tainer_me.py

# Copyright 2021 Ran Cheng <ran.cheng2@mail.mcgill.ca>
# Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated
# documentation files (the "Software"), to deal in the Software without restriction, including without limitation the
# rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit
# persons to whom the Software is furnished to do so, subject to the following conditions:
#
# The above copyright notice and this permission notice shall be included in all copies or substantial portions of the
# Software.
#
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE
# WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR
# COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR
# OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.

import argparse
import numpy as np

import torch
import torch.optim as optim
from torch.utils.data import Dataset, DataLoader
import MinkowskiEngine as ME

from module.SparseConvNet import PointNet
from module.ViT import ViT


def plot(C, L):
    import matplotlib.pyplot as plt
    mask = L == 0
    cC = C[mask].t().numpy()
    plt.scatter(cC[0], cC[1], c='r', s=0.1)
    mask = L == 1
    cC = C[mask].t().numpy()
    plt.scatter(cC[0], cC[1], c='b', s=0.1)
    plt.show()


class RandomLineDataset(Dataset):

    # Warning: read using mutable obects for default input arguments in python.
    def __init__(
        self,
        angle_range_rad=[-np.pi, np.pi],
        line_params=[
            -1,  # Start
            1,  # end
        ],
        is_linear_noise=True,
        dataset_size=100,
        num_samples=10000,
        quantization_size=0.005):

        self.angle_range_rad = angle_range_rad
        self.is_linear_noise = is_linear_noise
        self.line_params = line_params
        self.dataset_size = dataset_size
        self.rng = np.random.RandomState(0)

        self.num_samples = num_samples
        self.num_data = int(0.2 * num_samples)
        self.num_noise = num_samples - self.num_data

        self.quantization_size = quantization_size

    def __len__(self):
        return self.dataset_size

    def _uniform_to_angle(self, u):
        return (self.angle_range_rad[1] -
                self.angle_range_rad[0]) * u + self.angle_range_rad[0]

    def _sample_noise(self, num, noise_params):
        noise = noise_params[0] + self.rng.randn(num, 1) * noise_params[1]
        return noise

    def _sample_xs(self, num):
        """Return random numbers between line_params[0], line_params[1]"""
        return (self.line_params[1] - self.line_params[0]) * self.rng.rand(
            num, 1) + self.line_params[0]

    def __getitem__(self, i):
        # Regardless of the input index, return randomized data
        angle, intercept = np.tan(self._uniform_to_angle(
            self.rng.rand())), self.rng.rand()

        # Line as x = cos(theta) * t, y = sin(theta) * t + intercept and random t's
        # Drop some samples
        xs_data = self._sample_xs(self.num_data)
        ys_data = angle * xs_data + intercept + self._sample_noise(
            self.num_data, [0, 0.1])

        noise = 4 * (self.rng.rand(self.num_noise, 2) - 0.5)

        # Concatenate data
        input = np.vstack([np.hstack([xs_data, ys_data]), noise])
        feats = input
        labels = np.vstack(
            [np.ones((self.num_data, 1)),
             np.zeros((self.num_noise, 1))]).astype(np.int32)

        # Quantize the input
        discrete_coords, unique_feats, unique_labels = ME.utils.sparse_quantize(
            coords=input,
            feats=feats,
            labels=labels,
            quantization_size=self.quantization_size,
            ignore_label=-100)

        return discrete_coords, unique_feats, unique_labels


def collation_fn(data_labels):
    coords, feats, labels = list(zip(*data_labels))
    coords_batch, feats_batch, labels_batch = [], [], []

    # Generate batched coordinates
    coords_batch = ME.utils.batched_coordinates(coords)

    # Concatenate all lists
    feats_batch = torch.from_numpy(np.concatenate(feats, 0)).float()
    labels_batch = torch.from_numpy(np.concatenate(labels, 0))

    return coords_batch, feats_batch, labels_batch


def main(config):
    # Binary classification
    net = PointNet(
        2,  # in nchannel
        2)  # out_nchannel

    optimizer = optim.SGD(
        net.parameters(),
        lr=config.lr,
        momentum=config.momentum,
        weight_decay=config.weight_decay)

    criterion = torch.nn.CrossEntropyLoss(ignore_index=-100)

    # Dataset, data loader
    train_dataset = RandomLineDataset()

    train_dataloader = DataLoader(
        train_dataset,
        batch_size=config.batch_size,
        # 1) collate_fn=collation_fn,
        # 2) collate_fn=ME.utils.batch_sparse_collate,
        # 3) collate_fn=ME.utils.SparseCollation(),
        collate_fn=ME.utils.batch_sparse_collate,
        num_workers=1)

    accum_loss, accum_iter, tot_iter = 0, 0, 0

    for epoch in range(config.max_epochs):
        train_iter = iter(train_dataloader)

        # Training
        net.train()
        for i, data in enumerate(train_iter):
            coords, feats, labels = data
            out = net(ME.SparseTensor(feats.float(), coords))
            optimizer.zero_grad()
            loss = criterion(out.F.squeeze(), labels.long())
            loss.backward()
            optimizer.step()

            accum_loss += loss.item()
            accum_iter += 1
            tot_iter += 1

            if tot_iter % 10 == 0 or tot_iter == 1:
                print(
                    f'Epoch: {epoch} iter: {tot_iter}, Loss: {accum_loss / accum_iter}'
                )
                accum_loss, accum_iter = 0, 0


if __name__ == '__main__':
    parser = argparse.ArgumentParser()
    parser.add_argument('--batch_size', default=12, type=int)
    parser.add_argument('--max_epochs', default=10, type=int)
    parser.add_argument('--lr', default=0.1, type=float)
    parser.add_argument('--momentum', type=float, default=0.9)
    parser.add_argument('--weight_decay', type=float, default=1e-4)

    config = parser.parse_args()
    main(config)