提交 d9461a0b 编写于 作者: W weishengyu

Merge branch 'develop' into update_readme_cn

...@@ -12,3 +12,4 @@ build/ ...@@ -12,3 +12,4 @@ build/
log/ log/
nohup.out nohup.out
.DS_Store .DS_Store
.idea
...@@ -20,10 +20,10 @@ ...@@ -20,10 +20,10 @@
## 近期更新 ## 近期更新
- 📢将于**<u>6月15-6月17日晚20:30</u>**进行为期三天的课程直播,详细介绍超轻量图像分类方案,对各场景模型优化原理及使用方式进行拆解,之后还有产业案例全流程实操,对各类痛难点解决方案进行手把手教学,加上现场互动答疑,抓紧扫码上车吧! - 📢将于**6月15-6月17日晚20:30** 进行为期三天的课程直播,详细介绍超轻量图像分类方案,对各场景模型优化原理及使用方式进行拆解,之后还有产业案例全流程实操,对各类痛难点解决方案进行手把手教学,加上现场互动答疑,抓紧扫码上车吧!
<div align="center"> <div align="center">
<img src="https://user-images.githubusercontent.com/80816848/173404459-9426c0ed-4801-4f75-876f-2e6ec47255f5.png" width = "200" height = "200"/> <img src="https://user-images.githubusercontent.com/45199522/173483779-2332f990-4941-4f8d-baee-69b62035fc31.png" width = "200" height = "200"/>
</div> </div>
- 🔥️ 2022.6.15 发布[PULC超轻量图像分类实用方案](docs/zh_CN/PULC/PULC_train.md),CPU推理3ms,精度比肩SwinTransformer,覆盖人、车、OCR场景九大常见任务。 - 🔥️ 2022.6.15 发布[PULC超轻量图像分类实用方案](docs/zh_CN/PULC/PULC_train.md),CPU推理3ms,精度比肩SwinTransformer,覆盖人、车、OCR场景九大常见任务。
...@@ -47,11 +47,11 @@ PaddleClas发布了[PP-HGNet](docs/zh_CN/models/PP-HGNet.md)、[PP-LCNetv2](docs ...@@ -47,11 +47,11 @@ PaddleClas发布了[PP-HGNet](docs/zh_CN/models/PP-HGNet.md)、[PP-LCNetv2](docs
## 欢迎加入技术交流群 ## 欢迎加入技术交流群
* 您可以扫描下面的QQ/微信二维码(添加小助手微信并回复“C”),加入PaddleClas微信交流群,获得更高效的问题答疑,与各行各业开发者充分交流,期待您的加入。 * 您可以扫描下面的微信/QQ二维码(添加小助手微信并回复“C”),加入PaddleClas微信交流群,获得更高效的问题答疑,与各行各业开发者充分交流,期待您的加入。
<div align="center"> <div align="center">
<img src="https://user-images.githubusercontent.com/80816848/164383225-e375eb86-716e-41b4-a9e0-4b8a3976c1aa.jpg" width="200"/>
<img src="https://user-images.githubusercontent.com/48054808/160531099-9811bbe6-cfbb-47d5-8bdb-c2b40684d7dd.png" width="200"/> <img src="https://user-images.githubusercontent.com/48054808/160531099-9811bbe6-cfbb-47d5-8bdb-c2b40684d7dd.png" width="200"/>
<img src="https://user-images.githubusercontent.com/80816848/164383225-e375eb86-716e-41b4-a9e0-4b8a3976c1aa.jpg" width="200"/>
</div> </div>
## 快速体验 ## 快速体验
......
...@@ -33,8 +33,8 @@ For the introduction of PP-LCNet, please refer to [paper](https://arxiv.org/pdf/ ...@@ -33,8 +33,8 @@ For the introduction of PP-LCNet, please refer to [paper](https://arxiv.org/pdf/
## Features ## Features
PaddleClas release PP-HGNet、PP-LCNetv2、 PP-LCNet and **S**imple **S**emi-supervised **L**abel **D**istillation algorithms, and support plenty of PaddleClas release PP-HGNet、PP-LCNetv2、 PP-LCNet and **S**imple **S**emi-supervised **L**abel **D**istillation algorithms, and support plenty of
image classification and image recognition algorithms. image classification and image recognition algorithms.
Based on th algorithms above, PaddleClas release PP-ShiTu image recognition system and [**P**ractical **U**ltra **L**ight-weight image **C**lassification solutions](docs/en/PULC/PULC_quickstart_en.md). Based on th algorithms above, PaddleClas release PP-ShiTu image recognition system and [**P**ractical **U**ltra **L**ight-weight image **C**lassification solutions](docs/en/PULC/PULC_quickstart_en.md).
...@@ -59,6 +59,10 @@ Quick experience of **P**ractical **U**ltra **L**ight-weight image **C**lassific ...@@ -59,6 +59,10 @@ Quick experience of **P**ractical **U**ltra **L**ight-weight image **C**lassific
- [Install Paddle](./docs/en/installation/install_paddle_en.md) - [Install Paddle](./docs/en/installation/install_paddle_en.md)
- [Install PaddleClas Environment](./docs/en/installation/install_paddleclas_en.md) - [Install PaddleClas Environment](./docs/en/installation/install_paddleclas_en.md)
- [Practical Ultra Light-weight image Classification solutions](./docs/en/PULC/PULC_quickstart_en.md) - [Practical Ultra Light-weight image Classification solutions](./docs/en/PULC/PULC_quickstart_en.md)
- [PULC Quick Start](docs/en/PULC/PULC_quickstart_en.md)
- [PULC Model Zoo](docs/en/PULC/PULC_model_list_en.md)
- [PULC Classification Model of Someone or Nobody](docs/en/PULC/PULC_person_exists_en.md)
- [Quick Start of Recognition](./docs/en/tutorials/quick_start_recognition_en.md)
- [Quick Start of Recognition](./docs/en/quick_start/quick_start_recognition_en.md) - [Quick Start of Recognition](./docs/en/quick_start/quick_start_recognition_en.md)
- [Introduction to Image Recognition Systems](#Introduction_to_Image_Recognition_Systems) - [Introduction to Image Recognition Systems](#Introduction_to_Image_Recognition_Systems)
- [Image Recognition Demo images](#Rec_Demo_images) - [Image Recognition Demo images](#Rec_Demo_images)
......
# Paddle2ONNX: Converting To ONNX and Deployment
This section introduce that how to convert the Paddle Inference Model ResNet50_vd to ONNX model and deployment based on ONNX engine.
## 1. Installation
First, you need to install Paddle2ONNX and onnxruntime. Paddle2ONNX is a toolkit to convert Paddle Inference Model to ONNX model. Please refer to [Paddle2ONNX](https://github.com/PaddlePaddle/Paddle2ONNX/blob/develop/README_en.md) for more information.
- Paddle2ONNX Installation
```
python3.7 -m pip install paddle2onnx
```
- ONNX Installation
```
python3.7 -m pip install onnxruntime
```
## 2. Converting to ONNX
Download the Paddle Inference Model ResNet50_vd:
```
cd deploy
mkdir models && cd models
wget -nc https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/ResNet50_vd_infer.tar && tar xf ResNet50_vd_infer.tar
cd ..
```
Converting to ONNX model:
```
paddle2onnx --model_dir=./models/ResNet50_vd_infer/ \
--model_filename=inference.pdmodel \
--params_filename=inference.pdiparams \
--save_file=./models/ResNet50_vd_infer/inference.onnx \
--opset_version=10 \
--enable_onnx_checker=True
```
After running the above command, the ONNX model file converted would be save in `./models/ResNet50_vd_infer/`.
## 3. Deployment
Deployment with ONNX model, command is as shown below.
```
python3.7 python/predict_cls.py \
-c configs/inference_cls.yaml \
-o Global.use_onnx=True \
-o Global.use_gpu=False \
-o Global.inference_model_dir=./models/ResNet50_vd_infer
```
The prediction results:
```
ILSVRC2012_val_00000010.jpeg: class id(s): [153, 204, 229, 332, 155], score(s): [0.69, 0.10, 0.02, 0.01, 0.01], label_name(s): ['Maltese dog, Maltese terrier, Maltese', 'Lhasa, Lhasa apso', 'Old English sheepdog, bobtail', 'Angora, Angora rabbit', 'Shih-Tzu']
```
# 使用镜像:
# registry.baidubce.com/paddlepaddle/paddle:latest-dev-cuda10.1-cudnn7-gcc82
# 编译Serving Server:
# client和app可以直接使用release版本
# server因为加入了自定义OP,需要重新编译
# 默认编译时的${PWD}=PaddleClas/deploy/paddleserving/
python_name=${1:-'python'}
apt-get update
apt install -y libcurl4-openssl-dev libbz2-dev
wget -nc https://paddle-serving.bj.bcebos.com/others/centos_ssl.tar
tar xf centos_ssl.tar
rm -rf centos_ssl.tar
mv libcrypto.so.1.0.2k /usr/lib/libcrypto.so.1.0.2k
mv libssl.so.1.0.2k /usr/lib/libssl.so.1.0.2k
ln -sf /usr/lib/libcrypto.so.1.0.2k /usr/lib/libcrypto.so.10
ln -sf /usr/lib/libssl.so.1.0.2k /usr/lib/libssl.so.10
ln -sf /usr/lib/libcrypto.so.10 /usr/lib/libcrypto.so
ln -sf /usr/lib/libssl.so.10 /usr/lib/libssl.so
# 安装go依赖
rm -rf /usr/local/go
wget -qO- https://paddle-ci.cdn.bcebos.com/go1.17.2.linux-amd64.tar.gz | tar -xz -C /usr/local
export GOROOT=/usr/local/go
export GOPATH=/root/gopath
export PATH=$PATH:$GOPATH/bin:$GOROOT/bin
go env -w GO111MODULE=on
go env -w GOPROXY=https://goproxy.cn,direct
go install github.com/grpc-ecosystem/grpc-gateway/protoc-gen-grpc-gateway@v1.15.2
go install github.com/grpc-ecosystem/grpc-gateway/protoc-gen-swagger@v1.15.2
go install github.com/golang/protobuf/protoc-gen-go@v1.4.3
go install google.golang.org/grpc@v1.33.0
go env -w GO111MODULE=auto
# 下载opencv库
wget https://paddle-qa.bj.bcebos.com/PaddleServing/opencv3.tar.gz
tar -xvf opencv3.tar.gz
rm -rf opencv3.tar.gz
export OPENCV_DIR=$PWD/opencv3
# clone Serving
git clone https://github.com/PaddlePaddle/Serving.git -b develop --depth=1
cd Serving # PaddleClas/deploy/paddleserving/Serving
export Serving_repo_path=$PWD
git submodule update --init --recursive
${python_name} -m pip install -r python/requirements.txt
# set env
export PYTHON_INCLUDE_DIR=$(${python_name} -c "from distutils.sysconfig import get_python_inc; print(get_python_inc())")
export PYTHON_LIBRARIES=$(${python_name} -c "import distutils.sysconfig as sysconfig; print(sysconfig.get_config_var('LIBDIR'))")
export PYTHON_EXECUTABLE=`which ${python_name}`
export CUDA_PATH='/usr/local/cuda'
export CUDNN_LIBRARY='/usr/local/cuda/lib64/'
export CUDA_CUDART_LIBRARY='/usr/local/cuda/lib64/'
export TENSORRT_LIBRARY_PATH='/usr/local/TensorRT6-cuda10.1-cudnn7/targets/x86_64-linux-gnu/'
# cp 自定义OP代码
\cp ../preprocess/general_clas_op.* ${Serving_repo_path}/core/general-server/op
\cp ../preprocess/preprocess_op.* ${Serving_repo_path}/core/predictor/tools/pp_shitu_tools
# 编译Server
mkdir server-build-gpu-opencv
cd server-build-gpu-opencv
cmake -DPYTHON_INCLUDE_DIR=$PYTHON_INCLUDE_DIR \
-DPYTHON_LIBRARIES=$PYTHON_LIBRARIES \
-DPYTHON_EXECUTABLE=$PYTHON_EXECUTABLE \
-DCUDA_TOOLKIT_ROOT_DIR=${CUDA_PATH} \
-DCUDNN_LIBRARY=${CUDNN_LIBRARY} \
-DCUDA_CUDART_LIBRARY=${CUDA_CUDART_LIBRARY} \
-DTENSORRT_ROOT=${TENSORRT_LIBRARY_PATH} \
-DOPENCV_DIR=${OPENCV_DIR} \
-DWITH_OPENCV=ON \
-DSERVER=ON \
-DWITH_GPU=ON ..
make -j32
${python_name} -m pip install python/dist/paddle*
# export SERVING_BIN
export SERVING_BIN=$PWD/core/general-server/serving
cd ../../
\ No newline at end of file
// Copyright (c) 2020 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.
#include "core/general-server/op/general_clas_op.h"
#include "core/predictor/framework/infer.h"
#include "core/predictor/framework/memory.h"
#include "core/predictor/framework/resource.h"
#include "core/util/include/timer.h"
#include <algorithm>
#include <iostream>
#include <memory>
#include <sstream>
namespace baidu {
namespace paddle_serving {
namespace serving {
using baidu::paddle_serving::Timer;
using baidu::paddle_serving::predictor::MempoolWrapper;
using baidu::paddle_serving::predictor::general_model::Tensor;
using baidu::paddle_serving::predictor::general_model::Response;
using baidu::paddle_serving::predictor::general_model::Request;
using baidu::paddle_serving::predictor::InferManager;
using baidu::paddle_serving::predictor::PaddleGeneralModelConfig;
int GeneralClasOp::inference() {
VLOG(2) << "Going to run inference";
const std::vector<std::string> pre_node_names = pre_names();
if (pre_node_names.size() != 1) {
LOG(ERROR) << "This op(" << op_name()
<< ") can only have one predecessor op, but received "
<< pre_node_names.size();
return -1;
}
const std::string pre_name = pre_node_names[0];
const GeneralBlob *input_blob = get_depend_argument<GeneralBlob>(pre_name);
if (!input_blob) {
LOG(ERROR) << "input_blob is nullptr,error";
return -1;
}
uint64_t log_id = input_blob->GetLogId();
VLOG(2) << "(logid=" << log_id << ") Get precedent op name: " << pre_name;
GeneralBlob *output_blob = mutable_data<GeneralBlob>();
if (!output_blob) {
LOG(ERROR) << "output_blob is nullptr,error";
return -1;
}
output_blob->SetLogId(log_id);
if (!input_blob) {
LOG(ERROR) << "(logid=" << log_id
<< ") Failed mutable depended argument, op:" << pre_name;
return -1;
}
const TensorVector *in = &input_blob->tensor_vector;
TensorVector *out = &output_blob->tensor_vector;
int batch_size = input_blob->_batch_size;
output_blob->_batch_size = batch_size;
VLOG(2) << "(logid=" << log_id << ") infer batch size: " << batch_size;
Timer timeline;
int64_t start = timeline.TimeStampUS();
timeline.Start();
// only support string type
char *total_input_ptr = static_cast<char *>(in->at(0).data.data());
std::string base64str = total_input_ptr;
cv::Mat img = Base2Mat(base64str);
// RGB2BGR
cv::cvtColor(img, img, cv::COLOR_BGR2RGB);
// Resize
cv::Mat resize_img;
resize_op_.Run(img, resize_img, resize_short_size_);
// CenterCrop
crop_op_.Run(resize_img, crop_size_);
// Normalize
normalize_op_.Run(&resize_img, mean_, scale_, is_scale_);
// Permute
std::vector<float> input(1 * 3 * resize_img.rows * resize_img.cols, 0.0f);
permute_op_.Run(&resize_img, input.data());
float maxValue = *max_element(input.begin(), input.end());
float minValue = *min_element(input.begin(), input.end());
TensorVector *real_in = new TensorVector();
if (!real_in) {
LOG(ERROR) << "real_in is nullptr,error";
return -1;
}
std::vector<int> input_shape;
int in_num = 0;
void *databuf_data = NULL;
char *databuf_char = NULL;
size_t databuf_size = 0;
input_shape = {1, 3, resize_img.rows, resize_img.cols};
in_num = std::accumulate(input_shape.begin(), input_shape.end(), 1,
std::multiplies<int>());
databuf_size = in_num * sizeof(float);
databuf_data = MempoolWrapper::instance().malloc(databuf_size);
if (!databuf_data) {
LOG(ERROR) << "Malloc failed, size: " << databuf_size;
return -1;
}
memcpy(databuf_data, input.data(), databuf_size);
databuf_char = reinterpret_cast<char *>(databuf_data);
paddle::PaddleBuf paddleBuf(databuf_char, databuf_size);
paddle::PaddleTensor tensor_in;
tensor_in.name = in->at(0).name;
tensor_in.dtype = paddle::PaddleDType::FLOAT32;
tensor_in.shape = {1, 3, resize_img.rows, resize_img.cols};
tensor_in.lod = in->at(0).lod;
tensor_in.data = paddleBuf;
real_in->push_back(tensor_in);
if (InferManager::instance().infer(engine_name().c_str(), real_in, out,
batch_size)) {
LOG(ERROR) << "(logid=" << log_id
<< ") Failed do infer in fluid model: " << engine_name().c_str();
return -1;
}
int64_t end = timeline.TimeStampUS();
CopyBlobInfo(input_blob, output_blob);
AddBlobInfo(output_blob, start);
AddBlobInfo(output_blob, end);
return 0;
}
cv::Mat GeneralClasOp::Base2Mat(std::string &base64_data) {
cv::Mat img;
std::string s_mat;
s_mat = base64Decode(base64_data.data(), base64_data.size());
std::vector<char> base64_img(s_mat.begin(), s_mat.end());
img = cv::imdecode(base64_img, cv::IMREAD_COLOR); // CV_LOAD_IMAGE_COLOR
return img;
}
std::string GeneralClasOp::base64Decode(const char *Data, int DataByte) {
const char DecodeTable[] = {
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0,
62, // '+'
0, 0, 0,
63, // '/'
52, 53, 54, 55, 56, 57, 58, 59, 60, 61, // '0'-'9'
0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9,
10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, // 'A'-'Z'
0, 0, 0, 0, 0, 0, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36,
37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, // 'a'-'z'
};
std::string strDecode;
int nValue;
int i = 0;
while (i < DataByte) {
if (*Data != '\r' && *Data != '\n') {
nValue = DecodeTable[*Data++] << 18;
nValue += DecodeTable[*Data++] << 12;
strDecode += (nValue & 0x00FF0000) >> 16;
if (*Data != '=') {
nValue += DecodeTable[*Data++] << 6;
strDecode += (nValue & 0x0000FF00) >> 8;
if (*Data != '=') {
nValue += DecodeTable[*Data++];
strDecode += nValue & 0x000000FF;
}
}
i += 4;
} else // 回车换行,跳过
{
Data++;
i++;
}
}
return strDecode;
}
DEFINE_OP(GeneralClasOp);
} // namespace serving
} // namespace paddle_serving
} // namespace baidu
// Copyright (c) 2019 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 "core/general-server/general_model_service.pb.h"
#include "core/general-server/op/general_infer_helper.h"
#include "core/predictor/tools/pp_shitu_tools/preprocess_op.h"
#include "paddle_inference_api.h" // NOLINT
#include <string>
#include <vector>
#include "opencv2/core.hpp"
#include "opencv2/imgcodecs.hpp"
#include "opencv2/imgproc.hpp"
#include <chrono>
#include <iomanip>
#include <iostream>
#include <ostream>
#include <vector>
#include <cstring>
#include <fstream>
#include <numeric>
namespace baidu {
namespace paddle_serving {
namespace serving {
class GeneralClasOp
: public baidu::paddle_serving::predictor::OpWithChannel<GeneralBlob> {
public:
typedef std::vector<paddle::PaddleTensor> TensorVector;
DECLARE_OP(GeneralClasOp);
int inference();
private:
// clas preprocess
std::vector<float> mean_ = {0.485f, 0.456f, 0.406f};
std::vector<float> scale_ = {0.229f, 0.224f, 0.225f};
bool is_scale_ = true;
int resize_short_size_ = 256;
int crop_size_ = 224;
PaddleClas::ResizeImg resize_op_;
PaddleClas::Normalize normalize_op_;
PaddleClas::Permute permute_op_;
PaddleClas::CenterCropImg crop_op_;
// read pics
cv::Mat Base2Mat(std::string &base64_data);
std::string base64Decode(const char *Data, int DataByte);
};
} // namespace serving
} // namespace paddle_serving
} // namespace baidu
// Copyright (c) 2020 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.
