def test(model, test_loader):
print("Evaluation of Testset Starting...")
model.eval()
val_loss = 0
val_dice0 = 0
val_dice1 = 0
val_dice2 = 0
with torch.no_grad():
for data, target in tqdm(test_loader):
data, target = data.float(), target.float()
data, target = data.to(device), target.to(device)
output = model(data)
loss = metrics.DiceMeanLoss()(output, target)
dice0 = metrics.dice(output, target, 0)
dice1 = metrics.dice(output, target, 1)
dice2 = metrics.dice(output, target, 2)
val_loss += float(loss)
val_dice0 += float(dice0)
val_dice1 += float(dice1)
val_dice2 += float(dice2)
val_loss /= len(test_loader)
val_dice0 /= len(test_loader)
val_dice1 /= len(test_loader)
val_dice2 /= len(test_loader)
print('\nTest set: Average loss: {:.6f}, dice0: {:.6f}\tdice1: {:.6f}\tdice2: {:.6f}\t\n'.format(
val_loss, val_dice0, val_dice1, val_dice2))
def val(model, val_loader):
model.eval()
val_loss = 0
val_dice0 = 0
val_dice1 = 0
val_dice2 = 0
with torch.no_grad():
for idx, (data, target) in tqdm(enumerate(val_loader),
total=len(val_loader)):
target = common.to_one_hot_3d(target.long())
data, target = data.float(), target.float()
data, target = data.to(device), target.to(device)
output = model(data)
loss = metrics.DiceMeanLoss()(output, target)
dice0 = metrics.dice(output, target, 0)
dice1 = metrics.dice(output, target, 1)
dice2 = metrics.dice(output, target, 2)
val_loss += float(loss)
val_dice0 += float(dice0)
val_dice1 += float(dice1)
val_dice2 += float(dice2)
val_loss /= len(val_loader)
val_dice0 /= len(val_loader)
val_dice1 /= len(val_loader)
val_dice2 /= len(val_loader)
return OrderedDict({
'Val Loss': val_loss,
'Val dice0': val_dice0,
'Val dice1': val_dice1,
'Val dice2': val_dice2
})
def test(model, dataset, save_path, filename):
dataloader = DataLoader(dataset=dataset,
batch_size=4,
num_workers=0,
shuffle=False)
model.eval()
save_tool = Recompone_tool(save_path, filename, dataset.ori_shape,
dataset.new_shape, dataset.cut)
target = torch.from_numpy(np.expand_dims(dataset.label_np, axis=0)).long()
target = to_one_hot_3d(target)
with torch.no_grad():
for data in tqdm(dataloader, total=len(dataloader)):
data = data.unsqueeze(1)
data = data.float().to(device)
output = model(data)
save_tool.add_result(output.detach().cpu())
pred = save_tool.recompone_overlap()
pred = torch.unsqueeze(pred, dim=0)
val_loss = metrics.DiceMeanLoss()(pred, target)
val_dice0 = metrics.dice(pred, target, 0)
val_dice1 = metrics.dice(pred, target, 1)
val_dice2 = metrics.dice(pred, target, 2)
pred_img = torch.argmax(pred, dim=1)
img = sitk.GetImageFromArray(
np.squeeze(np.array(pred_img.numpy(), dtype='uint8'), axis=0))
sitk.WriteImage(img, os.path.join(save_path, filename))
# save_tool.save(filename)
print(
'\nAverage loss: {:.4f}\tdice0: {:.4f}\tdice1: {:.4f}\tdice2: {:.4f}\t\n'
.format(val_loss, val_dice0, val_dice1, val_dice2))
return val_loss, val_dice0, val_dice1, val_dice2
def val(model, val_loader, epoch, logger):
model.eval()
val_loss = 0
val_dice0 = 0
val_dice1 = 0
val_dice2 = 0
with torch.no_grad():
for data, target in val_loader:
data, target = data.float(), target.float()
data, target = data.to(device), target.to(device)
output = model(data)
loss = metrics.DiceMeanLoss()(output, target)
dice0 = metrics.dice(output, target, 0)
dice1 = metrics.dice(output, target, 1)
dice2 = metrics.dice(output, target, 2)
val_loss += float(loss)
val_dice0 += float(dice0)
val_dice1 += float(dice1)
val_dice2 += float(dice2)
val_loss /= len(val_loader)
val_dice0 /= len(val_loader)
val_dice1 /= len(val_loader)
val_dice2 /= len(val_loader)
logger.scalar_summary('val_loss', val_loss, epoch)
logger.scalar_summary('val_dice0', val_dice0, epoch)
logger.scalar_summary('val_dice1', val_dice1, epoch)
logger.scalar_summary('val_dice2', val_dice2, epoch)
print('\nVal set: Average loss: {:.6f}, dice0: {:.6f}\tdice1: {:.6f}\tdice2: {:.6f}\t\n'.format(
val_loss, val_dice0, val_dice1, val_dice2))
def train(model, train_loader, optimizer, epoch, logger):
print("=======Epoch:{}=======".format(epoch))
model.train()
train_loss = 0
train_dice0 = 0
train_dice1 = 0
train_dice2 = 0
for idx, (data, target) in tqdm(enumerate(train_loader),
total=len(train_loader)):
data = torch.squeeze(data, dim=0)
target = torch.squeeze(target, dim=0)
data, target = data.float(), target.float()
data, target = data.to(device), target.to(device)
output = model(data)
optimizer.zero_grad()
# loss = nn.CrossEntropyLoss()(output,target)
# loss=metrics.SoftDiceLoss()(output,target)
# loss=nn.MSELoss()(output,target)
loss = metrics.DiceMeanLoss()(output, target)
