Spaces:
Sleeping
Sleeping
File size: 8,089 Bytes
65d7391 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 |
import torch
import torch.nn as nn
from timm.models.layers import trunc_normal_
import math
# Feature Rectify Module
class ChannelWeights(nn.Module):
def __init__(self, dim, reduction=1):
super(ChannelWeights, self).__init__()
self.dim = dim
self.avg_pool = nn.AdaptiveAvgPool2d(1)
self.max_pool = nn.AdaptiveMaxPool2d(1)
self.mlp = nn.Sequential(
nn.Linear(self.dim * 4, self.dim * 4 // reduction),
nn.ReLU(inplace=True),
nn.Linear(self.dim * 4 // reduction, self.dim * 2),
nn.Sigmoid())
def forward(self, x1, x2):
B, _, H, W = x1.shape
x = torch.cat((x1, x2), dim=1)
avg = self.avg_pool(x).view(B, self.dim * 2)
max = self.max_pool(x).view(B, self.dim * 2)
y = torch.cat((avg, max), dim=1) # B 4C
y = self.mlp(y).view(B, self.dim * 2, 1)
channel_weights = y.reshape(B, 2, self.dim, 1, 1).permute(1, 0, 2, 3, 4) # 2 B C 1 1
return channel_weights
class SpatialWeights(nn.Module):
def __init__(self, dim, reduction=1):
super(SpatialWeights, self).__init__()
self.dim = dim
self.mlp = nn.Sequential(
nn.Conv2d(self.dim * 2, self.dim // reduction, kernel_size=1),
nn.ReLU(inplace=True),
nn.Conv2d(self.dim // reduction, 2, kernel_size=1),
nn.Sigmoid())
def forward(self, x1, x2):
B, _, H, W = x1.shape
x = torch.cat((x1, x2), dim=1) # B 2C H W
spatial_weights = self.mlp(x).reshape(B, 2, 1, H, W).permute(1, 0, 2, 3, 4) # 2 B 1 H W
return spatial_weights
class FeatureRectifyModule(nn.Module):
def __init__(self, dim, reduction=1, lambda_c=.5, lambda_s=.5):
super(FeatureRectifyModule, self).__init__()
self.lambda_c = lambda_c
self.lambda_s = lambda_s
self.channel_weights = ChannelWeights(dim=dim, reduction=reduction)
self.spatial_weights = SpatialWeights(dim=dim, reduction=reduction)
def _init_weights(self, m):
if isinstance(m, nn.Linear):
trunc_normal_(m.weight, std=.02)
if isinstance(m, nn.Linear) and m.bias is not None:
nn.init.constant_(m.bias, 0)
elif isinstance(m, nn.LayerNorm):
nn.init.constant_(m.bias, 0)
nn.init.constant_(m.weight, 1.0)
elif isinstance(m, nn.Conv2d):
fan_out = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
fan_out //= m.groups
m.weight.data.normal_(0, math.sqrt(2.0 / fan_out))
if m.bias is not None:
m.bias.data.zero_()
def forward(self, x1, x2):
channel_weights = self.channel_weights(x1, x2)
spatial_weights = self.spatial_weights(x1, x2)
out_x1 = x1 + self.lambda_c * channel_weights[1] * x2 + self.lambda_s * spatial_weights[1] * x2
out_x2 = x2 + self.lambda_c * channel_weights[0] * x1 + self.lambda_s * spatial_weights[0] * x1
return out_x1, out_x2
# Stage 1
class CrossAttention(nn.Module):
def __init__(self, dim, num_heads=8, qkv_bias=False, qk_scale=None):
super(CrossAttention, self).__init__()
assert dim % num_heads == 0, f"dim {dim} should be divided by num_heads {num_heads}."
