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Add EfficientNet (#590)

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* create psenet

create psenet with weight from tensorflow

* delete some useless code

* repalce tab with 4 blanks

* fix network bug, rewrite post-processing pse algorithm

* update readme

* update readme

* add RepVGG

* fix typo

* add hrnetseg w18 w32 w48

* add hrnetseg with ocr w18 w32 w48

* merge hrnet and small, add hrnet_ocr

* fix warning

* change project name

* add efficientnet
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cmake_minimum_required(VERSION 2.6)
project(efficientnet)
add_definitions(-std=c++11)
option(CUDA_USE_STATIC_CUDA_RUNTIME OFF)
set(CMAKE_CXX_STANDARD 11)
set(CMAKE_BUILD_TYPE Debug)
find_package(CUDA REQUIRED)
include_directories(${PROJECT_SOURCE_DIR}/include)
# include and link dirs of cuda and tensorrt, you need adapt them if yours are different
# cuda
include_directories(/usr/local/cuda/include)
link_directories(/usr/local/cuda/lib64)
# tensorrt
include_directories(/usr/include/x86_64-linux-gnu/)
link_directories(/usr/lib/x86_64-linux-gnu/)
add_executable(efficientnet ${PROJECT_SOURCE_DIR}/efficientnet.cpp)
target_link_libraries(efficientnet nvinfer)
target_link_libraries(efficientnet cudart)
add_definitions(-O2 -pthread)

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# EfficientNet
A TensorRT implementation of EfficientNet.
For the Pytorch implementation, you can refer to [EfficientNet-PyTorch](https://github.com/lukemelas/EfficientNet-PyTorch)
## How to run
1. install `efficientnet_pytorch`
```
pip install efficientnet_pytorch
```
2. gennerate `.wts` file
```
python gen_wts.py
```
3. build
```
mkdir build
cd build
cmake ..
make
```
4. serialize model to engine
```
./efficientnet -s [.wts] [.engine] [b0 b1 b2 b3 ... b7] // serialize model to engine file
```
such as
```
./efficientnet -s ../efficientnet-b3.wts efficientnet-b3.engine b3
```
5. deserialize and do infer
```
./efficientnet -d [.engine] [b0 b1 b2 b3 ... b7] // deserialize engine file and run inference
```
such as
```
./efficientnet -d efficientnet-b3.engine b3
```
6. see if the output is same as pytorch side
For more models, please refer to [tensorrtx](https://github.com/wang-xinyu/tensorrtx)