# loss=metrics.WeightDiceLoss()(output,target)
# loss=metrics.CrossEntropy()(output,target)
loss.backward()
optimizer.step()
train_loss += loss
train_dice0 += metrics.dice(output, target, 0)
train_dice1 += metrics.dice(output, target, 1)
train_dice2 += metrics.dice(output, target, 2)
train_loss /= len(train_loader)
train_dice0 /= len(train_loader)
train_dice1 /= len(train_loader)
train_dice2 /= len(train_loader)
print(
'Train Epoch: {} \tLoss: {:.4f}\tdice0: {:.4f}\tdice1: {:.4f}\tdice2: {:.4f}'
.format(epoch, train_loss, train_dice0, train_dice1, train_dice2))
logger.scalar_summary('train_loss', float(train_loss), epoch)
logger.scalar_summary('train_dice0', float(train_dice0), epoch)
logger.scalar_summary('train_dice1', float(train_dice1), epoch)
logger.scalar_summary('train_dice2', float(train_dice2), epoch)
def train(model, train_loader):
print("=======Epoch:{}=======".format(epoch))
model.train()
train_loss = 0
train_dice0 = 0
train_dice1 = 0
train_dice2 = 0
for idx, (data, target) in tqdm(enumerate(train_loader),
total=len(train_loader)):
data = torch.squeeze(data, dim=0)
target = torch.squeeze(target, dim=0)
data, target = data.float(), target.float()
data, target = data.to(device), target.to(device)
output = model(data)
optimizer.zero_grad()
# loss = nn.CrossEntropyLoss()(output,target)
# loss=metrics.SoftDiceLoss()(output,target)
# loss=nn.MSELoss()(output,target)
loss = metrics.DiceMeanLoss()(output, target)
# loss=metrics.WeightDiceLoss()(output,target)
# loss=metrics.CrossEntropy()(output,target)
loss.backward()
optimizer.step()
train_loss += float(loss)
train_dice0 += float(metrics.dice(output, target, 0))
train_dice1 += float(metrics.dice(output, target, 1))
train_dice2 += float(metrics.dice(output, target, 2))
train_loss /= len(train_loader)
train_dice0 /= len(train_loader)
train_dice1 /= len(train_loader)
train_dice2 /= len(train_loader)
return OrderedDict({
'Train Loss': train_loss,
'Train dice0': train_dice0,
'Train dice1': train_dice1,
'Train dice2': train_dice2
})
def train(model, train_loader, optimizer, epoch, logger):
model.train()
train_loss = 0
train_dice0 = 0
train_dice1 = 0
train_dice2 = 0
for batch_idx, (data, target) in enumerate(train_loader):
data = torch.squeeze(data, dim=0)
target = torch.squeeze(target, dim=0)
data, target = data.float(), target.float()
data, target = data.to(device), target.to(device)
output = model(data)
optimizer.zero_grad()
# loss = nn.CrossEntropyLoss()(output,target)
# loss=metrics.SoftDiceLoss()(output,target)
# loss=nn.MSELoss()(output,target)
loss = metrics.DiceMeanLoss()(output, target)
# loss=metrics.WeightDiceLoss()(output,target)
# loss=metrics.CrossEntropy()(output,target)
loss.backward()
optimizer.step()
train_loss = loss
train_dice0 = metrics.dice(output, target, 0)
train_dice1 = metrics.dice(output, target, 1)
train_dice2 = metrics.dice(output, target, 2)
print(
'Train Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}\tdice0: {:.6f}\tdice1: {:.6f}\tdice2: {:.6f}\tT: {:.6f}\tP: {:.6f}\tTP: {:.6f}'
.format(epoch, batch_idx,
len(train_loader), 100. * batch_idx / len(train_loader),
loss.item(), train_dice0, train_dice1, train_dice2,
metrics.T(output, target), metrics.P(output, target),
metrics.TP(output, target)))
logger.scalar_summary('train_loss', float(train_loss), epoch)
logger.scalar_summary('train_dice0', float(train_dice0), epoch)
logger.scalar_summary('train_dice1', float(train_dice1), epoch)
logger.scalar_summary('train_dice2', float(train_dice2), epoch)
def val(model, val_loader, epoch, logger):
model.eval()
val_loss = 0
val_dice0 = 0
val_dice1 = 0
val_dice2 = 0
with torch.no_grad():
for idx, (data, target) in tqdm(enumerate(val_loader),
total=len(val_loader)):
data = torch.squeeze(data, dim=0)
target = torch.squeeze(target, dim=0)
data, target = data.float(), target.float()
data, target = data.to(device), target.to(device)
output = model(data)
loss = metrics.DiceMeanLoss()(output, target)
dice0 = metrics.dice(output, target, 0)
dice1 = metrics.dice(output, target, 1)
dice2 = metrics.dice(output, target, 2)
val_loss += float(loss)
val_dice0 += float(dice0)
val_dice1 += float(dice1)
val_dice2 += float(dice2)
val_loss /= len(val_loader)
val_dice0 /= len(val_loader)
val_dice1 /= len(val_loader)
val_dice2 /= len(val_loader)
logger.scalar_summary('val_loss', val_loss, epoch)
logger.scalar_summary('val_dice0', val_dice0, epoch)
logger.scalar_summary('val_dice1', val_dice1, epoch)
logger.scalar_summary('val_dice2', val_dice2, epoch)
print(
'Val performance: Average loss: {:.4f}\tdice0: {:.4f}\tdice1: {:.4f}\tdice2: {:.4f}\t\n'
.format(val_loss, val_dice0, val_dice1, val_dice2))