self.dim = dim
self.num_heads = num_heads
head_dim = dim // num_heads
self.scale = qk_scale or head_dim ** -0.5
self.kv1 = nn.Linear(dim, dim * 2, bias=qkv_bias)
self.kv2 = nn.Linear(dim, dim * 2, bias=qkv_bias)
def forward(self, x1, x2):
B, N, C = x1.shape
q1 = x1.reshape(B, -1, self.num_heads, C // self.num_heads).permute(0, 2, 1, 3).contiguous()
q2 = x2.reshape(B, -1, self.num_heads, C // self.num_heads).permute(0, 2, 1, 3).contiguous()
k1, v1 = self.kv1(x1).reshape(B, -1, 2, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4).contiguous()
k2, v2 = self.kv2(x2).reshape(B, -1, 2, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4).contiguous()
# q,k,v B H N C
ctx1 = (k1.transpose(-2, -1) @ v1) * self.scale # B H C C
ctx1 = ctx1.softmax(dim=-2)
ctx2 = (k2.transpose(-2, -1) @ v2) * self.scale # B H C C
ctx2 = ctx2.softmax(dim=-2)
x1 = (q1 @ ctx2).permute(0, 2, 1, 3).reshape(B, N, C).contiguous()
x2 = (q2 @ ctx1).permute(0, 2, 1, 3).reshape(B, N, C).contiguous()
return x1, x2
class CrossPath(nn.Module):
def __init__(self, dim, reduction=1, num_heads=None, norm_layer=nn.LayerNorm):
super().__init__()
self.channel_proj1 = nn.Linear(dim, dim // reduction * 2)
self.channel_proj2 = nn.Linear(dim, dim // reduction * 2)
self.act1 = nn.ReLU(inplace=True)
self.act2 = nn.ReLU(inplace=True)
self.cross_attn = CrossAttention(dim // reduction, num_heads=num_heads)
self.end_proj1 = nn.Linear(dim // reduction * 2, dim)
self.end_proj2 = nn.Linear(dim // reduction * 2, dim)
self.norm1 = norm_layer(dim)
self.norm2 = norm_layer(dim)
def forward(self, x1, x2):
y1, u1 = self.act1(self.channel_proj1(x1)).chunk(2, dim=-1)
y2, u2 = self.act2(self.channel_proj2(x2)).chunk(2, dim=-1)
v1, v2 = self.cross_attn(u1, u2)
y1 = torch.cat((y1, v1), dim=-1)
y2 = torch.cat((y2, v2), dim=-1)
out_x1 = self.norm1(x1 + self.end_proj1(y1))
out_x2 = self.norm2(x2 + self.end_proj2(y2))
return out_x1, out_x2
# Stage 2
class ChannelEmbed(nn.Module):
def __init__(self, in_channels, out_channels, reduction=1, norm_layer=nn.BatchNorm2d):
super(ChannelEmbed, self).__init__()
self.out_channels = out_channels
self.residual = nn.Conv2d(in_channels, out_channels, kernel_size=1, bias=False)
self.channel_embed = nn.Sequential(
nn.Conv2d(in_channels, out_channels//reduction, kernel_size=1, bias=True),
nn.Conv2d(out_channels//reduction, out_channels//reduction, kernel_size=3, stride=1, padding=1, bias=True, groups=out_channels//reduction),
nn.ReLU(inplace=True),
nn.Conv2d(out_channels//reduction, out_channels, kernel_size=1, bias=True),
norm_layer(out_channels)
)
self.norm = norm_layer(out_channels)
def forward(self, x, H, W):
B, N, _C = x.shape
x = x.permute(0, 2, 1).reshape(B, _C, H, W).contiguous()
residual = self.residual(x)
x = self.channel_embed(x)
out = self.norm(residual + x)
return out
class FeatureFusionModule(nn.Module):
def __init__(self, dim, reduction=1, num_heads=None, norm_layer=nn.BatchNorm2d):
super().__init__()
self.cross = CrossPath(dim=dim, reduction=reduction, num_heads=num_heads)
self.channel_emb = ChannelEmbed(in_channels=dim*2, out_channels=dim, reduction=reduction, norm_layer=norm_layer)
self.apply(self._init_weights)
def _init_weights(self, m):
if isinstance(m, nn.Linear):
trunc_normal_(m.weight, std=.02)
if isinstance(m, nn.Linear) and m.bias is not None:
nn.init.constant_(m.bias, 0)
elif isinstance(m, nn.LayerNorm):
nn.init.constant_(m.bias, 0)
nn.init.constant_(m.weight, 1.0)
elif isinstance(m, nn.Conv2d):
fan_out = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
fan_out //= m.groups
m.weight.data.normal_(0, math.sqrt(2.0 / fan_out))
if m.bias is not None:
m.bias.data.zero_()
def forward(self, x1, x2):
B, C, H, W = x1.shape
x1 = x1.flatten(2).transpose(1, 2)
x2 = x2.flatten(2).transpose(1, 2)
x1, x2 = self.cross(x1, x2)
merge = torch.cat((x1, x2), dim=-1)
merge = self.channel_emb(merge, H, W)
return merge
|