def tensor_to_inference_result(t):
r = cpp_shape_inference_pb2.CppShapeInferenceResult()
r.shape.CopyFrom(t.get_shape().as_proto())
# pylint: disable=protected-access
if t._handle_data is not None:
r.handle_data.CopyFrom(t._handle_data)
# pylint: enable=protected-access
return r.SerializeToString()
def _call_cpp_shape_fn_impl(op, input_tensors_needed,
input_tensors_as_shapes_needed, require_shape_fn):
"""Core implementaton of call_cpp_shape_fn."""
graph_def_version = op.graph.graph_def_versions.producer
node_def_str = op.node_def.SerializeToString()
def tensor_to_inference_result(t):
r = cpp_shape_inference_pb2.CppShapeInferenceResult()
r.shape.CopyFrom(t.get_shape().as_proto())
# pylint: disable=protected-access
if t._handle_data is not None:
r.handle_data.CopyFrom(t._handle_data)
# pylint: enable=protected-access
return r.SerializeToString()
input_shapes = [tensor_to_inference_result(i) for i in op.inputs]
input_tensors = [None for i in input_shapes]
for idx in input_tensors_needed:
v = tensor_util.constant_value(op.inputs[idx])
if v is not None:
input_tensors[idx] = np.asarray(v)
serialized_unknown_shape = (
tensor_shape.TensorShape(None).as_proto().SerializeToString())
arr = [serialized_unknown_shape for i in input_shapes]
for idx in input_tensors_as_shapes_needed:
s = tensor_util.constant_value_as_shape(op.inputs[idx])
if s is not None:
arr[idx] = s.as_proto().SerializeToString()
input_tensors_as_shapes = arr
missing_shape_fn = False
try:
with errors.raise_exception_on_not_ok_status() as status:
output = pywrap_tensorflow.RunCppShapeInference(
graph_def_version, node_def_str, input_shapes, input_tensors,
input_tensors_as_shapes, status)
except errors.InvalidArgumentError as err:
if err.message.startswith("No shape inference function exists for op"):
missing_shape_fn = True
else:
raise ValueError(err.message)
if missing_shape_fn:
if require_shape_fn:
raise RuntimeError(
"No C++ shape function registered for standard op: %s" %
op.type)
return unknown_shape(op)
output_shapes = output[:-1]
# Convert TensorShapeProto values in output_shapes.
result_protos = [
cpp_shape_inference_pb2.CppShapeInferenceResult().FromString(s)
for s in output_shapes
]
result = [r.shape for r in result_protos]
result_handle_data = [
r.handle_data if r.handle_data.is_set else None for r in result_protos
]
return {
"shapes": result,
"handle_data": result_handle_data,
"inputs_needed": output[-1]
}
def _call_cpp_shape_fn_impl(
op, input_tensors_needed,
input_tensors_as_shapes_needed,
debug_python_shape_fn, require_shape_fn):
"""Core implementaton of call_cpp_shape_fn."""
node_def_str = op.node_def.SerializeToString()
def tensor_to_inference_result(t):
r = cpp_shape_inference_pb2.CppShapeInferenceResult()
r.shape.CopyFrom(t.get_shape().as_proto())
# pylint: disable=protected-access
r.handle_shape.CopyFrom(t._handle_shape)
r.handle_dtype = t._handle_dtype
# pylint: enable=protected-access
return r.SerializeToString()
input_shapes = [tensor_to_inference_result(i) for i in op.inputs]
input_tensors = [None for i in input_shapes]
for idx in input_tensors_needed:
v = tensor_util.constant_value(op.inputs[idx])
if v is not None:
input_tensors[idx] = np.asarray(v)
serialized_unknown_shape = (
tensor_shape.TensorShape(None).as_proto().SerializeToString())
arr = [serialized_unknown_shape for i in input_shapes]
for idx in input_tensors_as_shapes_needed:
s = tensor_util.constant_value_as_shape(op.inputs[idx])
if s is not None:
arr[idx] = s.as_proto().SerializeToString()
input_tensors_as_shapes = arr
missing_shape_fn = False
try:
with errors.raise_exception_on_not_ok_status() as status:
output = pywrap_tensorflow.RunCppShapeInference(
node_def_str, input_shapes, input_tensors, input_tensors_as_shapes,
status)
except errors.InvalidArgumentError as err:
if err.message.startswith("No shape inference function exists for op"):
missing_shape_fn = True
else:
raise ValueError(err.message)
if missing_shape_fn:
if require_shape_fn:
raise RuntimeError(
"No C++ shape function registered for standard op: %s" % op.type)
return unknown_shape(op)
output_shapes = output[:-1]
