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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)
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