TypeError: ‘Tensor’ object is not callable
how can i handle this,floks
Could you post a code snippet throwing this error?
I would generally recommend to use the factory method torch.tensor instead of torch.Tensor, since the latter will return uninitialized values if you provide a tensor shape.
the code is following:
if(epoch+1 )%20 == 0:
print(‘Epoch[{}/{}], loss: {:.6f}’
.format(epoch+1, num_epochs, loss.data()))
.data is an attribute not a method so you would have to remove the parentheses.
However, the usage of .data is generally not recommended anymore and you should use .item() instead.
wow great. it’s really a great help to me .thanks a lot.
when i remove the parantheses,it’s really worked!! Bravo
When I excute testing code, an error occured.
File "/home/mamingrui/code/MyOwn/train_v1.py", line 61, in train
grad_loss = grad_loss(flow)
TypeError: 'Tensor' object is not callable
This is the loss function.
def gradient_loss(s, penalty='l2'):
dy = torch.abs(s[:, :, 1:, :, :] - s[:, :, :-1, :, :])
dx = torch.abs(s[:, :, :, 1:, :] - s[:, :, :, :-1, :])
dz = torch.abs(s[:, :, :, :, 1:] - s[:, :, :, :, :-1])
if (penalty == 'l2'):
dy = dy * dy
dx = dx * dx
dz = dz * dz
d = torch.mean(dx) + torch.mean(dy) + torch.mean(dz)
return d / 3.0
and this is the train.
for epoch in range(iters):
start_time = time.time()
loss_batch = 0
for batch_moving in train_set:
print("^^^^^^moving size is {}^^^^^^".format(batch_moving.size()))
wrap, flow = model(batch_moving, fixed)
recon_loss = similarity_loss(wrap, fixed)
grad_loss = grad_loss(flow)
loss = recon_loss + reg_param * grad_loss
loss_batch += loss
print("The loss without average is {}".format(loss))
opt.zero_grad()
loss.backward()
opt.step()
How can I fix this bug? very appreciate!
You are reusing the variable name grad_loss which will overwrite the function name here:
grad_loss = grad_loss(flow)
Change the return value to grad_loss_value or any other unused name.
2 Likes
WOW ! It worked well ! Thanks a lot ^-^
@ptrblck
Facing the same issue while implementing TableNET please help me out.
Reference I took : Extract Tables from Images Using Tablenet — An End-to-End Solution | by Namratesh Shrivastav | DataDrivenInvestor
Here are the code snippets.
It seems you are using Keras, so I would recommend to post the question in their discussion board or StackOverflow, as you might find Keras experts there. 
Based on the error message I guess you are trying to cal a module, while it’s a tensor.
The tensor object is not callable. Got an error while trying to calculate accuracy.
I am new to PyTorch.
def accuracy(outputs, labels):
_, preds = torch.max(outputs, dim=1)
return torch.tensor(torch.sum(preds == labels).item() / len(preds))
n_total_steps = len(train_loader)
for epoch in range(epochs):
for i, (images, labels) in enumerate(train_loader):
images = images.to(DEVICE)
labels = labels.to(DEVICE)
# Forward pass
outputs = model(images)
loss = criterion(outputs, labels)
accuracy = accuracy(outputs, labels)
# Backward and optimize
optimizer.zero_grad()
loss.backward()
optimizer.step()
if (i+1) % 185 == 0:
print (f'Epoch [{epoch+1}/{epochs}], Step [{i+1}/{n_total_steps}], Loss: {loss.item():.4f}, accuracy: {accuracy:.2f}')
You are defining accuracy as a function name and are then assigning the result to a tensor with the same name. In the next iteration accuracy would thus be a tensor (not the function anymore) and the error is raised. Change either the function or tensor name and it should work.
hello everyone
i get this error when i use from SGD-loss function. plz help me…
criterion = nn.CrossEntropyLoss()
optimizer = optim.SGD(model.parameters(), lr=0.002, momentum=0.9)
num_epochs = 10
losses_SGD = []
for epoch in range(num_epochs):
for i, (inputs, targets) in enumerate(train_dl):
inputs = to_var(inputs)
targets = to_var(targets)
# forwad pass
optimizer.zero_grad()
outputs = model(inputs)
# loss
loss = criterion(outputs, targets)
losses += [loss.data]
# backward pass
loss.backward()
# update parameters
optimizer.step()
# report
if (i + 1) % 50 == 0:
print('Epoch [%2d/%2d], Step [%3d/%3d], Loss: %.4f'
% (epoch + 1, num_epochs, i + 1, len(train_ds) // batch_size, loss.data))
I’m not sure where the “‘Tensor’ object is not callable” error is raised, but your code will fail in:
losses_SGD += [loss.item_()]
since tensor.item_() is not a valid method:
criterion = nn.CrossEntropyLoss()
output = torch.randn(1, 10, requires_grad=True)
target = torch.randint(0, 10, (1,))
loss = criterion(output, target)
loss.item_()
# > AttributeError: 'Tensor' object has no attribute 'item_'
Oh! Sorry!! The code was modified. 
In this code, in line
losses += [loss.data]
the error is raised.
Thanks
Judging by the line, it appears that target is a tensor and not a function/callable that returns a tensor as it is being treated.