model.py

"""DeepSeeNet model definition."""

from torch import Tensor, nn

try:
    import timm
except ImportError:  # pragma: no cover - handled when timm is absent.
    timm = None


class DeepSeeNet(nn.Module):
    """DeepSeeNet risk-factor classifier in PyTorch.

    Args:
        n_classes: Number of output classes.
        backbone: Any timm model name that supports ``num_classes=0``. The
            default uses InceptionV3.
        pretrained: Load ImageNet weights for the backbone.
        dropout: Dropout probability used by the classifier head.
        freeze_backbone: If true, keep the backbone frozen and train only the
            classifier head.
    """

    def __init__(
        self,
        n_classes: int = 2,
        backbone: str = "inception_v3",
        pretrained: bool = True,
        dropout: float = 0.5,
        freeze_backbone: bool = False,
    ) -> None:
        super().__init__()
        if n_classes < 1:
            raise ValueError("n_classes must be positive")
        if timm is None:
            raise ImportError("timm is required to build DeepSeeNet")

        self.backbone_name = backbone
        self.backbone = timm.create_model(
            backbone,
            pretrained=pretrained,
            num_classes=0,
            global_pool="avg",
        )
        in_features = self.backbone.num_features
        self.classifier = nn.Sequential(
            nn.Linear(in_features, 256),
            nn.ReLU(inplace=True),
            nn.Dropout(dropout),
            nn.Linear(256, 128),
            nn.ReLU(inplace=True),
            nn.Dropout(dropout),
            nn.Linear(128, n_classes),
        )

        if freeze_backbone:
            self.backbone.requires_grad_(False)

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