def validate(val_loader, model, criterion, epoch, key, evaluator):
'''
Run evaluation
'''
# Switch to evaluate mode
model.eval()
for i, (img, gt) in enumerate(val_loader):
# Process the network inputs and outputs
img = utils.normalize(img, torch.Tensor([0.295, 0.204, 0.197]), torch.Tensor([0.221, 0.188, 0.182]))
gt_temp = gt * 255
label = utils.generateLabel4CE(gt_temp, key)
oneHotGT = utils.generateOneHot(gt_temp, key)
img, label = Variable(img), Variable(label)
if use_gpu:
img = img.cuda()
label = label.cuda()
# Compute output
seg = model(img)
loss = model.dice_loss(seg, label)
print('[%d/%d][%d/%d] Loss: %.4f'
% (epoch, args.epochs-1, i, len(val_loader)-1, loss.mean().data))
utils.displaySamples(img, seg, gt, use_gpu, key, args.saveTest, epoch,
i, args.save_dir)
evaluator.addBatch(seg, oneHotGT)
def train(train_loader, model, criterion, optimizer, scheduler, epoch, key):
'''
Run one training epoch
'''
# Switch to train mode
model.train()
for i, (img, seg_gt, class_gt) in enumerate(train_loader):
# For TenCrop Data Augmentation
img = img.view(-1, 3, args.resizedImageSize, args.resizedImageSize)
img = utils.normalize(img, torch.Tensor([0.295, 0.204, 0.197]),
torch.Tensor([0.221, 0.188, 0.182]))
seg_gt = seg_gt.view(-1, 3, args.resizedImageSize,
args.resizedImageSize)
# Process the network inputs and outputs
gt_temp = seg_gt * 255
seg_label = utils.generateLabel4CE(gt_temp, key)
class_label = class_gt
for _ in range(9):
class_label = torch.cat((class_label, class_gt), 0)
img, seg_label, class_label = Variable(img), Variable(
seg_label), Variable(class_label).float()
if use_gpu:
img = img.cuda()
seg_label = seg_label.cuda()
class_label = class_label.cuda()
# Compute output
classified, segmented = model(img)
seg_loss = model.dice_loss(segmented, seg_label)
class_loss = criterion(classified, class_label)
total_loss = seg_loss + class_loss
# Compute gradient and do SGD step
optimizer.zero_grad()
total_loss.backward()
optimizer.step()
scheduler.step(total_loss.mean().data)
print(
'[{:d}/{:d}][{:d}/{:d}] Total Loss: {:.4f}, Segmentation Loss: {:.4f}, Classification Loss: {:.4f}'
.format(epoch, args.epochs - 1, i,
len(train_loader) - 1,
total_loss.mean().data,
seg_loss.mean().data,
class_loss.mean().data))
utils.displaySamples(img, segmented, seg_gt, use_gpu, key, False,
epoch, i, args.save_dir)
def train(train_loader, model, criterion, optimizer, scheduler, epoch, key):
'''
Run one training epoch
'''
# Switch to train mode
model.train()
epoch_loss = 0
for i, (img, gt) in enumerate(train_loader):
# For TenCrop Data Augmentation
img = img.view(-1,3,args.resizedImageSize,args.resizedImageSize)
img = utils.normalize(img, torch.Tensor([0.295, 0.204, 0.197]), torch.Tensor([0.221, 0.188, 0.182]))
gt = gt.view(-1,3,args.resizedImageSize,args.resizedImageSize)
# Process the network inputs and outputs
gt_temp = gt * 255
label = utils.generateLabel4CE(gt_temp, key)
oneHotGT = utils.generateOneHot(gt_temp, key)
img, label = Variable(img), Variable(label)
if use_gpu:
img = img.cuda()
label = label.cuda()
# Compute output
seg = model(img)
loss = model.dice_loss(seg, label)
# Compute gradient and do SGD step
optimizer.zero_grad()
loss.backward()
optimizer.step()
scheduler.step(loss.mean().item())
epoch_loss += loss.mean().item()
print('[%d/%d][%d/%d] Loss: %.4f'
% (epoch, args.epochs-1, i, len(train_loader)-1, loss.mean().item()))
utils.displaySamples(img, seg, gt, use_gpu, key, False, epoch,
i, args.save_dir)
writer.add_scalar('Train Epoch Loss', epoch_loss / (i+1), epoch)
def validate(val_loader, model, criterion, epoch, key, evaluator):
'''
Run evaluation
'''
# Switch to evaluate mode
model.eval()
for i, (img, seg_gt, class_gt) in enumerate(val_loader):
# Process the network inputs and outputs
img = utils.normalize(img, torch.Tensor([0.295, 0.204, 0.197]),
torch.Tensor([0.221, 0.188, 0.182]))
gt_temp = seg_gt * 255
seg_label = utils.generateLabel4CE(gt_temp, key)
oneHotGT = utils.generateOneHot(gt_temp, key)
img, seg_label, class_label = Variable(img), Variable(
seg_label), Variable(class_gt).float()
if use_gpu:
img = img.cuda()
seg_label = seg_label.cuda()
class_label = class_label.cuda()
# Compute output
classified, segmented = model(img)
seg_loss = model.dice_loss(segmented, seg_label)
class_loss = criterion(classified, class_label)
total_loss = seg_loss + class_loss
print(
'[{:d}/{:d}][{:d}/{:d}] Total Loss: {:.4f}, Segmentation Loss: {:.4f}, Classification Loss: {:.4f}'
.format(epoch, args.epochs - 1, i,
len(val_loader) - 1,
total_loss.mean().data,
seg_loss.mean().data,
class_loss.mean().data))
utils.displaySamples(img, segmented, seg_gt, use_gpu, key,
args.saveTest, epoch, i, args.save_dir)
evaluator.addBatch(segmented, oneHotGT)