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Download test_load.py from NMundhra/OCT-Image-Classifier-Model: direct link, hf CLI and curl.
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- Download file 889 Bytes
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https://huggingface.co/spaces/NMundhra/OCT-Image-Classifier-Model/resolve/main/test_load.py
- Command line
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hf download hf://spaces/NMundhra/OCT-Image-Classifier-Model/test_load.py
-
curl -L -o test_load.py https://huggingface.co/spaces/NMundhra/OCT-Image-Classifier-Model/resolve/main/test_load.py
889 Bytes
| import torch | |
| from models.level1_gatekeeper import GatekeeperModel | |
| from models.level2_router import DiseaseRouterModel | |
| try: | |
| print("Testing Level 1 Gatekeeper...") | |
| l1_model = GatekeeperModel(pretrained=False, freeze_backbone=False) | |
| ckpt = torch.load('weights/level1.pth', map_location='cpu') | |
| if 'model_state_dict' in ckpt: ckpt = ckpt['model_state_dict'] | |
| l1_model.load_state_dict(ckpt) | |
| print("Level 1 loaded successfully!") | |
| except Exception as e: | |
| print(f"Level 1 failed: {e}") | |
| try: | |
| print("Testing Level 2 Router...") | |
| l2_model = DiseaseRouterModel(pretrained=False, freeze_backbone=False) | |
| ckpt = torch.load('weights/level2.pth', map_location='cpu') | |
| if 'model_state_dict' in ckpt: ckpt = ckpt['model_state_dict'] | |
| l2_model.load_state_dict(ckpt) | |
| print("Level 2 loaded successfully!") | |
| except Exception as e: | |
| print(f"Level 2 failed: {e}") | |