提交 52993878 编写于 作者: T tensor-tang 提交者: Yan Chunwei

add feature/vis infer demos (#11708)

上级 8df303c0
......@@ -14,3 +14,43 @@
#
inference_api_test(simple_on_word2vec ARGS test_word2vec)
set(DEMO_INSTALL_DIR "${PADDLE_BINARY_DIR}/inference_demo")
set(URL_ROOT http://paddlemodels.bj.bcebos.com/inference-vis-demos%2F)
function(inference_download_test_demo TARGET)
if (NOT WITH_TESTING)
return()
endif()
set(options "")
set(oneValueArgs URL)
set(multiValueArgs SRCS)
cmake_parse_arguments(tests "${options}" "${oneValueArgs}" "${multiValueArgs}" ${ARGN})
set(test_dir "${DEMO_INSTALL_DIR}/${TARGET}")
message(STATUS "inference demo ${test_dir}")
if(NOT EXISTS "${test_dir}")
message(STATUS "Download ${TARGET} model from ${tests_URL}")
execute_process(COMMAND bash -c "mkdir -p ${test_dir}")
execute_process(COMMAND bash -c "cd ${test_dir}; wget -q ${tests_URL}")
execute_process(COMMAND bash -c "cd ${test_dir}; tar xzf *.tar.gz")
endif()
cc_test(${TARGET} SRCS "${tests_SRCS}"
DEPS paddle_inference_api paddle_fluid
ARGS --data=${test_dir}/data.txt
--modeldir=${test_dir}/model
--refer=${test_dir}/result.txt)
endfunction()
# disable mobilenet test
#inference_download_test_demo(mobilenet_inference_demo
# SRCS vis_demo.cc
# URL ${URL_ROOT}mobilenet.tar.gz)
inference_download_test_demo(se_resnext50_inference_demo
SRCS vis_demo.cc
URL ${URL_ROOT}se_resnext50.tar.gz)
inference_download_test_demo(ocr_inference_demo
SRCS vis_demo.cc
URL ${URL_ROOT}ocr.tar.gz)
# Infernce Demos
Input data format:
- Each line contains a single record
- Each record's format is
```
<space splitted floats as data>\t<space splitted ints as shape>
```
Follow the C++ codes in `vis_demo.cc`.
## MobileNet
To execute the demo, simply run
```sh
./mobilenet_inference_demo --modeldir <model> --data <datafile>
```
## SE-ResNeXt-50
To execute the demo, simply run
```sh
./se_resnext50_inference_demo --modeldir <model> --data <datafile>
```
## OCR
To execute the demo, simply run
```sh
./ocr_inference_demo --modeldir <model> --data <datafile>
```
......@@ -21,6 +21,7 @@ limitations under the License. */
#include <memory>
#include <thread>
#include "paddle/contrib/inference/paddle_inference_api.h"
namespace paddle {
namespace demo {
......
// Copyright (c) 2018 PaddlePaddle 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.
#pragma once
#include <string>
#include <vector>
#include "paddle/contrib/inference/paddle_inference_api.h"
namespace paddle {
namespace demo {
static void split(const std::string& str,
char sep,
std::vector<std::string>* pieces) {
pieces->clear();
if (str.empty()) {
return;
}
size_t pos = 0;
size_t next = str.find(sep, pos);
while (next != std::string::npos) {
pieces->push_back(str.substr(pos, next - pos));
pos = next + 1;
next = str.find(sep, pos);
}
if (!str.substr(pos).empty()) {
pieces->push_back(str.substr(pos));
}
}
/*
* Get a summary of a PaddleTensor content.
*/
static std::string SummaryTensor(const PaddleTensor& tensor) {
std::stringstream ss;
int num_elems = tensor.data.length() / PaddleDtypeSize(tensor.dtype);
ss << "data[:10]\t";
switch (tensor.dtype) {
case PaddleDType::INT64: {
for (int i = 0; i < std::min(num_elems, 10); i++) {
ss << static_cast<int64_t*>(tensor.data.data())[i] << " ";
}
break;
}
case PaddleDType::FLOAT32:
for (int i = 0; i < std::min(num_elems, 10); i++) {
ss << static_cast<float*>(tensor.data.data())[i] << " ";
}
break;
}
return ss.str();
}
} // namespace demo
} // namespace paddle
/* Copyright (c) 2018 PaddlePaddle 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. */
/*
* This file contains demo for mobilenet, se-resnext50 and ocr.
*/
#include <gflags/gflags.h>
#include <glog/logging.h> // use glog instead of PADDLE_ENFORCE to avoid importing other paddle header files.
