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PaddleDetection
未验证 提交 71a95461 编写于 作者: C cnn 提交者： GitHub
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[deploy] support dynamic_shape and add tensorrt tutorial (#2367) 

* TensorRT support dynamic shape

* add TensorRT doc

* update model visual tool, test=fix_document

* python support trt6

* cpp support trt6

* fix code error

* clean code

* python trt dynamic shape and fix code style

* disable log and update doc style

* update opencv lib url

* solov2 trt support dynamic shape

* support tensorrt_int8

* add TensorRT int8 calib data for ppyolo

* set workspace_size=1 << 30 as default

* remove content

* remove some content, and fix code style

* remove images

* update doc style
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dygraph/deploy/cpp/include/object_detector.h
浏览文件 @ 71a95461
......@@ -59,12 +59,17 @@ class ObjectDetector {
explicit ObjectDetector(const std::string& model_dir,
bool use_gpu=false,
const std::string& run_mode="fluid",
const int gpu_id=0) {
const int gpu_id=0,
bool use_dynamic_shape=false,
const int trt_min_shape=1,
const int trt_max_shape=1280,
const int trt_opt_shape=640) {
config_.load_config(model_dir);
threshold_ = config_.draw_threshold_;
image_shape_ = config_.image_shape_;
preprocessor_.Init(config_.preprocess_info_, image_shape_);
LoadModel(model_dir, use_gpu, config_.min_subgraph_size_, 1, run_mode, gpu_id);
LoadModel(model_dir, use_gpu, config_.min_subgraph_size_, 1, run_mode, gpu_id,
use_dynamic_shape, trt_min_shape, trt_max_shape, trt_opt_shape);
}
// Load Paddle inference model
......@@ -74,7 +79,11 @@ class ObjectDetector {
const int min_subgraph_size,
const int batch_size = 1,
const std::string& run_mode = "fluid",
const int gpu_id=0);
const int gpu_id=0,
bool use_dynamic_shape=false,
const int trt_min_shape=1,
const int trt_max_shape=1280,
const int trt_opt_shape=640);
// Run predictor
void Predict(const cv::Mat& im,
......
dygraph/deploy/cpp/include/preprocess_op.h
浏览文件 @ 71a95461
......@@ -37,8 +37,8 @@ class ImageBlob {
std::vector<float> im_shape_;
// Buffer for image data after preprocessing
std::vector<float> im_data_;
// input image width, height
std::vector<int> input_shape_;
// in net data shape(after pad)
std::vector<int> in_net_shape_;
// Evaluation image width and height
//std::vector<float> eval_im_size_f_;
// Scale factor for image size to origin image size
......@@ -90,7 +90,7 @@ class Resize : public PreprocessOp {
keep_ratio_ = item["keep_ratio"].as<bool>();
target_size_ = item["target_size"].as<std::vector<int>>();
if (item["keep_ratio"]) {
input_shape_ = image_shape;
in_net_shape_ = image_shape;
}
}
......@@ -103,7 +103,7 @@ class Resize : public PreprocessOp {
int interp_;
bool keep_ratio_;
std::vector<int> target_size_;
std::vector<int> input_shape_;
std::vector<int> in_net_shape_;
};
// Models with FPN need input shape % stride == 0
......
dygraph/deploy/cpp/scripts/build.sh
浏览文件 @ 71a95461
......@@ -35,11 +35,11 @@ then
echo "set OPENCV_DIR for x86_64"
# linux系统通过以下命令下载预编译的opencv
mkdir -p $(pwd)/deps && cd $(pwd)/deps
wget -c https://bj.bcebos.com/paddleseg/deploy/opencv3.4.6gcc4.8ffmpeg.tar.gz2
tar xvfj opencv3.4.6gcc4.8ffmpeg.tar.gz2 && cd ..
