def test_assert_binary_exception_raised(self, dtype):
"""Checks that assert_binary raises exceptions for invalid input."""
tensor_size = np.random.randint(3) + 1
tensor_shape = np.random.randint(1, 10, size=(tensor_size)).tolist()
num_elements = np.prod(tensor_shape)
# Vector with all ones except for a single negative entry.
vector_with_negative = np.ones(num_elements)
vector_with_negative[np.random.randint(num_elements)] = -1
vector_with_negative = vector_with_negative.reshape(tensor_shape)
vector_with_negative = tf.convert_to_tensor(value=vector_with_negative,
dtype=dtype)
# Vector with all zeros except for a single 0.5 (or 2 in case dtype=int).
vector = np.zeros(num_elements)
vector[np.random.randint(num_elements)] = 2
vector = vector.reshape(tensor_shape)
vector = tf.convert_to_tensor(value=vector, dtype=dtype)
vector = vector - tf.compat.v1.div(vector, 4) * 3
with self.subTest(name="has_negative_number"):
with self.assertRaises(tf.errors.InvalidArgumentError):
self.evaluate(asserts.assert_binary(vector_with_negative))
with self.subTest(name="has_non_binary_number"):
with self.assertRaises(tf.errors.InvalidArgumentError):
self.evaluate(asserts.assert_binary(vector))
def evaluate(ground_truth_labels: type_alias.TensorLike,
predicted_labels: type_alias.TensorLike,
grid_size: int = 1,
name: str = "intersection_over_union_evaluate") -> tf.Tensor:
"""Computes the Intersection-Over-Union metric for the given ground truth and predicted labels.
Note:
In the following, A1 to An are optional batch dimensions, which must be
broadcast compatible, and G1 to Gm are the grid dimensions.
Args:
ground_truth_labels: A tensor of shape `[A1, ..., An, G1, ..., Gm]`, where
the last m axes represent a grid of ground truth attributes. Each
attribute can either be 0 or 1.
predicted_labels: A tensor of shape `[A1, ..., An, G1, ..., Gm]`, where the
last m axes represent a grid of predicted attributes. Each attribute can
either be 0 or 1.
grid_size: The number of grid dimensions. Defaults to 1.
name: A name for this op. Defaults to "intersection_over_union_evaluate".
Returns:
A tensor of shape `[A1, ..., An]` that stores the intersection-over-union
metric of the given ground truth labels and predictions.
Raises:
ValueError: if the shape of `ground_truth_labels`, `predicted_labels` is
not supported.
"""
with tf.name_scope(name):
ground_truth_labels = tf.convert_to_tensor(value=ground_truth_labels)
predicted_labels = tf.convert_to_tensor(value=predicted_labels)
shape.compare_batch_dimensions(tensors=(ground_truth_labels,
predicted_labels),
tensor_names=("ground_truth_labels",
"predicted_labels"),
last_axes=-grid_size,
broadcast_compatible=True)
ground_truth_labels = asserts.assert_binary(ground_truth_labels)
predicted_labels = asserts.assert_binary(predicted_labels)
sum_ground_truth = tf.math.reduce_sum(input_tensor=ground_truth_labels,
axis=list(range(-grid_size, 0)))
sum_predictions = tf.math.reduce_sum(input_tensor=predicted_labels,
axis=list(range(-grid_size, 0)))
intersection = tf.math.reduce_sum(input_tensor=ground_truth_labels *
predicted_labels,
axis=list(range(-grid_size, 0)))
union = sum_ground_truth + sum_predictions - intersection
return tf.where(tf.math.equal(union, 0), tf.ones_like(union),
intersection / union)
def test_assert_binary_passthrough(self):
"""Checks that the assert is a passthrough when the flag is False."""
vector_input = _pick_random_vector()
vector_output = asserts.assert_binary(vector_input)
self.assertIs(vector_input, vector_output)