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1182 lines
40 KiB
1182 lines
40 KiB
# pylint: skip-file |
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import math |
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import re |
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|
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import numpy as np |
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import torch |
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import torch.nn as nn |
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import torch.utils.checkpoint as checkpoint |
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from einops import rearrange |
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from einops.layers.torch import Rearrange |
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from torch import Tensor |
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from torch.nn import functional as F |
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|
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from .timm.drop import DropPath |
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from .timm.weight_init import trunc_normal_ |
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|
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|
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def img2windows(img, H_sp, W_sp): |
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""" |
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Input: Image (B, C, H, W) |
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Output: Window Partition (B', N, C) |
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""" |
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B, C, H, W = img.shape |
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img_reshape = img.view(B, C, H // H_sp, H_sp, W // W_sp, W_sp) |
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img_perm = ( |
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img_reshape.permute(0, 2, 4, 3, 5, 1).contiguous().reshape(-1, H_sp * W_sp, C) |
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) |
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return img_perm |
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|
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|
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def windows2img(img_splits_hw, H_sp, W_sp, H, W): |
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""" |
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Input: Window Partition (B', N, C) |
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Output: Image (B, H, W, C) |
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""" |
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B = int(img_splits_hw.shape[0] / (H * W / H_sp / W_sp)) |
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|
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img = img_splits_hw.view(B, H // H_sp, W // W_sp, H_sp, W_sp, -1) |
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img = img.permute(0, 1, 3, 2, 4, 5).contiguous().view(B, H, W, -1) |
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return img |
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|
|
|
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class SpatialGate(nn.Module): |
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"""Spatial-Gate. |
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Args: |
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dim (int): Half of input channels. |
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""" |
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|
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def __init__(self, dim): |
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super().__init__() |
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self.norm = nn.LayerNorm(dim) |
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self.conv = nn.Conv2d( |
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dim, dim, kernel_size=3, stride=1, padding=1, groups=dim |
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) # DW Conv |
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|
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def forward(self, x, H, W): |
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# Split |
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x1, x2 = x.chunk(2, dim=-1) |
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B, N, C = x.shape |
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x2 = ( |
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self.conv(self.norm(x2).transpose(1, 2).contiguous().view(B, C // 2, H, W)) |
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.flatten(2) |
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.transpose(-1, -2) |
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.contiguous() |
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) |
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|
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return x1 * x2 |
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|
|
|
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class SGFN(nn.Module): |
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"""Spatial-Gate Feed-Forward Network. |
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Args: |
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in_features (int): Number of input channels. |
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hidden_features (int | None): Number of hidden channels. Default: None |
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out_features (int | None): Number of output channels. Default: None |
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act_layer (nn.Module): Activation layer. Default: nn.GELU |
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drop (float): Dropout rate. Default: 0.0 |
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""" |
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|
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def __init__( |
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self, |
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in_features, |
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hidden_features=None, |
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out_features=None, |
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act_layer=nn.GELU, |
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drop=0.0, |
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): |
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super().__init__() |
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out_features = out_features or in_features |
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hidden_features = hidden_features or in_features |
