File size: 2,955 Bytes
93cf011 | 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 | import torch
import torch.nn as nn
from torchvision import models
class ChannelAttention(nn.Module):
def __init__(self, channels: int, reduction: int = 16):
super().__init__()
hidden = max(channels // reduction, 16)
self.avg_pool = nn.AdaptiveAvgPool2d(1)
self.max_pool = nn.AdaptiveMaxPool2d(1)
self.mlp = nn.Sequential(
nn.Conv2d(channels, hidden, kernel_size=1, bias=False),
nn.ReLU(inplace=True),
nn.Conv2d(hidden, channels, kernel_size=1, bias=False),
)
self.sigmoid = nn.Sigmoid()
def forward(self, x):
attn = self.mlp(self.avg_pool(x)) + self.mlp(self.max_pool(x))
return x * self.sigmoid(attn)
class SpatialAttention(nn.Module):
def __init__(self, kernel_size: int = 7):
super().__init__()
padding = kernel_size // 2
self.conv = nn.Conv2d(2, 1, kernel_size=kernel_size, padding=padding, bias=False)
self.sigmoid = nn.Sigmoid()
def forward(self, x):
avg_map = torch.mean(x, dim=1, keepdim=True)
max_map, _ = torch.max(x, dim=1, keepdim=True)
attn = torch.cat([avg_map, max_map], dim=1)
attn = self.sigmoid(self.conv(attn))
return x * attn
class CBAMBlock(nn.Module):
def __init__(self, channels: int, reduction: int = 16, kernel_size: int = 7):
super().__init__()
self.ca = ChannelAttention(channels, reduction)
self.sa = SpatialAttention(kernel_size)
def forward(self, x):
return self.sa(self.ca(x))
class ConvNeXtWithCBAM(nn.Module):
def __init__(self, backbone: nn.Module, n_class: int, dropout: float = 0.65):
super().__init__()
self.features = backbone.features
self.avgpool = backbone.avgpool
self.norm = backbone.classifier[0]
out_channels = backbone.classifier[2].in_features
self.attn = CBAMBlock(out_channels, reduction=16, kernel_size=7)
self.head = nn.Sequential(
nn.Flatten(1),
nn.Dropout(p=dropout),
nn.Linear(out_channels, n_class),
)
def forward(self, x):
x = self.features(x)
x = self.attn(x)
x = self.avgpool(x)
x = self.norm(x)
x = self.head(x)
return x
def set_convnext_stochastic_depth(model: nn.Module, p: float = 0.2):
for m in model.modules():
if hasattr(m, "stochastic_depth") and hasattr(m.stochastic_depth, "p"):
m.stochastic_depth.p = p
def build_convnext_cbam(
n_class: int,
dropout: float = 0.65,
sd_prob: float = 0.2,
weights=models.ConvNeXt_Small_Weights.IMAGENET1K_V1,
):
backbone = models.convnext_small(weights=weights)
set_convnext_stochastic_depth(backbone, p=sd_prob)
model = ConvNeXtWithCBAM(backbone=backbone, n_class=n_class, dropout=dropout)
return model |