#include "opencv2/core.hpp"
#include "opencv2/imgcodecs.hpp"
#include "opencv2/imgproc.hpp"
#include "paddle_api.h"
#include "paddle_inference_api.h"
#include <chrono>
#include <iomanip>
#include <iostream>
#include <ostream>
#include <vector>
#include <cstring>
#include <fstream>
#include <math.h>
#include <numeric>
#include "preprocess_op.h"
namespace Feature {
void Permute::Run(const cv::Mat *im, float *data) {
int rh = im->rows;
int rw = im->cols;
int rc = im->channels();
for (int i = 0; i < rc; ++i) {
cv::extractChannel(*im, cv::Mat(rh, rw, CV_32FC1, data + i * rh * rw), i);
}
}
void Normalize::Run(cv::Mat *im, const std::vector<float> &mean,
const std::vector<float> &std, float scale) {
(*im).convertTo(*im, CV_32FC3, scale);
for (int h = 0; h < im->rows; h++) {
for (int w = 0; w < im->cols; w++) {
im->at<cv::Vec3f>(h, w)[0] =
(im->at<cv::Vec3f>(h, w)[0] - mean[0]) / std[0];
im->at<cv::Vec3f>(h, w)[1] =
(im->at<cv::Vec3f>(h, w)[1] - mean[1]) / std[1];
im->at<cv::Vec3f>(h, w)[2] =
(im->at<cv::Vec3f>(h, w)[2] - mean[2]) / std[2];
}
}
}
void CenterCropImg::Run(cv::Mat &img, const int crop_size) {
int resize_w = img.cols;
int resize_h = img.rows;
int w_start = int((resize_w - crop_size) / 2);
int h_start = int((resize_h - crop_size) / 2);
cv::Rect rect(w_start, h_start, crop_size, crop_size);
img = img(rect);
}
void ResizeImg::Run(const cv::Mat &img, cv::Mat &resize_img,
int resize_short_size, int size) {
int resize_h = 0;
int resize_w = 0;
if (size > 0) {
resize_h = size;
resize_w = size;
} else {
int w = img.cols;
int h = img.rows;
float ratio = 1.f;
if (h < w) {
ratio = float(resize_short_size) / float(h);
} else {
ratio = float(resize_short_size) / float(w);
}
resize_h = round(float(h) * ratio);
resize_w = round(float(w) * ratio);
}
cv::resize(img, resize_img, cv::Size(resize_w, resize_h));
}
} // namespace Feature
namespace PaddleClas {
void Permute::Run(const cv::Mat *im, float *data) {
int rh = im->rows;
int rw = im->cols;
int rc = im->channels();
for (int i = 0; i < rc; ++i) {
cv::extractChannel(*im, cv::Mat(rh, rw, CV_32FC1, data + i * rh * rw), i);
}
}
void Normalize::Run(cv::Mat *im, const std::vector<float> &mean,
const std::vector<float> &scale, const bool is_scale) {
double e = 1.0;
if (is_scale) {
e /= 255.0;
}
(*im).convertTo(*im, CV_32FC3, e);
for (int h = 0; h < im->rows; h++) {
for (int w = 0; w < im->cols; w++) {
im->at<cv::Vec3f>(h, w)[0] =
(im->at<cv::Vec3f>(h, w)[0] - mean[0]) / scale[0];
im->at<cv::Vec3f>(h, w)[1] =
(im->at<cv::Vec3f>(h, w)[1] - mean[1]) / scale[1];
im->at<cv::Vec3f>(h, w)[2] =
(im->at<cv::Vec3f>(h, w)[2] - mean[2]) / scale[2];
}
}
}
void CenterCropImg::Run(cv::Mat &img, const int crop_size) {
int resize_w = img.cols;
int resize_h = img.rows;
int w_start = int((resize_w - crop_size) / 2);
int h_start = int((resize_h - crop_size) / 2);
cv::Rect rect(w_start, h_start, crop_size, crop_size);
img = img(rect);
}
void ResizeImg::Run(const cv::Mat &img, cv::Mat &resize_img,
int resize_short_size) {
int w = img.cols;
int h = img.rows;
float ratio = 1.f;
if (h < w) {
ratio = float(resize_short_size) / float(h);
} else {
ratio = float(resize_short_size) / float(w);
}
int resize_h = round(float(h) * ratio);
int resize_w = round(float(w) * ratio);
cv::resize(img, resize_img, cv::Size(resize_w, resize_h));
}
} // namespace PaddleClas
// Copyright (c) 2020 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 "opencv2/core.hpp"
#include "opencv2/imgcodecs.hpp"
#include "opencv2/imgproc.hpp"
#include <chrono>
#include <iomanip>
#include <iostream>
#include <ostream>
#include <vector>
#include <cstring>
#include <fstream>
#include <numeric>
namespace Feature {
class Normalize {
public:
virtual void Run(cv::Mat *im, const std::vector<float> &mean,
const std::vector<float> &std, float scale);
};
// RGB -> CHW
class Permute {
public:
virtual void Run(const cv::Mat *im, float *data);
};
class CenterCropImg {
public:
virtual void Run(cv::Mat &im, const int crop_size = 224);
};
class ResizeImg {
public:
virtual void Run(const cv::Mat &img, cv::Mat &resize_img, int max_size_len,
int size = 0);
};
} // namespace Feature
namespace PaddleClas {
class Normalize {
public:
virtual void Run(cv::Mat *im, const std::vector<float> &mean,
const std::vector<float> &scale, const bool is_scale = true);
};
// RGB -> CHW
class Permute {
public:
virtual void Run(const cv::Mat *im, float *data);
};
class CenterCropImg {
public:
virtual void Run(cv::Mat &im, const int crop_size = 224);
};
class ResizeImg {
public:
virtual void Run(const cv::Mat &img, cv::Mat &resize_img, int max_size_len);
};
} // namespace PaddleClas
feed_var {
name: "x"
alias_name: "x"
is_lod_tensor: false
feed_type: 1
shape: 3
shape: 224
shape: 224
}
feed_var {
name: "boxes"
alias_name: "boxes"
is_lod_tensor: false
feed_type: 1
shape: 6
}
fetch_var {
name: "save_infer_model/scale_0.tmp_1"
alias_name: "features"
is_lod_tensor: false
fetch_type: 1
shape: 512
}
fetch_var {
name: "boxes"
alias_name: "boxes"
is_lod_tensor: false
fetch_type: 1
shape: 6
}
feed_var {
name: "x"
alias_name: "x"
is_lod_tensor: false
feed_type: 1
shape: 3
shape: 224
shape: 224
}
feed_var {
name: "boxes"
alias_name: "boxes"
is_lod_tensor: false
feed_type: 1
shape: 6
}
fetch_var {
name: "save_infer_model/scale_0.tmp_1"
alias_name: "features"
is_lod_tensor: false
fetch_type: 1
shape: 512
}
fetch_var {
name: "boxes"
alias_name: "boxes"
is_lod_tensor: false
fetch_type: 1
shape: 6
}
feed_var {
name: "im_shape"
alias_name: "im_shape"
is_lod_tensor: false
feed_type: 1
shape: 2
}
feed_var {
name: "image"
alias_name: "image"
is_lod_tensor: false
feed_type: 7
shape: -1
shape: -1
shape: 3
}
fetch_var {
name: "save_infer_model/scale_0.tmp_1"
alias_name: "save_infer_model/scale_0.tmp_1"
is_lod_tensor: true
fetch_type: 1
shape: -1
}
fetch_var {
name: "save_infer_model/scale_1.tmp_1"
alias_name: "save_infer_model/scale_1.tmp_1"
is_lod_tensor: false
fetch_type: 2
}
feed_var {
name: "im_shape"
alias_name: "im_shape"
is_lod_tensor: false
feed_type: 1
shape: 2
}
feed_var {
name: "image"
alias_name: "image"
is_lod_tensor: false
feed_type: 7
shape: -1
shape: -1
shape: 3
}
fetch_var {
name: "save_infer_model/scale_0.tmp_1"
alias_name: "save_infer_model/scale_0.tmp_1"
is_lod_tensor: true
fetch_type: 1
shape: -1
}
fetch_var {
name: "save_infer_model/scale_1.tmp_1"
alias_name: "save_infer_model/scale_1.tmp_1"
is_lod_tensor: false
fetch_type: 2
}
...@@ -12,7 +12,6 @@ ...@@ -12,7 +12,6 @@
# See the License for the specific language governing permissions and # See the License for the specific language governing permissions and
# limitations under the License. # limitations under the License.
import sys
import numpy as np import numpy as np
from paddle_serving_client import Client from paddle_serving_client import Client
...@@ -22,181 +21,101 @@ import faiss ...@@ -22,181 +21,101 @@ import faiss
import os import os
import pickle import pickle
rec_nms_thresold = 0.05
class MainbodyDetect(): rec_score_thres = 0.5
""" feature_normalize = True
pp-shitu mainbody detect. return_k = 1
include preprocess, process, postprocess index_dir = "../../drink_dataset_v1.0/index"
return detect results
Attention: Postprocess include num limit and box filter; no nms
""" def init_index(index_dir):
assert os.path.exists(os.path.join(
def __init__(self): index_dir, "vector.index")), "vector.index not found ..."
self.preprocess = DetectionSequential([ assert os.path.exists(os.path.join(
DetectionFile2Image(), DetectionNormalize( index_dir, "id_map.pkl")), "id_map.pkl not found ... "
[0.485, 0.456, 0.406], [0.229, 0.224, 0.225], True),
DetectionResize( searcher = faiss.read_index(os.path.join(index_dir, "vector.index"))
(640, 640), False, interpolation=2), DetectionTranspose(
(2, 0, 1)) with open(os.path.join(index_dir, "id_map.pkl"), "rb") as fd:
]) id_map = pickle.load(fd)
return searcher, id_map
self.client = Client()
self.client.load_client_config(
"../../models/picodet_PPLCNet_x2_5_mainbody_lite_v1.0_client/serving_client_conf.prototxt" #get box
) def nms_to_rec_results(results, thresh=0.1):
self.client.connect(['127.0.0.1:9293']) filtered_results = []
self.max_det_result = 5 x1 = np.array([r["bbox"][0] for r in results]).astype("float32")
self.conf_threshold = 0.2 y1 = np.array([r["bbox"][1] for r in results]).astype("float32")
x2 = np.array([r["bbox"][2] for r in results]).astype("float32")
def predict(self, imgpath): y2 = np.array([r["bbox"][3] for r in results]).astype("float32")
im, im_info = self.preprocess(imgpath) scores = np.array([r["rec_scores"] for r in results])
im_shape = np.array(im.shape[1:]).reshape(-1)
scale_factor = np.array(list(im_info['scale_factor'])).reshape(-1) areas = (x2 - x1 + 1) * (y2 - y1 + 1)
order = scores.argsort()[::-1]
fetch_map = self.client.predict( while order.size > 0:
feed={ i = order[0]
"image": im, xx1 = np.maximum(x1[i], x1[order[1:]])
"im_shape": im_shape, yy1 = np.maximum(y1[i], y1[order[1:]])
"scale_factor": scale_factor, xx2 = np.minimum(x2[i], x2[order[1:]])
}, yy2 = np.minimum(y2[i], y2[order[1:]])
fetch=["save_infer_model/scale_0.tmp_1"],
batch=False) w = np.maximum(0.0, xx2 - xx1 + 1)
return self.postprocess(fetch_map, imgpath) h = np.maximum(0.0, yy2 - yy1 + 1)
inter = w * h
def postprocess(self, fetch_map, imgpath): ovr = inter / (areas[i] + areas[order[1:]] - inter)
#1. get top max_det_result inds = np.where(ovr <= thresh)[0]
det_results = fetch_map["save_infer_model/scale_0.tmp_1"] order = order[inds + 1]
if len(det_results) > self.max_det_result: filtered_results.append(results[i])
boxes_reserved = fetch_map[ return filtered_results
"save_infer_model/scale_0.tmp_1"][:self.max_det_result]
else:
boxes_reserved = det_results def postprocess(fetch_dict, feature_normalize, det_boxes, searcher, id_map,
return_k, rec_score_thres, rec_nms_thresold):
#2. do conf threshold batch_features = fetch_dict["features"]
boxes_list = []
for i in range(boxes_reserved.shape[0]): #do feature norm
if (boxes_reserved[i, 1]) > self.conf_threshold: if feature_normalize:
boxes_list.append(boxes_reserved[i, :]) feas_norm = np.sqrt(
np.sum(np.square(batch_features), axis=1, keepdims=True))
#3. add origin image box batch_features = np.divide(batch_features, feas_norm)
origin_img = cv2.imread(imgpath)
boxes_list.append( scores, docs = searcher.search(batch_features, return_k)
np.array([0, 1.0, 0, 0, origin_img.shape[1], origin_img.shape[0]]))
return np.array(boxes_list) results = []
for i in range(scores.shape[0]):
pred = {}
class ObjectRecognition(): if scores[i][0] >= rec_score_thres:
""" pred["bbox"] = [int(x) for x in det_boxes[i, 2:]]
pp-shitu object recognion for all objects detected by MainbodyDetect. pred["rec_docs"] = id_map[docs[i][0]].split()[1]
include preprocess, process, postprocess pred["rec_scores"] = scores[i][0]
preprocess include preprocess for each image and batching. results.append(pred)
Batch process
postprocess include retrieval and nms #do nms
""" results = nms_to_rec_results(results, rec_nms_thresold)
return results
def __init__(self):
self.client = Client()
self.client.load_client_config( #do client
"../../models/general_PPLCNet_x2_5_lite_v1.0_client/serving_client_conf.prototxt"
)
self.client.connect(["127.0.0.1:9294"])
self.seq = Sequential([
BGR2RGB(), Resize((224, 224)), Div(255),
Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225],
False), Transpose((2, 0, 1))
])
self.searcher, self.id_map = self.init_index()
self.rec_nms_thresold = 0.05
self.rec_score_thres = 0.5
self.feature_normalize = True
self.return_k = 1
def init_index(self):
index_dir = "../../drink_dataset_v1.0/index"
assert os.path.exists(os.path.join(
index_dir, "vector.index")), "vector.index not found ..."
