changed csv_file -> csv_path_file in various places and merged carole…

… changes

doc/source/config_spec.md

@@ -119,7 +119,7 @@ within each section.
 
  Name | Type | Example | Default
  ---- | ---- | ------- | -------
-[csv_file](#csv-file) | `string` | `csv_file=file_list.csv` | `''`
+[csv_path_file](#csv-path-file) | `string` | `csv_path_file=file_list.csv` | `''`
 [path_to_search](#path-to-search) | `string` | `path_to_search=my_data/fold_1` | NiftyNet home folder
 [filename_contains](#filename-contains) | `string` or `string array` | `filename_contains=foo, bar` | `''`
 [filename_not_contains](#filename-not-contains) | `string` or `string array` | `filename_not_contains=foo` | `''`
@@ -130,7 +130,7 @@ within each section.
 [spatial_window_size](#spatial-window-size) | `integer array` | `spatial_window_size=64, 64, 64` | `''`
 [loader](#loader) | `string` | `loader=simpleitk` | `None`
 
-###### `csv_file`
+###### `csv_path_file`
 A file path to a list of input images.  If the file exists, input image name
 list will be loaded from the file; the filename based input image search will
 be disabled; [path_to_search](#path-to-search),
@@ -221,8 +221,8 @@ with an interpolation order of `3`.
 
 A CSV file with the matched filenames and extracted subject names will be
 generated to `T1Image.csv` in [`model_dir`](#model-dir) (by default; the CSV
-file location can be specified by setting [csv_file](#csv-file)).  To exclude
-particular images, the [csv_file](#csv-file) can be edited manually.
+file location can be specified by setting [csv_path_file](#csv-path-file)).  To exclude
+particular images, the [csv_path_file](#csv-path-file) can be edited manually.
 
 This input source can be used alone, as a `T1` MRI input to an application.
 It can also be used along with other modalities, a multi-modality example

niftynet/contrib/csv_reader/classification_application.py

@@ -269,7 +269,6 @@ def switch_sampler(for_training):
                 data_dict = switch_sampler(for_training=True)
 
             image = tf.cast(data_dict['image'], tf.float32)
-            print(self.sampler[0][0]()['label'])
             net_args = {'is_training': self.is_training,
                         'keep_prob': self.net_param.keep_prob}
             net_out = self.net(image, **net_args)

niftynet/io/image_reader.py

@@ -410,7 +410,7 @@ def _filename_to_image_list(file_list, mod_dict, data_param):
     if not volume_list:
         tf.logging.fatal(
             "Empty filename lists, please check the csv "
-            "files. (removing csv_file keyword if it is in the config file "
+            "files. (removing csv_path_file keyword if it is in the config file "
             "to automatically search folders and generate new csv "
             "files again)\n\n"
             "Please note in the matched file names, each subject id are "

niftynet/layer/bn.py

@@ -135,5 +135,5 @@ def layer_op(self, inputs):
             variables_collections=None,
             outputs_collections=None,
             trainable=True,
-            data_format='NHWC',
+            data_format='NWC',
             scope=None)

niftynet/layer/loss_classification.py

@@ -79,6 +79,6 @@ def cross_entropy(prediction,
     :return: the loss
     """
     ground_truth = tf.to_int64(ground_truth)
-    loss = tf.nn.sparse_softmax_cross_entropy_with_logits(logits=prediction, labels=ground_truth) 
+    loss = tf.nn.sparse_softmax_cross_entropy_with_logits(logits=prediction, labels=ground_truth)
     return loss
 

niftynet/layer/spatial_transformer.py

@@ -78,7 +78,6 @@ def layer_op(self,field):
                     for d in [0, 1, 2]]
     resampled=tf.stack(resampled_list,5)
     permuted_shape=[batch_size]+[f-3 for f in self._coeff_shape]+self._knot_spacing+[spatial_rank]
-    print(permuted_shape)
     permuted=tf.transpose(tf.reshape(resampled,permuted_shape),[0,1,4,2,5,3,6,7])
     valid_size=[(f-3)*k for f,k in zip(self._coeff_shape,self._knot_spacing)]
     reshaped=tf.reshape(permuted,[batch_size]+valid_size+[spatial_rank])

tests/image_reader_test.py

@@ -18,7 +18,7 @@
 # test multiple modalities
 MULTI_MOD_DATA = {
     'T1': ParserNamespace(
-        csv_file=os.path.join('testing_data', 'T1reader.csv'),
+        csv_path_file=os.path.join('testing_data', 'T1reader.csv'),
         path_to_search='testing_data',
         filename_contains=('_o_T1_time',),
         filename_not_contains=('Parcellation',),
@@ -28,7 +28,7 @@
         loader=None
     ),
     'FLAIR': ParserNamespace(
-        csv_file=os.path.join('testing_data', 'FLAIRreader.csv'),
+        csv_path_file=os.path.join('testing_data', 'FLAIRreader.csv'),
         path_to_search='testing_data',
         filename_contains=('FLAIR_',),
         filename_not_contains=('Parcellation',),
@@ -43,7 +43,7 @@
 # test single modalities
 SINGLE_MOD_DATA = {
     'lesion': ParserNamespace(
-        csv_file=os.path.join('testing_data', 'lesion.csv'),
+        csv_path_file=os.path.join('testing_data', 'lesion.csv'),
         path_to_search='testing_data',
         filename_contains=('Lesion',),
         filename_not_contains=('Parcellation',),
@@ -57,7 +57,7 @@
 
