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未验证 提交 5a33e458 编写于 作者: Z Zhang Jun 提交者： GitHub
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Merge branch 'develop' into xpu-fix

上级 40f2ca3e 0c45b440
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Showing with 320 addition and 172 deletion
core/general-server/op/general_detection_op.cpp
浏览文件 @ 5a33e458
......@@ -22,7 +22,6 @@
#include "core/predictor/framework/resource.h"
#include "core/util/include/timer.h"
/*
#include "opencv2/imgcodecs/legacy/constants_c.h"
#include "opencv2/imgproc/types_c.h"
......@@ -52,7 +51,7 @@ int GeneralDetectionOp::inference() {
}
const std::string pre_name = pre_node_names[0];
const GeneralBlob *input_blob = get_depend_argument<GeneralBlob>(pre_name);
const GeneralBlob* input_blob = get_depend_argument<GeneralBlob>(pre_name);
if (!input_blob) {
LOG(ERROR) << "input_blob is nullptr,error";
return -1;
......@@ -60,7 +59,7 @@ int GeneralDetectionOp::inference() {
uint64_t log_id = input_blob->GetLogId();
VLOG(2) << "(logid=" << log_id << ") Get precedent op name: " << pre_name;
GeneralBlob *output_blob = mutable_data<GeneralBlob>();
GeneralBlob* output_blob = mutable_data<GeneralBlob>();
if (!output_blob) {
LOG(ERROR) << "output_blob is nullptr,error";
return -1;
......@@ -73,7 +72,7 @@ int GeneralDetectionOp::inference() {
return -1;
}
const TensorVector *in = &input_blob->tensor_vector;
const TensorVector* in = &input_blob->tensor_vector;
TensorVector* out = &output_blob->tensor_vector;
int batch_size = input_blob->_batch_size;
......@@ -81,20 +80,18 @@ int GeneralDetectionOp::inference() {
output_blob->_batch_size = batch_size;
VLOG(2) << "(logid=" << log_id << ") infer batch size: " << batch_size;
std::vector<int> input_shape;
int in_num =0;
int in_num = 0;
void* databuf_data = NULL;
char* databuf_char = NULL;
size_t databuf_size = 0;
// now only support single string
char* total_input_ptr = static_cast<char*>(in->at(0).data.data());
std::string base64str = total_input_ptr;
std::string* input_ptr = static_cast<std::string*>(in->at(0).data.data());
std::string base64str = input_ptr[0];
float ratio_h{};
float ratio_w{};
cv::Mat img = Base2Mat(base64str);
cv::Mat srcimg;
cv::Mat resize_img;
......@@ -103,16 +100,19 @@ int GeneralDetectionOp::inference() {
cv::Mat crop_img;
img.copyTo(srcimg);
this->resize_op_.Run(img, resize_img, this->max_side_len_, ratio_h, ratio_w,
this->resize_op_.Run(img,
resize_img,
this->max_side_len_,
ratio_h,
ratio_w,
this->use_tensorrt_);
this->normalize_op_.Run(&resize_img, this->mean_det, this->scale_det,
this->is_scale_);
this->normalize_op_.Run(
&resize_img, this->mean_det, this->scale_det, this->is_scale_);
std::vector<float> input(1 * 3 * resize_img.rows * resize_img.cols, 0.0f);
this->permute_op_.Run(&resize_img, input.data());
TensorVector* real_in = new TensorVector();
if (!real_in) {
LOG(ERROR) << "real_in is nullptr,error";
......@@ -121,14 +121,15 @@ int GeneralDetectionOp::inference() {
for (int i = 0; i < in->size(); ++i) {
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);
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);
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;
......@@ -150,14 +151,16 @@ int GeneralDetectionOp::inference() {
<< ") Failed do infer in fluid model: " << engine_name().c_str();
return -1;
}
delete real_in;
std::vector<int> output_shape;
int out_num =0;
int out_num = 0;
void* databuf_data_out = NULL;
char* databuf_char_out = NULL;
