torchlayers.upsample module¶

class
torchlayers.upsample.
ConvPixelShuffle
(in_channels, out_channels, upscale_factor: int = 2, kernel_size: int = 3, stride: int = 1, padding: Union[Tuple[int, int], int, str] = 'same', dilation: int = 1, groups: int = 1, bias: bool = True, padding_mode: str = 'zeros', initializer: Callable[[torch.Tensor], torch.Tensor] = None)[source]¶ Two dimensional convolution with ICNR initialization followed by PixelShuffle.
Increases
height
andwidth
ofinput
tensor by scale, acts like learnable upsampling. Due to ICNR weight initialization ofconvolution
it has similar starting point to nearest neighbour upsampling.kernel_size
got a default value of3
,upscale_factor
got a default value of2
Note
Currently only
4D
input is allowed ([batch, channels, height, width]
), due totorch.nn.PixelShuffle
not supporting1D
or3D
versions. See [this PyTorch PR](https://github.com/pytorch/pytorch/pull/6340/files) for example of dimensionagnostic implementation. Parameters
in_channels (int) – Number of channels in the input image
out_channels (int) – Number of channels produced after PixelShuffle
upscale_factor (int, optional) – Factor to increase spatial resolution by. Default:
2
kernel_size (int or tuple, optional) – Size of the convolving kernel. Default:
3
stride (int or tuple, optional) – Stride of the convolution. Default: 1
padding (int or tuple, optional) – Zeropadding added to both sides of the input. Default: 0
padding_mode (string, optional) – Accepted values
zeros
andcircular
Default:zeros
dilation (int or tuple, optional) – Spacing between kernel elements. Default: 1
groups (int, optional) – Number of blocked connections from input channels to output channels. Default: 1
bias (bool, optional) – If
True
, adds a learnable bias to the output. Default:True
initializer (typing.Callable[[torch.Tensor,], torch.Tensor], optional) – Initializer for ICNR initialization, can be a function from
torch.nn.init
. Gets and returns tensor after initialization. Default:torch.nn.init.kaiming_normal_

forward
(inputs)[source]¶ Defines the computation performed at every call.
Should be overridden by all subclasses.
Note
Although the recipe for forward pass needs to be defined within this function, one should call the
Module
instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.

icnr_initialization
(tensor)[source]¶ ICNR initializer for checkerboard artifact free sub pixel convolution.
Originally presented in Checkerboard artifact free subpixel convolution: A note on subpixel convolution, resize convolution and convolution resize Initializes convolutional layer prior to
torch.nn.PixelShuffle
. Weights are initialized according toinitializer
passed to to__init__
. Parameters
tensor (torch.Tensor) – Tensor to be initialized using ICNR init.
 Returns
Tensor initialized using ICNR.
 Return type