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| """ Shifted Window Attn | |
| This is a WIP experiment to apply windowed attention from the Swin Transformer | |
| to a stand-alone module for use as an attn block in conv nets. | |
| Based on original swin window code at https://github.com/microsoft/Swin-Transformer | |
| Swin Transformer paper: https://arxiv.org/pdf/2103.14030.pdf | |
| """ | |
| from typing import Optional | |
| import torch | |
| import torch.nn as nn | |
| from .drop import DropPath | |
| from .helpers import to_2tuple | |
| from .weight_init import trunc_normal_ | |
| def window_partition(x, win_size: int): | |
| """ | |
| Args: | |
| x: (B, H, W, C) | |
| win_size (int): window size | |
| Returns: | |
| windows: (num_windows*B, window_size, window_size, C) | |
| """ | |
| B, H, W, C = x.shape | |
| x = x.view(B, H // win_size, win_size, W // win_size, win_size, C) | |
| windows = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(-1, win_size, win_size, C) | |
| return windows | |
| def window_reverse(windows, win_size: int, H: int, W: int): | |
| """ | |
| Args: | |
| windows: (num_windows*B, window_size, window_size, C) | |
| win_size (int): Window size | |
| H (int): Height of image | |
| W (int): Width of image | |
| Returns: | |
| x: (B, H, W, C) | |
| """ | |
| B = int(windows.shape[0] / (H * W / win_size / win_size)) | |
| x = windows.view(B, H // win_size, W // win_size, win_size, win_size, -1) | |
| x = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(B, H, W, -1) | |
| return x | |
| class WindowAttention(nn.Module): | |
| r""" Window based multi-head self attention (W-MSA) module with relative position bias. | |
| It supports both of shifted and non-shifted window. | |
| Args: | |
| dim (int): Number of input channels. | |
| win_size (int): The height and width of the window. | |
| num_heads (int): Number of attention heads. | |
| qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True | |
| attn_drop (float, optional): Dropout ratio of attention weight. Default: 0.0 | |
| """ | |
| def __init__( | |
| self, dim, dim_out=None, feat_size=None, stride=1, win_size=8, shift_size=None, num_heads=8, | |
| qkv_bias=True, attn_drop=0.): | |
| super().__init__() | |
| self.dim_out = dim_out or dim | |
| self.feat_size = to_2tuple(feat_size) | |
| self.win_size = win_size | |
| self.shift_size = shift_size or win_size // 2 | |
| if min(self.feat_size) <= win_size: | |
| # if window size is larger than input resolution, we don't partition windows | |
| self.shift_size = 0 | |
| self.win_size = min(self.feat_size) | |
| assert 0 <= self.shift_size < self.win_size, "shift_size must in 0-window_size" | |
| self.num_heads = num_heads | |
| head_dim = self.dim_out // num_heads | |
| self.scale = head_dim ** -0.5 | |
| if self.shift_size > 0: | |
| # calculate attention mask for SW-MSA | |
| H, W = self.feat_size | |
| img_mask = torch.zeros((1, H, W, 1)) # 1 H W 1 | |
| h_slices = ( | |
| slice(0, -self.win_size), | |
| slice(-self.win_size, -self.shift_size), | |
| slice(-self.shift_size, None)) | |
| w_slices = ( | |
| slice(0, -self.win_size), | |
| slice(-self.win_size, -self.shift_size), | |
| slice(-self.shift_size, None)) | |
| cnt = 0 | |
| for h in h_slices: | |
| for w in w_slices: | |
| img_mask[:, h, w, :] = cnt | |
| cnt += 1 | |
| mask_windows = window_partition(img_mask, self.win_size) # num_win, window_size, window_size, 1 | |
| mask_windows = mask_windows.view(-1, self.win_size * self.win_size) | |
| attn_mask = mask_windows.unsqueeze(1) - mask_windows.unsqueeze(2) | |