size_t databuf_size_out = 0;
//this is special add for PaddleOCR postprecess
// this is special add for PaddleOCR postprecess
int infer_outnum = out->size();
for (int k = 0;k <infer_outnum; ++k) {
for (int k = 0; k < infer_outnum; ++k) {
int n2 = out->at(k).shape[2];
int n3 = out->at(k).shape[3];
int n = n2 * n3;
......@@ -171,17 +174,19 @@ int GeneralDetectionOp::inference() {
cbuf[i] = (unsigned char)((out_data[i]) * 255);
}
cv::Mat cbuf_map(n2, n3, CV_8UC1, (unsigned char *)cbuf.data());
cv::Mat pred_map(n2, n3, CV_32F, (float *)pred.data());
cv::Mat cbuf_map(n2, n3, CV_8UC1, (unsigned char*)cbuf.data());
cv::Mat pred_map(n2, n3, CV_32F, (float*)pred.data());
const double threshold = this->det_db_thresh_ * 255;
const double maxvalue = 255;
cv::Mat bit_map;
cv::threshold(cbuf_map, bit_map, threshold, maxvalue, cv::THRESH_BINARY);
cv::Mat dilation_map;
cv::Mat dila_ele = cv::getStructuringElement(cv::MORPH_RECT, cv::Size(2, 2));
cv::Mat dila_ele =
cv::getStructuringElement(cv::MORPH_RECT, cv::Size(2, 2));
cv::dilate(bit_map, dilation_map, dila_ele);
boxes = post_processor_.BoxesFromBitmap(pred_map, dilation_map,
boxes = post_processor_.BoxesFromBitmap(pred_map,
dilation_map,
this->det_db_box_thresh_,
this->det_db_unclip_ratio_);
......@@ -192,25 +197,28 @@ int GeneralDetectionOp::inference() {
float wh_ratio = float(crop_img.cols) / float(crop_img.rows);
this->resize_op_rec.Run(crop_img, resize_img_rec, wh_ratio, this->use_tensorrt_);
this->resize_op_rec.Run(
crop_img, resize_img_rec, wh_ratio, this->use_tensorrt_);
this->normalize_op_.Run(&resize_img_rec, this->mean_rec, this->scale_rec,
this->is_scale_);
this->normalize_op_.Run(
&resize_img_rec, this->mean_rec, this->scale_rec, this->is_scale_);
std::vector<float> output_rec(1 * 3 * resize_img_rec.rows * resize_img_rec.cols, 0.0f);
std::vector<float> output_rec(
1 * 3 * resize_img_rec.rows * resize_img_rec.cols, 0.0f);
this->permute_op_.Run(&resize_img_rec, output_rec.data());
// Inference.
output_shape = {1, 3, resize_img_rec.rows, resize_img_rec.cols};
out_num = std::accumulate(output_shape.begin(), output_shape.end(), 1, std::multiplies<int>());
databuf_size_out = out_num*sizeof(float);
out_num = std::accumulate(
output_shape.begin(), output_shape.end(), 1, std::multiplies<int>());
databuf_size_out = out_num * sizeof(float);
databuf_data_out = MempoolWrapper::instance().malloc(databuf_size_out);
if (!databuf_data_out) {
LOG(ERROR) << "Malloc failed, size: " << databuf_size_out;
return -1;
}
memcpy(databuf_data_out,output_rec.data(),databuf_size_out);
memcpy(databuf_data_out, output_rec.data(), databuf_size_out);
databuf_char_out = reinterpret_cast<char*>(databuf_data_out);
paddle::PaddleBuf paddleBuf(databuf_char_out, databuf_size_out);
paddle::PaddleTensor tensor_out;
......@@ -221,8 +229,7 @@ int GeneralDetectionOp::inference() {
out->push_back(tensor_out);
}
}
out->erase(out->begin(),out->begin()+infer_outnum);
out->erase(out->begin(), out->begin() + infer_outnum);
int64_t end = timeline.TimeStampUS();
CopyBlobInfo(input_blob, output_blob);
......@@ -231,58 +238,52 @@ int GeneralDetectionOp::inference() {
return 0;
}
cv::Mat GeneralDetectionOp::Base2Mat(std::string &base64_data)
{
cv::Mat GeneralDetectionOp::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
img = cv::imdecode(base64_img, cv::IMREAD_COLOR); // CV_LOAD_IMAGE_COLOR
return img;
}
std::string GeneralDetectionOp::base64Decode(const char* Data, int DataByte)
{
const char DecodeTable[] =
