Improved Utilities

.github/workflows/test_and_deploy.yml

@@ -7,6 +7,7 @@ on:
   push:
     branches:
       - main
+      - cy/utils
     tags:
       - "v*" # Push events to matching v*, i.e. v1.0, v20.15.10
   pull_request:
@@ -51,7 +52,7 @@ jobs:
         run: |
           python -m pip install --upgrade pip
           python -m pip install setuptools tox tox-gh-actions
-#         pip install git+https://github.com/lucasb-eyer/pydensecrf.git@master#egg=pydensecrf
+#         pip install git+https://github.com/kodalli/pydensecrf.git@master#egg=pydensecrf
 
 # this runs the platform-specific tests declared in tox.ini
       - name: Test with tox

napari_cellseg3d/_tests/test_inference.py

@@ -66,18 +66,27 @@ def test_inference_on_folder():
     config.images_filepaths = [
         str(Path(__file__).resolve().parent / "res/test.tif")
     ]
+    config.sliding_window_config.window_size = 64
 
-    def mock_work(x):
-        return x
+    class mock_work:
+        def __call__(self, x):
+            return x
+
+        def eval(self):
+            return None
 
     worker = InferenceWorker(worker_config=config)
-    worker.aniso_transform = mock_work
+    worker.aniso_transform = mock_work()
 
     image = torch.Tensor(rand_gen.random((1, 1, 64, 64, 64)))
     res = worker.inference_on_folder(
-        {"image": image}, 0, model=mock_work, post_process_transforms=mock_work
+        {"image": image},
+        0,
+        model=mock_work(),
+        post_process_transforms=mock_work(),
     )
     assert isinstance(res, InferenceResult)
+    assert res.result is not None
 
 
 def test_post_processing():

napari_cellseg3d/_tests/test_models.py

@@ -115,7 +115,7 @@ def test_pretrained_weights_compatibility():
     for model_name in MODEL_LIST:
         file_name = MODEL_LIST[model_name].weights_file
         WeightsDownloader().download_weights(model_name, file_name)
-        model = MODEL_LIST[model_name](input_img_size=[128, 128, 128])
+        model = MODEL_LIST[model_name](input_img_size=[64, 64, 64])
         try:
             model.load_state_dict(
                 torch.load(

napari_cellseg3d/code_models/crf.py

@@ -7,7 +7,7 @@
 Philipp Krähenbühl and Vladlen Koltun
 NIPS 2011
 
-Implemented using the pydense libary available at https://github.com/lucasb-eyer/pydensecrf.
+Implemented using the pydense libary available at https://github.com/kodalli/pydensecrf.
 """
 from warnings import warn
 

napari_cellseg3d/code_models/models/model_SegResNet.py

@@ -3,7 +3,7 @@
 
 class SegResNet_(SegResNetVAE):
     use_default_training = True
-    weights_file = "SegResNet.pth"
+    weights_file = "SegResNet_latest.pth"
 
     def __init__(
         self, input_img_size, out_channels=1, dropout_prob=0.3, **kwargs

napari_cellseg3d/code_models/models/model_SwinUNetR.py

@@ -7,7 +7,7 @@
 
 class SwinUNETR_(SwinUNETR):
     use_default_training = True
-    weights_file = "Swin64_best_metric.pth"
+    weights_file = "SwinUNetR_latest.pth"
 
     def __init__(
         self,

napari_cellseg3d/code_models/models/model_TRAILMAP_MS.py

@@ -4,7 +4,7 @@
 
 class TRAILMAP_MS_(UNet3D):
     use_default_training = True
-    weights_file = "TRAILMAP_MS_best_metric_epoch_26.pth"
+    weights_file = "TRAILMAP_MS_best_metric.pth"
 
     # original model from Liqun Luo lab, transferred to pytorch and trained on mesoSPIM-acquired data (mostly TPH2 as of July 2022)
 

napari_cellseg3d/code_models/models/model_VNet.py

@@ -3,7 +3,7 @@
 
 class VNet_(VNet):
     use_default_training = True
-    weights_file = "VNet_40e.pth"
+    weights_file = "VNet_latest.pth"
 
     def __init__(self, in_channels=1, out_channels=1, **kwargs):
         try:

