repeat -> n_example

chainercv/utils/testing/assertions/assert_is_detection_dataset.py

@@ -5,7 +5,7 @@
 from chainercv.utils.testing.assertions.assert_is_image import assert_is_image
 
 
-def assert_is_detection_dataset(dataset, n_fg_class, repeat=10):
+def assert_is_detection_dataset(dataset, n_fg_class, n_example=None):
     """Checks if a dataset satisfies detection dataset APIs.
 
     This function checks if a given dataset satisfies detection dataset APIs
@@ -16,34 +16,41 @@ def assert_is_detection_dataset(dataset, n_fg_class, repeat=10):
     Args:
         dataset: A dataset to be checked.
         n_fg_class (int): The number of foreground classes.
-        repeat (int): The number of trials. This function picks
-            an example randomly and checks it. This argmuments determines,
-            how many times this function picks and checks.
-            The default value is :obj:`10`.
+        n_example (int): The number of examples to be checked.
+            If this argument is specified, this function picks
+            examples ramdomly and checks them. Otherwise,
+            this function checks all examples.
+
     """
 
     assert len(dataset) > 0, 'The length of dataset must be greater than zero.'
 
-    for _ in six.moves.range(repeat):
-        i = np.random.randint(0, len(dataset))
-        sample = dataset[i]
-
-        assert len(sample) >= 3, \
-            'Each example must have at least three elements:' \
-            'img, bbox and label.'
-
-        img, bbox, label = sample[:3]
-
-        assert_is_image(img, color=True)
-        assert_is_bbox(bbox, size=img.shape[1:])
-
-        assert isinstance(label, np.ndarray), \
-            'label must be a numpy.ndarray.'
-        assert label.dtype == np.int32, \
-            'The type of label must be numpy.int32.'
-        assert label.shape[1:] == (), \
-            'The shape of label must be (*,).'
-        assert len(label) == len(bbox), \
-            'The length of label must be same as that of bbox.'
-        assert label.min() >= 0 and label.max() < n_fg_class, \
-            'The value of label must be in [0, n_fg_class - 1].'
+    if n_example:
+        for _ in six.moves.range(n_example):
+            i = np.random.randint(0, len(dataset))
+            _check_example(dataset[i], n_fg_class)
+    else:
+        for i in six.moves.range(len(dataset)):
+            _check_example(dataset[i], n_fg_class)
+
+
+def _check_example(example, n_fg_class):
+    assert len(example) >= 3, \
+        'Each example must have at least three elements:' \
+        'img, bbox and label.'
+
+    img, bbox, label = example[:3]
+
+    assert_is_image(img, color=True)
+    assert_is_bbox(bbox, size=img.shape[1:])
+
+    assert isinstance(label, np.ndarray), \
+        'label must be a numpy.ndarray.'
+    assert label.dtype == np.int32, \
+        'The type of label must be numpy.int32.'
+    assert label.shape[1:] == (), \
+        'The shape of label must be (*,).'
+    assert len(label) == len(bbox), \
+        'The length of label must be same as that of bbox.'
+    assert label.min() >= 0 and label.max() < n_fg_class, \
+        'The value of label must be in [0, n_fg_class - 1].'

chainercv/utils/testing/assertions/assert_is_semantic_segmentation_dataset.py

@@ -4,7 +4,7 @@
 from chainercv.utils.testing.assertions.assert_is_image import assert_is_image
 
 
-def assert_is_semantic_segmentation_dataset(dataset, n_class, repeat=10):
+def assert_is_semantic_segmentation_dataset(dataset, n_class, n_example=None):
     """Checks if a dataset satisfies semantic segmentation dataset APIs.
 
     This function checks if a given dataset satisfies semantic segmentation
@@ -15,31 +15,38 @@ def assert_is_semantic_segmentation_dataset(dataset, n_class, repeat=10):
     Args:
         dataset: A dataset to be checked.
         n_class (int): The number of classes including background.
-        repeat (int): The number of trials. This function picks
-            an example randomly and checks it. This argmuments determines,
-            how many times this function picks and checks.
-            The default value is :obj:`10`.
+        n_example (int): The number of examples to be checked.
+            If this argument is specified, this function picks
+            examples ramdomly and checks them. Otherwise,
+            this function checks all examples.
+
     """
 
     assert len(dataset) > 0, 'The length of dataset must be greater than zero.'
 
-    for _ in six.moves.range(repeat):
-        i = np.random.randint(0, len(dataset))
-        sample = dataset[i]
+    if n_example:
+        for _ in six.moves.range(n_example):
+            i = np.random.randint(0, len(dataset))
+            _check_example(dataset[i], n_class)
+    else:
+        for i in six.moves.range(len(dataset)):
+            _check_example(dataset[i], n_class)
+
 
-        assert len(sample) >= 2, \
-            'Each example must have at least two elements:' \
-            'img and label.'
+def _check_example(example, n_class):
+    assert len(example) >= 2, \
+        'Each example must have at least two elements:' \
+        'img and label.'
 
-        img, label = sample[:2]
+    img, label = example[:2]
 
-        assert_is_image(img, color=True)
+    assert_is_image(img, color=True)
 
-        assert isinstance(label, np.ndarray), \
-            'label must be a numpy.ndarray.'
-        assert label.dtype == np.int32, \
-            'The type of label must be numpy.int32.'
-        assert label.shape == img.shape[1:], \
-            'The shape of label must be (H, W).'
-        assert label.min() >= -1 and label.max() < n_class, \
-            'The value of label must be in [-1, n_class - 1].'
+    assert isinstance(label, np.ndarray), \
+        'label must be a numpy.ndarray.'
+    assert label.dtype == np.int32, \
+        'The type of label must be numpy.int32.'
+    assert label.shape == img.shape[1:], \
+        'The shape of label must be (H, W).'
+    assert label.min() >= -1 and label.max() < n_class, \
+        'The value of label must be in [-1, n_class - 1].'

tests/utils_tests/testing_tests/assertions_tests/test_assert_is_detection_dataset.py

@@ -14,7 +14,7 @@ def __init__(self, *options):
         self.options = options
 
     def __len__(self):
-        return 100
+        return 10
 
     def get_example(self, i):
         img = np.random.randint(0, 256, size=(3, 48, 64))

tests/utils_tests/testing_tests/assertions_tests/test_assert_is_semantic_segmentation_dataset.py

@@ -13,7 +13,7 @@ def __init__(self, *options):
         self.options = options
 
     def __len__(self):
-        return 100
+        return 10
 
     def get_example(self, i):
         img = np.random.randint(0, 256, size=(3, 48, 64))