diff options
Diffstat (limited to 'tools/tflite_benchmark_model')
| -rw-r--r-- | tools/tflite_benchmark_model/.FORMATDENY | 0 | ||||
| -rw-r--r-- | tools/tflite_benchmark_model/CMakeLists.txt | 6 | ||||
| -rw-r--r-- | tools/tflite_benchmark_model/README.md | 209 | ||||
| -rw-r--r-- | tools/tflite_benchmark_model/benchmark_main.cc | 53 | ||||
| -rw-r--r-- | tools/tflite_benchmark_model/benchmark_model.cc | 175 | ||||
| -rw-r--r-- | tools/tflite_benchmark_model/benchmark_model.h | 177 | ||||
| -rw-r--r-- | tools/tflite_benchmark_model/benchmark_params.cc | 73 | ||||
| -rw-r--r-- | tools/tflite_benchmark_model/benchmark_params.h | 118 | ||||
| -rw-r--r-- | tools/tflite_benchmark_model/benchmark_tflite_model.cc | 360 | ||||
| -rw-r--r-- | tools/tflite_benchmark_model/benchmark_tflite_model.h | 95 | ||||
| -rw-r--r-- | tools/tflite_benchmark_model/command_line_flags.cc | 214 | ||||
| -rw-r--r-- | tools/tflite_benchmark_model/command_line_flags.h | 141 | ||||
| -rw-r--r-- | tools/tflite_benchmark_model/logging.h | 92 | ||||
| -rw-r--r-- | tools/tflite_benchmark_model/profile_summarizer.cc | 164 | ||||
| -rw-r--r-- | tools/tflite_benchmark_model/profile_summarizer.h | 55 |
15 files changed, 0 insertions, 1932 deletions
diff --git a/tools/tflite_benchmark_model/.FORMATDENY b/tools/tflite_benchmark_model/.FORMATDENY deleted file mode 100644 index e69de29bb..000000000 --- a/tools/tflite_benchmark_model/.FORMATDENY +++ /dev/null diff --git a/tools/tflite_benchmark_model/CMakeLists.txt b/tools/tflite_benchmark_model/CMakeLists.txt deleted file mode 100644 index d52690460..000000000 --- a/tools/tflite_benchmark_model/CMakeLists.txt +++ /dev/null @@ -1,6 +0,0 @@ -file(GLOB_RECURSE SOURCES "*.cc") - -add_executable(tflite_benchmark_model ${SOURCES}) -target_compile_definitions(tflite_benchmark_model PUBLIC "TFLITE_PROFILING_ENABLED") -target_link_libraries(tflite_benchmark_model tensorflow-lite ${LIB_PTHREAD} dl nnfw_util nnfw_support_tflite) -install(TARGETS tflite_benchmark_model DESTINATION bin) diff --git a/tools/tflite_benchmark_model/README.md b/tools/tflite_benchmark_model/README.md deleted file mode 100644 index 93769305b..000000000 --- a/tools/tflite_benchmark_model/README.md +++ /dev/null @@ -1,209 +0,0 @@ -# TFLite Model Benchmark Tool - -## Description - -A simple C++ binary to benchmark a TFLite model and its individual operators, -both on desktop machines and on Android. The binary takes a TFLite model, -generates random inputs and then repeatedly runs the model for specified number -of runs. Aggregrate latency statistics are reported after running the benchmark. - -The instructions below are for running the binary on Desktop and Android, -for iOS please use the -[iOS benchmark app] (https://github.com/tensorflow/tensorflow/tree/master/tensorflow/contrib/lite/tools/benchmark/ios). - -## Parameters - -The binary takes the following required parameters: - -* `graph`: `string` \ - The path to the TFLite model file. -* `input_layer`: `string` \ - The name of the input layer, this is typically the first layer of the model. -* `input_layer_shape`: `string` \ - The shape of the input layer. This is a comma separated string of the shape - of tensor of input layer. - -and the following optional parameters: - -* `num_threads`: `int` (default=1) \ - The number of threads to use for running TFLite interpreter. -* `warmup_runs`: `int` (default=1) \ - The number of warmup runs to do before starting the benchmark. -* `run_delay`: `float` (default=-1.0) \ - The delay in seconds between subsequent benchmark runs. Non-positive values - mean use no delay. -* `use_nnapi`: `bool` (default=false) \ - Whether to use [Android NNAPI] (https://developer.android.com/ndk/guides/neuralnetworks/). - This API is available on recent Android devices. - -## To build/install/run - -### On Android: - -(0) Refer to https://github.com/tensorflow/tensorflow/tree/master/tensorflow/examples/android to edit the `WORKSPACE` to configure the android NDK/SDK. - -(1) Build for your specific platform, e.g.: - -``` -bazel build -c opt \ - --config=android_arm \ - --cxxopt='--std=c++11' \ - tensorflow/contrib/lite/tools/benchmark:benchmark_model -``` - -(2) Connect your phone. Push the binary to your phone with adb push - (make the directory if required): - -``` -adb push bazel-bin/tensorflow/contrib/lite/tools/benchmark/benchmark_model /data/local/tmp -``` - -(3) Make the binary executable. - -``` -adb shell chmod +x /data/local/tmp/benchmark_model -``` - -(4) Push the compute graph that you need to test. For example: - -``` -adb push mobilenet_quant_v1_224.tflite /data/local/tmp -``` - -(5) Run the benchmark. For example: - -``` -adb shell /data/local/tmp/benchmark_model \ - --graph=/data/local/tmp/mobilenet_quant_v1_224.tflite \ - --input_layer="input" \ - --input_layer_shape="1,224,224,3" \ - --num_threads=4 -``` - -### On desktop: -(1) build the binary - -``` -bazel build -c opt tensorflow/contrib/lite/tools/benchmark:benchmark_model -``` - -(2) Run on your compute graph, similar to the Android case but without the need of adb shell. -For example: - -``` -bazel-bin/tensorflow/contrib/lite/tools/benchmark/benchmark_model \ - --graph=mobilenet_quant_v1_224.tflite \ - --input_layer="Placeholder" \ - --input_layer_shape="1,224,224,3" \ - --num_threads=4 -``` - -The MobileNet graph used as an example here may be downloaded from -https://storage.googleapis.com/download.tensorflow.org/models/tflite/mobilenet_v1_224_android_quant_2017_11_08.zip - - -## Reducing variance between runs on Android. - -Most modern Android phones use [ARM big.LITTLE](https://en.wikipedia.org/wiki/ARM_big.LITTLE) -architecture where some cores are more power hungry but faster than other cores. -When running benchmarks on these phones there can be significant variance -between different runs of the benchmark. One way to reduce variance between runs -is to set the [CPU affinity](https://en.wikipedia.org/wiki/Processor_affinity) -before running the benchmark. On Android this can be done using the `taskset` -command. -E.g. for running the benchmark on big cores on Pixel 2 with a single thread one -can use the following command: - -``` -adb shell tasket f0 /data/local/tmp/benchmark_model \ - --graph=/data/local/tmp/mobilenet_quant_v1_224.tflite \ - --input_layer="input" \ - --input_layer_shape="1,224,224,3" \ - --num_threads=1 -``` - -where `f0` is the affinity mask for big cores on Pixel 2. -Note: The affinity mask varies with the device. - -## Profiling model operators -The benchmark model binary also allows you to profile operators and give execution times of each operator. To do this, -compile the binary with a compiler flag that enables profiling to be compiled in. Pass **--copt=-DTFLITE_PROFILING_ENABLED** -to compile benchmark with profiling support. -For example, to compile with profiling support on Android, add this flag to the previous command: - -``` -bazel build -c opt \ - --config=android_arm \ - --cxxopt='--std=c++11' \ - --copt=-DTFLITE_PROFILING_ENABLED \ - tensorflow/contrib/lite/tools/benchmark:benchmark_model -``` -This compiles TFLite with profiling enabled, now you can run the benchmark binary like before. The binary will produce detailed statistics for each operation similar to those shown below: - -``` - -============================== Run