wget -c https://paddledet.bj.bcebos.com/data/opencv3.4.6gcc8.2ffmpeg.zip
unzip opencv3.4.6gcc8.2ffmpeg.zip && cd ..
# set OPENCV_DIR
OPENCV_DIR=$(pwd)/deps/opencv3.4.6gcc4.8ffmpeg/
OPENCV_DIR=$(pwd)/deps/opencv3.4.6gcc8.2ffmpeg
elif [ "$MACHINE_TYPE" = "aarch64" ]
then
......
dygraph/deploy/cpp/src/main.cc
浏览文件 @ 71a95461
......@@ -42,6 +42,10 @@ DEFINE_int32(camera_id, -1, "Device id of camera to predict");
DEFINE_bool(run_benchmark, false, "Whether to predict a image_file repeatedly for benchmark");
DEFINE_double(threshold, 0.5, "Threshold of score.");
DEFINE_string(output_dir, "output", "Directory of output visualization files.");
DEFINE_bool(use_dynamic_shape, false, "Trt use dynamic shape or not");
DEFINE_int32(trt_min_shape, 1, "Min shape of TRT DynamicShapeI");
DEFINE_int32(trt_max_shape, 1280, "Max shape of TRT DynamicShapeI");
DEFINE_int32(trt_opt_shape, 640, "Opt shape of TRT DynamicShapeI");
static std::string DirName(const std::string &filepath) {
auto pos = filepath.rfind(OS_PATH_SEP);
......@@ -198,14 +202,17 @@ int main(int argc, char** argv) {
return -1;
}
if (!(FLAGS_run_mode == "fluid" || FLAGS_run_mode == "trt_fp32"
|| FLAGS_run_mode == "trt_fp16")) {
std::cout << "run_mode should be 'fluid', 'trt_fp32' or 'trt_fp16'.";
|| FLAGS_run_mode == "trt_fp16" || FLAGS_run_mode == "trt_int8")) {
std::cout << "run_mode should be 'fluid', 'trt_fp32', 'trt_fp16' or 'trt_int8'.";
return -1;
}
// Load model and create a object detector
PaddleDetection::ObjectDetector det(FLAGS_model_dir, FLAGS_use_gpu,
FLAGS_run_mode, FLAGS_gpu_id);
const std::vector<int> trt_min_shape = {1, FLAGS_trt_min_shape, FLAGS_trt_min_shape};
const std::vector<int> trt_max_shape = {1, FLAGS_trt_max_shape, FLAGS_trt_max_shape};
const std::vector<int> trt_opt_shape = {1, FLAGS_trt_opt_shape, FLAGS_trt_opt_shape};
PaddleDetection::ObjectDetector det(FLAGS_model_dir, FLAGS_use_gpu, FLAGS_run_mode,
FLAGS_gpu_id, FLAGS_use_dynamic_shape, FLAGS_trt_min_shape,
FLAGS_trt_max_shape, FLAGS_trt_opt_shape);
// Do inference on input video or image
if (!FLAGS_video_path.empty() || FLAGS_use_camera) {
PredictVideo(FLAGS_video_path, &det);
......
dygraph/deploy/cpp/src/object_detector.cc
浏览文件 @ 71a95461
......@@ -27,7 +27,11 @@ void ObjectDetector::LoadModel(const std::string& model_dir,
const int min_subgraph_size,
const int batch_size,
const std::string& run_mode,
const int gpu_id) {
const int gpu_id,
bool use_dynamic_shape,
const int trt_min_shape,
const int trt_max_shape,
const int trt_opt_shape) {
paddle_infer::Config config;
std::string prog_file = model_dir + OS_PATH_SEP + "model.pdmodel";
std::string params_file = model_dir + OS_PATH_SEP + "model.pdiparams";
......@@ -35,26 +39,48 @@ void ObjectDetector::LoadModel(const std::string& model_dir,
if (use_gpu) {
config.EnableUseGpu(200, gpu_id);
config.SwitchIrOptim(true);
// use tensorrt
bool use_calib_mode = false;
if (run_mode != "fluid") {
auto precision = paddle_infer::Config::Precision::kFloat32;
if (run_mode == "trt_fp16") {
if (run_mode == "trt_fp32") {
precision = paddle_infer::Config::Precision::kFloat32;
}
else if (run_mode == "trt_fp16") {
precision = paddle_infer::Config::Precision::kHalf;
} else if (run_mode == "trt_int8") {
printf("TensorRT int8 mode is not supported now, "
