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Merge pull request #43513 from vsilyaev:pull_request/Use_std_endl_for_flush

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PiperOrigin-RevId: 341725520
This commit is contained in:
TensorFlower Gardener 2020-11-10 16:58:11 -08:00
commit 0accbd6e73
4 changed files with 80 additions and 42 deletions
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1
tensorflow/lite/examples/label_image/BUILD
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@ -63,6 +63,7 @@ cc_library(
"bitmap_helpers.h",
"bitmap_helpers_impl.h",
"label_image.h",
"log.h",
],
deps = [
"//tensorflow/lite:builtin_op_data",

16
tensorflow/lite/examples/label_image/bitmap_helpers.cc
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@ -13,17 +13,17 @@ See the License for the specific language governing permissions and
limitations under the License.
==============================================================================*/
#include "tensorflow/lite/examples/label_image/bitmap_helpers.h"
#include <unistd.h> // NOLINT(build/include_order)
#include <cstdint>
#include <cstdio>
#include <cstdlib>
#include <fstream>
#include <iostream>
#include <unistd.h> // NOLINT(build/include_order)
#include "tensorflow/lite/examples/label_image/bitmap_helpers.h"
#define LOG(x) std::cerr
#include "tensorflow/lite/examples/label_image/log.h"
namespace tflite {
namespace label_image {
@ -76,7 +76,7 @@ std::vector<uint8_t> read_bmp(const std::string& input_bmp_name, int* width,
std::ifstream file(input_bmp_name, std::ios::in | std::ios::binary);
if (!file) {
LOG(FATAL) << "input file " << input_bmp_name << " not found\n";
LOG(FATAL) << "input file " << input_bmp_name << " not found";
exit(-1);
}
@ -85,7 +85,7 @@ std::vector<uint8_t> read_bmp(const std::string& input_bmp_name, int* width,
end = file.tellg();
size_t len = end - begin;
if (s->verbose) LOG(INFO) << "len: " << len << "\n";
if (s->verbose) LOG(INFO) << "len: " << len;
std::vector<uint8_t> img_bytes(len);
file.seekg(0, std::ios::beg);
@ -100,7 +100,7 @@ std::vector<uint8_t> read_bmp(const std::string& input_bmp_name, int* width,
if (s->verbose)
LOG(INFO) << "width, height, channels: " << *width << ", " << *height
<< ", " << *channels << "\n";
<< ", " << *channels;
// there may be padding bytes when the width is not a multiple of 4 bytes
// 8 * channels == bits per pixel

66
tensorflow/lite/examples/label_image/label_image.cc
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@ -52,7 +52,7 @@ limitations under the License.
#include "tensorflow/lite/delegates/gpu/delegate.h"
#endif
#define LOG(severity) (std::cerr << (#severity) << ": ")
#include "tensorflow/lite/examples/label_image/log.h"
namespace tflite {
namespace label_image {
@ -98,7 +98,7 @@ class DelegateProviders {
// It's possible that a delegate of certain type won't be created as
// user-specified benchmark params tells not to.
if (ptr == nullptr) continue;
LOG(INFO) << delegate->GetName() << " delegate created.\n";
LOG(INFO) << delegate->GetName() << " delegate created.";
delegates_map.emplace(delegate->GetName(), std::move(ptr));
}
return delegates_map;
@ -134,7 +134,7 @@ TfLiteDelegatePtrMap GetDelegates(Settings* s,
if (s->gl_backend) {
auto delegate = CreateGPUDelegate(s);
if (!delegate) {
LOG(INFO) << "GPU acceleration is unsupported on this platform.\n";
LOG(INFO) << "GPU acceleration is unsupported on this platform.";
} else {
delegates.emplace("GPU", std::move(delegate));
}
@ -145,7 +145,7 @@ TfLiteDelegatePtrMap GetDelegates(Settings* s,
options.allow_fp16 = s->allow_fp16;
auto delegate = evaluation::CreateNNAPIDelegate(options);
if (!delegate) {
LOG(INFO) << "NNAPI acceleration is unsupported on this platform.\n";
