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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 | |