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self.fc1 = nn.Linear(in_features, hidden_features) |
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self.act = act_layer() |
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self.sg = SpatialGate(hidden_features // 2) |
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self.fc2 = nn.Linear(hidden_features // 2, out_features) |
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self.drop = nn.Dropout(drop) |
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|
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def forward(self, x, H, W): |
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""" |
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Input: x: (B, H*W, C), H, W |
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Output: x: (B, H*W, C) |
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""" |
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x = self.fc1(x) |
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x = self.act(x) |
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x = self.drop(x) |
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|
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x = self.sg(x, H, W) |
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x = self.drop(x) |
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|
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x = self.fc2(x) |
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x = self.drop(x) |
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return x |
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|
|
|
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class DynamicPosBias(nn.Module): |
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# The implementation builds on Crossformer code https://github.com/cheerss/CrossFormer/blob/main/models/crossformer.py |
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"""Dynamic Relative Position Bias. |
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Args: |
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dim (int): Number of input channels. |
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num_heads (int): Number of attention heads. |
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residual (bool): If True, use residual strage to connect conv. |
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""" |
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|
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def __init__(self, dim, num_heads, residual): |
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super().__init__() |
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self.residual = residual |
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self.num_heads = num_heads |
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self.pos_dim = dim // 4 |
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self.pos_proj = nn.Linear(2, self.pos_dim) |
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self.pos1 = nn.Sequential( |
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nn.LayerNorm(self.pos_dim), |
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nn.ReLU(inplace=True), |
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nn.Linear(self.pos_dim, self.pos_dim), |
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) |
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self.pos2 = nn.Sequential( |
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nn.LayerNorm(self.pos_dim), |
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nn.ReLU(inplace=True), |
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nn.Linear(self.pos_dim, self.pos_dim), |
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) |
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self.pos3 = nn.Sequential( |
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nn.LayerNorm(self.pos_dim), |
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nn.ReLU(inplace=True), |
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nn.Linear(self.pos_dim, self.num_heads), |
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) |
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|
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def forward(self, biases): |
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if self.residual: |
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pos = self.pos_proj(biases) # 2Gh-1 * 2Gw-1, heads |
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pos = pos + self.pos1(pos) |
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pos = pos + self.pos2(pos) |
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pos = self.pos3(pos) |
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else: |
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pos = self.pos3(self.pos2(self.pos1(self.pos_proj(biases)))) |
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return pos |
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|
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|
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class Spatial_Attention(nn.Module): |
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"""Spatial Window Self-Attention. |
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It supports rectangle window (containing square window). |
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Args: |
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dim (int): Number of input channels. |
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idx (int): The indentix of window. (0/1) |
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split_size (tuple(int)): Height and Width of spatial window. |
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dim_out (int | None): The dimension of the attention output. Default: None |
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num_heads (int): Number of attention heads. Default: 6 |
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attn_drop (float): Dropout ratio of attention weight. Default: 0.0 |
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proj_drop (float): Dropout ratio of output. Default: 0.0 |
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qk_scale (float | None): Override default qk scale of head_dim ** -0.5 if set |
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position_bias (bool): The dynamic relative position bias. Default: True |
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""" |
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|
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def __init__( |
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self, |
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dim, |
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idx, |