 EXISTING_DATA = {
     'lesion': ParserNamespace(
-        csv_file=os.path.join('testing_data', 'lesion.csv'),
+        csv_path_file=os.path.join('testing_data', 'lesion.csv'),
         interp_order=3,
         pixdim=None,
         axcodes=None,
@@ -68,7 +68,7 @@
 # test labels
 LABEL_DATA = {
     'parcellation': ParserNamespace(
-        csv_file=os.path.join('testing_data', 'labels.csv'),
+        csv_path_file=os.path.join('testing_data', 'labels.csv'),
         path_to_search='testing_data',
         filename_contains=('Parcellation',),
         filename_not_contains=('Lesion',),
@@ -82,7 +82,7 @@
 
 BAD_DATA = {
     'lesion': ParserNamespace(
-        csv_file=os.path.join('testing_data', 'lesion.csv'),
+        csv_path_file=os.path.join('testing_data', 'lesion.csv'),
         path_to_search='testing_data',
         filename_contains=('Lesion',),
         filename_not_contains=('Parcellation',),
@@ -96,7 +96,7 @@
 
 IMAGE_2D_DATA = {
     'color_images': ParserNamespace(
-        csv_file=os.path.join('testing_data', 'images_2d_u.csv'),
+        csv_path_file=os.path.join('testing_data', 'images_2d_u.csv'),
         path_to_search=os.path.join('testing_data', 'images2d'),
         filename_contains=('_u.png',),
         interp_order=1,
@@ -105,7 +105,7 @@
         loader=None
     ),
     'gray_images': ParserNamespace(
-        csv_file=os.path.join('testing_data', 'images_2d_g.csv'),
+        csv_path_file=os.path.join('testing_data', 'images_2d_g.csv'),
         path_to_search=os.path.join('testing_data', 'images2d'),
         filename_contains=('_g.png',),
         interp_order=1,
@@ -114,7 +114,7 @@
         loader=None
     ),
     'seg_masks': ParserNamespace(
-        csv_file=os.path.join('testing_data', 'images_2d_m.csv'),
+        csv_path_file=os.path.join('testing_data', 'images_2d_m.csv'),
         path_to_search=os.path.join('testing_data', 'images2d'),
         filename_contains=('_m.png',),
         interp_order=0,

tests/reader_modular_test.py

@@ -138,7 +138,7 @@ def test_reader_field(self):
 
     def test_input_properties(self):
         data_param = {'mr': {'path_to_search': IMAGE_PATH_2D,
-                             'csv_file': '2d_test.csv'}}
+                             'csv_path_file': '2d_test.csv'}}
         reader = ImageReader().initialise(data_param)
         self.default_property_asserts(reader)
         idx, data, interp = reader()
@@ -151,7 +151,7 @@ def test_input_properties(self):
 
     def test_no_2d_resampling_properties(self):
         data_param = {'mr': {'path_to_search': IMAGE_PATH_2D,
-                             'csv_file': '2d_test.csv',
+                             'csv_path_file': '2d_test.csv',
                              'pixdim': (2, 2, 2),
                              'axcodes': 'RAS'}}
         reader = ImageReader().initialise(data_param)
@@ -199,7 +199,7 @@ class Read2D_1DTest(tf.test.TestCase):
     # loading 2d images of rank 3: [x, y, 1]
     def test_no_2d_resampling_properties(self):
         data_param = {'mr': {'path_to_search': IMAGE_PATH_2D_1,
-                             'csv_file': '2d_test.csv',
+                             'csv_path_file': '2d_test.csv',
                              'filename_contains': '_img',
                              'pixdim': (2, 2, 2),
                              'axcodes': 'RAS'}}
@@ -282,7 +282,7 @@ class Read2D_colorTest(tf.test.TestCase):
     # loading 2d images of rank 3: [x, y, 3] or [x, y, 4]
     def test_no_2d_resampling_properties(self):
         data_param = {'mr': {'path_to_search': IMAGE_PATH_2D,
-                             'csv_file': '2d_test.csv',
+                             'csv_path_file': '2d_test.csv',
                              'filename_contains': '_u',
                              'pixdim': (2, 2, 2),
                              'axcodes': 'RAS'}}