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-rw-r--r--tests/tools/tflite_vanilla_run/src/tflite_vanilla_run.cc276
1 files changed, 0 insertions, 276 deletions
diff --git a/tests/tools/tflite_vanilla_run/src/tflite_vanilla_run.cc b/tests/tools/tflite_vanilla_run/src/tflite_vanilla_run.cc
deleted file mode 100644
index d44ea60cf..000000000
--- a/tests/tools/tflite_vanilla_run/src/tflite_vanilla_run.cc
+++ /dev/null
@@ -1,276 +0,0 @@
-/*
- * Copyright (c) 2020 Samsung Electronics Co., Ltd. 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/lite/model.h"
-#include "tensorflow/lite/kernels/register.h"
-
-#include "args.h"
-#include "tensor_view.h"
-#include "misc/EnvVar.h"
-#include "misc/RandomGenerator.h"
-#include "misc/tensor/IndexIterator.h"
-#include "misc/tensor/Object.h"
-#include "benchmark.h"
-
-#include <iostream>
-#include <chrono>
-#include <algorithm>
-#include <vector>
-#include <memory>
-
-using namespace std::placeholders; // for _1, _2 ...
-
-#define TFLITE_ENSURE(exp) \
- { \
- const TfLiteStatus status = (exp); \
- \
- if (status != kTfLiteOk) \
- { \
- std::ostringstream ss; \
- ss << #exp << " failed (" << __FILE__ << ":" << __LINE__ << ")"; \
- throw std::runtime_error{ss.str()}; \
- } \
- }
-
-namespace
-{
-
-void print_max_idx(float *f, int size)
-{
- float *p = std::max_element(f, f + size);
- std::cout << "max:" << p - f;
-}
-
-static const char *default_backend_cand = "tflite_cpu";
-
-// Verifies whether the model is a flatbuffer file.
-class BMFlatBufferVerifier : public tflite::TfLiteVerifier
-{
-public:
- bool Verify(const char *data, int length, tflite::ErrorReporter *reporter) override
- {
-
- flatbuffers::Verifier verifier(reinterpret_cast<const uint8_t *>(data), length);
- if (!tflite::VerifyModelBuffer(verifier))
- {
- reporter->Report("The model is not a valid Flatbuffer file");
- return false;
- }
- return true;
- }
-};
-
-} // namespace anonymous
-
-int main(const int argc, char **argv)
-{
- tflite::StderrReporter error_reporter;
-
- TFLiteVanillaRun::Args args(argc, argv);
-
- std::chrono::milliseconds t_model_load(0), t_prepare(0);
-
- // TODO Apply verbose level to phases
- const int verbose = args.getVerboseLevel();
- benchmark::Phases phases(
- benchmark::PhaseOption{args.getMemoryPoll(), args.getGpuMemoryPoll(), args.getRunDelay()});
-
- std::unique_ptr<tflite::FlatBufferModel> model;
- std::unique_ptr<tflite::Interpreter> interpreter;
- std::unique_ptr<tflite::TfLiteVerifier> verifier{new BMFlatBufferVerifier};
-
- try
- {
- phases.run("MODEL_LOAD", [&](const benchmark::Phase &, uint32_t) {
- if (args.getModelValidate())
- {
- model = tflite::FlatBufferModel::VerifyAndBuildFromFile(args.getTFLiteFilename().c_str(),
- verifier.get(), &error_reporter);
- }
- else
- {
- model = tflite::FlatBufferModel::BuildFromFile(args.getTFLiteFilename().c_str(),
- &error_reporter);
- }
- if (model == nullptr)
- {
- throw std::runtime_error{"Cannot create model"};
- }
-
- // Use tflite's resolver, not onert's one
- tflite::ops::builtin::BuiltinOpResolver resolver;
- tflite::InterpreterBuilder builder(*model, resolver);
- TFLITE_ENSURE(builder(&interpreter))
- interpreter->SetNumThreads(nnfw::misc::EnvVar("THREAD").asInt(-1));
- });
- }
- catch (const std::exception &e)
- {
- std::cerr << e.what() << '\n';
- return 1;
- }
-
- const bool use_nnapi = nnfw::misc::EnvVar("USE_NNAPI").asBool(false);
-
- try
- {
- phases.run("PREPARE", [&](const benchmark::Phase &, uint32_t) {
- interpreter->UseNNAPI(use_nnapi);
- interpreter->AllocateTensors();
- });
- }
- catch (const std::exception &e)
- {
- std::cerr << e.what() << '\n';
- return 1;
- }
-
- const int seed = 1; /* TODO Add an option for seed value */
- nnfw::misc::RandomGenerator randgen{seed, 0.0f, 2.0f};
-
- // No input specified. So we fill the input tensors with random values.