assert os.path.exists(os.path.join(
index_dir, "id_map.pkl")), "id_map.pkl not found ... "
searcher = faiss.read_index(os.path.join(index_dir, "vector.index"))
with open(os.path.join(index_dir, "id_map.pkl"), "rb") as fd:
id_map = pickle.load(fd)
return searcher, id_map
def predict(self, det_boxes, imgpath):
#1. preprocess
batch_imgs = []
origin_img = cv2.imread(imgpath)
for i in range(det_boxes.shape[0]):
box = det_boxes[i]
x1, y1, x2, y2 = [int(x) for x in box[2:]]
cropped_img = origin_img[y1:y2, x1:x2, :].copy()
tmp = self.seq(cropped_img)
batch_imgs.append(tmp)
batch_imgs = np.array(batch_imgs)
#2. process
fetch_map = self.client.predict(
feed={"x": batch_imgs}, fetch=["features"], batch=True)
batch_features = fetch_map["features"]
#3. postprocess
if self.feature_normalize:
feas_norm = np.sqrt(
np.sum(np.square(batch_features), axis=1, keepdims=True))
batch_features = np.divide(batch_features, feas_norm)
scores, docs = self.searcher.search(batch_features, self.return_k)
results = []
for i in range(scores.shape[0]):
pred = {}
if scores[i][0] >= self.rec_score_thres:
pred["bbox"] = [int(x) for x in det_boxes[i, 2:]]
pred["rec_docs"] = self.id_map[docs[i][0]].split()[1]
pred["rec_scores"] = scores[i][0]
results.append(pred)
return self.nms_to_rec_results(results)
def nms_to_rec_results(self, results):
filtered_results = []
x1 = np.array([r["bbox"][0] for r in results]).astype("float32")
y1 = np.array([r["bbox"][1] for r in results]).astype("float32")
x2 = np.array([r["bbox"][2] for r in results]).astype("float32")
y2 = np.array([r["bbox"][3] for r in results]).astype("float32")
scores = np.array([r["rec_scores"] for r in results])
areas = (x2 - x1 + 1) * (y2 - y1 + 1)
order = scores.argsort()[::-1]
while order.size > 0:
i = order[0]
xx1 = np.maximum(x1[i], x1[order[1:]])
yy1 = np.maximum(y1[i], y1[order[1:]])
xx2 = np.minimum(x2[i], x2[order[1:]])
yy2 = np.minimum(y2[i], y2[order[1:]])
w = np.maximum(0.0, xx2 - xx1 + 1)
h = np.maximum(0.0, yy2 - yy1 + 1)
inter = w * h
ovr = inter / (areas[i] + areas[order[1:]] - inter)
inds = np.where(ovr <= self.rec_nms_thresold)[0]
order = order[inds + 1]
filtered_results.append(results[i])
return filtered_results
if __name__ == "__main__": if __name__ == "__main__":
det = MainbodyDetect() client = Client()
rec = ObjectRecognition() client.load_client_config([
"../../models/picodet_PPLCNet_x2_5_mainbody_lite_v1.0_client",
#1. get det_results "../../models/general_PPLCNet_x2_5_lite_v1.0_client"
imgpath = "../../drink_dataset_v1.0/test_images/001.jpeg" ])
det_results = det.predict(imgpath) client.connect(['127.0.0.1:9400'])
#2. get rec_results im = cv2.imread("../../drink_dataset_v1.0/test_images/001.jpeg")
rec_results = rec.predict(det_results, imgpath) im_shape = np.array(im.shape[:2]).reshape(-1)
print(rec_results) fetch_map = client.predict(
feed={"image": im,
"im_shape": im_shape},
fetch=["features", "boxes"],
batch=False)
#add retrieval procedure
det_boxes = fetch_map["boxes"]
searcher, id_map = init_index(index_dir)
results = postprocess(fetch_map, feature_normalize, det_boxes, searcher,
id_map, return_k, rec_score_thres, rec_nms_thresold)
print(results)
...@@ -12,16 +12,20 @@ ...@@ -12,16 +12,20 @@
# See the License for the specific language governing permissions and # See the License for the specific language governing permissions and
# limitations under the License. # limitations under the License.
import sys import base64
import time
from paddle_serving_client import Client from paddle_serving_client import Client
#app
from paddle_serving_app.reader import Sequential, URL2Image, Resize def bytes_to_base64(image: bytes) -> str:
from paddle_serving_app.reader import CenterCrop, RGB2BGR, Transpose, Div, Normalize """encode bytes into base64 string
import time """
return base64.b64encode(image).decode('utf8')
client = Client() client = Client()
client.load_client_config("./ResNet50_vd_serving/serving_server_conf.prototxt") client.load_client_config("./ResNet50_client/serving_client_conf.prototxt")
client.connect(["127.0.0.1:9292"]) client.connect(["127.0.0.1:9292"])
label_dict = {} label_dict = {}
...@@ -31,22 +35,17 @@ with open("imagenet.label") as fin: ...@@ -31,22 +35,17 @@ with open("imagenet.label") as fin:
label_dict[label_idx] = line.strip() label_dict[label_idx] = line.strip()
label_idx += 1 label_idx += 1
#preprocess image_file = "./daisy.jpg"
seq = Sequential([
URL2Image(), Resize(256), CenterCrop(224), RGB2BGR(), Transpose((2, 0, 1)),
Div(255), Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225], True)
])
start = time.time()
image_file = "https://paddle-serving.bj.bcebos.com/imagenet-example/daisy.jpg"
for i in range(1): for i in range(1):
img = seq(image_file) start = time.time()
fetch_map = client.predict( with open(image_file, 'rb') as img_file:
feed={"inputs": img}, fetch=["prediction"], batch=False) image_data = img_file.read()
image = bytes_to_base64(image_data)
prob = max(fetch_map["prediction"][0]) fetch_dict = client.predict(
label = label_dict[fetch_map["prediction"][0].tolist().index(prob)].strip( feed={"inputs": image}, fetch=["prediction"], batch=False)
).replace(",", "") prob = max(fetch_dict["prediction"][0])
print("prediction: {}, probability: {}".format(label, prob)) label = label_dict[fetch_dict["prediction"][0].tolist().index(
end = time.time() prob)].strip().replace(",", "")
print(end - start) print("prediction: {}, probability: {}".format(label, prob))
end = time.time()
print(end - start)
此差异已折叠。
...@@ -14,7 +14,7 @@ ...@@ -14,7 +14,7 @@
- [3.3 EDA strategy](#3.3) - [3.3 EDA strategy](#3.3)
- [3.4 SKL-UGI knowledge distillation](#3.4) - [3.4 SKL-UGI knowledge distillation](#3.4)
- [3.5 Summary](#3.5) - [3.5 Summary](#3.5)
- [4. Hyperparameter Search](#4) - [4. Hyperparameters Searching](#4)
- [4.1 Search based on default configuration](#4.1) - [4.1 Search based on default configuration](#4.1)
- [4.2 Custom search configuration](#4.2) - [4.2 Custom search configuration](#4.2)
...@@ -31,7 +31,7 @@ The PULC solution has been verified to be effective in many scenarios, such as h ...@@ -31,7 +31,7 @@ The PULC solution has been verified to be effective in many scenarios, such as h
<img src="https://user-images.githubusercontent.com/19523330/173011854-b10fcd7a-b799-4dfd-a1cf-9504952a3c44.png" width = "800" /> <img src="https://user-images.githubusercontent.com/19523330/173011854-b10fcd7a-b799-4dfd-a1cf-9504952a3c44.png" width = "800" />
</div> </div>
The solution mainly includes 4 parts, namely: PP-LCNet lightweight backbone network, SSLD pre-trained model, Ensemble Data Augmentation (EDA) and SKL-UGI knowledge distillation algorithm. In addition, we also adopt the method of hyperparameter search to efficiently optimize the hyperparameters in training. Below, we take the person exists or not scene as an example to illustrate the solution. The solution mainly includes 4 parts, namely: PP-LCNet lightweight backbone network, SSLD pre-trained model, Ensemble Data Augmentation (EDA) and SKL-UGI knowledge distillation algorithm. In addition, we also adopt the method of hyperparameters searching to efficiently optimize the hyperparameters in training. Below, we take the person exists or not scene as an example to illustrate the solution.
**Note**:For some specific scenarios, we provide basic training documents for reference, such as [person exists or not classification model](PULC_person_exists_en.md), etc. You can find these documents [here](./PULC_model_list_en.md). If the methods in these documents do not meet your needs, or if you need a custom training task, you can refer to this document. **Note**:For some specific scenarios, we provide basic training documents for reference, such as [person exists or not classification model](PULC_person_exists_en.md), etc. You can find these documents [here](./PULC_model_list_en.md). If the methods in these documents do not meet your needs, or if you need a custom training task, you can refer to this document.
...@@ -201,22 +201,22 @@ We also used the same optimization strategy in the other 8 scenarios and got the ...@@ -201,22 +201,22 @@ We also used the same optimization strategy in the other 8 scenarios and got the
| Text Image Orientation Classification | SwinTransformer_tiny |99.12 | PPLCNet_x1_0 | 99.06 | | Text Image Orientation Classification | SwinTransformer_tiny |99.12 | PPLCNet_x1_0 | 99.06 |
| Text-line Orientation Classification | SwinTransformer_tiny | 93.61 | PPLCNet_x1_0 | 96.01 | | Text-line Orientation Classification | SwinTransformer_tiny | 93.61 | PPLCNet_x1_0 | 96.01 |
| Language Classification | SwinTransformer_tiny | 98.12 | PPLCNet_x1_0 | 99.26 | | Language Classification | SwinTransformer_tiny | 98.12 | PPLCNet_x1_0 | 99.26 |
It can be seen from the results that the PULC scheme can improve the model accuracy in multiple application scenarios. Using the PULC scheme can greatly reduce the workload of model optimization and quickly obtain models with higher accuracy. It can be seen from the results that the PULC scheme can improve the model accuracy in multiple application scenarios. Using the PULC scheme can greatly reduce the workload of model optimization and quickly obtain models with higher accuracy.
<a name="4"></a> <a name="4"></a>
### 4. Hyperparameter Search ### 4. Hyperparameters Searching
In the above training process, we adjusted parameters such as learning rate, data augmentation probability, and stage learning rate mult list. The optimal values of these parameters may not be the same in different scenarios. We provide a quick hyperparameter search script to automate the process of hyperparameter tuning. This script traverses the parameters in the search value list to replace the parameters in the default configuration, then trains in sequence, and finally selects the parameters corresponding to the model with the highest accuracy as the search result. In the above training process, we adjusted parameters such as learning rate, data augmentation probability, and stage learning rate mult list. The optimal values of these parameters may not be the same in different scenarios. We provide a quick hyperparameters searching script to automate the process of hyperparameter tuning. This script traverses the parameters in the search value list to replace the parameters in the default configuration, then trains in sequence, and finally selects the parameters corresponding to the model with the highest accuracy as the search result.
<a name="4.1"></a> <a name="4.1"></a>
#### 4.1 Search based on default configuration #### 4.1 Search based on default configuration
The configuration file [search.yaml](../../../ppcls/configs/PULC/person_exists/search.yaml) defines the configuration of hyperparameter search in person exists or not scenarios. Use the following commands to complete hyperparameter search. The configuration file [search.yaml](../../../ppcls/configs/PULC/person_exists/search.yaml) defines the configuration of hyperparameters searching in person exists or not scenarios. Use the following commands to complete hyperparameters searching.
```bash ```bash
python3 tools/search_strategy.py -c ppcls/configs/PULC/person_exists/search.yaml python3 tools/search_strategy.py -c ppcls/configs/PULC/person_exists/search.yaml
...@@ -228,8 +228,8 @@ python3 tools/search_strategy.py -c ppcls/configs/PULC/person_exists/search.yaml ...@@ -228,8 +228,8 @@ python3 tools/search_strategy.py -c ppcls/configs/PULC/person_exists/search.yaml
#### 4.2 Custom search configuration #### 4.2 Custom search configuration
You can also modify the configuration of hyperparameter search based on training results or your parameter tuning experience. You can also modify the configuration of hyperparameters searching based on training results or your parameter tuning experience.
Modify the `search_values` field in `lrs` to modify the list of learning rate search values; Modify the `search_values` field in `lrs` to modify the list of learning rate search values;
......
...@@ -8,6 +8,8 @@ PaddleClas supports Python wheel package for prediction. At present, PaddleClas ...@@ -8,6 +8,8 @@ PaddleClas supports Python wheel package for prediction. At present, PaddleClas
- [1. Installation](#1) - [1. Installation](#1)
- [2. Quick Start](#2) - [2. Quick Start](#2)
- [2.1 ImageNet1k models](#2.1)
- [2.2 PULC models](#2.2)
- [3. Definition of Parameters](#3) - [3. Definition of Parameters](#3)
- [4. More usage](#4) - [4. More usage](#4)
- [4.1 View help information](#4.1) - [4.1 View help information](#4.1)
...@@ -75,7 +77,6 @@ filename: docs/images/inference_deployment/whl_demo.jpg, top-5, class_ids: [8, 7 ...@@ -75,7 +77,6 @@ filename: docs/images/inference_deployment/whl_demo.jpg, top-5, class_ids: [8, 7
Predict complete! Predict complete!
``` ```
<a name="2.2"></a> <a name="2.2"></a>
### 2.2 PULC models ### 2.2 PULC models
......
