Add assert_is_*_link

chainercv/links/model/segnet/segnet_basic.py

@@ -176,6 +176,6 @@ def predict(self, imgs):
                 dtype = score.dtype
                 score = resize(score, (H, W)).astype(dtype)
 
-            label = np.argmax(score, axis=0)
+            label = np.argmax(score, axis=0).astype(np.int32)
             labels.append(label)
         return labels

chainercv/links/model/ssd/ssd.py

@@ -182,9 +182,9 @@ def _suppress(self, raw_bbox, raw_score):
             label.append(xp.array((l,) * len(bbox_l)))
             score.append(score_l)
 
-        bbox = xp.vstack(bbox)
-        label = xp.hstack(label).astype(int)
-        score = xp.hstack(score)
+        bbox = xp.vstack(bbox).astype(np.float32)
+        label = xp.hstack(label).astype(np.int32)
+        score = xp.hstack(score).astype(np.float32)
 
         return bbox, label, score
 

chainercv/utils/__init__.py

@@ -8,7 +8,9 @@
 from chainercv.utils.iterator import unzip  # NOQA
 from chainercv.utils.testing import assert_is_bbox  # NOQA
 from chainercv.utils.testing import assert_is_detection_dataset  # NOQA
+from chainercv.utils.testing import assert_is_detection_link  # NOQA
 from chainercv.utils.testing import assert_is_image  # NOQA
 from chainercv.utils.testing import assert_is_semantic_segmentation_dataset  # NOQA
+from chainercv.utils.testing import assert_is_semantic_segmentation_link  # NOQA
 from chainercv.utils.testing import ConstantStubLink  # NOQA
 from chainercv.utils.testing import generate_random_bbox  # NOQA

chainercv/utils/testing/__init__.py

@@ -1,6 +1,8 @@
 from chainercv.utils.testing.assertions import assert_is_bbox  # NOQA
 from chainercv.utils.testing.assertions import assert_is_detection_dataset  # NOQA
+from chainercv.utils.testing.assertions import assert_is_detection_link  # NOQA
 from chainercv.utils.testing.assertions import assert_is_image  # NOQA
 from chainercv.utils.testing.assertions import assert_is_semantic_segmentation_dataset  # NOQA
+from chainercv.utils.testing.assertions import assert_is_semantic_segmentation_link  # NOQA
 from chainercv.utils.testing.constant_stub_link import ConstantStubLink  # NOQA
 from chainercv.utils.testing.generate_random_bbox import generate_random_bbox  # NOQA

chainercv/utils/testing/assertions/__init__.py

@@ -1,4 +1,6 @@
 from chainercv.utils.testing.assertions.assert_is_bbox import assert_is_bbox  # NOQA
 from chainercv.utils.testing.assertions.assert_is_detection_dataset import assert_is_detection_dataset  # NOQA
+from chainercv.utils.testing.assertions.assert_is_detection_link import assert_is_detection_link  # NOQA
 from chainercv.utils.testing.assertions.assert_is_image import assert_is_image  # NOQA
 from chainercv.utils.testing.assertions.assert_is_semantic_segmentation_dataset import assert_is_semantic_segmentation_dataset  # NOQA
+from chainercv.utils.testing.assertions.assert_is_semantic_segmentation_link import assert_is_semantic_segmentation_link  # NOQA

