Extract model persistance

src/editor/models/__init__.py

@@ -1,3 +1,4 @@
+from typing import Any, Dict, Tuple
 from .v1 import HistogramRestorationNet as v1
 from .simple_cnn import SimpleCNN
 from .residual import Residual
@@ -9,6 +10,10 @@ from .attention2 import EnhancedAestheticHistogramNet
 from .attention import PhotoEnhanceNetAdvanced
 from .advanced_attention import PhotoEnhanceNetAdvanced as advanced_attention
 import torch
+import torch.nn as nn
+from pathlib import Path
+import logging
+import json
 
 
 MODELS = {
@@ -29,9 +34,39 @@ def create_model(type: str, bin_count: int):
     return MODELS[type](bin_count)
 
 
-def test_models():
+def save_model(model: nn.Module, hyperparameters: Dict[str, Any], path: Path):
+    model_path = path.with_suffix(".pth")
+    params_path = path.with_suffix(".json")
+
+    logging.info(f"Saving model to {model_path}")
+    with open(model_path, "wb") as f:
+        torch.save(model.state_dict(), f)
+    with open(params_path, "w") as f:
+        json.dump(hyperparameters, f, indent=2)
+
+
+def load_model(path: Path, device: torch.device) -> Tuple[nn.Module, Dict[str, Any]]:
+    logging.info(f"Loading model from {path}")
+
+    params_path = path.with_suffix(".json")
+    with open(params_path) as f:
+        hyperparameters = json.load(f)
+    logging.info(f"Hyperparameters: {hyperparameters}")
+
+    model_path = path.with_suffix(".pth")
+    model = create_model(
+        type=hyperparameters["model_type"],
+        bin_count=hyperparameters["bin_count"],
+    ).to(device)
+    model.load_state_dict(torch.load(model_path))
+    model.eval()
+
+    return model, hyperparameters
+
+
+def _test_models():
     for model_name, model_constructor in MODELS.items():
-        print(f"Testing model {model_name}")
+        logging.info(f"Testing model {model_name}")
         _test_network_dimensions(model_constructor)
 
 
@@ -48,3 +83,7 @@ def _test_network_dimensions(constructor):
             input_tensor.shape == output.shape
         ), f"Expected output shape {input_tensor.shape}, but got {output.shape}"
     print("Test passed! Output shape matches input shape.")
+
+
+if __name__ == "__main__":
+    _test_models()