std::string GeneralDetectionOp::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,
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'
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')
{
while (i < DataByte) {
if (*Data != '\r' && *Data != '\n') {
nValue = DecodeTable[*Data++] << 18;
nValue += DecodeTable[*Data++] << 12;
strDecode += (nValue & 0x00FF0000) >> 16;
if (*Data != '=')
{
if (*Data != '=') {
nValue += DecodeTable[*Data++] << 6;
strDecode += (nValue & 0x0000FF00) >> 8;
if (*Data != '=')
{
if (*Data != '=') {
nValue += DecodeTable[*Data++];
strDecode += nValue & 0x000000FF;
}
}
i += 4;
}
else// 回车换行,跳过
} else // 回车换行,跳过
{
Data++;
i++;
......@@ -291,8 +292,8 @@ std::string GeneralDetectionOp::base64Decode(const char* Data, int DataByte)
return strDecode;
}
cv::Mat GeneralDetectionOp::GetRotateCropImage(const cv::Mat &srcimage,
std::vector<std::vector<int>> box) {
cv::Mat GeneralDetectionOp::GetRotateCropImage(
const cv::Mat& srcimage, std::vector<std::vector<int>> box) {
cv::Mat image;
srcimage.copyTo(image);
std::vector<std::vector<int>> points = box;
......@@ -332,7 +333,9 @@ cv::Mat GeneralDetectionOp::GetRotateCropImage(const cv::Mat &srcimage,
cv::Mat M = cv::getPerspectiveTransform(pointsf, pts_std);
cv::Mat dst_img;
cv::warpPerspective(img_crop, dst_img, M,
cv::warpPerspective(img_crop,
dst_img,
M,
cv::Size(img_crop_width, img_crop_height),
cv::BORDER_REPLICATE);
......
core/general-server/op/general_reader_op.cpp
浏览文件 @ 5a33e458
......@@ -77,9 +77,6 @@ int GeneralReaderOp::inference() {
uint64_t log_id = req->log_id();
int input_var_num = 0;
std::vector<int64_t> elem_type;
std::vector<int64_t> elem_size;
std::vector<int64_t> databuf_size;
GeneralBlob *res = mutable_data<GeneralBlob>();
if (!res) {
......@@ -119,40 +116,44 @@ int GeneralReaderOp::inference() {
}
*/
// package tensor
elem_type.resize(var_num);
elem_size.resize(var_num);
databuf_size.resize(var_num);
// prepare basic information for input
// specify the memory needed for output tensor_vector
// fill the data into output general_blob
int data_len = 0;
int64_t elem_type = 0;
int64_t elem_size = 0;
int64_t databuf_size = 0;
for (int i = 0; i < var_num; ++i) {
paddle::PaddleTensor lod_tensor;
paddle::PaddleTensor paddleTensor;
const Tensor &tensor = req->insts(0).tensor_array(i);
data_len = 0;
elem_type[i] = tensor.elem_type();
VLOG(2) << "var[" << i << "] has elem type: " << elem_type[i];
if (elem_type[i] == P_INT64) { // int64
elem_size[i] = sizeof(int64_t);
lod_tensor.dtype = paddle::PaddleDType::INT64;
elem_type = 0;
elem_size = 0;
databuf_size = 0;
elem_type = tensor.elem_type();
VLOG(2) << "var[" << i << "] has elem type: " << elem_type;
if (elem_type == P_INT64) { // int64
elem_size = sizeof(int64_t);
paddleTensor.dtype = paddle::PaddleDType::INT64;
data_len = tensor.int64_data_size();
} else if (elem_type[i] == P_FLOAT32) {
elem_size[i] = sizeof(float);
lod_tensor.dtype = paddle::PaddleDType::FLOAT32;
} else if (elem_type == P_FLOAT32) {
elem_size = sizeof(float);
paddleTensor.dtype = paddle::PaddleDType::FLOAT32;
data_len = tensor.float_data_size();
} else if (elem_type[i] == P_INT32) {
elem_size[i] = sizeof(int32_t);
lod_tensor.dtype = paddle::PaddleDType::INT32;
} else if (elem_type == P_INT32) {
elem_size = sizeof(int32_t);
paddleTensor.dtype = paddle::PaddleDType::INT32;
data_len = tensor.int_data_size();
} else if (elem_type[i] == P_STRING) {
} else if (elem_type == P_STRING) {
// use paddle::PaddleDType::UINT8 as for String.
