PixelShuffle¶

class paddle.nn. PixelShuffle ( upscale_factor: int, data_format: DataLayout2D = 'NCHW', name: str | None = None ) [source]

Rearranges elements in a tensor of shape $[N, C, H, W]$ to a tensor of shape $[N, C/upscale_factor^2, H*upscale_factor, W*upscale_factor]$ , or from shape $[N, H, W, C]$ to $[N, H*upscale_factor, W*upscale_factor, C/upscale_factor^2]$ . This is useful for implementing efficient sub-pixel convolution with a stride of 1/upscale_factor. Please refer to the paper: Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional Neural Network . by Shi et. al (2016) for more details.

Parameters

upscale_factor (int) – factor to increase spatial resolution.
data_format (str, optional) – The data format of the input and output data. An optional string from: ‘NCHW’`, 'NHWC'. When it is 'NCHW', the data is stored in the order of: [batch_size, input_channels, input_height, input_width]. Default: 'NCHW'.
name (str|None, optional) – Name for the operation (optional, default is None). For more information, please refer to Name.

Shape:

x: 4-D tensor with shape of $(N, C, H, W)$ or $(N, H, W, C)$ .
out: 4-D tensor with shape of $(N, C/upscale_factor^2, H*upscale_factor, W*upscale_factor)$ or $(N, H*upscale_factor, W*upscale_factor, C/upscale_factor^2)$ .

Examples

>>> import paddle
>>> import paddle.nn as nn

>>> x = paddle.randn(shape=[2, 9, 4, 4])
>>> pixel_shuffle = nn.PixelShuffle(3)
>>> out = pixel_shuffle(x)
>>> print(out.shape)
[2, 1, 12, 12]

forward ( x: Tensor ) → Tensor forward¶

Defines the computation performed at every call. Should be overridden by all subclasses.

Parameters

*inputs (tuple) – unpacked tuple arguments
**kwargs (dict) – unpacked dict arguments

extra_repr ( ) → str extra_repr¶: Extra representation of this layer, you can have custom implementation of your own layer.

PixelShuffle¶

forward¶

extra_repr¶