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#include "NvInfer.h"
#include "cuda_runtime_api.h"
#include "logging.h"
#include <fstream>
#include <iostream>
#include <map>
#include <sstream>
#include <vector>
#include <chrono>
#include "utils.hpp"
#define USE_FP32 //USE_FP16
#define INPUT_NAME "data"
#define OUTPUT_NAME "prob"
#define MAX_BATCH_SIZE 8
using namespace nvinfer1;
static Logger gLogger;
static std::vector<BlockArgs>
block_args_list = {
BlockArgs{1, 3, 1, 1, 32, 16, 0.25, true},
BlockArgs{2, 3, 2, 6, 16, 24, 0.25, true},
BlockArgs{2, 5, 2, 6, 24, 40, 0.25, true},
BlockArgs{3, 3, 2, 6, 40, 80, 0.25, true},
BlockArgs{3, 5, 1, 6, 80, 112, 0.25, true},
BlockArgs{4, 5, 2, 6, 112, 192, 0.25, true},
BlockArgs{1, 3, 1, 6, 192, 320, 0.25, true}};
static std::map<std::string, GlobalParams>
global_params_map = {
// input_h,input_w,num_classes,batch_norm_epsilon,
// width_coefficient,depth_coefficient,depth_divisor, min_depth
{"b0", GlobalParams{224, 224, 1000, 0.001, 1.0, 1.0, 8, -1}},
{"b1", GlobalParams{240, 240, 1000, 0.001, 1.0, 1.1, 8, -1}},
{"b2", GlobalParams{260, 260, 1000, 0.001, 1.1, 1.2, 8, -1}},
{"b3", GlobalParams{300, 300, 1000, 0.001, 1.2, 1.4, 8, -1}},
{"b4", GlobalParams{380, 380, 1000, 0.001, 1.4, 1.8, 8, -1}},
{"b5", GlobalParams{456, 456, 1000, 0.001, 1.6, 2.2, 8, -1}},
{"b6", GlobalParams{528, 528, 1000, 0.001, 1.8, 2.6, 8, -1}},
{"b7", GlobalParams{600, 600, 1000, 0.001, 2.0, 3.1, 8, -1}},
{"b8", GlobalParams{672, 672, 1000, 0.001, 2.2, 3.6, 8, -1}},
{"l2", GlobalParams{800, 800, 1000, 0.001, 4.3, 5.3, 8, -1}},
};
ICudaEngine *createEngine(unsigned int maxBatchSize, IBuilder *builder, IBuilderConfig *config, DataType dt, std::string path_wts, std::vector<BlockArgs> block_args_list, GlobalParams global_params)
{
float bn_eps = global_params.batch_norm_epsilon;
DimsHW image_size = DimsHW{global_params.input_h, global_params.input_w};
std::map<std::string, Weights> weightMap = loadWeights(path_wts);
Weights emptywts{DataType::kFLOAT, nullptr, 0};
INetworkDefinition *network = builder->createNetworkV2(0U);
ITensor *data = network->addInput(INPUT_NAME, dt, Dims3{3, global_params.input_h, global_params.input_w});
assert(data);
int out_channels = roundFilters(32, global_params);
auto conv_stem = addSamePaddingConv2d(network, weightMap, *data, out_channels, 3, 2, 1, 1, image_size, "_conv_stem");
auto bn0 = addBatchNorm2d(network, weightMap, *conv_stem->getOutput(0), "_bn0", bn_eps);
auto swish0 = addSwish(network, *bn0->getOutput(0));
ITensor *x = swish0->getOutput(0);
image_size = calculateOutputImageSize(image_size, 2);
int block_id = 0;
for (int i = 0; i < block_args_list.size(); i++)
{
BlockArgs block_args = block_args_list[i];
block_args.input_filters = roundFilters(block_args.input_filters, global_params);
block_args.output_filters = roundFilters(block_args.output_filters, global_params);
block_args.num_repeat = roundRepeats(block_args.num_repeat, global_params);
x = MBConvBlock(network, weightMap, *x, "_blocks." + std::to_string(block_id), block_args, global_params, image_size);
assert(x);
block_id++;
image_size = calculateOutputImageSize(image_size, block_args.stride);
if (block_args.num_repeat > 1)
{
block_args.input_filters = block_args.output_filters;
block_args.stride = 1;
}
for (int r = 0; r < block_args.num_repeat - 1; r++)
{
x = MBConvBlock(network, weightMap, *x, "_blocks." + std::to_string(block_id), block_args, global_params, image_size);
block_id++;
}
}
out_channels = roundFilters(1280, global_params);
auto conv_head = addSamePaddingConv2d(network, weightMap, *x, out_channels, 1, 1, 1, 1, image_size, "_conv_head", false);
auto bn1 = addBatchNorm2d(network, weightMap, *conv_head->getOutput(0), "_bn1", bn_eps);
auto swish1 = addSwish(network, *bn1->getOutput(0));
auto avg_pool = network->addPoolingNd(*swish1->getOutput(0), PoolingType::kAVERAGE, image_size);
IFullyConnectedLayer *final = network->addFullyConnected(*avg_pool->getOutput(0), global_params.num_classes, weightMap["_fc.weight"], weightMap["_fc.bias"]);
assert(final);
final->getOutput(0)->setName(OUTPUT_NAME);
network->markOutput(*final->getOutput(0));
// Build engine
builder->setMaxBatchSize(maxBatchSize);
config->setMaxWorkspaceSize(1 << 20);
#ifdef USE_FP16
config->setFlag(BuilderFlag::kFP16);
#endif
std::cout << "build engine ..." << std::endl;
ICudaEngine *engine = builder->buildEngineWithConfig(*network, *config);
assert(engine != nullptr);
std::cout << "build finished" << std::endl;
// Don't need the network any more
network->destroy();
// Release host memory
for (auto &mem : weightMap)
{
free((void *)(mem.second.values));
}
return engine;
}
void APIToModel(unsigned int maxBatchSize, IHostMemory **modelStream, std::string wtsPath, std::vector<BlockArgs> block_args_list, GlobalParams global_params)
{
// Create builder
IBuilder *builder = createInferBuilder(gLogger);