elem_size[i] = sizeof(uint8_t);
lod_tensor.dtype = paddle::PaddleDType::UINT8;
elem_size = sizeof(char);
paddleTensor.dtype = paddle::PaddleDType::UINT8;
// this is for vector<String>, cause the databuf_size !=
// vector<String>.size()*sizeof(char);
// data_len should be +1 cause '\0'
// now only support single string
for (int idx = 0; idx < tensor.data_size(); idx++) {
data_len += tensor.data()[idx].length();
data_len += tensor.data()[idx].length() + 1;
}
}
// implement lod tensor here
......@@ -160,29 +161,29 @@ int GeneralReaderOp::inference() {
// TODO(HexToString): support 2-D lod
if (tensor.lod_size() > 0) {
VLOG(2) << "(logid=" << log_id << ") var[" << i << "] is lod_tensor";
lod_tensor.lod.resize(1);
paddleTensor.lod.resize(1);
for (int k = 0; k < tensor.lod_size(); ++k) {
lod_tensor.lod[0].push_back(tensor.lod(k));
paddleTensor.lod[0].push_back(tensor.lod(k));
}
}
for (int k = 0; k < tensor.shape_size(); ++k) {
int dim = tensor.shape(k);
VLOG(2) << "(logid=" << log_id << ") shape for var[" << i << "]: " << dim;
lod_tensor.shape.push_back(dim);
paddleTensor.shape.push_back(dim);
}
lod_tensor.name = model_config->_feed_name[i];
out->push_back(lod_tensor);
paddleTensor.name = model_config->_feed_name[i];
out->push_back(paddleTensor);
VLOG(2) << "(logid=" << log_id << ") tensor size for var[" << i
<< "]: " << data_len;
databuf_size[i] = data_len * elem_size[i];
out->at(i).data.Resize(data_len * elem_size[i]);
databuf_size = data_len * elem_size;
out->at(i).data.Resize(databuf_size);
if (out->at(i).lod.size() > 0) {
VLOG(2) << "(logid=" << log_id << ") var[" << i
<< "] has lod_tensor and len=" << out->at(i).lod[0].back();
}
if (elem_type[i] == P_INT64) {
if (elem_type == P_INT64) {
int64_t *dst_ptr = static_cast<int64_t *>(out->at(i).data.data());
VLOG(2) << "(logid=" << log_id << ") first element data in var[" << i
<< "] is " << tensor.int64_data(0);
......@@ -190,14 +191,14 @@ int GeneralReaderOp::inference() {
LOG(ERROR) << "dst_ptr is nullptr";
return -1;
}
memcpy(dst_ptr, tensor.int64_data().data(), databuf_size[i]);
memcpy(dst_ptr, tensor.int64_data().data(), databuf_size);
/*
int elem_num = tensor.int64_data_size();
for (int k = 0; k < elem_num; ++k) {
dst_ptr[k] = tensor.int64_data(k);
}
*/
} else if (elem_type[i] == P_FLOAT32) {
} else if (elem_type == P_FLOAT32) {
float *dst_ptr = static_cast<float *>(out->at(i).data.data());
VLOG(2) << "(logid=" << log_id << ") first element data in var[" << i
<< "] is " << tensor.float_data(0);
......@@ -205,12 +206,12 @@ int GeneralReaderOp::inference() {
LOG(ERROR) << "dst_ptr is nullptr";
return -1;
}
memcpy(dst_ptr, tensor.float_data().data(), databuf_size[i]);
memcpy(dst_ptr, tensor.float_data().data(), databuf_size);
/*int elem_num = tensor.float_data_size();
for (int k = 0; k < elem_num; ++k) {
dst_ptr[k] = tensor.float_data(k);
}*/
} else if (elem_type[i] == P_INT32) {
} else if (elem_type == P_INT32) {
int32_t *dst_ptr = static_cast<int32_t *>(out->at(i).data.data());
VLOG(2) << "(logid=" << log_id << ") first element data in var[" << i
<< "] is " << tensor.int_data(0);
......@@ -218,15 +219,9 @@ int GeneralReaderOp::inference() {
LOG(ERROR) << "dst_ptr is nullptr";
return -1;
}
memcpy(dst_ptr, tensor.int_data().data(), databuf_size[i]);
/*
int elem_num = tensor.int_data_size();
for (int k = 0; k < elem_num; ++k) {
dst_ptr[k] = tensor.int_data(k);
}
*/
} else if (elem_type[i] == P_STRING) {
std::string *dst_ptr = static_cast<std::string *>(out->at(i).data.data());
memcpy(dst_ptr, tensor.int_data().data(), databuf_size);
} else if (elem_type == P_STRING) {
char *dst_ptr = static_cast<char *>(out->at(i).data.data());
VLOG(2) << "(logid=" << log_id << ") first element data in var[" << i
<< "] is " << tensor.data(0);
if (!dst_ptr) {
......@@ -234,8 +229,12 @@ int GeneralReaderOp::inference() {
return -1;
}
int elem_num = tensor.data_size();
int offset = 0;
for (int k = 0; k < elem_num; ++k) {
dst_ptr[k] = tensor.data(k);
memcpy(dst_ptr + offset,
tensor.data(k).c_str(),
strlen(tensor.data(k).c_str()) + 1);
offset += strlen(tensor.data(k).c_str()) + 1;
}
}
}
......