Order ============================== - [node type] [start] [first] [avg ms] [%] [cdf%] [mem KB] [times called] [Name] - CONV_2D 0.000 4.269 4.269 0.107% 0.107% 0.000 0 [MobilenetV1/MobilenetV1/Conv2d_0/Relu6] - DEPTHWISE_CONV_2D 4.270 2.150 2.150 0.054% 0.161% 0.000 0 [MobilenetV1/MobilenetV1/Conv2d_1_depthwise/Relu6] - CONV_2D 6.421 6.107 6.107 0.153% 0.314% 0.000 0 [MobilenetV1/MobilenetV1/Conv2d_1_pointwise/Relu6] - DEPTHWISE_CONV_2D 12.528 1.366 1.366 0.034% 0.348% 0.000 0 [MobilenetV1/MobilenetV1/Conv2d_2_depthwise/Relu6] - CONV_2D 13.895 4.195 4.195 0.105% 0.454% 0.000 0 [MobilenetV1/MobilenetV1/Conv2d_2_pointwise/Relu6] - DEPTHWISE_CONV_2D 18.091 1.260 1.260 0.032% 0.485% 0.000 0 [MobilenetV1/MobilenetV1/Conv2d_3_depthwise/Relu6] - CONV_2D 19.352 6.652 6.652 0.167% 0.652% 0.000 0 [MobilenetV1/MobilenetV1/Conv2d_3_pointwise/Relu6] - DEPTHWISE_CONV_2D 26.005 0.698 0.698 0.018% 0.670% 0.000 0 [MobilenetV1/MobilenetV1/Conv2d_4_depthwise/Relu6] - CONV_2D 26.703 3.344 3.344 0.084% 0.754% 0.000 0 [MobilenetV1/MobilenetV1/Conv2d_4_pointwise/Relu6] - DEPTHWISE_CONV_2D 30.047 0.646 0.646 0.016% 0.770% 0.000 0 [MobilenetV1/MobilenetV1/Conv2d_5_depthwise/Relu6] - CONV_2D 30.694 5.800 5.800 0.145% 0.915% 0.000 0 [MobilenetV1/MobilenetV1/Conv2d_5_pointwise/Relu6] - DEPTHWISE_CONV_2D 36.495 0.331 0.331 0.008% 0.924% 0.000 0 [MobilenetV1/MobilenetV1/Conv2d_6_depthwise/Relu6] - CONV_2D 36.826 2.838 2.838 0.071% 0.995% 0.000 0 [MobilenetV1/MobilenetV1/Conv2d_6_pointwise/Relu6] - DEPTHWISE_CONV_2D 39.665 0.439 0.439 0.011% 1.006% 0.000 0 [MobilenetV1/MobilenetV1/Conv2d_7_depthwise/Relu6] - CONV_2D 40.105 5.293 5.293 0.133% 1.139% 0.000 0 [MobilenetV1/MobilenetV1/Conv2d_7_pointwise/Relu6] - DEPTHWISE_CONV_2D 45.399 0.352 0.352 0.009% 1.147% 0.000 0 [MobilenetV1/MobilenetV1/Conv2d_8_depthwise/Relu6] - CONV_2D 45.752 5.322 5.322 0.133% 1.281% 0.000 0 [MobilenetV1/MobilenetV1/Conv2d_8_pointwise/Relu6] - DEPTHWISE_CONV_2D 51.075 0.357 0.357 0.009% 1.290% 0.000 0 [MobilenetV1/MobilenetV1/Conv2d_9_depthwise/Relu6] - CONV_2D 51.432 5.693 5.693 0.143% 1.433% 0.000 0 [MobilenetV1/MobilenetV1/Conv2d_9_pointwise/Relu6] - DEPTHWISE_CONV_2D 57.126 0.366 0.366 0.009% 1.442% 0.000 0 [MobilenetV1/MobilenetV1/Conv2d_10_depthwise/Relu6] - CONV_2D 57.493 5.472 5.472 0.137% 1.579% 0.000 0 [MobilenetV1/MobilenetV1/Conv2d_10_pointwise/Relu6] - DEPTHWISE_CONV_2D 62.966 0.364 0.364 0.009% 1.588% 0.000 0 [MobilenetV1/MobilenetV1/Conv2d_11_depthwise/Relu6] - CONV_2D 63.330 5.404 5.404 0.136% 1.724% 0.000 0 [MobilenetV1/MobilenetV1/Conv2d_11_pointwise/Relu6] - DEPTHWISE_CONV_2D 68.735 0.155 0.155 0.004% 1.728% 0.000 0 [MobilenetV1/MobilenetV1/Conv2d_12_depthwise/Relu6] - CONV_2D 68.891 2.970 2.970 0.074% 1.802% 0.000 0 [MobilenetV1/MobilenetV1/Conv2d_12_pointwise/Relu6] - DEPTHWISE_CONV_2D 71.862 0.206 0.206 0.005% 1.807% 0.000 0 [MobilenetV1/MobilenetV1/Conv2d_13_depthwise/Relu6] - CONV_2D 72.069 5.888 5.888 0.148% 1.955% 0.000 0 [MobilenetV1/MobilenetV1/Conv2d_13_pointwise/Relu6] - AVERAGE_POOL_2D 77.958 0.036 0.036 0.001% 1.956% 0.000 0 [MobilenetV1/Logits/AvgPool_1a/AvgPool] - CONV_2D 77.994 1.445 1.445 0.036% 1.992% 0.000 0 [MobilenetV1/Logits/Conv2d_1c_1x1/BiasAdd] - RESHAPE 79.440 0.002 0.002 0.000% 1.992% 0.000 0 [MobilenetV1/Predictions/Reshape] - SOFTMAX 79.443 0.029 0.029 0.001% 1.993% 0.000 0 [MobilenetV1/Predictions/Softmax] - -============================== Top by Computation Time ============================== - [node type] [start] [first] [avg ms] [%] [cdf%] [mem KB] [times called] [Name] - CONV_2D 19.352 6.652 6.652 0.167% 0.167% 0.000 0 [MobilenetV1/MobilenetV1/Conv2d_3_pointwise/Relu6] - CONV_2D 6.421 6.107 6.107 0.153% 0.320% 0.000 0 [MobilenetV1/MobilenetV1/Conv2d_1_pointwise/Relu6] - CONV_2D 72.069 5.888 5.888 0.148% 0.468% 0.000 0 [MobilenetV1/MobilenetV1/Conv2d_13_pointwise/Relu6] - CONV_2D 30.694 5.800 5.800 0.145% 0.613% 0.000 0 [MobilenetV1/MobilenetV1/Conv2d_5_pointwise/Relu6] - CONV_2D 51.432 5.693 5.693 0.143% 0.756% 0.000 0 [MobilenetV1/MobilenetV1/Conv2d_9_pointwise/Relu6] - CONV_2D 57.493 5.472 5.472 0.137% 0.893% 0.000 0 [MobilenetV1/MobilenetV1/Conv2d_10_pointwise/Relu6] - CONV_2D 63.330 5.404 5.404 0.136% 1.029% 0.000 0 [MobilenetV1/MobilenetV1/Conv2d_11_pointwise/Relu6] - CONV_2D 45.752 5.322 5.322 0.133% 1.162% 0.000 0 [MobilenetV1/MobilenetV1/Conv2d_8_pointwise/Relu6] - CONV_2D 40.105 5.293 5.293 0.133% 1.295% 0.000 0 [MobilenetV1/MobilenetV1/Conv2d_7_pointwise/Relu6] - CONV_2D 0.000 4.269 4.269 0.107% 1.402% 0.000 0 [MobilenetV1/MobilenetV1/Conv2d_0/Relu6] - -Number of nodes executed: 31 -============================== Summary by node type ============================== - [Node type] [count] [avg ms] [avg %] [cdf %] [mem KB] [times called] - CONV_2D 15 1.406 89.270% 89.270% 0.000 0 - DEPTHWISE_CONV_2D 13 0.169 10.730% 100.000% 0.000 0 - SOFTMAX 1 0.000 0.000% 100.000% 0.000 0 - RESHAPE 1 0.000 0.000% 100.000% 0.000 0 - AVERAGE_POOL_2D 1 0.000 0.000% 100.000% 0.000 0 - -Timings (microseconds): count=50 first=79449 curr=81350 min=77385 max=88213 avg=79732 std=1929 -Memory (bytes): count=0 -31 nodes observed - - -Average inference timings in us: Warmup: 83235, Init: 38467, no stats: 79760.9 -``` - - diff --git a/tools/tflite_benchmark_model/benchmark_main.cc b/tools/tflite_benchmark_model/benchmark_main.cc deleted file mode 100644 index 7e4231c48..000000000 --- a/tools/tflite_benchmark_model/benchmark_main.cc +++ /dev/null @@ -1,53 +0,0 @@ -/* - * Copyright (c) 2018 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. - */ - -/* Copyright 2018 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 "benchmark_tflite_model.h" -#include "logging.h" - -namespace nnfw { -namespace benchmark { - -int Main(int argc, char** argv) { -#ifdef TFLITE_CUSTOM_OPS_HEADER - TFLITE_LOG(INFO) << "STARTING with custom ops!"; -#else - TFLITE_LOG(INFO) << "STARTING!"; -#endif - BenchmarkTfLiteModel benchmark; - BenchmarkLoggingListener listener; - benchmark.AddListener(&listener); - benchmark.Run(argc, argv); - return 0; -} -} // namespace benchmark -} // namespace nnfw - -int main(int argc, char** argv) { return nnfw::benchmark::Main(argc, argv); } diff --git a/tools/tflite_benchmark_model/benchmark_model.cc b/tools/tflite_benchmark_model/benchmark_model.cc deleted file mode 100644 index 7869180bf..000000000 --- a/tools/tflite_benchmark_model/benchmark_model.cc +++ /dev/null @@ -1,175 +0,0 @@ -/* - * Copyright (c) 2018 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. - */ - -/* Copyright 2018 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 "benchmark_model.h" - -#include <time.h> - -#include <iostream> -#include <sstream> - -#include "tensorflow/contrib/lite/profiling/time.h" -#include "logging.h" - -namespace { -void SleepForSeconds(double sleep_seconds) { - if (sleep_seconds <= 0.0) { - return; - } - // Convert the run_delay string into a timespec. - timespec req; - req.tv_sec = static_cast<time_t>(sleep_seconds); - req.tv_nsec = (sleep_seconds - req.tv_sec) * 1000000000; - // If requested, sleep between runs for an arbitrary amount of time. - // This can be helpful to determine the effect of mobile processor - // scaling and thermal throttling. -#ifdef PLATFORM_WINDOWS - Sleep(sleep_seconds * 1000); -#else - nanosleep(&req, nullptr); -#endif -} - -} // namespace - -namespace nnfw { -namespace benchmark { -using tensorflow::Stat; - -BenchmarkParams BenchmarkModel::DefaultParams() { - BenchmarkParams params; - params.AddParam("num_runs", BenchmarkParam::Create<int32_t>(50)); - params.AddParam("run_delay", BenchmarkParam::Create<float>(-1.0f)); - params.AddParam("num_threads", BenchmarkParam::Create<int32_t>(1)); - params.AddParam("benchmark_name", BenchmarkParam::Create<std::string>("")); - params.AddParam("output_prefix", BenchmarkParam::Create<std::string>("")); - params.AddParam("warmup_runs", BenchmarkParam::Create<int32_t>(1)); - return params; -} - -BenchmarkModel::BenchmarkModel() : params_(DefaultParams()) {} - -void BenchmarkLoggingListener::OnBenchmarkEnd(const BenchmarkResults &results) { - auto inference_us = results.inference_time_us(); - auto init_us = results.startup_latency_us(); - auto warmup_us = results.warmup_time_us(); - TFLITE_LOG(INFO) << "Average inference timings in us: " - << "Warmup: " << warmup_us.avg() << ", " - << "Init: " << init_us << ", " - << "no stats: " << inference_us.avg(); -} - -std::vector<Flag> BenchmarkModel::GetFlags() { - return { - CreateFlag<int32_t>("num_runs", ¶ms_, "number of runs"), - CreateFlag<float>("run_delay", ¶ms_, "delay between runs in seconds"), - CreateFlag<int32_t>("num_threads", ¶ms_, "number of threads"), - CreateFlag<std::string>("benchmark_name", ¶ms_, "benchmark name"), - CreateFlag<std::string>("output_prefix", ¶ms_, - "benchmark output prefix"), - CreateFlag<int32_t>("warmup_runs", ¶ms_, - "how many runs to initialize model"), - }; -} - -void BenchmarkModel::LogFlags() { - TFLITE_LOG(INFO) << "Num runs: [" << params_.Get<int32_t>("num_runs") << "]"; - TFLITE_LOG(INFO) << "Inter-run delay (seconds): [" - << params_.Get<float>("run_delay") << "]"; - TFLITE_LOG(INFO) << "Num threads: [" << params_.Get<int32_t>("num_threads") - << "]"; - TFLITE_LOG(INFO) << "Benchmark name: [" - << params_.Get<std::string>("benchmark_name") << "]"; - TFLITE_LOG(INFO) << "Output prefix: [" - << params_.Get<std::string>("output_prefix") << "]"; - TFLITE_LOG(INFO) << "Warmup runs: [" << params_.Get<int32_t>("warmup_runs") - << "]"; -} - -Stat<int64_t> BenchmarkModel::Run(int num_times, RunType run_type) { - Stat<int64_t> run_stats; - TFLITE_LOG(INFO) << "Running benchmark for " << num_times << " iterations "; - for (int run = 0; run < num_times; run++) { - listeners_.OnSingleRunStart(run_type); - int64_t start_us = tflite::profiling::time::NowMicros(); - RunImpl(); - int64_t end_us = tflite::profiling::time::NowMicros(); - listeners_.OnSingleRunEnd(); - - run_stats.UpdateStat(end_us - start_us); - SleepForSeconds(params_.Get<float>("run_delay")); - } - - std::stringstream stream; - run_stats.OutputToStream(&stream); - TFLITE_LOG(INFO) << stream.str() << std::endl; - - return run_stats; -} - -void BenchmarkModel::Run(int argc, char **argv) { - if (!ParseFlags(argc, argv)) { - return; - } - - LogFlags(); - - listeners_.OnBenchmarkStart(params_); - int64_t initialization_start_us = tflite::profiling::time::NowMicros(); - Init(); - int64_t initialization_end_us = tflite::profiling::time::NowMicros(); - int64_t startup_latency_us = initialization_end_us - initialization_start_us; - TFLITE_LOG(INFO) << "Initialized session in " << startup_latency_us / 1e3 - << "ms"; - - uint64_t input_bytes = ComputeInputBytes(); - Stat<int64_t> warmup_time_us = - Run(params_.Get<int32_t>("warmup_runs"), WARMUP); - Stat<int64_t> inference_time_us = - Run(params_.Get<int32_t>("num_runs"), REGULAR); - listeners_.OnBenchmarkEnd( - {startup_latency_us, input_bytes, warmup_time_us, inference_time_us}); -} - -bool BenchmarkModel::ParseFlags(int argc, char **argv) { - auto flag_list = GetFlags(); - const bool parse_result = - Flags::Parse(&argc, const_cast<const char **>(argv), flag_list); - if (!parse_result) { - std::string usage = Flags::Usage(argv[0], flag_list); - TFLITE_LOG(ERROR) << usage; - return false; - } - return ValidateFlags(); -} - -} // namespace benchmark -} // namespace nnfw diff --git a/tools/tflite_benchmark_model/benchmark_model.h b/tools/tflite_benchmark_model/benchmark_model.h deleted file mode 100644 index 5645e2910..000000000 --- a/tools/tflite_benchmark_model/benchmark_model.h +++ /dev/null @@ -1,177 +0,0 @@ -/* - * Copyright (c) 2018 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. - */ - -/* 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. -==============================================================================*/ - -#ifndef __TFLITE_BENCHMARK_MODEL_BENCHMARK_MODEL_H__ -#define __TFLITE_BENCHMARK_MODEL_BENCHMARK_MODEL_H__ - -#include <cmath> -#include <limits> -#include <ostream> -#include <string> -#include <unordered_set> -#include <vector> - -#include "benchmark_params.h" -#include "command_line_flags.h" -#include "tensorflow/core/util/stats_calculator.h" - -namespace nnfw { -namespace benchmark { - -enum RunType { - WARMUP, - REGULAR, -}; - -class BenchmarkResults { - public: - BenchmarkResults(int64_t startup_latency_us, uint64_t input_bytes, - tensorflow::Stat<int64_t> warmup_time_us, - tensorflow::Stat<int64_t> inference_time_us) - : startup_latency_us_(startup_latency_us), - input_bytes_(input_bytes), - warmup_time_us_(warmup_time_us), - inference_time_us_(inference_time_us) {} - - tensorflow::Stat<int64_t> inference_time_us() const { - return inference_time_us_; - } - tensorflow::Stat<int64_t> warmup_time_us() const { return warmup_time_us_; } - int64_t startup_latency_us() const { return startup_latency_us_; } - uint64_t input_bytes() const { return input_bytes_; } - double throughput_MB_per_second() const { - double bytes_per_sec = (input_bytes_ * inference_time_us_.count() * 1e6) / - inference_time_us_.sum(); - return bytes_per_sec / (1024.0 * 1024.0); - } - - private: - int64_t startup_latency_us_; - uint64_t input_bytes_; - tensorflow::Stat<int64_t> warmup_time_us_; - tensorflow::Stat<int64_t> inference_time_us_; -}; - -class BenchmarkListener { - public: - virtual void OnBenchmarkStart(const BenchmarkParams& params) {} - virtual void OnSingleRunStart(RunType runType) {} - virtual void OnSingleRunEnd() {} - virtual void OnBenchmarkEnd(const BenchmarkResults& results) {} - virtual ~BenchmarkListener() {} -}; - -// A listener that forwards its method calls to a collection of listeners. -class BenchmarkListeners : public BenchmarkListener { - public: - // Added a listener to the listener collection. - // |listener| is not owned by the instance of |BenchmarkListeners|. - // |listener| should not be null and should outlast the instance of - // |BenchmarkListeners|. - void AddListener(BenchmarkListener* listener) { - listeners_.push_back(listener); - } - - void OnBenchmarkStart(const BenchmarkParams& params) override { - for (auto listener : listeners_) { - listener->OnBenchmarkStart(params); - } - } - - void OnSingleRunStart(RunType runType) override { - for (auto listener : listeners_) { - listener->OnSingleRunStart(runType); - } - } - - void OnSingleRunEnd() override { - for (auto listener : listeners_) { - listener->OnSingleRunEnd(); - } - } - - void OnBenchmarkEnd(const BenchmarkResults& results) override { - for (auto listener : listeners_) { - listener->OnBenchmarkEnd(results); - } - } - - ~BenchmarkListeners() {} - - private: - // Use