src/IDH/model.py

import torch
import torch.nn as nn
from monai.networks.nets import ViT, UNETR
import os


class ViTUNETRSegmentationModel(nn.Module):
    def __init__(self, simclr_ckpt_path: str, img_size=(96, 96, 96), in_channels=1, out_channels=1):
        super().__init__()
        # Load ViT backbone
        self.vit = ViT(
            in_channels=in_channels,
            img_size=img_size,
            patch_size=(16, 16, 16),
            hidden_size=768,
            mlp_dim=3072,
            num_layers=12,
            num_heads=12,
            save_attn=False,
        )
        
        # Load SimCLR weights if provided
        if False:#simclr_ckpt_path and os.path.exists(simclr_ckpt_path):
            ckpt = torch.load(simclr_ckpt_path, map_location='cpu', weights_only=False)
            state_dict = ckpt.get('state_dict', ckpt)
            backbone_state_dict = {k[9:]: v for k, v in state_dict.items() if k.startswith('backbone.')}
            missing, unexpected = self.vit.load_state_dict(backbone_state_dict, strict=False)
            print(f"Loaded SimCLR backbone weights. Missing: {len(missing)}, Unexpected: {len(unexpected)}")
        else:
            print("Warning: SimCLR checkpoint not found or not provided. Using randomly initialized backbone.")
        
        # UNETR decoder
        self.unetr = UNETR(
            in_channels=in_channels,
            out_channels=out_channels,
            img_size=img_size,
            feature_size=16,
            hidden_size=768,
            mlp_dim=3072,
            num_heads=12,
            norm_name='instance',
            res_block=True,
            dropout_rate=0.0
        )
        
        # Transfer ViT weights to UNETR encoder
        self.unetr.vit.load_state_dict(self.vit.state_dict(), strict=True)
        print("="*10)
        print("ViT loaded for segmentation")
        print("="*10)

    def forward(self, x):
        return self.unetr(x)