fix conflicts and add MaskDilatorStream

Author: apyskir

pipelines.py

@@ -5,8 +5,8 @@
 from steps.preprocessing.misc import XYSplit
 from utils import squeeze_inputs
 from models import PyTorchUNet, PyTorchUNetStream
-from postprocessing import Resizer, CategoryMapper, MulticlassLabeler, MaskDilator\
-    ResizerStream, CategoryMapperStream, MulticlassLabelerStream
+from postprocessing import Resizer, CategoryMapper, MulticlassLabeler, MaskDilator, \
+    ResizerStream, CategoryMapperStream, MulticlassLabelerStream, MaskDilatorStream
 
 
 def unet(config, train_mode):
@@ -27,7 +27,7 @@ def unet(config, train_mode):
     mask_postprocessed = mask_postprocessing(unet, config, save_output=save_output)
     if config.postprocessor["dilate_selem_size"] > 0:
         mask_postprocessed = Step(name='mask_dilation',
-                                  transformer=MaskDilator(**config.postprocessor),
+                                  transformer=MaskDilatorStream(**config.postprocessor) if config.execution.stream_mode else MaskDilator(**config.unet),
                                   input_steps=[mask_postprocessed],
                                   adapter={'images': ([(mask_postprocessed.name, 'categorized_images')]),
                                            },

postprocessing.py

@@ -76,6 +76,15 @@ def _transform(self, images):
             yield categorize_image(image)
 
 
+class MaskDilatorStream(BaseTransformer):
+    def transform(self, images):
+        return {'categorized_images': self._transform(images)}
+
+    def _transform(self, images):
+        for image in tqdm(images):
+            yield dilate_image(image)
+
+
 def label(mask):
     labeled, nr_true = ndi.label(mask)
     return labeled