Conversation
Codecov Report
@@ Coverage Diff @@
## master #333 +/- ##
==========================================
- Coverage 92.01% 91.94% -0.07%
==========================================
Files 34 34
Lines 2491 2496 +5
==========================================
+ Hits 2292 2295 +3
- Misses 199 201 +2
Flags with carried forward coverage won't be shown. Click here to find out more.
|
|
Kudos, SonarCloud Quality Gate passed!
|
There was a problem hiding this comment.
Hi @zakajd,
As you mentioned in #332, we pass the 4D tensor, not 3D. I would suggest to update the doc string instead of expanding the function to a non standard kernel sizes, which could require additional testing. Edge cases: kernels.dim() = 5 # or 2.
I checked the library for every metric that uses gradient_map. Usually, we consider only luminance channel to be provided for the function. I assume that there are other cases outside the library, where it could be helpful to compute gradient map per channel, i.e. using the gradient_map as standalone function.
If we follow this path, let's add assert on kernel size and tests to cover the features of the function.
| kernels = kernels.repeat(C, 1, 1, 1) | ||
|
|
||
| # Process each channel separately using group convolution. | ||
| grads = torch.nn.functional.conv2d(x, kernels.to(x), groups=C, padding=padding) |
There was a problem hiding this comment.
Implicit transfer between devices is not needed as current implementation in master handles the device and dtype properties.
| grads = torch.nn.functional.conv2d(x, kernels.to(x), groups=C, padding=padding) | |
| grads = torch.nn.functional.conv2d(x, kernels, groups=C, padding=padding) |
| Args: | ||
| x: Tensor with shape (N, C, H, W). | ||
| kernels: Stack of tensors for gradient computation with shape (k_N, k_H, k_W) | ||
| kernels: Stack of tensors for gradient computation with shape (k_N, k_H, k_W) or (k_N, 1, k_H, k_W) |
There was a problem hiding this comment.
| kernels: Stack of tensors for gradient computation with shape (k_N, k_H, k_W) or (k_N, 1, k_H, k_W) | |
| kernels: Stack of tensors for gradient computation with shape (k_C_out, k_C_in, k_H, k_W). k_C_in equals 1. |
| padding = kernels.size(-1) // 2 | ||
| grads = torch.nn.functional.conv2d(x, kernels, padding=padding) | ||
| N, C, H, W = x.shape | ||
|
|
||
| # Expand kernel if this is not done already and repeat to match number of groups | ||
| if kernels.dim() != 4: | ||
| kernels = kernels.unsqueeze(1) | ||
|
|
There was a problem hiding this comment.
| padding = kernels.size(-1) // 2 | |
| grads = torch.nn.functional.conv2d(x, kernels, padding=padding) | |
| N, C, H, W = x.shape | |
| # Expand kernel if this is not done already and repeat to match number of groups | |
| if kernels.dim() != 4: | |
| kernels = kernels.unsqueeze(1) | |
| assert kernels.dim() == 4, f'Expected 4D kernel, got {kernels.dim()}D tensor ' | |
| assert kernels.size(1) == 1, f'Expected dimension size of kernel to be equal one for input number of channels, got kernel {kernel.size()} ' | |
| assert kernels.size(-1) == kernel_size(-2), f'Expected squared kernel along coast two dimensions, got {kernel.size()}' | |
| padding = kernels.size(-1) // 2 | |
| N, C, H, W = x.shape |
2 participants
Closes #332
Proposed Changes