vector so listeners are invoked in the order they are added. - std::vector<BenchmarkListener*> listeners_; -}; - -// Benchmark listener that just logs the results of benchmark run. -class BenchmarkLoggingListener : public BenchmarkListener { - void OnBenchmarkEnd(const BenchmarkResults& results) override; -}; - -template <typename T> -Flag CreateFlag(const char* name, BenchmarkParams* params, - const std::string& usage) { - return Flag(name, [params, name](const T& val) { params->Set<T>(name, val); }, - params->Get<T>(name), usage); -} - -// Benchmarks a model. -// -// Subclasses need to implement initialization and running of the model. -// The results can be collected by adding BenchmarkListener(s). -class BenchmarkModel { - public: - static BenchmarkParams DefaultParams(); - BenchmarkModel(); - BenchmarkModel(BenchmarkParams params) : params_(std::move(params)) {} - virtual ~BenchmarkModel() {} - bool ParseFlags(int argc, char** argv); - virtual void Init() = 0; - void Run(int argc, char** argv); - void AddListener(BenchmarkListener* listener) { - listeners_.AddListener(listener); - } - - protected: - virtual void LogFlags(); - virtual bool ValidateFlags() { return true; } - virtual std::vector<Flag> GetFlags(); - virtual uint64_t ComputeInputBytes() = 0; - virtual tensorflow::Stat<int64_t> Run(int num_times, RunType run_type); - virtual void RunImpl() = 0; - BenchmarkParams params_; - BenchmarkListeners listeners_; -}; - -} // namespace benchmark -} // namespace nnfw - -#endif //__TFLITE_BENCHMARK_MODEL_BENCHMARK_MODEL_H__ diff --git a/tools/tflite_benchmark_model/benchmark_params.cc b/tools/tflite_benchmark_model/benchmark_params.cc deleted file mode 100644 index 7b667a442..000000000 --- a/tools/tflite_benchmark_model/benchmark_params.cc +++ /dev/null @@ -1,73 +0,0 @@ -/* - * Copyright (c) 2018 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. - */ - -/* Copyright 2018 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 "benchmark_params.h" - -#include <string> -#include <unordered_map> -#include <vector> - -#include "logging.h" - -namespace nnfw { -namespace benchmark { - -void BenchmarkParam::AssertHasSameType(BenchmarkParam::ParamType a, - BenchmarkParam::ParamType b) { - TFLITE_BENCHMARK_CHECK(a == b) << "Type mismatch while accessing parameter."; -} - -template <> -BenchmarkParam::ParamType BenchmarkParam::GetValueType<int32_t>() { - return BenchmarkParam::ParamType::TYPE_INT32; -} - -template <> -BenchmarkParam::ParamType BenchmarkParam::GetValueType<bool>() { - return BenchmarkParam::ParamType::TYPE_BOOL; -} - -template <> -BenchmarkParam::ParamType BenchmarkParam::GetValueType<float>() { - return BenchmarkParam::ParamType::TYPE_FLOAT; -} - -template <> -BenchmarkParam::ParamType BenchmarkParam::GetValueType<std::string>() { - return BenchmarkParam::ParamType::TYPE_STRING; -} - -void BenchmarkParams::AssertParamExists(const std::string& name) const { - TFLITE_BENCHMARK_CHECK(HasParam(name)) << name << " was not found."; -} - -} // namespace benchmark -} // namespace nnfw diff --git a/tools/tflite_benchmark_model/benchmark_params.h b/tools/tflite_benchmark_model/benchmark_params.h deleted file mode 100644 index 1ac3f4af6..000000000 --- a/tools/tflite_benchmark_model/benchmark_params.h +++ /dev/null @@ -1,118 +0,0 @@ -/* - * Copyright (c) 2018 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. - */ - -/* Copyright 2018 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. -==============================================================================*/ - -#ifndef __TFLITE_BENCHMARK_MODEL_BENCHMARK_PARAMS_H__ -#define __TFLITE_BENCHMARK_MODEL_BENCHMARK_PARAMS_H__ -#include <memory> -#include <string> -#include <unordered_map> -#include <vector> - -#include "logging.h" - -namespace nnfw { -namespace benchmark { - -template <typename T> -class TypedBenchmarkParam; - -class BenchmarkParam { - protected: - enum class ParamType { TYPE_INT32, TYPE_FLOAT, TYPE_BOOL, TYPE_STRING }; - - public: - template <typename T> - static std::unique_ptr<BenchmarkParam> Create(const T& default_value) { - return std::unique_ptr<BenchmarkParam>( - new TypedBenchmarkParam<T>(default_value)); - } - - template <typename T> - TypedBenchmarkParam<T>* AsTyped() { - AssertHasSameType(GetValueType<T>(), type_); - return static_cast<TypedBenchmarkParam<T>*>(this); - } - virtual ~BenchmarkParam() {} - BenchmarkParam(ParamType type) : type_(type) {} - - private: - static void AssertHasSameType(ParamType a, ParamType b); - protected: - template <typename T> - static ParamType GetValueType(); - - const ParamType type_; -}; - -template <typename T> -class TypedBenchmarkParam : public BenchmarkParam { - public: - TypedBenchmarkParam(const T& value) - : BenchmarkParam(GetValueType<T>()), value_(value) {} - void Set(const T& value) { value_ = value; } - - T Get() { return value_; } - - private: - T value_; -}; - -class BenchmarkParams { - public: - void AddParam(const std::string& name, - std::unique_ptr<BenchmarkParam> value) { - params_[name] = std::move(value); - } - - bool HasParam(const std::string& name) const { - return params_.find(name) != params_.end(); - } - - template <typename T> - void Set(const std::string& name, const T& value) { - AssertParamExists(name); - params_.at(name)->AsTyped<T>()->Set(value); - } - - template <typename T> - T Get(const std::string& name) const { - AssertParamExists(name); - return params_.at(name)->AsTyped<T>()->Get(); - } - - private: - void AssertParamExists(const std::string& name) const; - std::unordered_map<std::string, std::unique_ptr<BenchmarkParam>> params_; -}; - -} // namespace benchmark -} // namespace nnfw -#endif // __TFLITE_BENCHMARK_MODEL_BENCHMARK_PARAMS_H__ diff --git a/tools/tflite_benchmark_model/benchmark_tflite_model.cc b/tools/tflite_benchmark_model/benchmark_tflite_model.cc deleted file mode 100644 index d277795a3..000000000 --- a/tools/tflite_benchmark_model/benchmark_tflite_model.cc +++ /dev/null @@ -1,360 +0,0 @@ -/* - * Copyright (c) 2018 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. - */ - -/* Copyright 2018 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 "benchmark_tflite_model.h" - -#include <cstdarg> -#include <cstdlib> -#include <iostream> -#include <memory> -#include <string> -#include <unordered_set> -#include <vector> - -#include "support/tflite/kernels/register.h" -#include "tensorflow/contrib/lite/model.h" -#include "tensorflow/contrib/lite/op_resolver.h" -#include "tensorflow/contrib/lite/string_util.h" -#include "logging.h" -#include "util/profiling/profiling.h" -#include "support/tflite/nnapi_delegate.h" - -#ifdef TFLITE_CUSTOM_OPS_HEADER -void RegisterSelectedOps(::tflite::MutableOpResolver* resolver); -#endif - -namespace nnfw { -namespace benchmark { - -void ProfilingListener::SetInterpreter(tflite::Interpreter* interpreter) { - TFLITE_BENCHMARK_CHECK(interpreter); - interpreter_ = interpreter; - interpreter_->SetProfiler(&profiler_); -} - -void ProfilingListener::OnSingleRunStart(RunType run_type) { - if (run_type == REGULAR) { - profiler_.Reset(); - profiler_.StartProfiling(); - } -} - -void ProfilingListener::OnBenchmarkEnd(const BenchmarkResults& results) { - if (has_profiles_) { - TFLITE_LOG(INFO) << summarizer_.GetOutputString(); - } -} - -void