I am trying to make a Sobel-type filter by adapting the follwoing Tensorflow code into Pytorch.

```
def get_gradient_filters():
np_grad_x = np.asarray([[-3,0,3], [-10,0,10], [-3,0,3]], dtype=np.float32).reshape((3, 3, 1, 1))
np_grad_x /= np.sum(np.abs(np_grad_x), keepdims=True)
tf_grad = tf.constant((np.concatenate([np_grad_x, np_grad_x.transpose((1, 0, 2, 3))], axis=-1)))
return tf_grad
tf_grad_filter = get_gradient_filters()
tf_grad_filter = tf.tile(tf_grad_filter, (1, 1, 3, 1))
image=skimage.data.astronaut().astype(np.float32)/255.
tf_f=tf.expand_dims(tf.constant(image),0)
tf_f_grad = tf.nn.depthwise_conv2d(tf_f, tf_grad_filter, [1, 1, 1, 1], "SAME")/(1./32)
tf_f_grad=tf.reshape(tf_f_grad,(1,512,512,3,2))
```

Here is my attempt which does not give the same result.

```
def gradient_filter(C):
np_grad_x = np.asarray([[-3, 0, 3], [-10, 0, 10], [-3, 0, 3]], dtype=np.float32).reshape((3, 3, 1, 1))
np_grad_x /= np.sum(np.abs(np_grad_x), keepdims=True)
np_grad = np.concatenate([np_grad_x, np_grad_x.transpose((1, 0, 2, 3))], axis=-1)
np_grad = np.tile(np_grad, (1, 1, C, 1))
torch_grad = torch.Tensor(np_grad).reshape(1, 3, 3, 2 * C).permute(3, 0, 1, 2)
filter = torch.nn.Conv2d(in_channels=C, out_channels=2 * C, kernel_size=(3, 3), padding=(1, 1), padding_mode='zeros',
groups=C, bias=False)
filter.weight.data = torch_grad
return filter
filter = gradient_filter(3)
def img_gradient(torch_f, filter):
M, N, _ = torch_f.shape
return filter(torch_f.unsqueeze(dim=0).permute(0, 3, 1, 2)) /(1./max(M,N))
torch_f=torch.Tensor(image)
torch_f_grad=img_gradient(torch_f,filter)
torch_f_grad=torch_f_grad.permute(0,2,3,1).reshape(1,512,512,3,2)
```

I also tried some minor variations (first permute then reshape) but it is still not the same. Any ideas?