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import math
from math import prod
import torch
import torch.nn as nn
import torch.nn.functional as F
from architecture.grl_common.ops import (
calculate_mask,
get_relative_coords_table,
get_relative_position_index,
window_partition,
window_reverse,
)
from architecture.grl_common.swin_v1_block import Mlp
from timm.models.layers import DropPath, to_2tuple
class WindowAttentionV2(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.
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=0.0,
proj_drop=0.0,
pretrained_window_size=[0, 0],
use_pe=True,
):
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.use_pe = use_pe
self.logit_scale = nn.Parameter(
torch.log(10 * torch.ones((num_heads, 1, 1))), requires_grad=True
)
if self.use_pe:
# 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),
)
table = get_relative_coords_table(window_size, pretrained_window_size)
index = get_relative_position_index(window_size)
self.register_buffer("relative_coords_table", table)
self.register_buffer("relative_position_index", index)
self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)
# 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 projection
# 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 = self.qkv(x)
qkv = qkv.reshape(B_, N, 3, self.num_heads, -1).permute(2, 0, 3, 1, 4)
q, k, v = qkv[0], qkv[1], qkv[2]
# cosine attention map
attn = F.normalize(q, dim=-1) @ F.normalize(k, dim=-1).transpose(-2, -1)
logit_scale = torch.clamp(self.logit_scale, max=math.log(1.0 / 0.01)).exp()
attn = attn * logit_scale
# positional encoding
if self.use_pe:
bias_table = self.cpb_mlp(self.relative_coords_table)
bias_table = bias_table.view(-1, self.num_heads)
win_dim = prod(self.window_size)
bias = bias_table[self.relative_position_index.view(-1)]
bias = bias.view(win_dim, win_dim, -1).permute(2, 0, 1).contiguous()
# nH, Wh*Ww, Wh*Ww
bias = 16 * torch.sigmoid(bias)
attn = attn + bias.unsqueeze(0)
# shift attention mask
if mask is not None:
nW = mask.shape[0]
mask = mask.unsqueeze(1).unsqueeze(0)
attn = attn.view(B_ // nW, nW, self.num_heads, N, N) + mask
attn = attn.view(-1, self.num_heads, N, N)
# attention
attn = self.softmax(attn)
attn = self.attn_drop(attn)
x = (attn @ v).transpose(1, 2).reshape(B_, N, C)
# output projection
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}, 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
class WindowAttentionWrapperV2(WindowAttentionV2):
def __init__(self, shift_size, input_resolution, **kwargs):
super(WindowAttentionWrapperV2, self).__init__(**kwargs)
self.shift_size = shift_size
self.input_resolution = input_resolution
if self.shift_size > 0:
attn_mask = calculate_mask(input_resolution, self.window_size, shift_size)
else:
attn_mask = None
self.register_buffer("attn_mask", attn_mask)
def forward(self, x, x_size):
H, W = x_size
B, L, C = x.shape
x = x.view(B, H, W, C)
# cyclic shift
if self.shift_size > 0:
x = torch.roll(x, shifts=(-self.shift_size, -self.shift_size), dims=(1, 2))
# partition windows
x = window_partition(x, self.window_size) # nW*B, wh, ww, C
x = x.view(-1, prod(self.window_size), C) # nW*B, wh*ww, C
# W-MSA/SW-MSA
if self.input_resolution == x_size:
attn_mask = self.attn_mask
else:
attn_mask = calculate_mask(x_size, self.window_size, self.shift_size)
attn_mask = attn_mask.to(x.device)
# attention
x = super(WindowAttentionWrapperV2, self).forward(x, mask=attn_mask)
# nW*B, wh*ww, C
# merge windows
x = x.view(-1, *self.window_size, C)
x = window_reverse(x, self.window_size, x_size) # B, H, W, C
# reverse cyclic shift
if self.shift_size > 0:
x = torch.roll(x, shifts=(self.shift_size, self.shift_size), dims=(1, 2))
x = x.view(B, H * W, C)
return x
class SwinTransformerBlockV2(nn.Module):
r"""Swin Transformer Block.
Args:
dim (int): Number of input channels.
input_resolution (tuple[int]): Input resulotion.
num_heads (int): Number of attention heads.
window_size (int): Window size.
shift_size (int): Shift size for SW-MSA.
mlp_ratio (float): Ratio of mlp hidden dim to embedding dim.
qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True
drop (float, optional): Dropout rate. Default: 0.0
attn_drop (float, optional): Attention dropout rate. Default: 0.0
drop_path (float, optional): Stochastic depth rate. Default: 0.0
act_layer (nn.Module, optional): Activation layer. Default: nn.GELU
norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm
pretrained_window_size (int): Window size in pre-training.
"""
def __init__(
self,
dim,
input_resolution,
num_heads,
window_size=7,
shift_size=0,
mlp_ratio=4.0,
qkv_bias=True,
drop=0.0,
attn_drop=0.0,
drop_path=0.0,
act_layer=nn.GELU,
norm_layer=nn.LayerNorm,
pretrained_window_size=0,
use_pe=True,
res_scale=1.0,
):
super().__init__()
self.dim = dim
self.input_resolution = input_resolution
self.num_heads = num_heads
self.window_size = window_size
self.shift_size = shift_size
self.mlp_ratio = mlp_ratio
if min(self.input_resolution) <= self.window_size:
# if window size is larger than input resolution, we don't partition windows
self.shift_size = 0
self.window_size = min(self.input_resolution)
assert (
0 <= self.shift_size < self.window_size
), "shift_size must in 0-window_size"
self.res_scale = res_scale
self.attn = WindowAttentionWrapperV2(
shift_size=self.shift_size,
input_resolution=self.input_resolution,
dim=dim,
window_size=to_2tuple(self.window_size),
num_heads=num_heads,
qkv_bias=qkv_bias,
attn_drop=attn_drop,
proj_drop=drop,
pretrained_window_size=to_2tuple(pretrained_window_size),
use_pe=use_pe,
)
self.norm1 = norm_layer(dim)
self.drop_path = DropPath(drop_path) if drop_path > 0.0 else nn.Identity()
self.mlp = Mlp(
in_features=dim,
hidden_features=int(dim * mlp_ratio),
act_layer=act_layer,
drop=drop,
)
self.norm2 = norm_layer(dim)
def forward(self, x, x_size):
# Window attention
x = x + self.res_scale * self.drop_path(self.norm1(self.attn(x, x_size)))
# FFN
x = x + self.res_scale * self.drop_path(self.norm2(self.mlp(x)))
return x
def extra_repr(self) -> str:
return (
f"dim={self.dim}, input_resolution={self.input_resolution}, num_heads={self.num_heads}, "
f"window_size={self.window_size}, shift_size={self.shift_size}, mlp_ratio={self.mlp_ratio}, res_scale={self.res_scale}"
)
def flops(self):
flops = 0
H, W = self.input_resolution
# norm1
flops += self.dim * H * W
# W-MSA/SW-MSA
nW = H * W / self.window_size / self.window_size
flops += nW * self.attn.flops(self.window_size * self.window_size)
# mlp
flops += 2 * H * W * self.dim * self.dim * self.mlp_ratio
# norm2
flops += self.dim * H * W
return flops
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