Call NumpyToTensor last

ivadomed/scripts/download_data.py

@@ -47,7 +47,7 @@
         "url": ["https://github.com/ivadomed/model_find_disc_t2/archive/r20200928.zip"],
         "description": "Intervertebral disc detection model trained on T2-weighted images."},
     "data_functional_testing": {
-        "url": ["https://github.com/ivadomed/data_functional_testing/archive/r20210204.zip"],
+        "url": ["https://github.com/ivadomed/data_functional_testing/archive/r20210617.zip"],
         "description": "Data used for functional testing in Ivadomed."
     }
 

ivadomed/scripts/visualize_transforms.py

@@ -5,6 +5,7 @@
 import nibabel as nib
 import numpy as np
 import random
+import torch
 
 from ivadomed import config_manager as imed_config_manager
 from ivadomed.loader import utils as imed_loader_utils
@@ -158,7 +159,10 @@ def run_visualization(input, config, number, output, roi):
                 before = np.rot90(imed_maths.rescale_values_array(data[0], 0.0, 1.0))
             else:
                 before = after
-            after = np.rot90(imed_maths.rescale_values_array(stack_im[0], 0.0, 1.0))
+            if isinstance(stack_im[0], torch.Tensor):
+                after = np.rot90(imed_maths.rescale_values_array(stack_im[0].numpy(), 0.0, 1.0))
+            else:
+                after = np.rot90(imed_maths.rescale_values_array(stack_im[0], 0.0, 1.0))
             # Plot
             imed_utils.plot_transformed_sample(before,
                                                after,

ivadomed/transforms.py

@@ -142,13 +142,17 @@ def __init__(self, dict_transforms, requires_undo=False):
             "gt": torchvision_transforms.Compose(list_tr_gt),
             "roi": torchvision_transforms.Compose(list_tr_roi)}
 
-    def __call__(self, sample, metadata, data_type='im'):
+    def __call__(self, sample, metadata, data_type='im', preprocessing=False):
         if self.transform[data_type] is None or len(metadata) == 0:
             # In case self.transform[data_type] is None
             return None, None
         else:
             for tr in self.transform[data_type].transforms:
                 sample, metadata = tr(sample, metadata)
+
+            if not preprocessing:
+                numpy_to_tensor = NumpyToTensor()
+                sample, metadata = numpy_to_tensor(sample, metadata)
             return sample, metadata
 
 
@@ -172,6 +176,8 @@ def __call__(self, sample, metadata, data_type='gt'):
             # In case self.transforms.transform[data_type] is None
             return None, None
         else:
+            numpy_to_tensor = NumpyToTensor()
+            sample, metadata = numpy_to_tensor.undo_transform(sample, metadata)
             for tr in self.transforms.transform[data_type].transforms[::-1]:
                 sample, metadata = tr.undo_transform(sample, metadata)
             return sample, metadata
@@ -1067,17 +1073,20 @@ def apply_preprocessing_transforms(transforms, seg_pair, roi_pair=None):
     if roi_pair is not None:
         stack_roi, metadata_roi = transforms(sample=roi_pair["gt"],
                                              metadata=roi_pair['gt_metadata'],
-                                             data_type="roi")
+                                             data_type="roi",
+                                             preprocessing=True)
         metadata_input = imed_loader_utils.update_metadata(metadata_roi, metadata_input)
     # Run transforms on images
     stack_input, metadata_input = transforms(sample=seg_pair["input"],
                                              metadata=metadata_input,
-                                             data_type="im")
+                                             data_type="im",
+                                             preprocessing=True)
     # Run transforms on images
     metadata_gt = imed_loader_utils.update_metadata(metadata_input, seg_pair['gt_metadata'])
     stack_gt, metadata_gt = transforms(sample=seg_pair["gt"],
                                        metadata=metadata_gt,
-                                       data_type="gt")
+                                       data_type="gt",
+                                       preprocessing=True)
 
     seg_pair = {
         'input': stack_input,

testing/functional_tests/test_segment_volume.py

@@ -59,7 +59,6 @@ def test_segment_volume_2d(download_functional_test_files):
                 "applied_to": ["im", "gt"],
                 "dataset_type": ["training"]
             },
-            "NumpyToTensor": {},
             "NormalizeInstance": {"applied_to": ["im"]}
         },
         "postprocessing": {},
@@ -117,7 +116,6 @@ def test_segment_volume_2d_with_patches(download_functional_test_files, center_c
                 "applied_to": ["im", "gt"],
                 "dataset_type": ["training"]
             },
-            "NumpyToTensor": {},
             "NormalizeInstance": {"applied_to": ["im"]}
         },
         "postprocessing": {},
@@ -182,7 +180,6 @@ def test_segment_volume_3d(download_functional_test_files, center_crop):
                 "applied_to": ["im", "gt"],
                 "dataset_type": ["training"]
                 },
-            "NumpyToTensor": {},
             "NormalizeInstance": {"applied_to": ["im"]}
         },
         "postprocessing": {},

testing/unit_tests/test_orientation.py

@@ -53,7 +53,6 @@ def test_image_orientation(download_data_testing_test_files, loader_parameters):
             {
                 "size": [176, 128, 160]
             },
-        "NumpyToTensor": {},
         "NormalizeInstance": {"applied_to": ['im']}
     }
 

testing/unit_tests/test_testing.py

@@ -36,7 +36,6 @@ def setup_function():
         "CenterCrop": {
                 "size": [48, 48]
             },
-        "NumpyToTensor": {},
         "NormalizeInstance": {"applied_to": ["im"]}
     }])
 @pytest.mark.parametrize('test_lst', [['sub-unf01']])