Hi! I’m having trouble understanding why I’m getting nonetype for my “resampled” function right after I call loss.backward().

Below is the code:

```
def problem_l2loss(data,target):
not_done = True
learning_rate=0.07
count = 1
cd = torch.tensor([0,0]).type(torch.FloatTensor)
cd = torch.unsqueeze(cd,dim=1).cuda()
scale_a = torch.unsqueeze(torch.tensor([.5]).type(torch.FloatTensor),dim=1).cuda()
cd.requires_grad_()
scale_a.requires_grad_()
while(not_done):
one_zero = torch.tensor([1,0]).cuda()
zero_one = torch.tensor([0,1]).cuda()
temp_a = one_zero*scale_a
temp_b = zero_one*scale_a
two_by_two = torch.cat((temp_a,temp_b),dim=0)
M= torch.cat((torch.tensor(two_by_two),cd),dim=1)[None,:,:]
#The None,:,: is for the batch size
pdb.set_trace()
grid = F.affine_grid(M,data.shape)
grid.requires_grad_()
resampled = F.grid_sample(data.cuda(), grid, mode='bilinear')[0]
criterion = torch.nn.MSELoss()
loss = -1*criterion(resampled, batch_cat_alone.cuda())
loss.backward()
#why is the gradient of resampled None here?
scale_a_grad = scale_a.grad.data
cd_grad = cd.grad.data
scale_a.data = scale_a.data + learning_rate*scale_a_grad
cd_grad.data = cd_grad.data + learning_rate*cd_grad_grad
count = count + 1
if count == 100:
print("here")
not_done = False
return
```

Further comments: I made sure to send everything to cuda before saying that I wanted gradient_true() on all my variables. I also checked, and if I multiply two variables where one has gradient_true and the other I didn’t explicitly set, the product has gradient_true. Also, “resampled” has resampled.requires_grad = True, yet it has none type for resampled.grad … Obviously scale_a and cd also have None type, despite both also having requires_grad = True when I check them after loss.backward() with a pdb trace… Thanks!