#include <gtest/gtest.h>
#include <fstream>
#include <iostream>
#include "paddle/contrib/inference/demo/utils.h"
#include "paddle/contrib/inference/paddle_inference_api.h"
#ifdef PADDLE_WITH_CUDA
DECLARE_double(fraction_of_gpu_memory_to_use);
#endif
namespace paddle {
namespace demo {
DEFINE_string(modeldir, "", "Directory of the inference model.");
DEFINE_string(refer, "", "path to reference result for comparison.");
DEFINE_string(
data,
"",
"path of data; each line is a record, format is "
"'<space splitted floats as data>\t<space splitted ints as shape'");
struct Record {
std::vector<float> data;
std::vector<int32_t> shape;
};
void split(const std::string& str, char sep, std::vector<std::string>* pieces);
Record ProcessALine(const std::string& line) {
LOG(INFO) << "process a line";
std::vector<std::string> columns;
split(line, '\t', &columns);
CHECK_EQ(columns.size(), 2UL)
<< "data format error, should be <data>\t<shape>";
Record record;
std::vector<std::string> data_strs;
split(columns[0], ' ', &data_strs);
for (auto& d : data_strs) {
record.data.push_back(std::stof(d));
}
std::vector<std::string> shape_strs;
split(columns[1], ' ', &shape_strs);
for (auto& s : shape_strs) {
record.shape.push_back(std::stoi(s));
}
LOG(INFO) << "data size " << record.data.size();
LOG(INFO) << "data shape size " << record.shape.size();
return record;
}
void CheckOutput(const std::string& referfile, const PaddleTensor& output) {
std::string line;
std::ifstream file(referfile);
std::getline(file, line);
auto refer = ProcessALine(line);
file.close();
size_t numel = output.data.length() / PaddleDtypeSize(output.dtype);
LOG(INFO) << "predictor output numel " << numel;
LOG(INFO) << "reference output numel " << refer.data.size();
EXPECT_EQ(numel, refer.data.size());
switch (output.dtype) {
case PaddleDType::INT64: {
for (size_t i = 0; i < numel; ++i) {
EXPECT_EQ(static_cast<int64_t*>(output.data.data())[i], refer.data[i]);
}
break;
}
case PaddleDType::FLOAT32:
for (size_t i = 0; i < numel; ++i) {
EXPECT_NEAR(
static_cast<float*>(output.data.data())[i], refer.data[i], 1e-5);
}
break;
}
}
/*
* Use the native fluid engine to inference the demo.
*/
void Main(bool use_gpu) {
NativeConfig config;
config.param_file = FLAGS_modeldir + "/__params__";
config.prog_file = FLAGS_modeldir + "/__model__";
config.use_gpu = use_gpu;
config.device = 0;
#ifdef PADDLE_WITH_CUDA
config.fraction_of_gpu_memory = FLAGS_fraction_of_gpu_memory_to_use;
#endif
LOG(INFO) << "init predictor";
auto predictor =
CreatePaddlePredictor<NativeConfig, PaddleEngineKind::kNative>(config);
LOG(INFO) << "begin to process data";
// Just a single batch of data.
std::string line;
std::ifstream file(FLAGS_data);
std::getline(file, line);
auto record = ProcessALine(line);
file.close();
// Inference.
PaddleTensor input{
.name = "xx",
.shape = record.shape,
.data = PaddleBuf(record.data.data(), record.data.size() * sizeof(float)),
.dtype = PaddleDType::FLOAT32};
LOG(INFO) << "run executor";
std::vector<PaddleTensor> output;
predictor->Run({input}, &output);
LOG(INFO) << "output.size " << output.size();
auto& tensor = output.front();
LOG(INFO) << "output: " << SummaryTensor(tensor);
// compare with reference result
CheckOutput(FLAGS_refer, tensor);
}
TEST(demo, vis_demo_cpu) { Main(false /*use_gpu*/); }
#ifdef PADDLE_WITH_CUDA
TEST(demo, vis_demo_gpu) { Main(true /*use_gpu*/); }
#endif
} // namespace demo
} // namespace paddle
......@@ -16,6 +16,19 @@ limitations under the License. */
namespace paddle {
int PaddleDtypeSize(PaddleDType dtype) {
switch (dtype) {
case PaddleDType::FLOAT32:
return sizeof(float);
case PaddleDType::INT64:
return sizeof(int64_t);
default:
//
assert(false);
return -1;
}
}
PaddleBuf::PaddleBuf(PaddleBuf&& other)
: data_(other.data_),
length_(other.length_),
......@@ -62,4 +75,4 @@ void PaddleBuf::Free() {
}
}
} // namespace paddle
\ No newline at end of file
} // namespace paddle
......@@ -15,7 +15,7 @@ limitations under the License. */
/*
* This file contains the definition of a simple Inference API for Paddle.
*
* ATTENTION: It requires some C++ features, for lower version C++ or C, we
* ATTENTION: It requires some C++11 features, for lower version C++ or C, we
* might release another API.
*/
......@@ -140,4 +140,7 @@ struct AnakinConfig : public PaddlePredictor::Config {
// Similarly, each engine kind should map to a unique predictor implementation.
template <typename ConfigT, PaddleEngineKind engine = PaddleEngineKind::kNative>
std::unique_ptr<PaddlePredictor> CreatePaddlePredictor(const ConfigT& config);
int PaddleDtypeSize(PaddleDType dtype);
} // namespace paddle
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