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(doc/fluxion/conv) add/convert docstrings to mkdocstrings format
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@ -4,6 +4,33 @@ from refiners.fluxion.layers.module import WeightedModule
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class Conv2d(nn.Conv2d, WeightedModule):
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class Conv2d(nn.Conv2d, WeightedModule):
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"""2D Convolutional layer.
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This layer wraps [`torch.nn.Conv2d`][torch.nn.Conv2d].
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Receives:
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(Real[Tensor, "batch in_channels in_height in_width"]):
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Returns:
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(Real[Tensor, "batch out_channels out_height out_width"]):
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Example:
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```py
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conv2d = fl.Conv2d(
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in_channels=3,
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out_channels=32,
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kernel_size=3,
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stride=1,
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padding=1,
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)
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tensor = torch.randn(2, 3, 128, 128)
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output = conv2d(tensor)
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assert output.shape == (2, 32, 128, 128)
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```
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"""
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def __init__(
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def __init__(
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self,
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self,
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in_channels: int,
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in_channels: int,
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@ -19,22 +46,49 @@ class Conv2d(nn.Conv2d, WeightedModule):
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dtype: DType | None = None,
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dtype: DType | None = None,
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) -> None:
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) -> None:
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super().__init__( # type: ignore
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super().__init__( # type: ignore
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in_channels,
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in_channels=in_channels,
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out_channels,
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out_channels=out_channels,
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kernel_size,
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kernel_size=kernel_size,
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stride,
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stride=stride,
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padding,
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padding=padding,
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dilation,
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dilation=dilation,
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groups,
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groups=groups,
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use_bias,
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bias=use_bias,
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padding_mode,
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padding_mode=padding_mode,
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device,
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device=device,
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dtype,
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dtype=dtype,
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)
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)
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self.use_bias = use_bias
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self.use_bias = use_bias
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class ConvTranspose2d(nn.ConvTranspose2d, WeightedModule):
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class ConvTranspose2d(nn.ConvTranspose2d, WeightedModule):
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"""2D Transposed Convolutional layer.
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This layer wraps [`torch.nn.ConvTranspose2d`][torch.nn.ConvTranspose2d].
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Receives:
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(Real[Tensor, "batch in_channels in_height in_width"]):
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Returns:
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(Real[Tensor, "batch out_channels out_height out_width"]):
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Example:
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```py
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conv2d = fl.ConvTranspose2d(
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in_channels=3,
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out_channels=32,
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kernel_size=3,
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stride=1,
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padding=1,
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)
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tensor = torch.randn(2, 3, 128, 128)
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output = conv2d(tensor)
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assert output.shape == (2, 32, 128, 128)
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```
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"""
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def __init__(
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def __init__(
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self,
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self,
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in_channels: int,
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in_channels: int,
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@ -64,3 +118,4 @@ class ConvTranspose2d(nn.ConvTranspose2d, WeightedModule):
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device=device,
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device=device,
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dtype=dtype,
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dtype=dtype,
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)
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)
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self.use_bias = use_bias
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