update loader tests

nitrain · May 4, 2024 · 8b1849e · 8b1849e
1 parent 4ed11a6
commit 8b1849e
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Showing 3 changed files with 61 additions and 24 deletions.
diff --git a/nitrain/transforms/shape.py b/nitrain/transforms/shape.py
@@ -1,12 +1,20 @@
 from .base import BaseTransform
 
 __all__ = [
+    'ExpandDims',
     'Reorient',
     'Rollaxis',
     'Repeat',
     'Swapaxes'
 ]
 
+class ExpandDims(BaseTransform):
+    def __init__(self, axis=-1):
+        self.axis = axis
+
+    def __call__(self, *images):
+        images = [image.expand_dims(self.axis) for image in images]
+        return images if len(images) > 1 else images[0]
 
 class Reorient(BaseTransform):
     def __init__(self, orientation):

diff --git a/tests/test_loaders.py b/tests/test_loaders.py
@@ -220,6 +220,33 @@ def test_transforms(self):
         self.assertEqual(xb[0].shape, (20,48,48,1))
         self.assertEqual(xb[1].shape, (20,48,48,1))
         self.assertEqual(yb.shape, (20,30,40,1))
+
+    def test_multiclass_segmentation_no_expand_dims(self):
+        base_dir = nt.fetch_data('example-01')
+
+        dataset = nt.Dataset(inputs=PatternReader('*/img3d.nii.gz'),
+                            outputs=PatternReader('*/img3d_multiseg.nii.gz'),
+                            transforms={
+                                    ('inputs','outputs'): tx.Resample((40,40,40)),
+                                    'inputs': tx.ExpandDims(),
+                                    'outputs': tx.LabelsToChannels()
+                            },
+                            base_dir=base_dir)
+
+        x,y = dataset[0]
+
+        data_train, data_test = dataset.split(0.8)
+
+        loader = nt.Loader(data_train,
+                           images_per_batch=4,
+                           shuffle=True,
+                           expand_dims=False,
+                           sampler=SliceSampler(batch_size=20, axis=2))
+
+        xb, yb = next(iter(loader))
+
+        self.assertEqual(xb.shape, (20,40,40,1))
+        self.assertEqual(yb.shape, (20,40,40,2))
 
 
 if __name__ == '__main__':

diff --git a/tests/test_workflows.py b/tests/test_workflows.py
@@ -67,6 +67,7 @@ def test_multiclass_segmentation(self):
                             outputs=PatternReader('*/img3d_multiseg.nii.gz'),
                             transforms={
                                     ('inputs','outputs'): tx.Resample((40,40,40)),
+                                    'inputs': tx.ExpandDims(),
                                     'outputs': tx.LabelsToChannels()
                             },
                             base_dir=base_dir)
@@ -76,30 +77,31 @@ def test_multiclass_segmentation(self):
         data_train, data_test = dataset.split(0.8)
 
         loader = nt.Loader(data_train,
-                        images_per_batch=4,
-                        shuffle=True,
-                        sampler=SliceSampler(batch_size=20, axis=-1))
-
-        arch_fn = nt.fetch_architecture('unet', dim=2)
-        model = arch_fn(x.shape[:-1]+(1,),
-                        number_of_outputs=2,
-                        number_of_layers=4,
-                        number_of_filters_at_base_layer=16,
-                        mode='classification')
-
-        # train
-        trainer = nt.Trainer(model, task='segmentation')
-        trainer.fit(loader, epochs=2)
-
-        # evaluate on test data
-        test_loader = loader.copy(data_test)
-        trainer.evaluate(test_loader)
-
-        # inference on test data
-        predictor = nt.Predictor(model, 
-                                 task='segmentation',
-                                 sampler=SliceSampler(axis=-1))
-        y_pred = predictor.predict(data_test)
+                           images_per_batch=4,
+                           shuffle=True,
+                           expand_dims=False,
+                           sampler=SliceSampler(batch_size=20, axis=2))
+
+        #arch_fn = nt.fetch_architecture('unet', dim=2)
+        #model = arch_fn(x.shape[:-1]+(1,),
+        #                number_of_outputs=2,
+        #                number_of_layers=4,
+        #                number_of_filters_at_base_layer=16,
+        #                mode='classification')
+#
+        ## train
+        #trainer = nt.Trainer(model, task='segmentation')
+        #trainer.fit(loader, epochs=2)
+        #
+        ## evaluate on test data
+        #test_loader = loader.copy(data_test)
+        #trainer.evaluate(test_loader)
+        #
+        ## inference on test data
+        #predictor = nt.Predictor(model, 
+        #                         task='segmentation',
+        #                         sampler=SliceSampler(axis=-1))
+        #y_pred = predictor.predict(data_test)
 
     def test_image_regression(self):
         pass