| """Small scratch-only CNN used for both DeepWeeds comparison runs.""" | |
| from __future__ import annotations | |
| from torch import Tensor, nn | |
| class SmallDeepWeedsCNN(nn.Module): | |
| """Three-block CNN with no pretrained parameters or regularization layers.""" | |
| provenance = "scratch" | |
| def __init__(self, num_classes: int = 9, in_channels: int = 3) -> None: | |
| super().__init__() | |
| if num_classes <= 0: | |
| raise ValueError("num_classes must be positive") | |
| if in_channels not in (1, 3): | |
| raise ValueError("in_channels must be 1 (grayscale) or 3 (RGB)") | |
| self.features = nn.Sequential( | |
| nn.Conv2d(in_channels, 16, kernel_size=3, padding=1), | |
| nn.ReLU(), | |
| nn.MaxPool2d(kernel_size=2), | |
| nn.Conv2d(16, 32, kernel_size=3, padding=1), | |
| nn.ReLU(), | |
| nn.MaxPool2d(kernel_size=2), | |
| nn.Conv2d(32, 64, kernel_size=3, padding=1), | |
| nn.ReLU(), | |
| nn.AdaptiveAvgPool2d(output_size=1), | |
| ) | |
| self.classifier = nn.Sequential( | |
| nn.Flatten(), | |
| nn.Linear(64, num_classes), | |
| ) | |
| def forward(self, inputs: Tensor) -> Tensor: | |
| """Return unnormalized class logits.""" | |
| return self.classifier(self.features(inputs)) | |