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| """ | |
| 📌 MODEL DESIGNATION: | |
| Figure2CNN is validated ONLY for RAMAN spectra input. | |
| Any use for FTIR modeling is invalid and deprecated. | |
| See milestone: @figure2cnn-raman-only-milestone | |
| """ | |
| import torch | |
| import torch.nn as nn | |
| class ResidualBlock1D(nn.Module): | |
| """ | |
| Basic 1-D residual block: | |
| Conv1d -> ReLU -> Conv1d (+ skip connection). | |
| If channel count changes, a 1x1 Conv aligns the skip path. | |
| """ | |
| def __init__(self, in_channels: int, out_channels: int, kernel_size: int = 3): | |
| super().__init__() | |
| padding = kernel_size // 2 | |
| self.conv1 = nn.Conv1d(in_channels, out_channels, | |
| kernel_size, padding=padding) | |
| self.relu = nn.ReLU(inplace=True) | |
| self.conv2 = nn.Conv1d(out_channels, out_channels, | |
| kernel_size, padding=padding) | |
| self.skip = ( | |
| nn.Identity() | |
| if in_channels == out_channels | |
| else nn.Conv1d(in_channels, out_channels, kernel_size=1) | |
| ) | |
| def forward(self, x: torch.Tensor) -> torch.Tensor: | |
| identity = self.skip(x) | |
| out = self.relu(self.conv1(x)) | |
| out = self.conv2(out) | |
| return self.relu(out + identity) | |
| def describe_model(self): | |
| """Print architecture and flattened size (for debug). """ | |
| print(r"\n Model Summary:") | |
| print(r" - Conv Block: 4 Layers") | |
| print(f" - Input length: {self.flattened_size} after conv/pool") | |
| print(f" - Classifier: {self.classifier}\n") | |
| class ResNet1D(nn.Module): | |
| """ | |
| Lightweight 1-D ResNet for Raman spectra (length 500, single channel). | |
| """ | |
| def __init__(self, input_length: int = 500, num_classes: int = 2): | |
| super().__init__() | |
| # Three residual stages | |
| self.stage1 = ResidualBlock1D(1, 16) | |
| self.stage2 = ResidualBlock1D(16, 32) | |
| self.stage3 = ResidualBlock1D(32, 64) | |
| # Global aggregation + classifier | |
| self.global_pool = nn.AdaptiveAvgPool1d(1) # -> [B, 64, 1] | |
| self.fc = nn.Linear(64, num_classes) | |
| def forward(self, x: torch.Tensor) -> torch.Tensor: | |
| x = self.stage1(x) | |
| x = self.stage2(x) | |
| x = self.stage3(x) | |
| x = self.global_pool(x).squeeze(-1) # -> [B, 64] | |
| return self.fc(x) | |