minor changes to cli

spotiflow/cli/train.py

@@ -24,7 +24,7 @@
 log.addHandler(console_handler)
 
 ALLOWED_EXTENSIONS = ("tif", "tiff", "png", "jpg", "jpeg")
-PRETRAINED_MODELS = list_registered()
+PRETRAINED_MODELS = tuple(["general"]+sorted([m for m in list_registered() if m != "general"]))
 
 
 def get_data(
@@ -59,8 +59,8 @@ def get_args() -> argparse.Namespace:
         help="Path to directory containing images and annotations. Please refer to the documentation (https://weigertlab.github.io/spotiflow/train.html#data-format) to see the required format.",
     )
     required.add_argument(
-        "-s",
-        "--save-dir",
+        "-o",
+        "--outdir",
         type=Path,
         required=True,
         help="Output directory where the model will be stored.",
@@ -182,7 +182,14 @@ def get_args() -> argparse.Namespace:
         default=42,
         help="Seed for reproducibility. Defaults to 42.",
     )
-
+    train_args.add_argument(
+        "--logger",
+        type=str,
+        required=False,
+        choices=["none", "tensorboard", "wandb"],
+        default="tensorboard",
+        help="Logger to use for monitoring training. Defaults to 'tensorboard'.",
+    )
     args = parser.parse_args()
     return args
 
@@ -195,20 +202,18 @@ def main():
 
     log.info("Loading training data...")
     train_images, train_spots = get_data(args.data_dir / "train", is_3d=args.is_3d)
-    assert len(train_images) == len(
-        train_spots
-    ), "Number of images and spots do not match."
-    assert (
-        len(train_images) > 0
-    ), "No images were found in the 'train' subfolder of the given directory."
+    if len(train_images) != len(train_spots):
+        raise ValueError(f"Number of images and spots in {args.data_dir/'train'} do not match.")
+    if len(train_images) == 0:
+        raise ValueError(f"No images were found in the {args.data_dir/'train'}.")
     log.info(f"Training data loaded (N={len(train_images)}).")
 
     log.info("Loading validation data...")
     val_images, val_spots = get_data(args.data_dir / "val", is_3d=args.is_3d)
-    assert len(val_images) == len(val_spots), "Number of images and spots do not match."
-    assert (
-        len(val_images) > 0
-    ), "No images were found in the 'val' subfolder of the given directory."
+    if len(val_images) != len(val_spots):
+        raise ValueError(f"Number of images and spots in {args.data_dir/'val'} do not match.")
+    if len(val_images) == 0:
+        raise ValueError(f"No images were found in the {args.data_dir/'val'}.")
     log.info(f"Validation data loaded (N={len(val_images)}).")
 
     if args.finetune_from is None:
@@ -236,12 +241,14 @@ def main():
                 verbose=True,
             )
         else:
-            assert (
-                Path(args.finetune_from) != args.save_dir
-            ), "The save directory cannot be the same as the pre-trained model to be finetuned!"
-            assert (
-                Path(args.finetune_from).exists()
-            ), f"Given pre-trained model '{args.finetune_from}' does not exist!"
+            if Path(args.finetune_from) == args.outdir:
+                err_msg = "The save directory cannot be the same as the pre-trained model to be finetuned!"
+                raise ValueError(err_msg)
+            if not Path(args.finetune_from).is_dir():
+                err_msg = f"Given pre-trained model '{args.finetune_from}' does not exist! Please provide either one of the pre-trained models ({', '.join(PRETRAINED_MODELS)}) or a valid directory containing a model.".strip().replace(
+                    "\n", " "
+                )
+                raise ValueError(err_msg)
             log.info(f"Finetuning local model '{args.finetune_from}' to be fine-tuned.")
             model = Spotiflow.from_folder(
                 args.finetune_from,
@@ -256,8 +263,9 @@ def main():
         train_spots,
         val_images,
         val_spots,
-        save_dir=args.save_dir,
+        save_dir=args.outdir,
         device=args.device,
+        logger=args.logger,
         train_config={
             "batch_size": args.batch_size,
             "crop_size": args.crop_size,