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split_size=[8, 8], |
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dim_out=None, |
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num_heads=6, |
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attn_drop=0.0, |
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proj_drop=0.0, |
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qk_scale=None, |
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position_bias=True, |
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): |
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super().__init__() |
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self.dim = dim |
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self.dim_out = dim_out or dim |
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self.split_size = split_size |
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self.num_heads = num_heads |
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self.idx = idx |
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self.position_bias = position_bias |
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|
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head_dim = dim // num_heads |
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self.scale = qk_scale or head_dim**-0.5 |
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|
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if idx == 0: |
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H_sp, W_sp = self.split_size[0], self.split_size[1] |
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elif idx == 1: |
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W_sp, H_sp = self.split_size[0], self.split_size[1] |
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else: |
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print("ERROR MODE", idx) |
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exit(0) |
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self.H_sp = H_sp |
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self.W_sp = W_sp |
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|
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if self.position_bias: |
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self.pos = DynamicPosBias(self.dim // 4, self.num_heads, residual=False) |
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# generate mother-set |
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position_bias_h = torch.arange(1 - self.H_sp, self.H_sp) |
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position_bias_w = torch.arange(1 - self.W_sp, self.W_sp) |
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biases = torch.stack(torch.meshgrid([position_bias_h, position_bias_w])) |
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biases = biases.flatten(1).transpose(0, 1).contiguous().float() |
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self.register_buffer("rpe_biases", biases) |
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|
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# get pair-wise relative position index for each token inside the window |
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coords_h = torch.arange(self.H_sp) |
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coords_w = torch.arange(self.W_sp) |
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coords = torch.stack(torch.meshgrid([coords_h, coords_w])) |
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coords_flatten = torch.flatten(coords, 1) |
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relative_coords = coords_flatten[:, :, None] - coords_flatten[:, None, :] |
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relative_coords = relative_coords.permute(1, 2, 0).contiguous() |
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relative_coords[:, :, 0] += self.H_sp - 1 |
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relative_coords[:, :, 1] += self.W_sp - 1 |
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relative_coords[:, :, 0] *= 2 * self.W_sp - 1 |
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relative_position_index = relative_coords.sum(-1) |
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self.register_buffer("relative_position_index", relative_position_index) |
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|
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self.attn_drop = nn.Dropout(attn_drop) |
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|
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def im2win(self, x, H, W): |
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B, N, C = x.shape |
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x = x.transpose(-2, -1).contiguous().view(B, C, H, W) |
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x = img2windows(x, self.H_sp, self.W_sp) |
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x = ( |
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x.reshape(-1, self.H_sp * self.W_sp, self.num_heads, C // self.num_heads) |
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.permute(0, 2, 1, 3) |
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.contiguous() |
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) |
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return x |
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|
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def forward(self, qkv, H, W, mask=None): |
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""" |
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Input: qkv: (B, 3*L, C), H, W, mask: (B, N, N), N is the window size |
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Output: x (B, H, W, C) |
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""" |
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q, k, v = qkv[0], qkv[1], qkv[2] |
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|
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B, L, C = q.shape |
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assert L == H * W, "flatten img_tokens has wrong size" |
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|
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# partition the q,k,v, image to window |
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q = self.im2win(q, H, W) |
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k = self.im2win(k, H, W) |
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v = self.im2win(v, H, W) |
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|
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q = q * self.scale |
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attn = q @ k.transpose(-2, -1) # B head N C @ B head C N --> B head N N |
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|