...@@ -54,22 +54,22 @@ ...@@ -54,22 +54,22 @@
| PPLCNet_x1_0 | 95.48 | 2.12 | 6.5 | 使用 SSLD 预训练模型+EDA 策略| | PPLCNet_x1_0 | 95.48 | 2.12 | 6.5 | 使用 SSLD 预训练模型+EDA 策略|
| <b>PPLCNet_x1_0<b> | <b>95.92<b> | <b>2.12<b> | <b>6.5<b> | 使用 SSLD 预训练模型+EDA 策略+SKL-UGI 知识蒸馏策略| | <b>PPLCNet_x1_0<b> | <b>95.92<b> | <b>2.12<b> | <b>6.5<b> | 使用 SSLD 预训练模型+EDA 策略+SKL-UGI 知识蒸馏策略|
从表中可以看出,backbone 为 SwinTranformer_tiny 时精度较高,但是推理速度较慢。将 backboone 替换为轻量级模型 MobileNetV3_small_x0_35 后,速度可以大幅提升,但是会导致精度大幅下降。将 backbone 替换为速度更快的 PPLCNet_x1_0 时,精度较 MobileNetV3_small_x0_35 高 13 个百分点,与此同时速度依旧可以快 20% 以上。在此基础上,使用 SSLD 预训练模型后,在不改变推理速度的前提下,精度可以提升约 0.7 个百分点,进一步地,在使用 SKL-UGI 知识蒸馏后,精度可以继续提升 0.44 个百分点。此时,PPLCNet_x1_0 达到了接近 SwinTranformer_tiny 模型的精度,但是速度快 40 多倍。关于 PULC 的训练方法和推理部署方法将在下面详细介绍。 从表中可以看出,backbone 为 SwinTranformer_tiny 时精度较高,但是推理速度较慢。将 backbone 替换为轻量级模型 MobileNetV3_small_x0_35 后,速度可以大幅提升,但是会导致精度大幅下降。将 backbone 替换为速度更快的 PPLCNet_x1_0 时,精度较 MobileNetV3_small_x0_35 高 13 个百分点,与此同时速度依旧可以快 20% 以上。在此基础上,使用 SSLD 预训练模型后,在不改变推理速度的前提下,精度可以提升约 0.7 个百分点,进一步地,在使用 SKL-UGI 知识蒸馏后,精度可以继续提升 0.44 个百分点。此时,PPLCNet_x1_0 达到了接近 SwinTranformer_tiny 模型的精度,但是速度快 40 多倍。关于 PULC 的训练方法和推理部署方法将在下面详细介绍。
**备注:** **备注:**
* `Tpr`指标的介绍可以参考 [3.3节](#3.3)的备注部分,延时是基于 Intel(R) Xeon(R) Gold 6148 CPU @ 2.40GHz 测试得到,开启 MKLDNN 加速策略,线程数为10。 * `Tpr`指标的介绍可以参考 [3.3节](#3.3)的备注部分,延时是基于 Intel(R) Xeon(R) Gold 6148 CPU @ 2.40GHz 测试得到,开启 MKLDNN 加速策略,线程数为10。
* 关于PP-LCNet的介绍可以参考[PP-LCNet介绍](../models/PP-LCNet.md),相关论文可以查阅[PP-LCNet paper](https://arxiv.org/abs/2109.15099) * 关于PP-LCNet的介绍可以参考[PP-LCNet介绍](../models/PP-LCNet.md),相关论文可以查阅[PP-LCNet paper](https://arxiv.org/abs/2109.15099)
<a name="2"></a> <a name="2"></a>
## 2. 模型快速体验 ## 2. 模型快速体验
<a name="2.1"></a> <a name="2.1"></a>
### 2.1 安装 paddlepaddle ### 2.1 安装 paddlepaddle
- 您的机器安装的是 CUDA9 或 CUDA10,请运行以下命令安装 - 您的机器安装的是 CUDA9 或 CUDA10,请运行以下命令安装
```bash ```bash
...@@ -81,11 +81,11 @@ python3 -m pip install paddlepaddle-gpu -i https://mirror.baidu.com/pypi/simple ...@@ -81,11 +81,11 @@ python3 -m pip install paddlepaddle-gpu -i https://mirror.baidu.com/pypi/simple
```bash ```bash
python3 -m pip install paddlepaddle -i https://mirror.baidu.com/pypi/simple python3 -m pip install paddlepaddle -i https://mirror.baidu.com/pypi/simple
``` ```
更多的版本需求,请参照[飞桨官网安装文档](https://www.paddlepaddle.org.cn/install/quick)中的说明进行操作。 更多的版本需求,请参照[飞桨官网安装文档](https://www.paddlepaddle.org.cn/install/quick)中的说明进行操作。
<a name="2.2"></a> <a name="2.2"></a>
### 2.2 安装 paddleclas ### 2.2 安装 paddleclas
使用如下命令快速安装 paddleclas 使用如下命令快速安装 paddleclas
...@@ -93,11 +93,11 @@ python3 -m pip install paddlepaddle -i https://mirror.baidu.com/pypi/simple ...@@ -93,11 +93,11 @@ python3 -m pip install paddlepaddle -i https://mirror.baidu.com/pypi/simple
``` ```
pip3 install paddleclas pip3 install paddleclas
``` ```
<a name="2.3"></a> <a name="2.3"></a>
### 2.3 预测 ### 2.3 预测
点击[这里](https://paddleclas.bj.bcebos.com/data/PULC/pulc_demo_imgs.zip)下载 demo 数据并解压,然后在终端中切换到相应目录。 点击[这里](https://paddleclas.bj.bcebos.com/data/PULC/pulc_demo_imgs.zip)下载 demo 数据并解压,然后在终端中切换到相应目录。
* 使用命令行快速预测 * 使用命令行快速预测
...@@ -130,7 +130,7 @@ print(next(result)) ...@@ -130,7 +130,7 @@ print(next(result))
>>> result >>> result
[{'class_ids': [1], 'scores': [0.9871138], 'label_names': ['contains_car'], 'filename': 'pulc_demo_imgs/car_exists/objects365_00001507.jpeg'}] [{'class_ids': [1], 'scores': [0.9871138], 'label_names': ['contains_car'], 'filename': 'pulc_demo_imgs/car_exists/objects365_00001507.jpeg'}]
``` ```
<a name="3"></a> <a name="3"></a>
...@@ -326,7 +326,7 @@ python3 -m paddle.distributed.launch \ ...@@ -326,7 +326,7 @@ python3 -m paddle.distributed.launch \
## 5. 超参搜索 ## 5. 超参搜索
[3.3 节](#3.3)[4.1 节](#4.1)所使用的超参数是根据 PaddleClas 提供的 `SHAS 超参数搜索策略` 搜索得到的,如果希望在自己的数据集上得到更好的结果,可以参考[SHAS 超参数搜索策略](PULC_train.md#4-超参搜索)来获得更好的训练超参数。 [3.3 节](#3.3)[4.1 节](#4.1)所使用的超参数是根据 PaddleClas 提供的 `超参数搜索策略` 搜索得到的,如果希望在自己的数据集上得到更好的结果,可以参考[超参数搜索策略](PULC_train.md#4-超参搜索)来获得更好的训练超参数。
**备注:** 此部分内容是可选内容,搜索过程需要较长的时间,您可以根据自己的硬件情况来选择执行。如果没有更换数据集,可以忽略此节内容。 **备注:** 此部分内容是可选内容,搜索过程需要较长的时间,您可以根据自己的硬件情况来选择执行。如果没有更换数据集,可以忽略此节内容。
......
...@@ -49,7 +49,7 @@ ...@@ -49,7 +49,7 @@
| PPLCNet_x1_0 | 99.12 | 2.58 | 6.5 | 使用SSLD预训练模型+EDA策略 | | PPLCNet_x1_0 | 99.12 | 2.58 | 6.5 | 使用SSLD预训练模型+EDA策略 |
| **PPLCNet_x1_0** | **99.26** | **2.58** | **6.5** | 使用SSLD预训练模型+EDA策略+SKL-UGI知识蒸馏策略 | | **PPLCNet_x1_0** | **99.26** | **2.58** | **6.5** | 使用SSLD预训练模型+EDA策略+SKL-UGI知识蒸馏策略 |
从表中可以看出,backbone 为 SwinTranformer_tiny 时精度比较高,但是推理速度较慢。将 backboone 替换为轻量级模型 MobileNetV3_small_x0_35 后,速度提升明显,但精度有了大幅下降。将 backbone 替换为 PPLCNet_x1_0 且调整预处理输入尺寸和网络的下采样stride时,速度略为提升,同时精度较 MobileNetV3_large_x1_0 高2.43个百分点。在此基础上,使用 SSLD 预训练模型后,在不改变推理速度的前提下,精度可以提升 0.35 个百分点,进一步地,当融合EDA策略后,精度可以再提升 0.42 个百分点,最后,在使用 SKL-UGI 知识蒸馏后,精度可以继续提升 0.14 个百分点。此时,PPLCNet_x1_0 超过了 SwinTranformer_tiny 模型的精度,并且速度有了明显提升。关于 PULC 的训练方法和推理部署方法将在下面详细介绍。 从表中可以看出,backbone 为 SwinTranformer_tiny 时精度比较高,但是推理速度较慢。将 backbone 替换为轻量级模型 MobileNetV3_small_x0_35 后,速度提升明显,但精度有了大幅下降。将 backbone 替换为 PPLCNet_x1_0 且调整预处理输入尺寸和网络的下采样stride时,速度略为提升,同时精度较 MobileNetV3_large_x1_0 高2.43个百分点。在此基础上,使用 SSLD 预训练模型后,在不改变推理速度的前提下,精度可以提升 0.35 个百分点,进一步地,当融合EDA策略后,精度可以再提升 0.42 个百分点,最后,在使用 SKL-UGI 知识蒸馏后,精度可以继续提升 0.14 个百分点。此时,PPLCNet_x1_0 超过了 SwinTranformer_tiny 模型的精度,并且速度有了明显提升。关于 PULC 的训练方法和推理部署方法将在下面详细介绍。
**备注:** **备注:**
...@@ -60,9 +60,9 @@ ...@@ -60,9 +60,9 @@
## 2. 模型快速体验 ## 2. 模型快速体验
<a name="2.1"></a> <a name="2.1"></a>
### 2.1 安装 paddlepaddle ### 2.1 安装 paddlepaddle
- 您的机器安装的是 CUDA9 或 CUDA10,请运行以下命令安装 - 您的机器安装的是 CUDA9 或 CUDA10,请运行以下命令安装
```bash ```bash
...@@ -74,23 +74,23 @@ python3 -m pip install paddlepaddle-gpu -i https://mirror.baidu.com/pypi/simple ...@@ -74,23 +74,23 @@ python3 -m pip install paddlepaddle-gpu -i https://mirror.baidu.com/pypi/simple
```bash ```bash
python3 -m pip install paddlepaddle -i https://mirror.baidu.com/pypi/simple python3 -m pip install paddlepaddle -i https://mirror.baidu.com/pypi/simple
``` ```
更多的版本需求,请参照[飞桨官网安装文档](https://www.paddlepaddle.org.cn/install/quick)中的说明进行操作。 更多的版本需求,请参照[飞桨官网安装文档](https://www.paddlepaddle.org.cn/install/quick)中的说明进行操作。
<a name="2.2"></a> <a name="2.2"></a>
### 2.2 安装 paddleclas ### 2.2 安装 paddleclas
使用如下命令快速安装 paddleclas 使用如下命令快速安装 paddleclas
``` ```
pip3 install paddleclas pip3 install paddleclas
``` ```
<a name="2.3"></a> <a name="2.3"></a>
### 2.3 预测 ### 2.3 预测
点击[这里](https://paddleclas.bj.bcebos.com/data/PULC/pulc_demo_imgs.zip)下载 demo 数据并解压,然后在终端中切换到相应目录。 点击[这里](https://paddleclas.bj.bcebos.com/data/PULC/pulc_demo_imgs.zip)下载 demo 数据并解压,然后在终端中切换到相应目录。
* 使用命令行快速预测 * 使用命令行快速预测
...@@ -309,7 +309,7 @@ python3 -m paddle.distributed.launch \ ...@@ -309,7 +309,7 @@ python3 -m paddle.distributed.launch \
## 5. 超参搜索 ## 5. 超参搜索
[3.2 节](#3.2)[4.1 节](#4.1)所使用的超参数是根据 PaddleClas 提供的 `SHAS 超参数搜索策略` 搜索得到的,如果希望在自己的数据集上得到更好的结果,可以参考[SHAS 超参数搜索策略](PULC_train.md#4-超参搜索)来获得更好的训练超参数。 [3.2 节](#3.2)[4.1 节](#4.1)所使用的超参数是根据 PaddleClas 提供的 `超参数搜索策略` 搜索得到的,如果希望在自己的数据集上得到更好的结果,可以参考[超参数搜索策略](PULC_train.md#4-超参搜索)来获得更好的训练超参数。
**备注:** 此部分内容是可选内容,搜索过程需要较长的时间,您可以根据自己的硬件情况来选择执行。如果没有更换数据集,可以忽略此节内容。 **备注:** 此部分内容是可选内容,搜索过程需要较长的时间,您可以根据自己的硬件情况来选择执行。如果没有更换数据集,可以忽略此节内容。
......
...@@ -67,9 +67,9 @@ ...@@ -67,9 +67,9 @@
## 2. 模型快速体验 ## 2. 模型快速体验
<a name="2.1"></a> <a name="2.1"></a>
### 2.1 安装 paddlepaddle ### 2.1 安装 paddlepaddle
- 您的机器安装的是 CUDA9 或 CUDA10,请运行以下命令安装 - 您的机器安装的是 CUDA9 或 CUDA10,请运行以下命令安装
```bash ```bash
...@@ -81,23 +81,23 @@ python3 -m pip install paddlepaddle-gpu -i https://mirror.baidu.com/pypi/simple ...@@ -81,23 +81,23 @@ python3 -m pip install paddlepaddle-gpu -i https://mirror.baidu.com/pypi/simple
```bash ```bash
python3 -m pip install paddlepaddle -i https://mirror.baidu.com/pypi/simple python3 -m pip install paddlepaddle -i https://mirror.baidu.com/pypi/simple
``` ```
更多的版本需求,请参照[飞桨官网安装文档](https://www.paddlepaddle.org.cn/install/quick)中的说明进行操作。 更多的版本需求,请参照[飞桨官网安装文档](https://www.paddlepaddle.org.cn/install/quick)中的说明进行操作。
<a name="2.2"></a> <a name="2.2"></a>
### 2.2 安装 paddleclas ### 2.2 安装 paddleclas
使用如下命令快速安装 paddleclas 使用如下命令快速安装 paddleclas
``` ```
pip3 install paddleclas pip3 install paddleclas
``` ```
<a name="2.3"></a> <a name="2.3"></a>
### 2.3 预测 ### 2.3 预测
点击[这里](https://paddleclas.bj.bcebos.com/data/PULC/pulc_demo_imgs.zip)下载 demo 数据并解压,然后在终端中切换到相应目录。 点击[这里](https://paddleclas.bj.bcebos.com/data/PULC/pulc_demo_imgs.zip)下载 demo 数据并解压,然后在终端中切换到相应目录。
* 使用命令行快速预测 * 使用命令行快速预测
...@@ -313,7 +313,7 @@ python3 -m paddle.distributed.launch \ ...@@ -313,7 +313,7 @@ python3 -m paddle.distributed.launch \
## 5. 超参搜索 ## 5. 超参搜索
[3.2 节](#3.2)[4.1 节](#4.1)所使用的超参数是根据 PaddleClas 提供的 `SHAS 超参数搜索策略` 搜索得到的,如果希望在自己的数据集上得到更好的结果,可以参考[SHAS 超参数搜索策略](PULC_train.md#4-超参搜索)来获得更好的训练超参数。 [3.2 节](#3.2)[4.1 节](#4.1)所使用的超参数是根据 PaddleClas 提供的 `超参数搜索策略` 搜索得到的,如果希望在自己的数据集上得到更好的结果,可以参考[超参数搜索策略](PULC_train.md#4-超参搜索)来获得更好的训练超参数。
**备注:** 此部分内容是可选内容,搜索过程需要较长的时间,您可以根据自己的硬件情况来选择执行。如果没有更换数据集,可以忽略此节内容。 **备注:** 此部分内容是可选内容,搜索过程需要较长的时间,您可以根据自己的硬件情况来选择执行。如果没有更换数据集,可以忽略此节内容。
......
...@@ -54,7 +54,7 @@ ...@@ -54,7 +54,7 @@
| PPLCNet_x1_0 | 93.43 | 2.12 | 6.5 | 使用 SSLD 预训练模型+EDA 策略| | PPLCNet_x1_0 | 93.43 | 2.12 | 6.5 | 使用 SSLD 预训练模型+EDA 策略|
| <b>PPLCNet_x1_0<b> | <b>95.60<b> | <b>2.12<b> | <b>6.5<b> | 使用 SSLD 预训练模型+EDA 策略+SKL-UGI 知识蒸馏策略| | <b>PPLCNet_x1_0<b> | <b>95.60<b> | <b>2.12<b> | <b>6.5<b> | 使用 SSLD 预训练模型+EDA 策略+SKL-UGI 知识蒸馏策略|
从表中可以看出,backbone 为 SwinTranformer_tiny 时精度较高,但是推理速度较慢。将 backboone 替换为轻量级模型 MobileNetV3_small_x0_35 后,速度可以大幅提升,但是会导致精度大幅下降。将 backbone 替换为速度更快的 PPLCNet_x1_0 时,精度较 MobileNetV3_small_x0_35 高 20 多个百分点,与此同时速度依旧可以快 20% 以上。在此基础上,使用 SSLD 预训练模型后,在不改变推理速度的前提下,精度可以提升约 2.6 个百分点,进一步地,当融合EDA策略后,精度可以再提升 1.3 个百分点,最后,在使用 SKL-UGI 知识蒸馏后,精度可以继续提升 2.2 个百分点。此时,PPLCNet_x1_0 达到了 SwinTranformer_tiny 模型的精度,但是速度快 40 多倍。关于 PULC 的训练方法和推理部署方法将在下面详细介绍。 从表中可以看出,backbone 为 SwinTranformer_tiny 时精度较高,但是推理速度较慢。将 backbone 替换为轻量级模型 MobileNetV3_small_x0_35 后,速度可以大幅提升,但是会导致精度大幅下降。将 backbone 替换为速度更快的 PPLCNet_x1_0 时,精度较 MobileNetV3_small_x0_35 高 20 多个百分点,与此同时速度依旧可以快 20% 以上。在此基础上,使用 SSLD 预训练模型后,在不改变推理速度的前提下,精度可以提升约 2.6 个百分点,进一步地,当融合EDA策略后,精度可以再提升 1.3 个百分点,最后,在使用 SKL-UGI 知识蒸馏后,精度可以继续提升 2.2 个百分点。此时,PPLCNet_x1_0 达到了 SwinTranformer_tiny 模型的精度,但是速度快 40 多倍。关于 PULC 的训练方法和推理部署方法将在下面详细介绍。
**备注:** **备注:**
...@@ -67,9 +67,9 @@ ...@@ -67,9 +67,9 @@
## 2. 模型快速体验 ## 2. 模型快速体验
<a name="2.1"></a> <a name="2.1"></a>
### 2.1 安装 paddlepaddle ### 2.1 安装 paddlepaddle
- 您的机器安装的是 CUDA9 或 CUDA10,请运行以下命令安装 - 您的机器安装的是 CUDA9 或 CUDA10,请运行以下命令安装
```bash ```bash
...@@ -81,23 +81,23 @@ python3 -m pip install paddlepaddle-gpu -i https://mirror.baidu.com/pypi/simple ...@@ -81,23 +81,23 @@ python3 -m pip install paddlepaddle-gpu -i https://mirror.baidu.com/pypi/simple
```bash ```bash
python3 -m pip install paddlepaddle -i https://mirror.baidu.com/pypi/simple python3 -m pip install paddlepaddle -i https://mirror.baidu.com/pypi/simple
``` ```
更多的版本需求,请参照[飞桨官网安装文档](https://www.paddlepaddle.org.cn/install/quick)中的说明进行操作。 更多的版本需求,请参照[飞桨官网安装文档](https://www.paddlepaddle.org.cn/install/quick)中的说明进行操作。
<a name="2.2"></a> <a name="2.2"></a>
### 2.2 安装 paddleclas ### 2.2 安装 paddleclas
使用如下命令快速安装 paddleclas 使用如下命令快速安装 paddleclas
``` ```
pip3 install paddleclas pip3 install paddleclas
``` ```
<a name="2.3"></a> <a name="2.3"></a>
### 2.3 预测 ### 2.3 预测
点击[这里](https://paddleclas.bj.bcebos.com/data/PULC/pulc_demo_imgs.zip)下载 demo 数据并解压,然后在终端中切换到相应目录。 点击[这里](https://paddleclas.bj.bcebos.com/data/PULC/pulc_demo_imgs.zip)下载 demo 数据并解压,然后在终端中切换到相应目录。
* 使用命令行快速预测 * 使用命令行快速预测
...@@ -328,7 +328,7 @@ python3 -m paddle.distributed.launch \ ...@@ -328,7 +328,7 @@ python3 -m paddle.distributed.launch \
## 5. 超参搜索 ## 5. 超参搜索
[3.3 节](#3.3)[4.1 节](#4.1)所使用的超参数是根据 PaddleClas 提供的 `SHAS 超参数搜索策略` 搜索得到的,如果希望在自己的数据集上得到更好的结果,可以参考[SHAS 超参数搜索策略](PULC_train.md#4-超参搜索)来获得更好的训练超参数。 [3.3 节](#3.3)[4.1 节](#4.1)所使用的超参数是根据 PaddleClas 提供的 `超参数搜索策略` 搜索得到的,如果希望在自己的数据集上得到更好的结果,可以参考[超参数搜索策略](PULC_train.md#4-超参搜索)来获得更好的训练超参数。
**备注:** 此部分内容是可选内容,搜索过程需要较长的时间,您可以根据自己的硬件情况来选择执行。如果没有更换数据集,可以忽略此节内容。 **备注:** 此部分内容是可选内容,搜索过程需要较长的时间,您可以根据自己的硬件情况来选择执行。如果没有更换数据集,可以忽略此节内容。
......