| attn_mask = attn_mask.masked_fill(attn_mask != 0, float(-100.0)).masked_fill(attn_mask == 0, float(0.0)) | |
| else: | |
| attn_mask = None | |
| self.register_buffer("attn_mask", attn_mask) | |
| # define a parameter table of relative position bias | |
| self.relative_position_bias_table = nn.Parameter( | |
| # 2 * Wh - 1 * 2 * Ww - 1, nH | |
| torch.zeros((2 * self.win_size - 1) * (2 * self.win_size - 1), num_heads)) | |
| trunc_normal_(self.relative_position_bias_table, std=.02) | |
| # get pair-wise relative position index for each token inside the window | |
| coords_h = torch.arange(self.win_size) | |
| coords_w = torch.arange(self.win_size) | |
| coords = torch.stack(torch.meshgrid([coords_h, coords_w])) # 2, Wh, Ww | |
| coords_flatten = torch.flatten(coords, 1) # 2, Wh*Ww | |
| relative_coords = coords_flatten[:, :, None] - coords_flatten[:, None, :] # 2, Wh*Ww, Wh*Ww | |
| relative_coords = relative_coords.permute(1, 2, 0).contiguous() # Wh*Ww, Wh*Ww, 2 | |
| relative_coords[:, :, 0] += self.win_size - 1 # shift to start from 0 | |
| relative_coords[:, :, 1] += self.win_size - 1 | |
| relative_coords[:, :, 0] *= 2 * self.win_size - 1 | |
| relative_position_index = relative_coords.sum(-1) # Wh*Ww, Wh*Ww | |
| self.register_buffer("relative_position_index", relative_position_index) | |
| self.qkv = nn.Linear(dim, self.dim_out * 3, bias=qkv_bias) | |
| self.attn_drop = nn.Dropout(attn_drop) | |
| self.softmax = nn.Softmax(dim=-1) | |
| self.pool = nn.AvgPool2d(2, 2) if stride == 2 else nn.Identity() | |
| def reset_parameters(self): | |
| trunc_normal_(self.qkv.weight, std=self.qkv.weight.shape[1] ** -0.5) | |
| trunc_normal_(self.relative_position_bias_table, std=.02) | |
| def forward(self, x): | |
| B, C, H, W = x.shape | |
| x = x.permute(0, 2, 3, 1) | |
| # cyclic shift | |
| if self.shift_size > 0: | |
| shifted_x = torch.roll(x, shifts=(-self.shift_size, -self.shift_size), dims=(1, 2)) | |
| else: | |
| shifted_x = x | |
| # partition windows | |
| win_size_sq = self.win_size * self.win_size | |
| x_windows = window_partition(shifted_x, self.win_size) # num_win * B, window_size, window_size, C | |
| x_windows = x_windows.view(-1, win_size_sq, C) # num_win * B, window_size*window_size, C | |
| BW, N, _ = x_windows.shape | |
| qkv = self.qkv(x_windows) | |
| qkv = qkv.reshape(BW, N, 3, self.num_heads, self.dim_out // self.num_heads).permute(2, 0, 3, 1, 4) | |
| q, k, v = qkv[0], qkv[1], qkv[2] | |
| q = q * self.scale | |
| attn = (q @ k.transpose(-2, -1)) | |
| relative_position_bias = self.relative_position_bias_table[ | |
| self.relative_position_index.view(-1)].view(win_size_sq, win_size_sq, -1) | |
| relative_position_bias = relative_position_bias.permute(2, 0, 1).contiguous() # nH, Wh * Ww, Wh * Ww | |
| attn = attn + relative_position_bias.unsqueeze(0) | |
| if self.attn_mask is not None: | |
| num_win = self.attn_mask.shape[0] | |
| attn = attn.view(B, num_win, self.num_heads, N, N) + self.attn_mask.unsqueeze(1).unsqueeze(0) | |
| attn = attn.view(-1, self.num_heads, N, N) | |
| attn = self.softmax(attn) | |
| attn = self.attn_drop(attn) | |
| x = (attn @ v).transpose(1, 2).reshape(BW, N, self.dim_out) | |
| # merge windows | |
| x = x.view(-1, self.win_size, self.win_size, self.dim_out) | |
| shifted_x = window_reverse(x, self.win_size, H, W) # B H' W' C | |
| # reverse cyclic shift | |
| if self.shift_size > 0: | |
| x = torch.roll(shifted_x, shifts=(self.shift_size, self.shift_size), dims=(1, 2)) | |
| else: | |
| x = shifted_x | |
| x = x.view(B, H, W, self.dim_out).permute(0, 3, 1, 2) | |
| x = self.pool(x) | |
| return x | |