doc/LOD.md 0 → 100644
浏览文件 @ 5a33e458
# Lod Introduction
(English|[简体中文](LOD_CN.md))
## Principle
LoD(Level-of-Detail) Tensor is an advanced feature of paddle and an extension of tensor. LoD Tensor improves training efficiency by sacrificing flexibility.
**Notice：** For most users, there is no need to pay attention to the usage of LoD Tensor. Currently, serving only supports the usage of one-dimensional LOD.
## Use
**Prerequisite：** Your prediction model needs to support variable length tensor input.
Take the visual task as an example. In the visual task, we often need to process video and image. These elements are high-dimensional objects.
Suppose that an existing Mini batch contains three videos, each video contains three frames, one frame and two frames respectively.
If each frame has the same size: 640x480, the mini batch can be expressed as:
```
3 1 2
口口口 口 口口
```
The size of the bottom tenor is (3 + 1 + 2) x640x480, and each 口 represents a 640x480 image.
Then, the shape of tensor is [6,640,480], lod=[0,3,4,6].
Where 0 is the starting value and 3-0 = 3; 4-3=1; 6-4 = 2, these three values just represent your variable length information.
The last element 6 in LOD should be equal to the total length of the first dimension in shape.
The variable length information recorded in LOD and the first dimension information of shape in tensor should be aligned in the above way.
doc/LOD_CN.md 0 → 100644
浏览文件 @ 5a33e458
# Lod字段说明
(简体中文|[English](LOD.md))
## 概念
LoD(Level-of-Detail) Tensor是Paddle的高级特性，是对Tensor的一种扩充。LoDTensor通过牺牲灵活性来提升训练的效率。
**注：** 对于大部分用户来说，无需关注LoDTensor的用法，目前Serving中仅支持一维Lod的用法。
## 使用
**前提：** 首先您的预测模型需要支持变长Tensor的输入。
以视觉任务为例，在视觉任务中，时常需要处理视频和图像这些元素是高维的对象，假设现存的一个mini-batch包含3个视频，分别有3个，1个和2个帧。
每个帧都具有相同大小：640x480，则这个mini-batch可以被表示为：
```
3 1 2
口口口 口 口口
```
最底层tensor大小为（3+1+2）x640x480，每一个 口 表示一个640x480的图像。
那么此时，Tensor的shape为[6,640,480],lod=[0,3,4,6].
其中0为起始值，3-0=3;4-3=1;6-4=2，这三个值正好表示您的变长信息，lod中的最后一个元素6，应等于shape中第一维度的总长度。
lod中记录的变长信息与Tensor中shape的第一维度的信息应按照上述方式对齐。
doc/LOW_PRECISION_DEPLOYMENT.md
浏览文件 @ 5a33e458
......@@ -17,7 +17,7 @@ python -m paddle_serving_client.convert --dirname ResNet50_quant
```
Start RPC service, specify the GPU id and precision mode
```
python -m paddle_serving_server.serve --model serving_server --port 9393 --gpu_ids 0 --use_gpu --use_trt --precision int8
python -m paddle_serving_server.serve --model serving_server --port 9393 --gpu_ids 0 --use_trt --precision int8
```
Request the serving service with Client
```
......@@ -27,7 +27,7 @@ from paddle_serving_app.reader import RGB2BGR, Transpose, Div, Normalize
client = Client()
client.load_client_config(
"resnet_v2_50_imagenet_client/serving_client_conf.prototxt")
"serving_client/serving_client_conf.prototxt")
client.connect(["127.0.0.1:9393"])
seq = Sequential([
......@@ -37,8 +37,8 @@ seq = Sequential([
image_file = "daisy.jpg"
img = seq(image_file)
fetch_map = client.predict(feed={"image": img}, fetch=["score"])
print(fetch_map["score"].reshape(-1))
fetch_map = client.predict(feed={"image": img}, fetch=["save_infer_model/scale_0.tmp_0"])
print(fetch_map["save_infer_model/scale_0.tmp_0"].reshape(-1))
```
## Reference
......
