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/* Copyright 2017 The TensorFlow Authors. All Rights Reserved.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
==============================================================================*/
#include "tensorflow/contrib/lite/testing/generate_testspec.h"
#include "tensorflow/contrib/lite/testing/join.h"
#include "tensorflow/contrib/lite/testing/split.h"
#include "tensorflow/contrib/lite/testing/tf_driver.h"
#include "tensorflow/core/framework/types.h"
namespace tflite {
namespace testing {
template <typename T>
void GenerateCsv(const std::vector<int>& shape, float min, float max,
string* out) {
auto random_float = [](int min, int max) {
static unsigned int seed;
return min + (max - min) * static_cast<float>(rand_r(&seed)) / RAND_MAX;
};
std::function<T(int)> random_t = [&](int) {
return static_cast<T>(random_float(min, max));
};
std::vector<T> data = GenerateRandomTensor(shape, random_t);
*out = Join(data.data(), data.size(), ",");
}
bool GenerateTestSpecFromTensorflowModel(
std::iostream& stream, const string& tensorflow_model_path,
const string& tflite_model_path, const std::vector<string>& input_layer,
const std::vector<string>& input_layer_type,
const std::vector<string>& input_layer_shape,
const std::vector<string>& output_layer) {
CHECK_EQ(input_layer.size(), input_layer_type.size());
CHECK_EQ(input_layer.size(), input_layer_shape.size());
// Generate inputs.
std::vector<string> input_values;
input_values.resize(input_layer.size());
for (int i = 0; i < input_layer.size(); i++) {
tensorflow::DataType type;
CHECK(DataTypeFromString(input_layer_type[i], &type));
auto shape = Split<int>(input_layer_shape[i], ",");
switch (type) {
case tensorflow::DT_FLOAT:
GenerateCsv<float>(shape, -0.5, 0.5, &input_values[i]);
break;
case tensorflow::DT_UINT8:
GenerateCsv<uint8_t>(shape, 0, 255, &input_values[i]);
break;
case tensorflow::DT_INT32:
GenerateCsv<int32_t>(shape, -100, 100, &input_values[i]);
break;
case tensorflow::DT_INT64:
GenerateCsv<int64_t>(shape, -100, 100, &input_values[i]);
break;
case tensorflow::DT_BOOL:
GenerateCsv<int>(shape, 0.01, 1.99, &input_values[i]);
break;
default:
fprintf(stderr, "Unsupported type %d (%s) when generating testspec.\n",
type, input_layer_type[i].c_str());
return false;
}
}
// Invoke tensorflow model.
TfDriver runner(input_layer, input_layer_type, input_layer_shape,
output_layer);
if (!runner.IsValid()) {
cerr << runner.GetErrorMessage() << endl;
return false;
}
runner.LoadModel(tensorflow_model_path);
if (!runner.IsValid()) {
cerr << runner.GetErrorMessage() << endl;
return false;
}
for (int i = 0; i < input_values.size(); i++) {
runner.SetInput(i, input_values[i]);
if (!runner.IsValid()) {
cerr << runner.GetErrorMessage() << endl;
return false;
}
}
runner.Invoke();
if (!runner.IsValid()) {
cerr << runner.GetErrorMessage() << endl;
return false;
}
// Write test spec.
stream << "load_model: " << tflite_model_path << "\n";
stream << "reshape {\n";
for (const auto& shape : input_layer_shape) {
stream << " input: \"" << shape << "\"\n";
}
stream << "}\n";
stream << "invoke {\n";
for (const auto& value : input_values) {
stream << " input: \"" << value << "\"\n";
}
for (int i = 0; i < output_layer.size(); i++) {
stream << " output: \"" << runner.ReadOutput(i) << "\"\n";
if (!runner.IsValid()) {
cerr << runner.GetErrorMessage() << endl;
return false;
}
}
stream << "}\n";
return true;
}
} // namespace testing
} // namespace tflite
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