File size: 9,336 Bytes
00801a0 | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 | """
Backbone feature extractor for ACL-LKNet.
Uses timm for standard pretrained backbones (ConvNeXt, ResNet, EfficientNet).
Includes optional large-kernel modification for kernel-size ablation.
Design decisions:
- ConvNeXt-Tiny is the default (28M params, 7×7 DW conv, T4-friendly)
- ResNet-50 included for RadImageNet comparison (RadImageNet provides ResNet50 weights)
- Large-kernel variant replaces ConvNeXt DW convolutions with larger kernels
- All backbones output a single feature vector per input image via global avg pool
"""
import torch
import torch.nn as nn
import torch.nn.functional as F
import timm
from torch.utils.checkpoint import checkpoint as grad_checkpoint
class BackboneWrapper(nn.Module):
"""
Wraps a timm backbone to:
1. Accept 1-channel grayscale input (converts to 3-channel)
2. Strip the classification head
3. Return a feature vector via global average pooling
4. Support gradient checkpointing for T4 memory
"""
def __init__(
self,
name: str = "convnext_tiny",
pretrained: bool = True,
use_grad_checkpoint: bool = True,
):
super().__init__()
self.name = name
self.use_grad_checkpoint = use_grad_checkpoint
# Create backbone from timm (no classification head)
self.backbone = timm.create_model(
name,
pretrained=pretrained,
num_classes=0, # Remove classifier → feature extractor
global_pool="avg", # Global average pooling
)
self.feature_dim = self.backbone.num_features
# 1-channel → 3-channel adapter
# We use a lightweight conv instead of simple replication so the model
# can learn an optimal channel mapping for grayscale MRI
self.channel_adapter = nn.Sequential(
nn.Conv2d(1, 3, kernel_size=1, bias=False),
nn.BatchNorm2d(3),
)
# Initialize adapter to approximate channel replication
nn.init.constant_(self.channel_adapter[0].weight, 1.0 / 3.0)
if use_grad_checkpoint and hasattr(self.backbone, "set_grad_checkpointing"):
self.backbone.set_grad_checkpointing(enable=True)
def forward(self, x: torch.Tensor) -> torch.Tensor:
"""
Args:
x: (B, 1, H, W) grayscale MRI slices
Returns:
features: (B, D) feature vectors
"""
# Grayscale → 3-channel
x = self.channel_adapter(x)
# Extract features
features = self.backbone(x)
return features
class LargeKernelBlock(nn.Module):
"""
Large-kernel depth-wise convolution block inspired by RepLKNet.
Uses depth-wise separable convolution with a large kernel,
plus SE (Squeeze-and-Excitation) channel attention.
For efficiency, large DW convolutions are decomposed into:
depth-wise conv (large kernel) + point-wise conv (1×1)
The large DW conv has very few parameters (kernel_size² × channels).
"""
def __init__(self, dim: int, kernel_size: int = 31, drop_path: float = 0.0):
super().__init__()
padding = kernel_size // 2
self.norm = nn.BatchNorm2d(dim)
# Large-kernel depth-wise convolution
self.dw_conv = nn.Conv2d(
dim, dim, kernel_size=kernel_size, padding=padding,
groups=dim, bias=False
)
# SE attention
self.se = nn.Sequential(
nn.AdaptiveAvgPool2d(1),
nn.Flatten(),
nn.Linear(dim, dim // 4),
nn.GELU(),
nn.Linear(dim // 4, dim),
nn.Sigmoid(),
)
# Point-wise (1×1) expansion
self.pw_conv1 = nn.Conv2d(dim, dim * 4, kernel_size=1)
self.act = nn.GELU()
self.pw_conv2 = nn.Conv2d(dim * 4, dim, kernel_size=1)
# Stochastic depth
self.drop_path = DropPath(drop_path) if drop_path > 0.0 else nn.Identity()
def forward(self, x: torch.Tensor) -> torch.Tensor:
residual = x
x = self.norm(x)
x = self.dw_conv(x)
# SE attention
se_weight = self.se(x).unsqueeze(-1).unsqueeze(-1)
x = x * se_weight
# FFN
x = self.pw_conv1(x)
x = self.act(x)
x = self.pw_conv2(x)
x = self.drop_path(x) + residual
return x
class DropPath(nn.Module):
"""Stochastic depth — drops entire residual branches during training."""
def __init__(self, drop_prob: float = 0.0):
super().__init__()
self.drop_prob = drop_prob
def forward(self, x: torch.Tensor) -> torch.Tensor:
if not self.training or self.drop_prob == 0.0:
return x
keep_prob = 1 - self.drop_prob
shape = (x.shape[0],) + (1,) * (x.ndim - 1)
mask = torch.empty(shape, device=x.device).bernoulli_(keep_prob)
return x * mask / keep_prob
class LKNetBackbone(nn.Module):
"""
Custom Large-Kernel Network backbone for kernel-size ablation.
