save best, final, checkpoint models

README.md

@@ -16,6 +16,11 @@ Loading a trained model:
 python -m src --load_model 20230303_100000_run_name
 ```
 
+Loading a specific trained model (e.g. best model, final model, checkpoint):
+```
+python -m src --load_model --model_name=final 20230303_100000_run_name
+```
+
 Loading a trained model, but compute weights again:
 ```
 python -m src --load_model --load_weights 20230303_100000_run_name
@@ -60,6 +65,7 @@ Parameter         | Description
 `lr_decay_factor` | Step scheduler: Decay factor
 `epochs`          | Number of epochs
 `train_samples`   | Total number of samples used for training (alternative to number of epochs)
+`checkpoint_interval` | If value n set, save the model after every n epochs
 
 ### Evaluation
 

src/__main__.py

@@ -20,6 +20,7 @@ def main():
     parser = argparse.ArgumentParser()
     parser.add_argument("paramfile")
     parser.add_argument("--load_model", action="store_true")
+    parser.add_argument("--model_name", type=str, default="best")
     parser.add_argument("--load_weights", action="store_true")
     args = parser.parse_args()
 
@@ -44,17 +45,16 @@ def main():
         print(f"  Val points: {len(data.val_true)} truth, {len(data.val_fake)} generated")
 
         print("  Building model")
-        training = DiscriminatorTraining(params, device, data)
+        model_dir = doc.get_file(f"model_{data.suffix}.pth")
+        os.makedirs(model_dir, exist_ok=True)
+        training = DiscriminatorTraining(params, device, data, model_dir)
 
         if args.load_model:
             print("  Loading model")
-            training.load(doc.get_file(f"model_{data.suffix}.pth"))
-
-        if not args.load_model:
+            training.load(args.model_name)
+        else:
             print("  Running training")
             training.train()
-            print("  Saving model")
-            training.save(doc.get_file(f"model_{data.suffix}.pth"))
 
         if args.load_model and args.load_weights:
             print("  Loading weights")

src/train.py

@@ -1,3 +1,4 @@
+import os
 import torch
 import torch.nn as nn
 import numpy as np
@@ -15,7 +16,8 @@ def __init__(
         self,
         params: dict,
         device: torch.device,
-        data: DiscriminatorData
+        data: DiscriminatorData,
+        model_path: str
     ):
         """
         Build the network and data loaders.
@@ -24,10 +26,12 @@ def __init__(
             params: Dict with architecture and training hyperparameters
             device: Pytorch device used for training and evaluation
             data: DiscriminatorData object containing the training and evaluation data
+            model_path: Path to a directory where models are saved
         """
         self.params = params
         self.device = device
         self.data = data
+        self.model_path = model_path
 
         self.init_data_loaders()
         self.model = Discriminator(data.dim, params)
@@ -150,6 +154,8 @@ def train(self):
         """
         Main training loop
         """
+        best_val_loss = 1e20
+        checkpoint_interval = self.params.get("checkpoint_interval")
         for epoch in range(self.epochs):
             self.model.train()
             epoch_losses, epoch_bce_losses, epoch_kl_losses = [], [], []
@@ -179,6 +185,15 @@ def train(self):
             print(f"    Epoch {epoch:3d}: train loss {train_loss:.6f}, " +
                   f"val loss {val_loss:.6f}")
 
+            if val_loss < best_val_loss:
+                best_val_loss = val_loss
+                self.save("best")
+
+            if checkpoint_interval is not None and (epoch+1) % checkpoint_interval == 0:
+                self.save(f"epoch_{epoch}")
+
+        self.save("final")
+
 
     def val_loss(self) -> tuple[torch.Tensor, ...]:
         """
@@ -267,27 +282,29 @@ def predict_single(self):
             return w_true.cpu().numpy(), w_fake.cpu().numpy(), clf_score.cpu().numpy()
 
 
-    def save(self, file: str):
+    def save(self, name: str):
         """
         Saves the model, optimizer and losses.
 
         Args:
-            file: Output file name
+            name: File name for the model (without path and extension)
         """
+        file = os.path.join(self.model_path, f"{name}.pth")
         torch.save({
             "model": self.model.state_dict(),
             "optimizer": self.optimizer.state_dict(),
             "losses": self.losses
         }, file)
 
 
-    def load(self, file: str):
+    def load(self, name: str):
         """
         Loads the model, optimizer and losses.
 
         Args:
-            file: Input file name
+            name: File name for the model (without path and extension)
         """
+        file = os.path.join(self.model_path, f"{name}.pth")
         state_dicts = torch.load(file, map_location=self.device)
         self.optimizer.load_state_dict(state_dicts["optimizer"])
         self.model.load_state_dict(state_dicts["model"])