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[docs] refactoring docstrings in community/hd_painter.py (#9593)

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* [docs] refactoring docstrings in community/hd_painter.py

* Update examples/community/hd_painter.py

Co-authored-by: Aryan <contact.aryanvs@gmail.com>

* make style

---------

Co-authored-by: Aryan <contact.aryanvs@gmail.com>
Co-authored-by: Aryan <aryan@huggingface.co>
This commit is contained in:
wony617
2024-10-15 22:20:12 +09:00
committed by GitHub
parent 355bb641e3
commit fff4be8e23
1 changed files with 17 additions and 10 deletions
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examples/community/hd_painter.py
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@@ -898,13 +898,16 @@ class GaussianSmoothing(nn.Module):
Apply gaussian smoothing on a
1d, 2d or 3d tensor. Filtering is performed seperately for each channel
in the input using a depthwise convolution.
Arguments:
channels (int, sequence): Number of channels of the input tensors. Output will
have this number of channels as well.
kernel_size (int, sequence): Size of the gaussian kernel.
sigma (float, sequence): Standard deviation of the gaussian kernel.
dim (int, optional): The number of dimensions of the data.
Default value is 2 (spatial).
Args:
channels (`int` or `sequence`):
Number of channels of the input tensors. The output will have this number of channels as well.
kernel_size (`int` or `sequence`):
Size of the Gaussian kernel.
sigma (`float` or `sequence`):
Standard deviation of the Gaussian kernel.
dim (`int`, *optional*, defaults to `2`):
The number of dimensions of the data. Default is 2 (spatial dimensions).
"""
def __init__(self, channels, kernel_size, sigma, dim=2):
@@ -944,10 +947,14 @@ class GaussianSmoothing(nn.Module):
def forward(self, input):
"""
Apply gaussian filter to input.
Arguments:
input (torch.Tensor): Input to apply gaussian filter on.
Args:
input (`torch.Tensor` of shape `(N, C, H, W)`):
Input to apply Gaussian filter on.
Returns:
filtered (torch.Tensor): Filtered output.
`torch.Tensor`:
The filtered output tensor with the same shape as the input.
"""
return self.conv(input, weight=self.weight.to(input.dtype), groups=self.groups, padding="same")