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# calculate drpe |
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if self.position_bias: |
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pos = self.pos(self.rpe_biases) |
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# select position bias |
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relative_position_bias = pos[self.relative_position_index.view(-1)].view( |
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self.H_sp * self.W_sp, self.H_sp * self.W_sp, -1 |
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) |
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relative_position_bias = relative_position_bias.permute( |
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2, 0, 1 |
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).contiguous() |
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attn = attn + relative_position_bias.unsqueeze(0) |
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|
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N = attn.shape[3] |
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|
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# use mask for shift window |
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if mask is not None: |
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nW = mask.shape[0] |
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attn = attn.view(B, nW, self.num_heads, N, N) + mask.unsqueeze(1).unsqueeze( |
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0 |
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) |
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attn = attn.view(-1, self.num_heads, N, N) |
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|
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attn = nn.functional.softmax(attn, dim=-1, dtype=attn.dtype) |
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attn = self.attn_drop(attn) |
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|
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x = attn @ v |
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x = x.transpose(1, 2).reshape( |
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-1, self.H_sp * self.W_sp, C |
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) # B head N N @ B head N C |
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|
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# merge the window, window to image |
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x = windows2img(x, self.H_sp, self.W_sp, H, W) # B H' W' C |
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|
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return x |
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|
|
|
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class Adaptive_Spatial_Attention(nn.Module): |
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# The implementation builds on CAT code https://github.com/Zhengchen1999/CAT |
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"""Adaptive Spatial Self-Attention |
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Args: |
|
dim (int): Number of input channels. |
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num_heads (int): Number of attention heads. Default: 6 |
|
split_size (tuple(int)): Height and Width of spatial window. |
|
shift_size (tuple(int)): Shift size for spatial window. |
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qkv_bias (bool): If True, add a learnable bias to query, key, value. Default: True |
|
qk_scale (float | None): Override default qk scale of head_dim ** -0.5 if set. |
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drop (float): Dropout rate. Default: 0.0 |
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attn_drop (float): Attention dropout rate. Default: 0.0 |
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rg_idx (int): The indentix of Residual Group (RG) |
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b_idx (int): The indentix of Block in each RG |
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""" |
|
|
|
def __init__( |
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self, |
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dim, |
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num_heads, |
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reso=64, |
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split_size=[8, 8], |
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shift_size=[1, 2], |
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qkv_bias=False, |
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qk_scale=None, |
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drop=0.0, |
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attn_drop=0.0, |
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rg_idx=0, |
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b_idx=0, |
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): |
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super().__init__() |
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self.dim = dim |
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self.num_heads = num_heads |
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self.split_size = split_size |
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self.shift_size = shift_size |
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self.b_idx = b_idx |
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self.rg_idx = rg_idx |
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self.patches_resolution = reso |
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self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias) |
|
|
|
assert ( |
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0 <= self.shift_size[0] < self.split_size[0] |
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), "shift_size must in 0-split_size0" |
|
assert ( |
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0 <= self.shift_size[1] < self.split_size[1] |
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), "shift_size must in 0-split_size1" |
|
|
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self.branch_num = 2 |
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|
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self.proj = nn.Linear(dim, dim) |
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self.proj_drop = nn.Dropout(drop) |
|
|
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self.attns = nn.ModuleList( |
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[ |
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Spatial_Attention( |
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dim // 2, |
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idx=i, |
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split_size=split_size, |