# Convert TensorShapeProto values in output_shapes.
result_protos = [
cpp_shape_inference_pb2.CppShapeInferenceResult().FromString(s)
for s in output_shapes
]
result = [r.shape for r in result_protos]
result_handle_shapes = [r.handle_shape for r in result_protos]
result_handle_dtypes = [r.handle_dtype for r in result_protos]
if debug_python_shape_fn:
try:
python_result = [tensor_shape.as_shape(s)
for s in debug_python_shape_fn(op)]
except Exception as err:
raise AssertionError("Python shape function return error but "
"C++ shape functon did not: %s" % str(err))
result_as_shapes = [tensor_shape.as_shape(s) for s in result]
if str(result_as_shapes) != str(python_result):
raise ValueError(
("Python vs CPP shape mismatch. "
"CPP: %s vs python: %s on node %s "
"with input shapes %s") % (
str(result_as_shapes), str(python_result), str(op.node_def),
",".join([str(i.get_shape()) for i in op.inputs])))
return {"shapes": result,
"handle_shapes": result_handle_shapes,
"handle_dtypes": result_handle_dtypes,
"inputs_needed": output[-1]}
def call_cpp_shape_fn(op,
input_tensors_needed=None,
input_tensors_as_shapes_needed=None,
debug_python_shape_fn=None,
require_shape_fn=True):
"""A shape function that delegates to the registered C++ shape function.
Args:
op: the node in the graph for which to compute output shapes.
input_tensors_needed: a list of input tensor indices for which to compute
the input tensor's value and pass to the C++ shape function.
input_tensors_as_shapes_needed: a list of input tensor indices for which to
compute the constant_value_as_shape and pass to the C++ shape function.
debug_python_shape_fn: For testing only during migration to using
call_cpp_shape_fn. Do not submit calls that set this,
as the comparison is slow. If non-None, the python shape function;
this function will be called and its output compared to that of
the C++ shape function.
require_shape_fn: If true, and the C++ shape function is not registered
in the current binary then an exception is raised; otherwise, if the
C++ shape function is not registered then unknown_shape is used.
Returns:
A dictionary with the following keys:
shapes: A TensorShape list of the output shapes of the op, as computed
using the C++ shape inference function registered for the op.
handle_shapes: A TensorShape list of the shapes for handle outputs, if
any.
handle_dtypes: A list of DataType enums for the handle outputs, if any.
Raises:
ValueError: If the C++ shape function returned an error (e.g. because the
shapes of the inputs are of the wrong rank or otherwise incompatible
according to the shape function).
RuntimeError: If the C++ shape function is not registered and
<require_shape_fn> is True.
"""
if op.type == "Const":
# To avoid serializing large constants, we special-case constant
# here, even though it has a C++ shape function. When Python
# calls the C / C-API directly, we should be able to remove this.
return {
"shapes":
[tensor_shape.TensorShape(op.get_attr("value").tensor_shape)],
"handle_shapes": [tensor_shape.TensorShape(None).as_proto()],
"handle_dtypes": [types_pb2.DT_INVALID]
}
node_def_str = op.node_def.SerializeToString()
def tensor_to_inference_result(t):
r = cpp_shape_inference_pb2.CppShapeInferenceResult()
r.shape.CopyFrom(t.get_shape().as_proto())
# pylint: disable=protected-access
r.handle_shape.CopyFrom(t._handle_shape)
r.handle_dtype = t._handle_dtype
# pylint: enable=protected-access
return r.SerializeToString()
input_shapes = [tensor_to_inference_result(i) for i in op.inputs]
input_tensors = [None for i in input_shapes]
if input_tensors_needed:
for idx in input_tensors_needed:
v = tensor_util.constant_value(op.inputs[idx])
if v is not None:
input_tensors[idx] = np.asarray(v)
serialized_unknown_shape = (
tensor_shape.TensorShape(None).as_proto().SerializeToString())
arr = [serialized_unknown_shape for i in input_shapes]
if input_tensors_as_shapes_needed:
for idx in input_tensors_as_shapes_needed:
s = tensor_util.constant_value_as_shape(op.inputs[idx])
if s is not None:
arr[idx] = s.as_proto().SerializeToString()
input_tensors_as_shapes = arr
missing_shape_fn = False
try:
with errors.raise_exception_on_not_ok_status() as status:
output_shapes = pywrap_tensorflow.RunCppShapeInference(
node_def_str, input_shapes, input_tensors,
input_tensors_as_shapes, status)
except errors.InvalidArgumentError as err:
if err.message.startswith("No shape inference function exists for op"):
missing_shape_fn = True
else:
raise ValueError(err.message)
if missing_shape_fn:
if require_shape_fn:
raise RuntimeError(
"No C++ shape function registered for standard op: %s" %
op.type)
return unknown_shape(op)
