Hello everyone, i am just a beginner in Pytorch, recently i try to adjust STN to make it works on fish eye image, but i am trapped in problem of “one of the variables needed for gradient computation has been modified by an inplace operation: [torch.FloatTensor [1, 2, 28, 2]], which is output 0 of SliceBackward, is at version 14; expected version 13 instead. Hint: the backtrace further above shows the operation that failed to compute its gradient.” when debugging, i try to fix it with some suggestion here but it still doesn’t work, could someone point out the problem in my code for me, thanks

```
class Net(nn.Module):
def __init__(self):
super(Net, self).__init__()
self.conv1 = nn.Conv2d(1, 10, kernel_size=5)
self.conv2 = nn.Conv2d(10, 20, kernel_size=5)
self.conv2_drop = nn.Dropout2d()
self.fc1 = nn.Linear(320, 50)
self.fc2 = nn.Linear(50, 10)
# Spatial transformer localization-network
self.localization = nn.Sequential(
nn.Conv2d(1, 8, kernel_size=7),
nn.MaxPool2d(2, stride=2),
nn.ReLU(True),
nn.Conv2d(8, 10, kernel_size=5),
nn.MaxPool2d(2, stride=2),
nn.ReLU(True)
)
# Regressor for the 3 * 2 affine matrix
self.fc_loc = nn.Sequential(
nn.Linear(10 * 3 * 3, 2*14),
# nn.ReLU(True),
# nn.Linear(32, 3 * 2)
)
# Initialize the weights/bias with identity transformation
self.fc_loc[0].weight.data.zero_()
self.fc_loc[0].bias.data.copy_(torch.tensor([1,0,1,0,1,0,1,0,1,0,1,0,1,0,1,0,1,0,1,0,1,0,1,0,1,0,1,0], dtype=torch.float))
# Spatial transformer network forward function
def stn(self, x):
xs = self.localization(x)
xs = xs.view(-1, 10 * 3 * 3)
theta = self.fc_loc(xs)
theta = theta.view(-1, 14, 2)
# print('theta size:',theta.size())
output_2 = torch.zeros(x.size())
print(type(output_2))
#grid = torch.zeros(14, 1, 1, 2, 28,2)
grid = torch.zeros(14, 1, 2, 28, 2)
beta = torch.zeros(14,2,3)
for i in range(14):
beta[i,0,0] = theta[0,i,0]
beta[i,0,2] = theta[0,i,1]
for i in range(14):
# print('grid_size:',grid[i,:,:,:,:].size())
grid[i,:,:,:,:] = F.affine_grid(beta[i,:,:].unsqueeze(0), [1, 1, 2, 28]).clone()
output_2[:,:,2*i:2*i+2,:] = F.grid_sample(x[:,:,2*i:2*i+2,:], grid[i,:,:,:,:]).clone()
return output_2
def forward(self, x_temp):
# transform the input
x = self.stn(x_temp)
# Perform the usual forward pass
x = F.relu(F.max_pool2d(self.conv1(x), 2))
x = F.relu(F.max_pool2d(self.conv2_drop(self.conv2(x)), 2))
x = x.view(-1, 320)
x = F.relu(self.fc1(x))
x = F.dropout(x, training=self.training)
x = self.fc2(x)
return F.log_softmax(x, dim=1)
```