File size: 7,570 Bytes
bc30c66 f270b46 bc30c66 |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 |
import torch
import torch.nn as nn
import torch.nn.functional as F
import torchvision.models as models
# -------------------------------
# 1. SWIN WINDOW UTILS
# -------------------------------
def window_partition(x, window_size):
"""Partitions input tensor into windows of shape (B * num_windows, window_size*window_size, C)."""
B, H, W, C = x.shape
x = x.view(B, H // window_size, window_size, W // window_size, window_size, C)
x = x.permute(0, 1, 3, 2, 4, 5).contiguous()
windows = x.view(-1, window_size * window_size, C)
return windows
def window_reverse(windows, window_size, H, W):
"""Reverses the window partition operation."""
B = int(windows.shape[0] / (H * W / window_size / window_size))
x = windows.view(B, H // window_size, W // window_size, window_size, window_size, -1)
x = x.permute(0, 1, 3, 2, 4, 5).contiguous()
x = x.view(B, H, W, -1)
return x
# -------------------------------
# 2. SWIN WINDOW ATTENTION
# -------------------------------
class SwinWindowAttention(nn.Module):
"""Swin-style window attention block."""
def __init__(self, embed_dim, window_size, num_heads, dropout=0.0):
super(SwinWindowAttention, self).__init__()
self.embed_dim = embed_dim
self.window_size = window_size
self.num_heads = num_heads
self.mha = nn.MultiheadAttention(embed_dim, num_heads, dropout=dropout, batch_first=True)
self.dropout = nn.Dropout(dropout)
def forward(self, x):
"""Perform multi-head self-attn within windows."""
B, C, H, W = x.shape
x = x.permute(0, 2, 3, 1).contiguous()
pad_h = (self.window_size - H % self.window_size) % self.window_size
pad_w = (self.window_size - W % self.window_size) % self.window_size
if pad_h or pad_w:
x = F.pad(x, (0, 0, 0, pad_w, 0, pad_h))
Hp, Wp = x.shape[1], x.shape[2]
windows = window_partition(x, self.window_size) # (B*n_wins, win_size^2, C)
attn_windows, _ = self.mha(windows, windows, windows)
attn_windows = self.dropout(attn_windows)
x = window_reverse(attn_windows, self.window_size, Hp, Wp)
if pad_h or pad_w:
x = x[:, :H, :W, :].contiguous()
return x.permute(0, 3, 1, 2).contiguous()
# -------------------------------
# 3. GLAM
# -------------------------------
class GLAM(nn.Module):
"""Global-Local Attention Module (GLAM)."""
def __init__(self, in_channels, reduction_ratio=8):
super(GLAM, self).__init__()
# Local Channel Attention
self.local_channel_conv = nn.Conv2d(in_channels, in_channels // reduction_ratio, kernel_size=1)
self.local_channel_act = nn.Sigmoid()
self.local_channel_expand = nn.Conv2d(in_channels // reduction_ratio, in_channels, kernel_size=1)
# Local Spatial Attention
self.local_spatial_conv3 = nn.Conv2d(in_channels, in_channels, kernel_size=3, padding=3, dilation=3)
self.local_spatial_conv5 = nn.Conv2d(in_channels, in_channels, kernel_size=3, padding=5, dilation=5)
self.local_spatial_merge = nn.Conv2d(in_channels * 3, in_channels, kernel_size=1)
self.local_spatial_act = nn.Sigmoid()
# Global Channel Attention
self.global_avg_pool = nn.AdaptiveAvgPool2d(1)
self.global_channel_fc1 = nn.Linear(in_channels, in_channels // reduction_ratio)
self.global_channel_fc2 = nn.Linear(in_channels // reduction_ratio, in_channels)