4-stage hierarchical design with configurable kernel sizes per stage.
Used when we need to isolate the effect of kernel size independently
of the backbone architecture (ConvNeXt vs ResNet, etc.).
Stage dims: [64, 128, 256, 512]
Stage depths: [2, 2, 6, 2]
Default kernels: [7, 13, 21, 31]
"""
def __init__(
self,
in_channels: int = 1,
dims: list = None,
depths: list = None,
kernel_sizes: list = None,
drop_path_rate: float = 0.1,
):
super().__init__()
dims = dims or [64, 128, 256, 512]
depths = depths or [2, 2, 6, 2]
kernel_sizes = kernel_sizes or [7, 13, 21, 31]
self.feature_dim = dims[-1]
# Stem: 4× downsampling with small kernels (stable)
self.stem = nn.Sequential(
nn.Conv2d(in_channels, dims[0], kernel_size=4, stride=4),
nn.BatchNorm2d(dims[0]),
)
# Build stages
self.stages = nn.ModuleList()
dp_rates = [x.item() for x in torch.linspace(0, drop_path_rate, sum(depths))]
cur = 0
for i in range(4):
# Downsampling between stages (except first)
if i > 0:
downsample = nn.Sequential(
nn.BatchNorm2d(dims[i - 1]),
nn.Conv2d(dims[i - 1], dims[i], kernel_size=2, stride=2),
)
else:
downsample = nn.Identity()
# Stack of LK blocks
blocks = nn.Sequential(*[
LargeKernelBlock(dims[i], kernel_sizes[i], dp_rates[cur + j])
for j in range(depths[i])
])
cur += depths[i]
self.stages.append(nn.Sequential(downsample, blocks))
self.norm = nn.LayerNorm(dims[-1])
self.pool = nn.AdaptiveAvgPool2d(1)
def forward(self, x: torch.Tensor) -> torch.Tensor:
"""
Args:
x: (B, C, H, W)
Returns:
features: (B, feature_dim)
"""
x = self.stem(x)
for stage in self.stages:
x = stage(x)
x = self.pool(x).flatten(1)
x = self.norm(x)
return x
def create_backbone(
name: str = "convnext_tiny",
pretrained: bool = True,
use_grad_checkpoint: bool = True,
use_large_kernels: bool = False,
large_kernel_sizes: list = None,
) -> nn.Module:
"""
Factory function to create a backbone feature extractor.
Args:
name: Backbone name ('convnext_tiny', 'resnet18', 'resnet50',
'efficientnet_b0', 'lknet')
pretrained: Load ImageNet pretrained weights (for timm models)
use_grad_checkpoint: Enable gradient checkpointing
use_large_kernels: Replace DW convolutions with larger kernels
large_kernel_sizes: Kernel sizes per stage [s1, s2, s3, s4]
Returns:
backbone: nn.Module with .feature_dim attribute
"""
if name == "lknet":
# Custom LK backbone (no pretrained weights — train from scratch or MSM)
backbone = LKNetBackbone(
in_channels=1,
kernel_sizes=large_kernel_sizes or [7, 13, 21, 31],
)
return backbone
# Standard timm backbone
backbone = BackboneWrapper(
name=name,
pretrained=pretrained,
use_grad_checkpoint=use_grad_checkpoint,
)
return backbone
def load_radimagenet_weights(model: nn.Module, weights_path: str):
"""
Load RadImageNet pretrained weights into a ResNet/DenseNet backbone.
RadImageNet provides weights for ResNet50, DenseNet121, InceptionV3.
These are loaded into the backbone's internal model.
Args:
model: BackboneWrapper with a timm ResNet50 backbone
weights_path: Path to RadImageNet .pt/.h5 weights file
"""
state_dict = torch.load(weights_path, map_location="cpu", weights_only=True)
# RadImageNet weights may have different key names — attempt flexible loading
model_dict = model.backbone.state_dict()
filtered = {k: v for k, v in state_dict.items() if k in model_dict and v.shape == model_dict[k].shape}
model_dict.update(filtered)
model.backbone.load_state_dict(model_dict, strict=False)
print(f"Loaded {len(filtered)}/{len(model_dict)} layers from RadImageNet weights")
|