diff options
Diffstat (limited to 'tests/tools/tflite_run/src/tflite_run.cc')
| -rw-r--r-- | tests/tools/tflite_run/src/tflite_run.cc | 228 |
1 files changed, 59 insertions, 169 deletions
diff --git a/tests/tools/tflite_run/src/tflite_run.cc b/tests/tools/tflite_run/src/tflite_run.cc index e72966db5..a1e3d2eb6 100644 --- a/tests/tools/tflite_run/src/tflite_run.cc +++ b/tests/tools/tflite_run/src/tflite_run.cc @@ -14,9 +14,6 @@ * limitations under the License. */ -#include "tflite/ext/kernels/register.h" -#include "tensorflow/lite/model.h" - #include "args.h" #include "tensor_dumper.h" #include "tensor_loader.h" @@ -26,8 +23,8 @@ #include "tflite/Diff.h" #include "tflite/Assert.h" #include "tflite/Session.h" +#include "tflite/RandomInputInitializer.h" #include "tflite/InterpreterSession.h" -#include "tflite/NNAPISession.h" #include "misc/tensor/IndexIterator.h" #include "misc/tensor/Object.h" #include "benchmark.h" @@ -54,31 +51,10 @@ void print_max_idx(float *f, int size) 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 +} // namespace int main(const int argc, char **argv) { - const bool use_nnapi = nnfw::misc::EnvVar("USE_NNAPI").asBool(false); - - StderrReporter error_reporter; - TFLiteRun::Args args(argc, argv); std::chrono::milliseconds t_model_load(0), t_prepare(0); @@ -86,33 +62,14 @@ int main(const int argc, char **argv) // TODO Apply verbose level to phases const int verbose = args.getVerboseLevel(); benchmark::Phases phases( - benchmark::PhaseOption{args.getMemoryPoll(), args.getGpuMemoryPoll(), args.getRunDelay()}); + benchmark::PhaseOption{args.getMemoryPoll(), args.getGpuMemoryPoll(), args.getRunDelay()}); - std::unique_ptr<FlatBufferModel> model; - std::unique_ptr<Interpreter> interpreter; - std::unique_ptr<tflite::TfLiteVerifier> verifier{new BMFlatBufferVerifier}; + TfLiteModel *model = nullptr; try { phases.run("MODEL_LOAD", [&](const benchmark::Phase &, uint32_t) { - if (args.getModelValidate()) - { - model = FlatBufferModel::VerifyAndBuildFromFile(args.getTFLiteFilename().c_str(), - verifier.get(), &error_reporter); - } - else - { - model = FlatBufferModel::BuildFromFile(args.getTFLiteFilename().c_str(), &error_reporter); - } - if (model == nullptr) - { - throw std::runtime_error{"Cannot create model"}; - } - - BuiltinOpResolver resolver; - InterpreterBuilder builder(*model, resolver); - TFLITE_ENSURE(builder(&interpreter)) - interpreter->SetNumThreads(nnfw::misc::EnvVar("THREAD").asInt(-1)); + model = TfLiteModelCreateFromFile(args.getTFLiteFilename().c_str()); }); } catch (const std::exception &e) @@ -121,17 +78,16 @@ int main(const int argc, char **argv) return 1; } - std::shared_ptr<nnfw::tflite::Session> sess; - - if (use_nnapi) - { - sess = std::make_shared<nnfw::tflite::NNAPISession>(interpreter.get()); - } - else + if (model == nullptr) { - sess = std::make_shared<nnfw::tflite::InterpreterSession>(interpreter.get()); + throw std::runtime_error{"Cannot create model"}; } + auto options = TfLiteInterpreterOptionsCreate(); + TfLiteInterpreterOptionsSetNumThreads(options, nnfw::misc::EnvVar("THREAD").asInt(1)); + + TfLiteInterpreter *interpreter = TfLiteInterpreterCreate(model, options); + auto sess = std::make_shared<nnfw::tflite::InterpreterSession>(interpreter); try { phases.run("PREPARE", [&](const benchmark::Phase &, uint32_t) { sess->prepare(); }); @@ -144,27 +100,28 @@ int main(const int argc, char **argv) if (args.getInputShapes().size() != 0) { - const int dim_values = args.getInputShapes().size(); - int offset = 0; + const auto dim_values = args.getInputShapes().size(); + int32_t offset = 0; - for (const auto &id : interpreter->inputs()) + auto const input_count = TfLiteInterpreterGetInputTensorCount(interpreter); + for (int32_t id = 0; id < input_count; id++) { - TfLiteTensor *tensor = interpreter->tensor(id); + TfLiteTensor *tensor = TfLiteInterpreterGetInputTensor(interpreter, id); std::vector<int32_t> new_dim; - new_dim.resize(tensor->dims->size); + new_dim.resize(TfLiteTensorNumDims(tensor)); - for (uint32_t axis = 0; axis < tensor->dims->size; axis++, offset++) + for (int32_t axis = 0; axis < TfLiteTensorNumDims(tensor); axis++, offset++) { new_dim[axis] = - ((offset < dim_values) ? args.getInputShapes()[offset] : tensor->dims->data[axis]); + ((offset < dim_values) ? args.getInputShapes()[offset] : TfLiteTensorDim(tensor, axis)); } - interpreter->ResizeInputTensor(id, new_dim); + TfLiteInterpreterResizeInputTensor(interpreter, id, new_dim.data(), new_dim.size()); if (offset >= dim_values) break; } - interpreter->AllocateTensors(); + TfLiteInterpreterAllocateTensors(interpreter); } TFLiteRun::TensorLoader