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num_heads=num_heads // 2, |
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dim_out=dim // 2, |
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qk_scale=qk_scale, |
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attn_drop=attn_drop, |
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proj_drop=drop, |
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position_bias=True, |
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) |
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for i in range(self.branch_num) |
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] |
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) |
|
|
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if (self.rg_idx % 2 == 0 and self.b_idx > 0 and (self.b_idx - 2) % 4 == 0) or ( |
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self.rg_idx % 2 != 0 and self.b_idx % 4 == 0 |
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): |
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attn_mask = self.calculate_mask( |
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self.patches_resolution, self.patches_resolution |
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) |
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self.register_buffer("attn_mask_0", attn_mask[0]) |
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self.register_buffer("attn_mask_1", attn_mask[1]) |
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else: |
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attn_mask = None |
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self.register_buffer("attn_mask_0", None) |
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self.register_buffer("attn_mask_1", None) |
|
|
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self.dwconv = nn.Sequential( |
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nn.Conv2d(dim, dim, kernel_size=3, stride=1, padding=1, groups=dim), |
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nn.BatchNorm2d(dim), |
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nn.GELU(), |
|
) |
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self.channel_interaction = nn.Sequential( |
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nn.AdaptiveAvgPool2d(1), |
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nn.Conv2d(dim, dim // 8, kernel_size=1), |
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nn.BatchNorm2d(dim // 8), |
|
nn.GELU(), |
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nn.Conv2d(dim // 8, dim, kernel_size=1), |
|
) |
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self.spatial_interaction = nn.Sequential( |
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nn.Conv2d(dim, dim // 16, kernel_size=1), |
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nn.BatchNorm2d(dim // 16), |
|
nn.GELU(), |
|
nn.Conv2d(dim // 16, 1, kernel_size=1), |
|
) |
|
|
|
def calculate_mask(self, H, W): |
|
# The implementation builds on Swin Transformer code https://github.com/microsoft/Swin-Transformer/blob/main/models/swin_transformer.py |
|
# calculate attention mask for shift window |
|
img_mask_0 = torch.zeros((1, H, W, 1)) # 1 H W 1 idx=0 |
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img_mask_1 = torch.zeros((1, H, W, 1)) # 1 H W 1 idx=1 |
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h_slices_0 = ( |
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slice(0, -self.split_size[0]), |
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slice(-self.split_size[0], -self.shift_size[0]), |
|
slice(-self.shift_size[0], None), |
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) |
|
w_slices_0 = ( |
|
slice(0, -self.split_size[1]), |
|
slice(-self.split_size[1], -self.shift_size[1]), |
|
slice(-self.shift_size[1], None), |
|
) |
|
|
|
h_slices_1 = ( |
|
slice(0, -self.split_size[1]), |
|
slice(-self.split_size[1], -self.shift_size[1]), |
|
slice(-self.shift_size[1], None), |
|
) |
|
w_slices_1 = ( |
|
slice(0, -self.split_size[0]), |
|
slice(-self.split_size[0], -self.shift_size[0]), |
|
slice(-self.shift_size[0], None), |
|
) |
|
cnt = 0 |
|
for h in h_slices_0: |
|
for w in w_slices_0: |
|
img_mask_0[:, h, w, :] = cnt |
|
cnt += 1 |
|
cnt = 0 |
|
for h in h_slices_1: |
|
for w in w_slices_1: |
|
img_mask_1[:, h, w, :] = cnt |
|
cnt += 1 |
|
|
|
# calculate mask for window-0 |
|
img_mask_0 = img_mask_0.view( |
|
1, |
|
H // self.split_size[0], |
|
self.split_size[0], |
|
W // self.split_size[1], |
|
self.split_size[1], |
|
1, |
|
) |
|
img_mask_0 = ( |
|
img_mask_0.permute(0, 1, 3, 2, 4, 5) |
|
.contiguous() |
|
.view(-1, self.split_size[0], self.split_size[1], 1) |
|
) # nW, sw[0], sw[1], 1 |
|
mask_windows_0 = img_mask_0.view(-1, self.split_size[0] * self.split_size[1]) |
|
attn_mask_0 = mask_windows_0.unsqueeze(1) - mask_windows_0.unsqueeze(2) |
|
attn_mask_0 = attn_mask_0.masked_fill( |
|
attn_mask_0 != 0, float(-100.0) |
|
).masked_fill(attn_mask_0 == 0, float(0.0)) |
|
|
|
# calculate mask for window-1 |
|
img_mask_1 = img_mask_1.view( |
|
1, |
|
H // self.split_size[1], |
|
self.split_size[1], |
|
W // self.split_size[0], |
|
self.split_size[0], |
|
1, |
|
) |
|
img_mask_1 = ( |
|
img_mask_1.permute(0, 1, 3, 2, 4, 5) |
|
.contiguous() |
|
.view(-1, self.split_size[1], self.split_size[0], 1) |
|
) # nW, sw[1], sw[0], 1 |
|
mask_windows_1 = img_mask_1.view(-1, self.split_size[1] * self.split_size[0]) |
|
attn_mask_1 = mask_windows_1.unsqueeze(1) - mask_windows_1.unsqueeze(2) |
|
attn_mask_1 = attn_mask_1.masked_fill( |
|
attn_mask_1 != 0, float(-100.0) |
|
).masked_fill(attn_mask_1 == 0, float(0.0)) |
|
|
|
return attn_mask_0, attn_mask_1 |
|
|
|
def forward(self, x, H, W): |
|
""" |
|
Input: x: (B, H*W, C), H, W |
|
Output: x: (B, H*W, C) |
|
""" |
|
B, L, C = x.shape |
|
assert L == H * W, "flatten img_tokens has wrong size" |
|
|
|
qkv = self.qkv(x).reshape(B, -1, 3, C).permute(2, 0, 1, 3) # 3, B, HW, C |
|
# V without partition |
|
v = qkv[2].transpose(-2, -1).contiguous().view(B, C, H, W) |
|
|
|
# image padding |
|
max_split_size = max(self.split_size[0], self.split_size[1]) |
|
pad_l = pad_t = 0 |
|
pad_r = (max_split_size - W % max_split_size) % max_split_size |
|
pad_b = (max_split_size - H % max_split_size) % max_split_size |
|
|
|
qkv = qkv.reshape(3 * B, H, W, C).permute(0, 3, 1, 2) # 3B C H W |
|
qkv = ( |
|
F.pad(qkv, (pad_l, pad_r, pad_t, pad_b)) |
|
.reshape(3, B, C, -1) |
|
.transpose(-2, -1) |
|
) # l r t b |
|
_H = pad_b + H |
|
_W = pad_r + W |
|
_L = _H * _W |
|
|
|
# window-0 and window-1 on split channels [C/2, C/2]; for square windows (e.g., 8x8), window-0 and window-1 can be merged |
|
# shift in block: (0, 4, 8, ...), (2, 6, 10, ...), (0, 4, 8, ...), (2, 6, 10, ...), ... |
|
