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| import torch | |
| import torch.nn as nn | |
| from transformers import ViTModel | |
| from src.models.segmentation_head import SegmentationHead | |
| class ViTSegmentation(nn.Module): | |
| def __init__(self, image_size: int = 224, num_classes: int = 18) -> None: | |
| super().__init__() | |
| self.mean = [0.5, 0.5, 0.5] | |
| self.std = [0.5, 0.5, 0.5] | |
| self.backbone = ViTModel.from_pretrained("google/vit-base-patch16-224") | |
| self.segmentation_head = SegmentationHead(in_channels=768, num_classes=num_classes) | |
| for param in self.backbone.parameters(): | |
| param.requires_grad = False | |
| def forward(self, x: torch.Tensor) -> torch.Tensor: | |
| batch_size, channels, height, width = x.size() | |
| assert height == width == self.backbone.config.image_size, "The image must match the size required by the ViT model" | |
| outputs = self.backbone(pixel_values=x).last_hidden_state | |
| patch_dim = int(height / self.backbone.config.patch_size) | |
| outputs = outputs[:, 1:, :] | |
| outputs = outputs.permute(0, 2, 1).view(batch_size, -1, patch_dim, patch_dim) | |
| masks = self.segmentation_head(outputs) | |
| return masks | |
| def main() -> None: | |
| model = ViTSegmentation(image_size=224, num_classes=18) | |
| num_params = sum([p.numel() for p in model.parameters()]) | |
| print(f"params: {num_params/1e6:.2f} M") | |
| if __name__ == "__main__": | |
| main() |