tensor_loader(*interpreter); @@ -175,101 +132,31 @@ int main(const int argc, char **argv) { if (!args.getInputFilename().empty()) { - tensor_loader.loadRawTensors(args.getInputFilename(), interpreter->inputs()); + tensor_loader.loadRawInputTensors(args.getInputFilename()); } else { tensor_loader.loadDumpedTensors(args.getCompareFilename()); } - - for (const auto &o : interpreter->inputs()) - { - const auto &tensor_view = tensor_loader.get(o); - TfLiteTensor *tensor = interpreter->tensor(o); - - memcpy(reinterpret_cast<void *>(tensor->data.f), - reinterpret_cast<const void *>(tensor_view._base), tensor->bytes); - } } else { 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 = nnfw::tflite::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 = nnfw::tflite::TensorView<uint8_t>::make(*interpreter, o); - - auto fp = static_cast<uint8_t (nnfw::misc::RandomGenerator::*)( - const ::nnfw::misc::tensor::Shape &, const ::nnfw::misc::tensor::Index &)>( - &nnfw::misc::RandomGenerator::generate<uint8_t>); - const nnfw::misc::tensor::Object<uint8_t> 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 if (tensor->type == kTfLiteBool) - { - // Generate bool input - auto tensor_view = nnfw::tflite::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>(); - } - } - } + RandomInputInitializer initializer{randgen}; + initializer.run(*interpreter); } TFLiteRun::TensorDumper tensor_dumper; // Must be called before `interpreter->Invoke()` - tensor_dumper.addTensors(*interpreter, interpreter->inputs()); + tensor_dumper.addInputTensors(*interpreter); std::cout << "input tensor indices = ["; - for (const auto &o : interpreter->inputs()) + auto const input_count = TfLiteInterpreterGetInputTensorCount(interpreter); + for (int32_t idx = 0; idx < input_count; idx++) { - std::cout << o << ","; + std::cout << idx << ","; } std::cout << "]" << std::endl; @@ -277,40 +164,42 @@ int main(const int argc, char **argv) // only warmup. if (verbose == 0) { - phases.run("WARMUP", [&](const benchmark::Phase &, uint32_t) { sess->run(); }, - args.getWarmupRuns()); - phases.run("EXECUTE", [&](const benchmark::Phase &, uint32_t) { sess->run(); }, - args.getNumRuns(), true); + phases.run( + "WARMUP", [&](const benchmark::Phase &, uint32_t) { sess->run(); }, args.getWarmupRuns()); + phases.run( + "EXECUTE", [&](const benchmark::Phase &, uint32_t) { sess->run(); }, args.getNumRuns(), true); } else { - phases.run("WARMUP", [&](const benchmark::Phase &, uint32_t) { sess->run(); }, - [&](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) { sess->run(); }, - [&](const benchmark::Phase &phase, uint32_t nth) { - std::cout << "... " - << "run " << nth + 1 << " takes " << phase.time[nth] / 1e3 << " ms" - << std::endl; - }, - args.getNumRuns(), true); + phases.run( + "WARMUP", [&](const benchmark::Phase &, uint32_t) { sess->run(); }, + [&](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) { sess->run(); }, + [&](const benchmark::Phase &phase, uint32_t nth) { + std::cout << "... " + << "run " << nth + 1 << " takes " << phase.time[nth] / 1e3 << " ms" << std::endl; + }, + args.getNumRuns(), true); } sess->teardown(); // Must be called after `interpreter->Invoke()` - tensor_dumper.addTensors(*interpreter, interpreter->outputs()); + tensor_dumper.addOutputTensors(*interpreter); std::cout << "output tensor indices = ["; - for (const auto &o : interpreter->outputs()) + auto const output_count = TfLiteInterpreterGetOutputTensorCount(interpreter); + for (int32_t idx = 0; idx < output_count; idx++) { - std::cout << o << "("; - - print_max_idx(interpreter->tensor(o)->data.f, interpreter->tensor(o)->bytes / sizeof(float)); + auto tensor = TfLiteInterpreterGetOutputTensor(interpreter, idx); + print_max_idx(reinterpret_cast<float *>(TfLiteTensorData(tensor)), + TfLiteTensorByteSize(tensor) / sizeof(float)); std::cout << "),"; } @@ -374,12 +263,13 @@ int main(const int argc, char **argv) TfLiteInterpMatchApp app(comparator); bool res = true; - for (const auto &o : interpreter->outputs()) + for (int32_t idx = 0; idx < output_count; idx++) { - auto expected = tensor_loader.get(o); - auto obtained = nnfw::tflite::TensorView<float>::make(*interpreter, o); + auto expected = tensor_loader.getOutput(idx); + auto const tensor = TfLiteInterpreterGetOutputTensor(interpreter, idx); + auto obtained = nnfw::tflite::TensorView<float>::make(tensor); - res = res && app.compareSingleTensorView(expected, obtained, o); + res = res && app.compareSingleTensorView(expected, obtained, idx); } if (!res) |