if (self.rg_idx % 2 == 0 and self.b_idx > 0 and (self.b_idx - 2) % 4 == 0) or ( |
|
self.rg_idx % 2 != 0 and self.b_idx % 4 == 0 |
|
): |
|
qkv = qkv.view(3, B, _H, _W, C) |
|
qkv_0 = torch.roll( |
|
qkv[:, :, :, :, : C // 2], |
|
shifts=(-self.shift_size[0], -self.shift_size[1]), |
|
dims=(2, 3), |
|
) |
|
qkv_0 = qkv_0.view(3, B, _L, C // 2) |
|
qkv_1 = torch.roll( |
|
qkv[:, :, :, :, C // 2 :], |
|
shifts=(-self.shift_size[1], -self.shift_size[0]), |
|
dims=(2, 3), |
|
) |
|
qkv_1 = qkv_1.view(3, B, _L, C // 2) |
|
|
|
if self.patches_resolution != _H or self.patches_resolution != _W: |
|
mask_tmp = self.calculate_mask(_H, _W) |
|
x1_shift = self.attns[0](qkv_0, _H, _W, mask=mask_tmp[0].to(x.device)) |
|
x2_shift = self.attns[1](qkv_1, _H, _W, mask=mask_tmp[1].to(x.device)) |
|
else: |
|
x1_shift = self.attns[0](qkv_0, _H, _W, mask=self.attn_mask_0) |
|
x2_shift = self.attns[1](qkv_1, _H, _W, mask=self.attn_mask_1) |
|
|
|
x1 = torch.roll( |
|
x1_shift, shifts=(self.shift_size[0], self.shift_size[1]), dims=(1, 2) |
|
) |
|
x2 = torch.roll( |
|
x2_shift, shifts=(self.shift_size[1], self.shift_size[0]), dims=(1, 2) |
|
) |
|
x1 = x1[:, :H, :W, :].reshape(B, L, C // 2) |
|
x2 = x2[:, :H, :W, :].reshape(B, L, C // 2) |
|
# attention output |
|
attened_x = torch.cat([x1, x2], dim=2) |
|
|
|
else: |
|
x1 = self.attns[0](qkv[:, :, :, : C // 2], _H, _W)[:, :H, :W, :].reshape( |
|
B, L, C // 2 |
|
) |
|
x2 = self.attns[1](qkv[:, :, :, C // 2 :], _H, _W)[:, :H, :W, :].reshape( |
|
B, L, C // 2 |
|
) |
|
# attention output |
|
attened_x = torch.cat([x1, x2], dim=2) |
|
|
|
# convolution output |
|
conv_x = self.dwconv(v) |
|
|
|
# Adaptive Interaction Module (AIM) |
|
# C-Map (before sigmoid) |
|
channel_map = ( |
|
self.channel_interaction(conv_x) |
|
.permute(0, 2, 3, 1) |
|
.contiguous() |
|
.view(B, 1, C) |
|
) |
|
# S-Map (before sigmoid) |
|
attention_reshape = attened_x.transpose(-2, -1).contiguous().view(B, C, H, W) |
|
spatial_map = self.spatial_interaction(attention_reshape) |
|
|
|
# C-I |
|
attened_x = attened_x * torch.sigmoid(channel_map) |
|
# S-I |
|
conv_x = torch.sigmoid(spatial_map) * conv_x |
|
conv_x = conv_x.permute(0, 2, 3, 1).contiguous().view(B, L, C) |
|
|
|
x = attened_x + conv_x |
|
|
|
x = self.proj(x) |
|
x = self.proj_drop(x) |
|
|
|
return x |
|
|
|
|
|
class Adaptive_Channel_Attention(nn.Module): |
|
# The implementation builds on XCiT code https://github.com/facebookresearch/xcit |
|
"""Adaptive Channel Self-Attention |
|
Args: |
|
dim (int): Number of input channels. |
|
num_heads (int): Number of attention heads. Default: 6 |
|
qkv_bias (bool): If True, add a learnable bias to query, key, value. Default: True |
|
qk_scale (float | None): Override default qk scale of head_dim ** -0.5 if set. |
|
attn_drop (float): Attention dropout rate. Default: 0.0 |
|
drop_path (float): Stochastic depth rate. Default: 0.0 |
|
""" |
|
|
|
def __init__( |
|
self, |
|
dim, |
|
num_heads=8, |
|
qkv_bias=False, |
|
qk_scale=None, |
|
attn_drop=0.0, |
|
proj_drop=0.0, |
|
): |
|
super().__init__() |
|
self.num_heads = num_heads |
|
self.temperature = nn.Parameter(torch.ones(num_heads, 1, 1)) |
|
|
|
self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias) |
|
self.attn_drop = nn.Dropout(attn_drop) |
|
self.proj = nn.Linear(dim, dim) |
|
self.proj_drop = nn.Dropout(proj_drop) |
|
|
|
self.dwconv = nn.Sequential( |
|
nn.Conv2d(dim, dim, kernel_size=3, stride=1, padding=1, groups=dim), |
|
nn.BatchNorm2d(dim), |
|
nn.GELU(), |
|
) |
|
self.channel_interaction = nn.Sequential( |
|
nn.AdaptiveAvgPool2d(1), |
|
nn.Conv2d(dim, dim // 8, kernel_size=1), |
|
nn.BatchNorm2d(dim // 8), |
|
nn.GELU(), |
|
nn.Conv2d(dim // 8, dim, kernel_size=1), |
|
) |
|
self.spatial_interaction = nn.Sequential( |
|
nn.Conv2d(dim, dim // 16, kernel_size=1), |
|
nn.BatchNorm2d(dim // 16), |
|
nn.GELU(), |
|
nn.Conv2d(dim // 16, 1, kernel_size=1), |
|
) |
|
|
|
def forward(self, x, H, W): |
|
""" |
|
Input: x: (B, H*W, C), H, W |
|
Output: x: (B, H*W, C) |
|
""" |
|
B, N, C = x.shape |
|
qkv = self.qkv(x).reshape(B, N, 3, self.num_heads, C // self.num_heads) |
|
qkv = qkv.permute(2, 0, 3, 1, 4) |
|
q, k, v = qkv[0], qkv[1], qkv[2] |
|
|
|
q = q.transpose(-2, -1) |
|
k = k.transpose(-2, -1) |
|
v = v.transpose(-2, -1) |
|
|
|
v_ = v.reshape(B, C, N).contiguous().view(B, C, H, W) |
|
|
|
q = torch.nn.functional.normalize(q, dim=-1) |
|
k = torch.nn.functional.normalize(k, dim=-1) |
|
|
|
attn = (q @ k.transpose(-2, -1)) * self.temperature |
|
attn = attn.softmax(dim=-1) |
|
attn = self.attn_drop(attn) |
|
|
|
# attention output |
|
attened_x = (attn @ v).permute(0, 3, 1, 2).reshape(B, N, C) |
|
|
|
# convolution output |
|
conv_x = self.dwconv(v_) |
|
|
|
# Adaptive Interaction Module (AIM) |
|
# C-Map (before sigmoid) |
|
attention_reshape = attened_x.transpose(-2, -1).contiguous().view(B, C, H, W) |
|
channel_map = self.channel_interaction(attention_reshape) |
|
# S-Map (before sigmoid) |
|
spatial_map = ( |
|
self.spatial_interaction(conv_x) |
|
.permute(0, 2, 3, 1) |
|
.contiguous() |
|
.view(B, N, 1) |
|
) |
|
|
|
# S-I |
|
attened_x = attened_x * torch.sigmoid(spatial_map) |
|
# C-I |
|
conv_x = conv_x * torch.sigmoid(channel_map) |
|
conv_x = conv_x.permute(0, 2, 3, 1).contiguous().view(B, N, C) |
|
|
|
x = attened_x + conv_x |
|
|
|
x = self.proj(x) |
|
x = self.proj_drop(x) |
|
|
|
return x |
|
|
|
|
|
class DATB(nn.Module): |
|
def __init__( |
|
self, |
|
dim, |
|
num_heads, |
|
reso=64, |
|
split_size=[2, 4], |
|
shift_size=[1, 2], |
|
expansion_factor=4.0, |
|
qkv_bias=False, |
|
qk_scale=None, |
|
drop=0.0, |
|
attn_drop=0.0, |
|
drop_path=0.0, |
|
act_layer=nn.GELU, |
|
norm_layer=nn.LayerNorm, |
|
rg_idx=0, |
|
b_idx=0, |
|
): |
|
super().__init__() |
|
|
|
self.norm1 = norm_layer(dim) |
|
|
|
if b_idx % 2 == 0: |
|
# DSTB |
|
self.attn = Adaptive_Spatial_Attention( |
|
dim, |
|
num_heads=num_heads, |
|
reso=reso, |
|
split_size=split_size, |
|
shift_size=shift_size, |
|
qkv_bias=qkv_bias, |
|
qk_scale=qk_scale, |
|
drop=drop, |
|
attn_drop=attn_drop, |
|
rg_idx=rg_idx, |
|
b_idx=b_idx, |
|
) |
|
else: |
|
# DCTB |
|
self.attn = Adaptive_Channel_Attention( |
|
dim, |
|
num_heads=num_heads, |
|
qkv_bias=qkv_bias, |
|
qk_scale=qk_scale, |
|
attn_drop=attn_drop, |
|
proj_drop=drop, |
|
) |
|
self.drop_path = DropPath(drop_path) if drop_path > 0.0 else nn.Identity() |
|
|
|
ffn_hidden_dim = int(dim * expansion_factor) |
|
self.ffn = SGFN( |
|
in_features=dim, |
|
hidden_features=ffn_hidden_dim, |
|
out_features=dim, |
|
act_layer=act_layer, |
|
) |
|
self.norm2 = norm_layer(dim) |
|
|
|
def forward(self, x, x_size): |
|
""" |
|
Input: x: (B, H*W, C), x_size: (H, W) |
|
Output: x: (B, H*W, C) |
|
""" |
|
H, W = x_size |
|
x = x + self.drop_path(self.attn(self.norm1(x), H, W)) |
|
x = x + self.drop_path(self.ffn(self.norm2(x), H, W)) |
|
|
|
return x |
|
|
|
|
|
class ResidualGroup(nn.Module): |