# Convert TensorShapeProto values in output_shapes.
result_protos = [
cpp_shape_inference_pb2.CppShapeInferenceResult().FromString(s)
for s in output_shapes
]
result = [r.shape for r in result_protos]
result_handle_shapes = [r.handle_shape for r in result_protos]
result_handle_dtypes = [r.handle_dtype for r in result_protos]
if debug_python_shape_fn:
try:
python_result = [
tensor_shape.as_shape(s) for s in debug_python_shape_fn(op)
]
except Exception as err:
raise AssertionError("Python shape function return error but "
"C++ shape functon did not: %s" % str(err))
result_as_shapes = [tensor_shape.as_shape(s) for s in result]
if str(result_as_shapes) != str(python_result):
raise ValueError(
("Python vs CPP shape mismatch. "
"CPP: %s vs python: %s on node %s "
"with input shapes %s") %
(str(result_as_shapes), str(python_result), str(op.node_def),
",".join([str(i.get_shape()) for i in op.inputs])))
return {
"shapes": result,
"handle_shapes": result_handle_shapes,
"handle_dtypes": result_handle_dtypes
}
def call_cpp_shape_fn(op,
input_tensors_needed=None,
input_tensors_as_shapes_needed=None,
debug_python_shape_fn=None):
"""A shape function that delegates to the registered C++ shape function.
Args:
op: the node in the graph for which to compute output shapes.
input_tensors_needed: a list of input tensor indices for which to compute
the input tensor's value and pass to the C++ shape function.
input_tensors_as_shapes_needed: a list of input tensor indices for which to
compute the constant_value_as_shape and pass to the C++ shape function.
debug_python_shape_fn: For testing only during migration to using
call_cpp_shape_fn. Do not submit calls that set this,
as the comparison is slow. If non-None, the python shape function;
this function will be called and its output compared to that of
the C++ shape function.
Returns:
A dictionary with the following keys:
shapes: A TensorShape list of the output shapes of the op, as computed
using the C++ shape inference function registered for the op.
handle_shapes: A TensorShape list of the shapes for handle outputs, if
any.
handle_dtypes: A list of DataType enums for the handle outputs, if any.
Raises:
ValueError: If the C++ shape function returned an error (e.g. because the
shapes of the inputs are of the wrong rank or otherwise incompatible
according to the shape function).
"""
node_def_str = op.node_def.SerializeToString()
def tensor_to_inference_result(t):
r = cpp_shape_inference_pb2.CppShapeInferenceResult()
r.shape.CopyFrom(t.get_shape().as_proto())
# pylint: disable=protected-access
r.handle_shape.CopyFrom(t._handle_shape)
r.handle_dtype = t._handle_dtype
# pylint: enable=protected-access
return r.SerializeToString()
input_shapes = [tensor_to_inference_result(i) for i in op.inputs]
input_tensors = [None for i in input_shapes]
if input_tensors_needed:
for idx in input_tensors_needed:
v = tensor_util.constant_value(op.inputs[idx])
if v is not None:
input_tensors[idx] = np.asarray(v)
serialized_unknown_shape = (
tensor_shape.TensorShape(None).as_proto().SerializeToString())
arr = [serialized_unknown_shape for i in input_shapes]
if input_tensors_as_shapes_needed:
for idx in input_tensors_as_shapes_needed:
s = tensor_util.constant_value_as_shape(op.inputs[idx])
if s is not None:
arr[idx] = s.as_proto().SerializeToString()
input_tensors_as_shapes = arr
try:
with errors.raise_exception_on_not_ok_status() as status:
output_shapes = pywrap_tensorflow.RunCppShapeInference(
node_def_str, input_shapes, input_tensors,
input_tensors_as_shapes, status)
except errors.InvalidArgumentError as err:
raise ValueError(err.message)
# Convert TensorShapeProto values in output_shapes.
result_protos = [
cpp_shape_inference_pb2.CppShapeInferenceResult().FromString(s)
for s in output_shapes
]
result = [r.shape for r in result_protos]
result_handle_shapes = [r.handle_shape for r in result_protos]
result_handle_dtypes = [r.handle_dtype for r in result_protos]
if debug_python_shape_fn:
try:
python_result = [
tensor_shape.as_shape(s) for s in debug_python_shape_fn(op)
]
except Exception as err:
raise AssertionError("Python shape function return error but "
"C++ shape functon did not: %s" % str(err))
if str(result) != str(python_result):
raise ValueError(
("Python vs CPP shape mismatch. "
"CPP: %s vs python: %s on node %s "
"with input shapes %s") %
(str(result), str(python_result), str(op.node_def), ",".join(
[str(i.get_shape()) for i in op.inputs])))
return {
"shapes": result,
"handle_shapes": result_handle_shapes,
"handle_dtypes": result_handle_dtypes
}
