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valid_loader.py
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"""
Parts of the code is borrowed from /~https://github.com/lochenchou/DORN_radar
For further details please visit /~https://github.com/lochenchou/DORN_radar
"""
import os
import time
import torch
import numpy as np
import utils
from tqdm import tqdm
from metrics import AverageMeter, Result, compute_errors
import torch.nn.functional as F
from torchvision import transforms
from loss import OrdinalRegressionLoss
from tensorboardX import SummaryWriter
import matplotlib.pylab as plt
BATCH_SIZE = 1
WORKERS = 9
PRINT_FREQ = 250
in_size = (350,800)
n_sweeps = 5
ORD_NUM = 80
GAMMA = 0.3
ALPHA = 1
BETA = 80
epoch = 1
RGB_ONLY = False
inv_normalize = transforms.Normalize(
mean=[-0.485/0.229, -0.456/0.224, -0.406/0.225],
std=[1/0.229, 1/0.224, 1/0.225]
)
# validation
# set dataloader
DATA_ROOT = '/home/mlv/work/data/sets/nuscenes'
output_dir = os.path.join('./result','radar'.format(n_sweeps, in_size[0], in_size[1]))
#train_dir = os.path.join(output_dir, 'train')
val_dir = os.path.join(output_dir, 'valid')
logdir = os.path.join(output_dir, 'log')
if not os.path.exists(output_dir):
os.makedirs(output_dir)
if not os.path.exists(val_dir):
os.makedirs(val_dir)
if not os.path.exists(logdir):
os.makedirs(logdir)
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
logger = SummaryWriter(logdir)
i = 0
def validation(data_loader, model):
#model.to(device)
ord_loss = OrdinalRegressionLoss(ord_num=ORD_NUM, beta=BETA)
avg80_sparse = AverageMeter()
avg80_dense = AverageMeter()
#print(model)
model.eval()
end = time.time()
skip =int(len(data_loader)/10)
img_list = []
evalbar = tqdm(total=len(data_loader))
for i, data in enumerate(data_loader):
_rgb, _sparse_depth, _dense_depth = data['RGB'].to(device), data['SPARSE'].to(device), data['DENSE'].to(device)
_radar_depth = data['RADAR'].to(device)
torch.cuda.synchronize()
data_time = time.time() - end
#_rgb = inv_normalize(_rgb)
# compute output
end = time.time()
with torch.no_grad():
if RGB_ONLY:
_pred_prob, _pred_label = model(_rgb)
else:
_pred_prob, _pred_label = model(_rgb, _radar_depth)
loss = ord_loss(_pred_prob, _dense_depth)
torch.cuda.synchronize()
gpu_time = time.time() - end
pred_depth = utils.label2depth_sid(_pred_label, K=ORD_NUM, alpha=1.0, beta=BETA, gamma=GAMMA)
s_abs_rel, s_sq_rel, s_rmse, s_rmse_log, s_a1, s_a2, s_a3 = compute_errors(_sparse_depth, pred_depth.to(device))
d_abs_rel, d_sq_rel, d_rmse, d_rmse_log, d_a1, d_a2, d_a3 = compute_errors(_dense_depth, pred_depth.to(device))
# measure accuracy and record loss
result80_sparse = Result()
result80_sparse.evaluate(pred_depth, _sparse_depth.data, cap=80)
avg80_sparse.update(result80_sparse, gpu_time, data_time, _rgb.size(0))
result80_dense = Result()
result80_dense.evaluate(pred_depth, _dense_depth.data, cap=80)
avg80_dense.update(result80_dense, gpu_time, data_time, _rgb.size(0))
end = time.time()
# save images for visualization
#if i == 0:
i = i+1
filename = os.path.join(output_dir,'eval_{}.png'.format(int(epoch+i)))
