mirror of
https://github.com/Laurent2916/REVA-QCAV.git
synced 2024-11-08 14:39:00 +00:00
147 lines
4.3 KiB
Python
147 lines
4.3 KiB
Python
import sys
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import os
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from optparse import OptionParser
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import numpy as np
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import torch
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import torch.backends.cudnn as cudnn
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import torch.nn as nn
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from torch import optim
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from eval import eval_net
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from unet import UNet
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from utils import get_ids, split_ids, split_train_val, get_imgs_and_masks, batch
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def train_net(net,
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epochs=5,
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batch_size=1,
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lr=0.1,
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val_percent=0.05,
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save_cp=True,
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gpu=False,
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img_scale=0.5):
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dir_img = 'data/train/'
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dir_mask = 'data/train_masks/'
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dir_checkpoint = 'checkpoints/'
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ids = get_ids(dir_img)
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ids = split_ids(ids)
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iddataset = split_train_val(ids, val_percent)
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print('''
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Starting training:
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Epochs: {}
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Batch size: {}
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Learning rate: {}
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Training size: {}
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Validation size: {}
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Checkpoints: {}
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CUDA: {}
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'''.format(epochs, batch_size, lr, len(iddataset['train']),
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len(iddataset['val']), str(save_cp), str(gpu)))
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N_train = len(iddataset['train'])
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optimizer = optim.SGD(net.parameters(),
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lr=lr,
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momentum=0.9,
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weight_decay=0.0005)
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criterion = nn.BCELoss()
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for epoch in range(epochs):
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print('Starting epoch {}/{}.'.format(epoch + 1, epochs))
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net.train()
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# reset the generators
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train = get_imgs_and_masks(iddataset['train'], dir_img, dir_mask, img_scale)
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val = get_imgs_and_masks(iddataset['val'], dir_img, dir_mask, img_scale)
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epoch_loss = 0
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for i, b in enumerate(batch(train, batch_size)):
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imgs = np.array([i[0] for i in b]).astype(np.float32)
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true_masks = np.array([i[1] for i in b])
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imgs = torch.from_numpy(imgs)
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true_masks = torch.from_numpy(true_masks)
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if gpu:
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imgs = imgs.cuda()
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true_masks = true_masks.cuda()
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masks_pred = net(imgs)
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masks_probs_flat = masks_pred.view(-1)
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true_masks_flat = true_masks.view(-1)
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loss = criterion(masks_probs_flat, true_masks_flat)
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epoch_loss += loss.item()
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print('{0:.4f} --- loss: {1:.6f}'.format(i * batch_size / N_train, loss.item()))
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optimizer.zero_grad()
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loss.backward()
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optimizer.step()
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print('Epoch finished ! Loss: {}'.format(epoch_loss / i))
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if 1:
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val_dice = eval_net(net, val, gpu)
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print('Validation Dice Coeff: {}'.format(val_dice))
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if save_cp:
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torch.save(net.state_dict(),
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dir_checkpoint + 'CP{}.pth'.format(epoch + 1))
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print('Checkpoint {} saved !'.format(epoch + 1))
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def get_args():
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parser = OptionParser()
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parser.add_option('-e', '--epochs', dest='epochs', default=5, type='int',
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help='number of epochs')
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parser.add_option('-b', '--batch-size', dest='batchsize', default=10,
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type='int', help='batch size')
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parser.add_option('-l', '--learning-rate', dest='lr', default=0.1,
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type='float', help='learning rate')
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parser.add_option('-g', '--gpu', action='store_true', dest='gpu',
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default=False, help='use cuda')
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parser.add_option('-c', '--load', dest='load',
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default=False, help='load file model')
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parser.add_option('-s', '--scale', dest='scale', type='float',
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default=0.5, help='downscaling factor of the images')
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(options, args) = parser.parse_args()
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return options
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if __name__ == '__main__':
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args = get_args()
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net = UNet(n_channels=3, n_classes=1)
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if args.load:
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net.load_state_dict(torch.load(args.load))
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print('Model loaded from {}'.format(args.load))
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if args.gpu:
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net.cuda()
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# cudnn.benchmark = True # faster convolutions, but more memory
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try:
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train_net(net=net,
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epochs=args.epochs,
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batch_size=args.batchsize,
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lr=args.lr,
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gpu=args.gpu,
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img_scale=args.scale)
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except KeyboardInterrupt:
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torch.save(net.state_dict(), 'INTERRUPTED.pth')
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print('Saved interrupt')
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try:
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sys.exit(0)
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except SystemExit:
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os._exit(0)
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