IBuilderConfig *config = builder->createBuilderConfig();
// Create model to populate the network, then set the outputs and create an engine
ICudaEngine *engine = createEngine(maxBatchSize, builder, config, DataType::kFLOAT, wtsPath, block_args_list, global_params);
assert(engine != nullptr);
// Serialize the engine
(*modelStream) = engine->serialize();
// Close everything down
engine->destroy();
builder->destroy();
config->destroy();
}
void doInference(IExecutionContext &context, float *input, float *output, int batchSize, GlobalParams global_params)
{
const ICudaEngine &engine = context.getEngine();
// Pointers to input and output device buffers to pass to engine.
// Engine requires exactly IEngine::getNbBindings() number of buffers.
assert(engine.getNbBindings() == 2);
void *buffers[2];
// In order to bind the buffers, we need to know the names of the input and output tensors.
// Note that indices are guaranteed to be less than IEngine::getNbBindings()
const int inputIndex = engine.getBindingIndex(INPUT_NAME);
const int outputIndex = engine.getBindingIndex(OUTPUT_NAME);
// Create GPU buffers on device
CHECK(cudaMalloc(&buffers[inputIndex], batchSize * 3 * global_params.input_h * global_params.input_w * sizeof(float)));
CHECK(cudaMalloc(&buffers[outputIndex], batchSize * global_params.num_classes * sizeof(float)));
// Create stream
cudaStream_t stream;
CHECK(cudaStreamCreate(&stream));
// DMA input batch data to device, infer on the batch asynchronously, and DMA output back to host
CHECK(cudaMemcpyAsync(buffers[inputIndex], input, batchSize * 3 * global_params.input_h * global_params.input_w * sizeof(float), cudaMemcpyHostToDevice, stream));
context.enqueue(batchSize, buffers, stream, nullptr);
CHECK(cudaMemcpyAsync(output, buffers[outputIndex], batchSize * global_params.num_classes * sizeof(float), cudaMemcpyDeviceToHost, stream));
cudaStreamSynchronize(stream);
// Release stream and buffers
cudaStreamDestroy(stream);
CHECK(cudaFree(buffers[inputIndex]));
CHECK(cudaFree(buffers[outputIndex]));
}
bool parse_args(int argc, char **argv, std::string &wts, std::string &engine, std::string &backbone)
{
if (std::string(argv[1]) == "-s" && argc == 5)
{
wts = std::string(argv[2]);
engine = std::string(argv[3]);
backbone = std::string(argv[4]);
}
else if (std::string(argv[1]) == "-d" && argc == 4)
{
engine = std::string(argv[2]);
backbone = std::string(argv[3]);
}
else
{
return false;
}
return true;
}
int main(int argc, char **argv)
{
std::string wtsPath = "";
std::string engine_name = "";
std::string backbone = "";
if (!parse_args(argc, argv, wtsPath, engine_name, backbone))
{
std::cerr << "arguments not right!" << std::endl;
std::cerr << "./efficientnet -s [.wts] [.engine] [b0 b1 b2 b3 ... b7] // serialize model to engine file" << std::endl;
std::cerr << "./efficientnet -d [.engine] [b0 b1 b2 b3 ... b7] // deserialize engine file and run inference" << std::endl;
return -1;
}
GlobalParams global_params = global_params_map[backbone];
// create a model using the API directly and serialize it to a stream
if (!wtsPath.empty())
{
IHostMemory *modelStream{nullptr};
APIToModel(MAX_BATCH_SIZE, &modelStream, wtsPath, block_args_list, global_params);
assert(modelStream != nullptr);
std::ofstream p(engine_name, std::ios::binary);
if (!p)
{
std::cerr << "could not open plan output file" << std::endl;
return -1;
}
p.write(reinterpret_cast<const char *>(modelStream->data()), modelStream->size());
modelStream->destroy();
return 1;
}
char *trtModelStream{nullptr};
size_t size{0};
std::ifstream file(engine_name, std::ios::binary);
if (file.good())
{
file.seekg(0, file.end);
size = file.tellg();
file.seekg(0, file.beg);
trtModelStream = new char[size];
assert(trtModelStream);
file.read(trtModelStream, size);
file.close();
}
else
{
std::cerr << "could not open plan file" << std::endl;
return -1;
}
// dummy input
float *data = new float[3 * global_params.input_h * global_params.input_w];
for (int i = 0; i < 3 * global_params.input_h * global_params.input_w; i++)
data[i] = 0.1;
IRuntime *runtime = createInferRuntime(gLogger);
assert(runtime != nullptr);
ICudaEngine *engine = runtime->deserializeCudaEngine(trtModelStream, size, nullptr);
assert(engine != nullptr);
IExecutionContext *context = engine->createExecutionContext();
assert(context != nullptr);
delete[] trtModelStream;
// Run inference
float *prob = new float[global_params.num_classes];
for (int i = 0; i < 100; i++)
{
auto start = std::chrono::system_clock::now();
doInference(*context, data, prob, 1, global_params);
auto end = std::chrono::system_clock::now();
std::cout << std::chrono::duration_cast<std::chrono::milliseconds>(end - start).count() << "ms" << std::endl;
}
for (unsigned int i = 0; i < 20; i++)
{
std::cout << prob[i] << ", ";
}
std::cout << std::endl;
// Destroy the engine
context->destroy();
engine->destroy();
runtime->destroy();
delete data;
delete prob;
return 0;
}

