Добавлена реализация LeNet и Alexnet

MyNNExamplePL.py

@@ -178,6 +178,8 @@ test_loader = DataLoader(dataset=test_dataset,
 # %% Запуск обучения
 
 model = MyNNExample(model=MyTorchCNNModel())
+model.compile()
+
 callbacks = [
     ModelCheckpoint(dirpath="models",
                     filename="{epoch}_{val_accuracy:.4f}_{val_loss:.4f}",
@@ -206,10 +208,10 @@ trainer = pl.Trainer(
 trainer.fit(model=model, 
             train_dataloaders=train_loader,
             val_dataloaders=test_loader)
-        
+
 # %% Инференс модели
         
-checkpoint_path = "./models/6_0.9460_0.1643.ckpt"
+checkpoint_path = "./models/7_0.9720_0.0958.ckpt"
 device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
 model = MyNNExample.load_from_checkpoint(checkpoint_path,
                                          model=MyTorchCNNModel(output_size=10))
@@ -226,4 +228,29 @@ with torch.no_grad():
     
 print(f"Предсказание: {pred_label}")
 print(f"Истинная метка: {true_label}")
-print(f"Уверенность: {probs[0, pred_label].item():.2%}")
+print(f"Уверенность: {probs[0, pred_label].item():.2%}")
+
+# %% Matlab export
+
+pytorch_model = model.model
+pytorch_model.eval()
+pytorch_model = pytorch_model.cpu()
+
+dummy_input = torch.randn(1, 1, 28, 28, dtype=torch.float32)
+
+with torch.no_grad():
+    _ = pytorch_model(dummy_input)
+    
+traced_model = torch.jit.trace(pytorch_model, dummy_input)
+
+with torch.no_grad():
+    y_original = pytorch_model(dummy_input)
+    y_traced = traced_model(dummy_input)
+    
+print("Original:", y_original.shape)
+print("Traced:", y_traced.shape)
+print("Max difference:", torch.max(torch.abs(y_original - y_traced)).item())
+
+traced_model.save("./models/my_model_matlab.pt")
+
+