fix remap

Author: C-Achard

napari_cellseg3d/code_models/worker_inference.py

@@ -34,6 +34,7 @@
     ONNXModelWrapper,
     QuantileNormalization,
     QuantileNormalizationd,
+    RemapTensor,
     WeightsDownloader,
 )
 
@@ -717,15 +718,15 @@ def inference(self):
             if not post_process_config.thresholding.enabled:
                 post_process_transforms = Compose(
                     [
-                        # RemapTensor(new_max=1.0, new_min=0.0),
+                        RemapTensor(new_max=1.0, new_min=0.0),
                         EnsureType(),
                     ]
                 )
             else:
                 t = post_process_config.thresholding.threshold_value
                 post_process_transforms = Compose(
                     [
-                        # RemapTensor(new_max=1.0, new_min=0.0),
+                        RemapTensor(new_max=1.0, new_min=0.0),
                         AsDiscrete(threshold=t),
                         EnsureType(),
                     ]

napari_cellseg3d/code_models/worker_training.py

@@ -641,14 +641,17 @@ def get_loader_func(num_samples):
                             post_pred = Compose(
                                 [
                                     RemapTensor(new_max=1, new_min=0),
-                                    AsDiscrete(threshold=0.5),  # needed ?
+                                    AsDiscrete(threshold=0.25),  # needed ?
                                     EnsureType(),
                                 ]
                             )  #
                             post_label = EnsureType()
 
-                            # output_raw = [RemapTensor(0, 1)(t) for t in pred]
-                            output_raw = pred
+                            output_raw = [
+                                RemapTensor(new_max=1, new_min=0)(t)
+                                for t in pred
+                            ]
+                            # output_raw = pred
 
                             val_outputs = [
                                 post_pred(res_tensor) for res_tensor in pred