...@@ -53,12 +53,12 @@ ...@@ -53,12 +53,12 @@
| PPLCNet_x1_0 | 99.30 | 2.03 | 6.5 | 使用SSLD预训练模型+EDA策略| | PPLCNet_x1_0 | 99.30 | 2.03 | 6.5 | 使用SSLD预训练模型+EDA策略|
| <b>PPLCNet_x1_0<b> | <b>99.38<b> | <b>2.03<b> | <b>6.5<b> | 使用SSLD预训练模型+EDA策略+UDML知识蒸馏策略| | <b>PPLCNet_x1_0<b> | <b>99.38<b> | <b>2.03<b> | <b>6.5<b> | 使用SSLD预训练模型+EDA策略+UDML知识蒸馏策略|
从表中可以看出,在使用服务器端大模型作为 backbone 时,SwinTranformer_tiny 精度较低,Res2Net200_vd_26w_4s 精度较高,但服务器端大模型推理速度普遍较慢。将 backboone 替换为轻量级模型 MobileNetV3_small_x0_35 后,速度可以大幅提升,但是精度显著降低。在将 backbone 替换为 PPLCNet_x1_0 后,精度较 MobileNetV3_small_x0_35 提高约 8.5 个百分点,与此同时速度快 20% 以上。在此基础上,将 PPLCNet_x1_0 的预训练模型替换为 SSLD 预训练模型后,在对推理速度无影响的前提下,精度提升约 4.9 个百分点,进一步地使用 EDA 策略后,精度可以再提升 1.1 个百分点。此时,PPLCNet_x1_0 已经超过 Res2Net200_vd_26w_4s 模型的精度,但是速度快 70+ 倍。最后,在使用 UDML 知识蒸馏后,精度可以再提升 0.08 个百分点。下面详细介绍关于 PULC 安全帽模型的训练方法和推理部署方法。 从表中可以看出,在使用服务器端大模型作为 backbone 时,SwinTranformer_tiny 精度较低,Res2Net200_vd_26w_4s 精度较高,但服务器端大模型推理速度普遍较慢。将 backbone 替换为轻量级模型 MobileNetV3_small_x0_35 后,速度可以大幅提升,但是精度显著降低。在将 backbone 替换为 PPLCNet_x1_0 后,精度较 MobileNetV3_small_x0_35 提高约 8.5 个百分点,与此同时速度快 20% 以上。在此基础上,将 PPLCNet_x1_0 的预训练模型替换为 SSLD 预训练模型后,在对推理速度无影响的前提下,精度提升约 4.9 个百分点,进一步地使用 EDA 策略后,精度可以再提升 1.1 个百分点。此时,PPLCNet_x1_0 已经超过 Res2Net200_vd_26w_4s 模型的精度,但是速度快 70+ 倍。最后,在使用 UDML 知识蒸馏后,精度可以再提升 0.08 个百分点。下面详细介绍关于 PULC 安全帽模型的训练方法和推理部署方法。
**备注:** **备注:**
* `Tpr`指标的介绍可以参考 [3.3小节](#3.3)的备注部分,延时是基于 Intel(R) Xeon(R) Gold 6148 CPU @ 2.40GHz 测试得到,开启MKLDNN加速策略,线程数为10。 * `Tpr`指标的介绍可以参考 [3.3小节](#3.3)的备注部分,延时是基于 Intel(R) Xeon(R) Gold 6148 CPU @ 2.40GHz 测试得到,开启MKLDNN加速策略,线程数为10。
* 关于PP-LCNet的介绍可以参考[PP-LCNet介绍](../models/PP-LCNet.md),相关论文可以查阅[PP-LCNet paper](https://arxiv.org/abs/2109.15099) * 关于PP-LCNet的介绍可以参考[PP-LCNet介绍](../models/PP-LCNet.md),相关论文可以查阅[PP-LCNet paper](https://arxiv.org/abs/2109.15099)
...@@ -67,9 +67,9 @@ ...@@ -67,9 +67,9 @@
## 2. 模型快速体验 ## 2. 模型快速体验
<a name="2.1"></a> <a name="2.1"></a>
### 2.1 安装 paddlepaddle ### 2.1 安装 paddlepaddle
- 您的机器安装的是 CUDA9 或 CUDA10,请运行以下命令安装 - 您的机器安装的是 CUDA9 或 CUDA10,请运行以下命令安装
```bash ```bash
...@@ -81,23 +81,23 @@ python3 -m pip install paddlepaddle-gpu -i https://mirror.baidu.com/pypi/simple ...@@ -81,23 +81,23 @@ python3 -m pip install paddlepaddle-gpu -i https://mirror.baidu.com/pypi/simple
```bash ```bash
python3 -m pip install paddlepaddle -i https://mirror.baidu.com/pypi/simple python3 -m pip install paddlepaddle -i https://mirror.baidu.com/pypi/simple
``` ```
更多的版本需求,请参照[飞桨官网安装文档](https://www.paddlepaddle.org.cn/install/quick)中的说明进行操作。 更多的版本需求,请参照[飞桨官网安装文档](https://www.paddlepaddle.org.cn/install/quick)中的说明进行操作。
<a name="2.2"></a> <a name="2.2"></a>
### 2.2 安装 paddleclas ### 2.2 安装 paddleclas
使用如下命令快速安装 paddleclas 使用如下命令快速安装 paddleclas
``` ```
pip3 install paddleclas pip3 install paddleclas
``` ```
<a name="2.3"></a> <a name="2.3"></a>
### 2.3 预测 ### 2.3 预测
点击[这里](https://paddleclas.bj.bcebos.com/data/PULC/pulc_demo_imgs.zip)下载 demo 数据并解压,然后在终端中切换到相应目录。 点击[这里](https://paddleclas.bj.bcebos.com/data/PULC/pulc_demo_imgs.zip)下载 demo 数据并解压,然后在终端中切换到相应目录。
* 使用命令行快速预测 * 使用命令行快速预测
...@@ -295,7 +295,7 @@ python3 -m paddle.distributed.launch \ ...@@ -295,7 +295,7 @@ python3 -m paddle.distributed.launch \
## 5. 超参搜索 ## 5. 超参搜索
[3.2 节](#3.2)[4.1 节](#4.1)所使用的超参数是根据 PaddleClas 提供的 `SHAS 超参数搜索策略` 搜索得到的,如果希望在自己的数据集上得到更好的结果,可以参考[SHAS 超参数搜索策略](PULC_train.md#4-超参搜索)来获得更好的训练超参数。 [3.2 节](#3.2)[4.1 节](#4.1)所使用的超参数是根据 PaddleClas 提供的 `超参数搜索策略` 搜索得到的,如果希望在自己的数据集上得到更好的结果,可以参考[超参数搜索策略](PULC_train.md#4-超参搜索)来获得更好的训练超参数。
**备注**:此部分内容是可选内容,搜索过程需要较长的时间,您可以根据自己的硬件情况来选择执行。如果没有更换数据集,可以忽略此节内容。 **备注**:此部分内容是可选内容,搜索过程需要较长的时间,您可以根据自己的硬件情况来选择执行。如果没有更换数据集,可以忽略此节内容。
......
...@@ -38,7 +38,7 @@ ...@@ -38,7 +38,7 @@
在诸如文档扫描、证照拍摄等过程中,有时为了拍摄更清晰,会将拍摄设备进行旋转,导致得到的图片也是不同方向的。此时,标准的OCR流程无法很好地应对这些数据。利用图像分类技术,可以预先判断含文字图像的方向,并将其进行方向调整,从而提高OCR处理的准确性。该案例提供了用户使用 PaddleClas 的超轻量图像分类方案(PULC,Practical Ultra Lightweight Classification)快速构建轻量级、高精度、可落地的含文字图像方向的分类模型。该模型可以广泛应用于金融、政务等行业的旋转图片的OCR处理场景中。 在诸如文档扫描、证照拍摄等过程中,有时为了拍摄更清晰,会将拍摄设备进行旋转,导致得到的图片也是不同方向的。此时,标准的OCR流程无法很好地应对这些数据。利用图像分类技术,可以预先判断含文字图像的方向,并将其进行方向调整,从而提高OCR处理的准确性。该案例提供了用户使用 PaddleClas 的超轻量图像分类方案(PULC,Practical Ultra Lightweight Classification)快速构建轻量级、高精度、可落地的含文字图像方向的分类模型。该模型可以广泛应用于金融、政务等行业的旋转图片的OCR处理场景中。
下表列出了判断含文字图像方向分类模型的相关指标,前两行展现了使用 SwinTranformer_tiny 和 MobileNetV3_small_x0_35 作为 backbone 训练得到的模型的相关指标,第三行至第五行依次展现了替换 backbone 为 PPLCNet_x1_0、使用 SSLD 预训练模型、使用 SHAS 超参数搜索策略训练得到的模型的相关指标。 下表列出了判断含文字图像方向分类模型的相关指标,前两行展现了使用 SwinTranformer_tiny 和 MobileNetV3_small_x0_35 作为 backbone 训练得到的模型的相关指标,第三行至第五行依次展现了替换 backbone 为 PPLCNet_x1_0、使用 SSLD 预训练模型、使用 超参数搜索策略训练得到的模型的相关指标。
| 模型 | 精度(%) | 延时(ms) | 存储(M) | 策略 | | 模型 | 精度(%) | 延时(ms) | 存储(M) | 策略 |
| ----------------------- | --------- | ---------- | --------- | ------------------------------------- | | ----------------------- | --------- | ---------- | --------- | ------------------------------------- |
...@@ -48,9 +48,9 @@ ...@@ -48,9 +48,9 @@
| PPLCNet_x1_0 | 98.02 | 2.16 | 6.5 | 使用SSLD预训练模型 | | PPLCNet_x1_0 | 98.02 | 2.16 | 6.5 | 使用SSLD预训练模型 |
| **PPLCNet_x1_0** | **99.06** | **2.16** | **6.5** | 使用SSLD预训练模型+SHAS超参数搜索策略 | | **PPLCNet_x1_0** | **99.06** | **2.16** | **6.5** | 使用SSLD预训练模型+SHAS超参数搜索策略 |
从表中可以看出,backbone 为 SwinTranformer_tiny 时精度比较高,但是推理速度较慢。将 backboone 替换为轻量级模型 MobileNetV3_small_x0_35 后,速度提升明显,但精度有了大幅下降。将 backbone 替换为 PPLCNet_x1_0 时,速度略为提升,同时精度较 MobileNetV3_small_x0_35 高了 14.24 个百分点。在此基础上,使用 SSLD 预训练模型后,在不改变推理速度的前提下,精度可以提升 0.17 个百分点,进一步地,当使用SHAS超参数搜索策略搜索最优超参数后,精度可以再提升 1.04 个百分点。此时,PPLCNet_x1_0 与 SwinTranformer_tiny 的精度差别不大,但是速度明显变快。关于 PULC 的训练方法和推理部署方法将在下面详细介绍。 从表中可以看出,backbone 为 SwinTranformer_tiny 时精度比较高,但是推理速度较慢。将 backbone 替换为轻量级模型 MobileNetV3_small_x0_35 后,速度提升明显,但精度有了大幅下降。将 backbone 替换为 PPLCNet_x1_0 时,速度略为提升,同时精度较 MobileNetV3_small_x0_35 高了 14.24 个百分点。在此基础上,使用 SSLD 预训练模型后,在不改变推理速度的前提下,精度可以提升 0.17 个百分点,进一步地,当使用SHAS超参数搜索策略搜索最优超参数后,精度可以再提升 1.04 个百分点。此时,PPLCNet_x1_0 与 SwinTranformer_tiny 的精度差别不大,但是速度明显变快。关于 PULC 的训练方法和推理部署方法将在下面详细介绍。
**备注:** **备注:**
* 关于PP-LCNet的介绍可以参考[PP-LCNet介绍](../models/PP-LCNet.md),相关论文可以查阅[PP-LCNet paper](https://arxiv.org/abs/2109.15099) * 关于PP-LCNet的介绍可以参考[PP-LCNet介绍](../models/PP-LCNet.md),相关论文可以查阅[PP-LCNet paper](https://arxiv.org/abs/2109.15099)
...@@ -59,9 +59,9 @@ ...@@ -59,9 +59,9 @@
## 2. 模型快速体验 ## 2. 模型快速体验
<a name="2.1"></a> <a name="2.1"></a>
### 2.1 安装 paddlepaddle ### 2.1 安装 paddlepaddle
- 您的机器安装的是 CUDA9 或 CUDA10,请运行以下命令安装 - 您的机器安装的是 CUDA9 或 CUDA10,请运行以下命令安装
```bash ```bash
...@@ -73,23 +73,23 @@ python3 -m pip install paddlepaddle-gpu -i https://mirror.baidu.com/pypi/simple ...@@ -73,23 +73,23 @@ python3 -m pip install paddlepaddle-gpu -i https://mirror.baidu.com/pypi/simple
```bash ```bash
python3 -m pip install paddlepaddle -i https://mirror.baidu.com/pypi/simple python3 -m pip install paddlepaddle -i https://mirror.baidu.com/pypi/simple
``` ```
更多的版本需求,请参照[飞桨官网安装文档](https://www.paddlepaddle.org.cn/install/quick)中的说明进行操作。 更多的版本需求,请参照[飞桨官网安装文档](https://www.paddlepaddle.org.cn/install/quick)中的说明进行操作。
<a name="2.2"></a> <a name="2.2"></a>
### 2.2 安装 paddleclas ### 2.2 安装 paddleclas
使用如下命令快速安装 paddleclas 使用如下命令快速安装 paddleclas
``` ```
pip3 install paddleclas pip3 install paddleclas
``` ```
<a name="2.3"></a> <a name="2.3"></a>
### 2.3 预测 ### 2.3 预测
点击[这里](https://paddleclas.bj.bcebos.com/data/PULC/pulc_demo_imgs.zip)下载 demo 数据并解压,然后在终端中切换到相应目录。 点击[这里](https://paddleclas.bj.bcebos.com/data/PULC/pulc_demo_imgs.zip)下载 demo 数据并解压,然后在终端中切换到相应目录。
* 使用命令行快速预测 * 使用命令行快速预测
...@@ -319,7 +319,7 @@ python3 -m paddle.distributed.launch \ ...@@ -319,7 +319,7 @@ python3 -m paddle.distributed.launch \
## 5. 超参搜索 ## 5. 超参搜索
[3.2 节](#3.2)[4.1 节](#4.1)所使用的超参数是根据 PaddleClas 提供的 `SHAS 超参数搜索策略` 搜索得到的,如果希望在自己的数据集上得到更好的结果,可以参考[SHAS 超参数搜索策略](PULC_train.md#4-超参搜索)来获得更好的训练超参数。 [3.2 节](#3.2)[4.1 节](#4.1)所使用的超参数是根据 PaddleClas 提供的 `超参数搜索策略` 搜索得到的,如果希望在自己的数据集上得到更好的结果,可以参考[超参数搜索策略](PULC_train.md#4-超参搜索)来获得更好的训练超参数。
**备注:** 此部分内容是可选内容,搜索过程需要较长的时间,您可以根据自己的硬件情况来选择执行。如果没有更换数据集,可以忽略此节内容。 **备注:** 此部分内容是可选内容,搜索过程需要较长的时间,您可以根据自己的硬件情况来选择执行。如果没有更换数据集,可以忽略此节内容。
......