doc/LOW_PRECISION_DEPLOYMENT_CN.md
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......@@ -16,7 +16,7 @@ python -m paddle_serving_client.convert --dirname ResNet50_quant
```
启动rpc服务, 设定所选GPU id、部署模型精度
```
python -m paddle_serving_server.serve --model serving_server --port 9393 --gpu_ids 0 --use_gpu --use_trt --precision int8
python -m paddle_serving_server.serve --model serving_server --port 9393 --gpu_ids 0 --use_trt --precision int8
```
使用client进行请求
```
......
python/examples/low_precision/resnet50/README.md 0 → 100644
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# resnet50 int8 example
(English|[简体中文](./README_CN.md))
## Obtain the quantized model through PaddleSlim tool
Train the low-precision models please refer to [PaddleSlim](https://paddleslim.readthedocs.io/zh_CN/latest/tutorials/quant/overview.html).
## Deploy the quantized model from PaddleSlim using Paddle Serving with Nvidia TensorRT int8 mode
Firstly, download the [Resnet50 int8 model](https://paddle-inference-dist.bj.bcebos.com/inference_demo/python/resnet50/ResNet50_quant.tar.gz) and convert to Paddle Serving's saved model。
```
wget https://paddle-inference-dist.bj.bcebos.com/inference_demo/python/resnet50/ResNet50_quant.tar.gz
tar zxvf ResNet50_quant.tar.gz
python -m paddle_serving_client.convert --dirname ResNet50_quant
```
Start RPC service, specify the GPU id and precision mode
```
python -m paddle_serving_server.serve --model serving_server --port 9393 --gpu_ids 0 --use_trt --precision int8
```
Request the serving service with Client
```
python resnet50_client.py
```
## Reference
* [PaddleSlim](https://github.com/PaddlePaddle/PaddleSlim)
* [Deploy the quantized model Using Paddle Inference on Intel CPU](https://paddle-inference.readthedocs.io/en/latest/optimize/paddle_x86_cpu_int8.html)
* [Deploy the quantized model Using Paddle Inference on Nvidia GPU](https://paddle-inference.readthedocs.io/en/latest/optimize/paddle_trt.html)
python/examples/low_precision/resnet50/README_CN.md 0 → 100644
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# resnet50 int8示例
(简体中文|[English](./README.md))
## 通过PaddleSlim量化生成低精度模型
详细见[PaddleSlim量化](https://paddleslim.readthedocs.io/zh_CN/latest/tutorials/quant/overview.html)
## 使用TensorRT int8加载PaddleSlim Int8量化模型进行部署
首先下载Resnet50 [PaddleSlim量化模型](https://paddle-inference-dist.bj.bcebos.com/inference_demo/python/resnet50/ResNet50_quant.tar.gz),并转换为Paddle Serving支持的部署模型格式。
```
wget https://paddle-inference-dist.bj.bcebos.com/inference_demo/python/resnet50/ResNet50_quant.tar.gz
tar zxvf ResNet50_quant.tar.gz
python -m paddle_serving_client.convert --dirname ResNet50_quant
```
启动rpc服务, 设定所选GPU id、部署模型精度
```
python -m paddle_serving_server.serve --model serving_server --port 9393 --gpu_ids 0 --use_trt --precision int8
```
使用client进行请求
```
python resnet50_client.py
```
## 参考文档
* [PaddleSlim](https://github.com/PaddlePaddle/PaddleSlim)
* PaddleInference Intel CPU部署量化模型[文档](https://paddle-inference.readthedocs.io/en/latest/optimize/paddle_x86_cpu_int8.html)
* PaddleInference NV GPU部署量化模型[文档](https://paddle-inference.readthedocs.io/en/latest/optimize/paddle_trt.html)
