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| """ | |
| Unified Inference Engine for MedAdapt-SAM Dashboard | |
| """ | |
| import torch | |
| import torch.nn as nn | |
| import numpy as np | |
| from typing import Dict, Any | |
| class MedAdaptEngine: | |
| """Unified engine to handle different model types for the dashboard""" | |
| def __init__(self, checkpoints_path: str = "checkpoints/"): | |
| self.device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') | |
| self.checkpoints_path = checkpoints_path | |
| self.models = {} | |
| def load_unet(self): | |
| from models.unet import UNet | |
| model = UNet(in_channels=3, out_channels=3) | |
| # Load weights if exist | |
| return model.to(self.device).eval() | |
| def load_sam_adapter(self): | |
| from models.sam_adapter import SAMAdapter | |
| model = SAMAdapter(model_type='vit_b') | |
| # Load weights if exist | |
| return model.to(self.device).eval() | |
| def predict(self, image_np: np.ndarray, model_type: str) -> Dict[str, np.ndarray]: | |
| """Generic prediction wrapper""" | |
| # 1. Preprocess | |
| img_tensor = torch.from_numpy(image_np).permute(2, 0, 1).float().divide(255.0).unsqueeze(0).to(self.device) | |
| # 2. Logic based on model_type | |
| # (This is where the actual multi-phase model logic connects) | |
| # Placeholder for real output | |
| # In production: with torch.no_grad(): out = self.models[model_type](img_tensor) | |
| # Returning dummy masks for structural validation | |
| h, w = image_np.shape[:2] | |
| return { | |
| 'ET': (np.random.rand(h, w) > 0.95).astype(np.float32), | |
| 'TC': (np.random.rand(h, w) > 0.90).astype(np.float32), | |
| 'WT': (np.random.rand(h, w) > 0.85).astype(np.float32) | |
| } | |