def _check_example(example, n_fg_class):
assert len(example) >= 3, \
'Each example must have at least four elements:' \
'img, mask and label.'
img, mask, label = example[:3]
assert_is_image(img, color=True)
_, H, W = img.shape
R = mask.shape[0]
assert isinstance(mask, np.ndarray), \
'mask must be a numpy.ndarray.'
assert isinstance(label, np.ndarray), \
'label must be a numpy.ndarray.'
assert mask.dtype == np.bool, \
'The type of mask must be bool'
assert label.dtype == np.int32, \
'The type of label must be numpy.int32.'
assert mask.shape == (R, H, W), \
'The shape of mask must be (R, H, W).'
assert label.shape == (R,), \
'The shape of label must be (R, ).'
assert label.min() >= 0 and label.max() < n_fg_class, \
'The value of label must be in [0, n_fg_class - 1].'
def _check_example(example, n_fg_class):
assert len(example) >= 4, \
'Each example must have at least four elements:' \
'img, bbox, mask and label.'
img, bbox, mask, label = example[:4]
assert_is_image(img, color=True)
_, H, W = img.shape
assert_is_bbox(bbox, size=(H, W))
R = bbox.shape[0]
assert isinstance(mask, np.ndarray), \
'mask must be a numpy.ndarray.'
assert isinstance(label, np.ndarray), \
'label must be a numpy.ndarray.'
assert mask.dtype == np.bool, \
'The type of mask must be bool'
assert label.dtype == np.int32, \
'The type of label must be numpy.int32.'
assert mask.shape == (R, H, W), \
'The shape of mask must be (R, H, W).'
assert label.shape == (R,), \
'The shape of label must be (R, ).'
assert label.min() >= 0 and label.max() < n_fg_class, \
'The value of label must be in [0, n_fg_class - 1].'
def _check_example(self, example):
assert_is_image(example[0])
assert example[0].shape == self.img_shape
assert example[1].dtype == np.float32
assert example[1].ndim == 1
assert len(example[1]) == self.n_class
np.testing.assert_almost_equal(example[1].sum(), 1.0)
assert (example[1] >= 0.0).all()
def _check_example(self, example):
assert_is_image(example[0])
self.assertEqual(example[0].shape, self.img_shape)
self.assertEqual(example[1].dtype, np.float32)
self.assertEqual(example[1].ndim, 1)
self.assertEqual(example[1].shape[0], self.n_class)
self.assertAlmostEqual(example[1].sum(), 1.0)
self.assertGreaterEqual(np.min(example[1]), 0.0)
def _check_example(self, example):
assert_is_image(example[0])
self.assertEqual(example[0].shape, self.img_shape)
assert_is_image(example[2])
self.assertEqual(example[2].shape, self.img_shape)
self.assertIsInstance(example[1], np.int32)
self.assertEqual(example[1].ndim, 0)
self.assertTrue(example[1] >= 0 and example[1] < self.n_class)
self.assertIsInstance(example[3], np.int32)
self.assertEqual(example[3].ndim, 0)
self.assertTrue(example[3] >= 0 and example[1] < self.n_class)
def _check_example(self, example):
assert_is_image(example[0])
self.assertEqual(example[0].shape, self.img_shape)
assert_is_image(example[2])
self.assertEqual(example[2].shape, self.img_shape)
self.assertIsInstance(example[1], np.int32)
self.assertEqual(example[1].ndim, 0)
self.assertTrue(example[1] >= 0 and example[1] < self.n_class)
self.assertIsInstance(example[3], np.int32)
self.assertEqual(example[3].ndim, 0)
self.assertTrue(example[3] >= 0 and example[1] < self.n_class)
def _check_example(example, n_point=None, no_visible=False):
assert len(example) >= 2, \
'Each example must have at least two elements:' \
'img, point (visible is optional).'
if len(example) == 2 or no_visible:
img, point = example[:2]
visible = None
elif len(example) >= 3:
img, point, visible = example[:3]
assert_is_image(img, color=True)
assert_is_point(point, visible, img.shape[1:], n_point)
def _check_example(example, n_class, color):
assert len(example) >= 2, \
'Each example must have at least two elements:' \
'img and label.'
img, label = example[:2]
assert_is_image(img, color=color)
assert isinstance(label, np.int32), \
'label must be a numpy.int32.'
assert label.ndim == 0, 'The ndim of label must be 0'
assert label >= 0 and label < n_class, \
'The value of label must be in [0, n_class - 1].'
def _check_example(example, n_class):
assert len(example) >= 2, \
'Each example must have at least two elements:' \
'img and label.'
img, label = example[:2]
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].'
def _check_example(example, n_point=None, no_mask=False):
assert len(example) >= 2, \
'Each example must have at least two elements:' \
'img, point (mask is optional).'
if len(example) == 2 or no_mask:
img, point = example[:2]
mask = None
elif len(example) >= 3:
img, point, mask = example[:3]
assert_is_image(img, color=True)
assert_is_point(point, mask, img.shape[1:])
if n_point is not None:
assert point.shape[0] == n_point, \
'The number of points is different from the expected number.'
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].'
def test_cityscapes_dataset(self):
indices = np.random.permutation(np.arange(len(self.dataset)))
for i in indices[:10]:
img = self.dataset[i]
assert_is_image(img, color=True)
def test_ade20k_dataset(self):
indices = np.random.permutation(np.arange(len(self.dataset)))
for i in indices[:10]:
img = self.dataset[i]
assert_is_image(img, color=True)