LOG(INFO) << "NNAPI acceleration is unsupported on this platform.";
} else {
delegates.emplace("NNAPI", std::move(delegate));
}
@ -157,7 +157,7 @@ TfLiteDelegatePtrMap GetDelegates(Settings* s,
evaluation::CreateHexagonDelegate(libhexagon_path, s->profiling);
if (!delegate) {
LOG(INFO) << "Hexagon acceleration is unsupported on this platform.\n";
LOG(INFO) << "Hexagon acceleration is unsupported on this platform.";
} else {
delegates.emplace("Hexagon", std::move(delegate));
}
@ -166,7 +166,7 @@ TfLiteDelegatePtrMap GetDelegates(Settings* s,
if (s->xnnpack_delegate) {
auto delegate = evaluation::CreateXNNPACKDelegate(s->number_of_threads);
if (!delegate) {
LOG(INFO) << "XNNPACK acceleration is unsupported on this platform.\n";
LOG(INFO) << "XNNPACK acceleration is unsupported on this platform.";
} else {
delegates.emplace("XNNPACK", std::move(delegate));
}
@ -195,7 +195,7 @@ TfLiteStatus ReadLabelsFile(const string& file_name,
size_t* found_label_count) {
std::ifstream file(file_name);
if (!file) {
LOG(ERROR) << "Labels file " << file_name << " not found\n";
LOG(ERROR) << "Labels file " << file_name << " not found";
return kTfLiteError;
}
result->clear();
@ -225,14 +225,13 @@ void PrintProfilingInfo(const profiling::ProfileEvent* e,
<< std::setprecision(3) << op_index << ", OpCode " << std::setw(3)
<< std::setprecision(3) << registration.builtin_code << ", "
<< EnumNameBuiltinOperator(
static_cast<BuiltinOperator>(registration.builtin_code))
<< "\n";
static_cast<BuiltinOperator>(registration.builtin_code));
}
void RunInference(Settings* settings,
const DelegateProviders& delegate_providers) {
if (!settings->model_name.c_str()) {
LOG(ERROR) << "no model file name\n";
LOG(ERROR) << "no model file name";
exit(-1);
}
@ -240,29 +239,29 @@ void RunInference(Settings* settings,
std::unique_ptr<tflite::Interpreter> interpreter;
model = tflite::FlatBufferModel::BuildFromFile(settings->model_name.c_str());
if (!model) {
LOG(ERROR) << "\nFailed to mmap model " << settings->model_name << "\n";
LOG(ERROR) << "Failed to mmap model " << settings->model_name;
exit(-1);
}
settings->model = model.get();
LOG(INFO) << "Loaded model " << settings->model_name << "\n";
LOG(INFO) << "Loaded model " << settings->model_name;
model->error_reporter();
LOG(INFO) << "resolved reporter\n";
LOG(INFO) << "resolved reporter";
tflite::ops::builtin::BuiltinOpResolver resolver;
tflite::InterpreterBuilder(*model, resolver)(&interpreter);
if (!interpreter) {
LOG(ERROR) << "Failed to construct interpreter\n";
LOG(ERROR) << "Failed to construct interpreter";
exit(-1);
}
interpreter->SetAllowFp16PrecisionForFp32(settings->allow_fp16);
if (settings->verbose) {
LOG(INFO) << "tensors size: " << interpreter->tensors_size() << "\n";
LOG(INFO) << "nodes size: " << interpreter->nodes_size() << "\n";
LOG(INFO) << "inputs: " << interpreter->inputs().size() << "\n";
LOG(INFO) << "input(0) name: " << interpreter->GetInputName(0) << "\n";
LOG(INFO) << "tensors size: " << interpreter->tensors_size();
LOG(INFO) << "nodes size: " << interpreter->nodes_size();
LOG(INFO) << "inputs: " << interpreter->inputs().size();
LOG(INFO) << "input(0) name: " << interpreter->GetInputName(0);
int t_size = interpreter->tensors_size();
for (int i = 0; i < t_size; i++) {
@ -271,7 +270,7 @@ void RunInference(Settings* settings,
<< interpreter->tensor(i)->bytes << ", "
<< interpreter->tensor(i)->type << ", "
<< interpreter->tensor(i)->params.scale << ", "
<< interpreter->tensor(i)->params.zero_point << "\n";
<< interpreter->tensor(i)->params.zero_point;
}
}
@ -286,29 +285,29 @@ void RunInference(Settings* settings,
&image_height, &image_channels, settings);
int input = interpreter->inputs()[0];
if (settings->verbose) LOG(INFO) << "input: " << input << "\n";
if (settings->verbose) LOG(INFO) << "input: " << input;
