByyZTIbbbrsNExMT2L9/P8zMdKvXz+D8uCqDFqBfoZ3GhdqL0Mw6QGOUKBAImN1TdHQ0XF1dGfdhGvlaNW5bUwnT4KV7GSSAFlZPZmJiArm5uVCpVPCx55ZMqqXcxYRWxegcknGIEQCBfqE1Y19/9uxZNNTXMf5BNIHjRE0fvjirKSX2dXWivLQEgYGBCHez1FyTXIWeEW6mZSQWMu8L24l4dFKJeq15FIIgEKnOtoQElWFG/Tsdt36ZD4Aavq4uL8G7qUZI35KMKFY5UUAQWJySiNmzZ8PBwQE9PT3IzMyEtxn1uRmzSnrZTQMcVQSAIr0ks6SYcpuHMDIpR686S+nu7kZhYSHm+FhBAMCCFazFQgFPlkhAEBzlCxovHKjhPeZobsiYVAJU38PS0hJ+fn5ITEyEubk5SJLEbFe++sHJmj6eZqOBSIibWDYe+8u6kNM0iN9KufNTKX42eCDFC2aMLQ0ln0RjYGAA+fn58PT0xH3zuAs9QA3+KrU2BEGOZhw352+z21DYOgiBQABbW1sEBQVhzpw5uHtRDFYHcBfThR5ijI2NMYGtr68PcXFx2LKQ6+ic1TiA3CZ+RvP4fG8myJR2UN5runBdkB08rDVBWK4k8djPZRidVGB0dBT5+flwdXXF+7dF48UV1H2PTCrxmZZRJNti5bW/zYKZgRDNQ0psP13LKyPSWB3uiBh3C0zIVXiNZU1DkiTq6uoglUoRGxsLY2O+cv6FYHJyEuvXr0d/fz8OHTrEMVWdwcXjqgxa4+PjehXaaVxoedDMUMQs9M3944yOGADExsbC2toaSqWSF7S0lR3EQgEjUcSe2ZIrSQzL5MxkvIWFBaKiopgeEw1tIVQHMwOEOZthnr8to2hBI0eqpGw75s6Fr68vZDIZLED9yIwIzT0PyxQYn1KgtrYWtbW1iI6OhpOTE4K1iBZ5zfyFgu5rWWllYdoafoBmFou+f7mSRFnHMMbGx5GbmwuxWIwFs+JgYGDAUNYBYFJ9nKGhIdzc3Jid8GxfKnCIoAlSShLYW8R/bXoWSyQAlCSJhNfPYMV/c9DW1oaysjKEhobikUXByHxqNm6K1swcjU8pOUQOGnSJkG2vVd8zzhBg2GBLJ5V3jqCpbxzt/SMoKKBmqJKTk3Hf8kQYiQUcexMhAdQ3NvNEVm+J4c6pAcC/DtXqZAjeyApwP+S1Q6FSobe3F4WFhfDz84Onpydm+VjDU0t3sqZ7DD/k8ckbL7DUL3bmtuP2bYX4F8v7iiAIWFpY4LVbYjmkjFN1A8jKysLJkyfR09MDPz8/GBgYYLaPNU+/cMvucoxolZwtjCQczcZ3TzTozLYIgsB/1gRzyokyuQpt3YPIy8uDq6srfHx8YG1igBujnJnf9BeZLTpHJwDAykSCR+ZR/bL9zQRCo+M5ZUTaYmVoaAjPL/WHSEDgRHUvituGQJIk6uvr0dHRgZiYmEsOWHK5HHfddRfa29tx+PDhaeXlZnBhuCqDlrGxMaKionQqtNO4GPfi+WrB25beUZw7d455XCwWM4QLF0vuj39Axw+B7mtZaFlZ7D5Xz+x+6WumszF6SPiLsy1Y9EEW8xyhgMCP98Ti/bWhCNEyuvNSGzuKRCI4ODhQ81XBngD4VvL//S0bUqkU8fHxzA8iWOt8Z+oHUNbBDZqRatFbf62FR5c7Lp1psWV+VCSwLS0b1tbWiIiIYIYiE7X0A7X7UGKxGNdFUPcyruTezMcn61BaWoquri7ms6VtKthSgMMyBc6W1FJD3/b2sDGRwNxQzNi/A1SGcrKaX+6McbeEtbEY2j3393VQshcF2XHe7+UfZWPR1jwQAiFjO2EsEWLH3TF4/5ZQ5jglCUgElAkguw/mY2PIMzUclil0DriyjR27R6ewL6ceRUXFCA4OZoSgBQSB9QluvOdW62AnLgi0Q5yW4O+Pee08gWMAeOo6zSDx2XY5YGwFsVgMa2trVFZWIiMjA+Xl5bg7mtuj6huTczIVGo8v8GY2hWUdI3hgZzEe/YlPkIhwtcBmFiFEKFCho66UGh/w0ZQ4BQSBf60MBEBtoF7X8Zo0bol1hq+dCQYn5NiW28UrI9IWK+2VefhbiDFeXeyGEEcT1NfXo729HbGxsZze08VAoVDgnnvuQX19PY4cOQIbG5vzP+kCkZGRgZUrV8LZ2RkEQWDv3r2cv5Mkieeffx5OTk4wMjLCwoULUVur/32i8dFHH8HT0xOGhoZISEhATk7OeZ/zR+OqDFoEQTCq4/pwMe7DdCZTWN8Ba2trREdHQyAQYHJSQ7ig3XvpvoMuhh7tDWUo5s6//FLUgbCwMHh4eDB9MA910KJZTEMyBdoGZegb4zfgA+y5geOcjjKLr/oYlZBbjjrWNImpqSnU1tYy6tMhTtTCSK+3+0qkWPtFAWf2Zl28K767O5pxYaWh0KFSEeZiBgFBlWPYOCE14MzIAECIsxmnD3WovAt3f1PIUfzwtDGCj50xEr2sOUOjPg5mEEuonhEthaMa7eOpyQPAyX4L3q41zMWcGRQHdPfAhAKCUftmI6Oun7fYWxqLMZdFBiHV/3JHrTjKBYGOppjja8OZQSsaNsbcuXN5fbA4G3514N3j9bwMxc7UAKFOmgD3nxOtePycAEPgLqCrIhyw/Y5ITi8x1Fn37OIGrVk1JUkpZWgj0s0CtuqsWkUCB2rHkZiYyNDp6XuynpLC2Zj7fdlbLEW5lvZllJsl/r1aM2idUT+Ao1U9PNNGALg7yY2ZhewYkqOZtIW3tzfvuHgvK0by69eSLrTrkDcDaPo9FYR35rQzckzsMiI9InBjqBUcld04efIkmpqa4OzsfMkagEqlEg888ADKyspw7Ngx2Nvz7W4uB2NjY4iIiMBHH32k8+9vvPEGPvjgA3zyySfIzs6GiYkJFi9ePK2s1I8//ogtW7bghRdeQEFBASIiIrB48WJ0d+u2hvmzcFUGLeDC3IsvNGiZkBSba1ApYRrP1tbWyMnJoXZZ7e2wNxGqX5d6TlM//0ewJMQeD831RLIWC0o6KeKQRADA3YpaaAfGuYvR9qw23nlpZXUauuZ46AHj9kEZR1y0bZxARHQszM3N0dJCkR5aq0thYSjgkRN0DXhaGUs45ck8lnIIDROJiMnI2H2o6r5JXmlGLBQgjkXHbu6XIbtpkLMDJwgCv94fj89uj+D0wJoGJhEYoJmdsrS0RHt7OzzE1PvBHr490ziks2d1XZAmIJ2u78ffvsrHe8e5w6PXBdnBzlQCJxZbxsFMAh8dwXGZFu0eoEpS2irwIoGAQzrYVyzFlFLF9MHoe1oWYg8tiUuMTCrxyekm3us8tYjOeEiMKSjl/cd2cU0RTSQixHtaYSnrOr8+16qzBJfiaw1vW25F4VhVLzJ0sDJvjdGUJzM6VMhuHsbPBe1UGZF1T2/eGIZge0253NuCgOFEN4aGhjjXEO9phRit8YkHvi/hifQKBAIsDdT8vnaWjfLKpzReWx0EAtRG4g0d5Bcayd7WmB9gCyVJ4o0jdTrnz+ihZkdHR4hEInh4eGB4eJjn1Hw+t3SACliPPPIIcnJycOzYsWkVey4VS5cuxauvvorrr7+e9zeSJPHee+/h2WefxerVqxEeHo5vvvkGHR0dvIyMjXfeeQebNm3C3XffjeDgYHzyyScwNjbGV1999btf/+Xgqg1a54NIJDpvT4umy0/2UlTWzmE5puQKqFQqREZGIjk5GdbW1ujo6EBrVTEATaYxMC7nfUFXhTviobleSHA35SiWW+uwdKDLg4MTco52oC5bB4lQwFGe6B2dwj/2VnLIG+7WRrgpygn3JDjBxVjFea5AYgRvb28kJiZi1qxZcHBwgIcZFQTZLEbt4U4a7MDRNyZHxxB/N0b3OUxFmtcmSeC3En42Q+sHsu/7pFqTkAa9KWG71HaPTDF0cRMTE8ZL66ZZ6l066+NQkcBXZ/hstIVquSY68ShqG8bX2W2c45K9rXHy8WRsYkkddY1M8fqYABDtIOKI1AIUueWnfH7fiO1uPDalxMfpTXj4Rw2rzsTEBMH+3lgW6ghbTr+NhONUB8dskCRJWCv74WQMsM1QmvsnkKMjE38oRXMvjX3jyG4a5B1DEAQ2JHvwHn/pQDVHqkqhUCDaqBdBVvQwrwr37izBSwdqeJJWUV52eP1GjaJGwxCJWukw8vPzcfr0aUZOS6lU4slFXP3CvjE53tJSoxgaGkKcseZ72j4ow/5S/ncMANytjRlm5LGqXp19SRpPXucDkYDAmfp+nUEaoMwyW1paEBsbCz8/P6aM6OPjw5QRtbURtaFSqfDEE08gPT0dx44dg4sLv495pdHY2AipVIqFCxcyj1lYWCAhIQFZWVk6n0PLzLGfIxAIsHDhQr3P+bNwzQat82VaCoUChYWF6OrqwtK5CZAIBZCrSLQNjDPzV8bGxsxQ6bJ5syASUOsiARID43IUVNZhbIzLphsbG0Nubi5czDQLcufQJG/xNDMUMcGMrZzQPijjqVQDYJTQ6RLT3hIp3mZlCGKhAA/GWSIAbRwPMJlCxVHIMDIygru7O5ICXNXn0yyOI5NKnMyv4O0Y6aBFC5iu+m82bvosl/m7SqWCt8E4UpwJpAZwyxwJ6rkrzmPqoMUe1pTJVTpVB+b4cuv8J6p7OQujTCaDoL8JhiJAobXJ3V/SiYyMDFRWVqKvrw8qlQpxnpYwMxCBXeWcVKg4dH6hgFBnZXac8qS2JmBvby8qS4sxy5MvFb81vYnHdvS1M+HMOn12tgXHq3uR1cCVq3p2qT8OPJQAMfO5ERgQ20KhUDB9sKysLDQ3N+NvOixOnv+NL+/kamXEIWXsyGmDTK5Av1Y5ekWoA2cmCqC+q/Scm1wuR0FBASRiEf6+LIxznIoEnme5/tLwsTPhvPaZPiOkpqYiJCQEAoGAUT5X9TQgxoWb6e3MbWeCyNDQEAoKChAT5I2nF2l6WO+daNAp5AsAdyW5Y1mIPUgA70wjx+RhbYw7E6n+33+O1PEGiZuamhizTDYdXSwWw8HBgVNG1NZGLCgoQGVlJVQqFZ5++mmkpaXh2LFjHIPTPxJSKcU21farc3BwYP6mjd7eXiiVyot6zp+FqzZoXU55cGJiAtnZ2VAoFEhMTISpiQnc1XTalgGZznObGBvByYL6QTmYU8eWtw3i3LlzyMzMRF1dHdra2pCdnU19id01ZSi5iuRQ32nQwYpNNyYBZDXwd8p+6p4VW9OwoJWq+ZMkicbGRlRUVCAsLAzJgdzdm65gEKzuhygJ7kd8sHoYhYWFOH36NLPYR7qqf6TqQDYuV6FSOgq5UgWFQoHi4mL4GMvw3rpE3D2bq5xwto6vHxjoSGkMyrX6Y//NaOIdG+RoyslU3z5ej7j/nEb3iIzZIFhbWuDWWFfcmeDK6d2kBjkhNJQiQJSXlyM9PR3VFeVIcOOX+d44wi8f2ZhIOFnmryVSqEgSo5MKSKVSFBcXIygoCDfHU/fM/taMTCp4Cg8AcEssf2f93G/chd5YIoSZoRgLAjUl5QPVQwgODkZKSgosLS0hk8kgFothP94MA61facuADD/n88kb97OyrePVvYh+7TRu/aqAc4xEJMCvD8TjxihN+W98UgErYzGz25ZIJIiMjESitzWPOHKooocjPk3jQRaBYm9xJ6QjU7CxsWGUz2kpsVXudGdQg63pjRgYGEBBQQF8fHzg4eGBOxLdGUWU7pEpjkuDNh6d7w2RgMDZhgFk6tCzpHHfHA/YmEjQ3D+BHTmaMn1TUxOampp4AUsb7DIie6g5LS0NiYmJcHV1xddff43XX3/9TwtYfwVctUHrfBAKhRy7exoDAxRF19LSEjExMQzLkBaSbe7X34ikabR26iCjMHVgygN9fX2orKyEQCCASqWCsyn11tH9pTIdBpIPpnji49vCMceH2wMTalMAAWaHzl7ox6eU6BicQGVlJVpbWxnR21CtheR0bR8GtHbUdNDq1ip5FfaoMHfuXISEUHJFZWVlaC7NhZmE4GQnJIBfCtqQn58PpVLJaPm5WxnBkVVz1OWey9YYZLvE5zQNQjrMff8JguCotdMJ4HN7K5Cbm0tR+IOD8dQiP/zfYj9OZna0qhfW1tbMnFF0dDSMjIzgZ0SVGNkqJ419EzoX2zWsPlRD7zjCXjmF1R9loaKiAhEREXByckKyjzVi3S0QzWLfiQUEh8pNY2mIPePgS6NjaFLnYnpnoiaLah2QIadpgLFsT0pKwuzZszF/ThIW+PIX0k9ON2JKK2NfGeaAAAcuUaNtQIZSLQ8vK2MJ7putWVTbhiZxorILeXl5MDY2Rnh4OFONuHcOf/H9x74qXrVgeag9k8GpSOCLM014+McS9I5OgiAImJmZwdvbGzdfl4Rv7wjnbADiLag5LCcnJ4YZCQCPzfdi2JsfnGzQ6SYNAG5WRrgtjtosvH28Xq9HlqmBCI8v8IaAAAbU2qHNzc1obGxEdHT0RQ/80kPNzzzzDB588EGQJIn58+fj4YcfhoODA+66665LMoS8XNA9tK4ublm1q6tLb3/N1tYWQqHwop7zZ+GaDVq02SP7S9HR0YG8vDz4+PggKCiI83daULa5ny+xQ4MOWnQvprprFEKhEENDQxgfH0dUVBRCQkKgUChADlE7XQMB9QMpaePvumf72mCunw1iPbgst45BfuD0UxMdtCnIWw8VYmhoiCN6625lxGGq/VzYidlvZ3JKfs4WBnhmsR++WBfB6Z942xlDIBDAxsYGQUG0jXg0guzU2oqsXfDHp+phbGzM0LsBKsgkemmCcGnHCJ7/rZI3WEpLB5mz5sAEAv78G6ApEbKzqMymQbh6ePF80tjeSn1jclR0jjLXZWFhAV9fX2xcngwDETcIA8Dbhyt55d6FQXYcEgwJoHNUAXf/UNja2qqvS4Bv7orGP5doZp2mlCRO6ugRWhiJsSCAP8D878O1vMU0XIvt+NSuEqzb1wtDZ39GecHExASb5gXC196Es9DHWE4i51wWpw9GEIQex+QK3mu7Whlx5qw+OVEFc3NzhIaGchhz8/xteS4DLQMT+FjLr4ogCHyxLpL57x/yO3G8ug/36dARjPG0xVx/zebjWIsKZmZm6OrqQnp6OkpLSyGVSmFvImL8vvrH5fipgN9HpHH/HA+YGghRKR3FAR2sURprIhzx6wPx2LLQBy0tLWhoaEB0dPR53SL0gSRJvP7669i5cyfOnDmDffv2oaurC3v37kV0dPSf4o/l5eUFR0dHHD9+nHlseHgY2dnZSEpK0vkciUSCmJgYznNUKhWOHz+u9zl/Fq7aoHUh5UGACkq0ugWtT+ju7g6VSsUELIFAAE/17JMuXTgaLmqVcvq1q7tGUVxcjJ6eHsTHx8PW1hZ2dnYICQnByrlxAIAxObUYnK1qR1lZGbq7u3m7Kx87Y87uO12HXJKTuQHTi2Efe7p1CnFxcTzPHu2yDQngeHUP55j1Ca5I8LJGHGt2qrh1mEeIsLCwwJxAikTANl/smiDRPjiBtrY2joIAraxOZ1G7CqV49RBXVSLV3wZPXueDW1kDtUoVX4UeoIRwBQQVCJhjSQLFg3yCS6qfLWfxfu9EPW75Mo9zT8YSIY/hCQCESoHMrHM4e/YswwazMBTpFOL9JJufQQY6msLNSvM57NXj9Lsmgr8zbegdx0GtnhlBELiPlcn0TJCQq4BnD3B7MwEOpth3XxwWBWuCYemIEfz8/ZlheXoeLM6e4CiDABR5Y28xv7z2MEstvqKfhMjGDXu1iDUCgsC9sz1gY6LZJBEAY7iofZ3z/Lk9ykrpKLIb+VkmewPQMkZAZeeHlJQUJltubGxEeno6ki2GmO/Zh6eaMCHXnblYGUuwMZmabXv/RKPOvjF9P962JmhtbUV9fT2io6MvWaGCZult3boVhw8fRlgY1QMUCoWYPXs2HnnkkUs674VgdHQURUVFKCoqAkCRL4qKitDS0gKCIPDYY4/h1Vdfxa+//orS0lLccccdcHZ2xpo1a5hzLFiwAFu3bmX+e8uWLfj888/x9ddfo7KyEg888ADGxsZw9913X7H7uBRctUHrfCAIAgKBAFNTUygqKkJnZycSExNha2vLUWind410ptXUp3ueAwBuinbG0UcS8cJy6gdV3zMG2ZQccXFxvAFDN2sqENHrbPs4AaFYgtpaSi6muLgYnZ2dkMvlEAsF8GfNYpXrmE8hCIIRS3Vmld+GpoBxbQYCqJkkAIwhIAC9bq3sMtbIpBINvfzATfd2JqbYJRgCuX1idHd34+zZs4yPVpi9WhmDdWhd7zhnoXCzMsLdSe64WUu5/ES17uxE2wEZAD493cwjuFgaizmKDWcbBlDaPoL9WrvrW2Nd8PQiX06QkRibYl4qpTIyOTmJwsJCZGRkINySP0ieVtGNnhF+VsjWJDxbP4Cf8tvwlhbdOtnHiikxs6HUQd1eFmQLLWF+NPdPoLSdr4h+V6JmkLh9aBL1oyKEhIQgJSWFmZ1qa25Egg3/ft48WsezV4l3MYIbi2Nyw+cFePbXKp7n1oowB+y7P54pt5IApDoyZgB4MMWL99iTeyp5pBUj1ThnxuuT082cbDkpKQmzZs2Cr6sDlnlLICRIjMgU2JpWzKPT07gj0Q32ZhJ0DMnwfa7+rKy1tRV1dXWIioq6rID10Ucf4a233kJaWhqio6Mv6TyXiry8PERFRSEqKgoAFXCioqLw/PPPAwCeeuopPPzww7j33nsRFxeH0dFRpKWlcTa/9fX16O3V/B5vueUWvPXWW3j++ecRGRmJoqIipKWl8cgZfzau2aAFUBlUUVERpqamkJSUBBMTE56lCA1aa69zSMabDaFhayqBi6URLIRyGAqp4UtL9wBIJPwFSCQQMOc0EgswqSABCyckJycjISEBZmZmaG5uRnp6OgoKCuBuprmWnlG5TrfUF5b746d1/vAz1pQPJUICZgYi3rH0AKmQVcqp6hrVuRONUSvd07h9Wz6e2M2d9wlxNoNYAExo9Q1sra0Yd2Z3d3cMDw+jobwQjlpcB5IE9ujIPOzNDDjK5YfKu6FQU7o1zyURZEmq71fz3LZBmU7SyuJg/qDme8e5M2hzfG1wR6IbVrEchnOaBjEmJzlssJCQEPgajUNIcBdBFQl8ztJ2pMH2k1IBePFALb7KauUwHkUCAe6f44n1CdxSnYnW5zg1NYWa8iIkOPMzSralPI1wF3NGlQUAfsrvAEmSGJtSMrNTycnJeHR5NO+HPTShQHplJ3PO0dFR5OXl4fZIfkb63K9cdiJlu2PAKc1uz2rlaRwC1Pdojg83c+0dm8L7JzWfT19fH4qKivDgbFcmEOY2D/J6jjQT9qVbkvDcUkrrcFf5MDJzCxhVDnZlw0gsxMNqR+JPTjfzgjQAtLW1obaWUlNhe4BdDEiSxOeff45//etfOHDgAOLj4y/pPJeD1NRUkCTJ+7d9+3YA1Cbn5ZdfhlQqhUwmw7Fjx+Dvz9WqbGpqwosvvsh5bPPmzWhubsbk5CSys7ORkJDwB93RheOqDVrnKw8ODg5CoVDAxMQEsbGxzNyWtqUIDWtjjbtvq57peYASYc3Pz4ePDdXjqe3RX06ka/20FmFp+whjt82em7KxsYGdiNqZ0uTAc3X88pPh5AC6m6qREqLZUY/LVWjWMehMlweHWUoKKpIyk9SGn70JI8BKPUfJ2U2TJImWxgY8EAL8cncILFmq7nSPTSKRwNnZGZGRkUhNTWVo7ex3+fPTTTxiDACOqkRx+zDCX01nAoJKpUJpaSm8DCfU96A5o0RIINyVX4bSpWjROTypU76IHeBUJHCsqoex0VAqlWhsbISVqRGPeg8AP+S2oaSmkaMi4GhuqNN48q2j3JLebXFUpkfLeAHAbpauo0wmQ25uLkxNTbFpfjDvfBXSUZyq4ZaRCYLAbSx24pHKHoS/egrXfcCdo/FytMKGZHdEsTJSsQAQ99bi7NmzKC0tRU5ODlxcXPC3uWGcbBSg1Ft26shUnliooaG3Dcp09vQALpOQxnfZbShoGURfXx/Dyrwh0Z+hoQPAp2d0VwokQgFuiHaBh7URRqZItBh4ISwsDCKRCDU1NYx6SltbG5YEWsPHzhjDMgVHUBoA2tvbUVNTc9kB6+uvv8bzzz+P3377DcnJyZd0nhlcOq7aoDUdOjs7kZubC4lEwrCN6N0WQRA6Ax7b3VdXiZAkSTQ1NaGsrAwhISEIV1Pata3t2fC2NYGBSMD0aXTJ0hgZGcHDwwNLEym2Hqle5g/kVHFs62tqapga+9xQd845tOd8AMrXiL0g0mBTeWkICALRWtmWXEmiqG0IKpWKGZZcvygeAW72nH6QrnKeUCjE/BBq8WSbL0pH5Ph6fzqjH0gPX9Kit2xS3bfZrcws3cTEBG6cH483rg/GG9drFvApJYlqHe+/q5URjyUHAB+eauQ95mtnwhFDfulANaJey0BBUy/y8vIgEokQHR2N5WFURsb+QQTaG2ByqBdnzpxBdnY281mxsy0auws7MSzj7uwJguAYRZ6u7UfP6CTG1ULDVlZWCA0NRZynFU80GaBUHrTnk9YnuHEybyVJZVGZWt+RLQt98JJanw+gSrljNoFwdXVlGGKtra2oqqzADSGWvNd+73gDrzzqZmXMuc7tWa2o6BzGt9lcH6oIVwusDHOAMSttdjQ3gDkhYwIW7e68IdmdEck9XdfPk4CiIRII8KBacmxbVivExuYICAjArFmzkJCQAEtLS3R0dCDr7BmscKXWgm+zW9ExSP3WOzqo4e2oqKhLFq0lSRI7d+7E//3f/2Hfvn1ISUm5pPNcKGjvQO1/Dz30kM7jt2/fzjv2Yt2VrwVcU0GL9rYpLy9HREQEjIyMoFAomN29rgyLDVp5XZuMoVKpUFlZyQwXOjg4wF+9KOravdPYNMsdeU+n4K4kardYqiNo0aA9rGiyQQ/M4e7ujqGhIWRlZaGlpQUODg4gCAK2JhLOQpulx84hzNkcEqGAMyzKLh+xQfe12P2TD040oKCgAOPj45y+XRKLmNA5PIn/pjeispN7b3GeliBAZYJs1CttYWhoyGjtFRYWwk4wClMDIYfN1zcmx8GMHBAEQVmXGxhgRZgDFgTawpil7XhEh4IIoBFBZuNkdS/adGTRbGIEXf18Zk8pzMzMGLHfVH8bvLDcHzew5pdqe6cQFhmNlJQUuLm5MQwss8E6DtOROi+JbVl8E8E1EZrzqQDct6MQs9/NhqEFxd6kFxe2bQmNCBdzaH+djSVCjlwUjWd/reIxBH3tTDgMwW+zW9DQ0Ag/Pz/MmzcPUVFRMDAwgL+4H0ZC7nNlChXe1DGs+yorEBa0DuGmz/Pxn8N1PO3E168PxmPzNRmXdHgShSXlCA4OZgIWQDkHrItnZ1tNvNeksSzEgcmivlYbNtKVDVo9JSUlBcuj3BHjKMZiVyUqi3KRn5+PyspKhIeHX1bA+vnnn/H444/j559/xrx58y7pPBeD3NxcdHZ2Mv+OHj0KALj55pv1Psfc3JzznOZm3dnrtYyrNmhpBx+lUoni4mJ0dHQgISEBdnZ2EIvFaG5uRmdn5wXZlNBT+00s2rtcLkdhIUUrT0hIYBqztIjtdEHLUCyEUEAgTN1fquse5zWcabA9rACgSjoGY3MryOVyWFhYICgoiDG9PHPmDHwsNB9NXvOQztmTF5b7I/fpOZwFvLFXd+mT7mux1TNymgcxRQoQGxsLAwNNkNQuf21Nb8Lz+7nCqpZGYgQ5mbJUHSgsDHFidOnoHbC0ox2+pvzP50ATicjISGZ8AaCUP+b6azK930o6cd+OIpRoERMWqEuE7JcnQflyaWNJCD8zah0F7Nx9GaKOiUSEW2JcOEO3k0oSJ2v6eKXR8OAAxDnzNwdfZ7XyXKIdzA0Yt2YAqOqagEwJfFY6xfmOr4lwxBMLvOHOKtU19I5BpEOw9dZYZ95j0uFJnXJH97PYibW9MigsXdEHcyhVGl3EeXNmccqONIqa+9Ddr6We4m6B2VpzhySAf+6r5D3/pihnRjyZBFA6Zatz5mdDshsz73isqhe13bqrG0IBwajAf5PdqtOShK6+fLNpFv69LhVuzo4YGBiAUChEaWkpSkpKGILUxWDfvn3YvHkzfvjhByxZsuSinnupsLOzg6OjI/Nv//798PHxwdy5c/U+hyAIznOuNhLF74GrNmixIZPJkJ2djcnJScYQUqlUIiAgAPb29mhra0NGRgby8/PR2tqKyUndzCY/e1P425swau10mUYgEPBo5TR5oGd0Sqd9AxsO5gawN5NASZLT6p8FOVL9GSrrIPHziVwYGhoiJiYGLi4uCAsLY9xk/ayphVwASr8wu7qN1y+yNpFALBQgniVQ29A3rrMBHeJsBgORgEMrV5FAC+x5syQulkZws+LK7dAKGWy8c2Mosv9vDmehPcLqqdE74ISEBKyK82Xuh0Zm+xS6B4Z5hIOFLKWIQZkKp+sH8LKWUWOQo6nO8miseqiZDV87E51iuLoUOsJdzDkKJvuK2vHRqUbGk0woFMLe3h7rZwfwnitTqPDt8SJ0dHRwFkWNj5bmPjMbBjgBzspYgo2zPHA7K+so6xxlVMnZ8LY1QQLrM6fx1tF6HhFnQaAdLI00n+/fD7Zj3ddFePkA16bizlmeWB/vwplZ8zYHSgvzcfbsWc48GE0tZ+NYdS+vtGcoFmKRv4adt79yQOd308pYwiGtfKantwVQYscBDqYYnVRiWyafKEODIAh0d3ejra2N8aaLjo6GiYkJQ5DKy8tDc3Mzxsf1960BYP/+/di0aRO+/fZbrFixYtpjrxSmpqbw3XffYcOGDdNWk0ZHR+Hh4QE3NzesXr0a5eXleo+9VnHVBy26fGZqasoQLmiGoJGREbMozpo1C7a2tpBKpTh9+jRyc3PR0tLCaaJfF2SHvffH45F53hgYGEBOTg5sbGx4u32AYnrRC3eNnp0fGzSbT1t9gI0QJ1O4WxvBWV3C61QYIywsjBM0aDfZJbEU04dWYTqU38AMXrL9pgAw6hM0dLnDSoQChLvwaeVndLDzAG6JEFBbVGjRyt2tjWAoFmIeK9M7Ud0LlZrJxMb8IGrHxw57ShL44AC1KNbU1DCaiHN8bTiLJ0AFzbFJTbZGEAS23hKGI48kcrKttHLd5cQUHfqBvxRJeRsSgiCwJlyzO82oH8RHGU34p5bmXrKPFWxMxBwiigBAqp81WlpamEWxpaUF/qaTMBZRqpY0SBL48CS/B7cy3IHT+9tdSDEyteeOaAUINnrHpvBtNren2dfbg1QHTaBQqBl/v5RIOYxHO1MDPLPEn1N6PNepRFTCLAQEBECpVKK0tBTp6ekwHm1DoB2/V/Lsb1zX5K6uLiSb9jF3LVOQ+Cmfb/QJUJYktB/aofIuvSIAAoJgGILf5bTpZOEClP4eXRK0sbFh6PQ+Pj4cYem+vj5kZmbqVXI/fPgwNmzYgK+++kqnovofhb1792JwcBB33XWX3mMCAgLw1VdfYd++ffjuu++gUqmQnJyMtjZ+n/taxlUbtAiCgFQqRU5ODjw8PBiNOXqYWLt/RRMe4uLiMGfOHDg6OqKnp4dpojc1NTE7qo6ODkbnTNsPig262V8zTYmQBs3m0yXnROPORDdsu9EDkeZUEGwaE03z2qYwFAkYs8IugpKlMjIyYvymioqoXb2VoQCulppF5Iwe/bUAayHMxSQsWGJ2oU78xRwAktQlQvYX5NPTunfAKSyl9mGZAuGvnsK/07g7eeVoP1xM+CVOR1dP+Pv7Y2pqipmbaqmvQZQLl2hBArwFOdjJDM4WRpysg7avZy+eUqkUTgr+cK1CReKbbP4PenUEv7/UOiDjSFCJBAK8e1MoPl+nUThXAagaM2YWRXt7e7S3t6OuugqJjvyf2s+FnTxZK0sjMZJYRJjv89oQ8a9TWPg+lyE4z9+WNwsmFACrWQG3q6sLpaWl2DgvmEOYAaj3Z6sO4srdSZpMb0pJYm9JN+zs7BhdxKioKBgaGiLVkV/ure4aY4JSV1cXysvLkRQTjo2zNJnZtzltOuWYrIwl+GBtGOb62VAyUDrGDTT3boMwZzNMyFU6j6NfOzw8nFE10YaRkRHc3NwQHR2N1NRUjpL7Y489hrVr1+If//gH1q1bh08++QRr167Vez1/BL788kssXboUzs780jCNpKQk3HHHHYiMjMTcuXOxZ88e2NnZ4dNPP/0Dr/TK46oNWoODgygtLUV4eDi8vLw4OoPnI1wYGBgw9u4pKSlwdXXFwMAAMjMzkZ6ejsrKSgQEBMDNje/6ygY9EFzdfQFBS53F6CNj0KK3VVVVWBZHlZaK24b0qleLhQKOo3Fe8xCMTEzh6+vL9ItoD6309HT4mml24mfq+nlzUI2NjYg16sVv94RxFuW0Cj5FHqCU2glwM6Pm/gmdPb4Yd0uO8aOKpIwnmec1N6OyshKp/vYwlQg5ih/GBiLY29szc1O0jJCPIT+73ZbVovP9WhbK1g+cQNirp3DT55RKfWtrKyoqKrAkKUJniVA7owMo1fIgR34wf/MIl5gQ62GJJC8rrkpGMZUZGRkZQSgUYmJiAmFhYbglnu4tsVQ/VCT+m97Ee517WbqAUwoSciWplqzSbIjEQgH+vToIzy/zY50PjCVJR0cHs3D7uDlhZTi/l/RdTjvatSTFvG1NOMzM7/PaIVcosbugk+Ol9dDq2XCz5Pf1Cuo70NTUhNLSUoSFhcHOzg53JrrBQP2Z945O4Tc9ViMAGE3EfcVSnRY5ALWhpbOtH/La0c1iOXZ3d6OsrGzagKUN2iE8NDQUKSkpuOGGGyCXy/HBBx9gamoK33//PT799FO0t+sfWL6SaG5uxrFjx3DPPfdc1PPEYjGioqJQV6ffa+xaxFUbtCwtLTFnzhzY29sz5UBaBeNiIJFI4OLiwnyJSZKEpaUlqqurGfX24WF+XwUAIlzNMdvHGsGO5xfSpEVsWwdkvGa8SqVCRUUF2traEBsbi1h/F5gbijAhV6FKqj8gRqgDoUQkwIRcySk9as+CxTNzUyS6RiaRUVyHiYkJkCSJqqoqtLS0IDGeEtydxZpJqu0e410vQClPBDvx7/s7HZmJRCRAslZzfnRSiUrpMGpra9HY2IiYmBg8uigQZ5+cjXn+msWETR6gNREDAwOxaRl/qHFkUonvz1Rjaop7vQsCbDlfZKWKRE3XGMqr61BXV4fo6Giq5BpszzGSBKjsRBdW6KC1H67s5iyQALWAsn20itqGccf2Aix49zQzE+To6Ig5oV7qQMgNkkX1nSgpLeUMyca4W/D0/gC+WvwsH2vcFO0MK5b01k/57Whra0NVVRUiIiIYc9Lb4/iahEoViXeO8xmCbMv79kEZIv6dgef2V3FkwoQCAs8tC8SmWdz+lrNkErW1tRCLxejv70d/fz+sjES4iUX935bVolfUNtLNAgmellCoSHw1Tc9qlo81ot0sMKlQ4Ud1dtfd3c0JlpcCgUAAiUSCrKws/Pe//0VpaSnmzZuHnTt3/mnlwW3btsHe3h7Lly+/qOfRJV02W/N/AVdt0AKojElfOfBiMDk5iby8PMjlciQnJyMmJoYpCYyPjyMvL4/XVwEoVYXPbo/A7TpESLVhbihm5sDYJUKanTgyMoL4+HiYmZlBQBCMbFK+DuVxGrEelohwMYeX+ry6jP0Aale/NIYiOtBzYBm1vTh79ixOnTqFrq4uhISEMCrWsR6WHEmejFrd5cQkHUO0tnoo9Sk6hnNf3VcCqVSKuLg4WFhYwNRABLFQgOuCNAtKTfcYXtxfzdPxc7IwQoiOoPlNXhfS0zOQm5uL5uZmTExMwNJYjEStHhwJ4MvMVsTFxTGDpLfHu+LM32dz7mtvsVTnhmVpiD20v22USga/RLoq3JFzbF7LEDpHFJDb+HAo1tdH8hePKC9bGBkaMvJfdMn3+nC+6keldJSnGiESCDjMv4LWYWQU1cLJJxiGZpoepp+9bvLGofJuXvl7foAtfO34c3DP/1bNea9m+9rgoblesGANo6c1TiEiIgLBwcGcPtgs6wmm99jQO67XiBEA7p/jCQDYVdCJnlHdpCqCIPDEQh/8e3UgHkjxQE9PDxOwLsfaPjc3FzfeeCP+9a9/4b777kNgYCD+/ve/IyMjA2fPnr3k814qVCoVtm3bhjvvvJPXd7/jjjvwzDPPMP/98ssv48iRI2hooEZZ1q1bh+bm5ovO0K52XLVB69ixY3jvvfeYOYNLDVgjIyPIyclh1MppSSahUAgHBweEh4dj7ty5CAgIYPoqp0+fRlVVFfr7+3UuaPqgTcaYmJhg2Im0tQcNem6qQIcnE41Uf1t8vzGGYZ/pIljQcDA34JhNNowbwMzMDBKJBBYWFiguLmaazVPjoxyW3ala3coGid5WcLMyYoSEAUA6pHsRYTsQ0yjqksM3JJKn2zjH15pDnvipoAOv67BL16V8sTDUBbNnz4KjoyN6e3sZTcQoW/73I10qgMRIk7FYGolhbijmSDu1Dshwtq4fH7JkhgBK+UKb4AJQ0knaQ7f2ZgaY5cO//3fSueWk1eGOOLQ5AUksCnxa9SA8vH0Y+S8LCwu0t7fDZqSep0kIAK8f5dvFr41x5gTNd0sFuPHrSmzZVcE5bl0Cf/MV4WLOkdkCqN/aHTqOHRiX82j1EpGAw/xrHSPQOWXA64O5WBlz+nqfnKrlkKTYiPe0RJSrOaaUKmzXMftGI8rNAmsinDDQ14eSkhKEhoZeVsAqLCzEmjVr8Nxzz2Hz5s28NYd2OvgjcezYMbS0tGDDhg28v7W0tKCzU7PZGxgYwKZNmxAUFIRly5ZheHgYmZmZCA7mK65cy7hqg5ZKpcLhw4cRERGB1NRUvPPOO6iv59urT4feXkr1wNnZGaGhoXptAoRCIeVTxdKjo+WF0tPTUVFRgd7eXp0SRWzQ81plHSMYGhpCTk4OrKysEBERwdslRaszrYIW3eKfbNBzU0VtQ3pVrgEgnrXI5rcMASIDJCYmIioqCqmpqfDz88Pk5CQKCgrgLNAEyzN1/TwnV4DKtA4/nIgNLIozzQ7UhpOFIeO+TIME8EspPyCaG4oZGSgaQxMK3tD3/ABbmEiEnIHpgpZBpolO26G7u7sjzFoFQstccEKu0jm7ND/AFkLWgnTv9yX4+HQz2ga5r79cR4kwyNEUFkb8xYtWyWAvcy0DMhSzLGvMDEXwsDbmiO6OyBRM1mFkbMIMyS6ZNxuzdTAeS9tHkK6VpdiZSpDkptmw0APfZ+v7OYzLVD9bfHF7BOayNhilHcPo1cHAWxHmwCk70vjPkTreLOI8FyHnvf+OpcrC7oP94/pY5vESqQw7DmmURkZGRpjfAeXj5QkA+CGvA4Pj+meqent7mYB1OTNJpaWlWLVqFZ566ils2bLlkjfJvzcWLVoEkiR5uoEAcOrUKUZrEADeffddRjdQKpXiwIEDjKDu/xKu2qC1ePFinDhxAm1tbdi0aRMyMjIQExOD5ORkvP7666iqqpp2sW9paUFJSQmCgoLg4+NzwV9Cuq9C7xJpQ7yKigqkp6frtR8BqOxpWYg94pwNkJ+fDw8PDwQGBursw4Wq1Sz6x+XTKs8DgIe1ERzNDSBXkihs0Z+ZxanLPyKCsqYfN3VhAjU9X0QH5mUxGh25sSkldqdTFizswEy/Z2yTxpFJBYrbdJNNkjz5itm7izp1fk66sqi3tRQY/O1NcPbJ2Xh0njfzWFnnKLpHJplzisViODs7IykqFIHW/E3JB8dq0NXNvS8zQxHmBfDLmc//xp0FWxRkB4lQwPG8qukaA6kVHFUqFRwVXTAUanvy8u8JoO6dTVz516EaRPwrHeu3a1yGDQ0NcedsX95zBaCMO6uqqtDX1welUom6ujrEWVFZCwHNNahIcIgeQgGBZB9rTrlbRUKnK7ChWMiRoKIxMC7HtixNr6mtrQ2dzXW4LkCzCTlc0cPr/QEUyeP9m0OxLITKhkrk9nBzc2OqIex5sNnelghyNMWEXMmTiaLRp86wQkJCLitgVVRUYOXKlXj44Yfx9NNPXzUBawa6cdUGLYBaNB0cHHDffffh8OHD6OzsxKOPPoq8vDwkJSUhPj4er776KsrKyphFaWpqCr/99htj7nY5rpsEQcDa2hqBgYGYM2cOoqKiIJFIUFNTg/T0dJSUlHA09oKdzPBInBk8lO0ICQlhtMN0QSISIEztSVTQOnje66CzrelKhN6mKviak/C2poZuz+qZwRIIBEgKdOXspAu65KiqqsKpU6d42oEuloYcSv3xKj7jcGRkBDaTVKmCXfqb72+r8z2YpyNonazp5agcEAQBiVCAeQG2nHMu/iAL8a+fZv6b7lkmuPCHjXvGVTiSW8F8XlKpFAqFAstC+ZlRdtMAZ1dvYSTGqS3J+NfqIOYxmULFEbKllVqUUxNYEsIvTeW1DHGyLQAwEAmxkk1NH5mCQkWiuG2YMzsV72kJd2sjxpQUoNic4QHeIEkS5eXlOHnyJFpaWrAo0hMe1ka8oPmtjlmmZB9rLbX4dqhIEmfr+6BgbcZui3WBUMfX96vMFozIFGhra2PIJo9fF8C8lwoViR/zdDPtrguyY9iRx6v7oDSyYqop7HmwjIwMLHWnzvhdThtPJootvHs5v/GamhqsXLkSGzduxAsvvHBFA9aLL77I0wYMDAyc9jk///wzAgMDYWhoiLCwMBw8ePCKXd+1gqs6aLFBEARsbGywYcMG7N+/H11dXXj66adRUVGB1NRUREdH46mnnsKcOXPw0ksvIT4+/pKVnPW9vqWlJfz9/TFr1izExsbC2NgY9fX1SE9PR1FREQoKChjR2wvZ+UUzZAz92RMNOmjpkikCqB2vtKESn98WgkcWUqWE03W6CRYANaTJNj8s7lEy92VkZMS5r46ODiwMZDnNVvdysqf+/n7k5eVhbqg7/O1NEMyiix+t6tGZaTlbGPJo5SqSojBrw9JIjEjWYPSkkrLjyGsaYPqGZmZmuHN+BI88YW0ixu1L5yA6OhrGxsZoaKCGtC3H2mAo4hYUSRL4KovLWLM0EiPZ2wqmrMDxxdlmLP4gC6+n1aCwsBAKhQIxMTG4PpLKTLSvQZcmId2nZB9LAviYlRkRBIEv10Vi5wauV9PxxnEEBgbCxsaGyTQ7OzoQZc4fE1CoSHyipfwhIAjckeDGvH7H0CSi/52OTTtK8DqL1m9nZoAVYY7QngpYGeaAwZ5O1NbWIjo6GlZWVvCwNuZkzz/md+icxwIoHc7ZPtZQkWA0BOkSPbsPNsfbHM4mBEYmlXhrXw5aW1shk8nQ39/PE969FNTX12PFihX429/+hn/9619/SIYVEhLC0QY8c+aM3mMzMzNx2223YePGjUy/bc2aNSgrK7vi13k145oJWmzQAWT9+vXYu3cvurq68PDDD+Pbb79FRUUFxsbG8MorryAnJ+e8fahLfX1zc3P4+voiOTkZcXFxGBsbQ39/P5RKJRoaGtDe3s6jZmuD1gO8kKBF94DKO0c4auIkSaKuro5ZQBwcHJDgaQmRgEDrwIReZQEAzBArAcpqoqFvgnNfiYmJsLCwQGtrK8xHNIt5S/8EIy/U1dWFoqIi+Pv7w8/HG3vvj+dkJt0jUzqV2gHdJcKvs1p1LnardAz8vnKwGjk5OYybtIOFERK8rDjBsG9MjkrpGGMuSN+Xg60VIu34X//vcto4fSCAmolaGqrJoso7R9E6KMP36gAbHR0NsViMGA9LOFsY8rIdXWW2IEdTBDiY8I79LqcNo6zXd7E0hJ+9KcJZM3v7S7uQX1yGBmkfomJiERQUhOTkZGxeFsvTggSAH/LbeXY8N0Y5wUgsYF6fHsb+uaCD0998ZJ4XDj6UyAnaRyu6UF1bx7P4uIs1mNw/LschPeokAJg+6S9FnbyeFf37DvD3x6OLqEykfljAqN3k5+fD2toapqamF9XjZqOpqQkrVqzAmjVr8Oabb170KM2lQiQScbQBp5sle//997FkyRI8+eSTCAoKwiuvvILo6GiO2/BfEddk0NJGRUUFXn31Vaxbtw79/f1455130NvbizVr1iA4OBhPPvkkzp49q7MPdbmYnJxEZWUlJBIJ5s6di+TkZFhbW6O9vR0ZGRnIy8tjdojaiHQzx8owB2xMdtc7t0LDwdwAXjbGUJGUYR5A9VLKy8vR2dmJuLg4hl5tYiBi2Ilnpsm26EyLfuVTWv5IJiYmjEzW7UuSYcpySf76OGX1XVZWhrCwMLi4aGjXvnYmnNLj0Up9Su38H6xEJNBJDFgQaMvb8df2TkBs5Qh/f39ml/zFugh8d3c0Z4D5t5IO/De9EaOTcs593TGXz6qSyVX46HAphxgAADeo6ersS5hSASNmHkzfUEAQWB3uAEetsQBdHmcEQeg+p5LUOQt3C4vWPiFX4f4Dvfi/s0r855hmM+Fsa8GUPdkwFZHIzy9AbW0t4/prYSTmzKLRkpRTSpJjb+NkYQh3ayOO+v2ATIUJS29eJSPazYJRhgGoAKwvqCR40j0rFRP8dWFpiD2+uD0C396TAF9fSuDY2dkZBEEgJycHZ86c4egiXgja29uxfPlyLFmyBB988MEfFrAAoLa2Fs7OzvD29sbtt9+Olhb9s2hZWVlYuHAh57HFixcjKytLzzP+GvifCFrp6en45z//ia1bt8LCwgI33ngjduzYgc7OTmzduhVjY2O45ZZb4O/vj8ceewzp6ekXpAp/PoyOjjJ0+piYGIjFYhgbG8PT0xPx8fGYPXs27O3tIZVKcebMGeTk5DCzRQDFonv9+mCsjXGG4AJKEwlqqvS5xgEoFAoUFRVhZGQEcXFxMDXlltpoJW59kk4AReteHurA0NW1TQfZMDMxxhw/TZAp6Faht7eXyfTq6+sxOjoKkiRBEASWsxbPwzoWbQAIdDCFswVXw25SodIphGtjIkGslicYAKQ1k5yyjoAgYCQWYg6Lgv5NTge2pjfhlYNcaalkH2uYG/JdoXtHJhhiAD27F+ZsBncdPSNt+45Nsz1w/LFkZjAcAPaXdWFKqeJtTFaEOUAk0OY8Al+oe0ZsLA62hwlr0zCpXp8PlHdxXIZvjXWGkAAMWXz5caUA4QFeDHM0IyMDFRUVWOrLn8UCKPIGu7cG8LUOf6sc5D2PIAjclaRhmpZ3jqBID2mHIAgm29qR06bXTVwkoAbXh4aGUFhYiICAAISEhDB9sMDAQM48mHY/VhudnZ1YtmwZ5s2bh//+979/aMBKSEjA9u3bkZaWho8//hiNjY2YM2cORkZ0S79JpVJem8HBwQFSKZ8481fC/0TQeuqpp/DII4/watJGRkZYtWoVtm/fDqlUiu3bt4MkSdxxxx3w8fHBQw89hKNHj563jKcL/f39yM3NhZOTEyM9pA1DQ0O4u7szeojOzs6c2aLGxkaMjZ1fjJcGQ8ZooHpIJEny1OlpzFYP++Y0DfDEVtl484ZgvLCckpUqahvSqY5Bg+1A3DSkhGcwpZ7t6emJ0dFRZGdnM7Ngs9w181ENveM61coJgsCqcAfOeYdlChToKZcuCqaHkjXL/N5iqc5rXqMjizlc0cMJHBKtQWca0QEeSE1NZTQRi4qKcPr0acTrGAFq6pvgDIgbioVUFsXKTEYnlVj0QRbCXjmFdNZMnJWxRGe2OT6lxOk67gbCQAgkOnHNHwHKzJMW1QUolfpTW2bhnlkaKSi5kkRBDxjmaFhYGAQCAeQ9TfA252dCY1NKfHOOm+2Rw93wZektn63v1+kAfl2QLZwtDBlPtO90mJLSWBxsB2cLQ/SPyzmyX9oYHBxEYWEh/P39GdNXQHcfzNDQEPX19Th16hQKCgrQ2trKLPJdXV1YsWIFEhIS8Pnnn+sdgblSWLp0KW6++WaEh4dj8eLFOHjwIAYHB/HTTz/9oddxreN/ImhdCCQSCRYvXozPP/8cnZ2d+PHHH2FoaIj7778fXl5euO+++3Dw4EG9A49sdHR0MD8iX1/fC2rgGhgYwNXVlTNbNDg4iKysLGRlZaG+vp5XktJGvAelB1jfOwGFyBhRUVG8+S8a/vYmsDOVYEKuOm/PzNnCEAEOplCR+tUxAIrSzr7TrNYJiMViODk5MTtfehZMIa2BKWvMR5+Z4yPzvPHxbeFwMNeU1NL0HBtsTvc+NFehUJE6reHn+FrD1EDIyWKmlCpeqW5ZiD18bI1hwcq49hZLOSMCKSkp8PHxQbAxHXi5n9FHOvQDFwfbcXQNu0emQAJ4TUtImFbJYH+FDEQEx55FqVSiqKgIc1yEWndP4YOTjYwmI0EQsDGR4IYoJ85xuwooqaOyzhEYmVkgMDAQs2fPxl3J3jrP+fnZZkYBv7GxEU1NTXhwHkXBFxJUdvhzAV+xXSQQ4Mv1Efj6rkgAwJGKHp4oMPvYOxOpILQ9q1VniZzOsHx9fTkBSxvsebDk5GSmTN/a2oqwsDCEh4cjPj4ebm5u2LZt2x8esHSBJnbp0wZ0dHRkXKZpdHV1XRZb8n8Bf5mgxYZIJML8+fPx8ccfo62tDfv27YOVlRW2bNkCLy8vbNiwAfv27eP57KhUKtTV1aG6uhqRkZGcPs7FgGZ80UO/dKaSk5PDZCp074ENcnIUC1yBB+OsEBaie/6LBkEQmK0u+03X16Ixz5/KdvSpY8jlcjRVl8HbUvOaJ6q5AYC90M9LnYsUHw078Ze8JpSXl6Onp0dnb3FZsCaNSSvv5qi0kySJ+vp6DHQ0IcyJX9JK0CE3ZSAS6sxi3jvRwHlfE72s8NuDCZzyV0n7MO75rgiLP6R6B8PDlIZiQoi3uuzHXeLPNQ7gRFEdJ2M3NxTrzOJaBmScDGWWjxVcLA3haa3JTCcVJPM50MagJEni+nI1uU8AAEy5SURBVLkxCHQ05ZUTB8bl+LWEu7g5mhtyVEpKOkYQ958M3PplAV74jTL0JAgCq2LcYWMi4Z3TQqRETl4+8vPz0dTUhOjoaCwKd4WHtRGU6vdvT2GnzqF0D2tjhDiZI87DEkqSxI95uu1IAOCGKCeYG4rQ3D+BE9Xc797Q0BDjxnA+cWtt0GX65ORkZGRkYGJiAiKRCJmZmfD398ejjz6KkpKSizrn743R0VHU19frZUAmJSXh+PHjnMeOHj2KpKSkP+Lyrlr8JYMWG0KhECkpKfjggw/Q1NSEtLQ0uLi44Nlnn4WnpyfWrVuHXbt2obe3F2vXrsWXX36JuLg42Njwh1MvBSKRiJOp+Pr6QiaTMQ7G1dXVGBwchFQqRUFBAZ5a5IfNSyNhYaRbA5CN2T7UNU7X16JBl+h0qWPIZDLk5uZCLBbj9iQfxr4+r3lIr1qBQCDA0ghNIGgfA7ongKqqKt7MFACsDNf8cPvH5UzJjSRJVFdXo62tDXFxcVgWxv+B1+nxO1sazK/nNfdPcDJPOkterkVgyGwYQOuADIcKG1FQUABfX194eXnpFNI1lRAY6e9FRgZXE5GWi9KedXrvuEYySiQQ4MBDCfjo1jDOMXuLKHfdI2dyIBQKERUVBbFYjJujKCaiNinlo/RGngL+rWryBs0opP9+uLKb6YNJhALcHO3EU7+YJMWwMzPEwMAAVCoVSkpKUFdbizWhVCAUCgj0j8t1zuzRWKceYv6poENviZpyjKbuiW3qODw8zAQsd3e+6eSFYmhoCPfddx8iIyPR1NSE3t5ebN26FTKZDI2NfGuWK4m///3vSE9PR1NTEzIzM3H99ddDKBTitttuA8DXEnz00UeRlpaGt99+G1VVVXjxxReRl5eHzZs3/6HXfbXhLx+02BAIBEhKSsLbb7/NCJgGBATg5ZdfhpeXF06fPg0XFxdGxPf3Bq2HSDsYBwYGQqFQID8/H6WlpbC2toaxsfEFs6SSvK0gIIC6njF06rF5oBHmYg4bEwk1/6RmJwLUbjA3NxeWlpYIDw/HrXGu+PfqIAQ4mEJJkjhZozszA4Bkb2uO8kOdzBSzZ89GXFwcZ2aqsLAQZsohjsVHWkU3VCoVysrK0Nvbi/j4eJiamuK6IDtEu1nAxVJD1vhNTz8kSQ/R4pPTTbzHfOxMEKjDjuT1o40IDAxkdvpLQ+w5ElAAMDpFIj4mErNnz+ZoIgq7q2FpKIBS66tyuLKbUzKTCAXwtDHmkDdO1fYj9j9n8PeMSaisPZly1vIwexiIBFBpnbN3dIpjXQIAs32tKSUV5mDqf+VKklPa2zjLHScfT2Z6pgAwKFMgs3EIiYmJTH9PLpfDXdEOA4GGIq9rro7GvAAbOFkYYGBcjoNl+unv6+JdGbbn6KQCw8PDyM/Ph7e392UFrJGREVx//fWwtrbG7t27YWBgAENDQyxfvhyffvopVq9efcnnvhS0tbXhtttuQ0BAANauXQsbGxucO3eOUaTX1hJMTk7Gzp078dlnnyEiIgK7du3C3r17GW/BvypmgpYe0CK39957L8RiMVJSUvDggw/im2++gaenJ2666SZ88803Fy2qezGvb2trC5FIBIFAgICAABgaGjJqAXSpbboAZmkkZtyKz54n2xIQBJNt0SzCwcFB5OXlwcnJCUFBQZxy5AJ16e14tf6gZSQWcixLjlT2gCAImJmZcWamLC0t0dbWhgBjTTn2SEU3CgqLMDY2hri4OBgZUdp6zhaG+O7uaGya5ckcW9g2rLNvIhEKOL0hGlkNA2jTQSJYrkMlo1tGYMrAkvlvaxMJFgbZIs6DK1l1sKwLhoaGcHbR9C19vL2Q7MLPiFUkdNpu3BRNZZG0X6Nc/bV66ZCmD2ZuKMbiYP492ZtJeM7UIoEAN6oJIWIhAbZk4Icnm5jMy0QigkQo4M2TlcssYGpqypR9Q0JCsHj+XCwL0VQZcpuHcPBMATo6OiCXc7NukUCAv6mzveno73ZmBtj/YAJ2bIgBOTWBgoICeHl5wcPDQ+fxF4KxsTHcdNNNMDY2xi+//KKTrPRH44cffkBHRwcmJyfR1taGH374AT4+Gjk1bS1BALj55ptRXV2NyclJlJWVYdmyZX/wVV99mAla02Bqagrz58/H4sWLcezYMbz++usoLS1FUVERkpOT8emnn8Lb2xurV6/Gl19+ie7u7t8tgNE03p6eHiQkJMDd3R1BQUFISUlhBHjpUluplh8TG7fGumDLAm9Gl3A6pNJ9rZpedHd3M+UZXWQTOhicre/nCaiysYDVVyrvHOEFC/Ys2J3zNS7A/eMK5DYPws7OTud9XRfE9dA6rMfMcqkOaaUIV3O4WhnxHqc18bQ/wY/SuWWkd24MwRfrIjlZ3PZzLYj+dzqS3qTkpWiCyqaFVNlPm+jwQ147qps7OZuOJcH2MBQLoNa7hUSdfdT3jHMMEW+KcoatqYTjXNwxNImKTr432w2RThAQVHYFaEqVgxNy/KqVoc7zt4G5geZKz9QP8qxBBAIBNsz24Tx2ukPFmJHm5+ejpaWFGeu4MdoZhiIBZasyjaOBm5URRkZGGM1OT09PvceeDxMTE4zT8K+//spzGZjBtY2ZoDUNJBIJY5FCl2cIgkBQUBCee+45FBQUoKKiAgsXLsS3334LPz8/LFu2DJ988gk6OjouOYDJ5XIUFBRAJpMhPj4exsaaJj1BELCyskJAQABmz56N6OhoGBoaoqamBqdOnUJxcTGnV7Qq3BH3zPKAhzXfVFAbSd5WkAgFaBuU4WhOGUJCQvQ2wAMcTOBiaYhJhQqZDfqzuFR/GywMtGVMDY9O0wOJ9rSBI4tFWC+3YOwVtBmWVsYSJLFkqPSpLyR4WfL6NSXtwxyNQxpOFoYIdeDvyNPKueaPBEFALBRgRZgmIPaNKSBTqDA6qUQZy706wMFUJ3nC10qMtkaqBE3391RTE4i0Viudgxr0pcG2Tolxt8DxR5M4DscAsLeEKi3lNw8ywdDJwpDjdcYuVX6U3sT0L0mSRFNDPZLVLTtTAyFUAI/gAVCl1CQvK7iqS7QnmyYQGROHWbNmwc7ODj09PcxYR19HCxYHUtn2dPT30dFR5Ofnw93dHV5eXnqPOx9kMhluu+02yGQy7N+/nze/OINrHzNB6zxgp+/aIAgCvr6+ePrpp5GdnY3a2lqsWrUKe/bsQWBgIBYtWoQPP/wQLS0tFxzAaC09sViMmJgYxv9L3+tbWFjAz8+Pci9W933YvSJdZRt9MBYLEeZALUR9Bs7T6icSBIGF6izq2DSByMpYgg/WhjHOuUf0ZEQAde+hFpqh0KzWCYRFRDCzYGNjY8jNzWWGfud6adQXStqHdZb86BLZApaqu4rki/7SQ9JhZlRGwyY6KEngm3N8/cBVavKINini1UNcWvtqNSGDfVjHmAopczQalnV1dTh37hySnQid59xf2sVkPXTQXB7mwHFi/q24E5H/OoX1Xxfi09Oa8iPtrCzUOmnv6BQqO6lNQE1NDaRSKR5YFA4C1GwZQMks6fruvntzCA5tToSLpSGGZQqkVXTDyMgI7u7uTHnUw8MDo6OjCBZT7/XRyh5UNkt5Je3R0VHk5eXBzc0N3t7evNe6UExOTmL9+vXo7+/HwYMHYWHBdx34PfHaa68hLi4OZmZmsLe3x5o1a1BdXT3tc7Zv384Tzb0aSpfXEmaC1u8EgiDg6emJJ554AqdPn0ZTUxNuueUWHDp0CGFhYUhNTcW7776LhoYGvQGMtmigPbguZpaE7hX5+PhwekV02aagoABtbW16B6lplp6fMbVo57Tp1yyksUBdIjxV08djrmljYaAtCADF7br7TyMjI8jNzcV8P032NDAuR07TIFNqow076aFfy7EWzuJ+sIyfFQDAlgU++GBtGKecdoBFDKDvvaOjA3dfFwWRgOARHTIbBnifW5izGTysjZhj6UspaR/mlPOWhzrAzcqQY7Y4LFPiXOMgTExNMSI0g1KphIuLC+aFusHWiGAyIvqcSpLSZWTD0kjMlGkNRQIMT6qYa/maZecxx88aDmYGnDECAHAwM0CYsxlqamrQ3d2N2NhY+DlbM6MSIgGBht5xnaoW5oZiCAUCrFUHRG1aO3t+72/L5iLK2QQqEvjsRCXTk+3u7mZIF25ubtNuEM8HuVyOu+66C+3t7Th8+DDHMfpKIT09HQ899BDOnTuHo0ePQi6XY9GiRecVDDA3N+eI5tJGtzO4MMwErSsAgiDg6uqKRx55BCdPnkRrays2btyIU6dOITo6GrNmzcIbb7yB6mqNffn+/ftx/PhxuLu7IzAw8LIVp+leUWJiImbNmgUbGxt0dHQweogtLS3MIDVteNnX14f1CyJxS4wz7ptz/iZ4lJsFrI3FGJYpOIxDXbAzM0CUWtX+aCWXvEETPtzc3LAyOZQTXA5rDRqzZ8GWLZyLeDdNtrUrp1HvLBhBEAxVH6CUQnpGJ6FSqRiTz7i4OLjaWTISWGxcF8S3WCEIgjF/FBLcXtiXZzULka2pBGmbE/HMYj/O819Lq0XYq+lY/20FXFxcKO83b2+sjfPQec4dOW1o6+YGzxvVFHh6MNfCiOqzUcoigwA02aaPrTEMWKlZ+5AMadnlTMCiy9BrY7i0erbihjauj3SCSECguH0YlVLdckRCoRB3z6ZKftm9IgSFhkMsFqO6uhrZ2dkQiUQwNDS8JGUagJpl27hxI+rr63H06NHfbRzlfEhLS8Ndd93FyEpt374dLS0tyM/Pn/Z5BEFwRHMvxwvsr4iZoHWFQX9B77//fhw5cgSdnZ14+OGHkZOTg8TERCQkJOD666/H+vXrMT4+Di8vr9/dIsHIyAgeHh6MHqKDgwO6u7tx5swZRnqJZul5OVjiheUBmON7/h++UEAwvljTsQhp0Ky3IywB3d7eXmYOytvbG0KBAH+Lc8ES5tgevVmcQCDAqijNLFjbGNA1AVRXV+ucBVsRpglaKhI4VNaF0tJSDA0NMZYsgMaxmP0p/Kajt8M+pzatfXdhJ88bLM7TkhOQ2wapcqYKQP2UGfO508FV+5yhtkJUlRYyZokDAwNI8LSEo7kB0/9iz829xdJFvHeOB359IJ4JsjQOVfZx7h2gZvYczDTnPFTezdMipGFrKmGGqH/K1z9EPM+fknfytjWBXGjEjI44OzvDyckJbW1tnDk37cF+fVAqlbj//vtRXl6OY8eOMfTxPwNDQxTRxNqav+lhY3R0FB4eHnBzc8Pq1atRXl7+R1ze/wxmgtYfCNoTbOPGjThw4ACkUikCAwNx4sQJkCSJN998Ey+++CKKioquiKUKQOkhurm5ITY2FgkJCZTkkkKBsbExFBQUoKGhAaOjfBaaPjDU96re8/bt6FJWQcsQekYn0dnZieLiYgQHB3MIH/fN8cQbNwTDxkSMoQkFsqcxvpwfYMuRS6ocM8asWbMQFxcHExMTNDY2Mv09Y/kg/Ow0hJRdOU2QyWSIjY3l9BXm+dsi1NkMYSwKeVP/BGq6+O+Lu7URIl3NeY9PKUl8ryUvJWBle4YiAmwHlrePaYgWrlZGiPew5J2zdVyA1LkpCAgIgEKhQHFxMc6czsAsZ6qMbCIRQklqgm1R2zCq1dcsEQo4yvI0CvqEEBtwBYrZVHlDkQATciXSyvX3Immq/G+lXTxbFxpCAYFdm2Lx3d3RsDYgkZ+fD2dnZwQHB8Pb2xsJCQnMnFtfXx9Dvqmrq8Pw8LDO75ZSqcTDDz+M3NxcHDt27E+VN1KpVHjssccwa9asaeeoAgIC8NVXX2Hfvn347rvvoFKpkJycjLY2