def smart_constant_value(pred):
"""Return the bool value for `pred`, or None if `pred` had a dynamic value.
Arguments:
pred: A scalar, either a Python bool or tensor.
Returns:
True or False if `pred` has a constant boolean value, None otherwise.
Raises:
TypeError: If `pred` is not a Tensor or bool.
"""
if isinstance(pred, ops.Tensor):
pred_value = tensor_util.constant_value(pred)
# TODO(skyewm): consider folding this into tensor_util.constant_value.
# pylint: disable=protected-access
if pred_value is None:
pred_value = c_api.TF_TryEvaluateConstant_wrapper(
pred.graph._c_graph, pred._as_tf_output())
# pylint: enable=protected-access
elif pred in {0, 1}: # Accept 1/0 as valid boolean values
pred_value = bool(pred)
elif isinstance(pred, bool):
pred_value = pred
else:
raise TypeError(
"`pred` must be a Tensor, or a Python bool, or 1 or 0. "
"Found instead: %s" % type(pred))
return pred_value
def smart_constant_value(pred):
"""Return the bool value for `pred`, or None if `pred` had a dynamic value.
Arguments:
pred: A scalar, either a Python bool or tensor.
Returns:
True or False if `pred` has a constant boolean value, None otherwise.
Raises:
TypeError: If `pred` is not a Tensor or bool.
"""
if pred in {0, 1}: # Accept 1/0 as valid boolean values
pred_value = bool(pred)
elif isinstance(pred, bool):
pred_value = pred
elif isinstance(pred, ops.Tensor):
pred_value = tensor_util.constant_value(pred)
# TODO(skyewm): consider folding this into tensor_util.constant_value when
# _USE_C_API is removed (there may be performance and correctness bugs, so I
# wanted to limit the change hidden behind _USE_C_API).
# pylint: disable=protected-access
if pred_value is None and ops._USE_C_API:
with errors.raise_exception_on_not_ok_status() as status:
pred_value = c_api.TF_TryEvaluateConstant_wrapper(
pred.graph._c_graph, pred._as_tf_output(), status)
# pylint: enable=protected-access
else:
raise TypeError(
"`pred` must be a Tensor, or a Python bool, or 1 or 0. "
"Found instead: %s" % pred)
return pred_value
def _TransposeGrad(op, grad):
"""Returns unshuffle(grad)."""
p = op.inputs[1]
if not context.executing_eagerly():
p_static = pywrap_tensorflow.TF_TryEvaluateConstant_wrapper(
p.graph._c_graph, p._as_tf_output()) # pylint: disable=protected-access
if p_static is not None:
p = constant_op.constant(p_static, dtype=p.dtype)
return [array_ops.transpose(grad, array_ops.invert_permutation(p)), None]
def _get_static_value(pred):
"""Helper function for getting static values from maybe-tensor objects."""
if tf.is_tensor(pred):
pred_value = tf.get_static_value(tf.convert_to_tensor(pred))
# TODO(jamieas): remove the dependency on `pywrap_tensorflow`.
# pylint: disable=protected-access
if pred_value is None:
pred_value = c_api.TF_TryEvaluateConstant_wrapper(pred.graph._c_graph,
pred._as_tf_output())
# pylint: enable=protected-access
return pred_value
return pred
def _BroadcastToGrad(op, grad):
input_value = op.inputs[0]
broadcast_shape = op.inputs[1]
input_value_shape = array_ops.shape(input_value)
if not context.executing_eagerly():
broadcast_shape_static = tensor_shape.TensorShape(
pywrap_tensorflow.TF_TryEvaluateConstant_wrapper(
broadcast_shape.graph._c_graph, broadcast_shape._as_tf_output())) # pylint: disable=protected-access
if broadcast_shape_static.is_fully_defined():
broadcast_shape = constant_op.constant(
broadcast_shape_static.as_list(), dtype=dtypes.int32)
_, reduction_axes = gen_array_ops.broadcast_gradient_args(
broadcast_shape, input_value_shape)
updates_grad_reshaped = math_ops.reduce_sum(
grad, axis=reduction_axes, keepdims=True)
updates_grad = array_ops.reshape(updates_grad_reshaped, input_value_shape)
return [updates_grad, None]
def _get_static_predicate(pred):
"""Helper function for statically evaluating predicates in `cond`."""
if pred in {0, 1}: # Accept 1/0 as valid boolean values
pred_value = bool(pred)
elif isinstance(pred, bool):
pred_value = pred
elif isinstance(pred, tf.Tensor):
pred_value = tf.get_static_value(pred)
# TODO(jamieas): remove the dependency on `pywrap_tensorflow`.
# pylint: disable=protected-access
if pred_value is None:
pred_value = c_api.TF_TryEvaluateConstant_wrapper(pred.graph._c_graph,
pred._as_tf_output())
# pylint: enable=protected-access
else:
raise TypeError("`pred` must be a Tensor, or a Python bool, or 1 or 0. "
"Found instead: %s" % pred)
return pred_value
def _get_static_predicate(pred):
"""Helper function for statically evaluating predicates in `cond`."""
if tf.is_tensor(pred):
pred_value = tf.get_static_value(tf.convert_to_tensor(pred))
# TODO(jamieas): remove the dependency on `pywrap_tensorflow`.
# pylint: disable=protected-access
if pred_value is None:
pred_value = c_api.TF_TryEvaluateConstant_wrapper(pred.graph._c_graph,
pred._as_tf_output())
# pylint: enable=protected-access
if pred_value in (0, 1, True, False):
pred_value = bool(pred_value)
elif pred in (0, 1, True, False): # Accept 1/0 as valid boolean values
# This branch also casts np.array(False), tf.EagerTensor(True), etc.
pred_value = bool(pred)
else:
raise TypeError('`pred` must be a Tensor, or a Python bool, or 1 or 0. '
'Found instead: {}'.format(pred))
return pred_value