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import torch
import struct
from efficientnet_pytorch import EfficientNet
model = EfficientNet.from_pretrained('efficientnet-b3')
model.eval()
f = open('efficientnet-b3.wts', 'w')
f.write('{}\n'.format(len(model.state_dict().keys())))
for k, v in model.state_dict().items():
vr = v.reshape(-1).cpu().numpy()
f.write('{} {} '.format(k, len(vr)))
for vv in vr:
f.write(' ')
f.write(struct.pack('>f',float(vv)).hex())
f.write('\n')
f.close()

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/*
* Copyright (c) 2019, NVIDIA CORPORATION. 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 TENSORRT_LOGGING_H
#define TENSORRT_LOGGING_H
#include "NvInferRuntimeCommon.h"
#include <cassert>
#include <ctime>
#include <iomanip>
#include <iostream>
#include <ostream>
#include <sstream>
#include <string>
using Severity = nvinfer1::ILogger::Severity;
class LogStreamConsumerBuffer : public std::stringbuf
{
public:
LogStreamConsumerBuffer(std::ostream& stream, const std::string& prefix, bool shouldLog)
: mOutput(stream)
, mPrefix(prefix)
, mShouldLog(shouldLog)
{
}
LogStreamConsumerBuffer(LogStreamConsumerBuffer&& other)
: mOutput(other.mOutput)
{
}
~LogStreamConsumerBuffer()
{
// std::streambuf::pbase() gives a pointer to the beginning of the buffered part of the output sequence
// std::streambuf::pptr() gives a pointer to the current position of the output sequence
// if the pointer to the beginning is not equal to the pointer to the current position,
// call putOutput() to log the output to the stream
if (pbase() != pptr())
{
putOutput();
}
}
// synchronizes the stream buffer and returns 0 on success
// synchronizing the stream buffer consists of inserting the buffer contents into the stream,
// resetting the buffer and flushing the stream
virtual int sync()
{
putOutput();
return 0;
}
void putOutput()
{
if (mShouldLog)
{
// prepend timestamp
std::time_t timestamp = std::time(nullptr);
tm* tm_local = std::localtime(&timestamp);
std::cout << "[";
std::cout << std::setw(2) << std::setfill('0') << 1 + tm_local->tm_mon << "/";
std::cout << std::setw(2) << std::setfill('0') << tm_local->tm_mday << "/";
std::cout << std::setw(4) << std::setfill('0') << 1900 + tm_local->tm_year << "-";
std::cout << std::setw(2) << std::setfill('0') << tm_local->tm_hour << ":";
std::cout << std::setw(2) << std::setfill('0') << tm_local->tm_min << ":";
std::cout << std::setw(2) << std::setfill('0') << tm_local->tm_sec << "] ";
// std::stringbuf::str() gets the string contents of the buffer
// insert the buffer contents pre-appended by the appropriate prefix into the stream
mOutput << mPrefix << str();
// set the buffer to empty
str("");
// flush the stream
mOutput.flush();
}
}
void setShouldLog(bool shouldLog)
{
mShouldLog = shouldLog;
}
private:
std::ostream& mOutput;
std::string mPrefix;
bool mShouldLog;
};
//!
//! \class LogStreamConsumerBase
//! \brief Convenience object used to initialize LogStreamConsumerBuffer before std::ostream in LogStreamConsumer
//!
class LogStreamConsumerBase
{
public:
LogStreamConsumerBase(std::ostream& stream, const std::string& prefix, bool shouldLog)
: mBuffer(stream, prefix, shouldLog)
{
}
protected:
LogStreamConsumerBuffer mBuffer;
};
//!
//! \class LogStreamConsumer
//! \brief Convenience object used to facilitate use of C++ stream syntax when logging messages.
//! Order of base classes is LogStreamConsumerBase and then std::ostream.
//! This is because the LogStreamConsumerBase class is used to initialize the LogStreamConsumerBuffer member field
//! in LogStreamConsumer and then the address of the buffer is passed to std::ostream.
//! This is necessary to prevent the address of an uninitialized buffer from being passed to std::ostream.
//! Please do not change the order of the parent classes.
//!
class LogStreamConsumer : protected LogStreamConsumerBase, public std::ostream
{
public:
//! \brief Creates a LogStreamConsumer which logs messages with level severity.
//! Reportable severity determines if the messages are severe enough to be logged.
LogStreamConsumer(Severity reportableSeverity, Severity severity)
: LogStreamConsumerBase(severityOstream(severity), severityPrefix(severity), severity <= reportableSeverity)
, std::ostream(&mBuffer) // links the stream buffer with the stream
, mShouldLog(severity <= reportableSeverity)
, mSeverity(severity)
{
}
LogStreamConsumer(LogStreamConsumer&& other)
: LogStreamConsumerBase(severityOstream(other.mSeverity), severityPrefix(other.mSeverity), other.mShouldLog)
, std::ostream(&mBuffer) // links the stream buffer with the stream
, mShouldLog(other.mShouldLog)
, mSeverity(other.mSeverity)
{
}
void setReportableSeverity(Severity reportableSeverity)
{
mShouldLog = mSeverity <= reportableSeverity;
mBuffer.setShouldLog(mShouldLog);
}
private:
