Small refactoring to load from ckpt (#203)

* Move checkpoint type computation to utils

* Refactor checkpointing in training script

* Get ckpt type if ckpt is passed

crabs/detection_tracking/detection_utils.py

@@ -244,16 +244,6 @@ def slurm_logs_as_artifacts(logger, slurm_job_id):
         )
 
 
-def log_data_augm_as_artifacts(logger, data_module):
-    """Log data augmentation transforms as artifacts in MLflow."""
-    for transform_str in ["train_transform", "test_val_transform"]:
-        logger.experiment.log_text(
-            text=str(getattr(data_module, f"_get_{transform_str}")()),
-            artifact_file=f"{transform_str}.txt",
-            run_id=logger.run_id,
-        )
-
-
 def get_checkpoint_type(checkpoint_path: Optional[str]) -> Optional[str]:
     """Get checkpoint type (full or weights) from the checkpoint path."""
     checkpoint = torch.load(checkpoint_path)  # fails if path doesn't exist
@@ -274,3 +264,13 @@ def get_checkpoint_type(checkpoint_path: Optional[str]) -> Optional[str]:
         )
 
     return checkpoint_type
+
+
+def log_data_augm_as_artifacts(logger, data_module):
+    """Log data augmentation transforms as artifacts in MLflow."""
+    for transform_str in ["train_transform", "test_val_transform"]:
+        logger.experiment.log_text(
+            text=str(getattr(data_module, f"_get_{transform_str}")()),
+            artifact_file=f"{transform_str}.txt",
+            run_id=logger.run_id,
+        )