/SSVGXAxE7T+JCiVSjz99NM4d+4c8vPz0dvbixdffBENDQ1YtGgRwsPD8Y9//AO5ublXJICNjY2huLgY1tbWSElJYfQQh4eHmeyrrq7uvHqISd5WMJYI0TUyibIO3eUhGk4Whgh3Mad067JqUVlZicjISJ2LjUigEbNNm4a8YW4o5pTzaBYh3d9LSkpCYmIirKys0N7ejgAjTeCpG1TCwSeER3Yxlgjx0z2xeHoR1+7+gJ6e2Up1tiXSIjp0DfMt52mVDJk6Yhmo1djbBmWokmqu7fpIJwQ6mMDWRHNt3SNTKO0cg4WVNWrk1OcWHh6OBT4UQ47OhgxYCu+fneH2S8JdzOBsKlDfpwADEwqcredvCm6Kpqjy9HX+UqS/RBjnYQkvG2OMTymxX897BACWxmKMj48jLy8PDg4O8PPz41QV6A1VdHQ05s6dCy8vL+b4M2fOoKqqiplZUqlU2LJlCzIyMnDs2LFLllT7vfDQQw+hrKwMP/zww7THJSUl4Y477kBkZCTmzp2LPXv2wM7ODp9++ukfdKXXPmaC1p8EuVwOuVyOrKwshIeHw9zcHLfddht27dqFrq4uvPHGG+ju7saqVasQHByMp556CpmZmb+LJ9jQ0BByc3Ph4OCAkJAQCAQCRg+R/jF5e3tjfHycw9bTpYdoIBLizkQ3PLHQB44WfEsRbSwKojKzo5U9iI6Onrb/sEQtw3Ssqkenxh1znHq+igClKK9tOmliYsLYxWxYEMH521dHi/Sq7Ue4msOZdU+HynXP4S0JocRxFVpEhxCWtxQNC2IC7qYk6HxoSqF5zscspY6V4Q7Yc188/hbPXYw/OtWIyH9n4J+/VqFrZBLW1tZIjQlBnIclAAIEABnrnGnl3aiVDgKgCCeVlZVIdqRe20hM9b90KYo4mhtirp8tAh1MICCAgtYhNPTqJhgQBMFkWz/k6R/1mJiYQH5+PhwcHDgeaLogFovh6OiI8PBwpKamIigoCCRJ4h//+Afc3NwQFhaGX375BXv37r2sIeTfA5s3b8b+/ftx8uTJaUV9dUEsFiMqKkqvaO4M+JgJWn8SjIyM8NVXX+mcgzIxMcFNN92EnTt3QiqVYuvWrRgZGcHatWsREBCAxx9/HBkZGZfkCdbX14f8/Hx4eXnxdro0aHdVbbZeQUEBTp8+jaqqKgwMaAgBD6d6YWOyO+xMpw9aJEnCR0JlY7VDgEo8/exYjLslbEwkGJYpcK5h+hJhgqcl/B0odt6hct27fZlMhv7WGvhYaYaCC/oIvWr7BEEwPSshQWVDJe18Jp2VsYQjTktjv1Yw6O7uRnFxMZNtsZ2DASo406xD2l9tZRg3Cy3r0AzovsKi1tPlPDbRAgC8zAnUleQhJycH2dnZ6O/vx32LIyESEOhTq7gfr+7l+XcBwFs3BmPPffGMUsovRfrtQ1ZFOMJAJEB116jO92hiYgJ5eXmws7M7b8DSBq0OExQUhB9++AGLFy9GT08PLC0tER8fjxUrVmDbtm0XfL7fCyRJYvPmzfjll19w4sSJS5ovo0UE9InmzoCPmaB1lYP2BPv6668hlUrx5ZdfQqlUYv369fD19cXmzZtx/PjxC2JedXZ2oqioCEFBQRe8O2Wz9ebOnYvg4GCoVCoUFxczZoJ9fX3nLWHSOoJC2SACHSj68/nIG0IBwZA39NmVAICpgQjb7ojCfXM8AejOiOhF08LCAjep2XkEgIb+KZi5+iM1NRVeXl68WbAUD4qgQCdRB/Ro6K0Md0SUqzlsTDSDzLktQ+hRDyVLpVKUlpYiLCwMtyT5QiQgMKGWvqCriiQJfKs1D+Ziacj0t4zEFK3dSK3zdKaunzFPvC7IDiYSIVPOo2NCxwSBpKQkkCSJ8fFxTE5OoqWmHLHOVA/PwkiESYWKQ46hYaT2xKLtU/YWS/WSYiyNxIz6yI9ahAw6w7K1tUVAQMAlE41IksRrr72Gs2fPIi8vD3V1dSgpKcHcuXNRVFR0See8HDz00EP47rvvsHPnTpiZmUEqlUIqlTJqIABfBPfll1/GkSNH0NDQgIKCAqxbtw7Nzc245557/vDrv1YxE7SuIUgkEixduhRffPEFOjs78f3330MikeDee++Ft7c37r//fhw6dAiTk/xeSlNTE9NDutRdHb3jpU33aDPB8vLyafUQaT8omqG4JIRisOmyodcGXSI8XtU7bYkQoFhvRmIh2gZlKGX112jRX/ral4Y4gICGTn6wrIuXXQYEBEAul2O4tRouJppj0yq6efNO9HXu2BDDKKvT/a20im50dHSgoqICERERsLe3h42JhOnDEQBnJmxCwb/HVWryhlhI/Vyt1QofChWJPepek5FYiGWhDghzNqMyOPU5ZXIVdp2tgFKpxKxZs5hB7TmuVLY5oSZO7CnQrw04188GNiZi9I1N4fQ0Njd0ifBQeTeG1MxJmUyG/Px8WFtbX9YoB0mSeP311/Hll1/i6NGjCAwMBAD4+/vjySefxPvvv39J570cfPzxxxgaGkJqaiqcnJyYfz/++CNzjLYI7sDAADZt2oSgoCAsW7aMUXwJDg7+w6//WgVBXgm11xn8oVAqlThz5gyjAj0yMoKlS5di9erVmDdvHp588klIJBK8+uqrMDfnM90uFyRJYmhoCN3d3ejq6oJcLoednR3s7e1hYWGB0tJSAEBkZCTEYjGa+sbx9rF6LA2xx7LQ6WdUlCoS89/LRM/oFD6+NQxz/fkeWWw8uaccB8q6cWeCK/5vsR9jceHq6gofHx9m0bzr60LkqGfL3KyMkLY5QeeCqlKpsPV4DT7J6oSAoALMc7MtsCDUFTY2NrwB8Ob+cSzdms0ExSA7QzwUMIHIyEgOFTqtohtbdpVDIiI4fa2nrvPhWNYDlPJ5yttnmSxKIiQYOrqDmQGOP5YEAUFArlRBLBTg+d+qsIs1WxVjL8CXG5I4hBOFSoUF72WhZ1STob8SDwS62cPe3h7W1tace3vzaB22ZbVinr8tz0aFBkmSuOGzPAgI4LXVQXC3ECEvLw/W1tYICgq6rID17rvv4t1338Xx48cRGRl5SeeZwf8GZjKt/wEIhULMnTsXH374IZqbm3Hw4EE4OTnhmWeegYuLC37++Wd4e3tPaxp5OaBdY/39/TF79mxm7qe2thanT5/G+Pg4nJ2dmUXL08YYH94Sdt6ABVAlwkXB52cR0liqzuLSKnrQ19+P/Px8eHp68kR/V4Y7Ypa3FSRCAq0DE3qZjwKBADfGeQLQZERZHXLU1NQgPT0dxcXF6OzsZPqLHtbGCHM2A021qOyRwdEnhDe7M8/fBmYGIk7AAii5Jm2YGoiwUM2kNDUQYkpJMrT2rpFJZNRSqvx0Jkb3zGgSYWkfiSmS+9mLBAKsVmdwluqB5BaBI0eIuaSkBJ2dlK8XTZXPqO3jiejSIAgC2+6IxK5NsfCwFCM/Px9WVlaXHbC2bt2Kt99+G2lpaTMBawZXR9D617/+heTkZBgbG8PS0lLnMS0tLVi+fDmMjY1hb2+PJ5988rxEhP7+ftx+++0wNzeHpaUlNm7ceFEzSNciBAIBkpOT8fLLL8PPzw8+Pj7YsGEDtm/fDk9PT9x666344YcfdDIBfw8QBAFzc3M4OzuDJEnY2trCxcUFLS0tOHXqFAoLC9He3n5R6gdMibC6R6+ZII3ZPtYwMxCha2QSu9OL4Ofnp1Mx/MYoJ3y+LpKZHdMnAQVQfaVoN42OXU7HFOISkhAXFwdTU1M0NTVx7m1pMJUNGqgTlbOtfNkqA5EQS0LsEeNmwXhJAUCFdJTjbEyD1i9k7EQMNFnQV1lcm5NIVzPYGwugIAFbEzGmlCSOV/H7h3QgGpqgfkdHa4c4Gw9jY2M0NTUhPT0dQ63VCLY3hJIk8WuxfkKGpZEYU1NTTP8wODj4sgLWZ599htdeew0HDx5EXFzcJZ1nBv9buCqC1tTUFG6++WY88MADOv+uVCqxfPlyTE1NITMzE19//TW2b9+O559/ftrz3n777SgvL8fRo0exf/9+ZGRk4N57770St3BVgSRJrF69GkqlErm5ufjoo49QVVWFrKwsRERE4O2334aXlxduvvlmfPvtt7+7JxitI2hvb4/IyEj4+voiKSkJycnJjHdWRkYG8vPz0draqrMHx0aUmwXszSQYnVTqnCliQyISYJYnNbfUoLQ6LwV5aSgVEA9VdDNSSLpAOxaLBASGZQqcbRjgzIIlJyczs2BmA7UQgIRMHV8P6lGgf36ZP769Oxrz1QPaJhIqEKXpOD7Rywp2phJMqjOzsUlN8FaqwARzlUqFstJSJDrSs1hUFqVrxszTxhgx7hbwdzCBgUiApr4JlLQPMxsP9udmbW2NBHsqYO4414jGxkadqhWTk5PIz8+HhYUFQkJCLitgbd++HS+88AJ+/fXXP9Ri/qOPPoKnpycMDQ2RkJCAnJycaY//+eefERgYCENDQ4SFheHgwYN/0JX+NXFVBK2XXnoJjz/+OMLCdNfKjxw5goqKCnz33XeIjIzE0qVL8corr+Cjjz7Su2OvrKxEWloavvjiC2ba/sMPP2SM2P6XQRAE3nrrLRw8eJDpYQkEAkREROCVV15BWVkZCgoKkJiYiE8++QTe3t5Ys2YNtm3bhp6enssKYLSOoLu7O4/abGxszHhnzZo1C7a2tpBKpTh9+jTHpl4bArWSw6pwBziYTU+r7+jogLeICmxnmsYYW3l9mONjA3NDEbpHppA/jX7i4mDuLJZ2ZmZsbAwPDw9YWlrCxkSMCCeKdUiARHXXKNKLqnmzYLTqOh0Q6bfqkA6mpFCg0Tk0MxByqPLuVoYwEAkZVufk5CQ2XUfNo9FZW1bDAPrH+L+VT24Lxy/3xTOD3LqsSIyNjeHp6YkHlyfASCyAdIxEdn0PT7WCDlhmZmaXHbB27NiBp59+Gvv27UNKSsolnedS8OOPP2LLli144YUXUFBQgIiICCxevBjd3bo3HpmZmbjtttuwceNGFBYWYs2aNVizZg3Kysr+sGv+q+GqCFrnQ1ZWFsLCwjjCkosXL8bw8LBe3a6srCxYWloiNjaWeWzhwoUQCATIzs6+4tf8ZyMqKgoGBroXeIIgEBwcjOeffx4FBQUoLy/H/Pnz8fXXX8PX15exI+/s1G1LoQ89PT0oKCiAn5/feTUUaT3EuLg4zJkzh2NTn52dzdvJPzLPG/9ZE6xzYJdGa2srqqqqsDYlAlbGYvSrVeKng0SkcTc+oCcjAqhZrLl+Noh2ozYBJ2t6OeaXtFJ8V1cX4uLicEsCNbNjqKaNH6nqw7lz5xilEbY00RxfKnCOTiohJIAq6Sgae/lZDN2DGlO/Lk2VP1rZC9mUHEVFRZiamkJ0dDR8HMwR7WYBEhRZQ0mSOo0yTQyoTIwuPx4s79LL0jQxEDGD3GXj5gwTkVatOH36NEiShLOzs87nXwhIksTPP/+MLVu2YNeuXZg3b94ln+tS8M4772DTpk24++67ERwcjE8++QTGxsb46quvdB7//vvvY8mSJXjyyScRFBSEV155BdHR0di6desfet1/JVwTQUsqlfKUkOn/lkp119elUins7bmutSKRCNbW1nqf81cEQRDw8/PDM888w3iCrVy5Ert27UJAQAAWL16MrVu3orW1ddoA1tnZiZKSEoSEhFy0KoCBgQHc3NwQExODlJQUuLq6YnBwkGP+ODo6Ou3rNzY2oq6uDtHR0bC3s8EideagzxySjWXqEuGRCv3ivADw/tpQfHtXNNysjDAhV+FUDdUnopUment7ERsbCxMTEywIsFXr9lHnK+gVICUlhVEaoaWJqqurMT4yzMyj2aoFdXUNSPvZmyLYyRSBDqYcqvy4XIkdxwugUCgQHR0NsZiixNOEDKX6fdMnQwVoyo9DEwqG2KELN6r7YIfKuzGlIuDk5MSUxiwsLGBpaYnS0lKkp6frVdyfDnv37sXmzZuZIeI/ElNTU8jPz8fChQuZxwQCARYuXIisrCydz8nKyuIcD1Aban3Hz+DyccWC1tNPP80zO9P+V1VVdaVefgaXALYn2JkzZ9DU1IS1a9fiwIEDCAkJwbx58/Dee++hsbGRE0DOnDnDzIBdrs2CRCKBi4sLoqKimJ386OgoRw+RnaWQJIna2lq0tLQgNjaWIfLQg65HK6eXgAKABE8r2JhIMDghR+Y0yhsC9fd2mfrcB8q6oVKpUF5ejoGBAY5auomBiOlVCQUEmvsnUNsr48yCBQYGQqFQoKioCG5KaiM1oFZp11UiBICv74zCrnvjEO9J3aedKRWgzrVPcQIWQJU0JUIBekenQICSYmL7fLEhFBBMmfJXHbJONKLcLOBlYwwrYzGa+ycYl20TExPExMQgJCQEc+fORUREBIeJSLMspzMk3b9/P+699158++23WLFihd7jrhR6e3uhVCp1bpCn2xxfzPEzuHyIzn/IpeGJJ57AXXfdNe0xF+pS6ujoyGuGdnV1MX/T9xztOrRCoUB/f/+fqgZ9rYDtCfbwww9DKpVi79692L17N1544QWEhoZi1apVqKmpwZEjR5CTk/O7+xjRRoJOTk5QKpXo7e1Fd3c38vLyIBaLYW9vj4mJCQwPDzMZDo0Yd0rENczFDKMyBaxN9DtACwUEloTYYUdOOw6WdTGyRfqwLNQBn55pxum6PpwrKAGmxhEbG8srxy4PdcDB8m4IBQSUKhIHyroQ7ESVN4VCIezs7GBnZweVSoXQgQF8U1uG3nEVBARQ3zOOc1UtiPNz4cxLmaiJFSvCHJDdNAhSSTH/SvpIqLT2oBZGYswLsMHhih7Ym0nQNTKFQ2Vd2DhLtxrK6nBHbMtqxamaPgxOyGFpJOYdQxAEvlgXAQdzAygVCuTn5zMEBHqkgiAIWFlZwcrKCv7+/hgdHUV3dzeamppQXl4Oa2tr5t5pdf20tDRs2LAB27Ztw/XXXz/t+z+DvzauWKZlZ2eHwMDAaf9NZyXPRlJSEkpLSzlB6OjRozA3N9c7SZ6UlITBwUGOIduJEyegUqmQkJBweTf3FwNBUGWgBx54AEePHkVnZycefPBBbN++HT/88AOsrKywbds2VFRUXBEaPUAt8g4ODggLC2MUK3p6ehjiSGtrK/r7+xk1DqGAwG8PxuM/a4KnDVg0lqvnu45X9zLSSPrgZ28CP3sTKFQkzjSN6gxYADDL1xrWxmJ4WFEL86Fy3QxFgUAAWxsbrImkyqpW6rmpXdmNOHXqlM4sZb6/DUQCoHeChJWxGCOTSmQ28NUq6F7VqFr5Yrq+nb+DKQIcTKFQkToZjDScLAw5ASs8PFzvDCDbUZvNRJRKpXjkkUcQHx+PO++8E+vWrcOnn36Km2++We/rXmnY2tpCKBQyG2IaXV1d026OL+b4GVw+roqeVktLC4qKitDS0sJI/hQVFTEzVYsWLUJwcDDWr1+P4uJiHD58GM8++yweeughZrHIyclBYGAg2tspD6OgoCAsWbIEmzZtQk5ODs6ePYsNGzaAJEm4uLjwSpW5ubl6ry81NZV3/P3333/l35irEDQd+uTJkzAwMEBxcTGee+45lJaWYs6cOYiJicFLL72E4uLiK+YJRhAEOjo6IBQKMXv2bISGhjLuy7QeYm9vL3ARAZRSdDfE+JQS6dP0dABqBCPKmgoClePGejdfEqEAhx9JxE+bYmEiEUI6PImi1iGdxwIaFuGgem6qbMQA8fHxzCxYeno6CgoK0NLSgpqyIkTYUcHNyZz6DegiWszyoQKnhZEYQgGBKumoXrV2AFgdTl3DvmlmseiSoIGBwbQBSxdoJmJcXByee+45JCQkYM+ePZDL5fjPf/7DkIP+DKEeiUSCmJgYHD9+nHlMpVLh+PHjein3SUlJnOMBakP9R1L0/2q4KoLW888/j6ioKLzwwgsYHR1FVFQUoqKikJeXB4DaZe/fvx9CoRBJSUlYt24d7rjjDrz88svMOcbHx1FdXc3Zje7YsQOBgYFYsGABli1bhgULFqCurg6dnZ3Mv3vuuQdeXl4clqEubNq0ifO8N95448q8GdcAjh49iurqapw5cwbh4eG48847sW/fPnR1deH5559HfX09rrvuOkREROCf//wn8vLyfrcAplQqUVhYiKmpKaaHZGNjw+ghRkREQCAQoKKiAunp6SgrK0N3d/d5yQDsXtVBPaK4AFViLiwsRLwDVbLLbR7SqxABUOU8A5FQM8R8nkzH394EShIQCwi09E+gdRScLMXS0pLp6yU5U0GrU92nOl7dgykt7UKxUIAf74nF0UeSMMvbCoB+0V+Acm0WEEBx+zCa+vgMRvr+xWIx815fKlpbW/Hzzz/jo48+Qm9vL/75z3+itrYWq1evnrb3dSWxZcsWfP755/j6669RWVmJBx54AGNjY7j77rsB8AVwH330UaSlpeHtt99GVVUVXnzxReTl5WHz5s1/yvX/FfCX1h6Uy+VwcXHBww8/jOeee07vcampqYiMjMR77733x13cVY6pqalpy7tjY2M4dOgQdu/ejQMHDsDKygqrVq3CmjVrEB8fz9PsuxDI5XIUFhZCIBAgMjISIpH+lixJkhgeHkZXVxe6u7sxNTUFW1tb2Nvbw9bWVudzq6SjuOXLPFwXaIc3b+ArOdCvLxQKERkZidu3F6GkfRjPLPbD+oTpGZOn6/pw384S2JiIcfLxZIj0LPZfnG3GO8cbYGUsxsC4HBuS3PD36ygzSoVCgYKCAgiFQgQFBaGzqwdrv2/AhAIwFQOjcuCdNX5YEq77Wn4rleL/fqmEu7URDj2kW2sRAO7dUYwz9f14YI4HHp6n6TvTry8SiRAREXFJnyGN3NxcrF69Gq+88go2b97MuRaVSnXFJMcuBFu3bsWbb74JqVSKyMhIfPDBB0xLITU1FZ6enti+fTtz/M8//4xnn30WTU1N8PPzwxtvvIFly5b9SVf/v4+/dNDavXs31q5di+bm5mlp2qmpqSgvLwdJknB0dMTKlSvx3HPPwdh4ej+oGVCYmJjAkSNHsHv3buzfvx9GRkZYuXIl1qxZg+Tk5GmDDw3az4suSV3MgkmSJEZHR5kANjExARsbG9jb28POzo5h3JEkibEpJUwN+NdDl8QkEgnz+t9mt+K1w3WIcDHH9xtjpr0GuVKFue9kYnBCji9uj0Cyj7XO4zqGZFj4fhYczQ0gHZ6Es4Uhjj6SyGQ42gHjn79W4pciKTytJGgamEKcHYn7Igxhb08J35qZmTEBYWxKgTlvUcK7P90Tg1Bn3eLJB8q68OSeCrhYGuLww4kQEATz+vSG4XICVmFhIVasWIFnn30WW7ZsueQh5Bn8NfGXDlr0buh8siufffYZPDw84OzsjJKSEvzf//0f4uPjsWfPnj/iMv+nMDk5iePHj2P37t3Yt28fhEIhVqxYgeuvvx5z5szhULZp0PYWZmZmCA0NvexdOM1m6+7uxujoKKytrZlFXlf2SM/vGBsbc1hyPSOTmPdeJlQkcOThRLhaGU37ui8dqMaP+R24IdIJr64K1HtcU9847M0kSHknE+NTSnx7RzgUXXWcgEkjs6Ef93xXDBOJUB1whdi93g+Dfb3o6elhWJb29vawtLTEE7srkFbRjTsT3fB/i3x1vv6EXImUt89CRZLYtSkO7lYGKCgo+F0CVmlpKZYtW4a///3vzFjMDGZwMfifCFpPP/00Xn/99WmPqaysZDx4AKCtrQ0eHh746aefcOONN17U6504cYLpj/n4+FzSNc+Ayl7S09MZSxW5XI4VK1YwlioGBgYoLy/HsWPHcN11112WWrg+jI+PMwFseHgYlpaWcHBwYOjYMpkMBQUFMDU11Rkw7/6mENlNg3hsvjfunT29sWZu0wDu/KYIZgYinH5iFiSi6YPvk3sqcKCsC4s8RLgz3FRnD4lt3WJpJMLghAIf3RKGeQG2UKlU6OvrQ3d3N3p6ekAQBBqnzPBG1hDszSQ4/mgyIyWljYKWQQQ4mMJQRKCwsBAApbJyOQGroqICS5cuxebNm/H888/PBKwZXBKuCiLG5eKJJ55AZWXltP+0Z8K2bdsGGxsbrFq16qJfj65v19XV/S7X/1eFWCzGwoUL8cknn6C9vR27d++Gubk5HnnkEXh5eeGGG27A/PnzUVVVdUUCFqBhs8XHx2P27Nmwt7dHV1cXzpw5g3PnziErKwsmJiacDIuN5Wp7lekYeTRiPCxhbybByKQCZ+r1mynSWBRIlRBzu5QI08PSEwo0BBIL9VwV7fIsEAhgZ2eHkJAQxrQzxtkIRkKge2QKu9IL9ZJUot0tYSgiUFRUBJIkLztgVVdXY8WKFdi0adOfFrCampqwceNGeHl5wcjICD4+PnjhhRfO6zgwwx6+unDFhov/SNCDihcKkiSxbds23HHHHTrLUecDbe2t7QDs6emJ5uZmzmOvvfYann76ab3nkslkeOKJJ/DDDz9gcnISixcvxn//+9/LVpa41iAUCpGamorU1FS899572LZtGx5++GFIJBLs3r0bY2NjWLNmDRYvXswZIv49YWhoCHd3d7i7u2NwcJAhXfT09CA7OxsODg6wt7fnvP6SEHvEe1rB3Xr60iBAKWosDbbH19ltOFjWxShm6MLU1BQMBxthLCIwICNR1DaCWA9LnccuD3PA19ltjJrGyZpeTCqUMBBpgoxAIIC1tTWsra2xtB7YUyTFuQ4FXCW1KC0t5ZBUxGIxM3qiUqkuO2DV1dVhxYoVWLduHV599dU/LcOqqqqCSqXCp59+Cl9fX5SVlWHTpk0YGxvDW2+9Ne1zN23axGErz/Sz/zz8T5QHLxbHjx/HwoULeSVDAGhvb8eCBQvwzTffID4+HvX19di5cyeWLVsGGxsblJSU4PHHH4erqyvS09M5z/X09MTGjRuxadMm5jEzM7NpF9kHHngABw4cwPbt22FhYYHNmzdDIBDg7Nmzv+9NX0NIT0/HqlWr8NJLL+GRRx5Bfn4+du3ahV9++QXt7e1YuHAh1qxZg6VLl14RJ+axsTHk5+fDwcEB/v7+UCgU6OnpQVdXF/r7+2FkZMQEMFNT04tahEvbh3HLl/kwEgtw+onZMJbwgwG7h/Zjoxh7S7pwW6wLnlvmr/OcJEniZE0fEr0ssfyjHHSNTGLrLWF6gyLdB7MwEuHU48mQyyY4PT4rKytMTk5CIBAgNjb2gogy+tDU1IQlS5Zg9erVeP/99/9UVqAuvPnmm/j444/R0NCg95gZ9vDVhavrG/QH4csvv0RycjIvYAFUn6W6uppRGJdIJDh27BgWLVqEwMBAPPHEE7jxxhvx22+/6Ty3mZkZHB0dmX/TBayhoSF8+eWXeOeddzB//nzExMRg27ZtyMzMxLlz536fm70GUVxcjHfffRePPfYYBAIB4uLi8Prrr6Oqqgpnz55FWFgY3nzzTXh6euLmm2/Gd999h4GBgd9lIHV0dBR5eXlwdnZmrFXEYjGcnZ0ZPURvb2+Mjo4yQ+u1tbUXbKoZ6mwGNytDjuAuG3TAokuSS9Xlx8MV3XptVgiCwPwAWxhLRIzL82E92oWARmtxaEKBrIYBmJqawtvbG4mJiUhKSoJMJoNMJsPo6CgKCwv1WsacD21tbVi+fDmWLVt2VQYsgPoNartK68KOHTtga2uL0NBQPPPMMzq9xGbwx+AvmWldKXh6ekImk0Eul8Pd3R1/+9vf8Pjjj+vdqdKEjoGBAY5js4eHBx577DE8/vjjf9CVX3sgSRIVFRVMBlZRUYHU1FSsWbMGK1asgI2NzUWXoYaHh1FQUAB3d/cL0sVUKpXo6+tDV1cXent7IRKJOEw9fa//3okGfHamGctD7fHmDSHM49p+VAKBgEuVXxeBZO/pF9jC1iHcvq0AJhIhzvx9FqdEyMa/02rxXU4bloc64M0bKCk02o+LtjdRqVTo6elBd3c3+vv7YWpqytyfqanptNfR2dmJJUuWYM6cOfj8888vq7x4pVBXV4eYmBi89dZbnOqINmbYw1cXZoLW74h33nkH0dHRsLa2RmZmJp555hncfffdeOedd3Qev3PnTtx999085974+HjMmzfvvIzIGVCgld7pAFZUVITZs2dj9erVWLVqFRwcHM4bwOgelpeXFzw9PS/6GnQx9egF3srKipNltA1MoKF3HEneVhALqcd1BSwaL+yvxs8FHbgpygkvr9RPlQcAFUli4ftZkA5P4sNbQrEgQHevt7htCLd9VQAjsTq4CQlOwNLu9crlciaA9fX1wcjICHZ2drC3t4e5uTnn/e3q6sLSpUsRGxuLr7/++ooHrEthD7e3t2Pu3LlITU3FF198cVGvN8Me/nMxE7TOg0v5QdD46quvcN9992F0dFSnoOpM0Pr9QZIkmpqasHv3buzZswc5OTlISkrCqlWrsHr1akZ3ko2BgQEUFhbC19cX7u7ul30NKpUKg4ODzDAzSZLMAm9jY8Mrk9FzaPos6s81DmDDt0UwNxQh44lZkAinL7P953Atvsluw4owB7xxvW5BaZIk8emZZqT62cLPzgilpaWQyWSIiYk5LzmJrbhPZ5j19fVwcHBAVFQUVq1ahZCQEOzYseOy+mEXip6eHvT1Ta8X6e3tzczgdXR0IDU1FYmJidi+fftFly3HxsZgamqKtLS0P9zzawYzQeu8uNgfBBvl5eUIDQ1FVVUVAgICeH+fKQ9eWZAkiba2NuzZswd79uxBZmYmYmJisHr1aqxevRoeHh7Yt28fMjMz8eijj8LFxeWKXMPQ0BATwBQKBWxtbeHg4AAbGxvI5XLk5+fD0tISwcF86SiAmsVKfTcTfWNT+Pi28PNapxS1DuFvF1AiBMAIDU9MTFxQwNL1/P7+frz++uv49ttvMT4+DldXV3z44YdYtGiRXvfsPwvt7e2YN28eYmJi8N13311SFnj27FnMnj0bxcXFCA8PvwJXOYPpMBO0riB27NiBO+64A729vbCysuL9fWhoCHZ2dvj++++ZAefq6moEBgYiKysLiYmJf/Ql/8+CJElIpVL88ssv2L17NzIyMuDm5obW1lY88cQTeO655644FZvWQ6SZejIZJXRrYWGBiIiIaQPGq4dqsDO3HavDHfHamqBpX+dCS4QqlQplZWUYGxtDTEzMBVsF6cLg4CBWrFgBsViM+Ph47Nu3D0NDQ7jpppvwxRdfXBWDxO3t7UhNTYWHhwevbElbiVwOe3gGfwyuPjrPNYqsrCy89957KC4uRkNDA3bs2IHHH38c69atYwJWe3s7AgMDGUNLCwsLbNy4EVu2bMHJkyeRn5+Pu+++G0lJSZyANTMUefmgPcEefPBBHDt2DJ999hna29sRFBSEd999F0lJSXjttdeuqCcYQRCwsLCAn58foqKiIBaLYWJigqmpKWRkZKCwsBAdHR06Fc6vj3DCQ3M9z6u6AVDzYIuDqECVVs63KwF+34A1PDyMG264AXZ2dkhPT8eHH36I5uZmHD9+nPkOXg04evQo6urqcPz4cbi6ujIGo+x5y8tlD8/gymMm0/qdUFBQgAcffBBVVVWYnJyEl5cX1q9fjy1btjAlkqamJnh5eeHkyZNITU0FoBku/v777znDxWwTubS0NPz444+47bbbOEOR69evn3YoMjU1Ff7+/ryhyCsx23Qt4fvvv8c999yDH374AStWrMDAwAB+/fVX7NmzB0ePHoWnpydWr16NNWvW/C5ah9qYmJhAXl4ebG1tERgYCIIgMDY2hu7ubnR1dTGzUrSc1KWU2OgSobFEiDNPzIKhWJNVkCSJsrIyjIyMIDY29rIC1ujoKG644QZIJBLs379/Zuh2BlccM0HrGsXMUOSlY+/evTAyMtLZRB8aGsL+/fuxZ88epKWlwcnJiQlgUVFRlx3A6IBlZ2eHgIAAnVnIxMQEE8CGh4dhYWHBDDPT9vTnA7tE+MHaUMbPiyRJlJeXY3h4GDExMZfVcxofH8dNN90ElUqFgwcPnpcGP4MZ/B6YCVrXKJ599lmkpaUxRpm6MGOpcnkYHR1lPMEOHjwIa2trxhMsLi7uopv44+PjyM/Ph729PTO4fD7IZDJGjWNwcBDm5uYMlf58n+PrR+rw9blWZh6MDlhDQ0OIjY29rIAlk8lwyy23YGxsDGlpaX/57H0GfxxmgtY1iJmhyD8e4+PjHE8wExMTxhMsKSnpvNRubWmoS+nzTE1NceSkzjfsW9w2hLeP1WN1hBNuiHRERUUFBgcHERMTc8EZmy5MTk7i9ttvR09PD44ePcphvs5gBlcaM0HrT8TMUOS1CZlMhuPHj2PPnj2MJ9jKlStx/fXXY/bs2TwWIB2wHB0d4efn97sQE+RyOXp7e9HV1cUM+9rb28PBwYGnh0iSJCorK9Hf34/Y2NjLClhyuRx33HEHWlpacOzYMdjYTE+/v5KYEaj+a2ImaP2JmBmKvPYhl8tx6tQp7N69G3v37oVCoWA8wVJTU1FZWYkXXngBr7322hWzV1EoFBw5KYlEwgQwMzMzVFVV/S4BS6FQYMOGDaiursaJEycuylnhSmBGoPqviZmgdY1gZijy6odCocCZM2cYU8uhoSFMTU1h7ty52LFjxxWzVGGD1kOk5aTon3dwcPAFyVlNd9777rsPRUVFOHHiBIfd+mfB09MTjz32GB577LELOp6ei9y5cyduuukmAGC82mbmIq8dzMxpXQOghyLd3d3x1ltvoaenB1KpFFKplHMMewasvr4er7zyCvLz89HU1IRff/0Vd9xxB1JSUhAeHo6PPvoInp6eMDQ0REJCAvM8ffj5558RGBgIQ0NDhIWF4eDBg1f0nq9FiEQipKamYuvWrTh48CDEYjEiIiJQXV0NHx8f3HXXXfjll18wNnZ+w8hLhVAohL29PUJCQuDo6AiBQABbW1tUVVUhIyMDFRUV6Ovrg0qPYrwuKJVKPPzww8jLy8PRo0evioBF4z//+Q9sbGwQFRWFN998EwqFQu+x+fn5kMvlWLhwIfNYYGAg3N3dkZWV9Udc7gx+B/xPmED+r4Meiqyrq4Orqyvnb/ROWt9Q5HvvvYexsTG4ubnhxhtvxLPPPosff/wRW7ZswSeffIKEhAS89957WLx4Maqrq2Fvb897/czMTNx222147bXXsGLFCuzcuRNr1qxBQUEBQkNDr/wbcI2hvLwcCxcuxKOPPooXXngBKpUKeXl52LVrF1588UXce++9WLRoEVavXo2lS5f+f3v3HtPU+cYB/FvkogQLcgdHFSd4IRo2kQ6iIQamgypijFPitHMqixO8gBd0omxTtyVuLhGnLsGShaAxE8rijSmiTEHxknpB8cJQJrYw1KJFM7B9fn8Yzm9dWxxyLTyf5CScc95z+h5C+nDOed/nwcCBAzv084kIt2/fRn19PaRSKQYMGAAiwpMnT1BXV4fy8nLo9Xp4eHjAy8sLrq6uFu/cDQYDVq5cieLiYpw6dapTUl29qWXLlpkkqFar1RYTVGs0Gtjb25sMHPHy8jL6B5D1bPx4sA+SSqUYP348MjIyALz6YvLz80NSUpLZl9gtQ5sPHTokbHvvvfcQHByM3bt3d1m/rcXDhw+hVCrx2WefmexrKf/RkpG+qqoKkZGRmD59OmQyGZydndv13qslYNXW1iIkJMTssPiWfIgtc8Gam5uFfIju7u5CADMYDFizZg0OHz6MoqKi/1Supb04QTV7HQ5afUxTUxMcHR3xyy+/IC4uTtgul8uh1WqRn59vcoxEIkFycrLRu4NNmzZBqVTiypUrXdDr3umfNcFyc3Nx8+ZNTJo0CXFxcZDJZG2uCdZSokWj0VgMWOaOefbsmRDAGhoasHv3bkRHR+P27dtCwAoICGjPpf5nnKCavQ4/Huxj6uvrodfrTYb4enl5oaKiwuwxGo3GbHt+pNI+IpEIQUFBCAoKwsaNG4WaYJmZmVi2bBkmTpyIuLg4TJs2DZ6enq0GMCLC3bt3oVar/3PAaumDWCyGWCzG8OHD8fDhQ4wYMQKbN2+GVqvFxIkTUVxcjEGDBsHd3b2jLt0iDw+PNx6VqFKpYGNjY/YRNwAhi31hYaFRgurq6mqEhYW9cZ9Z1+KBGIz1ACKRCIGBgVi/fj0uXLiAW7duITo6Gvv27UNgYCCio6Oxa9cu1NTUmCT0JSJUVlbi4cOHCAkJadcoRR8fH7i6usLOzg6HDh2CTCbDnj174OPjg507d7b3MjtMZyaoZj0b32n1MS3vLGpra42219bWWhwV5u3t3ab2rH1EIhGGDRuG1atXY9WqVfjzzz+FmmCpqakICQkR0klJJBKsXbsWHh4eWLJkSbsCFhFh+/bt+PHHH1FYWIjg4GDIZDKsXbsW1dXVHZ44uD0cHBywf/9+pKenCwmqV65cieTkZKHNvwcnAcD27dthY2ODmTNnGk0uZtaD32n1QVKpFKGhodixYweAVy/cJRIJEhMTLQ7EeP78uVE5hvDwcIwdO5YHYnQhIoJarUZeXh5yc3NRXFwMNzc3NDQ0YO/evYiNjX3jQRxEhIyMDHz77bcoKCjA+PHjO7j3jHUQYn3O/v37ycHBgbKysujGjRuUkJBALi4upNFoiIho3rx5lJqaKrQ/e/Ys2dra0rZt2+jmzZu0adMmsrOzo2vXrpmce+vWrRQSEkJOTk7k4eFB06dPp4qKilb7o1AoCIDR4uDg0LEX3csYDAZat24dOTk5UVhYGNnZ2dGYMWMoLS2NLl68SDqdjhobG//TotPp6PvvvydnZ2cqKSnp7ktjrFUctPqoHTt2kEQiIXt7ewoNDaVz584J+yIiIkgulxu1P3DgAAUGBpK9vT0FBQXR4cOHzZ53ypQppFAo6Pr166RSqSgmJoYkEgnpdDqLfVEoFCQWi0mtVgtLSwBl5n399dfk5uZGV69eJYPBQI8ePSKFQkFTp04lBwcHGjVqFKWmptL58+fp2bNnrQasjIwMGjhwIJ0+fbq7L4ux1+KgxTpVXV0dAWj1C1GhUJCzs3PXdaoX+Omnn0ilUpndp9VqKTs7m2bMmEGOjo40fPhwSklJod9//90ogOl0Otq9ezc5OTnRyZMnu/gK/q+oqMjkTrtlKSsrs3hcRESESftPP/20C3vOugO/02Kd6u7duwgICMC1a9csZs/IysrCokWLMHjwYBgMBrz77rvYunUrgoKCuri3vY9Op8ORI0eEmmDu7u7CII6qqiosW7YMBw8e7NYEyk1NTXj8+LHRtrS0NBQWFqKystLiezquzN03cdBincZgMCA2NhZarRZnzpyx2K60tBR37tzB2LFj0dDQgG3btqG4uBjl5eUmaavYm3v+/DkKCgpw8OBB5Ofno7GxEb/++iumTp3a3V0z0tzcjMGDByMpKQlpaWkW23Fl7r6JgxbrNEuWLMHRo0dx5syZNgWf5uZmjBo1CvHx8fjqq686sYd9V8to0NmzZ3d3V0wcPHgQH374Ie7fv9/q3w1X5u6bOGixTpGYmIj8/HwUFxfD39+/zcfPmjULtra22LdvXyf0jvVkMTExAPDaSgJcmbtv4snFrEMREZKSkpCXl4dTp069UcDS6/W4du2a8OXFrNObJL998OABCgoKcODAgdeePyEhQfh5zJgx8PHxQWRkJCorK7kydy/Wc6a4s15h6dKlyM7ORk5ODgYOHCjU/Xrx4oXQZv78+Vi3bp2w/uWXX+K3337DH3/8gcuXL+Ojjz7C/fv3sWjRIpPzp6enQyQSGS3mMn7/E9cC6x4pKSm4efNmq8u/M8crFAq4ubkhNja2zZ8nlUoBvBr8w3ovvtNiHWrXrl0AXr1v+CeFQoGPP/4YAExSAj158gSLFy+GRqPBoEGDMG7cOJSUlGD06NFmPyMoKAgnTpwQ1m1tLf8Zcy2w7tPW5LdEBIVCgfnz58POzq7Nn6dSqQC8yp/Iei9+p8WsSnp6OpRKpfAF9TpcC8x6FBYWIioqymy9rJqaGkRGRuLnn39GaGgoKisrkZOTg5iYGLi5ueHq1atYuXIl3nrrLZw+fbqbroB1BX48yKzOnTt34Ovri2HDhmHu3Lmorq622La0tNSovDoATJkyhcur90CZmZkIDw83+7jXUmXuyZMnY+TIkUhJScHMmTON8mOy3okfDzKrIpVKkZWVhREjRkCtVuOLL77AxIkTcf36dbNl67kWmPXIycmxuG/o0KFGJVn8/Pz4jqqP4qDFrEp0dLTw89ixYyGVSjFkyBAcOHAACxcu7MaeMca6Aj8eZFbNxcUFgYGBFkeMcS0wxnoXDlrMqul0OlRWVlocMRYWFobCwkKjbcePH+fy6oxZKQ5azKqsWrUKp0+fxr1791BSUoIZM2agX79+iI+PB2A6B2z58uU4duwYvvvuO1RUVCA9PR0XL15EYmKi2fMPHTrUZB6YSCTC0qVLzbbPysoyadu/f/+Ov/AeasuWLQgPD4ejoyNcXFzMtqmuroZMJoOjoyM8PT2xevVqvHz5stXzPn78GHPnzoVYLIaLiwsWLlwInU7XCVfArA2/02JW5cGDB4iPj8ejR4/g4eGBCRMm4Ny5c8J8oH/PAQsPD0dOTg42bNiA9evXIyAgAEql0uIcrQsXLkCv1wvr169fx/vvv49Zs2ZZ7JNYLMatW7eE9TetHmyNmpqaMGvWLISFhSEzM9Nkv16vh0wmg7e3N0pKSqBWq4V5WFu3brV43rlz50KtVuP48eNobm7GggULkJCQ0OpgDdZHdEc9FMasxfLly+ntt98mg8Fgdj/XAnvF0u/hyJEjZGNjY1TUc9euXSQWi+nvv/82e64bN24QALpw4YKw7ejRoyQSiaimpqbD+86sCz8eZMyCpqYmZGdn45NPPmn17kmn02HIkCHw8/PD9OnTUV5e3oW97NlKS0sxZswYo2kHU6ZMwdOnTy3+nkpLS+Hi4oKQkBBhW1RUFGxsbHD+/PlO7zPr2ThoMWaBUqmEVqsV0k+ZM2LECOzduxf5+fnIzs6GwWBAeHg4Hjx40HUd7cEszZNr2WfpGE9PT6Nttra2cHV15fl1jIMWY5ZkZmYiOjoavr6+FtuEhYVh/vz5CA4ORkREBHJzc+Hh4YE9e/Z0YU87VmpqqtnBKP9cKioqurubrI/igRiMmXH//n2cOHGizbWZ7Ozs8M4771h1pvGUlJRW7y4BmGRnt8Tb2xtlZWVG21rmzVmaK+ft7Y26ujqjbS9fvsTjx495fh3joMWYOQqFAp6enpDJZG06rjfUAmtrdvbWhIWFYcuWLairqxMe+R0/fhxisdhiFv+wsDBotVpcunQJ48aNAwCcPHkSBoNBKD/C+i5+PMjYvxgMBigUCsjlcpOyJ29SC6y4uBjTpk2Dr68vRCIRlEql0TmJCBs3boSPjw8GDBiAqKgo3Llz57X93LlzJ4YOHYr+/ftDKpWa3NF0herqaqhUKlRXV0Ov10OlUkGlUglzqiZPnozRo0dj3rx5uHLlCgoKCrBhwwYsXboUDg4OAICysjKMHDkSNTU1AIBRo0bhgw8+wOLFi1FWVoazZ88iMTERc+bMafVRLesjunv4ImM9TUFBAQGgW7dumeyLiIgguVwurK9YsYIkEgnZ29uTl5cXxcTE0OXLl42OOXLkCH3++eeUm5tLACgvL89o/zfffEPOzs6kVCrpypUrFBsbS/7+/vTixQuLfdy/fz/Z29vT3r17qby8nBYvXkwuLi5UW1vbrmtvK7lcTgBMlqKiIqHNvXv3KDo6mgYMGEDu7u6UkpJCzc3Nwv6ioiICQFVVVcK2R48eUXx8PDk5OZFYLKYFCxbQs2fPuvDKWE/F9bQY60IikQh5eXmIi4sD8Oouy9fXFykpKVi1ahUAoKGhAV5eXsjKysKcOXPMnkcqlWL8+PHIyMgA8Oru0M/PD0lJSUhNTe2Sa2GsO/DjQca6UVVVFTQajVHNL2dnZ0ilUos1v5qamnDp0iWjY2xsbBAVFcV1wlivx0GLsW7UMu+oLTW/6uvrodfruU4Y65M4aDHGGLMaHLQY60Yt847aUvPL3d0d/fr14zphrE/ioMVYN/L394e3t7dRza+nT5/i/PnzFmt+2dvbY9y4cUbHGAwGFBYWcp0w1uvx5GLGOplOpzPKkFFVVQWVSgVXV1dIJBKsWLECmzdvRkBAAPz9/ZGWlgZfX19hhCEAREZGYsaMGUIdsOTkZMjlcoSEhCA0NBQ//PADGhsbsWDBgq6+PMa6FActxjrZxYsXMWnSJGE9OTkZACCXy5GVlYU1a9agsbERCQkJ0Gq1mDBhAo4dO2ZUTLKyshL19fXC+uzZs/HXX39h48aN0Gg0CA4OxrFjx0wGZzDW2/A8LcYYY1aD32kxxhizGhy0GGOMWQ0OWowxxqwGBy3GGGNWg4MWY4wxq8FBizHGmNXgoMUYY8xqcNBijDFmNThoMcYYsxoctBhjjFkNDlqMMcasBgctxhhjVuN//1Rmf4Pogh0AAAAASUVORK5CYII=", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "test_estimator.peek_3D(length=0.15)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Old version of training loop" ] }, { @@ -325,6 +638,7 @@ " 'num_repeat' : 2,\n", " 'num_block' : 3,\n", " 'device' : device,\n", + " 'num_input' : 12,\n", "}\n", "train_percents = np.arange(1.0,1.01,0.1)\n", "RMSE_history_end = np.zeros(len(train_percents))\n", @@ -453,7 +767,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.7" + "version": "3.10.13" } }, "nbformat": 4, diff --git a/Modeling eMNS/Neural_network.py b/Modeling eMNS/Neural_network.py index b309655..123ff4a 100644 --- a/Modeling eMNS/Neural_network.py +++ b/Modeling eMNS/Neural_network.py @@ -5,10 +5,10 @@ import numpy as np # set up dataset class class eMNS_Dataset(torch.utils.data.Dataset): - def __init__(self,train_x,train_y): + def __init__(self,x,y): #data loading - self.x = train_x - self.y = train_y + self.x = x + self.y = y self.n_samples = self.x.shape[0] diff --git a/Modeling eMNS/Training_loop_v2.py b/Modeling eMNS/Training_loop_v2.py index 2442007..4c99da5 100644 --- a/Modeling eMNS/Training_loop_v2.py +++ b/Modeling eMNS/Training_loop_v2.py @@ -5,7 +5,7 @@ from torch.nn.parallel import DistributedDataParallel import torch.nn.functional as F from early_stopping import EarlyStopping, EarlyDecay -from utils import compute_discrete_curl, denorm, max_min_norm, denorm_ray +from utils import Jacobian3, grad_loss_Jacobain, check_rmse_CNN, compute_discrete_curl, compute_discrete_divergence, grad_loss, get_mean_of_dataloader, gridData_reshape from Neural_network import ResidualEMNSBlock_3d, BigBlock, Generative_net import numpy as np import ray @@ -84,7 +84,6 @@ def train_GM(config): """ #---------------unpack config--------------------- # print(config) - batch_size = config['batch_size'] epochs = config["epochs"] verbose = config['verbose'] lr_max = config['lr_max'] @@ -95,9 +94,6 @@ def train_GM(config): learning_rate_decay = config['learning_rate_decay'] maxB = config['maxB'] minB = config['minB'] - skip_spacing = config['skip_spacing'] - num_repeat = config['num_repeat'] - num_block = config['num_block'] device = config['device'] train_set = config['train_set'] valid_set = config['valid_set'] @@ -105,15 +101,7 @@ def train_GM(config): #################################################### #--------------model construction------------------ #################################################### - num_input = 12 - output_shape = (3,16,16,16) - SB_args = (64,64,skip_spacing,num_repeat) # (Cin, Cout, skip_spacing, num_repeat) - BB_args = (2,num_block) # (scale_factor, num_block) - SB_block = ResidualEMNSBlock_3d - BB_block = BigBlock - - - model = Generative_net(SB_args, BB_args, SB_block, BB_block, num_input=num_input, output_shape= output_shape) + model = construct_model_GM(config) @@ -226,20 +214,24 @@ def train_GM(config): print() adjust_epoch_count += 1 - # # create checkpoint - # base_model = (model.module - # if isinstance(model, DistributedDataParallel) else model) - # checkpoint_dir = tempfile.mkdtemp() - # # load back training state - # checkpoint_data = { - # "epoch": epoch, - # "net_state_dict": base_model.state_dict(), - # "optimizer_state_dict": optimizer.state_dict(), - # } - # torch.save(checkpoint_data, os.path.join(checkpoint_dir, "model.pt")) - # checkpoint = Checkpoint.from_directory(checkpoint_dir) - #Send the current training result back to Tune - train.report({'rmse_val':rmse_val.item(), 'rmse_train': rmse.item(), 'loss':loss.item()}) + if epoch % (epochs-1) == 0: + # create checkpoint only at the begin and the end of epochs + base_model = (model.module + if isinstance(model, DistributedDataParallel) else model) + checkpoint_dir = tempfile.mkdtemp() + # load back training state + checkpoint_data = { + "epoch": epoch, + "net_state_dict": base_model.state_dict(), + 'model': base_model + # "optimizer_state_dict": optimizer.state_dict(), + } + torch.save(checkpoint_data, os.path.join(checkpoint_dir, "model.pt")) + checkpoint = Checkpoint.from_directory(checkpoint_dir) + # Send the current training result back to Tune + train.report({'rmse_val':rmse_val.item(), 'rmse_train': rmse.item(), 'loss':loss.item()},checkpoint=checkpoint) + else: + train.report({'rmse_val':rmse_val.item(), 'rmse_train': rmse.item(), 'loss':loss.item()}) @@ -250,131 +242,23 @@ def train_GM(config): return rmse_history, rmse_val_history,loss_history, iter_history,mse_history, mse_val_history,epoch_stop,Rsquare #------------------------------------------------------------------------------------------------------- +def construct_model_GM(config): + num_input = config['num_input'] + skip_spacing = config['skip_spacing'] + num_repeat = config['num_repeat'] + num_block = config['num_block'] + output_shape = (3,16,16,16) + SB_args = (64,64,skip_spacing,num_repeat) # (Cin, Cout, skip_spacing, num_repeat) + BB_args = (2,num_block) # (scale_factor, num_block) + SB_block = ResidualEMNSBlock_3d + BB_block = BigBlock + model = Generative_net(SB_args, BB_args, SB_block, BB_block, num_input=num_input, output_shape= output_shape) + return model -def get_mean_of_dataloader(dataloader,model,device): - num_samples = 0 - b = torch.zeros(1,device=device) - model.eval() - for x,y in dataloader: - y = y.to(device=device,dtype=torch.float) - # use sum instead of mean, what do you think? - y_sum = y.sum(dim=0,keepdim=True) - num_samples += y.shape[0] - # print(y.shape[0]) - b =b+y_sum - return b/num_samples - - -def check_rmse_CNN(dataloader,model, grid_space, device, DF, maxB=[],minB=[]): - ''' - Check RMSE of CNN - ''' - mse_temp = 0 - R_temp=0 - Rsquare=0 - num_samples = 0 - # print(Bfield_mean) - - data = next(iter(dataloader)) - mean = data[0].mean() - - Bfield_mean=get_mean_of_dataloader(dataloader,model,device) - - model.eval() # set model to evaluation model - - with torch.no_grad(): - for x,y in dataloader: - x = x.to(device=device,dtype=torch.float) - y = y.to(device=device,dtype=torch.float) - num_samples += x.shape[0] - if DF: - _, scores = Jacobian3(model(x)) - else: - scores = model(x) - - # compute mse and R2 by de-normalize data - mse_temp += F.mse_loss(1e3*denorm(scores,maxB,minB,device), 1e3*denorm(y,maxB,minB, device) ,reduction='sum') - R_temp += F.mse_loss(1e3*denorm(Bfield_mean.expand_as(y),maxB,minB,device), 1e3*denorm(y,maxB,minB,device), reduction='sum') - - - rmse = torch.sqrt(mse_temp/num_samples/grid_space/3) - - Rsquare=1-mse_temp/R_temp/num_samples - print(f'Got rmse {rmse}') - - - - - return rmse, mse_temp/num_samples/grid_space/3, Rsquare #----------------------------------------------------------------- -#---------------------------------------------------------------- -def grad_loss(preds, y): - ''' - preds, y shape: (batch, dimension, grid_x, grid_y, grid_z) - This function computes lamda_g*| nabla(y) - nabla(preds)| - ''' - grad_preds = torch.gradient(preds,spacing=1.0) - grad_y = torch.gradient(y, spacing=1) - grad_loss = 0 - for i in range(2,5): - # accumulate grad loss for grad_x,y,z - grad_loss += torch.mean(torch.abs(grad_y[i]-grad_preds[i]))/3 - return grad_loss - -def grad_loss_Jacobain(preds,y): - ''' - preds, y shape: (batch, dimension, grid_x, grid_y, grid_z) - This function computes lamda_g*| nabla(y) - nabla(preds)| by Jacobian - ''' - Jaco_preds,_ = Jacobian3(preds) - Jaco_y ,_ = Jacobian3(y) - - grad_loss = torch.mean(torch.abs(Jaco_preds - Jaco_y)) - - return grad_loss - - -def Jacobian3(x): - ''' - Jacobian for 3D vector field - -------input---------- - x shape: (batch, dimension,grid_x, grid_y, grid_z) - ''' - - dudx = x[:, 0, 1:, :, :] - x[:, 0, :-1, :, :] - dvdx = x[:, 1, 1:, :, :] - x[:, 1, :-1, :, :] - dwdx = x[:, 2, 1:, :, :] - x[:, 2, :-1, :, :] - - dudy = x[:, 0, :, 1:, :] - x[:, 0, :, :-1, :] - dvdy = x[:, 1, :, 1:, :] - x[:, 1, :, :-1, :] - dwdy = x[:, 2, :, 1:, :] - x[:, 2, :, :-1, :] - - dudz = x[:, 0, :, :, 1:] - x[:, 0, :, :, :-1] - dvdz = x[:, 1, :, :, 1:] - x[:, 1, :, :, :-1] - dwdz = x[:, 2, :, :, 1:] - x[:, 2, :, :, :-1] - - dudx = torch.cat((dudx, torch.unsqueeze(dudx[:,-1],dim=1)), dim=1) - dvdx = torch.cat((dvdx, torch.unsqueeze(dvdx[:,-1],dim=1)), dim=1) - dwdx = torch.cat((dwdx, torch.unsqueeze(dwdx[:,-1],dim=1)), dim=1) - - dudy = torch.cat((dudy, torch.unsqueeze(dudy[:,:,-1],dim=2)), dim=2) - dvdy = torch.cat((dvdy, torch.unsqueeze(dvdy[:,:,-1],dim=2)), dim=2) - dwdy = torch.cat((dwdy, torch.unsqueeze(dwdy[:,:,-1],dim=2)), dim=2) - - dudz = torch.cat((dudz, torch.unsqueeze(dudz[:,:,:,-1],dim=3)), dim=3) - dvdz = torch.cat((dvdz, torch.unsqueeze(dvdz[:,:,:,-1],dim=3)), dim=3) - dwdz = torch.cat((dwdz, torch.unsqueeze(dwdz[:,:,:,-1],dim=3)), dim=3) - - u = dwdy - dvdz - v = dudz - dwdx - w = dvdx - dudy - - j = torch.stack([dudx,dudy,dudz,dvdx,dvdy,dvdz,dwdx,dwdy,dwdz],axis=1) - c = torch.stack([u,v,w],axis=1) #vorticity - - return j,c + diff --git a/Modeling eMNS/utils.py b/Modeling eMNS/utils.py index dd13c56..6c7d7c3 100644 --- a/Modeling eMNS/utils.py +++ b/Modeling eMNS/utils.py @@ -1,5 +1,7 @@ -import torch,ray +import torch,ray,os import matplotlib.pyplot as plt +import torch.nn.functional as F +import numpy as np def compute_discrete_curl(A_field, device): ''' A_field: (batch, Dimensions, grid_x, grid_y, grid_z) @@ -42,10 +44,10 @@ def plot_3D_vector_field(position, vectorField, figsize=(5,5), length=1): ''' Plot 3D vector field -----------input---------- - position: position of grids shape: (1,dimensions,grid_x,grid_y,grid_z) - vectorField: shape (1,dimensions,grid_x,grid_y,grid_z) + position: position of grids shape: (dimensions,grid_x,grid_y,grid_z) + vectorField: shape (dimensions,grid_x,grid_y,grid_z) ''' - fig = plt.figure(figsize=(5,5)) + fig = plt.figure(figsize=figsize) ax = fig.add_subplot(111,projection='3d') p = gridData_reshape(position) #(-1, dimension) vector = gridData_reshape(vectorField) #(-1, dimension) @@ -96,4 +98,195 @@ def plot_ray_results(results, metrics_names,legend=False, ylim=None, xlim=None): ax = None for data in dfs.values(): - ax = data[metrics_name].plot(ax=ax, legend=legend, ylim=ylim, xlim=xlim) \ No newline at end of file + ax = data[metrics_name].plot(ax=ax, legend=legend, ylim=ylim, xlim=xlim) + +#------------------------------------------------------------------------------------------------------- + +def get_mean_of_dataloader(dataloader,model,device): + num_samples = 0 + b = torch.zeros(1,device=device) + model.eval() + for x,y in dataloader: + y = y.to(device=device,dtype=torch.float) + # use sum instead of mean, what do you think? + y_sum = y.sum(dim=0,keepdim=True) + num_samples += y.shape[0] + # print(y.shape[0]) + b =b+y_sum + return b/num_samples + + + +def check_rmse_CNN(dataloader,model, grid_space, device, DF, maxB=[],minB=[]): + ''' + Check RMSE of CNN + ''' + mse_temp = 0 + R_temp=0 + Rsquare=0 + num_samples = 0 + # print(Bfield_mean) + + data = next(iter(dataloader)) + mean = data[0].mean() + + Bfield_mean=get_mean_of_dataloader(dataloader,model,device) + + model.eval() # set model to evaluation model + + with torch.no_grad(): + for x,y in dataloader: + x = x.to(device=device,dtype=torch.float) + y = y.to(device=device,dtype=torch.float) + num_samples += x.shape[0] + if DF: + _, scores = Jacobian3(model(x)) + else: + scores = model(x) + + # compute mse and R2 by de-normalize data + mse_temp += F.mse_loss(1e3*denorm(scores,maxB,minB,device), 1e3*denorm(y,maxB,minB, device) ,reduction='sum') + R_temp += F.mse_loss(1e3*denorm(Bfield_mean.expand_as(y),maxB,minB,device), 1e3*denorm(y,maxB,minB,device), reduction='sum') + + + rmse = torch.sqrt(mse_temp/num_samples/grid_space/3) + + Rsquare=1-mse_temp/R_temp/num_samples + print(f'Got rmse {rmse}') + + return rmse, mse_temp/num_samples/grid_space/3, Rsquare +class estimate_test_set(): + ''' + This class estimate the error of the test set + ''' + def __init__(self, checkpoint, test_set, train_loop_config) -> None: + + self.train_loop_config = train_loop_config + #--------------------create test loader------------ + self.test_set = test_set + self.test_loader = torch.utils.data.DataLoader(dataset=test_set,batch_size=train_loop_config['batch_size'],shuffle=True) + + # load checkpoint and model + if checkpoint: + with checkpoint.as_directory() as checkpoint_dir: + self.model = torch.load( + os.path.join(checkpoint_dir, "model.pt"),map_location=train_loop_config['device'])['model'] + + def fit(self): + # estimate rmse for test set + rmse_test, mse_test, R2_test = check_rmse_CNN( + self.test_loader, self.model, self.train_loop_config['grid_space'], self.train_loop_config['device'], self.train_loop_config['DF'], self.train_loop_config['maxB'], self.train_loop_config['minB']) + + print(f'rmse for test set: {rmse_test:.4f}mT') + print(f' mse for test set: {mse_test:.4f}mT') + print(f' R2 for test set: {R2_test:.4f}') + return rmse_test, mse_test, R2_test + + def peek_z(self, z_plane_index): + # for plotting a random choice sample in test set + plot_index = np.random.choice(self.test_set.indices) + plot_sample = self.test_set.dataset[plot_index] + + # prediction B field + + self.plot_B_pred = 1e3*denorm(self.model(torch.unsqueeze(plot_sample[0],0).to(dtype=torch.float)), self.train_loop_config['maxB'], self.train_loop_config['minB'], self.train_loop_config['device']) + self.plot_B = 1e3*denorm(plot_sample[1], self.train_loop_config['maxB'], self.train_loop_config['minB'], self.train_loop_config['device']) + + ylables=['Bx(mT)','By(mT)','Bz(mT)'] + plot_rmse = torch.sqrt(F.mse_loss(self.plot_B, torch.squeeze(self.plot_B_pred,0), reduction='mean')) + print(f'plot sample rmse: {plot_rmse:.4f}mT') + + f = plt.figure(figsize=(15,15)) + for i in range(1,4): + + B_est_temp =self.plot_B_pred[0,i-1,:,:,z_plane_index].detach() + ax = f.add_subplot(3,2,2*i-1) + img_plot = ax.imshow( B_est_temp ) + plt.ylabel(ylables[i-1]) + + Bfield_temp = self.plot_B[i-1,:,:,z_plane_index] + ax2 = f.add_subplot(3,2,2*i) + img_plot=ax2.imshow(Bfield_temp) + plt.colorbar(img_plot,ax=[ax,ax2]) + # plt.ylabel(ylables[i-1]) + plt.show() + + def peek_3D(self,length=0.1): + from utils import plot_3D_vector_field + x = torch.linspace(-10,10,16) + y = torch.linspace(-10,10,16) + z = torch.linspace(-10,10,16) + print(x.shape) + position = torch.cat(torch.meshgrid([x,y,z],indexing='ij')).reshape(3,16,16,16) + print(position.shape) + print(torch.squeeze(self.plot_B_pred,0).shape) + plot_3D_vector_field(position[:,:,:,::15], torch.squeeze(self.plot_B_pred,0).detach()[:,:,:,::15], length=length) + + plot_3D_vector_field(position[:,:,:,::15], self.plot_B[:,:,:,::15], length=length) + +#---------------------------------------------------------------- +def grad_loss(preds, y): + ''' + preds, y shape: (batch, dimension, grid_x, grid_y, grid_z) + This function computes lamda_g*| nabla(y) - nabla(preds)| + ''' + grad_preds = torch.gradient(preds,spacing=1.0) + grad_y = torch.gradient(y, spacing=1) + grad_loss = 0 + for i in range(2,5): + # accumulate grad loss for grad_x,y,z + grad_loss += torch.mean(torch.abs(grad_y[i]-grad_preds[i]))/3 + return grad_loss + +def grad_loss_Jacobain(preds,y): + ''' + preds, y shape: (batch, dimension, grid_x, grid_y, grid_z) + This function computes lamda_g*| nabla(y) - nabla(preds)| by Jacobian + ''' + Jaco_preds,_ = Jacobian3(preds) + Jaco_y ,_ = Jacobian3(y) + + grad_loss = torch.mean(torch.abs(Jaco_preds - Jaco_y)) + + return grad_loss + + +def Jacobian3(x): + ''' + Jacobian for 3D vector field + -------input---------- + x shape: (batch, dimension,grid_x, grid_y, grid_z) + ''' + + dudx = x[:, 0, 1:, :, :] - x[:, 0, :-1, :, :] + dvdx = x[:, 1, 1:, :, :] - x[:, 1, :-1, :, :] + dwdx = x[:, 2, 1:, :, :] - x[:, 2, :-1, :, :] + + dudy = x[:, 0, :, 1:, :] - x[:, 0, :, :-1, :] + dvdy = x[:, 1, :, 1:, :] - x[:, 1, :, :-1, :] + dwdy = x[:, 2, :, 1:, :] - x[:, 2, :, :-1, :] + + dudz = x[:, 0, :, :, 1:] - x[:, 0, :, :, :-1] + dvdz = x[:, 1, :, :, 1:] - x[:, 1, :, :, :-1] + dwdz = x[:, 2, :, :, 1:] - x[:, 2, :, :, :-1] + + dudx = torch.cat((dudx, torch.unsqueeze(dudx[:,-1],dim=1)), dim=1) + dvdx = torch.cat((dvdx, torch.unsqueeze(dvdx[:,-1],dim=1)), dim=1) + dwdx = torch.cat((dwdx, torch.unsqueeze(dwdx[:,-1],dim=1)), dim=1) + + dudy = torch.cat((dudy, torch.unsqueeze(dudy[:,:,-1],dim=2)), dim=2) + dvdy = torch.cat((dvdy, torch.unsqueeze(dvdy[:,:,-1],dim=2)), dim=2) + dwdy = torch.cat((dwdy, torch.unsqueeze(dwdy[:,:,-1],dim=2)), dim=2) + + dudz = torch.cat((dudz, torch.unsqueeze(dudz[:,:,:,-1],dim=3)), dim=3) + dvdz = torch.cat((dvdz, torch.unsqueeze(dvdz[:,:,:,-1],dim=3)), dim=3) + dwdz = torch.cat((dwdz, torch.unsqueeze(dwdz[:,:,:,-1],dim=3)), dim=3) + + u = dwdy - dvdz + v = dudz - dwdx + w = dvdx - dudy + + j = torch.stack([dudx,dudy,dudz,dvdx,dvdy,dvdz,dwdx,dwdy,dwdz],axis=1) + c = torch.stack([u,v,w],axis=1) #vorticity + + return j,c \ No newline at end of file diff --git a/SOFA_playground/Qubot/MagneticCatheterSim/mcr_radiologyInstrumentDOFs.py b/SOFA_playground/Qubot/MagneticCatheterSim/mcr_radiologyInstrumentDOFs.py index bf3de87..affa816 100644 --- a/SOFA_playground/Qubot/MagneticCatheterSim/mcr_radiologyInstrumentDOFs.py +++ b/SOFA_playground/Qubot/MagneticCatheterSim/mcr_radiologyInstrumentDOFs.py @@ -78,14 +78,6 @@ def Instrument_DOFs( indexFromEnd=True, showArrowSize=1e-2, showColor=[1,0,0,1]) - # PhysicsModel.addObject( - # 'ConstantForceField', - # name='CollectorMagneticForceField_test', - # indices=np.arange(topo_instruments[0].nbsections[-1]), - # forces=np.tile(np.array([100,1000,0,0,0,100]), (topo_instruments[0].nbsections[-1],1)), - # indexFromEnd=True, - # showArrowSize=1e-2, - # showColor=[1,0,0,1]) PhysicsModel.addObject( 'ConstantForceField', name='MagneticFieldVisual', From 946d0577458a57e1e4f84e1b3efad8d928eed2c2 Mon Sep 17 00:00:00 2001 From: wangjunang Date: Thu, 21 Mar 2024 14:54:38 +0800 Subject: [PATCH 15/16] tune v5 and plot v1 --- Modeling eMNS/Generative_model_v2.ipynb | 317 ++---------------------- 1 file changed, 14 insertions(+), 303 deletions(-) diff --git a/Modeling eMNS/Generative_model_v2.ipynb b/Modeling eMNS/Generative_model_v2.ipynb index 8fa372b..d8de9eb 100644 --- a/Modeling eMNS/Generative_model_v2.ipynb +++ b/Modeling eMNS/Generative_model_v2.ipynb @@ -18,25 +18,9 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Using cpu\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/qubot/.pyenv/versions/3.10.13/lib/python3.10/site-packages/torch/cuda/__init__.py:628: UserWarning: Can't initialize NVML\n", - " warnings.warn(\"Can't initialize NVML\")\n" - ] - } - ], + "outputs": [], "source": [ "%reload_ext autoreload\n", "%autoreload 2\n", @@ -54,19 +38,9 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "torch.Size([146, 6, 21, 21, 21])\n", - "current shape torch.Size([146, 12])\n", - "Bfield shape torch.Size([146, 3, 16, 16, 16])\n" - ] - } - ], + "outputs": [], "source": [ "from ReadData import ReadCurrentAndField_CNN\n", "import glob\n", @@ -134,185 +108,9 @@ }, { "cell_type": "code", - "execution_count": 38, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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Tune Status

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Current time:2024-03-21 11:57:19
Running for: 00:00:43.56
Memory: 10.8/31.0 GiB
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System Info

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Logical resource usage: 2.0/16 CPUs, 0/0 GPUs\n", - "
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Trial Status

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Trial name status loc iter total time (s) rmse_val rmse_train loss
TorchTrainer_0385f_00000TERMINATED192.168.8.117:20581 10 38.5563 3.16977 2.374340.0456446
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A sync will be triggered whenever a trial has checkpointed more than `num_to_keep` times since last sync or if 300 seconds have passed since last sync. If you have set `num_to_keep` in your `CheckpointConfig`, consider increasing the checkpoint frequency or keeping more checkpoints. You can supress this warning by changing the `TUNE_WARN_EXCESSIVE_EXPERIMENT_CHECKPOINT_SYNC_THRESHOLD_S` environment variable.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\u001b[36m(RayTrainWorker pid=20651)\u001b[0m Epoch 1, Iteration 32, loss = 0.1101, l1 loss=0.0793, grad loss=0.0308\n", - "\u001b[36m(RayTrainWorker pid=20651)\u001b[0m Got rmse 6.467669486999512\n", - "\u001b[36m(RayTrainWorker pid=20651)\u001b[0m Got rmse 5.52919864654541\n", - "\u001b[36m(RayTrainWorker pid=20651)\u001b[0m \n", - "\u001b[36m(RayTrainWorker pid=20651)\u001b[0m Epoch 2, Iteration 48, loss = 0.0885, l1 loss=0.0615, grad loss=0.0270\n", - "\u001b[36m(RayTrainWorker pid=20651)\u001b[0m Got rmse 5.83610725402832\n", - "\u001b[36m(RayTrainWorker pid=20651)\u001b[0m Got rmse 4.7611260414123535\n", - "\u001b[36m(RayTrainWorker pid=20651)\u001b[0m \n", - "\u001b[36m(RayTrainWorker pid=20651)\u001b[0m Epoch 3, Iteration 64, loss = 0.0745, l1 loss=0.0504, grad loss=0.0241\n", - "\u001b[36m(RayTrainWorker pid=20651)\u001b[0m Got rmse 4.861945152282715\n", - "\u001b[36m(RayTrainWorker pid=20651)\u001b[0m Got rmse 4.036675930023193\n", - "\u001b[36m(RayTrainWorker pid=20651)\u001b[0m \n", - "\u001b[36m(RayTrainWorker pid=20651)\u001b[0m Epoch 4, Iteration 80, loss = 0.0710, l1 loss=0.0473, grad loss=0.0238\n", - "\u001b[36m(RayTrainWorker pid=20651)\u001b[0m Got rmse 4.330577373504639\n", - "\u001b[36m(RayTrainWorker pid=20651)\u001b[0m Got rmse 3.654207944869995\n", - "\u001b[36m(RayTrainWorker pid=20651)\u001b[0m \n", - "\u001b[36m(RayTrainWorker pid=20651)\u001b[0m Epoch 5, Iteration 96, loss = 0.0633, l1 loss=0.0413, grad loss=0.0219\n", - "\u001b[36m(RayTrainWorker pid=20651)\u001b[0m Got rmse 3.7916672229766846\n", - "\u001b[36m(RayTrainWorker pid=20651)\u001b[0m Got rmse 3.040168285369873\n", - "\u001b[36m(RayTrainWorker pid=20651)\u001b[0m \n", - "\u001b[36m(RayTrainWorker pid=20651)\u001b[0m Epoch 6, Iteration 112, loss = 0.0529, l1 loss=0.0326, grad loss=0.0203\n", - "\u001b[36m(RayTrainWorker pid=20651)\u001b[0m Got rmse 3.4171268939971924\n", - "\u001b[36m(RayTrainWorker pid=20651)\u001b[0m Got rmse 2.6344687938690186\n", - "\u001b[36m(RayTrainWorker pid=20651)\u001b[0m \n", - "\u001b[36m(RayTrainWorker pid=20651)\u001b[0m Epoch 7, Iteration 128, loss = 0.0482, l1 loss=0.0292, grad loss=0.0191\n", - "\u001b[36m(RayTrainWorker pid=20651)\u001b[0m Got rmse 3.147188186645508\n", - "\u001b[36m(RayTrainWorker pid=20651)\u001b[0m Got rmse 2.4714787006378174\n", - "\u001b[36m(RayTrainWorker pid=20651)\u001b[0m \n", - "\u001b[36m(RayTrainWorker pid=20651)\u001b[0m Epoch 8, Iteration 144, loss = 0.0521, l1 loss=0.0321, grad loss=0.0200\n", - "\u001b[36m(RayTrainWorker pid=20651)\u001b[0m Got rmse 3.2194416522979736\n", - "\u001b[36m(RayTrainWorker pid=20651)\u001b[0m Got rmse 2.39068865776062\n", - "\u001b[36m(RayTrainWorker pid=20651)\u001b[0m \n", - "\u001b[36m(RayTrainWorker pid=20651)\u001b[0m Epoch 9, Iteration 160, loss = 0.0456, l1 loss=0.0271, grad loss=0.0186\n", - "\u001b[36m(RayTrainWorker pid=20651)\u001b[0m Got rmse 3.169771194458008\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\u001b[36m(RayTrainWorker pid=20651)\u001b[0m Checkpoint successfully created at: Checkpoint(filesystem=local, path=/home/qubot/Trained_model/EMS_CNN_s_2r_1b_3/TorchTrainer_0385f_00000_0_2024-03-21_11-56-36/checkpoint_000001)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\u001b[36m(RayTrainWorker pid=20651)\u001b[0m Got rmse 2.3743371963500977\n", - "\u001b[36m(RayTrainWorker pid=20651)\u001b[0m \n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2024-03-21 11:57:19,905\tWARNING experiment_state.py:323 -- Experiment checkpoint syncing has been triggered multiple times in the last 30.0 seconds. A sync will be triggered whenever a trial has checkpointed more than `num_to_keep` times since last sync or if 300 seconds have passed since last sync. If you have set `num_to_keep` in your `CheckpointConfig`, consider increasing the checkpoint frequency or keeping more checkpoints. You can supress this warning by changing the `TUNE_WARN_EXCESSIVE_EXPERIMENT_CHECKPOINT_SYNC_THRESHOLD_S` environment variable.