Alexander Soare
3 years ago
parent
ab3ac3f25b
commit
bc3d4eb403
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""" Shifted Window Attn
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This is a WIP experiment to apply windowed attention from the Swin Transformer
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to a stand-alone module for use as an attn block in conv nets.
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Based on original swin window code at https://github.com/microsoft/Swin-Transformer
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Swin Transformer paper: https://arxiv.org/pdf/2103.14030.pdf
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"""
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from typing import Optional
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import torch
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import torch.nn as nn
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from .drop import DropPath
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from .helpers import to_2tuple
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from .weight_init import trunc_normal_
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from timm.models.fx_helpers import fx_float_to_int
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def window_partition(x, win_size: int):
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"""
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Args:
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x: (B, H, W, C)
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win_size (int): window size
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Returns:
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windows: (num_windows*B, window_size, window_size, C)
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"""
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B, H, W, C = x.shape
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x = x.view(B, H // win_size, win_size, W // win_size, win_size, C)
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windows = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(-1, win_size, win_size, C)
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return windows
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def window_reverse(windows, win_size: int, H: int, W: int):
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"""
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Args:
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windows: (num_windows*B, window_size, window_size, C)
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win_size (int): Window size
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H (int): Height of image
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W (int): Width of image
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Returns:
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x: (B, H, W, C)
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"""
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B = fx_float_to_int(windows.shape[0] / (H * W / win_size / win_size))
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x = windows.view(B, H // win_size, W // win_size, win_size, win_size, -1)
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x = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(B, H, W, -1)
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return x
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class WindowAttention(nn.Module):
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r""" Window based multi-head self attention (W-MSA) module with relative position bias.
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It supports both of shifted and non-shifted window.
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Args:
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dim (int): Number of input channels.
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win_size (int): The height and width of the window.
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num_heads (int): Number of attention heads.
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qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True
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attn_drop (float, optional): Dropout ratio of attention weight. Default: 0.0
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"""
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def __init__(
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self, dim, dim_out=None, feat_size=None, stride=1, win_size=8, shift_size=None, num_heads=8,
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qkv_bias=True, attn_drop=0.):
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super().__init__()
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self.dim_out = dim_out or dim
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self.feat_size = to_2tuple(feat_size)
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self.win_size = win_size
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self.shift_size = shift_size or win_size // 2
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if min(self.feat_size) <= win_size:
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# if window size is larger than input resolution, we don't partition windows
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self.shift_size = 0
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self.win_size = min(self.feat_size)
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assert 0 <= self.shift_size < self.win_size, "shift_size must in 0-window_size"
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self.num_heads = num_heads
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head_dim = self.dim_out // num_heads
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self.scale = head_dim ** -0.5
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if self.shift_size > 0:
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# calculate attention mask for SW-MSA
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H, W = self.feat_size
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img_mask = torch.zeros((1, H, W, 1)) # 1 H W 1
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h_slices = (
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slice(0, -self.win_size),
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slice(-self.win_size, -self.shift_size),
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slice(-self.shift_size, None))
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w_slices = (
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slice(0, -self.win_size),
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slice(-self.win_size, -self.shift_size),
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slice(-self.shift_size, None))
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cnt = 0
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for h in h_slices:
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for w in w_slices:
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img_mask[:, h, w, :] = cnt
