Shortcuts

Note

You are reading the documentation for MMEditing 0.x, which will soon be deprecated by the end of 2022. We recommend you upgrade to MMEditing 1.0 to enjoy fruitful new features and better performance brought by OpenMMLab 2.0. Check out the changelog, code and documentation of MMEditing 1.0 for more details.

Source code for mmedit.models.transformers.search_transformer

# Copyright (c) OpenMMLab. All rights reserved.
import torch
import torch.nn as nn
import torch.nn.functional as F

from mmedit.models.registry import COMPONENTS


[docs]@COMPONENTS.register_module() class SearchTransformer(nn.Module): """Search texture reference by transformer. Include relevance embedding, hard-attention and soft-attention. """
[docs] def gather(self, inputs, dim, index): """Hard Attention. Gathers values along an axis specified by dim. Args: inputs (Tensor): The source tensor. (N, C*k*k, H*W) dim (int): The axis along which to index. index (Tensor): The indices of elements to gather. (N, H*W) results: outputs (Tensor): The result tensor. (N, C*k*k, H*W) """ views = [inputs.size(0) ] + [1 if i != dim else -1 for i in range(1, inputs.ndim)] expansion = [ -1 if i in (0, dim) else d for i, d in enumerate(inputs.size()) ] index = index.view(views).expand(expansion) outputs = torch.gather(inputs, dim, index) return outputs
[docs] def forward(self, lq_up, ref_downup, refs): """Texture transformer. Q = LTE(lq_up) K = LTE(ref_downup) V = LTE(ref), from V_level_n to V_level_1 Relevance embedding aims to embed the relevance between the LQ and Ref image by estimating the similarity between Q and K. Hard-Attention: Only transfer features from the most relevant position in V for each query. Soft-Attention: synthesize features from the transferred GT texture features T and the LQ features F from the backbone. Args: All args are features come from extractor (such as LTE). These features contain 3 levels. When upscale_factor=4, the size ratio of these features is level3:level2:level1 = 1:2:4. lq_up (Tensor): Tensor of 4x bicubic-upsampled lq image. (N, C, H, W) ref_downup (Tensor): Tensor of ref_downup. ref_downup is obtained by applying bicubic down-sampling and up-sampling with factor 4x on ref. (N, C, H, W) refs (Tuple[Tensor]): Tuple of ref tensors. [(N, C, H, W), (N, C/2, 2H, 2W), ...] Returns: soft_attention (Tensor): Soft-Attention tensor. (N, 1, H, W) textures (Tuple[Tensor]): Transferred GT textures. [(N, C, H, W), (N, C/2, 2H, 2W), ...] """ levels = len(refs) # query query = F.unfold(lq_up, kernel_size=(3, 3), padding=1) # key key = F.unfold(ref_downup, kernel_size=(3, 3), padding=1) key_t = key.permute(0, 2, 1) # values values = [ F.unfold( refs[i], kernel_size=3 * pow(2, i), padding=pow(2, i), stride=pow(2, i)) for i in range(levels) ] key_t = F.normalize(key_t, dim=2) # [N, H*W, C*k*k] query = F.normalize(query, dim=1) # [N, C*k*k, H*W] # Relevance embedding rel_embedding = torch.bmm(key_t, query) # [N, H*W, H*W] max_val, max_index = torch.max(rel_embedding, dim=1) # [N, H*W] # hard-attention textures = [self.gather(value, 2, max_index) for value in values] # to tensor h, w = lq_up.size()[-2:] textures = [ F.fold( textures[i], output_size=(h * pow(2, i), w * pow(2, i)), kernel_size=3 * pow(2, i), padding=pow(2, i), stride=pow(2, i)) / 9. for i in range(levels) ] soft_attention = max_val.view(max_val.size(0), 1, h, w) return soft_attention, textures
Read the Docs v: latest
Versions
latest
stable
1.x
v0.16.0
v0.15.2
v0.15.1
v0.15.0
v0.14.0
v0.13.0
v0.12.0
dev-1.x
Downloads
pdf
html
epub
On Read the Docs
Project Home
Builds

Free document hosting provided by Read the Docs.