chainercv/utils/testing/assertions/assert_is_detection_link.py

@@ -0,0 +1,59 @@
+import numpy as np
+import six
+
+from chainercv.utils.testing.assertions.assert_is_bbox import assert_is_bbox
+
+
+def assert_is_detection_link(link, n_fg_class):
+    """Checks if a link satisfies detection link APIs.
+
+    This function checks if a given link satisfies detection link APIs
+    or not.
+    If the link does not satifiy the APIs, this function raises an
+    :class:`AssertionError`.
+
+    Args:
+        link: A link to be checked.
+        n_fg_class (int): The number of foreground classes.
+
+    """
+
+    imgs = [
+        np.random.randint(0, 256, size=(3, 480, 640)).astype(np.float32),
+        np.random.randint(0, 256, size=(3, 480, 320)).astype(np.float32)]
+
+    result = link.predict(imgs)
+    assert len(result) == 3, \
+        'Link must return three elements: bboxes, labels and scores.'
+    bboxes, labels, scores = result
+
+    assert len(bboxes) == len(imgs), \
+        'The length of bboxes must be same as that of imgs.'
+    assert len(labels) == len(imgs), \
+        'The length of labels must be same as that of imgs.'
+    assert len(scores) == len(imgs), \
+        'The length of scores must be same as that of imgs.'
+
+    for bbox, label, score in six.moves.zip(bboxes, labels, scores):
+        assert_is_bbox(bbox)
+
+        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.'
+        if len(label) > 0:
+            assert label.min() >= 0 and label.max() < n_fg_class, \
+                'The value of label must be in [0, n_fg_class - 1].'
+
+        assert isinstance(score, np.ndarray), \
+            'score must be a numpy.ndarray.'
+        assert score.dtype == np.float32, \
+            'The type of score must be numpy.float32.'
+        assert score.shape[1:] == (), \
+            'The shape of score must be (*,).'
+        assert len(score) == len(bbox), \
+            'The length of score must be same as that of bbox.'

chainercv/utils/testing/assertions/assert_is_semantic_segmentation_link.py

@@ -0,0 +1,35 @@
+import numpy as np
+import six
+
+
+def assert_is_semantic_segmentation_link(link, n_class):
+    """Checks if a link satisfies semantic segmentation link APIs.
+
+    This function checks if a given link satisfies semantic segmentation link
+    APIs or not.
+    If the link does not satifiy the APIs, this function raises an
+    :class:`AssertionError`.
+
+    Args:
+        link: A link to be checked.
+        n_class (int): The number of classes including background.
+
+    """
+
+    imgs = [
+        np.random.randint(0, 256, size=(3, 480, 640)).astype(np.float32),
+        np.random.randint(0, 256, size=(3, 480, 320)).astype(np.float32)]
+
+    labels = link.predict(imgs)
+    assert len(labels) == len(imgs), \
+        'The length of labels must be same as that of imgs.'
+
+    for img, label in six.moves.zip(imgs, labels):
+        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() >= 0 and label.max() < n_class, \
+            'The value of label must be in [0, n_class - 1].'

docs/source/reference/utils.rst

@@ -57,19 +57,27 @@ Testing Utilities
 
 assert_is_bbox
 ~~~~~~~~~~~~~~
-.. autofunctions:: assert_is_bbox
+.. autofunction:: assert_is_bbox
 
 assert_is_detection_dataset
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~
-.. autofunctions:: assert_is_detection_dataset
+.. autofunction:: assert_is_detection_dataset
+
+assert_is_detection_link
+~~~~~~~~~~~~~~~~~~~~~~~~
+.. autofunction:: assert_is_detection_link
 
 assert_is_image
 ~~~~~~~~~~~~~~~
-.. autofunctions:: assert_is_image
+.. autofunction:: assert_is_image
 
 assert_is_semantic_segmentation_dataset
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-.. autofunctions:: assert_is_semantic_segmentation_dataset
+.. autofunction:: assert_is_semantic_segmentation_dataset
+
+assert_is_semantic_segmentation_link
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+.. autofunction:: assert_is_semantic_segmentation_link
 