if i == 300:
_rgb = inv_normalize(_rgb)
img_merge = utils.merge_into_row_with_radar(_rgb, _radar_depth, _dense_depth, pred_depth)
utils.save_image(img_merge, filename)
# if (i + 1) % PRINT_FREQ == 0:
# print('Test: [{0}/{1}]\t'
# 't_GPU={gpu_time:.3f}({average.gpu_time:.3f})\n\t'
# 'RMSE={result.rmse:.2f}({average.rmse:.2f}) '
# 'RMSE_log={result.rmse_log:.3f}({average.rmse_log:.3f}) '
# 'AbsRel={result.absrel:.2f}({average.absrel:.2f}) '
# 'SqRel={result.squared_rel:.2f}({average.squared_rel:.2f}) '
# 'SILog={result.silog:.2f}({average.silog:.2f}) '
# 'iRMSE={result.irmse:.2f}({average.irmse:.2f}) '
# 'Delta1={result.delta1:.3f}({average.delta1:.3f}) '
# 'Delta2={result.delta2:.3f}({average.delta2:.3f}) '
# 'Delta3={result.delta3:.3f}({average.delta3:.3f})'.format(
# i + 1, len(data_loader), gpu_time=gpu_time, result=result80_sparse, average=avg80_sparse.average()))
# update progress bar and show loss
evalbar.set_postfix(ORD_LOSS='{:.2f}||DENSE||RMSE={:.2f},delta={:.2f}/{:.2f}|||SPARSE||RMSE={:.2f},delta={:.2f}/{:.2f}|'.format(loss,d_rmse,d_a1,d_a2,s_rmse,s_a1,s_a2))
evalbar.update(1)
i = i+1
print('\n**** EVALUATE WITH SPARSE DEPTH ****\n'
'\n**** CAP=80 ****\n'
'RMSE={average.rmse:.3f}\n'
'RMSE_log={average.rmse_log:.3f}\n'
'AbsRel={average.absrel:.3f}\n'
'SqRel={average.squared_rel:.3f}\n'
'SILog={average.silog:.3f}\n'
'Delta1={average.delta1:.3f}\n'
'Delta2={average.delta2:.3f}\n'
'Delta3={average.delta3:.3f}\n'
'iRMSE={average.irmse:.3f}\n'
'iMAE={average.imae:.3f}\n'
't_GPU={average.gpu_time:.3f}\n'.format(
average=avg80_sparse.average()))
logger.add_scalar('VAL_CAP80/SPARSE_RMSE', avg80_sparse.average().rmse, epoch)
logger.add_scalar('VAL_CAP80/SPARSE_RMSE_log', avg80_sparse.average().rmse_log, epoch)
logger.add_scalar('VAL_CAP80/SPARSE_iRMSE', avg80_sparse.average().irmse, epoch)
logger.add_scalar('VAL_CAP80/SPARSE_SILog', avg80_sparse.average().silog, epoch)
logger.add_scalar('VAL_CAP80/SPARSE_AbsRel', avg80_sparse.average().absrel, epoch)
logger.add_scalar('VAL_CAP80/SPARSE_SqRel', avg80_sparse.average().squared_rel, epoch)
logger.add_scalar('VAL_CAP80/SPARSE_Delta1', avg80_sparse.average().delta1, epoch)
logger.add_scalar('VAL_CAP80/SPARSE_Delta2', avg80_sparse.average().delta2, epoch)
logger.add_scalar('VAL_CAP80/SPARSE_Delta3', avg80_sparse.average().delta3, epoch)
logger.add_scalar('VAL_CAP80/DENSE_RMSE', avg80_dense.average().rmse, epoch)
logger.add_scalar('VAL_CAP80/DENSE_RMSE_log', avg80_dense.average().rmse_log, epoch)
logger.add_scalar('VAL_CAP80/DENSE_iRMSE', avg80_dense.average().irmse, epoch)
logger.add_scalar('VAL_CAP80/DENSE_SILog', avg80_dense.average().silog, epoch)
logger.add_scalar('VAL_CAP80/DENSE_AbsRel', avg80_dense.average().absrel, epoch)
logger.add_scalar('VAL_CAP80/DENSE_SqRel', avg80_dense.average().squared_rel, epoch)
logger.add_scalar('VAL_CAP80/DENSE_Delta1', avg80_dense.average().delta1, epoch)
logger.add_scalar('VAL_CAP80/DENSE_Delta2', avg80_dense.average().delta2, epoch)
logger.add_scalar('VAL_CAP80/DENSE_Delta3', avg80_dense.average().delta3, epoch)