...@@ -55,11 +55,11 @@ ...@@ -55,11 +55,11 @@
| <b>PPLCNet_x1_0**<b> | <b>96.01<b> | <b>2.72<b> | <b>6.5<b> | 使用 SSLD 预训练模型+EDA 策略| | <b>PPLCNet_x1_0**<b> | <b>96.01<b> | <b>2.72<b> | <b>6.5<b> | 使用 SSLD 预训练模型+EDA 策略|
| PPLCNet_x1_0** | 95.86 | 2.72 | 6.5 | 使用 SSLD 预训练模型+EDA 策略+SKL-UGI 知识蒸馏策略| | PPLCNet_x1_0** | 95.86 | 2.72 | 6.5 | 使用 SSLD 预训练模型+EDA 策略+SKL-UGI 知识蒸馏策略|
从表中可以看出,backbone 为 SwinTranformer_tiny 时精度较高,但是推理速度较慢。将 backboone 替换为轻量级模型 MobileNetV3_small_x0_35 后,速度可以大幅提升,精度下降也比较明显。将 backbone 替换为 PPLCNet_x1_0 时,精度较 MobileNetV3_small_x0_35 高 8.6 个百分点,速度快10%左右。在此基础上,更改分辨率和stride, 速度变慢 27%,但是精度可以提升 4.5 个百分点(采用[PaddleOCR](https://github.com/PaddlePaddle/PaddleOCR)的方案),使用 SSLD 预训练模型后,精度可以继续提升约 0.05 个百分点 ,进一步地,当融合EDA策略后,精度可以再提升 1.9 个百分点。最后,融合SKL-UGI 知识蒸馏策略后,在该场景无效。关于 PULC 的训练方法和推理部署方法将在下面详细介绍。 从表中可以看出,backbone 为 SwinTranformer_tiny 时精度较高,但是推理速度较慢。将 backbone 替换为轻量级模型 MobileNetV3_small_x0_35 后,速度可以大幅提升,精度下降也比较明显。将 backbone 替换为 PPLCNet_x1_0 时,精度较 MobileNetV3_small_x0_35 高 8.6 个百分点,速度快10%左右。在此基础上,更改分辨率和stride, 速度变慢 27%,但是精度可以提升 4.5 个百分点(采用[PaddleOCR](https://github.com/PaddlePaddle/PaddleOCR)的方案),使用 SSLD 预训练模型后,精度可以继续提升约 0.05 个百分点 ,进一步地,当融合EDA策略后,精度可以再提升 1.9 个百分点。最后,融合SKL-UGI 知识蒸馏策略后,在该场景无效。关于 PULC 的训练方法和推理部署方法将在下面详细介绍。
**备注:** **备注:**
* 其中不带\*的模型表示分辨率为224x224,带\*的模型表示分辨率为48x192(h\*w),数据增强从网络中的 stride 改为 `[2, [2, 1], [2, 1], [2, 1], [2, 1]]`,其中,外层列表中的每一个元素代表网络结构下采样层的stride,该策略为 [PaddleOCR](https://github.com/PaddlePaddle/PaddleOCR) 提供的文本行方向分类器方案。带\*\*的模型表示分辨率为80x160(h\*w), 网络中的 stride 改为 `[2, [2, 1], [2, 1], [2, 1], [2, 1]]`,其中,外层列表中的每一个元素代表网络结构下采样层的stride,此分辨率是经过[SHAS 超参数搜索策略](PULC_train.md#4-超参搜索)搜索得到的。 * 其中不带\*的模型表示分辨率为224x224,带\*的模型表示分辨率为48x192(h\*w),数据增强从网络中的 stride 改为 `[2, [2, 1], [2, 1], [2, 1], [2, 1]]`,其中,外层列表中的每一个元素代表网络结构下采样层的stride,该策略为 [PaddleOCR](https://github.com/PaddlePaddle/PaddleOCR) 提供的文本行方向分类器方案。带\*\*的模型表示分辨率为80x160(h\*w), 网络中的 stride 改为 `[2, [2, 1], [2, 1], [2, 1], [2, 1]]`,其中,外层列表中的每一个元素代表网络结构下采样层的stride,此分辨率是经过[超参数搜索策略](PULC_train.md#4-超参搜索)搜索得到的。
* 延时是基于 Intel(R) Xeon(R) Gold 6148 CPU @ 2.40GHz 测试得到,开启 MKLDNN 加速策略,线程数为10。 * 延时是基于 Intel(R) Xeon(R) Gold 6148 CPU @ 2.40GHz 测试得到,开启 MKLDNN 加速策略,线程数为10。
* 关于PP-LCNet的介绍可以参考[PP-LCNet介绍](../models/PP-LCNet.md),相关论文可以查阅[PP-LCNet paper](https://arxiv.org/abs/2109.15099) * 关于PP-LCNet的介绍可以参考[PP-LCNet介绍](../models/PP-LCNet.md),相关论文可以查阅[PP-LCNet paper](https://arxiv.org/abs/2109.15099)
...@@ -68,9 +68,9 @@ ...@@ -68,9 +68,9 @@
## 2. 模型快速体验 ## 2. 模型快速体验
<a name="2.1"></a> <a name="2.1"></a>
### 2.1 安装 paddlepaddle ### 2.1 安装 paddlepaddle
- 您的机器安装的是 CUDA9 或 CUDA10,请运行以下命令安装 - 您的机器安装的是 CUDA9 或 CUDA10,请运行以下命令安装
```bash ```bash
...@@ -82,23 +82,23 @@ python3 -m pip install paddlepaddle-gpu -i https://mirror.baidu.com/pypi/simple ...@@ -82,23 +82,23 @@ python3 -m pip install paddlepaddle-gpu -i https://mirror.baidu.com/pypi/simple
```bash ```bash
python3 -m pip install paddlepaddle -i https://mirror.baidu.com/pypi/simple python3 -m pip install paddlepaddle -i https://mirror.baidu.com/pypi/simple
``` ```
更多的版本需求,请参照[飞桨官网安装文档](https://www.paddlepaddle.org.cn/install/quick)中的说明进行操作。 更多的版本需求,请参照[飞桨官网安装文档](https://www.paddlepaddle.org.cn/install/quick)中的说明进行操作。
<a name="2.2"></a> <a name="2.2"></a>
### 2.2 安装 paddleclas ### 2.2 安装 paddleclas
使用如下命令快速安装 paddleclas 使用如下命令快速安装 paddleclas
``` ```
pip3 install paddleclas pip3 install paddleclas
``` ```
<a name="2.3"></a> <a name="2.3"></a>
### 2.3 预测 ### 2.3 预测
点击[这里](https://paddleclas.bj.bcebos.com/data/PULC/pulc_demo_imgs.zip)下载 demo 数据并解压,然后在终端中切换到相应目录。 点击[这里](https://paddleclas.bj.bcebos.com/data/PULC/pulc_demo_imgs.zip)下载 demo 数据并解压,然后在终端中切换到相应目录。
* 使用命令行快速预测 * 使用命令行快速预测
...@@ -314,7 +314,7 @@ python3 -m paddle.distributed.launch \ ...@@ -314,7 +314,7 @@ python3 -m paddle.distributed.launch \
## 5. 超参搜索 ## 5. 超参搜索
[3.3 节](#3.3)[4.1 节](#4.1)所使用的超参数是根据 PaddleClas 提供的 `SHAS 超参数搜索策略` 搜索得到的,如果希望在自己的数据集上得到更好的结果,可以参考[SHAS 超参数搜索策略](PULC_train.md#4-超参搜索)来获得更好的训练超参数。 [3.3 节](#3.3)[4.1 节](#4.1)所使用的超参数是根据 PaddleClas 提供的 `超参数搜索策略` 搜索得到的,如果希望在自己的数据集上得到更好的结果,可以参考[超参数搜索策略](PULC_train.md#4-超参搜索)来获得更好的训练超参数。
**备注:** 此部分内容是可选内容,搜索过程需要较长的时间,您可以根据自己的硬件情况来选择执行。 **备注:** 此部分内容是可选内容,搜索过程需要较长的时间,您可以根据自己的硬件情况来选择执行。
......
...@@ -66,9 +66,9 @@ ...@@ -66,9 +66,9 @@
## 2. 模型快速体验 ## 2. 模型快速体验
<a name="2.1"></a> <a name="2.1"></a>
### 2.1 安装 paddlepaddle ### 2.1 安装 paddlepaddle
- 您的机器安装的是 CUDA9 或 CUDA10,请运行以下命令安装 - 您的机器安装的是 CUDA9 或 CUDA10,请运行以下命令安装
```bash ```bash
...@@ -80,23 +80,23 @@ python3 -m pip install paddlepaddle-gpu -i https://mirror.baidu.com/pypi/simple ...@@ -80,23 +80,23 @@ python3 -m pip install paddlepaddle-gpu -i https://mirror.baidu.com/pypi/simple
```bash ```bash
python3 -m pip install paddlepaddle -i https://mirror.baidu.com/pypi/simple python3 -m pip install paddlepaddle -i https://mirror.baidu.com/pypi/simple
``` ```
更多的版本需求,请参照[飞桨官网安装文档](https://www.paddlepaddle.org.cn/install/quick)中的说明进行操作。 更多的版本需求,请参照[飞桨官网安装文档](https://www.paddlepaddle.org.cn/install/quick)中的说明进行操作。
<a name="2.2"></a> <a name="2.2"></a>
### 2.2 安装 paddleclas ### 2.2 安装 paddleclas
使用如下命令快速安装 paddleclas 使用如下命令快速安装 paddleclas
``` ```
pip3 install paddleclas pip3 install paddleclas
``` ```
<a name="2.3"></a> <a name="2.3"></a>
### 2.3 预测 ### 2.3 预测
点击[这里](https://paddleclas.bj.bcebos.com/data/PULC/pulc_demo_imgs.zip)下载 demo 数据并解压,然后在终端中切换到相应目录。 点击[这里](https://paddleclas.bj.bcebos.com/data/PULC/pulc_demo_imgs.zip)下载 demo 数据并解压,然后在终端中切换到相应目录。
* 使用命令行快速预测 * 使用命令行快速预测
...@@ -344,7 +344,7 @@ python3 -m paddle.distributed.launch \ ...@@ -344,7 +344,7 @@ python3 -m paddle.distributed.launch \
## 5. 超参搜索 ## 5. 超参搜索
[3.2 节](#3.2)[4.1 节](#4.1)所使用的超参数是根据 PaddleClas 提供的 `SHAS 超参数搜索策略` 搜索得到的,如果希望在自己的数据集上得到更好的结果,可以参考[SHAS 超参数搜索策略](PULC_train.md#4-超参搜索)来获得更好的训练超参数。 [3.2 节](#3.2)[4.1 节](#4.1)所使用的超参数是根据 PaddleClas 提供的 `超参数搜索策略` 搜索得到的,如果希望在自己的数据集上得到更好的结果,可以参考[超参数搜索策略](PULC_train.md#4-超参搜索)来获得更好的训练超参数。
**备注:** 此部分内容是可选内容,搜索过程需要较长的时间,您可以根据自己的硬件情况来选择执行。如果没有更换数据集,可以忽略此节内容。 **备注:** 此部分内容是可选内容,搜索过程需要较长的时间,您可以根据自己的硬件情况来选择执行。如果没有更换数据集,可以忽略此节内容。
......
...@@ -337,7 +337,7 @@ python3 -m paddle.distributed.launch \ ...@@ -337,7 +337,7 @@ python3 -m paddle.distributed.launch \
## 5. 超参搜索 ## 5. 超参搜索
[3.3 节](#3.3)[4.1 节](#4.1)所使用的超参数是根据 PaddleClas 提供的 `SHAS 超参数搜索策略` 搜索得到的,如果希望在自己的数据集上得到更好的结果,可以参考[SHAS 超参数搜索策略](PULC_train.md#4-超参搜索)来获得更好的训练超参数。 [3.3 节](#3.3)[4.1 节](#4.1)所使用的超参数是根据 PaddleClas 提供的 `超参数搜索策略` 搜索得到的,如果希望在自己的数据集上得到更好的结果,可以参考[超参数搜索策略](PULC_train.md#4-超参搜索)来获得更好的训练超参数。
**备注:** 此部分内容是可选内容,搜索过程需要较长的时间,您可以根据自己的硬件情况来选择执行。如果没有更换数据集,可以忽略此节内容。 **备注:** 此部分内容是可选内容,搜索过程需要较长的时间,您可以根据自己的硬件情况来选择执行。如果没有更换数据集,可以忽略此节内容。
......
...@@ -233,7 +233,7 @@ class ShuffleNet(Layer): ...@@ -233,7 +233,7 @@ class ShuffleNet(Layer):
elif scale == 1.5: elif scale == 1.5:
stage_out_channels = [-1, 24, 176, 352, 704, 1024] stage_out_channels = [-1, 24, 176, 352, 704, 1024]
elif scale == 2.0: elif scale == 2.0:
stage_out_channels = [-1, 24, 224, 488, 976, 2048] stage_out_channels = [-1, 24, 244, 488, 976, 2048]
else: else:
raise NotImplementedError("This scale size:[" + str(scale) + raise NotImplementedError("This scale size:[" + str(scale) +
"] is not implemented!") "] is not implemented!")
......
...@@ -51,10 +51,10 @@ Optimizer: ...@@ -51,10 +51,10 @@ Optimizer:
one_dim_param_no_weight_decay: True one_dim_param_no_weight_decay: True
lr: lr:
name: Cosine name: Cosine
learning_rate: 1e-4 learning_rate: 5e-5
eta_min: 2e-6 eta_min: 1e-6
warmup_epoch: 5 warmup_epoch: 5
warmup_start_lr: 2e-7 warmup_start_lr: 1e-7
# data loader for train and eval # data loader for train and eval
......
...@@ -371,6 +371,11 @@ def run(dataloader, ...@@ -371,6 +371,11 @@ def run(dataloader,
"Except RuntimeError when reading data from dataloader, try to read once again..." "Except RuntimeError when reading data from dataloader, try to read once again..."
) )
continue continue
except IndexError:
logger.warning(
"Except IndexError when reading data from dataloader, try to read once again..."
)
continue
idx += 1 idx += 1
# ignore the warmup iters # ignore the warmup iters
if idx == 5: if idx == 5:
......
...@@ -112,4 +112,5 @@ bash test_tipc/test_train_inference_python.sh ./test_tipc/configs/MobileNetV3/Mo ...@@ -112,4 +112,5 @@ bash test_tipc/test_train_inference_python.sh ./test_tipc/configs/MobileNetV3/Mo
- [test_lite_arm_cpu_cpp 使用](docs/test_lite_arm_cpu_cpp.md): 测试基于Paddle-Lite的ARM CPU端c++预测部署功能. - [test_lite_arm_cpu_cpp 使用](docs/test_lite_arm_cpu_cpp.md): 测试基于Paddle-Lite的ARM CPU端c++预测部署功能.