\n", - "2024-03-21 11:57:19,911\tINFO tune.py:1042 -- Total run time: 43.58 seconds (42.73 seconds for the tuning loop).\n" - ] - } - ], + "outputs": [], "source": [ "from Neural_network import eMNS_Dataset\n", "from Training_loop_v2 import train_GM\n", @@ -453,32 +251,9 @@ }, { "cell_type": "code", - "execution_count": 44, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Result(\n", - " metrics={'rmse_val': 3.169771194458008, 'rmse_train': 2.3743371963500977, 'loss': 0.045644618570804596},\n", - " path='/home/qubot/Trained_model/EMS_CNN_s_2r_1b_3/TorchTrainer_0385f_00000_0_2024-03-21_11-56-36',\n", - " filesystem='local',\n", - " checkpoint=Checkpoint(filesystem=local, path=/home/qubot/Trained_model/EMS_CNN_s_2r_1b_3/TorchTrainer_0385f_00000_0_2024-03-21_11-56-36/checkpoint_000001)\n", - ")\n" - ] - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "from utils import plot_ray_results\n", "print(result)\n", @@ -487,21 +262,9 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "ename": "AttributeError", - "evalue": "'Result' object has no attribute 'get_best_result'", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[7], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m best_result \u001b[38;5;241m=\u001b[39m \u001b[43mresults\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_best_result\u001b[49m(metric\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mrmse_val\u001b[39m\u001b[38;5;124m'\u001b[39m,mode\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mmin\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[1;32m 2\u001b[0m \u001b[38;5;28mprint\u001b[39m(best_result)\n", - "\u001b[0;31mAttributeError\u001b[0m: 'Result' object has no attribute 'get_best_result'" - ] - } - ], + "outputs": [], "source": [ "best_result = results.get_best_result(metric='rmse_val',mode='min')\n", "print(best_result)" @@ -528,31 +291,9 @@ }, { "cell_type": "code", - "execution_count": 92, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Got rmse 2.705864906311035\n", - "rmse for test set: 2.7059mT\n", - " mse for test set: 7.3217mT\n", - " R2 for test set: 0.9840\n", - "plot sample rmse: 2.2255mT\n" - ] - }, - { - "data": { - "image/png": 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- "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "\n", "from utils import estimate_test_set \n", @@ -563,39 +304,9 @@ }, { "cell_type": "code", - "execution_count": 114, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "torch.Size([16])\n", - "torch.Size([3, 16, 16, 16])\n", - "torch.Size([3, 16, 16, 16])\n" - ] - }, - { - "data": { - "image/png": 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", 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "test_estimator.peek_3D(length=0.15)" ] From cd06c962a4952290abb7f8a78ac92ff6c0c9a91f Mon Sep 17 00:00:00 2001 From: windsong57 <1403250637@qq.com> Date: Thu, 21 Mar 2024 17:11:34 +0800 Subject: [PATCH 16/16] merge from main --- .vscode/launch.json | 3 +- Modeling eMNS/Generative_model_v2.ipynb | 67 ++- Modeling eMNS/Generative_model_v2_zj.ipynb | 665 +++++++++++++++++++++ Modeling eMNS/utils.py | 13 +- 4 files changed, 735 insertions(+), 13 deletions(-) create mode 100644 Modeling eMNS/Generative_model_v2_zj.ipynb diff --git a/.vscode/launch.json b/.vscode/launch.json index 0c442f4..dbf87f4 100644 --- a/.vscode/launch.json +++ b/.vscode/launch.json @@ -9,7 +9,8 @@ "type": "python", "request": "launch", "module": "enter-your-module-name", - "justMyCode": true + "justMyCode": true, + "env": {"PL_TORCH_DISTRIBUTED_BACKEND":"gloo"} } ] } \ No newline at end of file diff --git a/Modeling eMNS/Generative_model_v2.ipynb b/Modeling eMNS/Generative_model_v2.ipynb index d8de9eb..1e31fcf 100644 --- a/Modeling eMNS/Generative_model_v2.ipynb +++ b/Modeling eMNS/Generative_model_v2.ipynb @@ -18,9 +18,17 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Good to go\n" + ] + } + ], "source": [ "%reload_ext autoreload\n", "%autoreload 2\n", @@ -38,9 +46,19 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "torch.Size([146, 6, 21, 21, 21])\n", + "current shape torch.Size([146, 12])\n", + "Bfield shape torch.Size([146, 3, 16, 16, 16])\n" + ] + } + ], "source": [ "from ReadData import ReadCurrentAndField_CNN\n", "import glob\n", @@ -108,9 +126,46 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "c:\\Users\\14032\\.conda\\envs\\myenv\\lib\\site-packages\\tqdm\\auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n", + " from .autonotebook import tqdm as notebook_tqdm\n", + "2024-03-21 15:20:55,592\tINFO util.py:154 -- Missing packages: ['ipywidgets']. Run `pip install -U ipywidgets`, then restart the notebook server for rich notebook output.\n", + "2024-03-21 15:20:58,015\tINFO util.py:154 -- Missing packages: ['ipywidgets']. Run `pip install -U ipywidgets`, then restart the notebook server for rich notebook output.\n", + "2024-03-21 15:20:58,119\tINFO util.py:154 -- Missing packages: ['ipywidgets']. Run `pip install -U ipywidgets`, then restart the notebook server for rich notebook output.\n", + "2024-03-21 15:21:07,788\tINFO worker.py:1715 -- Started a local Ray instance. View the dashboard at \u001b[1m\u001b[32m127.0.0.1:8265 \u001b[39m\u001b[22m\n", + "2024-03-21 15:21:10,410\tINFO tune.py:220 -- Initializing Ray automatically. For cluster usage or custom Ray initialization, call `ray.init(...)` before `Trainer(...)`.\n", + "2024-03-21 15:21:10,412\tINFO tune.py:583 -- [output] This uses the legacy output and progress reporter, as Jupyter notebooks are not supported by the new engine, yet. For more information, please see /~https://github.com/ray-project/ray/issues/36949\n" + ] + }, + { + "ename": "ArrowInvalid", + "evalue": "URI has empty scheme: '~/Trained_model'", + "output_type": "error", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mArrowInvalid\u001b[0m Traceback (most recent call last)", + "Cell \u001b[1;32mIn[3], line 122\u001b[0m\n\u001b[0;32m 114\u001b[0m trainer \u001b[38;5;241m=\u001b[39m TorchTrainer(\n\u001b[0;32m 115\u001b[0m train_loop_per_worker \u001b[38;5;241m=\u001b[39m train_GM,\n\u001b[0;32m 116\u001b[0m train_loop_config \u001b[38;5;241m=\u001b[39m train_loop_config,\n\u001b[1;32m (...)\u001b[0m\n\u001b[0;32m 119\u001b[0m \n\u001b[0;32m 120\u001b[0m )\n\u001b[0;32m 121\u001b[0m \u001b[38;5;66;03m# train the model\u001b[39;00m\n\u001b[1;32m--> 122\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[43mtrainer\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfit\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 123\u001b[0m \u001b[38;5;66;03m#----------------------------------------------\u001b[39;00m\n\u001b[0;32m 124\u001b[0m \u001b[38;5;66;03m# tuner = tune.Tuner(\u001b[39;00m\n\u001b[0;32m 125\u001b[0m \u001b[38;5;66;03m# trainer,\u001b[39;00m\n\u001b[1;32m (...)\u001b[0m\n\u001b[0;32m 134\u001b[0m \u001b[38;5;66;03m# # tune the model \u001b[39;00m\n\u001b[0;32m 135\u001b[0m \u001b[38;5;66;03m# results = tuner.fit()\u001b[39;00m\n", + "File \u001b[1;32mc:\\Users\\14032\\.conda\\envs\\myenv\\lib\\site-packages\\ray\\train\\base_trainer.py:625\u001b[0m, in \u001b[0;36mBaseTrainer.fit\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m 619\u001b[0m restore_msg \u001b[38;5;241m=\u001b[39m TrainingFailedError\u001b[38;5;241m.\u001b[39m_RESTORE_MSG\u001b[38;5;241m.\u001b[39mformat(\n\u001b[0;32m 620\u001b[0m trainer_cls_name\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__class__\u001b[39m\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__name__\u001b[39m,\n\u001b[0;32m 621\u001b[0m path\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mstr\u001b[39m(experiment_local_path),\n\u001b[0;32m 622\u001b[0m )\n\u001b[0;32m 624\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m--> 625\u001b[0m result_grid \u001b[38;5;241m=\u001b[39m \u001b[43mtuner\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfit\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 626\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m TuneError \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[0;32m 627\u001b[0m \u001b[38;5;66;03m# Catch any `TuneError`s raised by the `Tuner.fit` call.\u001b[39;00m\n\u001b[0;32m 628\u001b[0m \u001b[38;5;66;03m# Unwrap the `TuneError` if needed.\u001b[39;00m\n\u001b[0;32m 629\u001b[0m parent_error \u001b[38;5;241m=\u001b[39m e\u001b[38;5;241m.\u001b[39m__cause__ \u001b[38;5;129;01mor\u001b[39;00m e\n", + "File \u001b[1;32mc:\\Users\\14032\\.conda\\envs\\myenv\\lib\\site-packages\\ray\\tune\\tuner.py:381\u001b[0m, in \u001b[0;36mTuner.fit\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m 379\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_is_ray_client:\n\u001b[0;32m 380\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m--> 381\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_local_tuner\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfit\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 382\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m TuneError \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[0;32m 383\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m TuneError(\n\u001b[0;32m 384\u001b[0m _TUNER_FAILED_MSG\u001b[38;5;241m.\u001b[39mformat(\n\u001b[0;32m 385\u001b[0m path\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_local_tuner\u001b[38;5;241m.\u001b[39mget_experiment_checkpoint_dir()\n\u001b[0;32m 386\u001b[0m )\n\u001b[0;32m 387\u001b[0m ) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01me\u001b[39;00m\n", + "File \u001b[1;32mc:\\Users\\14032\\.conda\\envs\\myenv\\lib\\site-packages\\ray\\tune\\impl\\tuner_internal.py:509\u001b[0m, in \u001b[0;36mTunerInternal.fit\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m 507\u001b[0m param_space \u001b[38;5;241m=\u001b[39m copy\u001b[38;5;241m.\u001b[39mdeepcopy(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mparam_space)\n\u001b[0;32m 508\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_is_restored:\n\u001b[1;32m--> 509\u001b[0m analysis \u001b[38;5;241m=\u001b[39m 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\u001b[0;36mrun\u001b[1;34m(run_or_experiment, name, metric, mode, stop, time_budget_s, config, resources_per_trial, num_samples, storage_path, storage_filesystem, search_alg, scheduler, checkpoint_config, verbose, progress_reporter, log_to_file, trial_name_creator, trial_dirname_creator, sync_config, export_formats, max_failures, fail_fast, restore, resume, reuse_actors, raise_on_failed_trial, callbacks, max_concurrent_trials, keep_checkpoints_num, checkpoint_score_attr, checkpoint_freq, checkpoint_at_end, chdir_to_trial_dir, local_dir, _remote, _remote_string_queue, _entrypoint)\u001b[0m\n\u001b[0;32m 770\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m i, exp \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28menumerate\u001b[39m(experiments):\n\u001b[0;32m 771\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(exp, Experiment):\n\u001b[1;32m--> 772\u001b[0m experiments[i] \u001b[38;5;241m=\u001b[39m 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resources_per_trial, num_samples, storage_path, storage_filesystem, sync_config, checkpoint_config, trial_name_creator, trial_dirname_creator, log_to_file, export_formats, max_failures, restore, local_dir)\u001b[0m\n\u001b[0;32m 163\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m name:\n\u001b[0;32m 164\u001b[0m name \u001b[38;5;241m=\u001b[39m StorageContext\u001b[38;5;241m.\u001b[39mget_experiment_dir_name(run)\n\u001b[1;32m--> 166\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mstorage \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_storage_context_cls\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m 167\u001b[0m \u001b[43m \u001b[49m\u001b[43mstorage_path\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mstorage_path\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 168\u001b[0m \u001b[43m 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\u001b[38;5;129;01mor\u001b[39;00m {}\n", + "File \u001b[1;32mc:\\Users\\14032\\.conda\\envs\\myenv\\lib\\site-packages\\ray\\train\\_internal\\storage.py:452\u001b[0m, in \u001b[0;36mStorageContext.__init__\u001b[1;34m(self, storage_path, experiment_dir_name, sync_config, storage_filesystem, trial_dir_name, current_checkpoint_index)\u001b[0m\n\u001b[0;32m 447\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcurrent_checkpoint_index \u001b[38;5;241m=\u001b[39m current_checkpoint_index\n\u001b[0;32m 448\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39msync_config \u001b[38;5;241m=\u001b[39m (\n\u001b[0;32m 449\u001b[0m dataclasses\u001b[38;5;241m.