def testStaticNonPositivePatchSizeRaisesError(self, patch_size): image_shape = [6, 7] with self.assertRaises(tf.errors.InvalidArgumentError): patch_ops.get_patch_mask(0, 0, patch_size=patch_size, image_shape=image_shape)
def testStaticCoordinatesOutsideImageRaisesError(self, y, x): image_shape = [15, 10] with self.assertRaises(tf.errors.InvalidArgumentError): patch_ops.get_patch_mask(y, x, patch_size=3, image_shape=image_shape)
def testHandleImageShapeWithChannels(self): image_shape = [15, 10, 3] mask = patch_ops.get_patch_mask(10, 5, patch_size=3, image_shape=image_shape) self.assertListEqual(mask.shape.as_list(), image_shape[:2])
def testMaskShape(self): image_shape = [15, 10] mask = patch_ops.get_patch_mask(10, 5, patch_size=3, image_shape=image_shape) self.assertListEqual(mask.shape.as_list(), image_shape)
def testDynamicNonPositivePatchSizeRaisesError(self): image_shape = [6, 7] patch_size = -1 * tf.random_uniform([], minval=0, maxval=3, dtype=tf.int32) mask = patch_ops.get_patch_mask( 0, 0, patch_size=patch_size, image_shape=image_shape) with self.assertRaises(tf.errors.InvalidArgumentError): self.evaluate(mask)
def testDynamicCoordinatesOutsideImageRaisesError(self): image_shape = [15, 10] x = tf.random_uniform([], minval=-2, maxval=-1, dtype=tf.int32) y = tf.random_uniform([], minval=0, maxval=1, dtype=tf.int32) mask = patch_ops.get_patch_mask( y, x, patch_size=3, image_shape=image_shape) with self.assertRaises(tf.errors.InvalidArgumentError): self.evaluate(mask)
def graph_fn(): image_shape = [6, 7] patch_size = -1 * tf.random_uniform( [], minval=0, maxval=3, dtype=tf.int32) mask = patch_ops.get_patch_mask(0, 0, patch_size=patch_size, image_shape=image_shape) return mask
def graph_fn(): image_shape = [15, 10] x = tf.random_uniform([], minval=-2, maxval=-1, dtype=tf.int32) y = tf.random_uniform([], minval=0, maxval=1, dtype=tf.int32) mask = patch_ops.get_patch_mask(y, x, patch_size=3, image_shape=image_shape) return mask
def testMaskAreaPartiallyOutsideImage(self): image_shape = [6, 7] mask = patch_ops.get_patch_mask(5, 6, patch_size=5, image_shape=image_shape) expected_mask = np.array([ [0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 1, 1, 1], [0, 0, 0, 0, 1, 1, 1], [0, 0, 0, 0, 1, 1, 1], ]).reshape(image_shape).astype(bool) self.assertAllEqual(mask, expected_mask)
def testMaskAreaWithOddPatchSize(self): image_shape = [6, 7] mask = patch_ops.get_patch_mask(2, 3, patch_size=3, image_shape=image_shape) expected_mask = np.array([ [0, 0, 0, 0, 0, 0, 0], [0, 0, 1, 1, 1, 0, 0], [0, 0, 1, 1, 1, 0, 0], [0, 0, 1, 1, 1, 0, 0], [0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0], ]).reshape(image_shape).astype(bool) self.assertAllEqual(mask, expected_mask)
def testMaskDType(self): mask = patch_ops.get_patch_mask(2, 3, patch_size=2, image_shape=[6, 7]) self.assertDTypeEqual(mask, bool)