basicsr.archs.duf_arch
- class basicsr.archs.duf_arch.DUF(scale=4, num_layer=52, adapt_official_weights=False)[source]
Bases:
Module
Network architecture for DUF
- Paper: Jo et.al. Deep Video Super-Resolution Network Using Dynamic
Upsampling Filters Without Explicit Motion Compensation, CVPR, 2018
- Code reference:
For all the models below, ‘adapt_official_weights’ is only necessary when loading the weights converted from the official TensorFlow weights. Please set it to False if you are training the model from scratch.
There are three models with different model size: DUF16Layers, DUF28Layers, and DUF52Layers. This class is the base class for these models.
- Parameters
scale (int) – The upsampling factor. Default: 4.
num_layer (int) – The number of layers. Default: 52.
adapt_official_weights_weights (bool) – Whether to adapt the weights translated from the official implementation. Set to false if you want to train from scratch. Default: False.
- forward(x)[source]
- Parameters
x (Tensor) – Input with shape (b, 7, c, h, w)
- Returns
Output with shape (b, c, h * scale, w * scale)
- Return type
Tensor
- training: bool
- class basicsr.archs.duf_arch.DenseBlocks(num_block, num_feat=64, num_grow_ch=16, adapt_official_weights=False)[source]
Bases:
Module
A concatenation of N dense blocks.
- Parameters
num_feat (int) – Number of channels in the blocks. Default: 64.
num_grow_ch (int) – Growing factor of the dense blocks. Default: 32.
num_block (int) – Number of dense blocks. The values are: DUF-S (16 layers): 3 DUF-M (18 layers): 9 DUF-L (52 layers): 21
adapt_official_weights (bool) – Whether to adapt the weights translated from the official implementation. Set to false if you want to train from scratch. Default: False.
- forward(x)[source]
- Parameters
x (Tensor) – Input tensor with shape (b, num_feat, t, h, w).
- Returns
Output with shape (b, num_feat + num_block * num_grow_ch, t, h, w).
- Return type
Tensor
- training: bool
- class basicsr.archs.duf_arch.DenseBlocksTemporalReduce(num_feat=64, num_grow_ch=32, adapt_official_weights=False)[source]
Bases:
Module
A concatenation of 3 dense blocks with reduction in temporal dimension.
Note that the output temporal dimension is 6 fewer the input temporal dimension, since there are 3 blocks.
- Parameters
num_feat (int) – Number of channels in the blocks. Default: 64.
num_grow_ch (int) – Growing factor of the dense blocks. Default: 32
adapt_official_weights (bool) – Whether to adapt the weights translated from the official implementation. Set to false if you want to train from scratch. Default: False.
- forward(x)[source]
- Parameters
x (Tensor) – Input tensor with shape (b, num_feat, t, h, w).
- Returns
Output with shape (b, num_feat + num_grow_ch * 3, 1, h, w).
- Return type
Tensor
- training: bool
- class basicsr.archs.duf_arch.DynamicUpsamplingFilter(filter_size=(5, 5))[source]
Bases:
Module
Dynamic upsampling filter used in DUF.
Ref: https://github.com/yhjo09/VSR-DUF. It only supports input with 3 channels. And it applies the same filters to 3 channels.
- Parameters
filter_size (tuple) – Filter size of generated filters. The shape is (kh, kw). Default: (5, 5).
- forward(x, filters)[source]
Forward function for DynamicUpsamplingFilter.
- Parameters
x (Tensor) – Input image with 3 channels. The shape is (n, 3, h, w).
filters (Tensor) –
Generated dynamic filters. The shape is (n, filter_prod, upsampling_square, h, w). filter_prod: prod of filter kernel size, e.g., 1*5*5=25. upsampling_square: similar to pixel shuffle,
upsampling_square = upsampling * upsampling e.g., for x 4 upsampling, upsampling_square= 4*4 = 16
- Returns
Filtered image with shape (n, 3*upsampling_square, h, w)
- Return type
Tensor
- training: bool