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train.py
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train.py
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import numpy as np
import torch
import torch.nn as nn
import torch.optim as optim
import torch.nn.functional as F
import torch.utils.data as data
from util import EarlyStopping, save_nets, save_predictions, load_best_weights
from model import UNet
from dataset import DataFolder
from tqdm import tqdm
import matplotlib.pyplot as plt
import matplotlib as mpl
import argparse
np. random.seed(1000)
if __name__ == '__main__':
parser = argparse.ArgumentParser(description='Train the UNet on images and target masks', \
formatter_class=argparse.ArgumentDefaultsHelpFormatter)
parser.add_argument('-tb','--batch_size', type=int, default=8,
help='Batch size for Training and Testing', dest='batch_size')
parser.add_argument('-e','--epochs', type=int, default=200,
help='Maximum number of epochs for training', dest='epochs')
parser.add_argument('-l','--lr', type=float, default=0.001,
help='Learning rate.', dest='lr')
parser.add_argument('-p','--patience', type=float, default=10,
help='Early stopping patience.', dest='patience')
parser.add_argument('-d','--min_delta', type=float, default=0.001,
help='Minimum loss improvement for each epoch.', dest='min_delta')
args = parser.parse_args()
print(args)
train_loader = data.DataLoader(
dataset=DataFolder('new_dataset/train/train_images_256/', 'new_dataset/train/train_masks_256/', 'train'),
batch_size=args.batch_size,
shuffle=True,
num_workers=4
)
valid_loader = data.DataLoader(
dataset=DataFolder('new_dataset/val/train_images_256/', 'new_dataset/val/train_masks_256/', 'validation'),
batch_size=args.batch_size,
shuffle=False,
num_workers=4
)
test_loader = data.DataLoader(
dataset=DataFolder('new_dataset/test/train_images_256/', 'new_dataset/test/train_masks_256/', 'test'),
batch_size=args.batch_size,
shuffle=False,
num_workers=4
)
model = UNet(1, shrink=1).cuda()
nets = [model]
params = [{'params': net.parameters()} for net in nets]
solver = optim.Adam(params, lr=args.lr)
criterion = nn.CrossEntropyLoss()
es = EarlyStopping(min_delta=args.min_delta, patience=args.patience)
train_allepoch_loss = []
valid_allepoch_loss = []
for epoch in range(1, args.epochs+1):
with tqdm(total=len(train_loader.dataset), desc=f'Epoch {epoch}/{args.epochs}', unit='img', position=0, leave=True) as pbar:
train_loss = []
valid_loss = []
for batch_idx, (img, mask, _) in enumerate(train_loader):
solver.zero_grad()
img = img.cuda()
mask = mask.cuda()
pred = model(img)
loss = criterion(pred, mask)
pbar.set_postfix(**{'loss (batch)': loss.item()})
loss.backward()
solver.step()
train_loss.append(loss.item())
pbar.update(img.shape[0])
with torch.no_grad():
for batch_idx, (img, mask, _) in enumerate(valid_loader):
img = img.cuda()
mask = mask.cuda()
pred = model(img)
loss = criterion(pred, mask)
valid_loss.append(loss.item())
train_loss_mean = np.mean(train_loss)
valid_loss_mean = np.mean(valid_loss)
print('[EPOCH {}/{}] Train Loss: {:.4f}; Valid Loss: {:.4f}'.format(
epoch, args.epochs, train_loss_mean, valid_loss_mean
))
flag, best, bad_epochs = es.step(torch.Tensor([valid_loss_mean]))
if flag:
print('Early stopping criterion met')
break
else:
if bad_epochs == 0:
save_nets(nets, 'saved_model')
print('Saving current best model')
print('Current Valid loss: {:.4f}; Current best: {:.4f}; Bad epochs: {}'.format(
valid_loss_mean, best.item(), bad_epochs
))
train_allepoch_loss.append(train_loss_mean)
valid_allepoch_loss.append(valid_loss_mean)
print('Training is over!!!')
plt.figure()
plt.plot(np.arange(1,len(train_allepoch_loss)+1), train_allepoch_loss, 'b-', label="Training Set Loss")
plt.plot(np.arange(1,len(valid_allepoch_loss)+1), valid_allepoch_loss, 'r-', label="Validation Set Loss")
plt.legend(loc="upper right")
plt.xlabel('Epochs')
plt.ylabel('Cross Entropy Loss')
plt.savefig('saved_images/trainval_loss.svg',transparent=True)
print('Training is over!!!')
with torch.no_grad():
test_loss = []
for batch_idx, (img, mask, img_fns) in enumerate(test_loader):
model = load_best_weights(model, 'saved_model')
img = img.cuda()
mask = mask.cuda()
pred = model(img)
loss = criterion(pred, mask)
test_loss.append(loss.item())
pred_mask = torch.argmax(F.softmax(pred, dim=1), dim=1)
pred_mask = torch.chunk(pred_mask, chunks=args.batch_size, dim=0)
save_predictions(pred_mask, img_fns, 'test_predictions')
print('[Testing {}/{}] Test Loss: {:.4f}'.format(
batch_idx+1, len(test_loader), loss.item()
))
print('Final Test Loss: {:.4f}'.format(np.mean(test_loss)))
fig = plt.figure(figsize=(15,15))
cmap = mpl.colors.ListedColormap(['black','blue','red', 'green', 'brown', 'cyan','yellow','royalblue'])
cmap.set_over('royalblue')
cmap.set_under('black')
bounds = [0,1,2,3,4,5,6,7,8]
cmaplist = [cmap(i) for i in range(cmap.N)]
cmap = mpl.colors.LinearSegmentedColormap.from_list('Custom cmap', cmaplist, cmap.N)
norm = mpl.colors.BoundaryNorm(bounds, cmap.N)
for idx, pred in enumerate(pred_mask):
pred = torch.squeeze(pred, dim=0)
ax = fig.add_subplot(3,3, idx+1)
img = ax.imshow(pred.cpu().numpy(), interpolation='none', cmap=cmap, norm=norm)
fig.colorbar(img)
plt.tight_layout()
plt.savefig('saved_images/predictions.svg',transparent=True)