def _convert_inputs_to_signature(inputs, input_signature,
flat_input_signature):
"""Converts inputs to pass into a function with an explicit signature."""
def format_error_message(inputs, input_signature):
return (" inputs: (\n" + " " +
",\n ".join(str(i) for i in inputs) + ")\n" +
" input_signature: (\n" + " " +
",\n ".join(str(i) for i in input_signature) + ")")
try:
flatten_inputs = nest.flatten_up_to(
input_signature,
inputs[:len(input_signature)],
expand_composites=True,
check_types=False) # lists are convert to tuples for `tf.data`.
except ValueError:
raise ValueError("Structure of Python function inputs does not match "
"input_signature:\n"
f"{format_error_message(inputs, input_signature)}.")
need_packing = False
for index, (value,
spec) in enumerate(zip(flatten_inputs, flat_input_signature)):
if (isinstance(spec, tensor_spec.TensorSpec)
and not _pywrap_utils.IsTensor(value)):
try:
flatten_inputs[index] = ops.convert_to_tensor(
value, dtype_hint=spec.dtype)
need_packing = True
except ValueError:
raise ValueError(
"When input_signature is provided, all inputs to "
"the Python function must be convertible to "
"tensors:\n"
f"{format_error_message(inputs, input_signature)}.")
if any(not spec.is_compatible_with(other)
for spec, other in zip(flat_input_signature, flatten_inputs)):
raise ValueError("Python inputs incompatible with input_signature:\n"
f"{format_error_message(inputs, input_signature)}.")
if need_packing:
inputs = nest.pack_sequence_as(structure=input_signature,
flat_sequence=flatten_inputs,
expand_composites=True)
flat_inputs = nest.flatten(inputs, expand_composites=True)
return (inputs, flat_inputs, [
t for t in flat_inputs
if isinstance(t, (ops.Tensor,
resource_variable_ops.BaseResourceVariable))
])
def watch(self, tensor):
"""Ensures that `tensor` is being traced by this tape.
Args:
tensor: a Tensor or list of Tensors.
Raises:
ValueError: if it encounters something that is not a tensor.
"""
for t in nest.flatten(tensor, expand_composites=True):
if not (_pywrap_utils.IsTensor(t) or _pywrap_utils.IsVariable(t)):
raise ValueError("Passed in object of type {}, not tf.Tensor".format(
type(t)))
if not backprop_util.IsTrainable(t):
logging.log_first_n(
logging.WARN, "The dtype of the watched tensor must be "
"floating (e.g. tf.float32), got %r", 5, t.dtype)
if hasattr(t, "handle"):
# There are many variable-like objects, all of them currently have
# `handle` attribute that points to a tensor. If this changes, internals
# of watch_variable need to change as well.
tape.watch_variable(self._tape, t)
else:
tape.watch(self._tape, t)