Fix tests issue with MONAI 1.3.0

Author: C-Achard

napari_cellseg3d/code_models/worker_inference.py

@@ -244,6 +244,7 @@ def load_layer(self):
             if self.config.model_info.name != "WNet"
             else lambda x: x
         )
+        volume = np.reshape(volume, newshape=(1, *volume.shape))
         if self.config.sliding_window_config.is_enabled():
             load_transforms = Compose(
                 [
@@ -278,6 +279,7 @@ def load_layer(self):
             )
 
         input_image = load_transforms(volume)
+        input_image = input_image.unsqueeze(0)
         logger.debug(f"INPUT IMAGE SHAPE : {input_image.shape}")
         logger.debug(f"INPUT IMAGE TYPE : {input_image.dtype}")
         self.log("Done")
@@ -579,7 +581,6 @@ def stats_csv(self, instance_labels):
     def inference_on_layer(self, image, model, post_process_transforms):
         self.log("-" * 10)
         self.log("Inference started on layer...")
-        image = image.view((1, 1, *image.shape))
         logger.debug(f"Layer shape @ inference input: {image.shape}")
         out = self.model_output(
             image,

napari_cellseg3d/code_models/worker_training.py

@@ -1246,7 +1246,7 @@ def get_patch_loader_func(num_samples):
                 logger.debug("Cache dataset : train")
                 train_dataset = CacheDataset(
                     data=self.train_files,
-                    transform=Compose(load_whole_images, train_transforms),
+                    transform=Compose([load_whole_images, train_transforms]),
                 )
                 logger.debug("Cache dataset : val")
                 validation_dataset = CacheDataset(

napari_cellseg3d/code_plugins/plugin_model_inference.py

@@ -609,22 +609,30 @@ def _display_results(self, result: InferenceResult):
                     # stats = result.stats
 
                     if self.worker_config.compute_stats and stats is not None:
-                        stats_dict = stats.get_dict()
-                        stats_df = pd.DataFrame(stats_dict)
-
-                        self.log.print_and_log(
-                            f"Number of instances in channel {i} : {stats.number_objects[0]}"
-                        )
-
-                        csv_name = f"/{method_name}_seg_results_{image_id}_channel_{i}_{utils.get_date_time()}.csv"
-                        stats_df.to_csv(
-                            self.worker_config.results_path + csv_name,
-                            index=False,
-                        )
-
-                    # self.log.print_and_log(
-                    #     f"OBJECTS DETECTED : {number_cells}\n"
-                    # )
+                        try:
+                            stats_dict = stats.get_dict()
+                            stats_df = pd.DataFrame(stats_dict)
+
+                            self.log.print_and_log(
+                                f"Number of instances in channel {i} : {stats.number_objects[0]}"
+                            )
+
+                            csv_name = f"/{method_name}_seg_results_{image_id}_channel_{i}_{utils.get_date_time()}.csv"
+                            stats_df.to_csv(
+                                self.worker_config.results_path + csv_name,
+                                index=False,
+                            )
+
+                            stats_df.to_csv(
+                                self.worker_config.results_path + csv_name,
+                                index=False,
+                            )
+                        except ValueError as e:
+                            logger.warning(f"Error saving stats to csv : {e}")
+                            logger.debug(
+                                f"Length of stats array : {[len(s) for s in stats.get_dict().values()]}"
+                            )
+                            # logger.debug(f"Stats dict : {stats.get_dict()}")
 
     def _setup_worker(self):
         if self.folder_choice.isChecked():