""" 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))
# 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)
trunc_normal_(self.relative_position_bias_table, std=.02)
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 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