static std::ostream& severityOstream(Severity severity)
{
return severity >= Severity::kINFO ? std::cout : std::cerr;
}
static std::string severityPrefix(Severity severity)
{
switch (severity)
{
case Severity::kINTERNAL_ERROR: return "[F] ";
case Severity::kERROR: return "[E] ";
case Severity::kWARNING: return "[W] ";
case Severity::kINFO: return "[I] ";
case Severity::kVERBOSE: return "[V] ";
default: assert(0); return "";
}
}
bool mShouldLog;
Severity mSeverity;
};
//! \class Logger
//!
//! \brief Class which manages logging of TensorRT tools and samples
//!
//! \details This class provides a common interface for TensorRT tools and samples to log information to the console,
//! and supports logging two types of messages:
//!
//! - Debugging messages with an associated severity (info, warning, error, or internal error/fatal)
//! - Test pass/fail messages
//!
//! The advantage of having all samples use this class for logging as opposed to emitting directly to stdout/stderr is
//! that the logic for controlling the verbosity and formatting of sample output is centralized in one location.
//!
//! In the future, this class could be extended to support dumping test results to a file in some standard format
//! (for example, JUnit XML), and providing additional metadata (e.g. timing the duration of a test run).
//!
//! TODO: For backwards compatibility with existing samples, this class inherits directly from the nvinfer1::ILogger
//! interface, which is problematic since there isn't a clean separation between messages coming from the TensorRT
//! library and messages coming from the sample.
//!
//! In the future (once all samples are updated to use Logger::getTRTLogger() to access the ILogger) we can refactor the
//! class to eliminate the inheritance and instead make the nvinfer1::ILogger implementation a member of the Logger
//! object.
class Logger : public nvinfer1::ILogger
{
public:
Logger(Severity severity = Severity::kWARNING)
: mReportableSeverity(severity)
{
}
//!
//! \enum TestResult
//! \brief Represents the state of a given test
//!
enum class TestResult
{
kRUNNING, //!< The test is running
kPASSED, //!< The test passed
kFAILED, //!< The test failed
kWAIVED //!< The test was waived
};
//!
//! \brief Forward-compatible method for retrieving the nvinfer::ILogger associated with this Logger
//! \return The nvinfer1::ILogger associated with this Logger
//!
//! TODO Once all samples are updated to use this method to register the logger with TensorRT,
//! we can eliminate the inheritance of Logger from ILogger
//!
nvinfer1::ILogger& getTRTLogger()
{
return *this;
}
//!
//! \brief Implementation of the nvinfer1::ILogger::log() virtual method
//!
//! Note samples should not be calling this function directly; it will eventually go away once we eliminate the
//! inheritance from nvinfer1::ILogger
//!
void log(Severity severity, const char* msg) override
{
LogStreamConsumer(mReportableSeverity, severity) << "[TRT] " << std::string(msg) << std::endl;
}
//!
//! \brief Method for controlling the verbosity of logging output
//!
//! \param severity The logger will only emit messages that have severity of this level or higher.
//!
void setReportableSeverity(Severity severity)
{
mReportableSeverity = severity;
}
//!
//! \brief Opaque handle that holds logging information for a particular test
//!
//! This object is an opaque handle to information used by the Logger to print test results.
//! The sample must call Logger::defineTest() in order to obtain a TestAtom that can be used
//! with Logger::reportTest{Start,End}().
//!
class TestAtom
{
public:
TestAtom(TestAtom&&) = default;
private:
friend class Logger;
TestAtom(bool started, const std::string& name, const std::string& cmdline)
: mStarted(started)
, mName(name)
, mCmdline(cmdline)
{
}
bool mStarted;
std::string mName;
std::string mCmdline;
};
//!
//! \brief Define a test for logging
//!
//! \param[in] name The name of the test. This should be a string starting with
//! "TensorRT" and containing dot-separated strings containing
//! the characters [A-Za-z0-9_].
//! For example, "TensorRT.sample_googlenet"
//! \param[in] cmdline The command line used to reproduce the test
//
//! \return a TestAtom that can be used in Logger::reportTest{Start,End}().
//!
static TestAtom defineTest(const std::string& name, const std::string& cmdline)
{
return TestAtom(false, name, cmdline);
}
//!
//! \brief A convenience overloaded version of defineTest() that accepts an array of command-line arguments
//! as input
//!
//! \param[in] name The name of the test
//! \param[in] argc The number of command-line arguments
//! \param[in] argv The array of command-line arguments (given as C strings)
//!
//! \return a TestAtom that can be used in Logger::reportTest{Start,End}().
static TestAtom defineTest(const std::string& name, int argc, char const* const* argv)