"please use 'trt_fp32' or 'trt_fp16' instead");
} else {
if (run_mode != "trt_fp32") {
printf("run_mode should be 'fluid', 'trt_fp32' or 'trt_fp16'");
}
else if (run_mode == "trt_int8") {
precision = paddle_infer::Config::Precision::kInt8;
use_calib_mode = true;
} else {
printf("run_mode should be 'fluid', 'trt_fp32', 'trt_fp16' or 'trt_int8'");
}
// set tensorrt
config.EnableTensorRtEngine(
1 << 10,
1 << 30,
batch_size,
min_subgraph_size,
precision,
false,
false);
use_calib_mode);
// set use dynamic shape
if (use_dynamic_shape) {
// set DynamicShsape for image tensor
const std::vector<int> min_input_shape = {1, trt_min_shape, trt_min_shape};
const std::vector<int> max_input_shape = {1, trt_max_shape, trt_max_shape};
const std::vector<int> opt_input_shape = {1, trt_opt_shape, trt_opt_shape};
const std::map<std::string, std::vector<int>> map_min_input_shape = {{"image", min_input_shape}};
const std::map<std::string, std::vector<int>> map_max_input_shape = {{"image", max_input_shape}};
const std::map<std::string, std::vector<int>> map_opt_input_shape = {{"image", opt_input_shape}};
config.SetTRTDynamicShapeInfo(map_min_input_shape,
map_max_input_shape,
map_opt_input_shape);
std::cout << "TensorRT dynamic shape enabled" << std::endl;
}
}
} else {
config.DisableGpu();
}
......@@ -171,8 +197,8 @@ void ObjectDetector::Predict(const cv::Mat& im,
for (const auto& tensor_name : input_names) {
auto in_tensor = predictor_->GetInputHandle(tensor_name);
if (tensor_name == "image") {
int rh = inputs_.input_shape_[0];
int rw = inputs_.input_shape_[1];
int rh = inputs_.in_net_shape_[0];
int rw = inputs_.in_net_shape_[1];
in_tensor->Reshape({1, 3, rh, rw});
in_tensor->CopyFromCpu(inputs_.im_data_.data());
} else if (tensor_name == "im_shape") {
......
dygraph/deploy/cpp/src/preprocess_op.cc
浏览文件 @ 71a95461
......@@ -25,7 +25,7 @@ void InitInfo::Run(cv::Mat* im, ImageBlob* data) {
static_cast<float>(im->cols)
};
data->scale_factor_ = {1., 1.};
data->input_shape_ = {
data->in_net_shape_ = {
static_cast<int>(im->rows),
static_cast<int>(im->cols)
};
......@@ -62,7 +62,11 @@ void Permute::Run(cv::Mat* im, ImageBlob* data) {
void Resize::Run(cv::Mat* im, ImageBlob* data) {
auto resize_scale = GenerateScale(*im);
data->input_shape_ = {
data->im_shape_ = {
static_cast<int>(im->cols * resize_scale.first),
static_cast<int>(im->rows * resize_scale.second)
};
data->in_net_shape_ = {
static_cast<int>(im->cols * resize_scale.first),
static_cast<int>(im->rows * resize_scale.second)
};
......@@ -121,7 +125,7 @@ void PadStride::Run(cv::Mat* im, ImageBlob* data) {
nw - rw,
cv::BORDER_CONSTANT,
cv::Scalar(0));
data->input_shape_ = {
data->in_net_shape_ = {
static_cast<int>(im->rows),
static_cast<int>(im->cols),
};
......
dygraph/deploy/python/infer.py
浏览文件 @ 71a95461
......@@ -46,6 +46,11 @@ class Detector(object):
model_dir (str): root path of model.pdiparams, model.pdmodel and infer_cfg.yml
use_gpu (bool): whether use gpu
run_mode (str): mode of running(fluid/trt_fp32/trt_fp16)
use_dynamic_shape (bool): use dynamic shape or not
trt_min_shape (int): min shape for dynamic shape in trt
trt_max_shape (int): max shape for dynamic shape in trt
trt_opt_shape (int): opt shape for dynamic shape in trt
run_mode (str): mode of running(fluid/trt_fp32/trt_fp16)
