PhysNet_train.py

"""
Training Script for PhysNet based models:
* PhysNet
* PhysNet_SpaTemp
* PhysNetDil
* PhysNetGlobal
"""
import argparse
import os
import glob
import time
import torch
import torch.nn as nn
import torch.nn.parallel
import torch.backends.cudnn as cudnn
import torch.optim
import torch.utils.data
import torchvision.transforms as transforms
from PhysNet import NegPearson
from PhysNet import PhysNet
import torchvision.datasets as datasets
import numpy as np
import matplotlib.pyplot as plt
import pulse_dataset_3d

parser = argparse.ArgumentParser(description='PyTorch ImageNet Training')
parser.add_argument('-j', '--workers', default=4, type=int, metavar='N',
                    help='number of data loading workers (default: 4)')
parser.add_argument('--epochs', default=15, type=int, metavar='N',
                    help='number of total epochs to run')
parser.add_argument('--start-epoch', default=0, type=int, metavar='N',
                    help='manual epoch number (useful on restarts)')
parser.add_argument('-b', '--batch-size', default=3, type=int,
                    metavar='N', help='mini-batch size (default: 128)')
parser.add_argument('--lr', '--learning-rate', default=0.0001, type=float,  # 0.0001 0.00007
                    metavar='LR', help='initial learning rate')
parser.add_argument('--momentum', default=0.9, type=float, metavar='M',
                    help='momentum')
parser.add_argument('--weight-decay', '--wd', default=5e-4, type=float,
                    metavar='W', help='weight decay (default: 5e-4)')
parser.add_argument('--print-freq', '-p', default=1, type=int,
                    metavar='N', help='print frequency (default: 20)')
parser.add_argument('--resume', default='', type=str, metavar='PATH',
                    help='path to latest checkpoint (default: none)')
parser.add_argument('-e', '--evaluate', dest='evaluate', action='store_true',
                    help='evaluate model on validation set')
parser.add_argument('--pretrained', dest='pretrained', action='store_true',
                    help='use pre-trained model')
parser.add_argument('--save-dir', dest='save_dir',
                    help='The directory used to save the trained models',
                    default='save_temp', type=str)

loss_global = []
loss_ = []
loss_global_test = []


def train(train_loader, model, criterion, optimizer, epoch):
    """
        Run one train epoch
    """
    batch_time = AverageMeter()
    data_time = AverageMeter()
    losses = AverageMeter()
    top1 = AverageMeter()

    # switch to train mode
    model.train()

    end = time.time()
    for i, (net_input, target) in enumerate(train_loader):

        # measure data loading time
        data_time.update(time.time() - end)
        net_input = net_input.cuda(non_blocking=True)
        target = target.cuda(non_blocking=True)

        # compute output
        output, x_visual, x,y = model(net_input)
        rPPG = (output - torch.mean(output)) / torch.std(output)  # normalize
        BVP_label = (target - torch.mean(target)) / torch.std(target)  # normalize
        print(rPPG.size(), BVP_label.size())

        loss = criterion(rPPG, BVP_label)

        # compute gradient and do SGD step
        optimizer.zero_grad()
        loss.backward()
        optimizer.step()

        loss = loss.float()

        # measure accuracy and record loss
        losses.update(loss.item(), net_input.size(0))

        # measure elapsed time
        batch_time.update(time.time() - end)
        end = time.time()

        if i % args.print_freq == 0:
            print('Epoch: [{0}][{1}/{2}]\t'
                  'Time {batch_time.val:.3f} ({batch_time.avg:.3f})\t'
                  'Data {data_time.val:.3f} ({data_time.avg:.3f})\t'
                  'Loss {loss.val:.4f} ({loss.avg:.4f})\t'
                  'Prec@1 {top1.val:.3f} ({top1.avg:.3f})'.format(
                      epoch, i, len(train_loader), batch_time=batch_time,
                      data_time=data_time, loss=losses, top1=top1))
    with open('train_log.csv', 'a') as log:
        log.write("{}, {}, {}, {}\n".format(losses.val, losses.avg, top1.val, top1.avg))
    loss_global.append(losses.avg)
    loss_.append(losses.val)


def validate(val_loader, model, criterion):
    """
    Run evaluation
    """
    batch_time = AverageMeter()
    losses = AverageMeter()
    top1 = AverageMeter()

    # switch to evaluate mode
    model.eval()

    end = time.time()
    for i, (net_input, target) in enumerate(val_loader):

        net_input = net_input.cuda(non_blocking=True)
        target = target.cuda(non_blocking=True)