{
auto cmdline = genCmdlineString(argc, argv);
return defineTest(name, cmdline);
}
//!
//! \brief Report that a test has started.
//!
//! \pre reportTestStart() has not been called yet for the given testAtom
//!
//! \param[in] testAtom The handle to the test that has started
//!
static void reportTestStart(TestAtom& testAtom)
{
reportTestResult(testAtom, TestResult::kRUNNING);
assert(!testAtom.mStarted);
testAtom.mStarted = true;
}
//!
//! \brief Report that a test has ended.
//!
//! \pre reportTestStart() has been called for the given testAtom
//!
//! \param[in] testAtom The handle to the test that has ended
//! \param[in] result The result of the test. Should be one of TestResult::kPASSED,
//! TestResult::kFAILED, TestResult::kWAIVED
//!
static void reportTestEnd(const TestAtom& testAtom, TestResult result)
{
assert(result != TestResult::kRUNNING);
assert(testAtom.mStarted);
reportTestResult(testAtom, result);
}
static int reportPass(const TestAtom& testAtom)
{
reportTestEnd(testAtom, TestResult::kPASSED);
return EXIT_SUCCESS;
}
static int reportFail(const TestAtom& testAtom)
{
reportTestEnd(testAtom, TestResult::kFAILED);
return EXIT_FAILURE;
}
static int reportWaive(const TestAtom& testAtom)
{
reportTestEnd(testAtom, TestResult::kWAIVED);
return EXIT_SUCCESS;
}
static int reportTest(const TestAtom& testAtom, bool pass)
{
return pass ? reportPass(testAtom) : reportFail(testAtom);
}
Severity getReportableSeverity() const
{
return mReportableSeverity;
}
private:
//!
//! \brief returns an appropriate string for prefixing a log message with the given severity
//!
static const char* severityPrefix(Severity severity)
{
switch (severity)
{
case Severity::kINTERNAL_ERROR: return "[F] ";
case Severity::kERROR: return "[E] ";
case Severity::kWARNING: return "[W] ";
case Severity::kINFO: return "[I] ";
case Severity::kVERBOSE: return "[V] ";
default: assert(0); return "";
}
}
//!
//! \brief returns an appropriate string for prefixing a test result message with the given result
//!
static const char* testResultString(TestResult result)
{
switch (result)
{
case TestResult::kRUNNING: return "RUNNING";
case TestResult::kPASSED: return "PASSED";
case TestResult::kFAILED: return "FAILED";
case TestResult::kWAIVED: return "WAIVED";
default: assert(0); return "";
}
}
//!
//! \brief returns an appropriate output stream (cout or cerr) to use with the given severity
//!
static std::ostream& severityOstream(Severity severity)
{
return severity >= Severity::kINFO ? std::cout : std::cerr;
}
//!
//! \brief method that implements logging test results
//!
static void reportTestResult(const TestAtom& testAtom, TestResult result)
{
severityOstream(Severity::kINFO) << "&&&& " << testResultString(result) << " " << testAtom.mName << " # "
<< testAtom.mCmdline << std::endl;
}
//!
//! \brief generate a command line string from the given (argc, argv) values
//!
static std::string genCmdlineString(int argc, char const* const* argv)
{
std::stringstream ss;
for (int i = 0; i < argc; i++)
{
if (i > 0)
ss << " ";
ss << argv[i];
}
return ss.str();
}
Severity mReportableSeverity;
};
namespace
{
//!
//! \brief produces a LogStreamConsumer object that can be used to log messages of severity kVERBOSE
//!
//! Example usage:
//!
//! LOG_VERBOSE(logger) << "hello world" << std::endl;
//!
inline LogStreamConsumer LOG_VERBOSE(const Logger& logger)
{
return LogStreamConsumer(logger.getReportableSeverity(), Severity::kVERBOSE);
}
//!
//! \brief produces a LogStreamConsumer object that can be used to log messages of severity kINFO
//!
//! Example usage:
//!
//! LOG_INFO(logger) << "hello world" << std::endl;
//!
inline LogStreamConsumer LOG_INFO(const Logger& logger)
{
return LogStreamConsumer(logger.getReportableSeverity(), Severity::kINFO);
}
//!
//! \brief produces a LogStreamConsumer object that can be used to log messages of severity kWARNING
//!
//! Example usage:
//!
//! LOG_WARN(logger) << "hello world" << std::endl;
//!
inline LogStreamConsumer LOG_WARN(const Logger& logger)
{
return LogStreamConsumer(logger.getReportableSeverity(), Severity::kWARNING);
}
//!
//! \brief produces a LogStreamConsumer object that can be used to log messages of severity kERROR
//!
//! Example usage:
//!
//! LOG_ERROR(logger) << "hello world" << std::endl;
//!
inline LogStreamConsumer LOG_ERROR(const Logger& logger)
{
return LogStreamConsumer(logger.getReportableSeverity(), Severity::kERROR);
}
//!
//! \brief produces a LogStreamConsumer object that can be used to log messages of severity kINTERNAL_ERROR
// ("fatal" severity)
//!
//! Example usage:
//!
//! LOG_FATAL(logger) << "hello world" << std::endl;
//!
inline LogStreamConsumer LOG_FATAL(const Logger& logger)
{
return LogStreamConsumer(logger.getReportableSeverity(), Severity::kINTERNAL_ERROR);
}
} // anonymous namespace
#endif // TENSORRT_LOGGING_H