|
"""ResidualGroup |
|
Args: |
|
dim (int): Number of input channels. |
|
reso (int): Input resolution. |
|
num_heads (int): Number of attention heads. |
|
split_size (tuple(int)): Height and Width of spatial window. |
|
expansion_factor (float): Ratio of ffn hidden dim to embedding dim. |
|
qkv_bias (bool): If True, add a learnable bias to query, key, value. Default: True |
|
qk_scale (float | None): Override default qk scale of head_dim ** -0.5 if set. Default: None |
|
drop (float): Dropout rate. Default: 0 |
|
attn_drop(float): Attention dropout rate. Default: 0 |
|
drop_paths (float | None): Stochastic depth rate. |
|
act_layer (nn.Module): Activation layer. Default: nn.GELU |
|
norm_layer (nn.Module): Normalization layer. Default: nn.LayerNorm |
|
depth (int): Number of dual aggregation Transformer blocks in residual group. |
|
use_chk (bool): Whether to use checkpointing to save memory. |
|
resi_connection: The convolutional block before residual connection. '1conv'/'3conv' |
|
""" |
|
|
|
def __init__( |
|
self, |
|
dim, |
|
reso, |
|
num_heads, |
|
split_size=[2, 4], |
|
expansion_factor=4.0, |
|
qkv_bias=False, |
|
qk_scale=None, |
|
drop=0.0, |
|
attn_drop=0.0, |
|
drop_paths=None, |
|
act_layer=nn.GELU, |
|
norm_layer=nn.LayerNorm, |
|
depth=2, |
|
use_chk=False, |
|
resi_connection="1conv", |
|
rg_idx=0, |
|
): |
|
super().__init__() |
|
self.use_chk = use_chk |
|
self.reso = reso |
|
|
|
self.blocks = nn.ModuleList( |
|
[ |
|
DATB( |
|
dim=dim, |
|
num_heads=num_heads, |
|
reso=reso, |
|
split_size=split_size, |
|
shift_size=[split_size[0] // 2, split_size[1] // 2], |
|
expansion_factor=expansion_factor, |
|
qkv_bias=qkv_bias, |
|
qk_scale=qk_scale, |
|
drop=drop, |
|
attn_drop=attn_drop, |
|
drop_path=drop_paths[i], |
|
act_layer=act_layer, |
|
norm_layer=norm_layer, |
|
rg_idx=rg_idx, |
|
b_idx=i, |
|
) |
|
for i in range(depth) |
|
] |
|
) |
|
|
|
if resi_connection == "1conv": |
|
self.conv = nn.Conv2d(dim, dim, 3, 1, 1) |
|
elif resi_connection == "3conv": |
|
self.conv = nn.Sequential( |
|
nn.Conv2d(dim, dim // 4, 3, 1, 1), |
|
nn.LeakyReLU(negative_slope=0.2, inplace=True), |
|
nn.Conv2d(dim // 4, dim // 4, 1, 1, 0), |
|
nn.LeakyReLU(negative_slope=0.2, inplace=True), |
|
nn.Conv2d(dim // 4, dim, 3, 1, 1), |
|
) |
|
|
|
def forward(self, x, x_size): |
|
""" |
|
Input: x: (B, H*W, C), x_size: (H, W) |
|
Output: x: (B, H*W, C) |
|
""" |
|
H, W = x_size |
|
res = x |
|
for blk in self.blocks: |
|
if self.use_chk: |
|
x = checkpoint.checkpoint(blk, x, x_size) |
|
else: |
|
x = blk(x, x_size) |
|
x = rearrange(x, "b (h w) c -> b c h w", h=H, w=W) |
|
x = self.conv(x) |
|
x = rearrange(x, "b c h w -> b (h w) c") |
|
x = res + x |
|
|
|
return x |
|
|
|
|
|
class Upsample(nn.Sequential): |
|
"""Upsample module. |
|
Args: |
|
scale (int): Scale factor. Supported scales: 2^n and 3. |
|
num_feat (int): Channel number of intermediate features. |
|
""" |
|
|
|
def __init__(self, scale, num_feat): |
|
m = [] |
|
if (scale & (scale - 1)) == 0: # scale = 2^n |
|
for _ in range(int(math.log(scale, 2))): |
|
m.append(nn.Conv2d(num_feat, 4 * num_feat, 3, 1, 1)) |
|
m.append(nn.PixelShuffle(2)) |
|
elif scale == 3: |
|
m.append(nn.Conv2d(num_feat, 9 * num_feat, 3, 1, 1)) |
|
m.append(nn.PixelShuffle(3)) |
|
else: |
|
raise ValueError( |
|
f"scale {scale} is not supported. " "Supported scales: 2^n and 3." |
|
) |
|
super(Upsample, self).__init__(*m) |
|
|
|
|
|
class UpsampleOneStep(nn.Sequential): |
|
"""UpsampleOneStep module (the difference with Upsample is that it always only has 1conv + 1pixelshuffle) |
|
Used in lightweight SR to save parameters. |
|
|
|
Args: |
|
scale (int): Scale factor. Supported scales: 2^n and 3. |
|
num_feat (int): Channel number of intermediate features. |
|
|
|
""" |
|
|
|
def __init__(self, scale, num_feat, num_out_ch, input_resolution=None): |
|
self.num_feat = num_feat |
|
self.input_resolution = input_resolution |
|
m = [] |
|
m.append(nn.Conv2d(num_feat, (scale**2) * num_out_ch, 3, 1, 1)) |
|
m.append(nn.PixelShuffle(scale)) |
|
super(UpsampleOneStep, self).__init__(*m) |
|
|
|
def flops(self): |
|
h, w = self.input_resolution |
|
flops = h * w * self.num_feat * 3 * 9 |
|
return flops |
|
|
|
|
|
class DAT(nn.Module): |
|
"""Dual Aggregation Transformer |
|
Args: |
|
img_size (int): Input image size. Default: 64 |
|
in_chans (int): Number of input image channels. Default: 3 |
|
embed_dim (int): Patch embedding dimension. Default: 180 |
|
depths (tuple(int)): Depth of each residual group (number of DATB in each RG). |
|
split_size (tuple(int)): Height and Width of spatial window. |
|
num_heads (tuple(int)): Number of attention heads in different residual groups. |
|
expansion_factor (float): Ratio of ffn hidden dim to embedding dim. Default: 4 |
|
qkv_bias (bool): If True, add a learnable bias to query, key, value. Default: True |
|
qk_scale (float | None): Override default qk scale of head_dim ** -0.5 if set. Default: None |
|
drop_rate (float): Dropout rate. Default: 0 |
|
attn_drop_rate (float): Attention dropout rate. Default: 0 |
|
drop_path_rate (float): Stochastic depth rate. Default: 0.1 |
|
act_layer (nn.Module): Activation layer. Default: nn.GELU |
|
norm_layer (nn.Module): Normalization layer. Default: nn.LayerNorm |
|
use_chk (bool): Whether to use checkpointing to save memory. |
|
upscale: Upscale factor. 2/3/4 for image SR |
|
img_range: Image range. 1. or 255. |
|
resi_connection: The convolutional block before residual connection. '1conv'/'3conv' |
|
""" |
|
|
|
def __init__(self, state_dict): |
|
super().__init__() |
|
|
|
# defaults |
|
img_size = 64 |
|
in_chans = 3 |
|
embed_dim = 180 |
|
split_size = [2, 4] |
|
depth = [2, 2, 2, 2] |
|
num_heads = [2, 2, 2, 2] |
|
expansion_factor = 4.0 |
|
qkv_bias = True |
|
qk_scale = None |
|
drop_rate = 0.0 |
|
attn_drop_rate = 0.0 |
|
drop_path_rate = 0.1 |
|
act_layer = nn.GELU |
|
norm_layer = nn.LayerNorm |
|
use_chk = False |
|
upscale = 2 |
|
img_range = 1.0 |
|
resi_connection = "1conv" |
|
upsampler = "pixelshuffle" |
|
|
|
self.model_arch = "DAT" |
|
self.sub_type = "SR" |
|
self.state = state_dict |
|
|
|
state_keys = state_dict.keys() |
|
if "conv_before_upsample.0.weight" in state_keys: |
|
if "conv_up1.weight" in state_keys: |
|
upsampler = "nearest+conv" |
|
else: |
|
upsampler = "pixelshuffle" |
|
supports_fp16 = False |
|
elif "upsample.0.weight" in state_keys: |
|
upsampler = "pixelshuffledirect" |
|
else: |
|
upsampler = "" |
|
|
|
num_feat = ( |
|
state_dict.get("conv_before_upsample.0.weight", None).shape[1] |
|
if state_dict.get("conv_before_upsample.weight", None) |
|
else 64 |
|
) |
|
|
|
num_in_ch = state_dict["conv_first.weight"].shape[1] |
|
in_chans = num_in_ch |
|
if "conv_last.weight" in state_keys: |
|
num_out_ch = state_dict["conv_last.weight"].shape[0] |
|
else: |
|
num_out_ch = num_in_ch |
|
|
|
upscale = 1 |
|