\u001b[39mreplace(sync_config) \u001b[38;5;28;01mif\u001b[39;00m sync_config \u001b[38;5;28;01melse\u001b[39;00m SyncConfig()\n\u001b[0;32m 450\u001b[0m )\n\u001b[1;32m--> 452\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mstorage_filesystem, 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\u001b[1;32mc:\\Users\\14032\\.conda\\envs\\myenv\\lib\\site-packages\\ray\\train\\_internal\\storage.py:306\u001b[0m, in \u001b[0;36mget_fs_and_path\u001b[1;34m(storage_path, storage_filesystem)\u001b[0m\n\u001b[0;32m 303\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m storage_filesystem:\n\u001b[0;32m 304\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m storage_filesystem, storage_path\n\u001b[1;32m--> 306\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mpyarrow\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfs\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mFileSystem\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfrom_uri\u001b[49m\u001b[43m(\u001b[49m\u001b[43mstorage_path\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[1;32mc:\\Users\\14032\\.conda\\envs\\myenv\\lib\\site-packages\\pyarrow\\_fs.pyx:348\u001b[0m, in \u001b[0;36mpyarrow._fs.FileSystem.from_uri\u001b[1;34m()\u001b[0m\n", + "File \u001b[1;32mc:\\Users\\14032\\.conda\\envs\\myenv\\lib\\site-packages\\pyarrow\\error.pxi:143\u001b[0m, in \u001b[0;36mpyarrow.lib.pyarrow_internal_check_status\u001b[1;34m()\u001b[0m\n", + "File \u001b[1;32mc:\\Users\\14032\\.conda\\envs\\myenv\\lib\\site-packages\\pyarrow\\error.pxi:99\u001b[0m, in \u001b[0;36mpyarrow.lib.check_status\u001b[1;34m()\u001b[0m\n", + "\u001b[1;31mArrowInvalid\u001b[0m: URI has empty scheme: '~/Trained_model'" + ] + } + ], "source": [ "from Neural_network import eMNS_Dataset\n", "from Training_loop_v2 import train_GM\n", diff --git a/Modeling eMNS/Generative_model_v2_zj.ipynb b/Modeling eMNS/Generative_model_v2_zj.ipynb new file mode 100644 index 0000000..0682a09 --- /dev/null +++ b/Modeling eMNS/Generative_model_v2_zj.ipynb @@ -0,0 +1,665 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Train ETH data to CNN generative network" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "!pip install -U \"ray[data,train,tune,serve]\"" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Good to go\n" + ] + } + ], + "source": [ + "%reload_ext autoreload\n", + "%autoreload 2\n", + "import numpy as np\n", + "import torch\n", + "import os\n", + "os.environ[\"PL_TORCH_DISTRIBUTED_BACKEND\"] = \"gloo\"\n", + "\n", + "if torch.cuda.device_count():\n", + " device = 'cuda'\n", + " use_gpu = True\n", + " print('Good to go')\n", + "else:\n", + " device = 'cpu'\n", + " use_gpu = False\n", + " print('Using cpu')" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "torch.Size([1200, 6, 21, 21, 21])\n", + "current shape torch.Size([1200, 12])\n", + "Bfield shape torch.Size([1200, 3, 16, 16, 16])\n" + ] + } + ], + "source": [ + "from ReadData import ReadCurrentAndField_CNN\n", + "import glob\n", + "import os \n", + "\n", + "# TODO zhoujing edit this Data loading \n", + "# print(os.getcwd())\n", + "foldername=\"./Data/\"\n", + "filepattern = \"MagneticField[0-9]*.txt\"\n", + "train_file_num= 1200\n", + "#data = ReadFolder(foldername,filepattern)\n", + "current,data = ReadCurrentAndField_CNN (foldername,filepattern,train_file_num)\n", + "\n", + "fileList = glob.glob(foldername+filepattern)\n", + "position = data[:,0:3,2:18,2:18,2:18]\n", + "Bfield = data[:,3:,2:18,2:18,2:18]\n", + "\n", + "# print(fileList)\n", + "print(data.shape)\n", + "print('current shape', current.shape)\n", + "print('Bfield shape', Bfield.shape)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from Neural_network import Generative_net,Generative_net_test ,ResidualEMNSBlock_3d, BigBlock, weight_init, eMNS_Dataset\n", + "###############################################\n", + "# Config the neural network\n", + "###############################################\n", + "num_input = 8\n", + "output_shape = (3,16,16,16)\n", + "SB_args = (64,64,1,4) # (Cin, Cout, num_repeat, num_block)\n", + "BB_args = (2,3) # (scale_factor, num_block)\n", + "SB_block = ResidualEMNSBlock_3d \n", + "BB_block = BigBlock\n", + "DF = False # whether using divergence free model\n", + "\n", + "Generative_network = Generative_net_test(SB_args, BB_args, SB_block, BB_block, num_input=num_input, output_shape= output_shape)\n", + "print(Generative_network)\n", + "\n", + "from torchviz import make_dot\n", + "import torch.nn.functional as F\n", + "from Training_loop import grad_loss_Jacobain\n", + "x = torch.randn(2,8)\n", + "y = Bfield[0:2]\n", + "preds = Generative_network(x)\n", + "print(preds.shape)\n", + "loss = F.l1_loss(preds,y)+grad_loss_Jacobain(preds,y)\n", + " # optimizer.zero_grad() #zero out all of gradient\n", + "loss.backward()\n", + "\n", + "make_dot(loss, params=dict(Generative_network.named_parameters()))\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Tune hyperparameters" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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Tune Status

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Current time:2024-03-21 17:00:38
Running for: 00:15:44.99
Memory: 21.1/31.7 GiB
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System Info

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Trial Status

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Trial name status loc iter total time (s) rmse_val rmse_train loss
TorchTrainer_499bb_00000TERMINATED127.0.0.1:11508 350 6.615 0.302903 0.09474340.00201093
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This metric should be reported with tune.report()\n", + " mode=\"min\",\n", + " max_t=350,\n", + " grace_period=350, # minimum stop epoch\n", + " reduction_factor=2,\n", + " )\n", + "param_space = {\n", + " \"scaling_config\": ScalingConfig(\n", + " num_workers = 1,\n", + " use_gpu = use_gpu,\n", + " resources_per_worker = {\"CPU\":4, \"GPU\":0}\n", + " ),\n", + " # You can even grid search various datasets in Tune.\n", + " # \"datasets\": {\n", + " # \"train\": tune.grid_search(\n", + " # [ds1, ds2]\n", + " # ),\n", + " # },\n", + " \"train_loop_config\": {\n", + " 'epochs': 350,\n", + " 'lr_max': tune.grid_search([1e-3,1e-4,5e-4]),\n", + " 'lr_min': tune.grid_search([1e-5,2.5e-6,2.5e-7]),\n", + " 'batch_size': 8,\n", + " 'L2_norm' : 0,\n", + " 'verbose': False,\n", + " 'DF' : False,\n", + " 'schedule': [],\n", + " 'grid_space': 16**3,\n", + " 'learning_rate_decay': 0.5,\n", + " 'skip_spacing': 2,\n", + " 'num_repeat' : 1,\n", + " 'num_block' : 3,\n", + " 'maxB' : extremes[2],\n", + " 'minB' : extremes[3],\n", + " 'train_set' : train_set,\n", + " 'valid_set' : valid_set,\n", + " 'num_input' : 12,\n", + " }\n", + "\n", + "}\n", + "\n", + "train_percents = np.arange(1.0,1.01,0.1)\n", + "RMSE_history_end = np.zeros(len(train_percents))\n", + "RMSE_val_history_end = np.zeros(len(train_percents))\n", + "loss_history_end = np.zeros(len(train_percents))\n", + "iter_history_end = np.zeros(len(train_percents))\n", + "mse_history_end = np.zeros(len(train_percents))\n", + "mse_val_history_end = np.zeros(len(train_percents))\n", + "train_stop_epoch = np.zeros(len(train_percents))\n", + "\n", + "################################################\n", + "# Train the neural network\n", + "################################################\n", + "\n", + "train_loop_config = {\n", + " 'epochs': 350,\n", + " 'lr_max': 5e-4,\n", + " 'lr_min': 2.5e-6,\n", + " 'batch_size': 8,\n", + " 'L2_norm' : 0,\n", + " 'verbose': False,\n", + " 'DF' : False,\n", + " 'schedule': [],\n", + " 'grid_space': 16**3,\n", + " 'learning_rate_decay': 0.5,\n", + " 'skip_spacing': 2,\n", + " 'num_repeat' : 1,\n", + " 'num_block' : 3,\n", + " 'maxB' : extremes[2],\n", + " 'minB' : extremes[3],\n", + " 'device' : device,\n", + " 'train_set' : train_set,\n", + " 'valid_set' : valid_set,\n", + " 'num_input' : 12,\n", + " # You can even grid search various datasets in Tune.\n", + " # \"datasets\": tune.grid_search(\n", + " # [ds1, ds2]\n", + " # ),\n", + "}\n", + "\n", + "scaling_config = ScalingConfig(\n", + " num_workers = 1,\n", + " use_gpu = use_gpu,\n", + " # resources_per_worker = {\"CPU\":4, \"GPU\":1}\n", + ")\n", + "\n", + "run_config = RunConfig(checkpoint_config=CheckpointConfig(num_to_keep=1))#,storage_path='D:\\Qubot\\Trained_model', \n", + " #name='EMS_CNN_'+'s_'+str(train_loop_config['skip_spacing'])+'r_'+str(train_loop_config['num_repeat'])+'b_'+str(train_loop_config['num_block']) )\n", + "#\n", + "# def train_loop_per_worker(params):\n", + "# train_GM(train_set=train_set, valid_set=valid_set, device=device, config=params)\n", + "torch_config = TorchConfig(backend=\"gloo\")\n", + "trainer = TorchTrainer(\n", + " train_loop_per_worker = train_GM,\n", + " train_loop_config = train_loop_config,\n", + " torch_config=torch_config,\n", + " scaling_config = scaling_config,\n", + " run_config = run_config,\n", + "\n", + ")\n", + "# train the model\n", + "result = trainer.fit()\n", + "#----------------------------------------------\n", + "# tuner = tune.Tuner(\n", + "# trainer,\n", + "# param_space = param_space,\n", + "# tune_config =tune.TuneConfig(\n", + "# scheduler=tune_schedule,\n", + "# num_samples=1, # number of samples of hyperparameter space\n", + "# ),\n", + "# # run_config = RunConfig(checkpoint_config=CheckpointConfig(num_to_keep=2),storage_path=\"/home/qubot/ray_results\", name=\"test_experiment\"),\n", + " # checkpoint_score_attribute='rmse_val', checkpoint_score_order='min\n", + "# )\n", + "# # tune the model \n", + "# results = tuner.fit()" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Result(\n", + " metrics={'rmse_val': 0.3029033839702606, 'rmse_train': 0.09474341571331024, 'loss': 0.00201093009673059},\n", + " path='C:/Users/14032/ray_results/TorchTrainer_2024-03-21_16-44-53/TorchTrainer_499bb_00000_0_2024-03-21_16-44-53',\n", + " filesystem='local',\n", + " checkpoint=Checkpoint(filesystem=local, path=C:/Users/14032/ray_results/TorchTrainer_2024-03-21_16-44-53/TorchTrainer_499bb_00000_0_2024-03-21_16-44-53/checkpoint_000001)\n", + ")\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from utils import plot_ray_results\n", + "print(result)\n", + "plot_ray_results(result, metrics_names=['rmse_train','rmse_val'],ylim=[0,5])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "best_result = results.get_best_result(metric='rmse_val',mode='min')\n", + "print(best_result)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from utils import plot_ray_results\n", + "plot_ray_results(best_result, metrics_names=['rmse_train','rmse_val'])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "!tensorboard --logdir=~/ray_results" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Got rmse 0.14411738514900208\n", + "rmse for test set: 0.1441mT\n", + " mse for test set: 0.0208mT\n", + " R2 for test set: 1.0000\n", + "plot sample rmse: 0.1878mT\n" + ] + }, + { + "data": { + "image/png": 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N5bcajWcfNhsPMG1Y/i7ldDb31SZqm/ve4LWPPSh3zRnmP/DsLPMf9l7V+lCz+bihFuMhvan1Hv3B04u3lBe1vjXHfF1uzfWm1tvGa70HL9IUZstyfzYB9gwAAAAAAADpJjAzAwAAAAAAaOMkcDYB5zSYGUAzAAAAAAAQOLaTwDIBNhAEAAAAACD9BH0DwfR/BAAAAAAAoEOYGQAAAAAACJygLxNIq5kBc+bMkWVZmjp1qt+pAAAAD1DrAQDJYn++gaDbke7SZmbA5s2b9fjjj+uCCy7wOxUAAOABaj0AIJmYGZAGDh48qIkTJ2rRokXq0qWL3+kAAADDqPUAgGRrawa4HekuLZoB5eXlGjdunEaPHn3S+0ajUTU0NLQbAAAgtVHrAQBIrpRfJvDss8+qurpamzdvPqX7V1RU6L777vM4KwAAYAq1HgDgB5YJpLCamhrdfvvtevrpp5WdnX1Kx8ycOVP19fXxUVNT43GWAADALWo9AMAvQV8mkNIzA6qqqlRXV6eLLroofl0sFtO6dev02GOPKRqNKhwOtzsmEokoEokkO1UAAOACtR4A4BdHcn1WAMdsKr5I6WbAFVdcoXfeeafddZMnT1b//v01Y8aMo74cAACA9EKtBwDAHyndDMjLy9PAgQPbXZebm6tu3boddT0AAEg/1HoAgF+CvmdASjcDAAAAAADwAs2ANPPGG2/4nQIAAPAQtR4AkAxBbwak9NkEAAAAAACAeWk3MwAAAAAAgEQFfWYAzQAAAAAAQOA4jiXH5X/q3R6XSgLTDBiQ9ZE6R8ytitiZ19NYrDaHWrOMx5Sk5lbzv+aDtvkXf4sHp49yMrxZCWNnefDm9+LzxDYf0vIgpiQ5Hvyq7CzzZ4CNZXsQs5M3T2o4r8VoPCtsNh5g2oDIh8rNNvdhsi+3wFisNgdbIsZjSlK0xXytr/eg1rdamcZjOiFvan0o6kHMFvPPqSd12aP/4zgenCk0FkmPWm/nxozHlKTMzs1G49khs/FSnS1LtssXvNvjUgl7BgAAAAAAEDCBmRkAAAAAAEAb9gwAAAAAACBg2DMAAAAAAICACfrMAPYMAAAAAAAgYJgZAAAAAAAIHJYJAAAAAAAQME4CywRoBgAAAAAAkIYcSY7j/th0x54BAAAAAAAEDDMDAAAAAACBY8uSJZdnE3B5XCqhGQAAAAAACBw2EAQAAAAAIGBsx5Ll8j/1bjceTCXsGQAAAAAAgIcqKio0bNgw5eXlqbCwUBMmTND27dvb3ef999/XN77xDfXo0UP5+fm69tprtW/fPs9yohkAAAAAAAgcx0lsdMTatWtVXl6ujRs3atWqVWppadGVV16pxsZGSVJjY6OuvPJKWZal119/Xf/93/+t5uZmjR8/XrZte/DoWSYAAAAAAAigZO4ZsGLFinaXFy9erMLCQlVVVenyyy/Xf//3f+uDDz7Q22+/rfz8fEnSU089pS5duuj111/X6NGjXeV5IswMAAAAAAAETlszwO2QpIaGhnYjGo2e0s+ur6+XJHXt2lWSFI1GZVmWIpFI/D7Z2dkKhUJav3694Ud+BM0AAAAAAABcKCkpUUFBQXxUVFSc9BjbtjV16lSNGDFCAwcOlCR98YtfVG5urmbMmKFDhw6psbFR//Iv/6JYLKaPPvrIk9xZJgAAAAAACBwTZxOoqamJT+uX1O4v+8dTXl6ubdu2tfuLf48ePfTCCy/o+9//vh555BGFQiFdd911uuiiixQKefM3fJoBAAAAAIDAcbMR4N8eK0n5+fntmgEnM2XKFC1fvlzr1q1T796929125ZVX6v3339cnn3yijIwMnXHGGSoqKtKZZ57pLsmToBkAAAAAAAicI80AtxsIdvT+jm699VYtXbpUb7zxhvr163fc+3bv3l2S9Prrr6uurk5XX321qxxPJjDNgMKwrbywwXiZDeaCfa5n9gHjMSWpLtLZeMxoTqbxmC0t7t6IJxJrNR9TkuTF2T08mP1jtZqPKY+eUseDx2978Aln57hsH59Itjeni8nJaTYaL+a0GI0HmFaa0ay8DHMfJn0inxiL1ebPOV2Mx5Sk/U25xmM2NZuv9U2t5j/sY958hMqxzOfqZJivIZZHj98LtsHv4vGYWeafUzvH/JMayvHiS5mUk222Nsdsb/LEkaUBS5Ys0SuvvKK8vDzV1tZKkgoKCpSTkyNJevLJJ3XeeeepR48e2rBhg26//XZNmzZN5557ric5BaYZAAAAAABAm2SeWrCyslKSNHLkyHbXP/nkk7rxxhslSdu3b9fMmTO1f/9+9e3bVz/+8Y81bdo0V/mdCpoBAAAAAIDAcT4fbo/t0P1PYV3BnDlzNGfOHHcJuUAzAAAAAAAQOMmcGZCKvDlHAQAAAAAASFnMDAAAAAAABE8y1wmkIJoBAAAAAIDgSWCZgE6DZQI0AwAAAAAAgeM4R4bbY9Ndyu8Z8OGHH+r6669Xt27dlJOTo0GDBmnLli1+pwUAAAyi3gMAkFwpPTPg008/1YgRIzRq1Ci99tpr6tGjh3bs2KEuXbr4nRoAADCEeg8A8EPQzyaQ0s2AuXPnqqSkRE8++WT8un79+p3wmGg0qmg0Gr/c0NDgWX4AACBxHa331HoAgBGO5X7t/2nQDEjpZQKvvvqqhg4dqmuuuUaFhYUaMmSIFi1adMJjKioqVFBQEB8lJSVJyhYAALjR0XpPrQcAmNC2Z4Dbke5Suhnwpz/9SZWVlTrnnHO0cuVKff/739dtt92mp5566rjHzJw5U/X19fFRU1OTxIwBAEBHdbTeU+sBAEhcSi8TsG1bQ4cO1QMPPCBJGjJkiLZt26YFCxaorKzsmMdEIhFFIpFkpgkAABLQ0XpPrQcAGOF8Ptwem+ZSemZAr169dP7557e77rzzztOePXt8yggAAJhGvQcA+KFtA0G3I92l9MyAESNGaPv27e2ue++999SnTx+fMgIAAKZR7wEAvjkN/sLvVkrPDJg2bZo2btyoBx54QDt37tSSJUu0cOFClZeX+50aAAAwhHoPAEDypXQzYNiwYVq6dKmeeeYZDRw4UPfff7/mzZuniRMn+p0aAAAwhHoPAPADywRS3FVXXaWrrrrK7zQAAICHqPcAgKQL+AaCKd8MAAAAAADAPOvz4fbY9JbSywQAAAAAAIB5zAwAAAAAAAQPywTc2bVrl958803t3r1bhw4dUo8ePTRkyBANHz5c2dnZJnM0onMoS3khcxMhijI+MxarTX7GYeMxJSk3q9l4zANZEeMxWyPme1NOqzeTX+xW89OCLA8+UJxM8zG9+uBzwh7EzDCfrBM2HzOUGTMeU5IyQrbReJbheIBpZ4SylW+w1p8RbjQWKx4z45DxmJLUKdN8rc/MbDUesznTfK23M72p9U7MgxriwYZj6bSHmSd12YOY8qDWhzO8qaFZGWa/Q8QMx0t5NAM65umnn9bDDz+sLVu2qGfPniouLlZOTo7