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#include "NvInfer.h"
#include "cuda_runtime_api.h"
#include "logging.h"
#include <math.h>
#include <string>
#include <algorithm>
using namespace nvinfer1;
#define CHECK(status) \
do \
{ \
auto ret = (status); \
if (ret != 0) \
{ \
std::cerr << "Cuda failure: " << ret << std::endl; \
abort(); \
} \
} while (0)
// Load weights from files shared with TensorRT samples.
// TensorRT weight files have a simple space delimited format:
// [type] [size] <data x size in hex>
std::map<std::string, Weights> loadWeights(const std::string file)
{
std::cout << "Loading weights: " << file << std::endl;
std::map<std::string, Weights> weightMap;
// Open weights file
std::ifstream input(file);
assert(input.is_open() && "Unable to load weight file.");
// Read number of weight blobs
int32_t count;
input >> count;
assert(count > 0 && "Invalid weight map file.");
while (count--)
{
Weights wt{DataType::kFLOAT, nullptr, 0};
uint32_t size;
// Read name and type of blob
std::string name;
input >> name >> std::dec >> size;
wt.type = DataType::kFLOAT;
// Load blob
uint32_t *val = reinterpret_cast<uint32_t *>(malloc(sizeof(val) * size));
for (uint32_t x = 0, y = size; x < y; ++x)
{
input >> std::hex >> val[x];
}
wt.values = val;
wt.count = size;
weightMap[name] = wt;
}
return weightMap;
}
struct BlockArgs
{
int num_repeat;
int kernel_size;
int stride;
float expand_ratio;
int input_filters;
int output_filters;
float se_ratio;
bool id_skip;
};
struct GlobalParams
{
int input_h;
int input_w;
int num_classes;
float batch_norm_epsilon;
float width_coefficient;
float depth_coefficient;
int depth_divisor;
int min_depth;
};
int roundFilters(int filters, GlobalParams global_params)
{
float multiplier = global_params.width_coefficient;
int divisor = global_params.depth_divisor;
int min_depth = global_params.min_depth;
filters = int(filters * multiplier);
if (min_depth < 0)
{
min_depth = divisor;
}
// follow the formula transferred from official TensorFlow implementation
int new_filters = std::max(min_depth, int(int(filters + divisor / 2) / divisor) * divisor);
if (new_filters < 0.9 * filters) // prevent rounding by more than 10%
new_filters += divisor;
return int(new_filters);
}
DimsHW calculateOutputImageSize(DimsHW image_size, int stride)
{
int image_h = int(ceil(float(image_size.h()) / float(stride)));
int image_w = int(ceil(float(image_size.w()) / float(stride)));
return DimsHW{image_h, image_w};
}
int roundRepeats(int repeats, GlobalParams global_params)
{
float multiplier = global_params.depth_coefficient;
// follow the formula transferred from official TensorFlow implementation
int new_repeats = int(ceil(multiplier * repeats));
return new_repeats;
}
IScaleLayer *addBatchNorm2d(INetworkDefinition *network, std::map<std::string, Weights> &weightMap, ITensor &input, std::string lname, float eps)
{
float *gamma = (float *)weightMap[lname + ".weight"].values;
float *beta = (float *)weightMap[lname + ".bias"].values;
float *mean = (float *)weightMap[lname + ".running_mean"].values;
float *var = (float *)weightMap[lname + ".running_var"].values;
int len = weightMap[lname + ".running_var"].count;
float *scval = reinterpret_cast<float *>(malloc(sizeof(float) * len));
for (int i = 0; i < len; i++)
{
scval[i] = gamma[i] / sqrt(var[i] + eps);
}
Weights scale{DataType::kFLOAT, scval, len};
float *shval = reinterpret_cast<float *>(malloc(sizeof(float) * len));
for (int i = 0; i < len; i++)
{
shval[i] = beta[i] - mean[i] * gamma[i] / sqrt(var[i] + eps);
}
Weights shift{DataType::kFLOAT, shval, len};
float *pval = reinterpret_cast<float *>(malloc(sizeof(float) * len));
for (int i = 0; i < len; i++)
{
pval[i] = 1.0;
}
Weights power{DataType::kFLOAT, pval, len};
weightMap[lname + ".scale"] = scale;
weightMap[lname + ".shift"] = shift;
weightMap[lname + ".power"] = power;
IScaleLayer *scale_1 = network->addScale(input, ScaleMode::kCHANNEL, shift, scale, power);
assert(scale_1);
return scale_1;
}
