wehub-resource-sync
81 lines
2.6 KiB
C++
81 lines
2.6 KiB
C++
/* Copyright 2020 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 <cstdint>
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#include <memory>
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#include <string>
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#include <vector>
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#include <gmock/gmock.h>
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#include <gtest/gtest.h>
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#include "absl/log/log.h"
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#include "tensorflow/core/platform/logging.h"
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#include "tensorflow/lite/core/c/common.h"
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#include "tensorflow/lite/core/interpreter_builder.h"
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#include "tensorflow/lite/create_op_resolver.h"
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#include "tensorflow/lite/interpreter.h"
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#include "tensorflow/lite/model_builder.h"
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#include "tensorflow/lite/mutable_op_resolver.h"
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namespace tflite {
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bool RunWithRandomInputs(const std::string& filename) {
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std::unique_ptr<tflite::FlatBufferModel> model =
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tflite::FlatBufferModel::BuildFromFile(filename.c_str());
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// Build the interpreter
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std::unique_ptr<MutableOpResolver> resolver = CreateOpResolver();
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std::unique_ptr<tflite::Interpreter> interpreter;
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if (tflite::InterpreterBuilder(*model, *resolver)(&interpreter) !=
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kTfLiteOk) {
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LOG(ERROR) << "Could not initialize interpreter for TFLite model.";
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return false;
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}
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// Resize input tensors, if desired.
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if (interpreter->AllocateTensors() != kTfLiteOk) {
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LOG(ERROR) << "Could not allocate tensor.";
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return false;
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}
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// Fill the random data.
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std::vector<std::vector<uint8_t>> sample;
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for (int tensor_idx : interpreter->inputs()) {
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auto tensor = interpreter->tensor(tensor_idx);
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std::vector<uint8_t> data(tensor->bytes);
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for (auto it = data.begin(); it != data.end(); ++it) {
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*it = random();
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}
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sample.push_back(data);
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tensor->data.raw = reinterpret_cast<char*>(sample.rbegin()->data());
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}
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// Running inference.
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if (interpreter->Invoke() != kTfLiteOk) {
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LOG(ERROR) << "Failed to run the model.";
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return false;
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}
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return true;
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}
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TEST(SelectiveBuiltTest, AddModel) {
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std::string model = "tensorflow/lite/testdata/add.bin";
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EXPECT_THAT(RunWithRandomInputs(model), true);
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}
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TEST(SelectiveBuiltTest, LSTMModel) {
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std::string model = "tensorflow/lite/testdata/lstm.bin";
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EXPECT_THAT(RunWithRandomInputs(model), true);
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}
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} // namespace tflite
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