ProfilingListener::OnSingleRunEnd() { - profiler_.StopProfiling(); - auto profile_events = profiler_.GetProfileEvents(); - has_profiles_ = !profile_events.empty(); - summarizer_.ProcessProfiles(profile_events, *interpreter_); -} - -namespace { - -std::vector<std::string> Split(const std::string& str, const char delim) { - std::istringstream input(str); - std::vector<std::string> results; - std::string item; - while (std::getline(input, item, delim)) { - results.push_back(item); - } - return results; -} - -template <typename T> -bool SplitAndParse(const std::string& str, char delim, std::vector<T>* values) { - std::istringstream input(str); - bool first = true; - while (!input.eof()) { - if (!first) { - char c; - input >> c; - if (c != delim) { - return false; - } - } else { - first = false; - } - T val; - input >> val; - if (!input.eof() && !input.good()) { - return false; - } - values->push_back(val); - } - return true; -} - -template <typename T> -void FillRandomValue(T* ptr, const std::vector<int>& sizes, - const std::function<T()>& random_func) { - int num_elements = 1; - for (int dim : sizes) { - num_elements *= dim; - } - for (int i = 0; i < num_elements; ++i) { - *ptr++ = random_func(); - } -} - -void FillRandomString(tflite::DynamicBuffer* buffer, - const std::vector<int>& sizes, - const std::function<std::string()>& random_func) { - int num_elements = 1; - for (int dim : sizes) { - num_elements *= dim; - } - for (int i = 0; i < num_elements; ++i) { - auto str = random_func(); - buffer->AddString(str.data(), str.length()); - } -} - -bool PopulateInputLayerInfo( - const std::string& names_string, const std::string& shapes_string, - std::vector<BenchmarkTfLiteModel::InputLayerInfo>* info) { - std::vector<std::string> names = Split(names_string, ','); - std::vector<std::string> shapes = Split(shapes_string, ':'); - - if (names.size() != shapes.size()) { - TFLITE_LOG(ERROR) << "The number of items in" - << " --input_layer_shape (" << shapes_string << ", with " - << shapes.size() << " items)" - << " must match the number of items in" - << " --input_layer (" << names_string << ", with " - << names.size() << " items)." - << " For example --input_layer=input1,input2" - << " --input_layer_shape=1,224,224,4:1,20"; - return false; - } - - for (int i = 0; i < names.size(); ++i) { - info->push_back(BenchmarkTfLiteModel::InputLayerInfo()); - BenchmarkTfLiteModel::InputLayerInfo& input = info->back(); - - input.name = names[i]; - - TFLITE_BENCHMARK_CHECK(SplitAndParse(shapes[i], ',', &input.shape)) - << "Incorrect size string specified: " << shapes[i]; - for (int dim : input.shape) { - if (dim == -1) { - TFLITE_LOG(ERROR) - << "Any unknown sizes in the shapes (-1's) must be replaced" - << " with the size you want to benchmark with."; - return false; - } - } - } - - return true; -} - -BenchmarkParams GetDefaultParams() { - BenchmarkParams default_params = BenchmarkModel::DefaultParams(); - default_params.AddParam("graph", BenchmarkParam::Create<std::string>("")); - default_params.AddParam("input_layer", - BenchmarkParam::Create<std::string>("")); - default_params.AddParam("input_layer_shape", - BenchmarkParam::Create<std::string>("")); - default_params.AddParam("use_nnapi", BenchmarkParam::Create<bool>(false)); - return default_params; -} - -} // namespace - -BenchmarkTfLiteModel::BenchmarkTfLiteModel() - : BenchmarkModel(GetDefaultParams()) { - AddListener(&profiling_listener_); -} - -BenchmarkTfLiteModel::BenchmarkTfLiteModel(BenchmarkParams params) - : BenchmarkModel(std::move(params)) { - AddListener(&profiling_listener_); -} - -std::vector<Flag> BenchmarkTfLiteModel::GetFlags() { - std::vector<Flag> flags = BenchmarkTfLiteModel::BenchmarkModel::GetFlags(); - std::vector<Flag> specific_flags = { - CreateFlag<std::string>("graph", ¶ms_, "graph file name"), - CreateFlag<std::string>("input_layer", ¶ms_, "input layer names"), - CreateFlag<std::string>("input_layer_shape", ¶ms_, - "input layer shape"), - CreateFlag<bool>("use_nnapi", ¶ms_, "use nnapi api")}; - - flags.insert(flags.end(), specific_flags.begin(), specific_flags.end()); - return flags; -} - -void BenchmarkTfLiteModel::LogFlags() { - BenchmarkModel::LogFlags(); - TFLITE_LOG(INFO) << "Graph: [" << params_.Get<std::string>("graph") << "]"; - TFLITE_LOG(INFO) << "Input layers: [" - << params_.Get<std::string>("input_layer") << "]"; - TFLITE_LOG(INFO) << "Input shapes: [" - << params_.Get<std::string>("input_layer_shape") << "]"; - TFLITE_LOG(INFO) << "Use nnapi : [" << params_.Get<bool>("use_nnapi") << "]"; -} - -bool BenchmarkTfLiteModel::ValidateFlags() { - if (params_.Get<std::string>("graph").empty()) { - TFLITE_LOG(ERROR) - << "Please specify the name of your TF Lite input file with --graph"; - return false; - } - return PopulateInputLayerInfo(params_.Get<std::string>("input_layer"), - params_.Get<std::string>("input_layer_shape"), - &inputs); -} - -uint64_t BenchmarkTfLiteModel::ComputeInputBytes() { - TFLITE_BENCHMARK_CHECK(interpreter); - uint64_t total_input_bytes = 0; - for (int input : interpreter->inputs()) { - auto* t = interpreter->tensor(input); - total_input_bytes += t->bytes; - } - return total_input_bytes; -} - -void BenchmarkTfLiteModel::Init() { - std::string graph = params_.Get<std::string>("graph"); - model = tflite::FlatBufferModel::BuildFromFile(graph.c_str()); - if (!model) { - TFLITE_LOG(FATAL) << "Failed to mmap model " << graph; - } - TFLITE_LOG(INFO) << "Loaded model " << graph; - model->error_reporter(); - TFLITE_LOG(INFO) << "resolved reporter"; - -#ifdef TFLITE_CUSTOM_OPS_HEADER - tflite::MutableOpResolver resolver; - RegisterSelectedOps(&resolver); -#else - tflite::ops::builtin::BuiltinOpResolver resolver; -#endif - - tflite::InterpreterBuilder(*model, resolver)(&interpreter); - if (!interpreter) { - TFLITE_LOG(FATAL) << "Failed to construct interpreter"; - } - profiling_listener_.SetInterpreter(interpreter.get()); - profiling::Context::get().setProfiler(interpreter->GetProfiler()); - - const int32_t num_threads = params_.Get<int32_t>("num_threads"); - - if (num_threads != -1) { - interpreter->SetNumThreads(num_threads); - } - - bool use_nnapi = params_.Get<bool>("use_nnapi"); - - interpreter->UseNNAPI(use_nnapi); - auto interpreter_inputs = interpreter->inputs(); - - if (!inputs.empty()) { - TFLITE_BENCHMARK_CHECK_EQ(inputs.size(), interpreter_inputs.size()) - << "Inputs mismatch: Model inputs #:" << interpreter_inputs.size() - << " expected: " << inputs.size(); - } - - // TFLITE_BENCHMARK_CHECK that all names and types match - for (int j = 0; j < inputs.size(); ++j) { - const InputLayerInfo& input = inputs[j]; - int i = interpreter_inputs[j]; - TfLiteTensor* t = interpreter->tensor(i); - TFLITE_BENCHMARK_CHECK_EQ(t->name, input.name) - << "Tensor # " << i << " is named " << t->name << " but flags call it " - << input.name; - } - - // Resize all non-string tensors. - for (int j = 0; j < inputs.size(); ++j) { - const InputLayerInfo& input = inputs[j]; - int i = interpreter_inputs[j]; - TfLiteTensor* t = interpreter->tensor(i); - if (t->type != kTfLiteString) { - interpreter->ResizeInputTensor(i, input.shape); - } - } - - if (interpreter->AllocateTensors() != kTfLiteOk) { - TFLITE_LOG(FATAL) << "Failed to allocate tensors!"; - } - - // Set the values of the input tensors. - for (int j = 0; j < inputs.size(); ++j) { - const