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 _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 not JAX_MODE and 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 try_evaluate_constant(tensor): # pylint: disable=invalid-name
"""Evaluates a symbolic tensor as a constant.
Args:
tensor: a symbolic Tensor.
Returns:
ndarray if the evaluation succeeds, or None if it fails.
"""
# pylint: disable=protected-access
with tensor.graph._c_graph.get() as c_graph:
return c_api.TF_TryEvaluateConstant_wrapper(c_graph,
tensor._as_tf_output())
def _BroadcastToGrad(op, grad):
input_value = op.inputs[0]
broadcast_shape = op.inputs[1]
input_value_shape = array_ops.shape(input_value)
if not isinstance(broadcast_shape, ops.EagerTensor):
broadcast_shape_static = tensor_shape.TensorShape(
pywrap_tf_session.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_value(pred):
"""Helper function for getting static values from maybe-tensor objects."""
if JAX_MODE:
try:
return np.asarray(pred)
except: # JAX sometimes raises raw Exception in __array__. # pylint: disable=bare-except
return None
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 _get_static_value(pred):
"""Helper function for getting static values from maybe-tensor objects."""
if JAX_MODE:
try:
return np.asarray(pred)
except: # JAX sometimes raises raw Exception in __array__. # pylint: disable=bare-except
return None
if tf.is_tensor(pred):
pred_value = tf.get_static_value(tf.convert_to_tensor(pred))
# Explicitly check for ops.Tensor, to avoid an AttributeError
# when requesting `KerasTensor.graph`.
if pred_value is None and isinstance(pred, ops.Tensor):
if hasattr(tensor_util, 'try_evaluate_constant'):
pred_value = tensor_util.try_evaluate_constant(pred)
else:
# TODO(feyu): remove this branch after try_evaluate_constant is in
# tf-nightly.
pred_value = c_api.TF_TryEvaluateConstant_wrapper(
pred.graph._c_graph, pred._as_tf_output()) # pylint: disable=protected-access
return pred_value
return pred
