Set training to load weights w/ strict

Author: C-Achard

napari_cellseg3d/code_models/worker_training.py

@@ -534,7 +534,8 @@ def get_loader_func(num_samples):
                         torch.load(
                             weights,
                             map_location=self.config.device,
-                        )
+                        ),
+                        strict=True,
                     )
                 except RuntimeError as e:
                     logger.error(f"Error when loading weights : {e}")

napari_cellseg3d/dev_scripts/crop_data.py

@@ -40,24 +40,24 @@ def crop_3d_image(image, roi_size):
         Path().home()
         # / "Desktop/Code/CELLSEG_BENCHMARK/TPH2_DATA/somatomotor_iso"
         # / "Desktop/Code/CELLSEG_BENCHMARK/TPH2_DATA/somatomotor_iso/labels/semantic"
-        / "Desktop/Code/CELLSEG_BENCHMARK/TPH2_mesospim/BENCHMARK/visual_small"
+        / "Desktop/Code/CELLSEG_BENCHMARK/TPH2_mesospim/visual_iso/labels/semantic"
     )
     if not image_path.exists() or not image_path.is_dir():
         raise ValueError(f"Image path {image_path} does not exist")
     image_list = image_path.glob("*.tif")
     for j in image_list:
         print(j)
         image = imread(str(j))
-        # crops = crop_3d_image(image, (64, 64, 64))
-        crops = [image]
+        crops = crop_3d_image(image, (64, 64, 64))
+        # crops = [image]
         # viewer = napari.Viewer()
         if not (image_path / "cropped").exists():
             (image_path / "cropped").mkdir(exist_ok=False)
         for i, im in enumerate(crops):
             print(im.shape)
             # viewer.add_image(im)
             imwrite(
-                str(image_path / f"cropped/{j.stem}_{i}_crop.tif"),
+                str(image_path / f"cropped/a_{j.stem}_{i}_crop.tif"),
                 im.astype(np.float32),
                 dtype="float32",
             )