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)