threshold (float): threshold to reserve the result for output.
"""
......@@ -54,13 +59,21 @@ class Detector(object):
model_dir,
use_gpu=False,
run_mode='fluid',
use_dynamic_shape=False,
trt_min_shape=1,
trt_max_shape=1280,
trt_opt_shape=640,
threshold=0.5):
self.pred_config = pred_config
self.predictor = load_predictor(
model_dir,
run_mode=run_mode,
min_subgraph_size=self.pred_config.min_subgraph_size,
use_gpu=use_gpu)
use_gpu=use_gpu,
use_dynamic_shape=use_dynamic_shape,
trt_min_shape=trt_min_shape,
trt_max_shape=trt_max_shape,
trt_opt_shape=trt_opt_shape)
def preprocess(self, im):
preprocess_ops = []
......@@ -154,6 +167,10 @@ class DetectorSOLOv2(Detector):
model_dir (str): root path of model.pdiparams, model.pdmodel and infer_cfg.yml
use_gpu (bool): whether use gpu
run_mode (str): mode of running(fluid/trt_fp32/trt_fp16)
use_dynamic_shape (bool): use dynamic shape or not
trt_min_shape (int): min shape for dynamic shape in trt
trt_max_shape (int): max shape for dynamic shape in trt
trt_opt_shape (int): opt shape for dynamic shape in trt
threshold (float): threshold to reserve the result for output.
"""
......@@ -162,13 +179,21 @@ class DetectorSOLOv2(Detector):
model_dir,
use_gpu=False,
run_mode='fluid',
use_dynamic_shape=False,
trt_min_shape=1,
trt_max_shape=1280,
trt_opt_shape=640,
threshold=0.5):
self.pred_config = pred_config
self.predictor = load_predictor(
model_dir,
run_mode=run_mode,
min_subgraph_size=self.pred_config.min_subgraph_size,
use_gpu=use_gpu)
use_gpu=use_gpu,
use_dynamic_shape=use_dynamic_shape,
trt_min_shape=trt_min_shape,
trt_max_shape=trt_max_shape,
trt_opt_shape=trt_opt_shape)
def predict(self,
image,
......@@ -287,11 +312,20 @@ def load_predictor(model_dir,
run_mode='fluid',
batch_size=1,
use_gpu=False,
min_subgraph_size=3):
min_subgraph_size=3,
use_dynamic_shape=False,
trt_min_shape=1,
trt_max_shape=1280,
trt_opt_shape=640):
"""set AnalysisConfig, generate AnalysisPredictor
Args:
model_dir (str): root path of __model__ and __params__
use_gpu (bool): whether use gpu
run_mode (str): mode of running(fluid/trt_fp32/trt_fp16)
use_dynamic_shape (bool): use dynamic shape or not
trt_min_shape (int): min shape for dynamic shape in trt
trt_max_shape (int): max shape for dynamic shape in trt
trt_opt_shape (int): opt shape for dynamic shape in trt
Returns:
predictor (PaddlePredictor): AnalysisPredictor
Raises:
......@@ -301,9 +335,12 @@ def load_predictor(model_dir,
raise ValueError(
"Predict by TensorRT mode: {}, expect use_gpu==True, but use_gpu == {}"
.format(run_mode, use_gpu))
if run_mode == 'trt_int8':
raise ValueError("TensorRT int8 mode is not supported now, "
"please use trt_fp32 or trt_fp16 instead.")
if run_mode == 'trt_int8' and not os.path.exists(
os.path.join(model_dir, '_opt_cache')):
raise ValueError(
"TensorRT int8 must calibration first, and model_dir must has _opt_cache dir"
)
use_calib_mode = True if run_mode == 'trt_int8' else False
config = Config(
os.path.join(model_dir, 'model.pdmodel'),
os.path.join(model_dir, 'model.pdiparams'))
......@@ -316,11 +353,7 @@ def load_predictor(model_dir,
# initial GPU memory(M), device ID
config.enable_use_gpu(200, 0)
