def train(train_loader, model):
running_loss = 0.0
data_size = train_data.__len__()
model.train()
# for inputs, masks, labels in progress_bar(train_loader, parent=mb):
for inputs, masks, labels in train_loader:
inputs, masks, labels = inputs.to(device), masks.to(device), labels.to(
device)
optimizer.zero_grad()
with torch.set_grad_enabled(True):
logit, logit1, logit2, logit3, logit4 = model(inputs)
loss0 = lovasz_hinge(logit.squeeze(1), masks.squeeze(1))
loss1 = lovasz_hinge(logit1.squeeze(1), masks.squeeze(1))
loss2 = lovasz_hinge(logit2.squeeze(1), masks.squeeze(1))
loss3 = lovasz_hinge(logit3.squeeze(1), masks.squeeze(1))
loss4 = lovasz_hinge(logit4.squeeze(1), masks.squeeze(1))
loss = loss0 + (0.6 * loss1 + 0.2 * loss2 + 0.1 * loss3 +
0.1 * loss4) * 0.01
loss.backward()
optimizer.step()
running_loss += loss.item() * inputs.size(0)
# mb.child.comment = 'loss: {}'.format(loss.item())
epoch_loss = running_loss / data_size
return epoch_loss
def test(test_loader, model, losstype = "lovasz"):
running_loss = 0.0
predicts = []
truths = []
model.eval()
for inputs, masks in test_loader:
inputs, masks = inputs.to(device), masks.to(device)
with torch.set_grad_enabled(False):
outputs = model(inputs)
outputs = outputs[:, :, pad_left:pad_left + fine_size,
pad_left:pad_left + fine_size].contiguous()
if (losstype != "bce_dice"):
loss = lovasz_hinge(outputs.squeeze(1), masks.squeeze(1))
predicts.append(F.sigmoid(outputs).detach().cpu().numpy())
else:
outputs = F.sigmoid(outputs)
loss = mixed_dice_bce_loss(outputs, masks)
predicts.append(outputs.detach().cpu().numpy())
truths.append(masks.detach().cpu().numpy())
running_loss += loss.item() * inputs.size(0)
predicts = np.concatenate(predicts).squeeze()
truths = np.concatenate(truths).squeeze()
precision, _, _ = do_kaggle_metric(predicts, truths, 0.5)
precision = precision.mean()
epoch_loss = running_loss / val_data.__len__()
return epoch_loss, precision
def train(train_loader, model, losstype="lovasz"):
running_loss = 0.0
data_size = train_data.__len__()
model.train()
# for inputs, masks, labels in progress_bar(train_loader, parent=mb):
for inputs, masks, labels in train_loader:
inputs, masks, labels = inputs.to(device), masks.to(device), labels.to(
device)
optimizer.zero_grad()
with torch.set_grad_enabled(True):
logit = model(inputs)
logit = logit[0]
if (losstype != "bce_dice"):
loss = lovasz_hinge(logit.squeeze(1), masks.squeeze(1))
else:
logit = F.sigmoid(logit)
loss = mixed_dice_bce_loss(logit, masks)
loss.backward()
optimizer.step()
running_loss += loss.item() * inputs.size(0)
# mb.child.comment = 'loss: {}'.format(loss.item())
epoch_loss = running_loss / data_size
return epoch_loss
def criterion(self,
logit,
logit_mask,
logit_clf,
truth_mask,
truth_clf,
is_average=True):
"""deep_supervised_criterion"""
loss_clf = F.binary_cross_entropy_with_logits(logit_clf,
truth_clf,
reduce=is_average)
loss_mask = 0
for l in logit_mask:
loss_mask = loss_mask + L.lovasz_loss_ignore_empty(
l.squeeze(1), truth_mask, truth_clf)
loss = L.lovasz_hinge(logit.squeeze(1), truth_mask)
return 0.05 * loss_clf + 0.1 * loss_mask + 1 * loss
def symmetric_lovasz(outputs, targets):
return (lovasz_hinge(outputs, targets) +
lovasz_hinge(-outputs, 1 - targets)) / 2