        # compute output
        with torch.no_grad():
            output, x_visual, x, y = model(net_input)
            rPPG = (output - torch.mean(output)) / torch.std(output)  # normalize
            BVP_label = (target - torch.mean(target)) / torch.std(target)  # normalize

            loss = criterion(rPPG, BVP_label)

        loss = loss.float()

        # measure accuracy and record loss
        losses.update(loss.item(), net_input.size(0))

        # measure elapsed time
        batch_time.update(time.time() - end)
        end = time.time()

        if i % args.print_freq == 0:
            print('Test: [{0}/{1}]\t'
                  'Time {batch_time.val:.3f} ({batch_time.avg:.3f})\t'
                  'Loss {loss.val:.4f} ({loss.avg:.4f})\t'
                  'Prec@1 {top1.val:.3f} ({top1.avg:.3f})'.format(
                      i, len(val_loader), batch_time=batch_time, loss=losses,
                      top1=top1))

    print(' * Prec@1 {top1.avg:.3f}'
          .format(top1=top1))
    with open('test_log.csv', 'a') as log:
        log.write("{}, {}\n".format(losses.avg, top1.avg))
    loss_global_test.append(losses.avg)

    return top1.avg


def save_checkpoint(state, is_best, filename='checkpoint.pth.tar'):
    """
    Save the training model
    """
    torch.save(state, filename)


class AverageMeter(object):
    """Computes and stores the average and current value"""
    def __init__(self):
        self.reset()

    def reset(self):
        self.val = 0
        self.avg = 0
        self.sum = 0
        self.count = 0

    def update(self, val, n=1):
        self.val = val
        self.sum += val * n
        self.count += n
        self.avg = self.sum / self.count


def adjust_learning_rate(optimizer, epoch, every):
    """Sets the learning rate to the initial LR decayed by 2 every 30 epochs"""
    lr = args.lr * (0.5 ** (epoch // every))
    for param_group in optimizer.param_groups:
        param_group['lr'] = lr


if __name__ == '__main__':
    train_sequence_list = "train_seq.txt"  # "sequence_test.txt"#   "sequence_test.txt"
    root_dir = 'E:/Datasets_PULSE/set_all/'
    seq_list = []
    end_indexes = []
    with open(train_sequence_list, 'r') as seq_list_file:
        for line in seq_list_file:
            seq_list.append(line.rstrip('\n'))

    i = 0
    for s in seq_list:
        i+=1
        sequence_dir = os.path.join(root_dir, s)
        if sequence_dir[-2:len(sequence_dir)] == '_1':
            fr_list = glob.glob(sequence_dir[0:-2] + '/cropped/*.png')
            fr_list = fr_list[0:len(fr_list) // 2]
        elif sequence_dir[-2:len(sequence_dir)] == '_2':
            fr_list = glob.glob(sequence_dir[0:-2] + '/cropped/*.png')
            fr_list = fr_list[len(fr_list) // 2: len(fr_list)]
        else:
            if os.path.exists(sequence_dir + '/cropped/'):
                fr_list = glob.glob(sequence_dir + '/cropped/*.png')
            else:
                fr_list = glob.glob(sequence_dir + '/*.png')
        # print(fr_list)
        end_indexes.append(len(fr_list))

    end_indexes = [0, *end_indexes]
    print(end_indexes)

    test_sequence_list = "test_seq.txt"
    root_dir = 'E:/Datasets_PULSE/set_all/'
    seq_list = []
    end_indexes_test = []
    with open(test_sequence_list, 'r') as seq_list_file:
        for line in seq_list_file:
            seq_list.append(line.rstrip('\n'))
    i = 0
    for s in seq_list:
        i += 1
        sequence_dir = os.path.join(root_dir, s)
        if sequence_dir[-2:len(sequence_dir)] == '_1':
            fr_list = glob.glob(sequence_dir[0:-2] + '/cropped/*.png')
            fr_list = fr_list[0:len(fr_list) // 2]
        elif sequence_dir[-2:len(sequence_dir)] == '_2':
            fr_list = glob.glob(sequence_dir[0:-2] + '/cropped/*.png')
            fr_list = fr_list[len(fr_list) // 2: len(fr_list)]
        else:
            if os.path.exists(sequence_dir + '/cropped/'):
                fr_list = glob.glob(sequence_dir + '/cropped/*.png')
            else:
                fr_list = glob.glob(sequence_dir + '/*.png')
        end_indexes_test.append(len(fr_list))

    end_indexes_test = [0, *end_indexes_test]
    print(end_indexes_test)

    global args, best_prec1
    best_prec1 = 0
    args = parser.parse_args()