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cnt += 1
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mask_windows = window_partition(img_mask, self.win_size) # num_win, window_size, window_size, 1
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mask_windows = mask_windows.view(-1, self.win_size * self.win_size)
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attn_mask = mask_windows.unsqueeze(1) - mask_windows.unsqueeze(2)
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attn_mask = attn_mask.masked_fill(attn_mask != 0, float(-100.0)).masked_fill(attn_mask == 0, float(0.0))
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else:
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attn_mask = None
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self.register_buffer("attn_mask", attn_mask)
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# define a parameter table of relative position bias
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self.relative_position_bias_table = nn.Parameter(
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# 2 * Wh - 1 * 2 * Ww - 1, nH
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torch.zeros((2 * self.win_size - 1) * (2 * self.win_size - 1), num_heads))
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trunc_normal_(self.relative_position_bias_table, std=.02)
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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.win_size)
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coords_w = torch.arange(self.win_size)
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coords = torch.stack(torch.meshgrid([coords_h, coords_w])) # 2, Wh, Ww
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coords_flatten = torch.flatten(coords, 1) # 2, Wh*Ww
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relative_coords = coords_flatten[:, :, None] - coords_flatten[:, None, :] # 2, Wh*Ww, Wh*Ww
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relative_coords = relative_coords.permute(1, 2, 0).contiguous() # Wh*Ww, Wh*Ww, 2
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relative_coords[:, :, 0] += self.win_size - 1 # shift to start from 0
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relative_coords[:, :, 1] += self.win_size - 1
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relative_coords[:, :, 0] *= 2 * self.win_size - 1
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relative_position_index = relative_coords.sum(-1) # Wh*Ww, Wh*Ww
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self.register_buffer("relative_position_index", relative_position_index)
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self.qkv = nn.Linear(dim, self.dim_out * 3, bias=qkv_bias)
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self.attn_drop = nn.Dropout(attn_drop)
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self.softmax = nn.Softmax(dim=-1)
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self.pool = nn.AvgPool2d(2, 2) if stride == 2 else nn.Identity()
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def reset_parameters(self):
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trunc_normal_(self.qkv.weight, std=self.qkv.weight.shape[1] ** -0.5)
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trunc_normal_(self.relative_position_bias_table, std=.02)
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def forward(self, x):
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B, C, H, W = x.shape
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x = x.permute(0, 2, 3, 1)
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# cyclic shift
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if self.shift_size > 0:
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shifted_x = torch.roll(x, shifts=(-self.shift_size, -self.shift_size), dims=(1, 2))
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else:
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shifted_x = x
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# partition windows
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win_size_sq = self.win_size * self.win_size
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x_windows = window_partition(shifted_x, self.win_size) # num_win * B, window_size, window_size, C
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x_windows = x_windows.view(-1, win_size_sq, C) # num_win * B, window_size*window_size, C
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BW, N, _ = x_windows.shape
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qkv = self.qkv(x_windows)
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qkv = qkv.reshape(BW, N, 3, self.num_heads, self.dim_out // self.num_heads).permute(2, 0, 3, 1, 4)
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q, k, v = qkv[0], qkv[1], qkv[2]
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q = q * self.scale
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attn = torch.matmul(q, k.transpose(-2, -1))
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relative_position_bias = self.relative_position_bias_table[
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self.relative_position_index.view(-1)].view(win_size_sq, win_size_sq, -1)
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relative_position_bias = relative_position_bias.permute(2, 0, 1).contiguous() # nH, Wh * Ww, Wh * Ww
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attn = attn + relative_position_bias.unsqueeze(0)
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if self.attn_mask is not None:
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num_win = self.attn_mask.shape[0]
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attn = attn.view(B, num_win, self.num_heads, N, N) + self.attn_mask.unsqueeze(1).unsqueeze(0)
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attn = attn.view(-1, self.num_heads, N, N)
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attn = self.softmax(attn)
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attn = self.attn_drop(attn)
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x = torch.matmul(attn, v).transpose(1, 2).reshape(BW, N, self.dim_out)
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# merge windows
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x = x.view(-1, self.win_size, self.win_size, self.dim_out)
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shifted_x = window_reverse(x, self.win_size, H, W) # B H' W' C
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# reverse cyclic shift
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if self.shift_size > 0:
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x = torch.roll(shifted_x, shifts=(self.shift_size, self.shift_size), dims=(1, 2))
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else:
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x = shifted_x
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x = x.view(B, H, W, self.dim_out).permute(0, 3, 1, 2)
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x = self.pool(x)
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return x
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