# optimize graph and fuse op
# FIXME(dkp): ir optimize may prune variable inside graph
# and incur error in Paddle 2.0, e.g. in SSDLite
# FCOS model, set as False currently and should
# be set as True after switch_ir_optim fixed
config.switch_ir_optim(False)
config.switch_ir_optim(True)
else:
config.disable_gpu()
......@@ -331,7 +364,16 @@ def load_predictor(model_dir,
min_subgraph_size=min_subgraph_size,
precision_mode=precision_map[run_mode],
use_static=False,
use_calib_mode=False)
use_calib_mode=use_calib_mode)
if use_dynamic_shape:
print('use_dynamic_shape')
min_input_shape = {'image': [1, 3, trt_min_shape, trt_min_shape]}
max_input_shape = {'image': [1, 3, trt_max_shape, trt_max_shape]}
opt_input_shape = {'image': [1, 3, trt_opt_shape, trt_opt_shape]}
config.set_trt_dynamic_shape_info(min_input_shape, max_input_shape,
opt_input_shape)
print('trt set dynamic shape done!')
# disable print log when predict
config.disable_glog_info()
......@@ -424,13 +466,21 @@ def main():
pred_config,
FLAGS.model_dir,
use_gpu=FLAGS.use_gpu,
run_mode=FLAGS.run_mode)
run_mode=FLAGS.run_mode,
use_dynamic_shape=FLAGS.use_dynamic_shape,
trt_min_shape=FLAGS.trt_min_shape,
trt_max_shape=FLAGS.trt_max_shape,
trt_opt_shape=FLAGS.trt_opt_shape)
if pred_config.arch == 'SOLOv2':
detector = DetectorSOLOv2(
pred_config,
FLAGS.model_dir,
use_gpu=FLAGS.use_gpu,
run_mode=FLAGS.run_mode)
run_mode=FLAGS.run_mode,
use_dynamic_shape=FLAGS.use_dynamic_shape,
trt_min_shape=FLAGS.trt_min_shape,
trt_max_shape=FLAGS.trt_max_shape,
trt_opt_shape=FLAGS.trt_opt_shape)
# predict from image
if FLAGS.image_file != '':
predict_image(detector)
......@@ -480,6 +530,23 @@ if __name__ == '__main__':
type=str,
default="output",
help="Directory of output visualization files.")
parser.add_argument(
"--use_dynamic_shape",
type=ast.literal_eval,
default=False,
help="Dynamic_shape for TensorRT.")
parser.add_argument(
"--trt_min_shape", type=int, default=1, help="min_shape for TensorRT.")
parser.add_argument(
"--trt_max_shape",
type=int,
default=1280,
help="max_shape for TensorRT.")
parser.add_argument(
"--trt_opt_shape",
type=int,
default=640,
help="opt_shape for TensorRT.")
FLAGS = parser.parse_args()
print_arguments(FLAGS)
......
dygraph/deploy/python/trt_int8_calib.py 0 → 100644
浏览文件 @ 71a95461
# 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.
import os
import argparse
import time
import yaml
import ast
from functools import reduce
from PIL import Image
import cv2
import numpy as np
import glob
import paddle
from preprocess import preprocess, Resize, NormalizeImage, Permute, PadStride
from visualize import visualize_box_mask
from paddle.inference import Config
from paddle.inference import create_predictor
# Global dictionary
SUPPORT_MODELS = {
'YOLO',
'RCNN',
'SSD',
'FCOS',
'SOLOv2',
'TTFNet',
}
class Detector(object):
"""
Args:
config (object): config of model, defined by `Config(model_dir)`
model_dir (str): root path of model.pdiparams, model.pdmodel and infer_cfg.yml
use_gpu (bool): whether use gpu
"""
def __init__(self, pred_config, model_dir, use_gpu=False):
self.pred_config = pred_config
self.predictor = load_predictor(
model_dir,
min_subgraph_size=self.pred_config.min_subgraph_size,
use_gpu=use_gpu)
def preprocess(self, im):
preprocess_ops = []
for op_info in self.pred_config.preprocess_infos:
