I changed torch.tensor(x) to torch.tensor(x).clone().Detach() but the problem is not solved.
Do you know what I am doing wrong here?
Thanks in advance!
for epoch in range(num_epochs):
outputs = []
outputs = torch.tensor(outputs, requires_grad=True)
outputs= outputs.clone().detach().cuda()
for fold in range(0, len(training_data), 5): # we take 5 images
xtrain = training_data[fold : fold+5]
xtrain = torch.tensor(xtrain, requires_grad=True).clone().detach().float().cuda()
xtrain = xtrain.view(5, 3, 120, 120, 120)
# Clear gradients
#optimizer.zero_grad()
# Forward propagation
optimizer.zero_grad()
v = model(xtrain)
v = torch.tensor(v, requires_grad=True).clone().detach()
outputs = torch.cat((outputs,v),dim=0)
# Calculate softmax and ross entropy loss
targets = torch.Tensor(targets).clone().detach()
labels = targets.cuda()
outputs = torch.tensor(outputs, requires_grad=True)
_, predicted = torch.max(outputs, 1) #prendre valeur maximale [0.96 0.04] ==> 0 (position de classe)
accuracy = accuracyCalc(predicted, targets)
labels = labels.long()
labels=labels.view(-1)
loss = nn.CrossEntropyLoss()
loss = loss(outputs, labels)
# Calculating gradients
loss.backward()
# Update parameters
optimizer.step()
loss_list_train.append(loss.clone())
accuracy_list_train.append(accuracy/100)
np.save('Datasets/brats/accuracy_list_train.npy', np.array(accuracy_list_train))
np.save('Datasets/brats/loss_list_train.npy', np.array(loss_list_train))
print('Iteration: {}/{} Loss: {} Accuracy: {} %'.format(epoch+1, num_epochs, loss.clone(), accuracy))
print('Model training : Finished')
result :
UserWarning: To copy construct from a tensor, it is recommended to use source
Tensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True),
rather than torch.tensor(sourceTensor)