python/examples/low_precision/resnet50/resnet50_client.py 0 → 100644
浏览文件 @ 5a33e458
# 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.
from paddle_serving_client import Client
from paddle_serving_app.reader import Sequential, File2Image, Resize, CenterCrop
from paddle_serving_app.reader import RGB2BGR, Transpose, Div, Normalize
client = Client()
client.load_client_config(
"serving_client/serving_client_conf.prototxt")
client.connect(["127.0.0.1:9303"])
seq = Sequential([
File2Image(), 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)
])
image_file = "daisy.jpg"
img = seq(image_file)
fetch_map = client.predict(feed={"image": img}, fetch=["save_infer_model/scale_0.tmp_0"])
print(fetch_map["save_infer_model/scale_0.tmp_0"].reshape(-1))
python/paddle_serving_server/server.py
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......@@ -386,8 +386,6 @@ class Server(object):
return
if not os.path.exists(self.server_path):
os.system("touch {}/{}.is_download".format(self.module_path,
folder_name))
print('Frist time run, downloading PaddleServing components ...')
r = os.system('wget ' + bin_url + ' --no-check-certificate')
......@@ -403,9 +401,10 @@ class Server(object):
tar = tarfile.open(tar_name)
tar.extractall()
tar.close()
open(download_flag, "a").close()
except:
if os.path.exists(exe_path):
os.remove(exe_path)
if os.path.exists(self.server_path):
os.remove(self.server_path)
raise SystemExit(
'Decompressing failed, please check your permission of {} or disk space left.'
.format(self.module_path))
......
python/pipeline/pipeline_server.py
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......@@ -56,7 +56,6 @@ class PipelineServicer(pipeline_service_pb2_grpc.PipelineServiceServicer):
resp = pipeline_service_pb2.Response()
resp.err_no = channel.ChannelDataErrcode.NO_SERVICE.value
resp.err_msg = "Failed to inference: Service name error."
resp.result = ""
return resp
resp = self._dag_executor.call(request)
return resp
......
tools/scripts/ipipe_py3.sh
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......@@ -323,7 +323,7 @@ function bert_rpc_cpu() {
link_data ${data_dir}
sed -i 's/8860/8861/g' bert_client.py
python3.6 -m paddle_serving_server.serve --model bert_seq128_model/ --port 8861 > ${dir}server_log.txt 2>&1 &
check_result server 3
check_result server 5
cp data-c.txt.1 data-c.txt
head data-c.txt | python3.6 bert_client.py --model bert_seq128_client/serving_client_conf.prototxt > ${dir}client_log.txt 2>&1
check_result client "bert_CPU_RPC server test completed"
......@@ -338,7 +338,7 @@ function pipeline_imagenet() {
data_dir=${data}imagenet/
link_data ${data_dir}
python3.6 resnet50_web_service.py > ${dir}server_log.txt 2>&1 &
check_result server 5
check_result server 8
nvidia-smi
python3.6 pipeline_rpc_client.py > ${dir}client_log.txt 2>&1
check_result client "pipeline_imagenet_GPU_RPC server test completed"
......@@ -355,7 +355,7 @@ function ResNet50_rpc() {
link_data ${data_dir}
sed -i 's/9696/8863/g' resnet50_rpc_client.py
python3.6 -m paddle_serving_server.serve --model ResNet50_vd_model --port 8863 --gpu_ids 0 > ${dir}server_log.txt 2>&1 &
check_result server 5
check_result server 8
nvidia-smi
python3.6 resnet50_rpc_client.py ResNet50_vd_client_config/serving_client_conf.prototxt > ${dir}client_log.txt 2>&1
check_result client "ResNet50_GPU_RPC server test completed"
......@@ -372,7 +372,7 @@ function ResNet101_rpc() {
link_data ${data_dir}
sed -i "22cclient.connect(['127.0.0.1:8864'])" image_rpc_client.py
python3.6 -m paddle_serving_server.serve --model ResNet101_vd_model --port 8864 --gpu_ids 0 > ${dir}server_log.txt 2>&1 &
check_result server 5
check_result server 8
nvidia-smi
python3.6 image_rpc_client.py ResNet101_vd_client_config/serving_client_conf.prototxt > ${dir}client_log.txt 2>&1