new_op_info = op_info.copy()
op_type = new_op_info.pop('type')
preprocess_ops.append(eval(op_type)(**new_op_info))
im, im_info = preprocess(im, preprocess_ops,
self.pred_config.input_shape)
inputs = create_inputs(im, im_info)
return inputs
def postprocess(self, np_boxes, np_masks, inputs, threshold=0.5):
# postprocess output of predictor
results = {}
if self.pred_config.arch in ['Face']:
h, w = inputs['im_shape']
scale_y, scale_x = inputs['scale_factor']
w, h = float(h) / scale_y, float(w) / scale_x
np_boxes[:, 2] *= h
np_boxes[:, 3] *= w
np_boxes[:, 4] *= h
np_boxes[:, 5] *= w
results['boxes'] = np_boxes
if np_masks is not None:
results['masks'] = np_masks
return results
def predict(self,
image,
threshold=0.5,
warmup=0,
repeats=1,
run_benchmark=False):
'''
Args:
image (str/np.ndarray): path of image/ np.ndarray read by cv2
threshold (float): threshold of predicted box' score
Returns:
results (dict): include 'boxes': np.ndarray: shape:[N,6], N: number of box,
matix element:[class, score, x_min, y_min, x_max, y_max]
MaskRCNN's results include 'masks': np.ndarray:
shape: [N, im_h, im_w]
'''
inputs = self.preprocess(image)
np_boxes, np_masks = None, None
input_names = self.predictor.get_input_names()
for i in range(len(input_names)):
input_tensor = self.predictor.get_input_handle(input_names[i])
input_tensor.copy_from_cpu(inputs[input_names[i]])
for i in range(warmup):
self.predictor.run()
output_names = self.predictor.get_output_names()
boxes_tensor = self.predictor.get_output_handle(output_names[0])
np_boxes = boxes_tensor.copy_to_cpu()
if self.pred_config.mask:
masks_tensor = self.predictor.get_output_handle(output_names[2])
np_masks = masks_tensor.copy_to_cpu()
t1 = time.time()
for i in range(repeats):
self.predictor.run()
output_names = self.predictor.get_output_names()
boxes_tensor = self.predictor.get_output_handle(output_names[0])
np_boxes = boxes_tensor.copy_to_cpu()
if self.pred_config.mask:
masks_tensor = self.predictor.get_output_handle(output_names[2])
np_masks = masks_tensor.copy_to_cpu()
t2 = time.time()
ms = (t2 - t1) * 1000.0 / repeats
print("Inference: {} ms per batch image".format(ms))
# do not perform postprocess in benchmark mode
results = []
if not run_benchmark:
if reduce(lambda x, y: x * y, np_boxes.shape) < 6:
print('[WARNNING] No object detected.')
results = {'boxes': np.array([])}
else:
results = self.postprocess(
np_boxes, np_masks, inputs, threshold=threshold)
return results
def create_inputs(im, im_info):
"""generate input for different model type
Args:
im (np.ndarray): image (np.ndarray)
im_info (dict): info of image
model_arch (str): model type
Returns:
inputs (dict): input of model
"""
inputs = {}
inputs['image'] = np.array((im, )).astype('float32')
inputs['im_shape'] = np.array((im_info['im_shape'], )).astype('float32')
inputs['scale_factor'] = np.array(
(im_info['scale_factor'], )).astype('float32')
return inputs
class PredictConfig():
"""set config of preprocess, postprocess and visualize
Args:
model_dir (str): root path of model.yml
"""
def __init__(self, model_dir):
# parsing Yaml config for Preprocess
deploy_file = os.path.join(model_dir, 'infer_cfg.yml')
with open(deploy_file) as f:
yml_conf = yaml.safe_load(f)
self.check_model(yml_conf)
self.arch = yml_conf['arch']