const std::vector<int> inputs = interpreter->inputs();
const std::vector<int> outputs = interpreter->outputs();
if (settings->verbose) {
LOG(INFO) << "number of inputs: " << inputs.size() << "\n";
LOG(INFO) << "number of outputs: " << outputs.size() << "\n";
LOG(INFO) << "number of inputs: " << inputs.size();
LOG(INFO) << "number of outputs: " << outputs.size();
}
auto delegates_ = GetDelegates(settings, delegate_providers);
for (const auto& delegate : delegates_) {
if (interpreter->ModifyGraphWithDelegate(delegate.second.get()) !=
kTfLiteOk) {
LOG(ERROR) << "Failed to apply " << delegate.first << " delegate.\n";
LOG(ERROR) << "Failed to apply " << delegate.first << " delegate.";
exit(-1);
} else {
LOG(INFO) << "Applied " << delegate.first << " delegate.\n";
LOG(INFO) << "Applied " << delegate.first << " delegate.";
}
}
if (interpreter->AllocateTensors() != kTfLiteOk) {
LOG(ERROR) << "Failed to allocate tensors!\n";
LOG(ERROR) << "Failed to allocate tensors!";
exit(-1);
}
@ -340,7 +339,7 @@ void RunInference(Settings* settings,
break;
default:
LOG(ERROR) << "cannot handle input type "
<< interpreter->tensor(input)->type << " yet\n";
<< interpreter->tensor(input)->type << " yet";
exit(-1);
}
auto profiler = absl::make_unique<profiling::Profiler>(
@ -351,7 +350,7 @@ void RunInference(Settings* settings,
if (settings->loop_count > 1) {
for (int i = 0; i < settings->number_of_warmup_runs; i++) {
if (interpreter->Invoke() != kTfLiteOk) {
LOG(ERROR) << "Failed to invoke tflite!\n";
LOG(ERROR) << "Failed to invoke tflite!";
exit(-1);
}
}
@ -361,16 +360,16 @@ void RunInference(Settings* settings,
gettimeofday(&start_time, nullptr);
for (int i = 0; i < settings->loop_count; i++) {
if (interpreter->Invoke() != kTfLiteOk) {
LOG(ERROR) << "Failed to invoke tflite!\n";
LOG(ERROR) << "Failed to invoke tflite!";
exit(-1);
}
}
gettimeofday(&stop_time, nullptr);
LOG(INFO) << "invoked\n";
LOG(INFO) << "invoked";
LOG(INFO) << "average time: "
<< (get_us(stop_time) - get_us(start_time)) /
(settings->loop_count * 1000)
<< " ms \n";
<< " ms";
if (settings->profiling) {
profiler->StopProfiling();
@ -413,7 +412,7 @@ void RunInference(Settings* settings,
break;
default:
LOG(ERROR) << "cannot handle output type "
<< interpreter->tensor(output)->type << " yet\n";
<< interpreter->tensor(output)->type << " yet";
exit(-1);
}
@ -427,7 +426,7 @@ void RunInference(Settings* settings,
for (const auto& result : top_results) {
const float confidence = result.first;
const int index = result.second;
LOG(INFO) << confidence << ": " << index << " " << labels[index] << "\n";
LOG(INFO) << confidence << ": " << index << " " << labels[index];
}
}
@ -449,8 +448,7 @@ void display_usage() {
<< "--threads, -t: number of threads\n"
<< "--verbose, -v: [0|1] print more information\n"
<< "--warmup_runs, -w: number of warmup runs\n"
<< "--xnnpack_delegate, -x [0:1]: xnnpack delegate\n"
<< "\n";
<< "--xnnpack_delegate, -x [0:1]: xnnpack delegate\n";
}
int Main(int argc, char** argv) {

39
tensorflow/lite/examples/label_image/log.h Normal file
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@ -0,0 +1,39 @@
/* 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 TENSORFLOW_LITE_EXAMPLES_LABEL_IMAGE_LOG_H_
#define TENSORFLOW_LITE_EXAMPLES_LABEL_IMAGE_LOG_H_
#include <iostream>
#include <sstream>
namespace tflite {
namespace label_image {
class Log {
std::stringstream stream_;
public:
explicit Log(const char* severity) { stream_ << severity << ": "; }
std::stringstream& Stream() { return stream_; }
~Log() { std::cerr << stream_.str() << std::endl; }
};
#define LOG(severity) tflite::label_image::Log(#severity).Stream()
} // namespace label_image
} // namespace tflite
#endif // TENSORFLOW_LITE_EXAMPLES_LABEL_IMAGE_LOG_H_