check_result client "ResNet101_GPU_RPC server test completed"
......@@ -482,7 +482,7 @@ function cascade_rcnn_rpc() {
link_data ${data_dir}
sed -i "s/9292/8879/g" test_client.py
python3.6 -m paddle_serving_server.serve --model serving_server --port 8879 --gpu_ids 0 --thread 2 > ${dir}server_log.txt 2>&1 &
check_result server 5
check_result server 8
nvidia-smi
python3.6 test_client.py > ${dir}client_log.txt 2>&1
nvidia-smi
......@@ -499,7 +499,7 @@ function deeplabv3_rpc() {
link_data ${data_dir}
sed -i "s/9494/8880/g" deeplabv3_client.py
python3.6 -m paddle_serving_server.serve --model deeplabv3_server --gpu_ids 0 --port 8880 --thread 2 > ${dir}server_log.txt 2>&1 &
check_result server 5
check_result server 10
nvidia-smi
python3.6 deeplabv3_client.py > ${dir}client_log.txt 2>&1
nvidia-smi
......@@ -516,7 +516,7 @@ function mobilenet_rpc() {
tar xf mobilenet_v2_imagenet.tar.gz
sed -i "s/9393/8881/g" mobilenet_tutorial.py
python3.6 -m paddle_serving_server.serve --model mobilenet_v2_imagenet_model --gpu_ids 0 --port 8881 > ${dir}server_log.txt 2>&1 &
check_result server 5
check_result server 8
nvidia-smi
python3.6 mobilenet_tutorial.py > ${dir}client_log.txt 2>&1
nvidia-smi
......@@ -533,7 +533,7 @@ function unet_rpc() {
link_data ${data_dir}
sed -i "s/9494/8882/g" seg_client.py
python3.6 -m paddle_serving_server.serve --model unet_model --gpu_ids 0 --port 8882 > ${dir}server_log.txt 2>&1 &
check_result server 5
check_result server 8
nvidia-smi
python3.6 seg_client.py > ${dir}client_log.txt 2>&1
nvidia-smi
......@@ -599,7 +599,7 @@ function criteo_ctr_rpc_gpu() {
link_data ${data_dir}
sed -i "s/8885/8886/g" test_client.py
python3.6 -m paddle_serving_server.serve --model ctr_serving_model/ --port 8886 --gpu_ids 0 > ${dir}server_log.txt 2>&1 &
check_result server 5
check_result server 8
nvidia-smi
python3.6 test_client.py ctr_client_conf/serving_client_conf.prototxt raw_data/part-0 > ${dir}client_log.txt 2>&1
nvidia-smi
......@@ -617,7 +617,7 @@ function yolov4_rpc_gpu() {
sed -i "s/9393/8887/g" test_client.py
python3.6 -m paddle_serving_server.serve --model yolov4_model --port 8887 --gpu_ids 0 > ${dir}server_log.txt 2>&1 &
nvidia-smi
check_result server 5
check_result server 8
python3.6 test_client.py 000000570688.jpg > ${dir}client_log.txt 2>&1
nvidia-smi
check_result client "yolov4_GPU_RPC server test completed"
......@@ -634,7 +634,7 @@ function senta_rpc_cpu() {
sed -i "s/9393/8887/g" test_client.py
python3.6 -m paddle_serving_server.serve --model yolov4_model --port 8887 --gpu_ids 0 > ${dir}server_log.txt 2>&1 &
nvidia-smi
check_result server 5
check_result server 8
python3.6 test_client.py 000000570688.jpg > ${dir}client_log.txt 2>&1
nvidia-smi
check_result client "senta_GPU_RPC server test completed"
......@@ -724,7 +724,7 @@ function bert_http() {
cp vocab.txt.1 vocab.txt
export CUDA_VISIBLE_DEVICES=0
python3.6 bert_web_service.py bert_seq128_model/ 8878 > ${dir}server_log.txt 2>&1 &
check_result server 5
check_result server 8
curl -H "Content-Type:application/json" -X POST -d '{"feed":[{"words": "hello"}], "fetch":["pooled_output"]}' http://127.0.0.1:8878/bert/prediction > ${dir}client_log.txt 2>&1
check_result client "bert_GPU_HTTP server test completed"
kill_server_process
......@@ -762,7 +762,7 @@ function grpc_yolov4() {
link_data ${data_dir}
echo -e "${GREEN_COLOR}grpc_impl_example_yolov4_GPU_gRPC server started${RES}"
python3.6 -m paddle_serving_server.serve --model yolov4_model --port 9393 --gpu_ids 0 --use_multilang > ${dir}server_log.txt 2>&1 &
check_result server 5
check_result server 10
echo -e "${GREEN_COLOR}grpc_impl_example_yolov4_GPU_gRPC client started${RES}"
python3.6 test_client.py 000000570688.jpg > ${dir}client_log.txt 2>&1
check_result client "grpc_yolov4_GPU_GRPC server test completed"
......
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