self.preprocess_infos = yml_conf['Preprocess']
self.min_subgraph_size = yml_conf['min_subgraph_size']
self.labels = yml_conf['label_list']
self.mask = False
if 'mask' in yml_conf:
self.mask = yml_conf['mask']
self.input_shape = yml_conf['image_shape']
self.print_config()
def check_model(self, yml_conf):
"""
Raises:
ValueError: loaded model not in supported model type
"""
for support_model in SUPPORT_MODELS:
if support_model in yml_conf['arch']:
return True
raise ValueError("Unsupported arch: {}, expect {}".format(yml_conf[
'arch'], SUPPORT_MODELS))
def print_config(self):
print('----------- Model Configuration -----------')
print('%s: %s' % ('Model Arch', self.arch))
print('%s: ' % ('Transform Order'))
for op_info in self.preprocess_infos:
print('--%s: %s' % ('transform op', op_info['type']))
print('--------------------------------------------')
def load_predictor(model_dir, batch_size=1, use_gpu=False, min_subgraph_size=3):
"""set AnalysisConfig, generate AnalysisPredictor
Args:
model_dir (str): root path of __model__ and __params__
use_gpu (bool): whether use gpu
Returns:
predictor (PaddlePredictor): AnalysisPredictor
Raises:
ValueError: predict by TensorRT need use_gpu == True.
"""
run_mode = 'trt_int8'
if not use_gpu and not run_mode == 'fluid':
raise ValueError(
"Predict by TensorRT mode: {}, expect use_gpu==True, but use_gpu == {}"
.format(run_mode, use_gpu))
config = Config(
os.path.join(model_dir, 'model.pdmodel'),
os.path.join(model_dir, 'model.pdiparams'))
precision_map = {
'trt_int8': Config.Precision.Int8,
'trt_fp32': Config.Precision.Float32,
'trt_fp16': Config.Precision.Half
}
if use_gpu:
# initial GPU memory(M), device ID
config.enable_use_gpu(200, 0)
# optimize graph and fuse op
config.switch_ir_optim(True)
else:
config.disable_gpu()
if run_mode in precision_map.keys():
config.enable_tensorrt_engine(
workspace_size=1 << 10,
max_batch_size=batch_size,
min_subgraph_size=min_subgraph_size,
precision_mode=precision_map[run_mode],
use_static=False,
use_calib_mode=True)
# disable print log when predict
config.disable_glog_info()
# enable shared memory
config.enable_memory_optim()
# disable feed, fetch OP, needed by zero_copy_run
config.switch_use_feed_fetch_ops(False)
predictor = create_predictor(config)
return predictor
def print_arguments(args):
print('----------- Running Arguments -----------')
for arg, value in sorted(vars(args).items()):
print('%s: %s' % (arg, value))
print('------------------------------------------')
def predict_image_dir(detector):
for image_file in glob.glob(FLAGS.image_dir + '/*.jpg'):
print('image_file is', image_file)
results = detector.predict(image_file, threshold=0.5)
def main():
pred_config = PredictConfig(FLAGS.model_dir)
detector = Detector(pred_config, FLAGS.model_dir, use_gpu=FLAGS.use_gpu)
# predict from image
if FLAGS.image_dir != '':
predict_image_dir(detector)
if __name__ == '__main__':
paddle.enable_static()
parser = argparse.ArgumentParser(description=__doc__)
parser.add_argument(
"--model_dir",
type=str,
default=None,
help=("Directory include:'model.pdiparams', 'model.pdmodel', "
"'infer_cfg.yml', created by tools/export_model.py."),
required=True)
parser.add_argument(
"--image_dir", type=str, default='', help="Directory of image file.")