self.global_channel_act = nn.Sigmoid()
# Global Spatial Attention
self.global_spatial_conv = nn.Conv2d(in_channels, 1, kernel_size=1)
self.global_spatial_softmax = nn.Softmax(dim=-1)
# Weights
self.local_attention_weight = nn.Parameter(torch.tensor(1.0))
self.global_attention_weight = nn.Parameter(torch.tensor(1.0))
def forward(self, x):
# Local Channel Attention
lca = self.local_channel_conv(x)
lca = self.local_channel_act(lca)
lca = self.local_channel_expand(lca)
lca_out = lca * x
# Local Spatial Attention
lsa3 = self.local_spatial_conv3(x)
lsa5 = self.local_spatial_conv5(x)
lsa_cat = torch.cat([x, lsa3, lsa5], dim=1)
lsa = self.local_spatial_merge(lsa_cat)
lsa = self.local_spatial_act(lsa)
lsa_out = lsa * lca_out
lsa_out = lsa_out + lca_out
# Global Channel Attention
B, C, H, W = x.size()
gca = self.global_avg_pool(x).view(B, C)
gca = F.relu(self.global_channel_fc1(gca), inplace=True)
gca = self.global_channel_fc2(gca)
gca = self.global_channel_act(gca)
gca = gca.view(B, C, 1, 1)
gca_out = gca * x
# Global Spatial Attention
gsa = self.global_spatial_conv(x) # [B, 1, H, W]
gsa = gsa.view(B, -1) # [B, H*W]
gsa = self.global_spatial_softmax(gsa)
gsa = gsa.view(B, 1, H, W)
gsa_out = gsa * gca_out
gsa_out = gsa_out + gca_out
# Final Fusion
out = lsa_out * self.local_attention_weight + gsa_out * self.global_attention_weight + x
return out
# -------------------------------
# 4. EFFICIENTNETB0_TRANSFORMERGLAM
# -------------------------------
class EfficientNetb0_TransformerGLAM(nn.Module):
"""EfficientNet-B0 + Swin-style Transformer + GLAM + Self-Adaptive Gating."""
def __init__(self,
num_classes=3,
embed_dim=512,
num_heads=8,
mlp_dim=512,
dropout=0.5,
window_size=7,
reduction_ratio=8):
super(EfficientNetb0_TransformerGLAM, self).__init__()
# EfficientNet Backbone
efficientnet = models.efficientnet_b0(weights=None)
self.feature_extractor = nn.Sequential(*list(efficientnet.features.children()))
self.conv1x1 = nn.Conv2d(1280, embed_dim, kernel_size=1)
# Transformer path
self.pre_attn_norm = nn.LayerNorm(embed_dim)
self.swin_attn = SwinWindowAttention(embed_dim, window_size, num_heads, dropout)
self.post_attn_norm = nn.LayerNorm(embed_dim)
# GLAM path
self.glam = GLAM(in_channels=embed_dim, reduction_ratio=reduction_ratio)
# Self-adaptive gating
self.gate_fc = nn.Linear(embed_dim, 1)
# Final classification
self.dropout = nn.Dropout(dropout)
self.fc = nn.Linear(embed_dim, num_classes)
def forward(self, x):
# Backbone
feats = self.feature_extractor(x) # [B, 1280, H', W']
feats = self.conv1x1(feats) # [B, embed_dim, H', W']
B, C, H, W = feats.shape
# Transformer path
x_perm = feats.permute(0, 2, 3, 1).contiguous()
x_norm = self.pre_attn_norm(x_perm) # LN
x_norm = x_norm.permute(0, 3, 1, 2).contiguous()
x_norm = self.dropout(x_norm)
T = self.swin_attn(x_norm)
T_perm = T.permute(0, 2, 3, 1).contiguous()
T_norm = self.post_attn_norm(T_perm) # LN
T_out = T_norm.permute(0, 3, 1, 2).contiguous()
# GLAM path
G_out = self.glam(feats)
# Gating
gap_feats = F.adaptive_avg_pool2d(feats, (1, 1)).view(B, C)
g = torch.sigmoid(self.gate_fc(gap_feats))
g = g.view(B, 1, 1, 1)
# Final Fusion
F_out = g * T_out + (1 - g) * G_out
pooled = F.adaptive_avg_pool2d(F_out, (1, 1)).view(B, -1)
return self.fc(pooled)
|