Trying to get the CNNTransformer to work, but it is hard.

1 files changed, 17 insertions, 11 deletions

diff --git a/src/training/run_experiment.py b/src/training/run_experiment.py
index c0f969d..0510d5c 100644
--- a/src/training/run_experiment.py
+++ b/src/training/run_experiment.py
@@ -73,7 +73,7 @@ def _create_experiment_dir(
     return experiment_dir, log_dir, model_dir
 
 
-def _load_modules_and_arguments(experiment_config: Dict) -> Tuple[Callable, Dict]:
+def _load_modules_and_arguments(experiment_config: Dict,) -> Tuple[Callable, Dict]:
     """Loads all modules and arguments."""
     # Load the dataset module.
     dataset_args = experiment_config.get("dataset", {})
@@ -104,7 +104,7 @@ def _load_modules_and_arguments(experiment_config: Dict) -> Tuple[Callable, Dict
         criterion_ = getattr(custom_loss_module, experiment_config["criterion"]["type"])
     else:
         criterion_ = getattr(torch.nn, experiment_config["criterion"]["type"])
-    criterion_args = experiment_config["criterion"].get("args", {})
+    criterion_args = experiment_config["criterion"].get("args", {}) or {}
 
     # Optimizers
     if experiment_config["optimizer"]["type"] == "AdaBelief":
@@ -187,18 +187,20 @@ def _save_config(experiment_dir: Path, experiment_config: Dict) -> None:
 
 
 def _load_from_checkpoint(
-    model: Type[Model], log_dir: Path, model_dir: Path, pretrained_weights: str = None
+    model: Type[Model], model_dir: Path, pretrained_weights: str = None,
 ) -> None:
     """If checkpoint exists, load model weights and optimizers from checkpoint."""
     # Get checkpoint path.
     if pretrained_weights is not None:
         logger.info(f"Loading weights from {pretrained_weights}.")
-        checkpoint_path = Path(pretrained_weights) / "model" / "best.pt"
+        checkpoint_path = (
+            EXPERIMENTS_DIRNAME / Path(pretrained_weights) / "model" / "best.pt"
+        )
     else:
         logger.info(f"Loading weights from {model_dir}.")
         checkpoint_path = model_dir / "last.pt"
     if checkpoint_path.exists():
-        logger.info("Loading and resuming training from last checkpoint.")
+        logger.info("Loading and resuming training from checkpoint.")
         model.load_from_checkpoint(checkpoint_path)
 
 
@@ -230,9 +232,9 @@ def run_experiment(
     experiment_config: Dict,
     save_weights: bool,
     device: str,
-    use_wandb: bool = False,
-    train: bool = True,
-    test: bool = False,
+    use_wandb: bool,
+    train: bool,
+    test: bool,
     verbose: int = 0,
     checkpoint: Optional[str] = None,
     pretrained_weights: Optional[str] = None,
@@ -264,7 +266,7 @@ def run_experiment(
     resume = False
     if checkpoint is not None or pretrained_weights is not None:
         resume = True
-        _load_from_checkpoint(model, log_dir, model_dir, pretrained_weights)
+        _load_from_checkpoint(model, model_dir, pretrained_weights)
 
     logger.info(f"The class mapping is {model.mapping}")
 
@@ -297,6 +299,7 @@ def run_experiment(
         max_epochs=experiment_config["train_args"]["max_epochs"],
         callbacks=callbacks,
         transformer_model=experiment_config["train_args"]["transformer_model"],
+        max_norm=experiment_config["train_args"]["max_norm"],
     )
 
     # Train the model.
@@ -309,7 +312,7 @@ def run_experiment(
         model.load_from_checkpoint(model_dir / "best.pt")
 
         logger.info("Running inference on test set.")
-        if experiment_config["criterion"]["type"] in custom_loss_module.__all__:
+        if experiment_config["criterion"]["type"] == "EmbeddingLoss":
             logger.info("Evaluating embedding.")
             score = evaluate_embedding(model)
         else:
@@ -341,13 +344,15 @@ def run_experiment(
 @click.option(
     "--nowandb", is_flag=False, help="If true, do not use wandb for this run."
 )
-@click.option("--notrain", is_flag=False, help="Do not train the model.")
 @click.option("--test", is_flag=True, help="If true, test the model.")
 @click.option("-v", "--verbose", count=True)
 @click.option("--checkpoint", type=str, help="Path to the experiment.")
 @click.option(
     "--pretrained_weights", type=str, help="Path to pretrained model weights."
 )
+@click.option(
+    "--notrain", is_flag=False, is_eager=True, help="Do not train the model.",
+)
 def run_cli(
     experiment_config: str,
     gpu: int,
@@ -367,6 +372,7 @@ def run_cli(
 
     experiment_config = json.loads(experiment_config)
     os.environ["CUDA_VISIBLE_DEVICES"] = f"{gpu}"
+
     run_experiment(
         experiment_config,
         save,