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# models/resnet18_vision.py
# 1D ResNet-18 style model for spectra: input (B, 1, L)
import torch
import torch.nn as nn
from typing import Callable, List
class BasicBlock1D(nn.Module):
expansion = 1
def __init__(self, in_planes: int, planes: int, stride: int = 1, downsample: nn.Module | None = None):
super().__init__()
self.conv1 = nn.Conv1d(in_planes, planes, kernel_size=3, stride=stride, padding=1, bias=False)
self.bn1 = nn.BatchNorm1d(planes)
self.relu = nn.ReLU(inplace=True)
self.conv2 = nn.Conv1d(planes, planes, kernel_size=3, stride=1, padding=1, bias=False)
self.bn2 = nn.BatchNorm1d(planes)
self.downsample = downsample
def forward(self, x: torch.Tensor) -> torch.Tensor:
identity = x
out = self.relu(self.bn1(self.conv1(x)))
out = self.bn2(self.conv2(out))
if self.downsample is not None:
identity = self.downsample(x)
out += identity
out = self.relu(out)
return out
def _make_layer(block: Callable[..., nn.Module], in_planes: int, planes: int, blocks: int, stride: int) -> nn.Sequential:
downsample = None
if stride != 1 or in_planes != planes * block.expansion:
downsample = nn.Sequential(
nn.Conv1d(in_planes, planes * block.expansion, kernel_size=1, stride=stride, bias=False),
nn.BatchNorm1d(planes * block.expansion),
)
layers: List[nn.Module] = [block(in_planes, planes, stride, downsample)]
in_planes = planes * block.expansion
for _ in range(1, blocks):
layers.append(block(in_planes, planes))
return nn.Sequential(*layers)
class ResNet18Vision(nn.Module):
def __init__(self, input_length: int = 500, num_classes: int = 2):
super().__init__()
# 1D stem
self.conv1 = nn.Conv1d(1, 64, kernel_size=7, stride=2, padding=3, bias=False)
self.bn1 = nn.BatchNorm1d(64)
self.relu = nn.ReLU(inplace=True)
self.maxpool = nn.MaxPool1d(kernel_size=3, stride=2, padding=1)
# ResNet-18: 2 blocks per layer
self.layer1 = _make_layer(BasicBlock1D, 64, 64, blocks=2, stride=1)
self.layer2 = _make_layer(BasicBlock1D, 64, 128, blocks=2, stride=2)
self.layer3 = _make_layer(BasicBlock1D, 128, 256, blocks=2, stride=2)
self.layer4 = _make_layer(BasicBlock1D, 256, 512, blocks=2, stride=2)
# Global pooling + classifier
self.avgpool = nn.AdaptiveAvgPool1d(1)
self.fc = nn.Linear(512 * BasicBlock1D.expansion, num_classes)
# Kaiming init
for m in self.modules():
if isinstance(m, nn.Conv1d):
nn.init.kaiming_normal_(m.weight, mode="fan_out", nonlinearity="relu")
elif isinstance(m, (nn.BatchNorm1d, nn.GroupNorm)):
nn.init.ones_(m.weight); nn.init.zeros_(m.bias)
def forward(self, x: torch.Tensor) -> torch.Tensor:
# x: (B, 1, L)
x = self.relu(self.bn1(self.conv1(x)))
x = self.maxpool(x)
x = self.layer1(x)
x = self.layer2(x)
x = self.layer3(x)
x = self.layer4(x)
x = self.avgpool(x) # (B, C, 1)
x = torch.flatten(x, 1) # (B, C)
x = self.fc(x) # (B, num_classes)
return x
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