IConvolutionLayer *addSamePaddingConv2d(INetworkDefinition *network, std::map<std::string, Weights> &weightMap, ITensor &input, int outch, int kernel_size, int stride, int dilation, int groups, DimsHW image_size, std::string lname, bool bias = true)
{
int ih = image_size.h();
int iw = image_size.w();
int kh = kernel_size;
int kw = kernel_size;
int sh = stride;
int sw = stride;
int oh = ceil(float(ih) / float(sh));
int ow = ceil(float(iw) / float(sw));
int pad_h = std::max((oh - 1) * stride + (kh - 1) * dilation + 1 - ih, 0);
int pad_w = std::max((ow - 1) * stride + (kw - 1) * dilation + 1 - iw, 0);
int pad_left = 0;
int pad_right = 0;
int pad_top = 0;
int pad_bottom = 0;
if (pad_h > 0 || pad_w > 0)
{
pad_left = int(pad_w / 2);
pad_right = pad_w - int(pad_w / 2);
pad_top = int(pad_h / 2);
pad_bottom = pad_h - int(pad_h / 2);
}
Weights bias_wt{DataType::kFLOAT, nullptr, 0};
if (bias)
{
bias_wt = weightMap[lname + ".bias"];
}
IConvolutionLayer *conv = network->addConvolutionNd(input, outch, DimsHW{kh, kw}, weightMap[lname + ".weight"], bias_wt);
conv->setPrePadding(DimsHW{pad_top, pad_left});
conv->setPostPadding(DimsHW{pad_bottom, pad_right});
conv->setStrideNd(DimsHW{stride, stride});
conv->setDilationNd(DimsHW{dilation, dilation});
conv->setNbGroups(groups);
return conv;
}
ILayer *addSwish(INetworkDefinition *network, ITensor &input)
{
//swish
auto *sigmoid = network->addActivation(input, ActivationType::kSIGMOID);
auto *ew = network->addElementWise(input, *sigmoid->getOutput(0), ElementWiseOperation::kPROD);
return ew;
}
ITensor *MBConvBlock(INetworkDefinition *network, std::map<std::string, Weights> &weightMap, ITensor &input, std::string lname, BlockArgs block_args, GlobalParams global_params, DimsHW image_size)
{
bool has_se = block_args.se_ratio > 0 && block_args.se_ratio <= 1;
bool id_skip = block_args.id_skip;
float bn_eps = global_params.batch_norm_epsilon;
int input_filters = block_args.input_filters;
int output_filters = block_args.output_filters;
Weights emptywts{DataType::kFLOAT, nullptr, 0};
ITensor *x = &input;
int inp = block_args.input_filters;
int oup = int(block_args.input_filters * block_args.expand_ratio);
// expand_ratio != 1
if (fabs(block_args.expand_ratio - 1) > 1e-5)
{
auto expand_conv = addSamePaddingConv2d(network, weightMap, input, oup, 1, 1, 1, 1, image_size, lname + "._expand_conv");
auto bn0 = addBatchNorm2d(network, weightMap, *expand_conv->getOutput(0), lname + "._bn0", bn_eps);
auto swish0 = addSwish(network, *bn0->getOutput(0));
x = swish0->getOutput(0);
}
int k = block_args.kernel_size;
int s = block_args.stride;
auto depthwise_conv = addSamePaddingConv2d(network, weightMap, *x, oup, k, s, 1, oup, image_size, lname + "._depthwise_conv", false);
auto bn1 = addBatchNorm2d(network, weightMap, *depthwise_conv->getOutput(0), lname + "._bn1", bn_eps);
//swish
auto swish1 = addSwish(network, *bn1->getOutput(0));
x = swish1->getOutput(0);
image_size = calculateOutputImageSize(image_size, s);
if (has_se)
{
auto avg_pool = network->addPoolingNd(*x, PoolingType::kAVERAGE, image_size);
int num_squeezed_channels = std::max(1, int(input_filters * block_args.se_ratio));
auto se_reduce = addSamePaddingConv2d(network, weightMap, *avg_pool->getOutput(0), num_squeezed_channels, 1, 1, 1, 1, DimsHW{1, 1}, lname + "._se_reduce");
auto swish2 = addSwish(network, *se_reduce->getOutput(0));
auto se_expand = addSamePaddingConv2d(network, weightMap, *swish2->getOutput(0), oup, 1, 1, 1, 1, DimsHW{1, 1}, lname + "._se_expand");
auto *sigmoid = network->addActivation(*se_expand->getOutput(0), ActivationType::kSIGMOID);
auto *ew = network->addElementWise(*x, *sigmoid->getOutput(0), ElementWiseOperation::kPROD);
x = ew->getOutput(0);
}
int final_oup = block_args.output_filters;
auto project_conv = addSamePaddingConv2d(network, weightMap, *x, final_oup, 1, 1, 1, 1, image_size, lname + "._project_conv");
auto bn2 = addBatchNorm2d(network, weightMap, *project_conv->getOutput(0), lname + "._bn2", bn_eps);
x = bn2->getOutput(0);
if (id_skip && block_args.stride == 1 && input_filters == output_filters)
{
auto *ew = network->addElementWise(input, *x, ElementWiseOperation::kSUM);
x = ew->getOutput(0);
}
return x;
}