    # Check the save_dir exists or not
    if not os.path.exists(args.save_dir):
        os.makedirs(args.save_dir)
    print("initialize model...")
    seq_len = 32
    model = PhysNet(seq_len)

    model = torch.nn.DataParallel(model)
    model.cuda()

    # optionally resume from a checkpoint
    if args.resume:
        if os.path.isfile(args.resume):
            print("=> loading checkpoint '{}'".format(args.resume))
            checkpoint = torch.load(args.resume)
            args.start_epoch = checkpoint['epoch']
            best_prec1 = checkpoint['best_prec1']
            model.load_state_dict(checkpoint['state_dict'])
            print("=> loaded checkpoint '{}' (epoch {})"
                  .format(args.evaluate, checkpoint['epoch']))
        else:
            print("=> no checkpoint found at '{}'".format(args.resume))

    cudnn.benchmark = True

    normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406],
                                     std=[0.229, 0.224, 0.225])
    trans = transforms.Compose([
        # transforms.CenterCrop(120),
        # transforms.RandomHorizontalFlip(p=0.5),
        # transforms.ColorJitter(0.2, 0.2),
        transforms.ToTensor(),
        normalize
    ])

    from pulse_sampler import PulseSampler

    sampler = PulseSampler(end_indexes, seq_len, False)
    sampler_test = PulseSampler(end_indexes_test, seq_len, False)

    pulse = pulse_dataset_3d.PulseDataset(train_sequence_list, root_dir, seq_len=seq_len,
                                          length=len(sampler), transform=trans)
    pulse_test = pulse_dataset_3d.PulseDataset(test_sequence_list, root_dir, seq_len=seq_len,
                                               length=len(sampler_test), transform=transforms.Compose([
                                                                                                transforms.ToTensor(),
                                                                                                normalize]))
    # Visualize frames
    # fig = plt.figure()
    # for i in range(len(pulse_test)):
    #     sample = pulse[i]
    #     # print(sample)
    #     data = sample[0]
    #     print(data.size())
    #     print(i, sample[0].shape, torch.mean(sample[1]))
    #     for b in range(data.size()[1]):
    #         # print(b)
    #         ax = plt.subplot(8, 8, b+1)
    #         # print(sample[0][0].size())
    #         img = data.permute(1, 2, 3, 0)
    #         print(img.size(),img[b].size())
    #         plt.imshow((img[b]))
    #         # plt.tight_layout()
    #         # ax.set_title('Sample #{}'.format(b))
    #         ax.axis('off')
    #     plt.show()

    train_loader = torch.utils.data.DataLoader(
        pulse,
        batch_size=args.batch_size, shuffle=False,
        num_workers=args.workers, pin_memory=True, sampler=sampler)

    val_loader = torch.utils.data.DataLoader(
        pulse_test,
        batch_size=args.batch_size, shuffle=False,
        num_workers=args.workers, pin_memory=True,  sampler=sampler_test)

    # define loss function (criterion) and optimizer
    # criterion = nn.MSELoss()
    criterion = NegPearson()
    criterion = criterion.cuda()

    # optimizer = torch.optim.SGD(model.parameters(), args.lr,
    #                             momentum=args.momentum,
    #                             weight_decay=args.weight_decay)
    optimizer = torch.optim.Adam(model.parameters(), args.lr,
                                 weight_decay=args.weight_decay)

    if args.evaluate:
        validate(val_loader, model, criterion)
    print('starting training...')
    for epoch in range(args.start_epoch, args.epochs):
        adjust_learning_rate(optimizer, epoch, 3)

        # train for one epoch
        train(train_loader, model, criterion, optimizer, epoch)

        # evaluate on validation set
        prec1 = validate(val_loader, model, criterion)

        # remember best prec@1 and save checkpoint
        is_best = prec1 > best_prec1
        best_prec1 = max(prec1, best_prec1)
        save_checkpoint({
            'epoch': epoch + 1,
            'state_dict': model.state_dict(),
            'best_prec1': best_prec1,
        }, is_best, filename=os.path.join(args.save_dir, 'drop_3d_{}.tar'.format(epoch)))

    plt.plot(loss_global)
    plt.title('average training loss')
    plt.show()

    plt.plot(loss_global_test)
    plt.title('testing loss')
    plt.show()

    plt.plot(loss_global_test)
    plt.plot(loss_global)
    plt.grid()
    plt.legend(['test loss', 'train loss'])
    plt.savefig('loss.jpg')