ml/models/vision/texture_classifier.py

# Optional CNN texture classifier (e.g. corrugation) if a labeled class is required.

import torch
import torch.nn as nn
from ml.core.registry import register_model


@register_model("texture_classifier")
class RailTextureClassifier(nn.Module):
    """Compact convolutional neural network for railhead texture & corrugation pattern classification."""

    def __init__(self, num_classes: int = 4):
        super().__init__()
        self.features = nn.Sequential(
            nn.Conv2d(3, 32, kernel_size=3, padding=1),
            nn.BatchNorm2d(32),
            nn.ReLU(inplace=True),
            nn.MaxPool2d(2, 2),
            nn.Conv2d(32, 64, kernel_size=3, padding=1),
            nn.BatchNorm2d(64),
            nn.ReLU(inplace=True),
            nn.AdaptiveAvgPool2d((1, 1)),
        )
        self.classifier = nn.Sequential(
            nn.Flatten(),
            nn.Linear(64, 32),
            nn.ReLU(inplace=True),
            nn.Linear(32, num_classes),
        )

    def forward(self, x: torch.Tensor) -> torch.Tensor:
        feat = self.features(x)
        logits = self.classifier(feat)
        return logits