InputLayerInfo& input = inputs[j]; - int i = interpreter_inputs[j]; - TfLiteTensor* t = interpreter->tensor(i); - std::vector<int> sizes = input.shape; - - // TODO(ahentz): below we ignore the O-th dimension (number of batches). - if (t->type == kTfLiteFloat32) { - FillRandomValue<float>( - interpreter->typed_tensor<float>(i), - std::vector<int>(sizes.begin() + 1, sizes.end()), - []() { return static_cast<float>(rand()) / RAND_MAX - 0.5f; }); - } else if (t->type == kTfLiteUInt8) { - FillRandomValue<uint8_t>( - interpreter->typed_tensor<uint8_t>(i), - std::vector<int>(sizes.begin() + 1, sizes.end()), - []() { return static_cast<uint8_t>(rand()) % 255; }); - } else if (t->type == kTfLiteString) { - tflite::DynamicBuffer buffer; - FillRandomString(&buffer, sizes, []() { - return "we're have some friends over saturday to hang out in the yard"; - }); - buffer.WriteToTensor(interpreter->tensor(i)); - } else { - TFLITE_LOG(FATAL) << "Don't know how to populate tensor " << t->name - << " of type " << t->type; - } - } -} - -void BenchmarkTfLiteModel::RunImpl() { - bool use_nnapi = params_.Get<bool>("use_nnapi"); - if (use_nnapi) { - if (nnfw::NNAPIDelegate().Invoke(interpreter.get()) != kTfLiteOk) { - TFLITE_LOG(FATAL) << "Failed to invoke!"; - } - } else { - if (interpreter->Invoke() != kTfLiteOk) { - TFLITE_LOG(FATAL) << "Failed to invoke!"; - } - } -} - -} // namespace benchmark -} // namespace nnfw diff --git a/tools/tflite_benchmark_model/benchmark_tflite_model.h b/tools/tflite_benchmark_model/benchmark_tflite_model.h deleted file mode 100644 index 7892de1f7..000000000 --- a/tools/tflite_benchmark_model/benchmark_tflite_model.h +++ /dev/null @@ -1,95 +0,0 @@ -/* - * Copyright (c) 2018 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. - */ - -/* Copyright 2018 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. -==============================================================================*/ - -#ifndef __TFLITE_BENCHMARK_MODEL_BENCHMARK_TFLITE_MODEL_H__ -#define __TFLITE_BENCHMARK_MODEL_BENCHMARK_TFLITE_MODEL_H__ - -#include <memory> -#include <string> -#include <vector> - -#include "tensorflow/contrib/lite/model.h" -#include "tensorflow/contrib/lite/profiling/profile_summarizer.h" -#include "benchmark_model.h" - -namespace nnfw { -namespace benchmark { - -// Dumps profiling events if profiling is enabled -class ProfilingListener : public BenchmarkListener { - public: - explicit ProfilingListener() : interpreter_(nullptr), has_profiles_(false) {} - - void SetInterpreter(tflite::Interpreter* interpreter); - - void OnSingleRunStart(RunType run_type) override; - - void OnSingleRunEnd() override; - - void OnBenchmarkEnd(const BenchmarkResults& results) override; - - private: - tflite::Interpreter* interpreter_; - tflite::profiling::Profiler profiler_; - tflite::profiling::ProfileSummarizer summarizer_; - bool has_profiles_; -}; - -// Benchmarks a TFLite model by running tflite interpreter. -class BenchmarkTfLiteModel : public BenchmarkModel { - public: - BenchmarkTfLiteModel(); - BenchmarkTfLiteModel(BenchmarkParams params); - - std::vector<Flag> GetFlags() override; - void LogFlags() override; - bool ValidateFlags() override; - uint64_t ComputeInputBytes() override; - void Init() override; - void RunImpl() override; - virtual ~BenchmarkTfLiteModel() {} - - struct InputLayerInfo { - std::string name; - std::vector<int> shape; - }; - - private: - std::unique_ptr<tflite::FlatBufferModel> model; - std::unique_ptr<tflite::Interpreter> interpreter; - std::vector<InputLayerInfo> inputs; - ProfilingListener profiling_listener_; -}; - -} // namespace benchmark -} // namespace nnfw - -#endif //__TFLITE_BENCHMARK_MODEL_BENCHMARK_TFLITE_MODEL_H__ diff --git a/tools/tflite_benchmark_model/command_line_flags.cc b/tools/tflite_benchmark_model/command_line_flags.cc deleted file mode 100644 index eacca9f73..000000000 --- a/tools/tflite_benchmark_model/command_line_flags.cc +++ /dev/null @@ -1,214 +0,0 @@ -/* - * Copyright (c) 2018 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. - */ - -/* Copyright 2018 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 "command_line_flags.h" - -#include <cstring> -#include <sstream> -#include <string> -#include <utility> -#include <vector> - -namespace nnfw { -namespace { - -template <typename T> -std::string ToString(T val) { - std::ostringstream stream; - stream << val; - return stream.str(); -} - -bool ParseFlag(const std::string& arg, const std::string& flag, - const std::function<bool(const std::string&)>& parse_func, - bool* value_parsing_ok) { - *value_parsing_ok = true; - std::string flag_prefix = "--" + flag + "="; - if (arg.find(flag_prefix) != 0) { - return false; - } - bool has_value = arg.size() >= flag_prefix.size(); - *value_parsing_ok = has_value; - if (has_value) { - *value_parsing_ok = parse_func(arg.substr(flag_prefix.size())); - } - return true; -} - -template <typename T> -bool ParseFlag(const std::string& flag_value, - const std::function<void(const T&)>& hook) { - std::istringstream stream(flag_value); - T read_value; - stream >> read_value; - if (!stream.eof() && !stream.good()) { - return false; - } - hook(read_value); - return true; -} - -bool ParseBoolFlag(const std::string& flag_value, - const std::function<void(const bool&)>& hook) { - if (flag_value != "true" && flag_value != "false") { - return false; - } - - hook(flag_value == "true"); - return true; -} -} // namespace - -Flag::Flag(const char* name, const std::function<void(const int32_t&)>& hook, - int32_t default_value, const std::string& usage_text) - : name_(name), - type_(TYPE_INT32), - value_hook_([hook](const std::string& flag_value) { - return ParseFlag<int32_t>(flag_value, hook); - }), - default_for_display_(ToString(default_value)), - usage_text_(usage_text) {} - -Flag::Flag(const char* name, const std::function<void(const int64_t&)>& hook, - int64_t default_value, const std::string& usage_text) - : name_(name), - type_(TYPE_INT64), - value_hook_([hook](const std::string& flag_value) { - return ParseFlag<int64_t>(flag_value, hook); - }), - default_for_display_(ToString(default_value)), - usage_text_(usage_text) {} - -Flag::Flag(const char* name, const std::function<void(const float&)>& hook, - float default_value, const std::string& usage_text) - : name_(name), - type_(TYPE_FLOAT), - value_hook_([hook](const std::string& flag_value) { - return ParseFlag<float>(flag_value, hook); - }), - default_for_display_(ToString(default_value)), - usage_text_(usage_text) {} - -Flag::Flag(const char* name, const std::function<void(const bool&)>& hook, - bool default_value, const std::string& usage_text) - : name_(name), - type_(TYPE_BOOL), - value_hook_([hook](const std::string& flag_value) { - return ParseBoolFlag(flag_value, hook); - }), - default_for_display_(default_value ? "true" : "false"), - usage_text_(usage_text) {} - -Flag::Flag(const char* name, - const std::function<void(const std::string&)>& hook, - const std::string& default_value, const std::string& usage_text) - : name_(name), - type_(TYPE_STRING), - value_hook_([hook](const std::string& flag_value) { - hook(flag_value); - return true; - }), - default_for_display_(default_value), - usage_text_(usage_text) {} - -bool Flag::Parse(const std::string& arg, bool* value_parsing_ok) const { - return ParseFlag(arg, name_, value_hook_, value_parsing_ok); -} - -std::string Flag::GetTypeName() const { - switch (type_) { - case TYPE_INT32: - return "int32"; - case TYPE_INT64: - return "int64"; - case TYPE_FLOAT: - return "float"; - case TYPE_BOOL: - return "bool"; - case TYPE_STRING: - return "string"; - } - - return "unknown"; -} - -/*static*/ bool Flags::Parse(int* argc, const char** argv, - const std::vector<Flag>& flag_list) { - bool result = true; - std::vector<const char*> unknown_flags; - for (int i = 1; i < *argc; ++i) { - if (std::string(argv[i]) == "--") { - while (i < *argc) { - unknown_flags.push_back(argv[i]); - ++i; - } - break; - } - - bool was_found = false; - for (const Flag& flag : flag_list) { - bool value_parsing_ok; - was_found = flag.Parse(argv[i], &value_parsing_ok); - if (!value_parsing_ok) { - result = false; - } - if (was_found) { - break; - } - } - if (!was_found) { - unknown_flags.push_back(argv[i]); - } - } - int dst = 1; // Skip argv[0] - for (auto f : unknown_flags) { - argv[dst++] = f; - } - argv[dst++] = nullptr; - *argc = unknown_flags.size() + 1; - return result && (*argc < 2 || std::strcmp(argv[1], "--help") != 0); -} - -/*static*/ std::string Flags::Usage(const std::string& cmdline, - const std::vector<Flag>& flag_list) { - std::ostringstream usage_text; - usage_text << "usage: " << cmdline << "\n"; - if (!flag_list.empty()) { - usage_text << "Flags:\n"; - } - - for (const Flag& flag : flag_list) { - auto type_name = flag.GetTypeName(); - usage_text << "\t"; - usage_text << "--" << flag.name_ << "=" << flag.default_for_display_; - usage_text << "\t" << type_name << "\t" << flag.usage_text_ << "\n"; - } - return usage_text.str(); -} - -} // namespace nnfw diff --git a/tools/tflite_benchmark_model/command_line_flags.h b/tools/tflite_benchmark_model/command_line_flags.h deleted file mode 100644 index 766417d87..000000000 --- a/tools/tflite_benchmark_model/command_line_flags.h +++ /dev/null @@ -1,141 +0,0 @@ -/* - * Copyright (c) 2018 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. - */ - -/* Copyright 2018 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. -==============================================================================*/ - -#ifndef __TFLITE_BENCHMARK_MODEL_COMMAND_LINE_FLAGS_H__ -#define __TFLITE_BENCHMARK_MODEL_COMMAND_LINE_FLAGS_H__ - -#include <functional> -#include <string> -#include <vector> - -namespace nnfw { -// A simple command-line argument parsing module. -// Dependency free simplified port of core/util/command_line_flags. -// This class is written for benchmarks and uses inefficient string -// concatenation. This was written to avoid dependency on tensorflow/core/util -// which transitively brings in a lot of other dependencies that are not -// necessary for tflite benchmarking code. -// The recommended way of using it is with local variables and an initializer -// list of Flag objects, for example: -// -// int some_int = 10; -// bool some_switch = false; -// std::string some_name = "something"; -// -// std::vector<tensorFlow::Flag> flag_list = { -// Flag::CreateFlag("some_int", &some_int, "an integer that affects X"), -// Flag::CreateFlag("some_switch", &some_switch, "a bool that affects Y"), -// Flag::CreateFlag("some_name", &some_name, "a string that affects Z") -// }; -// // Get usage message before ParseFlags() to capture default values. -// std::string usage = Flag::Usage(argv[0], flag_list); -// bool parsed_values_ok = Flags::Parse(&argc, argv, flag_list); -// -// tensorflow::port::InitMain(usage.c_str(), &argc, &argv); -// if (argc != 1 || !parsed_values_ok) { -// ...output usage and error message... -// } -// -// The argc and argv values are adjusted by the Parse function so all that -// remains is the program name (at argv[0]) and any unknown arguments fill the -// rest of the array. This means you can check for flags that weren't understood -// by seeing if argv is greater than 1. -// The result indicates if there were any errors parsing the values that were -// passed to the command-line switches. For example, --some_int=foo would return -// false because the argument is expected to be an integer. -// -// NOTE: Unlike gflags-style libraries, this library is intended to be -// used in the `main()` function of your binary. It does not handle -// flag definitions that are scattered around the source code. - -// A description of a single command line flag, holding its name, type, usage -// text, and a pointer to the corresponding variable. -class Flag { - public: - template <typename T> - static Flag CreateFlag(const char* name, T* val, const char* usage) { - return Flag(name, [val](const T& v) { *val = v; }, *val, usage); - } - - Flag(const char* name, const std::function<void(const int32_t&)>& hook, - int32_t default_value, const std::string& usage_text); - Flag(const char* name, const std::function<void(const int64_t&)>& hook, - int64_t default_value, const std::string& usage_text); - Flag(const char* name, const std::function<void(const float&)>& hook, - float default_value, const std::string& usage_text); - Flag(const char* name, const std::function<void(const bool&)>& hook, - bool default_value, const std::string& usage_text); - Flag(const char* name, const std::function<void(const std::string&)>& hook, - const std::string& default_value, const std::string& usage_text); - - private: - friend class Flags; - - bool Parse(const std::string& arg, bool* value_parsing_ok) const; - - std::string name_; - enum { - TYPE_INT32, - TYPE_INT64, - TYPE_BOOL, - TYPE_STRING, - TYPE_FLOAT, - } type_; - - std::string GetTypeName() const; - - std::function<bool(const std::string&)> value_hook_; - std::string default_for_display_; - - std::string usage_text_; -}; - -class Flags { - public: - // Parse the command line represented by argv[0, ..., (*argc)-1] to find flag - // instances matching flags in flaglist[]. Update the variables associated - // with matching flags, and remove the matching arguments from (*argc, argv). - // Return true iff all recognized flag values were parsed correctly, and the - // first remaining argument is not "--help". - static bool Parse(int* argc, const char** argv, - const std::vector<Flag>& flag_list); - - // Return a usage message with command line cmdline, and the - // usage_text strings in flag_list[]. - static std::string Usage(const std::string& cmdline, - const std::vector<Flag>& flag_list); -}; - -} // namespace nnfw - -#endif // __TFLITE_BENCHMARK_MODEL_COMMAND_LINE_FLAGS_H__ - - diff --git a/tools/tflite_benchmark_model/logging.h b/tools/tflite_benchmark_model/logging.h deleted file mode 100644 index e694a0926..000000000 --- a/tools/tflite_benchmark_model/logging.h +++ /dev/null @@ -1,92 +0,0 @@ -/* - * Copyright (c) 2018 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. - */ - -/* Copyright 2018 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. -==============================================================================*/ - -#ifndef __TFLITE_BENCHMARK_MODEL_LOGGING_H_ -#define __TFLITE_BENCHMARK_MODEL_LOGGING_H_ - -// LOG and CHECK macros for benchmarks. - -#include <cstdlib> -#include <iostream> -#include <sstream> - -namespace nnfw { -namespace logging { -// A wrapper that logs to stderr. -// -// Used for TFLITE_LOG and