dc86bb5296
If pybind11 is installed on the system its headers are already captured by @local_config_python//:python_headers, so the system lib only needs to depend on that. When installed correctly, includes should be #include "pybind11/...", the bundled pybind11 is based off the source repo which does not match the install paths. Use bazels strip_include_prefix to align the bundled headers correctly. Signed-off-by: Jason Zaman <jason@perfinion.com>
75 lines
2.7 KiB
C++
75 lines
2.7 KiB
C++
/* Copyright 2019 The TensorFlow Authors. All Rights Reserved.
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Licensed under the Apache License, Version 2.0 (the "License");
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you may not use this file except in compliance with the License.
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You may obtain a copy of the License at
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http://www.apache.org/licenses/LICENSE-2.0
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Unless required by applicable law or agreed to in writing, software
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distributed under the License is distributed on an "AS IS" BASIS,
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WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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See the License for the specific language governing permissions and
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limitations under the License.
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==============================================================================*/
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#include <string>
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#include <vector>
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#include "pybind11/pybind11.h"
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#include "tensorflow/core/lib/strings/str_util.h"
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#include "tensorflow/core/platform/types.h"
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#include "tensorflow/python/lib/core/pybind11_status.h"
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#include "tensorflow/tools/graph_transforms/transform_graph.h"
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namespace py = pybind11;
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namespace tensorflow {
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string TransformGraphWithStringInputs(string graph_def_string,
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string inputs_string,
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string outputs_string,
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string transforms_string) {
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GraphDef graph_def;
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if (!graph_def.ParseFromString(graph_def_string)) {
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MaybeRaiseFromStatus(
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errors::InvalidArgument("Couldn't interpret input as a GraphDef"));
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}
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graph_transforms::TransformParameters params_list;
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Status parse_status = graph_transforms::ParseTransformParameters(
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transforms_string, ¶ms_list);
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if (!parse_status.ok()) {
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MaybeRaiseFromStatus(parse_status);
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}
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std::vector<string> inputs = str_util::Split(inputs_string, ',');
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std::vector<string> outputs = str_util::Split(outputs_string, ',');
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Status transform_status = graph_transforms::TransformGraph(
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inputs, outputs, params_list, &graph_def);
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if (!transform_status.ok()) {
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MaybeRaiseFromStatus(transform_status);
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}
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string result;
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if (!graph_def.SerializeToString(&result)) {
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MaybeRaiseFromStatus(
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errors::InvalidArgument("Couldn't serialize output as a GraphDef"));
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}
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return result;
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}
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} // namespace tensorflow
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PYBIND11_MODULE(_pywrap_transform_graph, m) {
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m.def(
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"TransformGraphWithStringInputs",
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[](const py::object graph_def_string, const py::object inputs_string,
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const py::object outputs_string, const py::object transforms_string) {
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return py::bytes(tensorflow::TransformGraphWithStringInputs(
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graph_def_string.cast<std::string>(),
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inputs_string.cast<std::string>(),
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outputs_string.cast<std::string>(),
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transforms_string.cast<std::string>()));
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});
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};
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