- for (const auto &o : interpreter->inputs())
- {
- TfLiteTensor *tensor = interpreter->tensor(o);
- if (tensor->type == kTfLiteInt32)
- {
- // Generate singed 32-bit integer (s32) input
- auto tensor_view = TFLiteVanillaRun::TensorView<int32_t>::make(*interpreter, o);
-
- int32_t value = 0;
-
- nnfw::misc::tensor::iterate(tensor_view.shape())
- << [&](const nnfw::misc::tensor::Index &ind) {
- // TODO Generate random values
- // Gather operation: index should be within input coverage.
- tensor_view.at(ind) = value;
- value++;
- };
- }
- else if (tensor->type == kTfLiteUInt8)
- {
- // Generate unsigned 8-bit integer input
- auto tensor_view = TFLiteVanillaRun::TensorView<uint8_t>::make(*interpreter, o);
-
- uint8_t value = 0;
-
- nnfw::misc::tensor::iterate(tensor_view.shape())
- << [&](const nnfw::misc::tensor::Index &ind) {
- // TODO Generate random values
- tensor_view.at(ind) = value;
- value = (value + 1) & 0xFF;
- };
- }
- else if (tensor->type == kTfLiteBool)
- {
- // Generate bool input
- auto tensor_view = TFLiteVanillaRun::TensorView<bool>::make(*interpreter, o);
-
- auto fp = static_cast<bool (nnfw::misc::RandomGenerator::*)(
- const ::nnfw::misc::tensor::Shape &, const ::nnfw::misc::tensor::Index &)>(
- &nnfw::misc::RandomGenerator::generate<bool>);
- const nnfw::misc::tensor::Object<bool> data(tensor_view.shape(),
- std::bind(fp, randgen, _1, _2));
-
- nnfw::misc::tensor::iterate(tensor_view.shape())
- << [&](const nnfw::misc::tensor::Index &ind) {
- const auto value = data.at(ind);
- tensor_view.at(ind) = value;
- };
- }
- else
- {
- assert(tensor->type == kTfLiteFloat32);
-
- const float *end = reinterpret_cast<const float *>(tensor->data.raw_const + tensor->bytes);
- for (float *ptr = tensor->data.f; ptr < end; ptr++)
- {
- *ptr = randgen.generate<float>();
- }
- }
- }
-
- std::cout << "input tensor indices = [";
- for (const auto &o : interpreter->inputs())
- {
- std::cout << o << ",";
- }
- std::cout << "]" << std::endl;
-
- // NOTE: Measuring memory can't avoid taking overhead. Therefore, memory will be measured on the
- // only warmup.
- if (verbose == 0)
- {
- phases.run("WARMUP", [&](const benchmark::Phase &, uint32_t) { interpreter->Invoke(); },
- args.getWarmupRuns());
- phases.run("EXECUTE", [&](const benchmark::Phase &, uint32_t) { interpreter->Invoke(); },
- args.getNumRuns(), true);
- }
- else
- {
- phases.run("WARMUP", [&](const benchmark::Phase &, uint32_t) { interpreter->Invoke(); },
- [&](const benchmark::Phase &phase, uint32_t nth) {
- std::cout << "... "
- << "warmup " << nth + 1 << " takes " << phase.time[nth] / 1e3 << " ms"
- << std::endl;
- },
- args.getWarmupRuns());
- phases.run("EXECUTE", [&](const benchmark::Phase &, uint32_t) { interpreter->Invoke(); },
- [&](const benchmark::Phase &phase, uint32_t nth) {
- std::cout << "... "
- << "run " << nth + 1 << " takes " << phase.time[nth] / 1e3 << " ms"
- << std::endl;
- },
- args.getNumRuns(), true);
- }
-
- std::cout << "output tensor indices = [";
- for (const auto &o : interpreter->outputs())
- {
- std::cout << o << "(";
-
- print_max_idx(interpreter->tensor(o)->data.f, interpreter->tensor(o)->bytes / sizeof(float));
-
- std::cout << "),";
- }
- std::cout << "]" << std::endl;
-
- // TODO Apply verbose level to result
-
- // prepare result
- benchmark::Result result(phases);
-
- // to stdout
- benchmark::printResult(result);
-
- if (args.getWriteReport())
- {
- // prepare csv task
- std::string exec_basename;
- std::string model_basename;
- std::string backend_name = default_backend_cand;
- {
- std::vector<char> vpath(args.getTFLiteFilename().begin(), args.getTFLiteFilename().end() + 1);
- model_basename = basename(vpath.data());
- size_t lastindex = model_basename.find_last_of(".");
- model_basename = model_basename.substr(0, lastindex);
- exec_basename = basename(argv[0]);
- }
- benchmark::writeResult(result, exec_basename, model_basename, backend_name);
- }
-
- return 0;
-}
generated by cgit v1.2.3 (git 2.25.1) at 2024-07-04 18:23:07 +0000