TFLITE_BENCHMARK_CHECK macros. -class LoggingWrapper { - public: - enum class LogSeverity : int { - INFO = 0, - WARN = 1, - ERROR = 2, - FATAL = 3, - }; - LoggingWrapper(LogSeverity severity) - : severity_(severity), should_log_(true) {} - LoggingWrapper(LogSeverity severity, bool log) - : severity_(severity), should_log_(log) {} - std::stringstream& Stream() { return stream_; } - ~LoggingWrapper() { - if (should_log_) { - std::cerr << stream_.str() << std::endl; - if (severity_ == LogSeverity::FATAL) { - std::flush(std::cerr); - std::abort(); - } - } - } - - private: - std::stringstream stream_; - LogSeverity severity_; - bool should_log_; -}; - -} // namespace logging - -} // namespace nnfw - -#define TFLITE_LOG(severity) \ - nnfw::logging::LoggingWrapper( \ - nnfw::logging::LoggingWrapper::LogSeverity::severity) \ - .Stream() - -#define TFLITE_BENCHMARK_CHECK(condition) \ - nnfw::logging::LoggingWrapper( \ - nnfw::logging::LoggingWrapper::LogSeverity::FATAL, \ - (condition) ? false : true) \ - .Stream() - -#define TFLITE_BENCHMARK_CHECK_EQ(a, b) TFLITE_BENCHMARK_CHECK(a == b) - -#endif // __TFLITE_BENCHMARK_MODEL_BENCHMARK_LOGGING_H_ diff --git a/tools/tflite_benchmark_model/profile_summarizer.cc b/tools/tflite_benchmark_model/profile_summarizer.cc deleted file mode 100644 index 4d12b50af..000000000 --- a/tools/tflite_benchmark_model/profile_summarizer.cc +++ /dev/null @@ -1,164 +0,0 @@ -/* - * Copyright (c) 2018 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. - */ - -/* Copyright 2018 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/profiling/profile_summarizer.h" - -#include <sstream> - -#include "tensorflow/contrib/lite/schema/schema_generated.h" - -namespace tflite { -namespace profiling { -namespace { - -using Detail = tensorflow::StatsCalculator::Detail; - -struct OperatorDetails { - std::string name; - std::vector<std::string> inputs; - std::vector<std::string> outputs; -}; - -std::string GetTensorName(const tflite::Interpreter& interpreter, - int tensor_index) { - const auto tensor = interpreter.tensor(tensor_index); - if (tensor == nullptr || tensor->name == nullptr) { - return "Unknown"; - } - return tensor->name; -} -std::vector<std::string> GetTensorNames(const tflite::Interpreter& interpreter, - const TfLiteIntArray* tensor_indices) { - std::vector<std::string> tensors; - tensors.reserve(tensor_indices->size); - for (int i = 0; i < tensor_indices->size; i++) { - tensors.push_back(GetTensorName(interpreter, tensor_indices->data[i])); - } - return tensors; -} - -std::string ToString(const std::vector<std::string>& str_vector) { - std::stringstream stream; - stream << "["; - bool first = true; - for (const auto& s : str_vector) { - if (!first) { - stream << ", "; - } else { - first = false; - } - stream << s; - } - stream << "]"; - return stream.str(); -} - -OperatorDetails GetOperatorDetails(const tflite::Interpreter& interpreter, - int node_index) { - auto node_reg = interpreter.node_and_registration(node_index); - auto inputs = node_reg->first.inputs; - auto outputs = node_reg->first.outputs; - int code = node_reg->second.builtin_code; - const char* op_name = nullptr; - if (code == tflite::BuiltinOperator_CUSTOM) { - const char* custom_name = node_reg->second.custom_name; - op_name = custom_name ? custom_name : "UnknownCustomOp"; - } else { - op_name = tflite::EnumNamesBuiltinOperator()[code]; - } - OperatorDetails details; - details.name = op_name; - details.inputs = GetTensorNames(interpreter, inputs); - details.outputs = GetTensorNames(interpreter, outputs); - return details; -} - -} // namespace - -ProfileSummarizer::ProfileSummarizer() - : stats_calculator_(new ::tensorflow::StatsCalculator( - tensorflow::StatSummarizerOptions())) {} - -void ProfileSummarizer::ProcessProfiles( - const std::vector<const ProfileEvent*>& profile_stats, - const tflite::Interpreter& interpreter) { - std::vector<const ProfileEvent*> events; - std::copy_if(profile_stats.begin(), profile_stats.end(), - std::back_inserter(events), [](const ProfileEvent* e) { - return e->event_type == - ProfileEvent::EventType::OPERATOR_INVOKE_EVENT && - e->end_timestamp_us >= e->begin_timestamp_us; - }); - // Sort with begin_time. - std::sort(events.begin(), events.end(), - [](const ProfileEvent* const& a, const ProfileEvent* const& b) { - return a->begin_timestamp_us < b->begin_timestamp_us; - }); - if (events.empty()) { - return; - } - - int64_t base_start_us = events[0]->begin_timestamp_us; - int node_num = 0; - int64_t curr_total_us = 0; - std::map<std::string, Detail> details; - int prev_op_idx = -1; - int seq_no = 1; - for (auto event : events) { - auto op_details = GetOperatorDetails(interpreter, event->event_metadata); - bool is_continued = (prev_op_idx == event->event_metadata); - seq_no = is_continued ? seq_no + 1 : 1; - auto node_name = ToString(op_details.outputs) + "#" + std::to_string(seq_no); - auto result = details.emplace(node_name, Detail()); - Detail* detail = &(result.first->second); - detail->start_us.UpdateStat(event->begin_timestamp_us - base_start_us); - int64_t node_exec_time = - event->end_timestamp_us - event->begin_timestamp_us; - detail->rel_end_us.UpdateStat(node_exec_time); - curr_total_us += node_exec_time; - ++node_num; - - if (result.second) { - detail->name = node_name; - detail->type = op_details.name; - detail->run_order = node_num; - detail->times_called = 0; - } - if (!is_continued) { - ++detail->times_called; - } - prev_op_idx = event->event_metadata; - } - stats_calculator_->UpdateDetails(details); - stats_calculator_->UpdateRunTotalUs(curr_total_us); -} -} // namespace profiling -} // namespace tflite diff --git a/tools/tflite_benchmark_model/profile_summarizer.h b/tools/tflite_benchmark_model/profile_summarizer.h deleted file mode 100644 index a529ff874..000000000 --- a/tools/tflite_benchmark_model/profile_summarizer.h +++ /dev/null @@ -1,55 +0,0 @@ -/* Copyright 2018 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. -==============================================================================*/ - -#ifndef TENSORFLOW_CONTRIB_LITE_PROFILING_PROFILE_SUMMARIZER_H_ -#define TENSORFLOW_CONTRIB_LITE_PROFILING_PROFILE_SUMMARIZER_H_ - -#include <vector> - -#include "tensorflow/contrib/lite/interpreter.h" -#include "tensorflow/contrib/lite/profiling/profiler.h" -#include "tensorflow/core/util/stats_calculator.h" - -namespace tflite { -namespace profiling { - -// Creates a summary of operator invocations in the interpreter. -class ProfileSummarizer { - public: - ProfileSummarizer(); - virtual ~ProfileSummarizer() {} - - // Process profile events to update statistics for operator invocations. - void ProcessProfiles(const std::vector<const ProfileEvent*>& profile_stats, - const tflite::Interpreter& interpreter); - - // Returns a string detailing the accumulated runtime stats in a tab-separated - // format which can be pasted into a spreadsheet for further analysis. - std::string GetOutputString() const { - return stats_calculator_->GetOutputString(); - } - - std::string GetShortSummary() const { - return stats_calculator_->GetShortSummary(); - } - - private: - std::unique_ptr<tensorflow::StatsCalculator> stats_calculator_; -}; - -} // namespace profiling -} // namespace tflite - -#endif // TENSORFLOW_CONTRIB_LITE_PROFILING_PROFILE_SUMMARIZER_H_ |