- [test_paddle2onnx 使用](docs/test_paddle2onnx.md):测试Paddle2ONNX的模型转化功能,并验证正确性。 - [test_paddle2onnx 使用](docs/test_paddle2onnx.md):测试Paddle2ONNX的模型转化功能,并验证正确性。
- [test_serving_infer_python 使用](docs/test_serving_infer_python.md):测试python serving功能。 - [test_serving_infer_python 使用](docs/test_serving_infer_python.md):测试python serving功能。
- [test_serving_infer_cpp 使用](docs/test_serving_infer_cpp.md):测试cpp serving功能。
- [test_train_fleet_inference_python 使用](./docs/test_train_fleet_inference_python.md):测试基于Python的多机多卡训练与推理等基本功能。 - [test_train_fleet_inference_python 使用](./docs/test_train_fleet_inference_python.md):测试基于Python的多机多卡训练与推理等基本功能。
===========================serving_params===========================
model_name:MobileNetV3_large_x1_0
python:python3.7
inference_model_url:https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/MobileNetV3_large_x1_0_infer.tar
trans_model:-m paddle_serving_client.convert
--dirname:./deploy/paddleserving/MobileNetV3_large_x1_0_infer/
--model_filename:inference.pdmodel
--params_filename:inference.pdiparams
--serving_server:./deploy/paddleserving/MobileNetV3_large_x1_0_serving/
--serving_client:./deploy/paddleserving/MobileNetV3_large_x1_0_client/
serving_dir:./deploy/paddleserving
web_service:null
--use_gpu:0|null
pipline:test_cpp_serving_client.py
===========================serving_params===========================
model_name:PPShiTu
python:python3.7
cls_inference_model_url:https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/rec/models/inference/general_PPLCNet_x2_5_lite_v1.0_infer.tar
det_inference_model_url:https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/rec/models/inference/picodet_PPLCNet_x2_5_mainbody_lite_v1.0_infer.tar
trans_model:-m paddle_serving_client.convert
--dirname:./models/general_PPLCNet_x2_5_lite_v1.0_infer/
--dirname:./models/picodet_PPLCNet_x2_5_mainbody_lite_v1.0_infer/
--model_filename:inference.pdmodel
--params_filename:inference.pdiparams
--serving_server:./models/general_PPLCNet_x2_5_lite_v1.0_serving/
--serving_client:./models/general_PPLCNet_x2_5_lite_v1.0_client/
--serving_server:./models/picodet_PPLCNet_x2_5_mainbody_lite_v1.0_serving/
--serving_client:./models/picodet_PPLCNet_x2_5_mainbody_lite_v1.0_client/
serving_dir:./paddleserving/recognition
web_service:null
--use_gpu:0|null
pipline:test_cpp_serving_client.py
===========================serving_params===========================
model_name:PPHGNet_small
python:python3.7
inference_model_url:https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/PPHGNet_small_infer.tar
trans_model:-m paddle_serving_client.convert
--dirname:./deploy/paddleserving/PPHGNet_small_infer/
--model_filename:inference.pdmodel
--params_filename:inference.pdiparams
--serving_server:./deploy/paddleserving/PPHGNet_small_serving/
--serving_client:./deploy/paddleserving/PPHGNet_small_client/
serving_dir:./deploy/paddleserving
web_service:classification_web_service.py
--use_gpu:0|null
pipline:pipeline_http_client.py
===========================serving_params===========================
model_name:PPHGNet_tiny
python:python3.7
inference_model_url:https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/PPHGNet_tiny_infer.tar
trans_model:-m paddle_serving_client.convert
--dirname:./deploy/paddleserving/PPHGNet_tiny_infer/
--model_filename:inference.pdmodel
--params_filename:inference.pdiparams
--serving_server:./deploy/paddleserving/PPHGNet_tiny_serving/
--serving_client:./deploy/paddleserving/PPHGNet_tiny_client/
serving_dir:./deploy/paddleserving
web_service:classification_web_service.py
--use_gpu:0|null
pipline:pipeline_http_client.py
===========================serving_params===========================
model_name:PPLCNet_x0_25
python:python3.7
inference_model_url:https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/PPLCNet_x0_25_infer.tar
trans_model:-m paddle_serving_client.convert
--dirname:./deploy/paddleserving/PPLCNet_x0_25_infer/
--model_filename:inference.pdmodel
--params_filename:inference.pdiparams
--serving_server:./deploy/paddleserving/PPLCNet_x0_25_serving/
--serving_client:./deploy/paddleserving/PPLCNet_x0_25_client/
serving_dir:./deploy/paddleserving
web_service:null
--use_gpu:0|null
pipline:test_cpp_serving_client.py
===========================serving_params===========================
model_name:PPLCNet_x0_35
python:python3.7
inference_model_url:https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/PPLCNet_x0_35_infer.tar
trans_model:-m paddle_serving_client.convert
--dirname:./deploy/paddleserving/PPLCNet_x0_35_infer/
--model_filename:inference.pdmodel
--params_filename:inference.pdiparams
--serving_server:./deploy/paddleserving/PPLCNet_x0_35_serving/
--serving_client:./deploy/paddleserving/PPLCNet_x0_35_client/
serving_dir:./deploy/paddleserving
web_service:null
--use_gpu:0|null
pipline:test_cpp_serving_client.py
===========================serving_params===========================
model_name:PPLCNet_x0_5
python:python3.7
inference_model_url:https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/PPLCNet_x0_5_infer.tar
trans_model:-m paddle_serving_client.convert
--dirname:./deploy/paddleserving/PPLCNet_x0_5_infer/
--model_filename:inference.pdmodel
--params_filename:inference.pdiparams
--serving_server:./deploy/paddleserving/PPLCNet_x0_5_serving/
--serving_client:./deploy/paddleserving/PPLCNet_x0_5_client/
serving_dir:./deploy/paddleserving
web_service:null
--use_gpu:0|null
pipline:test_cpp_serving_client.py
===========================serving_params===========================
model_name:PPLCNet_x0_75
python:python3.7
inference_model_url:https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/PPLCNet_x0_75_infer.tar
trans_model:-m paddle_serving_client.convert
--dirname:./deploy/paddleserving/PPLCNet_x0_75_infer/
--model_filename:inference.pdmodel
--params_filename:inference.pdiparams
--serving_server:./deploy/paddleserving/PPLCNet_x0_75_serving/
--serving_client:./deploy/paddleserving/PPLCNet_x0_75_client/
serving_dir:./deploy/paddleserving
web_service:null
--use_gpu:0|null
pipline:test_cpp_serving_client.py
===========================serving_params===========================
model_name:PPLCNet_x1_0
python:python3.7
inference_model_url:https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/PPLCNet_x1_0_infer.tar
trans_model:-m paddle_serving_client.convert
--dirname:./deploy/paddleserving/PPLCNet_x1_0_infer/
--model_filename:inference.pdmodel
--params_filename:inference.pdiparams
--serving_server:./deploy/paddleserving/PPLCNet_x1_0_serving/
--serving_client:./deploy/paddleserving/PPLCNet_x1_0_client/
serving_dir:./deploy/paddleserving
web_service:null
--use_gpu:0|null
pipline:test_cpp_serving_client.py
===========================serving_params===========================
model_name:PPLCNet_x1_5
python:python3.7
inference_model_url:https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/PPLCNet_x1_5_infer.tar
trans_model:-m paddle_serving_client.convert
--dirname:./deploy/paddleserving/PPLCNet_x1_5_infer/
--model_filename:inference.pdmodel
--params_filename:inference.pdiparams
--serving_server:./deploy/paddleserving/PPLCNet_x1_5_serving/
--serving_client:./deploy/paddleserving/PPLCNet_x1_5_client/
serving_dir:./deploy/paddleserving
web_service:null
--use_gpu:0|null
pipline:test_cpp_serving_client.py
===========================serving_params===========================
model_name:PPLCNet_x2_0
python:python3.7
inference_model_url:https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/PPLCNet_x2_0_infer.tar
trans_model:-m paddle_serving_client.convert
--dirname:./deploy/paddleserving/PPLCNet_x2_0_infer/
--model_filename:inference.pdmodel
--params_filename:inference.pdiparams
--serving_server:./deploy/paddleserving/PPLCNet_x2_0_serving/
--serving_client:./deploy/paddleserving/PPLCNet_x2_0_client/
serving_dir:./deploy/paddleserving
web_service:null
--use_gpu:0|null
pipline:test_cpp_serving_client.py
===========================serving_params===========================
model_name:PPLCNet_x2_5
python:python3.7
inference_model_url:https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/PPLCNet_x2_5_infer.tar
trans_model:-m paddle_serving_client.convert
--dirname:./deploy/paddleserving/PPLCNet_x2_5_infer/
--model_filename:inference.pdmodel
--params_filename:inference.pdiparams
--serving_server:./deploy/paddleserving/PPLCNet_x2_5_serving/
--serving_client:./deploy/paddleserving/PPLCNet_x2_5_client/
serving_dir:./deploy/paddleserving
web_service:null
--use_gpu:0|null
pipline:test_cpp_serving_client.py
===========================serving_params===========================
model_name:PPLCNetV2_base
python:python3.7
inference_model_url:https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/PPLCNetV2_base_infer.tar
trans_model:-m paddle_serving_client.convert
--dirname:./deploy/paddleserving/PPLCNetV2_base_infer/
--model_filename:inference.pdmodel
--params_filename:inference.pdiparams
--serving_server:./deploy/paddleserving/PPLCNetV2_base_serving/
--serving_client:./deploy/paddleserving/PPLCNetV2_base_client/
serving_dir:./deploy/paddleserving
web_service:null
--use_gpu:0|null
pipline:test_cpp_serving_client.py
===========================serving_params===========================
model_name:ResNet50
python:python3.7
inference_model_url:https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/ResNet50_infer.tar
trans_model:-m paddle_serving_client.convert
--dirname:./deploy/paddleserving/ResNet50_infer/
--model_filename:inference.pdmodel
--params_filename:inference.pdiparams
--serving_server:./deploy/paddleserving/ResNet50_serving/
--serving_client:./deploy/paddleserving/ResNet50_client/
serving_dir:./deploy/paddleserving
web_service:null
--use_gpu:0|null
pipline:test_cpp_serving_client.py
===========================serving_params===========================
model_name:ResNet50_vd
python:python3.7
inference_model_url:https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/ResNet50_vd_infer.tar
trans_model:-m paddle_serving_client.convert
--dirname:./deploy/paddleserving/ResNet50_vd_infer/
--model_filename:inference.pdmodel
--params_filename:inference.pdiparams
--serving_server:./deploy/paddleserving/ResNet50_vd_serving/
--serving_client:./deploy/paddleserving/ResNet50_vd_client/
serving_dir:./deploy/paddleserving
web_service:null
--use_gpu:0|null
pipline:test_cpp_serving_client.py
===========================serving_params===========================
model_name:SwinTransformer_tiny_patch4_window7_224
python:python3.7
inference_model_url:https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/SwinTransformer_tiny_patch4_window7_224_infer.tar
trans_model:-m paddle_serving_client.convert
--dirname:./deploy/paddleserving/SwinTransformer_tiny_patch4_window7_224_infer/
--model_filename:inference.pdmodel
--params_filename:inference.pdiparams
--serving_server:./deploy/paddleserving/SwinTransformer_tiny_patch4_window7_224_serving/
--serving_client:./deploy/paddleserving/SwinTransformer_tiny_patch4_window7_224_client/
serving_dir:./deploy/paddleserving
web_service:null
--use_gpu:0|null
pipline:test_cpp_serving_client.py
# Linux GPU/CPU PYTHON 服务化部署测试
Linux GPU/CPU PYTHON 服务化部署测试的主程序为`test_serving_infer_cpp.sh`,可以测试基于Python的模型服务化部署功能。
## 1. 测试结论汇总
- 推理相关:
| 算法名称 | 模型名称 | device_CPU | device_GPU |
| :----: | :----: | :----: | :----: |
| MobileNetV3 | MobileNetV3_large_x1_0 | 支持 | 支持 |
| PP-ShiTu | PPShiTu_general_rec、PPShiTu_mainbody_det | 支持 | 支持 |
| PPHGNet | PPHGNet_small | 支持 | 支持 |
| PPHGNet | PPHGNet_tiny | 支持 | 支持 |
| PPLCNet | PPLCNet_x0_25 | 支持 | 支持 |
| PPLCNet | PPLCNet_x0_35 | 支持 | 支持 |
| PPLCNet | PPLCNet_x0_5 | 支持 | 支持 |
| PPLCNet | PPLCNet_x0_75 | 支持 | 支持 |
| PPLCNet | PPLCNet_x1_0 | 支持 | 支持 |
| PPLCNet | PPLCNet_x1_5 | 支持 | 支持 |
| PPLCNet | PPLCNet_x2_0 | 支持 | 支持 |
| PPLCNet | PPLCNet_x2_5 | 支持 | 支持 |
| PPLCNetV2 | PPLCNetV2_base | 支持 | 支持 |
| ResNet | ResNet50 | 支持 | 支持 |
| ResNet | ResNet50_vd | 支持 | 支持 |
| SwinTransformer | SwinTransformer_tiny_patch4_window7_224 | 支持 | 支持 |
## 2. 测试流程
### 2.1 准备数据
分类模型默认使用`./deploy/paddleserving/daisy.jpg`作为测试输入图片,无需下载
识别模型默认使用`drink_dataset_v1.0/test_images/001.jpeg`作为测试输入图片,在**2.2 准备环境**中会下载好。
### 2.2 准备环境
- 安装PaddlePaddle:如果您已经安装了2.2或者以上版本的paddlepaddle,那么无需运行下面的命令安装paddlepaddle。
```shell
# 需要安装2.2及以上版本的Paddle
# 安装GPU版本的Paddle
python3.7 -m pip install paddlepaddle-gpu==2.2.0
# 安装CPU版本的Paddle
python3.7 -m pip install paddlepaddle==2.2.0
```
- 安装依赖
```shell
python3.7 -m pip install -r requirements.txt
```
- 安装 PaddleServing 相关组件,包括serving_client、serving-app,自动编译并安装带自定义OP的 serving_server 包,以及自动下载并解压推理模型
```bash
bash test_tipc/prepare.sh test_tipc/configs/PPLCNet/PPLCNet_x1_0_linux_gpu_normal_normal_serving_cpp_linux_gpu_cpu.txt serving_infer
```
### 2.3 功能测试
测试方法如下所示,希望测试不同的模型文件,只需更换为自己的参数配置文件,即可完成对应模型的测试。
```bash
bash test_tipc/test_serving_infer_cpp.sh ${your_params_file}
```
`PPLCNet_x1_0``Linux GPU/CPU C++ 服务化部署测试`为例,命令如下所示。
```bash
bash test_tipc/test_serving_infer_cpp.sh test_tipc/configs/PPLCNet/PPLCNet_x1_0_linux_gpu_normal_normal_serving_cpp_linux_gpu_cpu.txt
```
输出结果如下,表示命令运行成功。
```
Run successfully with command - PPLCNet_x1_0 - python3.7 test_cpp_serving_client.py > ../../test_tipc/output/PPLCNet_x1_0/server_infer_cpp_gpu_pipeline_batchsize_1.log 2>&1 !
Run successfully with command - PPLCNet_x1_0 - python3.7 test_cpp_serving_client.py > ../../test_tipc/output/PPLCNet_x1_0/server_infer_cpp_cpu_pipeline_batchsize_1.log 2>&1 !
```
预测结果会自动保存在 `./test_tipc/output/PPLCNet_x1_0/server_infer_gpu_pipeline_http_batchsize_1.log` ,可以看到 PaddleServing 的运行结果:
```
WARNING: Logging before InitGoogleLogging() is written to STDERR
I0612 09:55:16.109890 38303 naming_service_thread.cpp:202] brpc::policy::ListNamingService("127.0.0.1:9292"): added 1
I0612 09:55:16.172924 38303 general_model.cpp:490] [client]logid=0,client_cost=60.772ms,server_cost=57.6ms.
prediction: daisy, probability: 0.9099399447441101
0.06275796890258789
```
如果运行失败,也会在终端中输出运行失败的日志信息以及对应的运行命令。可以基于该命令,分析运行失败的原因。
# Linux GPU/CPU PYTHON 服务化部署测试 # Linux GPU/CPU PYTHON 服务化部署测试
Linux GPU/CPU PYTHON 服务化部署测试的主程序为`test_serving_infer.sh`,可以测试基于Python的模型服务化部署功能。 Linux GPU/CPU PYTHON 服务化部署测试的主程序为`test_serving_infer_python.sh`,可以测试基于Python的模型服务化部署功能。
## 1. 测试结论汇总 ## 1. 测试结论汇总
...@@ -60,14 +60,14 @@ Linux GPU/CPU PYTHON 服务化部署测试的主程序为`test_serving_infer.sh ...@@ -60,14 +60,14 @@ Linux GPU/CPU PYTHON 服务化部署测试的主程序为`test_serving_infer.sh
测试方法如下所示,希望测试不同的模型文件,只需更换为自己的参数配置文件,即可完成对应模型的测试。 测试方法如下所示,希望测试不同的模型文件,只需更换为自己的参数配置文件,即可完成对应模型的测试。
```bash ```bash
bash test_tipc/test_serving_infer_python.sh ${your_params_file} lite_train_lite_infer bash test_tipc/test_serving_infer_python.sh ${your_params_file}
``` ```
`ResNet50``Linux GPU/CPU PYTHON 服务化部署测试`为例,命令如下所示。 `ResNet50``Linux GPU/CPU PYTHON 服务化部署测试`为例,命令如下所示。
```bash ```bash
bash test_tipc/test_serving_infer_python.sh test_tipc/configs/ResNet50/ResNet50_linux_gpu_normal_normal_serving_python_linux_gpu_cpu.txt serving_infer bash test_tipc/test_serving_infer_python.sh test_tipc/configs/ResNet50/ResNet50_linux_gpu_normal_normal_serving_python_linux_gpu_cpu.txt
``` ```
输出结果如下,表示命令运行成功。 输出结果如下,表示命令运行成功。
......
...@@ -200,15 +200,25 @@ fi ...@@ -200,15 +200,25 @@ fi
if [[ ${MODE} = "serving_infer" ]]; then if [[ ${MODE} = "serving_infer" ]]; then
# prepare serving env # prepare serving env
python_name=$(func_parser_value "${lines[2]}") python_name=$(func_parser_value "${lines[2]}")
${python_name} -m pip install paddle-serving-server-gpu==0.7.0.post102 ${python_name} -m pip install paddle_serving_client==0.9.0 -i https://pypi.tuna.tsinghua.edu.cn/simple
${python_name} -m pip install paddle_serving_client==0.7.0 ${python_name} -m pip install paddle-serving-app==0.9.0 -i https://pypi.tuna.tsinghua.edu.cn/simple
${python_name} -m pip install paddle-serving-app==0.7.0 python_name=$(func_parser_value "${lines[2]}")
if [[ ${FILENAME} =~ "cpp" ]]; then
pushd ./deploy/paddleserving
bash build_server.sh ${python_name}
popd
else
${python_name} -m pip install install paddle-serving-server-gpu==0.9.0.post101 -i https://pypi.tuna.tsinghua.edu.cn/simple
fi
if [[ ${model_name} =~ "ShiTu" ]]; then if [[ ${model_name} =~ "ShiTu" ]]; then
${python_name} -m pip install faiss-cpu==1.7.1post2 -i https://pypi.tuna.tsinghua.edu.cn/simple
cls_inference_model_url=$(func_parser_value "${lines[3]}") cls_inference_model_url=$(func_parser_value "${lines[3]}")
cls_tar_name=$(func_get_url_file_name "${cls_inference_model_url}") cls_tar_name=$(func_get_url_file_name "${cls_inference_model_url}")
det_inference_model_url=$(func_parser_value "${lines[4]}") det_inference_model_url=$(func_parser_value "${lines[4]}")
det_tar_name=$(func_get_url_file_name "${det_inference_model_url}") det_tar_name=$(func_get_url_file_name "${det_inference_model_url}")
cd ./deploy cd ./deploy
wget -nc https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/rec/data/drink_dataset_v1.0.tar --no-check-certificate
tar -xf drink_dataset_v1.0.tar
mkdir models mkdir models
cd models cd models
wget -nc ${cls_inference_model_url} && tar xf ${cls_tar_name} wget -nc ${cls_inference_model_url} && tar xf ${cls_tar_name}
......
