114 lines
3.8 KiB
C++
114 lines
3.8 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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#ifndef TENSORFLOW_LITE_TESTING_KERNEL_TEST_UTIL_H_
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#define TENSORFLOW_LITE_TESTING_KERNEL_TEST_UTIL_H_
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#include <fstream>
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#include "tensorflow/core/util/command_line_flags.h"
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#include "tensorflow/lite/core/c/common.h"
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#include "tensorflow/lite/testing/kernel_test/input_generator.h"
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#include "tensorflow/lite/testing/split.h"
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#include "tensorflow/lite/testing/tflite_driver.h"
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namespace tflite {
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namespace testing {
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namespace kernel_test {
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struct TestOptions {
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// Path of tensorflow lite model.
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string tflite_model;
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// Path of the input file. If empty, generate at runtime.
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string read_input_from_file;
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// Path to dump the input file.
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string dump_input_to_file;
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// Path to dump the output.
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string dump_output_to_file;
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// Input distribution.
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string input_distribution;
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// Kernel type.
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string kernel_type;
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};
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inline TestOptions ParseTfliteKernelTestFlags(int* argc, char** argv) {
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TestOptions options;
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std::vector<tensorflow::Flag> flags = {
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tensorflow::Flag("tflite_model", &options.tflite_model,
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"Path of tensorflow lite model."),
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tensorflow::Flag("read_input_from_file", &options.read_input_from_file,
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"File to read input data from. If empty, generates "
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"input at runtime."),
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tensorflow::Flag("dump_input_to_file", &options.dump_input_to_file,
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"File to dump randomly generated input."),
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tensorflow::Flag("dump_output_to_file", &options.dump_output_to_file,
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"File to dump output."),
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tensorflow::Flag("input_distribution", &options.input_distribution,
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"Input distribution. Default: Gaussian."),
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tensorflow::Flag("kernel_type", &options.kernel_type, "Kernel type."),
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};
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tensorflow::Flags::Parse(argc, argv, flags);
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return options;
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}
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inline TfLiteStatus RunKernelTest(const kernel_test::TestOptions& options,
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TestRunner* runner) {
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InputGenerator input_generator;
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if (options.read_input_from_file.empty()) {
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TF_LITE_ENSURE_STATUS(input_generator.LoadModel(options.tflite_model));
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TF_LITE_ENSURE_STATUS(
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input_generator.GenerateInput(options.input_distribution));
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} else {
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TF_LITE_ENSURE_STATUS(
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input_generator.ReadInputsFromFile(options.read_input_from_file));
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}
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runner->LoadModel(options.tflite_model);
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runner->AllocateTensors();
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if (!runner->IsValid()) return kTfLiteError;
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auto inputs = input_generator.GetInputs();
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runner->Invoke(inputs);
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if (!options.dump_input_to_file.empty()) {
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TF_LITE_ENSURE_STATUS(
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input_generator.WriteInputsToFile(options.dump_input_to_file));
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}
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if (!options.dump_output_to_file.empty()) {
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std::ofstream output_file;
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output_file.open(options.dump_output_to_file,
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std::fstream::out | std::fstream::trunc);
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if (!output_file) {
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return kTfLiteError;
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}
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for (const auto& name : runner->GetOutputNames()) {
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output_file << name << ":" << runner->ReadOutput(name) << "\n";
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}
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output_file.close();
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}
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return kTfLiteOk;
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}
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} // namespace kernel_test
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} // namespace testing
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} // namespace tflite
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#endif // TENSORFLOW_LITE_TESTING_KERNEL_TEST_UTIL_H_
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