Fix tests + new weights

Author: C-Achard

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/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"