...@@ -8,5 +8,11 @@ num_workers=8 ...@@ -8,5 +8,11 @@ num_workers=8
# get data # get data
bash test_tipc/static/${model_item}/benchmark_common/prepare.sh bash test_tipc/static/${model_item}/benchmark_common/prepare.sh
cd ./dataset/ILSVRC2012
cat train_list.txt >> tmp
for i in {1..10}; do cat tmp >> train_list.txt; done
cd ../../
# run # run
bash test_tipc/static/${model_item}/benchmark_common/run_benchmark.sh ${model_item} ${bs_item} ${fp_item} ${run_mode} ${device_num} ${max_epochs} ${num_workers} 2>&1; bash test_tipc/static/${model_item}/benchmark_common/run_benchmark.sh ${model_item} ${bs_item} ${fp_item} ${run_mode} ${device_num} ${max_epochs} ${num_workers} 2>&1;
...@@ -8,5 +8,11 @@ num_workers=8 ...@@ -8,5 +8,11 @@ num_workers=8
# get data # get data
bash test_tipc/static/${model_item}/benchmark_common/prepare.sh bash test_tipc/static/${model_item}/benchmark_common/prepare.sh
cd ./dataset/ILSVRC2012
cat train_list.txt >> tmp
for i in {1..10}; do cat tmp >> train_list.txt; done
cd ../../
# run # run
bash test_tipc/static/${model_item}/benchmark_common/run_benchmark.sh ${model_item} ${bs_item} ${fp_item} ${run_mode} ${device_num} ${max_epochs} ${num_workers} 2>&1; bash test_tipc/static/${model_item}/benchmark_common/run_benchmark.sh ${model_item} ${bs_item} ${fp_item} ${run_mode} ${device_num} ${max_epochs} ${num_workers} 2>&1;
...@@ -8,5 +8,11 @@ num_workers=8 ...@@ -8,5 +8,11 @@ num_workers=8
# get data # get data
bash test_tipc/static/${model_item}/benchmark_common/prepare.sh bash test_tipc/static/${model_item}/benchmark_common/prepare.sh
cd ./dataset/ILSVRC2012
cat train_list.txt >> tmp
for i in {1..10}; do cat tmp >> train_list.txt; done
cd ../../
# run # run
bash test_tipc/static/${model_item}/benchmark_common/run_benchmark.sh ${model_item} ${bs_item} ${fp_item} ${run_mode} ${device_num} ${max_epochs} ${num_workers} 2>&1; bash test_tipc/static/${model_item}/benchmark_common/run_benchmark.sh ${model_item} ${bs_item} ${fp_item} ${run_mode} ${device_num} ${max_epochs} ${num_workers} 2>&1;
#!/bin/bash
source test_tipc/common_func.sh
FILENAME=$1
dataline=$(awk 'NR==1, NR==19{print}' $FILENAME)
# parser params
IFS=$'\n'
lines=(${dataline})
function func_get_url_file_name(){
strs=$1
IFS="/"
array=(${strs})
tmp=${array[${#array[@]}-1]}
echo ${tmp}
}
# parser serving
model_name=$(func_parser_value "${lines[1]}")
python=$(func_parser_value "${lines[2]}")
trans_model_py=$(func_parser_value "${lines[4]}")
infer_model_dir_key=$(func_parser_key "${lines[5]}")
infer_model_dir_value=$(func_parser_value "${lines[5]}")
model_filename_key=$(func_parser_key "${lines[6]}")
model_filename_value=$(func_parser_value "${lines[6]}")
params_filename_key=$(func_parser_key "${lines[7]}")
params_filename_value=$(func_parser_value "${lines[7]}")
serving_server_key=$(func_parser_key "${lines[8]}")
serving_server_value=$(func_parser_value "${lines[8]}")
serving_client_key=$(func_parser_key "${lines[9]}")
serving_client_value=$(func_parser_value "${lines[9]}")
serving_dir_value=$(func_parser_value "${lines[10]}")
web_service_py=$(func_parser_value "${lines[11]}")
web_use_gpu_key=$(func_parser_key "${lines[12]}")
web_use_gpu_list=$(func_parser_value "${lines[12]}")
pipeline_py=$(func_parser_value "${lines[13]}")
function func_serving_cls(){
LOG_PATH="test_tipc/output/${model_name}"
mkdir -p ${LOG_PATH}
LOG_PATH="../../${LOG_PATH}"
status_log="${LOG_PATH}/results_serving.log"
IFS='|'
# pdserving
set_dirname=$(func_set_params "${infer_model_dir_key}" "${infer_model_dir_value}")
set_model_filename=$(func_set_params "${model_filename_key}" "${model_filename_value}")
set_params_filename=$(func_set_params "${params_filename_key}" "${params_filename_value}")
set_serving_server=$(func_set_params "${serving_server_key}" "${serving_server_value}")
set_serving_client=$(func_set_params "${serving_client_key}" "${serving_client_value}")
for python_ in ${python[*]}; do
if [[ ${python_} =~ "python" ]]; then
trans_model_cmd="${python_} ${trans_model_py} ${set_dirname} ${set_model_filename} ${set_params_filename} ${set_serving_server} ${set_serving_client}"
eval ${trans_model_cmd}
break
fi
done
# modify the alias_name of fetch_var to "outputs"
server_fetch_var_line_cmd="sed -i '/fetch_var/,/is_lod_tensor/s/alias_name: .*/alias_name: \"prediction\"/' ${serving_server_value}/serving_server_conf.prototxt"
eval ${server_fetch_var_line_cmd}
client_fetch_var_line_cmd="sed -i '/fetch_var/,/is_lod_tensor/s/alias_name: .*/alias_name: \"prediction\"/' ${serving_client_value}/serving_client_conf.prototxt"
eval ${client_fetch_var_line_cmd}
prototxt_dataline=$(awk 'NR==1, NR==3{print}' ${serving_server_value}/serving_server_conf.prototxt)
IFS=$'\n'
prototxt_lines=(${prototxt_dataline})
feed_var_name=$(func_parser_value "${prototxt_lines[2]}")
IFS='|'
cd ${serving_dir_value}
unset https_proxy
unset http_proxy
for item in ${python[*]}; do
if [[ ${item} =~ "python" ]]; then
python_=${item}
break
fi
done
serving_client_dir_name=$(func_get_url_file_name "$serving_client_value")
set_client_feed_type_cmd="sed -i '/feed_type/,/: .*/s/feed_type: .*/feed_type: 20/' ${serving_client_dir_name}/serving_client_conf.prototxt"
eval ${set_client_feed_type_cmd}
set_client_shape_cmd="sed -i '/shape: 3/,/shape: 3/s/shape: 3/shape: 1/' ${serving_client_dir_name}/serving_client_conf.prototxt"
eval ${set_client_shape_cmd}
set_client_shape224_cmd="sed -i '/shape: 224/,/shape: 224/s/shape: 224//' ${serving_client_dir_name}/serving_client_conf.prototxt"
eval ${set_client_shape224_cmd}
set_client_shape224_cmd="sed -i '/shape: 224/,/shape: 224/s/shape: 224//' ${serving_client_dir_name}/serving_client_conf.prototxt"
eval ${set_client_shape224_cmd}
set_pipeline_load_config_cmd="sed -i '/load_client_config/,/.prototxt/s/.\/.*\/serving_client_conf.prototxt/.\/${serving_client_dir_name}\/serving_client_conf.prototxt/' ${pipeline_py}"
eval ${set_pipeline_load_config_cmd}
set_pipeline_feed_var_cmd="sed -i '/feed=/,/: image}/s/feed={.*: image}/feed={${feed_var_name}: image}/' ${pipeline_py}"
eval ${set_pipeline_feed_var_cmd}
serving_server_dir_name=$(func_get_url_file_name "$serving_server_value")
for use_gpu in ${web_use_gpu_list[*]}; do
if [[ ${use_gpu} = "null" ]]; then
web_service_cpp_cmd="${python_} -m paddle_serving_server.serve --model ${serving_server_dir_name} --op GeneralClasOp --port 9292 &"
eval ${web_service_cpp_cmd}
sleep 5s
_save_log_path="${LOG_PATH}/server_infer_cpp_cpu_pipeline_batchsize_1.log"
pipeline_cmd="${python_} test_cpp_serving_client.py > ${_save_log_path} 2>&1 "
eval ${pipeline_cmd}
last_status=${PIPESTATUS[0]}
eval "cat ${_save_log_path}"
status_check ${last_status} "${pipeline_cmd}" "${status_log}" "${model_name}"
eval "${python_} -m paddle_serving_server.serve stop"
sleep 5s
else
web_service_cpp_cmd="${python_} -m paddle_serving_server.serve --model ${serving_server_dir_name} --op GeneralClasOp --port 9292 --gpu_id=${use_gpu} &"
eval ${web_service_cpp_cmd}
sleep 8s
_save_log_path="${LOG_PATH}/server_infer_cpp_gpu_pipeline_batchsize_1.log"
pipeline_cmd="${python_} test_cpp_serving_client.py > ${_save_log_path} 2>&1 "
eval ${pipeline_cmd}
last_status=${PIPESTATUS[0]}
eval "cat ${_save_log_path}"
status_check ${last_status} "${pipeline_cmd}" "${status_log}" "${model_name}"
sleep 5s
eval "${python_} -m paddle_serving_server.serve stop"
fi
done
}
function func_serving_rec(){
LOG_PATH="test_tipc/output/${model_name}"
mkdir -p ${LOG_PATH}
LOG_PATH="../../../${LOG_PATH}"
status_log="${LOG_PATH}/results_serving.log"
trans_model_py=$(func_parser_value "${lines[5]}")
cls_infer_model_dir_key=$(func_parser_key "${lines[6]}")
cls_infer_model_dir_value=$(func_parser_value "${lines[6]}")
det_infer_model_dir_key=$(func_parser_key "${lines[7]}")
det_infer_model_dir_value=$(func_parser_value "${lines[7]}")
model_filename_key=$(func_parser_key "${lines[8]}")
model_filename_value=$(func_parser_value "${lines[8]}")
params_filename_key=$(func_parser_key "${lines[9]}")
params_filename_value=$(func_parser_value "${lines[9]}")
cls_serving_server_key=$(func_parser_key "${lines[10]}")
cls_serving_server_value=$(func_parser_value "${lines[10]}")
cls_serving_client_key=$(func_parser_key "${lines[11]}")
cls_serving_client_value=$(func_parser_value "${lines[11]}")
det_serving_server_key=$(func_parser_key "${lines[12]}")
det_serving_server_value=$(func_parser_value "${lines[12]}")
det_serving_client_key=$(func_parser_key "${lines[13]}")
det_serving_client_value=$(func_parser_value "${lines[13]}")
serving_dir_value=$(func_parser_value "${lines[14]}")
web_service_py=$(func_parser_value "${lines[15]}")
web_use_gpu_key=$(func_parser_key "${lines[16]}")
web_use_gpu_list=$(func_parser_value "${lines[16]}")
pipeline_py=$(func_parser_value "${lines[17]}")
IFS='|'
for python_ in ${python[*]}; do
if [[ ${python_} =~ "python" ]]; then
python_interp=${python_}
break
fi
done
# pdserving
cd ./deploy
set_dirname=$(func_set_params "${cls_infer_model_dir_key}" "${cls_infer_model_dir_value}")
set_model_filename=$(func_set_params "${model_filename_key}" "${model_filename_value}")
set_params_filename=$(func_set_params "${params_filename_key}" "${params_filename_value}")
set_serving_server=$(func_set_params "${cls_serving_server_key}" "${cls_serving_server_value}")
set_serving_client=$(func_set_params "${cls_serving_client_key}" "${cls_serving_client_value}")
cls_trans_model_cmd="${python_interp} ${trans_model_py} ${set_dirname} ${set_model_filename} ${set_params_filename} ${set_serving_server} ${set_serving_client}"
eval ${cls_trans_model_cmd}
set_dirname=$(func_set_params "${det_infer_model_dir_key}" "${det_infer_model_dir_value}")
set_model_filename=$(func_set_params "${model_filename_key}" "${model_filename_value}")
set_params_filename=$(func_set_params "${params_filename_key}" "${params_filename_value}")
set_serving_server=$(func_set_params "${det_serving_server_key}" "${det_serving_server_value}")
set_serving_client=$(func_set_params "${det_serving_client_key}" "${det_serving_client_value}")
det_trans_model_cmd="${python_interp} ${trans_model_py} ${set_dirname} ${set_model_filename} ${set_params_filename} ${set_serving_server} ${set_serving_client}"
eval ${det_trans_model_cmd}
cp_prototxt_cmd="cp ./paddleserving/recognition/preprocess/general_PPLCNet_x2_5_lite_v1.0_serving/*.prototxt ${cls_serving_server_value}"
eval ${cp_prototxt_cmd}
cp_prototxt_cmd="cp ./paddleserving/recognition/preprocess/general_PPLCNet_x2_5_lite_v1.0_client/*.prototxt ${cls_serving_client_value}"
eval ${cp_prototxt_cmd}
cp_prototxt_cmd="cp ./paddleserving/recognition/preprocess/picodet_PPLCNet_x2_5_mainbody_lite_v1.0_client/*.prototxt ${det_serving_client_value}"
eval ${cp_prototxt_cmd}
cp_prototxt_cmd="cp ./paddleserving/recognition/preprocess/picodet_PPLCNet_x2_5_mainbody_lite_v1.0_serving/*.prototxt ${det_serving_server_value}"
eval ${cp_prototxt_cmd}
prototxt_dataline=$(awk 'NR==1, NR==3{print}' ${cls_serving_server_value}/serving_server_conf.prototxt)
IFS=$'\n'
prototxt_lines=(${prototxt_dataline})
feed_var_name=$(func_parser_value "${prototxt_lines[2]}")
IFS='|'
cd ${serving_dir_value}
unset https_proxy
unset http_proxy
export SERVING_BIN=${PWD}/../Serving/server-build-gpu-opencv/core/general-server/serving
for use_gpu in ${web_use_gpu_list[*]}; do
if [ ${use_gpu} = "null" ]; then
det_serving_server_dir_name=$(func_get_url_file_name "$det_serving_server_value")
web_service_cpp_cmd="${python_interp} -m paddle_serving_server.serve --model ../../${det_serving_server_value} ../../${cls_serving_server_value} --op GeneralPicodetOp GeneralFeatureExtractOp --port 9400 &"
eval ${web_service_cpp_cmd}
sleep 5s
_save_log_path="${LOG_PATH}/server_infer_cpp_cpu_batchsize_1.log"
pipeline_cmd="${python_interp} ${pipeline_py} > ${_save_log_path} 2>&1 "
eval ${pipeline_cmd}
last_status=${PIPESTATUS[0]}
eval "cat ${_save_log_path}"
status_check ${last_status} "${pipeline_cmd}" "${status_log}" "${model_name}"
eval "${python_} -m paddle_serving_server.serve stop"
sleep 5s
else
det_serving_server_dir_name=$(func_get_url_file_name "$det_serving_server_value")
web_service_cpp_cmd="${python_interp} -m paddle_serving_server.serve --model ../../${det_serving_server_value} ../../${cls_serving_server_value} --op GeneralPicodetOp GeneralFeatureExtractOp --port 9400 --gpu_id=${use_gpu} &"
eval ${web_service_cpp_cmd}
sleep 5s
_save_log_path="${LOG_PATH}/server_infer_cpp_gpu_batchsize_1.log"
pipeline_cmd="${python_interp} ${pipeline_py} > ${_save_log_path} 2>&1 "
eval ${pipeline_cmd}
last_status=${PIPESTATUS[0]}
eval "cat ${_save_log_path}"
status_check ${last_status} "${pipeline_cmd}" "${status_log}" "${model_name}"
eval "${python_} -m paddle_serving_server.serve stop"
sleep 5s
fi
done
}
# set cuda device
GPUID=$2
if [ ${#GPUID} -le 0 ];then
env=" "
else
env="export CUDA_VISIBLE_DEVICES=${GPUID}"
fi
set CUDA_VISIBLE_DEVICES
eval ${env}
echo "################### run test ###################"
export Count=0
IFS="|"
if [[ ${model_name} =~ "ShiTu" ]]; then
func_serving_rec
else
func_serving_cls
fi
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