279+v999/X9nZ2Zo4caJmzJjB+YEBAAAAAKnJsdx31NKpE3ccHWoGDBkyRJmZmbrxxhv10ksvqaSkpN3t0WhUGzZs0LPPPquhQ4fql7/8pa655hqjCQMAAAAAgMR0qBkwZ84cjRkz5ri3RyIRjRw5UiNHjtTs2bP1wQcfJJofAAAAAADGOc6R4fbYdNehRVZjxozRr3/9a0Wj0ZPet1u3brr44otdJwYAAAAAgGecBEea6/COK5MnT1Z9fb0XuQAAAAAAkBxtewa4HWmuw80A53SYDwEAAAAAQIC5Or+LZaV/FwQAAAAAEFyW4/703l6cFjzZXDUDrrjiCmVknPjQ6upqVwkBAAAAAOC5RNb+B7UZMGbMGHXu3Nl0LgAAAAAAJEcia/9Pgz0DXDUD7rzzThUWFprOBQAAAAAAJEGHmwHsFwAAAAAASHssE+gYziYAAAAAAEh7NAM6ZteuXerRo4cXuQAAAAAAkBw0AzqmT58+ko7MEHjxxRe1Zs0a1dXVybbtdvd7+eWXzWQIAAAAAIBpbCDoztSpU/X4449r1KhR6tmzJ3sJAAAAAACQJlw3A37zm9/o5Zdf1te+9jWT+QAAAAAA4DnLOTLcHpvuXDcDCgoKdOaZZ5rMBQAAAACA5Aj4ngEhtwfee++9uu+++3T48GGT+QAAAAAAAI+5nhlw7bXX6plnnlFhYaH69u2rzMzMdrdXV1cnnBwAAAAAADDPdTOgrKxMVVVVuv7669lAEAAAAACQViwlsGeA0Uz84boZ8J//+Z9auXKlvvSlL5nMxzMRK1MRy/WqiKPY7ldYHFdGyD75nVzIDMWMxwyHPcjVg104nJBHi3m8iNtyOnykuGeZf5nKCZuP6Ylg/+oBYzKtsDIN1vpMmf9gCnm045QXcT35aOLzLj0E/ffkwdvUOQ1OQ3da4tSC7pSUlCg/P99kLgAAAAAAJAcbCLrz85//XD/84Q/1wQcfGEwHAAAAAAB4zfXMgOuvv16HDh3SWWedpU6dOh21geD+/fsTTg4AAAAAAE8EfGaA62bAvHnzDKYBAAAAAEDyWE4CGwgGuRlQVlZmMg8AAAAAAJIn4DMDOrRnQGNjY4eCd/T+AAAAAADAex1qBpx99tmaM2eOPvroo+Pex3EcrVq1SmPHjtUjjzySUHKxWEyzZs1Sv379lJOTo7POOkv333+/HOc0aMMAAABJ1HsAgE+cBEea69AygTfeeEM/+tGPdO+992rw4MEaOnSoiouLlZ2drU8//VTvvvuuNmzYoIyMDM2cOVM333xzQsnNnTtXlZWVeuqppzRgwABt2bJFkydPVkFBgW677baEYgMAgNRAvQcA+IE9Azrg3HPP1UsvvaQ9e/bohRde0Jtvvqnf/e53Onz4sLp3764hQ4Zo0aJFGjt2rMLhcMLJ/e53v9PXv/51jRs3TpLUt29fPfPMM9q0aVPCsQEAQGqg3gMAfOFYR4bbY9Ocqw0ES0tLdccdd+iOO+4wnU87l112mRYuXKj33ntPX/jCF/T73/9e69ev10MPPXTcY6LRqKLRaPxyQ0ODpzkCAIDEdLTeU+sBAEic67MJNDU1KTs7+5i3ffTRR+rVq5frpNrcddddamhoUP/+/RUOhxWLxTR79mxNnDjxuMdUVFTovvvuS/hnAwCA5OhovafWAwCM4GwC7lx00UXaunXrUde/9NJLuuCCCxLJKe7555/X008/rSVLlqi6ulpPPfWUfvazn+mpp5467jEzZ85UfX19fNTU1BjJBQAAeKOj9Z5aDwAwoW3PALejIyoqKjRs2DDl5eWpsLBQEyZM0Pbt29vdp7a2VpMmTVJRUZFyc3N10UUX6aWXXjL4iNtzPTNg5MiR+uIXv6j77rtPM2bMUGNjo8rLy/X8889r9uzZRpK78847ddddd+k73/mOJGnQoEHavXu3KioqVFZWdsxjIpGIIpGIkZ8PAAC819F6T60HABiRxJkBa9euVXl5uYYNG6bW1lb96Ec/0pVXXql3331Xubm5kqQbbrhBn332mV599VV1795dS5Ys0bXXXqstW7ZoyJAhLhM9PtfNgF/+8pcaN26c/t//+39avny5PvroI3Xu3FmbNm3SwIEDjSR36NAhhULtJy+Ew2HZtm0kPgAA8B/1HgCQrv5+35rjNaxXrFjR7vLixYtVWFioqqoqXX755ZKObKhbWVmpSy65RJL0r//6r/rFL36hqqoqT5oBrpcJSNLYsWP1zW9+U//93/+tPXv2aO7cucYaAZI0fvx4zZ49W//5n/+pDz74QEuXLtVDDz2kb3zjG8Z+BgAA8Bf1HgDgi0SWCHw+M6CkpEQFBQXxUVFRcUo/ur6+XpLUtWvX+HWXXXaZnnvuOe3fv1+2bevZZ59VU1OTRo4cafiBH+F6ZsD777+v//t//69qa2u1cuVKrV27VldffbVuv/12zZ49W5mZmQkn9+ijj2rWrFn6wQ9+oLq6OhUXF+vmm2/W3XffnXBsAACQGqj3AABfGFgmUFNTo/z8/PjVp7KMzbZtTZ06VSNGjGj3x/Tnn39e3/72t9WtWzdlZGSoU6dOWrp0qc4++2yXSZ6Y62bAhRdeqHHjxmnlypU644wz9I//+I/62te+phtuuEGrVq3S22+/nXByeXl5mjdvnubNm5dwLAAAkJqo9wAAXxhoBuTn57drBpyK8vJybdu2TevXr293/axZs/TZZ5/pt7/9rbp3765ly5bp2muv1ZtvvqlBgwa5TPT4EtozYNKkSe2uu+yyy/T2229r6tSpieYFAAAAAMBpZcqUKVq+fLnWrVun3r17x69///339dhjj2nbtm0aMGCAJGnw4MF68803NX/+fC1YsMB4Lq6bAV/72tfi/66pqdGiRYt0+PBhXX311XriiSeMJAcAAAAAgBfcnCLwb4/tCMdxdOutt2rp0qV644031K9fv3a3Hzp0SJKSuqFuhzcQfOedd9S3b18VFhaqf//+2rp1q4YNG6Zf/OIXWrhwoUaNGqVly5Z5kCoAAAAAAOmnvLxc//7v/64lS5YoLy9PtbW1qq2t1eHDhyVJ/fv319lnn62bb75ZmzZt0vvvv6+f//znWrVqlSZMmOBJTh2eGfDDH/5QgwYN0tNPP63f/OY3uuqqqzRu3DgtWrRIknTrrbdqzpw5niXsVk3rQeW1JnTyhHY+ixUai9Vmf3Ou8ZiSdKgly3jMpqbEN4j8e05z2HjMUJO533m7uC2W8ZhWq/GQ7tdAnYj5h+5d3FbzQZ1m8zHtqPnXviRFs11P/jqmWIsXL1LAnKjToqhj7nP/kNPZWKw2h2Pm66cktcTMf460xszXUMfgd7E2VsybwuRFXbZi6RHTq1rvOB4E9iBkLNN80JgHr31Jiraafe/HDMdLeQb2DDhVlZWVknTUmQGefPJJ3XjjjcrMzNR//dd/6a677tL48eN18OBBnX322Xrqqafazco3qcPfFDdv3qzXX39dF1xwgQYPHqyFCxfqBz/4QXw6w6233qovfvGLxhMFAAAAACAdOc7JuwfnnHOOXnrppSRkc0SHmwH79+9XUVGRJKlz587Kzc1Vly5d4rd36dJFBw4cMJchAAAAAACGJXPPgFTkag6pZVknvAwAAAAAQMo7Df5T75arZsCNN96oSCQiSWpqatItt9yi3Nwj692j0ai57AAAAAAA8EIS9wxIRR1uBpSVlbW7fP311x91nxtuuMF9RgAAAAAAwFMdbgY8+eSTXuQBAAAAAEDSsGcAAAAAAABBwzIBAAAAAACCJegzA0J+JwAAAAAAAJKLmQEAAAAAgOBhmQAAAAAAAAFDMwAAAAAAgGBhzwAAAAAAABAozAwAAAAAAAQPywQAAAAAAAgYmgEAAAAAAAQLewYAAAAAAIBAYWYAAAAAACB4WCYAAAAAAECwBH2ZAM0AAAAAAEDwMDMgGD6JZakpZm6LhL3NXYzFalPfkm08piQdaIoYj9kaNf/SsQ6FjccMN1nGY0pS+LD5uJZtPKSsmPmY8uYplePBDiZWq/mYss0n2prhTTVpaswyGs8+7MGLFDDok1hUUYO1vq4131isNvtbco3HlKT6qPnvENFopvGYTpMHtd6Dmix58x0i1Jwe3x+84pj/9XvzXceDbdViljf/7TqcYfZ7vn3oNPgfLk5ZYJoBAAAAAADEMTMAAAAAAIBgseR+0qtHk2WTimYAAAAAACB4Aj4zwINVugAAAAAAIJUxMwAAAAAAEDicWhAAAAAAgKBhmYB/1q1bp/Hjx6u4uFiWZWnZsmXtbnccR3fffbd69eqlnJwcjR49Wjt27PAnWQAA0GHUegBASnNcjtOAr82AxsZGDR48WPPnzz/m7Q8++KAeeeQRLViwQG+99ZZyc3M1ZswYNTU1JTlTAADgBrUeAIDU5OsygbFjx2rs2LHHvM1xHM2bN0//+q//qq9//euSpF//+tfq2bOnli1bpu985zvJTBUAALhArQcApKqg7xmQsmcT2LVrl2prazV69Oj4dQUFBbr00ku1YcOG4x4XjUbV0NDQbgAAgNRDrQcA+MrtEoHTZKlAyjYDamtrJUk9e/Zsd33Pnj3jtx1LRUWFCgoK4qOkpMTTPAEAgDvUegCAn9pmBrgd6S5lmwFuzZw5U/X19fFRU1Pjd0oAAMAgaj0AAIlL2VMLFhUVSZL27dunXr16xa/ft2+fLrzwwuMeF4lEFIlEvE4PAAAkiFoPAPAVpxZMTf369VNRUZFWr14dv66hoUFvvfWWhg8f7mNmAADABGo9AMBPQV8m4OvMgIMHD2rnzp3xy7t27dLWrVvVtWtXlZaWaurUqfrpT3+qc845R/369dOsWbNUXFysCRMm+Jc0AAA4ZdR6AEDKCvjMAF+bAVu2bNGoUaPil6dPny5JKisr0+LFi/XDH/5QjY2N+t73vqfPPvtMX/rSl7RixQplZ2f7lTIAAOgAaj0AAKnJ12bAyJEj5TjHb6lYlqWf/OQn+slPfpLErAAAgCnUegBAymJmAAAAAAAAwZLI2n/2DAAAAAAAIB0xMwAAAAAAgGCxHEfWCZaynezYdJeypxYEAAAAAADeCMzMgPdbCtWpOWws3p+jXYzFalN3KM94TEk60OjBjswHM42HzDhkGY+ZecB8TEkKRz0I6kFz0Yp5ENOjJqhj7u0ZZ3sQM9Rs/jVlyYNEJbXahnM97MELCjDoTy15ym0x9376oKm7sVht9jYWGI8pSZ8dzDEeM+ZBrQ8fMP9558X3B0kKN3kQs9l8TC9qvVe8qPWxLA/qcqsHrymPvj+1Wmbfp3bQaj3LBAAAAAAACBY2EAQAAAAAIGgCPjOAPQMAAAAAAAgYZgYAAAAAAAKHZQIAAAAAAARNwJcJ0AwAAAAAAARO0GcGsGcAAAAAAAABw8wAAAAAAEDwsEwAAAAAAIDgOR2m+7vFMgEAAAAAQPA4TmKjAyoqKjRs2DDl5eWpsLBQEyZM0Pbt2+O3f/DBB7Is65jjhRdeMP3IJdEMAAAAAADAU2vXrlV5ebk2btyoVatWqaWlRVdeeaUaGxslSSUlJfroo4/ajfvuu0+dO3fW2LFjPcmJZQIAAAAAgMBJ5tkEVqxY0e7y4sWLVVhYqKqqKl1++eUKh8MqKipqd5+lS5fq2muvVefOnd0leRI0AwAAAAAAwWNgA8GGhoZ2V0ciEUUikZMeXl9fL0nq2rXrMW+vqqrS1q1bNX/+fJcJnhzLBAAAAAAAgWPZiQ3pyPT+goKC+KioqDjpz7VtW1OnTtWIESM0cODAY97niSee0HnnnafLLrvM5ENuh5kBAAAAAAC4UFNTo/z8/PjlU5kVUF5erm3btmn9+vXHvP3w4cNasmSJZs2aZSzPY6EZAAAAAAAIHgPLBPLz89s1A05mypQpWr58udatW6fevXsf8z4vvviiDh06pBtuuMFlcqeGZgAAAAAAIHCSuYGg4zi69dZbtXTpUr3xxhvq16/fce/7xBNP6Oqrr1aPHj3cJXeKaAYAAAAAAILHcY4Mt8d2QHl5uZYsWaJXXnlFeXl5qq2tlSQVFBQoJycnfr+dO3dq3bp1+q//+i93eXUAGwgCAAAAAOChyspK1dfXa+TIkerVq1d8PPfcc+3u96tf/Uq9e/fWlVde6XlOzAwAAAAAAAROspcJnIoHHnhADzzwgIuMOi4wzYCdTT0Vycg0Fq/2cJ6xWG0+OZhrPKYkxQ6Ye9xtsj4zP6kks8EyHjPjkPGQR+IedrvTyPG1nZ7EpFCr+ZiuN1k5GfO/fsWyzMe0zb+d1BL14MFLCkXDRuPFmszGA0x7J1qi7ExzX23+eKCnsVht9jac+iZTHRH9LNt4zIxPzX9NzDxg/vMus9F4SElS2INaH242HlJWzHxMrzgelBEvan0s2/zrNNTszYTsllazudpNgfnv4REGNhBMZwH7bQMAAAAAkNyZAamIPQMAAAAAAAgYZgYAAAAAAIIniWcTSEU0AwAAAAAAgRP0ZQI0AwAAAAAAwRPwDQTZMwAAAAAAgIDxtRmwbt06jR8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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "\n", + "from utils import estimate_test_set \n", + "test_estimator = estimate_test_set(result.checkpoint, test_set, train_loop_config)\n", + "test_estimator.fit()\n", + "test_estimator.peek_z(3)" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "torch.Size([16])\n", + "torch.Size([3, 16, 16, 16])\n", + "torch.Size([3, 16, 16, 16])\n" + ] + }, + { + "data": { + "image/png": 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", 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "test_estimator.peek_3D(length=0.15)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Old version of training loop" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from Neural_network import Generative_net, Generative_net_test, ResidualEMNSBlock_3d, BigBlock, weight_init, eMNS_Dataset\n", + "from Training_loop_v2 import train_GM\n", + "from tqdm import tqdm\n", + "\n", + "# construct dataset\n", + "dataset = eMNS_Dataset(\n", + " train_x=current,\n", + " train_y=Bfield\n", + ")\n", + "\n", + "config = {\n", + " 'epochs': 350,\n", + " 'lr_max': 1e-4,\n", + " 'lr_min': 2.5e-6,\n", + " 'batch_size': 8,\n", + " 'L2_norm' : 0,\n", + " 'verbose': False,\n", + " 'DF' : False,\n", + " 'schedule': [],\n", + " 'grid_space': 16**3,\n", + " 'learning_rate_decay': 0.5,\n", + " 'skip_spacing': 2,\n", + " 'num_repeat' : 2,\n", + " 'num_block' : 3,\n", + " 'device' : device,\n", + " 'num_input' : 12,\n", + "}\n", + "train_percents = np.arange(1.0,1.01,0.1)\n", + "RMSE_history_end = np.zeros(len(train_percents))\n", + "RMSE_val_history_end = np.zeros(len(train_percents))\n", + "loss_history_end = np.zeros(len(train_percents))\n", + "iter_history_end = np.zeros(len(train_percents))\n", + "mse_history_end = np.zeros(len(train_percents))\n", + "mse_val_history_end = np.zeros(len(train_percents))\n", + "train_stop_epoch = np.zeros(len(train_percents))\n", + "\n", + "################################################\n", + "# Train the neural network\n", + "################################################\n", + "index=0\n", + "for train_percent in train_percents:\n", + " epoch_stop = 0\n", + " print('train_percent',train_percent)\n", + "\n", + " # split the dataset to train, validation, test\n", + " train_set, valid_set = torch.utils.data.random_split(dataset, [0.9,0.1])\n", + "\n", + " # normailzation\n", + " extremes = dataset.train_norm(train_indices = train_set.indices)\n", + "\n", + " config['maxB'] = extremes[2]\n", + " config['minB'] = extremes[3]\n", + " config['train_set'] = train_set \n", + " config['valid_set'] = valid_set\n", + "\n", + "\n", + "\n", + " print(\"----------------------------\")\n", + " \n", + " print(\"----------------------------\")\n", + " # test_loader = torch.utils.data.DataLoader(dataset=test_set,batch_size=batch_size,shuffle=True)\n", + "\n", + "\n", + " \n", + " RMSE_history, RMSE_val_history, loss_history, iter_history, mse_history, mse_val_history,epoch_stop,Rsquare = train_GM(\n", + " config=config)\n", + " \n", + " \n", + " #save RMSE and loss after early stopping\n", + " RMSE_history_end[index] = RMSE_history[epoch_stop]\n", + " RMSE_val_history_end[index]= RMSE_val_history[epoch_stop]\n", + " loss_history_end[index] = loss_history[epoch_stop]\n", + " iter_history_end[index] = iter_history[epoch_stop]\n", + " mse_history_end[index] = mse_history[epoch_stop]\n", + " mse_val_history_end[index] = mse_val_history[epoch_stop]\n", + " index=index+1\n", + " print('training stop at epoch:',epoch_stop)\n", + " print('training stop at epoch:',Rsquare)\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "torch.save(Generative_network, 'EMS_CNN_ETH.pt')\t# 这里会存储迄今最优模型的参数" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "ave_site = 5\n", + "ave_kernel = 1/ave_site*np.ones(ave_site)\n", + "loss_history_conv = np.convolve(loss_history.numpy(),ave_kernel,'same')\n", + "\n", + "\n", + "plt.title('loss')\n", + "plt.plot(iter_history,loss_history,'-o')\n", + "plt.plot(iter_history,loss_history_conv,'-*')\n", + "plt.legend(['loss','loss_conv'])\n", + "plt.xlabel('iterations')\n", + "plt.ylabel('loss')\n", + "plt.ylim([0,10])\n", + "plt.show()\n", + "\n", + "plt.title('Train and Val RMSE(sample_num=1000)')\n", + "plt.plot(iter_history[0:epoch_stop],RMSE_history[0:epoch_stop],'-o')\n", + "plt.plot(iter_history[0:epoch_stop],RMSE_val_history[0:epoch_stop],'-*')\n", + "# plt.plot(2e-5*np.arange(epoch_stop),RMSE_history[0:epoch_stop]*1000,'-o')\n", + "# plt.plot(2e-5*np.arange(epoch_stop),RMSE_val_history[0:epoch_stop]*1000,'-*')\n", + "# plt.ylim([15,20])\n", + "plt.legend(['train CNN','val CNN'])\n", + "plt.xlabel('iterations')\n", + "plt.ylabel('RMSE(mT)')\n", + "plt.ylim([0,100])\n", + "plt.grid()\n", + "plt.show()\n", + "\n", + "plt.title('Train and Val loss(sample_num=1000)')\n", + "plt.plot(iter_history[0:epoch_stop],mse_history[0:epoch_stop]*1e6,'-o')\n", + "plt.plot(iter_history[0:epoch_stop],mse_val_history[0:epoch_stop]*1e6,'-*')\n", + "plt.legend(['train CNN','val CNN'])\n", + "plt.xlabel('iterations')\n", + "plt.ylabel('mse(mT^2)')\n", + "plt.grid()\n", + "plt.show()\n", + "print(epoch_stop)\n", + "\n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.13" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/Modeling eMNS/utils.py b/Modeling eMNS/utils.py index 6c7d7c3..552eef0 100644 --- a/Modeling eMNS/utils.py +++ b/Modeling eMNS/utils.py @@ -2,6 +2,7 @@ import matplotlib.pyplot as plt import torch.nn.functional as F import numpy as np + def compute_discrete_curl(A_field, device): ''' A_field: (batch, Dimensions, grid_x, grid_y, grid_z) @@ -189,8 +190,8 @@ def peek_z(self, z_plane_index): # prediction B field - self.plot_B_pred = 1e3*denorm(self.model(torch.unsqueeze(plot_sample[0],0).to(dtype=torch.float)), self.train_loop_config['maxB'], self.train_loop_config['minB'], self.train_loop_config['device']) - self.plot_B = 1e3*denorm(plot_sample[1], self.train_loop_config['maxB'], self.train_loop_config['minB'], self.train_loop_config['device']) + self.plot_B_pred = 1e3*denorm(self.model(torch.unsqueeze(plot_sample[0],0).to(device=self.train_loop_config['device'],dtype=torch.float)), self.train_loop_config['maxB'], self.train_loop_config['minB'], self.train_loop_config['device']) + self.plot_B = 1e3*denorm(plot_sample[1].to(device=self.train_loop_config['device']), self.train_loop_config['maxB'], self.train_loop_config['minB'], self.train_loop_config['device']) ylables=['Bx(mT)','By(mT)','Bz(mT)'] plot_rmse = torch.sqrt(F.mse_loss(self.plot_B, torch.squeeze(self.plot_B_pred,0), reduction='mean')) @@ -201,12 +202,12 @@ def peek_z(self, z_plane_index): B_est_temp =self.plot_B_pred[0,i-1,:,:,z_plane_index].detach() ax = f.add_subplot(3,2,2*i-1) - img_plot = ax.imshow( B_est_temp ) + img_plot = ax.imshow( B_est_temp.cpu() ) plt.ylabel(ylables[i-1]) Bfield_temp = self.plot_B[i-1,:,:,z_plane_index] ax2 = f.add_subplot(3,2,2*i) - img_plot=ax2.imshow(Bfield_temp) + img_plot=ax2.imshow(Bfield_temp.cpu()) plt.colorbar(img_plot,ax=[ax,ax2]) # plt.ylabel(ylables[i-1]) plt.show() @@ -220,9 +221,9 @@ def peek_3D(self,length=0.1): position = torch.cat(torch.meshgrid([x,y,z],indexing='ij')).reshape(3,16,16,16) print(position.shape) print(torch.squeeze(self.plot_B_pred,0).shape) - plot_3D_vector_field(position[:,:,:,::15], torch.squeeze(self.plot_B_pred,0).detach()[:,:,:,::15], length=length) + plot_3D_vector_field(position[:,:,:,::15], torch.squeeze(self.plot_B_pred,0).detach()[:,:,:,::15].cpu(), length=length) - plot_3D_vector_field(position[:,:,:,::15], self.plot_B[:,:,:,::15], length=length) + plot_3D_vector_field(position[:,:,:,::15], self.plot_B[:,:,:,::15].cpu(), length=length) #---------------------------------------------------------------- def grad_loss(preds, y):