napari_cellseg3d/code_models/models/pretrained/pretrained_model_urls.json

@@ -1,8 +1,8 @@
 {
-  "TRAILMAP_MS": "https://huggingface.co/C-Achard/cellseg3d/resolve/main/TRAILMAP_MS.tar.gz",
-  "SegResNet": "https://huggingface.co/C-Achard/cellseg3d/resolve/main/SegResNet.tar.gz",
-  "VNet": "https://huggingface.co/C-Achard/cellseg3d/resolve/main/VNet.tar.gz",
-  "SwinUNetR": "https://huggingface.co/C-Achard/cellseg3d/resolve/main/Swin64.tar.gz",
+  "TRAILMAP_MS": "https://huggingface.co/C-Achard/cellseg3d/resolve/main/TRAILMAP_latest.tar.gz",
+  "SegResNet": "https://huggingface.co/C-Achard/cellseg3d/resolve/main/SegResNet_latest.tar.gz",
+  "VNet": "https://huggingface.co/C-Achard/cellseg3d/resolve/main/VNet_latest.tar.gz",
+  "SwinUNetR": "https://huggingface.co/C-Achard/cellseg3d/resolve/main/SwinUNetR_latest.tar.gz",
   "WNet": "https://huggingface.co/C-Achard/cellseg3d/resolve/main/wnet.tar.gz",
   "WNet_ONNX": "https://huggingface.co/C-Achard/cellseg3d/resolve/main/wnet_onnx.tar.gz",
   "test": "https://huggingface.co/C-Achard/cellseg3d/resolve/main/test.tar.gz"

napari_cellseg3d/code_models/worker_inference.py

@@ -436,6 +436,8 @@ def save_image(
             + f"_{time}"
             + filetype
         )
+        if not Path(self.config.results_path).exists():
+            Path(self.config.results_path).mkdir(parents=True, exist_ok=True)
         try:
             imwrite(file_path, image)
         except ValueError as e:

napari_cellseg3d/code_models/workers_utils.py

@@ -151,7 +151,7 @@ def __init__(self, file_location):
         except ImportError as e:
             logger.error("ONNX is not installed but ONNX model was loaded")
             logger.error(e)
-            msg = "PLEASE INSTALL ONNX CPU OR GPU USING pip install napari-cellseg3d[onnx-cpu] OR napari-cellseg3d[onnx-gpu]"
+            msg = "PLEASE INSTALL ONNX CPU OR GPU USING: pip install napari-cellseg3d[onnx-cpu] OR pip install napari-cellseg3d[onnx-gpu]"
             logger.error(msg)
             raise ImportError(msg) from e
 
@@ -177,6 +177,8 @@ def to(self, device):
 
 
 class QuantileNormalizationd(MapTransform):
+    """MONAI-style dict transform to normalize each image in a batch individually by quantile normalization."""
+
     def __init__(self, keys, allow_missing_keys: bool = False):
         super().__init__(keys, allow_missing_keys)
 
@@ -199,6 +201,8 @@ def normalizer(self, image: torch.Tensor):
 
 
 class QuantileNormalization(Transform):
+    """MONAI-style transform to normalize each image in a batch individually by quantile normalization."""
+
     def __call__(self, img):
         return utils.quantile_normalization(img)
 

napari_cellseg3d/code_plugins/plugin_base.py

@@ -446,9 +446,10 @@ def _update_default_paths(self, path=None):
                 self.extract_dataset_paths(self.labels_filepaths),
                 self.results_path,
             ]
-            return
+            return utils.parse_default_path(self._default_path)
         if Path(path).is_dir():
             self._default_path.append(path)
+        return utils.parse_default_path(self._default_path)
 
     @staticmethod
     def extract_dataset_paths(paths):
@@ -458,3 +459,37 @@ def extract_dataset_paths(paths):
         if paths[0] is None:
             return None
         return str(Path(paths[0]).parent)
+
+
+class BasePluginUtils(BasePluginFolder):
+    """Small subclass used to have centralized widgets layer and result path selection in utilities"""
+
+    save_path = None
+    utils_default_paths = [Path.home() / "cellseg3d"]
+
+    def __init__(
+        self,
+        viewer: napari.viewer.Viewer,
+        parent=None,
+        loads_images=True,
+        loads_labels=True,
+    ):
+        super().__init__(
+            viewer=viewer,
+            loads_images=loads_images,
+            loads_labels=loads_labels,
+            parent=parent,
+        )
+        if parent is not None:
+            self.setParent(parent)
+        self.parent = parent
+
+        self.layer = None
+        """Should contain the layer associated with the results of the utility widget"""
+
+    def _update_default_paths(self, path=None):
+        """Override to also update utilities' pool of default paths"""
+        default_path = super()._update_default_paths(path)
+        logger.debug(f"Trying to update default with {default_path}")
+        if default_path is not None:
+            self.utils_default_paths.append(default_path)