Expose params in train script

spotiflow/cli/train.py

@@ -16,6 +16,7 @@
 
 log = logging.getLogger(__name__)
 log.setLevel(logging.INFO)
+logging.basicConfig(level=logging.INFO)
 
 console_handler = logging.StreamHandler(sys.stdout)
 console_handler.setLevel(logging.INFO)
@@ -58,6 +59,7 @@ def get_args() -> argparse.Namespace:
         type=Path,
         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(
         "-o",
         "--outdir",
@@ -130,6 +132,24 @@ def get_args() -> argparse.Namespace:
         title="Training arguments",
         description="Arguments to configure the training process.",
     )
+
+    train_args.add_argument(
+        "--subfolder",
+        type=Path,
+        nargs=2,
+        required=False,
+        default=['train', 'val'],
+        help="Subfolder names for training and validation data. Defaults to ['train', 'val'].",
+    )
+
+    train_args.add_argument(
+        "--train_samples",
+        type=int,
+        required=False,
+        default=None,
+        help="Number of training samples per epoch (defaults to None, which means all samples).",
+    )
+
     train_args.add_argument(
         "--crop-size",
         type=int,
@@ -161,6 +181,14 @@ def get_args() -> argparse.Namespace:
         choices=["auto", "cpu", "cuda", "mps"],
         help="Device to train the model on. Defaults to 'auto', which will infer based on the hardware.",
     )
+    train_args.add_argument(
+        "--augment",
+        type=str2bool,
+        required=False,
+        default=True,
+        help="Apply data augmentation during training. Defaults to True.",
+    )
+
     train_args.add_argument(
         "--pos-weight",
         type=float,
@@ -196,20 +224,21 @@ def get_args() -> argparse.Namespace:
 
 def main():
     args = get_args()
+
     log.info(f"Spotiflow - version {__version__}")
 
     pl.seed_everything(args.seed, workers=True)
 
     log.info("Loading training data...")
-    train_images, train_spots = get_data(args.data_dir / "train", is_3d=args.is_3d)
+    train_images, train_spots = get_data(args.data_dir / args.subfolder[0], is_3d=args.is_3d)
     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)
+    val_images, val_spots = get_data(args.data_dir / args.subfolder[1], is_3d=args.is_3d)
     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:
@@ -266,13 +295,15 @@ def main():
         save_dir=args.outdir,
         device=args.device,
         logger=args.logger,
+        augment_train=args.augment,
         train_config={
             "batch_size": args.batch_size,
             "crop_size": args.crop_size,
             "crop_size_z": args.crop_size_z,
             "lr": args.lr,
             "num_epochs": args.num_epochs,
             "pos_weight": args.pos_weight,
+            "num_train_samples":args.train_samples,
             "finetuned_from": args.finetune_from,
         },
     )

spotiflow/model/spotiflow.py

@@ -821,7 +821,7 @@ def predict(
             if verbose:
                 log.info("Normalizing...")
             x = normalizer(x)
-
+        
         pad_shape = tuple(int(d * np.ceil(s / d)) for s, d in zip(x.shape, div_by))
         if verbose:
             log.info(f"Padding to shape {pad_shape}")