 ConstantStubLink
 ~~~~~~~~~~~~~~~~

tests/links_tests/model_tests/faster_rcnn_tests/test_faster_rcnn.py

@@ -5,6 +5,8 @@
 from chainer import testing
 from chainer.testing import attr
 
+from chainercv.utils import assert_is_detection_link
+
 from dummy_faster_rcnn import DummyFasterRCNN
 
 
@@ -58,39 +60,13 @@ def test_call_gpu(self):
         self.link.to_gpu()
         self.check_call()
 
-    def check_predict(self):
-        imgs = [
-            _random_array(np, (3, 640, 480)),
-            _random_array(np, (3, 320, 320))]
-
-        bboxes, labels, scores = self.link.predict(imgs)
-
-        self.assertEqual(len(bboxes), len(imgs))
-        self.assertEqual(len(labels), len(imgs))
-        self.assertEqual(len(scores), len(imgs))
-
-        for bbox, label, score in zip(bboxes, labels, scores):
-            self.assertIsInstance(bbox, np.ndarray)
-            self.assertEqual(bbox.dtype, np.float32)
-            self.assertEqual(bbox.ndim, 2)
-            self.assertLessEqual(bbox.shape[0], self.n_roi)
-            self.assertEqual(bbox.shape[1], 4)
-
-            self.assertIsInstance(label, np.ndarray)
-            self.assertEqual(label.dtype, np.int32)
-            self.assertEqual(label.shape, (bbox.shape[0],))
-
-            self.assertIsInstance(score, np.ndarray)
-            self.assertEqual(score.dtype, np.float32)
-            self.assertEqual(score.shape, (bbox.shape[0],))
-
     def test_predict_cpu(self):
-        self.check_predict()
+        assert_is_detection_link(self.link, self.n_class - 1)
 
     @attr.gpu
     def test_predict_gpu(self):
         self.link.to_gpu()
-        self.check_predict()
+        assert_is_detection_link(self.link, self.n_class - 1)
 
 
 @testing.parameterize(

tests/links_tests/model_tests/segnet_tests/test_segnet_basic.py

@@ -6,6 +6,7 @@
 from chainer.testing import attr
 
 from chainercv.links import SegNetBasic
+from chainercv.utils import assert_is_semantic_segmentation_link
 
 
 @testing.parameterize(
@@ -37,29 +38,13 @@ def test_call_gpu(self):
         self.link.to_gpu()
         self.check_call()
 
-    def check_predict(self):
-        hs = np.random.randint(128, 160, size=(2,))
-        ws = np.random.randint(128, 160, size=(2,))
-        imgs = [
-            np.random.uniform(size=(3, hs[0], ws[0])).astype(np.float32),
-            np.random.uniform(size=(3, hs[1], ws[1])).astype(np.float32),
-        ]
-
-        labels = self.link.predict(imgs)
-
-        self.assertEqual(len(labels), 2)
-        for i in range(2):
-            self.assertIsInstance(labels[i], np.ndarray)
-            self.assertEqual(labels[i].shape, (hs[i], ws[i]))
-            self.assertEqual(labels[i].dtype, np.int64)
-
     def test_predict_cpu(self):
-        self.check_predict()
+        assert_is_semantic_segmentation_link(self.link, self.n_class)
 
     @attr.gpu
     def test_predict_gpu(self):
         self.link.to_gpu()
-        self.check_predict()
+        assert_is_semantic_segmentation_link(self.link, self.n_class)
 
 
 testing.run_module(__name__, __file__)

tests/links_tests/model_tests/ssd_tests/test_ssd.py

@@ -7,6 +7,7 @@
 
 from chainercv.links.model.ssd import Multibox
 from chainercv.links.model.ssd import SSD
+from chainercv.utils import assert_is_detection_link
 
 
 def _random_array(xp, shape):
@@ -154,38 +155,13 @@ def test_use_preset(self):
         with self.assertRaises(ValueError):
             self.link.use_preset('unknown')
 
-    def _check_predict(self):
-        imgs = [
-            _random_array(np, (3, 640, 480)),
-            _random_array(np, (3, 320, 320))]
-
