fix: make --continue_path work again (#131)

* fix: make --continue_path work again

There were errors when loading models with `--continue_path` because #121
changed
https://github.com/coqui-ai/Trainer/blob/47781f58d2714d8139dc00f57dbf64bcc14402b7/trainer/trainer.py#L1924
to save the `model_loss` as `{"train_loss": train_loss, "eval_loss": eval_loss}`
instead of just a float.
https://github.com/coqui-ai/Trainer/blob/47781f58d2714d8139dc00f57dbf64bcc14402b7/trainer/io.py#L195
still saves a float in `model_loss`, so loading the best model would still work
fine. Loading a model via `--restore-path` also works fine because in that case
the best loss is reset and not initialised from the saved model.

This fix:
- changes `save_best_model()` to also save a dict with train and eval loss, so
  that this is consistent everywhere
- ensures that the model loader can handle both float and dict
  `model_loss` for backwards compatibility
- adds relevant test cases

* fixup! fix: make --continue_path work again

tests/test_continue_train.py

@@ -14,8 +14,19 @@ def test_continue_train():
     continue_path = max(glob.glob(os.path.join(output_path, "*/")), key=os.path.getmtime)
     number_of_checkpoints = len(glob.glob(os.path.join(continue_path, "*.pth")))
 
-    command_continue = f"python tests/utils/train_mnist.py --continue_path {continue_path}"
+    # Continue training from the best model
+    command_continue = f"python tests/utils/train_mnist.py --continue_path {continue_path} --coqpit.run_eval_steps=1"
     run_cli(command_continue)
 
     assert number_of_checkpoints < len(glob.glob(os.path.join(continue_path, "*.pth")))
+
+    # Continue training from the last checkpoint
+    for best in glob.glob(os.path.join(continue_path, "best_model*")):
+        os.remove(best)
+    run_cli(command_continue)
+
+    # Continue training from a specific checkpoint
+    restore_path = os.path.join(continue_path, "checkpoint_5.pth")
+    command_continue = f"python tests/utils/train_mnist.py --restore_path {restore_path}"
+    run_cli(command_continue)
     shutil.rmtree(continue_path)

trainer/io.py

@@ -180,7 +180,10 @@ def save_best_model(
     save_func=None,
     **kwargs,
 ):
-    if current_loss < best_loss:
+    use_eval_loss = current_loss["eval_loss"] is not None and best_loss["eval_loss"] is not None
+    if (use_eval_loss and current_loss["eval_loss"] < best_loss["eval_loss"]) or (
+        not use_eval_loss and current_loss["train_loss"] < best_loss["train_loss"]
+    ):
         best_model_name = f"best_model_{current_step}.pth"
         checkpoint_path = os.path.join(out_path, best_model_name)
         logger.info(" > BEST MODEL : %s", checkpoint_path)

trainer/trainer.py

@@ -451,7 +451,7 @@ def __init__(  # pylint: disable=dangerous-default-value
         self.epochs_done = 0
         self.restore_step = 0
         self.restore_epoch = 0
-        self.best_loss = float("inf")
+        self.best_loss = {"train_loss": float("inf"), "eval_loss": float("inf") if self.config.run_eval else None}
         self.train_loader = None
         self.test_loader = None
         self.eval_loader = None
@@ -1724,8 +1724,15 @@ def _restore_best_loss(self):
             logger.info(" > Restoring best loss from %s ...", os.path.basename(self.args.best_path))
             ch = load_fsspec(self.args.restore_path, map_location="cpu")
             if "model_loss" in ch:
-                self.best_loss = ch["model_loss"]
-            logger.info(" > Starting with loaded last best loss %f", self.best_loss)
+                if isinstance(ch["model_loss"], dict):
+                    self.best_loss = ch["model_loss"]
+                # For backwards-compatibility:
+                elif isinstance(ch["model_loss"], float):
+                    if self.config.run_eval:
+                        self.best_loss = {"train_loss": None, "eval_loss": ch["model_loss"]}
+                    else:
+                        self.best_loss = {"train_loss": ch["model_loss"], "eval_loss": None}
+            logger.info(" > Starting with loaded last best loss %s", self.best_loss)
 
     def test(self, model=None, test_samples=None) -> None:
         """Run evaluation steps on the test data split. You can either provide the model and the test samples
@@ -1907,7 +1914,7 @@ def save_best_model(self) -> None:
 
         # save the model and update the best_loss
         self.best_loss = save_best_model(
-            eval_loss if eval_loss else train_loss,
+            {"train_loss": train_loss, "eval_loss": eval_loss},
             self.best_loss,
             self.config,
             self.model,