parser.add_argument(
"--use_gpu",
type=ast.literal_eval,
default=False,
help="Whether to predict with GPU.")
print('err?')
parser.add_argument(
"--output_dir",
type=str,
default="output",
help="Directory of output visualization files.")
FLAGS = parser.parse_args()
print_arguments(FLAGS)
main()
dygraph/deploy/tensorrt/README.md 0 → 100644
浏览文件 @ 71a95461
# TensorRT预测部署教程
TensorRT是NVIDIA提出的用于统一模型部署的加速库，可以应用于V100、JETSON Xavier等硬件，它可以极大提高预测速度。Paddle TensorRT教程请参考文档[使用Paddle-TensorRT库预测](https://paddle-inference.readthedocs.io/en/latest/optimize/paddle_trt.html#)
## 1. 安装PaddleInference预测库
- Python安装包，请从[这里](https://www.paddlepaddle.org.cn/documentation/docs/zh/install/Tables.html#whl-release) 下载带有tensorrt的安装包进行安装
- CPP预测库，请从[这里](https://www.paddlepaddle.org.cn/documentation/docs/zh/guides/05_inference_deployment/inference/build_and_install_lib_cn.html) 下载带有TensorRT编译的预测库
- 如果Python和CPP官网没有提供已编译好的安装包或预测库，请参考[源码安装](https://www.paddlepaddle.org.cn/documentation/docs/zh/install/compile/linux-compile.html) 自行编译
注意，您的机器上TensorRT的版本需要跟您使用的预测库中TensorRT版本保持一致。
## 2. 导出模型
模型导出具体请参考文档[PaddleDetection模型导出教程](../EXPORT_MODEL.md)。
## 3. 开启TensorRT加速
### 3.1 配置TensorRT
在使用Paddle预测库构建预测器配置config时，打开TensorRT引擎就可以了：
```
config->EnableUseGpu(100, 0); // 初始化100M显存，使用GPU ID为0
config->GpuDeviceId(); // 返回正在使用的GPU ID
// 开启TensorRT预测，可提升GPU预测性能，需要使用带TensorRT的预测库
config->EnableTensorRtEngine(1 << 20 /*workspace_size*/,
batch_size /*max_batch_size*/,
3 /*min_subgraph_size*/,
AnalysisConfig::Precision::kFloat32 /*precision*/,
false /*use_static*/,
false /*use_calib_mode*/);
```
### 3.2 TensorRT固定尺寸预测
TensorRT版本<=5时，使用TensorRT预测时，只支持固定尺寸输入。
在导出模型时指定模型输入尺寸，设置`TestReader.inputs_def.image_shape=[3,640,640]`，具体请参考[PaddleDetection模型导出教程](../EXPORT_MODEL.md) 。
`TestReader.inputs_def.image_shape`设置的是输入TensorRT引擎的数据尺寸（在像FasterRCNN中，`TestReader.inputs_def.image_shape`指定的是在`Pad`操作之前的图像数据尺寸）。
可以通过[visualdl](https://www.paddlepaddle.org.cn/paddle/visualdl/demo/graph) 打开`model.pdmodel`文件，查看输入的第一个Tensor尺寸是否是固定的，如果不指定，尺寸会用`？`表示，如下图所示：
![img](imgs/input_shape.png)
### 3.3 TensorRT动态尺寸预测
TensorRT版本>=6时，使用TensorRT预测时，可以支持动态尺寸输入。
Paddle预测库关于动态尺寸输入请查看[Paddle CPP预测](https://www.paddlepaddle.org.cn/documentation/docs/zh/guides/05_inference_deployment/inference/native_infer.html) 的`SetTRTDynamicShapeInfo`函数说明。
`python/infer.py`设置动态尺寸输入参数说明：
- use_dynamic_shape 用于设定TensorRT的输入尺寸是否是动态尺寸，默认值：False
- trt_min_shape 用于设定TensorRT的输入图像height、width中的最小尺寸，默认值：1
- trt_max_shape 用于设定TensorRT的输入图像height、width中的最大尺寸，默认值：1280
- trt_opt_shape 用于设定TensorRT的输入图像height、width中的最优尺寸，默认值：640
**注意：`TensorRT`中动态尺寸设置是4维的，这里只设置输入图像的尺寸。**
## 4、常见问题QA
**Q:** 提示没有`tensorrt_op`</br>
**A:** 请检查是否使用带有TensorRT的Paddle Python包或预测库。
**Q:** 提示`op out of memory`</br>
**A:** 检查GPU是否是别人也在使用，请尝试使用空闲GPU
**Q:** 提示`some trt inputs dynamic shape info not set`</br>
**A:** 这是由于`TensorRT`会把网络结果划分成多个子图，我们只设置了输入数据的动态尺寸，划分的其他子图的输入并未设置动态尺寸。有两个解决方法：
- 方法一：通过增大`min_subgraph_size`，跳过对这些子图的优化。根据提示，设置min_subgraph_size大于并未设置动态尺寸输入的子图中OP个数即可。
`min_subgraph_size`的意思是，在加载TensorRT引擎的时候，大于`min_subgraph_size`的OP才会被优化，并且这些OP是连续的且是TensorRT可以优化的。
- 方法二：找到子图的这些输入，按照上面方式也设置子图的输入动态尺寸。
**Q:** 如何打开日志</br>
**A:** 预测库默认是打开日志的，只要注释掉`config.disable_glog_info()`就可以打开日志
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