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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 Figure2CNN(nn.Module):
"""
CNN architecture based on Figure 2 of the referenced research paper.
Designed for 1D spectral data input of length 500
"""
def __init__(self, input_length=500, input_channels=1):
super(Figure2CNN, self).__init__()
self.input_channels = input_channels
self.conv_block = nn.Sequential(
nn.Conv1d(input_channels, 16, kernel_size=5, padding=2),
nn.ReLU(),
nn.MaxPool1d(kernel_size=2),
nn.Conv1d(16, 32, kernel_size=5, padding=2),
nn.ReLU(),
nn.MaxPool1d(kernel_size=2),
nn.Conv1d(32, 64, kernel_size=5, padding=2),
nn.ReLU(),
nn.MaxPool1d(kernel_size=2),
nn.Conv1d(64, 128, kernel_size=5, padding=2),
nn.ReLU(),
nn.MaxPool1d(kernel_size=2),
)
# Dynamically calculate flattened size after conv + pooling
self.flattened_size = self._get_flattened_size(input_channels, input_length)
self.classifier = nn.Sequential(
nn.Linear(self.flattened_size, 256),
nn.ReLU(),
nn.Linear(256, 128),
nn.ReLU(),
nn.Linear(128, 2) # Binary output
)
def _get_flattened_size(self,input_channels, input_length):
with torch.no_grad():
dummy_input = torch.zeros(1, input_channels, input_length)
out = self.conv_block(dummy_input)
return out.view(1, -1).shape[1]
def forward(self, x):
"""
Defines the forward pass of the Figure2CNN model.
Args:
x (torch.Tensor): Input tensor of shape (batch_size, channels, input_length).
Returns:
torch.Tensor: Output tensor containing class scores.
"""
x = self.conv_block(x)
x = x.view(x.size(0), -1) # Flatten
return self.classifier(x)
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")
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