fix wandb logging

spotiflow/cli/train.py

@@ -219,6 +219,13 @@ def get_args() -> argparse.Namespace:
         default="tensorboard",
         help="Logger to use for monitoring training. Defaults to 'tensorboard'.",
     )
+    train_args.add_argument(
+        "--smart-crop",
+        type=str2bool,
+        required=False,
+        default=False,
+        help="Use smart cropping for training. Defaults to False.",
+    )
     args = parser.parse_args()
     return args
 
@@ -306,6 +313,7 @@ def main():
             "pos_weight": args.pos_weight,
             "num_train_samples":args.train_samples,
             "finetuned_from": args.finetune_from,
+            "smart_crop": args.smart_crop,
         },
     )
     log.info("Done!")

spotiflow/model/spotiflow.py

@@ -6,6 +6,7 @@
 from types import SimpleNamespace
 from typing import Callable, Literal, Optional, Sequence, Tuple, Union
 
+import datetime
 import dask.array as da
 import lightning.pytorch as pl
 import numpy as np
@@ -467,9 +468,10 @@ def fit(
             ]
 
         if logger == "tensorboard":
-            logger = pl.loggers.TensorBoardLogger(save_dir=save_dir)
+            logger = pl.loggers.TensorBoardLogger(save_dir=save_dir, name=f"spotiflow-{datetime.datetime.now().strftime('%Y%m%d_%H%M')}")
         elif logger == "wandb":
-            logger = pl.loggers.WandbLogger(save_dir=save_dir)
+            Path(save_dir/"wandb").mkdir(parents=True, exist_ok=True)
+            logger = pl.loggers.WandbLogger(save_dir=save_dir, project="spotiflow", name=f"{datetime.datetime.now().strftime('%Y%m%d_%H%M')}")
         else:
             if logger != "none":
                 log.warning(f"Logger {logger} not implemented. Using no logger.")
@@ -1067,6 +1069,8 @@ def predict(
                                 s_src_corr[:actual_n_dims]
                             ]
                     points.append(p)
+                    del out, img_t, tile, y_tile, p
+                    torch.cuda.empty_cache()
 
             if scale is not None and scale != 1:
                 y = zoom(y, (1.0 / scale, 1.0 / scale), order=1)

spotiflow/model/trainer.py

@@ -362,7 +362,7 @@ def log_images(self):
                 self.logger.log_image(
                     key="flow",
                     images=[
-                        0.5 * (1 + np.squeeze(v, axis=0).transpose(1, 2, 0))
+                        0.5 * (1 + v.transpose(1, 2, 0))
                         for v in self._valid_flows[:n_images_to_log]
                     ],
                     step=self.global_step,

spotiflow/utils/fitting.py

@@ -9,6 +9,7 @@
 
 from tqdm.auto import tqdm
 from dataclasses import dataclass, fields
+from scipy.ndimage import map_coordinates
 
 FWHM_CONSTANT = 2 * np.sqrt(2 * np.log(2))
 
@@ -100,11 +101,12 @@ def _estimate_params_single2(
     y_range = np.arange(-window, window + 1)
     y, x = np.meshgrid(y_range, x_range, indexing="ij")
 
-    # Crop around the spot
-    region = image[
+    # Crop around the spot with interpolation
+    y_indices, x_indices = np.mgrid[
         center[0] - window : center[0] + window + 1,
-        center[1] - window : center[1] + window + 1,
+        center[1] - window : center[1] + window + 1
     ]
+    region = map_coordinates(image, [y_indices, x_indices], order=3, mode='reflect')
 
     try:
         mi, ma = np.min(region), np.max(region)
@@ -226,7 +228,7 @@ def estimate_params(
             peak_range (np.ndarray): peak range of the spots
     """
     img = np.pad(img, window, mode="reflect")
-    centers = np.asarray(centers).astype(int) + window
+    centers = np.asarray(centers) + window
     if max_workers == 1:
         params = tuple(
             _estimate_params_single(