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import torch
import torch.nn as nn
import torch.nn.functional as F
import numpy as np
class WindowAttention(nn.Module):
"""
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.
window_size (tuple[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
proj_drop (float, optional): Dropout ratio of output. Default: 0.0
pretrained_window_size (tuple[int]): The height and width of the
window in pre-training.
"""
def __init__(self,
dim,
window_size,
num_heads,
qkv_bias=True,
attn_drop=0.,
proj_drop=0.,
pretrained_window_size=[0, 0]):
super().__init__()
self.dim = dim
self.window_size = window_size # Wh, Ww
self.pretrained_window_size = pretrained_window_size
self.num_heads = num_heads
self.logit_scale = nn.Parameter(
torch.log(10 * torch.ones((num_heads, 1, 1))), requires_grad=True)
# mlp to generate continuous relative position bias
self.cpb_mlp = nn.Sequential(nn.Linear(2, 512, bias=True),
nn.ReLU(inplace=True),
nn.Linear(512, num_heads, bias=False))
# get relative_coords_table
relative_coords_h = torch.arange(
-(self.window_size[0] - 1),
self.window_size[0],
dtype=torch.float32)
relative_coords_w = torch.arange(
-(self.window_size[1] - 1),
self.window_size[1],
dtype=torch.float32)
# 1, 2*Wh-1, 2*Ww-1, 2
relative_coords_table = torch.stack(
torch.meshgrid(
[relative_coords_h,
relative_coords_w])).permute(1,
2,
0).contiguous().unsqueeze(0)
if pretrained_window_size[0] > 0:
relative_coords_table[:, :, :,
0] /= (pretrained_window_size[0] - 1)
relative_coords_table[:, :, :,
1] /= (pretrained_window_size[1] - 1)
else:
relative_coords_table[:, :, :, 0] /= (self.window_size[0] - 1)
relative_coords_table[:, :, :, 1] /= (self.window_size[1] - 1)
relative_coords_table *= 8 # normalize to -8, 8
relative_coords_table = torch.sign(relative_coords_table) * torch.log2(
torch.abs(relative_coords_table) + 1.0) / np.log2(8)
self.register_buffer("relative_coords_table", relative_coords_table)
# get pair-wise relative position index for each token inside
# the window
coords_h = torch.arange(self.window_size[0])
coords_w = torch.arange(self.window_size[1])
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.window_size[0] - \
1 # shift to start from 0
relative_coords[:, :, 1] += self.window_size[1] - 1
relative_coords[:, :, 0] *= 2 * self.window_size[1] - 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, dim * 3, bias=False)
if qkv_bias:
self.q_bias = nn.Parameter(torch.zeros(dim))
self.v_bias = nn.Parameter(torch.zeros(dim))
else:
self.q_bias = None
self.v_bias = None
self.attn_drop = nn.Dropout(attn_drop)
self.proj = nn.Linear(dim, dim)
self.proj_drop = nn.Dropout(proj_drop)
self.softmax = nn.Softmax(dim=-1)
def forward(self, x, mask=None):
"""
Args:
x: input features with shape of (num_windows*B, N, C)
mask: (0/-inf) mask with shape of (num_windows, Wh*Ww, Wh*Ww)
or None
"""
B_, N, C = x.shape
qkv_bias = None
if self.q_bias is not None:
qkv_bias = torch.cat((self.q_bias, torch.zeros_like(
self.v_bias, requires_grad=False), self.v_bias))
qkv = F.linear(input=x, weight=self.qkv.weight, bias=qkv_bias)
qkv = qkv.reshape(B_, N, 3, self.num_heads, -1).permute(2, 0, 3, 1, 4)
# make torchscript happy (cannot use tensor as tuple)
q, k, v = qkv[0], qkv[1], qkv[2]
# cosine attention
attn = (F.normalize(q, dim=-1) @
F.normalize(k, dim=-1).transpose(-2, -1))
# logit_scale = torch.clamp(
# self.logit_scale, max=torch.log(torch.tensor(1. / 0.01))).exp()
logit_scale = torch.clamp(self.logit_scale, max=torch.log(
torch.tensor(1. / 0.01))).exp() # .to(self.logit_scale.get_device())
attn = attn * logit_scale
relative_position_bias_table = self.cpb_mlp(
self.relative_coords_table).view(-1, self.num_heads)
relative_position_bias = \
relative_position_bias_table[
self.relative_position_index.view(-1)].view(
self.window_size[0] * self.window_size[1],
self.window_size[0] * self.window_size[1], -1)
# Wh*Ww,Wh*Ww,nH
relative_position_bias = relative_position_bias.permute(
2, 0, 1).contiguous() # nH, Wh*Ww, Wh*Ww
relative_position_bias = 16 * torch.sigmoid(relative_position_bias)
attn = attn + relative_position_bias.unsqueeze(0)
if mask is not None:
nW = mask.shape[0]
attn = attn.view(B_ // nW, nW, self.num_heads, N,
N) + mask.unsqueeze(1).unsqueeze(0)
attn = attn.view(-1, self.num_heads, N, N)
attn = self.softmax(attn)
else:
attn = self.softmax(attn)
attn = self.attn_drop(attn)
x = (attn @ v).transpose(1, 2).reshape(B_, N, C)
x = self.proj(x)
x = self.proj_drop(x)
return x
def extra_repr(self) -> str:
return f'dim={self.dim}, window_size={self.window_size}, ' \
f'pretrained_window_size={self.pretrained_window_size}, ' \
f'num_heads={self.num_heads}'
def flops(self, N):
# calculate flops for 1 window with token length of N
flops = 0
# qkv = self.qkv(x)
flops += N * self.dim * 3 * self.dim
# attn = (q @ k.transpose(-2, -1))
flops += self.num_heads * N * (self.dim // self.num_heads) * N
# x = (attn @ v)
flops += self.num_heads * N * N * (self.dim // self.num_heads)
# x = self.proj(x)
flops += N * self.dim * self.dim
return flops
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