-        bboxes, labels, scores = self.link.predict(imgs)
-
-        self.assertEqual(len(bboxes), len(imgs))
-        self.assertEqual(len(labels), len(imgs))
-        self.assertEqual(len(scores), len(imgs))
-
-        for bbox, label, score in zip(bboxes, labels, scores):
-            self.assertIsInstance(bbox, np.ndarray)
-            self.assertEqual(bbox.ndim, 2)
-            self.assertLessEqual(bbox.shape[0], self.n_bbox * self.n_fg_class)
-            self.assertEqual(bbox.shape[1], 4)
-
-            self.assertIsInstance(label, np.ndarray)
-            self.assertEqual(label.ndim, 1)
-            self.assertEqual(label.shape[0], bbox.shape[0])
-
-            self.assertIsInstance(score, np.ndarray)
-            self.assertEqual(score.ndim, 1)
-            self.assertEqual(score.shape[0], bbox.shape[0])
-
     def test_predict_cpu(self):
-        self._check_predict()
+        assert_is_detection_link(self.link, self.n_fg_class)
 
     @attr.gpu
     def test_predict_gpu(self):
         self.link.to_gpu()
-        self._check_predict()
+        assert_is_detection_link(self.link, self.n_fg_class)
 
 
 testing.run_module(__name__, __file__)

tests/utils_tests/testing_tests/assertions_tests/test_assert_is_detection_link.py

@@ -0,0 +1,79 @@
+import numpy as np
+import unittest
+
+import chainer
+from chainer import testing
+
+from chainercv.utils import assert_is_detection_link
+from chainercv.utils import generate_random_bbox
+
+
+class DetectionLink(chainer.Link):
+
+    def predict(self, imgs):
+        bboxes = list()
+        labels = list()
+        scores = list()
+
+        for img in imgs:
+            n_bbox = np.random.randint(0, 10)
+            bboxes.append(generate_random_bbox(
+                n_bbox, img.shape[1:], 4, 12))
+            labels.append(np.random.randint(
+                0, 20, size=n_bbox).astype(np.int32))
+            scores.append(np.random.uniform(
+                0, 1, size=n_bbox).astype(np.float32))
+
+        return bboxes, labels, scores
+
+
+class InvalidPredictionSizeLink(DetectionLink):
+
+    def predict(self, imgs):
+        bboxes, labels, scores = super(
+            InvalidPredictionSizeLink, self).predict(imgs)
+        return bboxes[1:], labels[1:], scores[1:]
+
+
+class InvalidLabelSizeLink(DetectionLink):
+
+    def predict(self, imgs):
+        bboxes, labels, scores = super(
+            InvalidLabelSizeLink, self).predict(imgs)
+        return bboxes, [label[1:] for label in labels], scores
+
+
+class InvalidLabelValueLink(DetectionLink):
+
+    def predict(self, imgs):
+        bboxes, labels, scores = super(
+            InvalidLabelValueLink, self).predict(imgs)
+        return bboxes, [label + 1000 for label in labels], scores
+
+
+class InvalidScoreSizeLink(DetectionLink):
+
+    def predict(self, imgs):
+        bboxes, labels, scores = super(
+            InvalidScoreSizeLink, self).predict(imgs)
+        return bboxes, labels, [score[1:] for score in scores]
+
+
+@testing.parameterize(
+    {'link': DetectionLink(), 'valid': True},
+    {'link': InvalidPredictionSizeLink(), 'valid': False},
+    {'link': InvalidLabelSizeLink(), 'valid': False},
+    {'link': InvalidLabelValueLink(), 'valid': False},
+    {'link': InvalidScoreSizeLink(), 'valid': False},
+)
+class TestAssertIsDetectionLink(unittest.TestCase):
+
+    def test_assert_is_detection_link(self):
+        if self.valid:
+            assert_is_detection_link(self.link, 20)
+        else:
+            with self.assertRaises(AssertionError):
+                assert_is_detection_link(self.link, 20)
+
+
+testing.run_module(__name__, __file__)