if upsampler == "nearest+conv": |
|
upsample_keys = [ |
|
x for x in state_keys if "conv_up" in x and "bias" not in x |
|
] |
|
|
|
for upsample_key in upsample_keys: |
|
upscale *= 2 |
|
elif upsampler == "pixelshuffle": |
|
upsample_keys = [ |
|
x |
|
for x in state_keys |
|
if "upsample" in x and "conv" not in x and "bias" not in x |
|
] |
|
for upsample_key in upsample_keys: |
|
shape = state_dict[upsample_key].shape[0] |
|
upscale *= math.sqrt(shape // num_feat) |
|
upscale = int(upscale) |
|
elif upsampler == "pixelshuffledirect": |
|
upscale = int( |
|
math.sqrt(state_dict["upsample.0.bias"].shape[0] // num_out_ch) |
|
) |
|
|
|
max_layer_num = 0 |
|
max_block_num = 0 |
|
for key in state_keys: |
|
result = re.match(r"layers.(\d*).blocks.(\d*).norm1.weight", key) |
|
if result: |
|
layer_num, block_num = result.groups() |
|
max_layer_num = max(max_layer_num, int(layer_num)) |
|
max_block_num = max(max_block_num, int(block_num)) |
|
|
|
depth = [max_block_num + 1 for _ in range(max_layer_num + 1)] |
|
|
|
if "layers.0.blocks.1.attn.temperature" in state_keys: |
|
num_heads_num = state_dict["layers.0.blocks.1.attn.temperature"].shape[0] |
|
num_heads = [num_heads_num for _ in range(max_layer_num + 1)] |
|
else: |
|
num_heads = depth |
|
|
|
embed_dim = state_dict["conv_first.weight"].shape[0] |
|
expansion_factor = float( |
|
state_dict["layers.0.blocks.0.ffn.fc1.weight"].shape[0] / embed_dim |
|
) |
|
|
|
# TODO: could actually count the layers, but this should do |
|
if "layers.0.conv.4.weight" in state_keys: |
|
resi_connection = "3conv" |
|
else: |
|
resi_connection = "1conv" |
|
|
|
if "layers.0.blocks.2.attn.attn_mask_0" in state_keys: |
|
attn_mask_0_x, attn_mask_0_y, attn_mask_0_z = state_dict[ |
|
"layers.0.blocks.2.attn.attn_mask_0" |
|
].shape |
|
|
|
img_size = int(math.sqrt(attn_mask_0_x * attn_mask_0_y)) |
|
|
|
if "layers.0.blocks.0.attn.attns.0.rpe_biases" in state_keys: |
|
split_sizes = ( |
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state_dict["layers.0.blocks.0.attn.attns.0.rpe_biases"][-1] + 1 |
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) |
|
split_size = [int(x) for x in split_sizes] |
|
|
|
self.in_nc = num_in_ch |
|
self.out_nc = num_out_ch |
|
self.num_feat = num_feat |
|
self.embed_dim = embed_dim |
|
self.num_heads = num_heads |
|
self.depth = depth |
|
self.scale = upscale |
|
self.upsampler = upsampler |
|
self.img_size = img_size |
|
self.img_range = img_range |
|
self.expansion_factor = expansion_factor |
|
self.resi_connection = resi_connection |
|
self.split_size = split_size |
|
|
|
self.supports_fp16 = False # Too much weirdness to support this at the moment |
|
self.supports_bfp16 = True |
|
self.min_size_restriction = 16 |
|
|
|
num_in_ch = in_chans |
|
num_out_ch = in_chans |
|
num_feat = 64 |
|
self.img_range = img_range |
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if in_chans == 3: |
|
rgb_mean = (0.4488, 0.4371, 0.4040) |
|
self.mean = torch.Tensor(rgb_mean).view(1, 3, 1, 1) |
|
else: |
|
self.mean = torch.zeros(1, 1, 1, 1) |
|
self.upscale = upscale |
|
self.upsampler = upsampler |
|
|
|
# ------------------------- 1, Shallow Feature Extraction ------------------------- # |
|
self.conv_first = nn.Conv2d(num_in_ch, embed_dim, 3, 1, 1) |
|
|
|
# ------------------------- 2, Deep Feature Extraction ------------------------- # |
|
self.num_layers = len(depth) |
|
self.use_chk = use_chk |
|
self.num_features = ( |
|
self.embed_dim |
|
) = embed_dim # num_features for consistency with other models |
|
heads = num_heads |
|
|
|
self.before_RG = nn.Sequential( |
|
Rearrange("b c h w -> b (h w) c"), nn.LayerNorm(embed_dim) |
|
) |
|
|
|
curr_dim = embed_dim |
|
dpr = [ |
|
x.item() for x in torch.linspace(0, drop_path_rate, np.sum(depth)) |
|
] # stochastic depth decay rule |
|
|
|
self.layers = nn.ModuleList() |
|
for i in range(self.num_layers): |
|
layer = ResidualGroup( |
|
dim=embed_dim, |
|
num_heads=heads[i], |
|
reso=img_size, |
|
split_size=split_size, |
|
expansion_factor=expansion_factor, |
|
qkv_bias=qkv_bias, |
|
qk_scale=qk_scale, |
|
drop=drop_rate, |
|
attn_drop=attn_drop_rate, |
|
drop_paths=dpr[sum(depth[:i]) : sum(depth[: i + 1])], |
|
act_layer=act_layer, |
|
norm_layer=norm_layer, |
|
depth=depth[i], |
|
use_chk=use_chk, |
|
resi_connection=resi_connection, |
|
rg_idx=i, |
|
) |
|
self.layers.append(layer) |
|
|
|
self.norm = norm_layer(curr_dim) |
|
# build the last conv layer in deep feature extraction |
|
if resi_connection == "1conv": |
|
self.conv_after_body = nn.Conv2d(embed_dim, embed_dim, 3, 1, 1) |
|
elif resi_connection == "3conv": |
|
# to save parameters and memory |
|
self.conv_after_body = nn.Sequential( |
|
nn.Conv2d(embed_dim, embed_dim // 4, 3, 1, 1), |
|
nn.LeakyReLU(negative_slope=0.2, inplace=True), |
|
nn.Conv2d(embed_dim // 4, embed_dim // 4, 1, 1, 0), |
|
nn.LeakyReLU(negative_slope=0.2, inplace=True), |
|
nn.Conv2d(embed_dim // 4, embed_dim, 3, 1, 1), |
|
) |
|
|
|
# ------------------------- 3, Reconstruction ------------------------- # |
|
if self.upsampler == "pixelshuffle": |
|
# for classical SR |
|
self.conv_before_upsample = nn.Sequential( |
|
nn.Conv2d(embed_dim, num_feat, 3, 1, 1), nn.LeakyReLU(inplace=True) |
|
) |
|
self.upsample = Upsample(upscale, num_feat) |
|
self.conv_last = nn.Conv2d(num_feat, num_out_ch, 3, 1, 1) |
|
elif self.upsampler == "pixelshuffledirect": |
|
# for lightweight SR (to save parameters) |
|
self.upsample = UpsampleOneStep( |
|
upscale, embed_dim, num_out_ch, (img_size, img_size) |
|
) |
|
|
|
self.apply(self._init_weights) |
|
self.load_state_dict(state_dict, strict=True) |
|
|
|
def _init_weights(self, m): |
|
if isinstance(m, nn.Linear): |
|
trunc_normal_(m.weight, std=0.02) |
|
if isinstance(m, nn.Linear) and m.bias is not None: |
|
nn.init.constant_(m.bias, 0) |
|
elif isinstance( |
|
m, (nn.LayerNorm, nn.BatchNorm2d, nn.GroupNorm, nn.InstanceNorm2d) |
|
): |
|
nn.init.constant_(m.bias, 0) |
|
nn.init.constant_(m.weight, 1.0) |
|
|
|
def forward_features(self, x): |
|
_, _, H, W = x.shape |
|
x_size = [H, W] |
|
x = self.before_RG(x) |
|
for layer in self.layers: |
|
x = layer(x, x_size) |
|
x = self.norm(x) |
|
x = rearrange(x, "b (h w) c -> b c h w", h=H, w=W) |
|
|
|
return x |
|
|
|
def forward(self, x): |
|
""" |
|
Input: x: (B, C, H, W) |
|
""" |
|
self.mean = self.mean.type_as(x) |
|
x = (x - self.mean) * self.img_range |
|
|
|
if self.upsampler == "pixelshuffle": |
|
# for image SR |
|
x = self.conv_first(x) |
|
x = self.conv_after_body(self.forward_features(x)) + x |
|
x = self.conv_before_upsample(x) |
|
x = self.conv_last(self.upsample(x)) |
|
elif self.upsampler == "pixelshuffledirect": |
|
# for lightweight SR |
|
x = self.conv_first(x) |
|
x = self.conv_after_body(self.forward_features(x)) + x |
|
x = self.upsample(x) |
|
|
|
x = x / self.img_range + self.mean |
|
return x
|
|
|