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Merge pull request #2921 from LDOUBLEV/bm_trt_dyg 

suport TensorRT inference
This commit is contained in:
Double_V 2021-06-01 09:21:26 +08:00 committed by GitHub
parent ac3768a62f c8cc0fb4b7
commit b38f353489
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GPG Key ID: 4AEE18F83AFDEB23
5 changed files with 153 additions and 65 deletions
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38
deploy/cpp_infer/src/ocr_det.cpp
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@ -30,6 +30,42 @@ void DBDetector::LoadModel(const std::string &model_dir) {
this->use_fp16_ ? paddle_infer::Config::Precision::kHalf
: paddle_infer::Config::Precision::kFloat32,
false, false);
std::map<std::string, std::vector<int>> min_input_shape = {
{"x", {1, 3, 50, 50}},
{"conv2d_92.tmp_0", {1, 96, 20, 20}},
{"conv2d_91.tmp_0", {1, 96, 10, 10}},
{"nearest_interp_v2_1.tmp_0", {1, 96, 10, 10}},
{"nearest_interp_v2_2.tmp_0", {1, 96, 20, 20}},
{"nearest_interp_v2_3.tmp_0", {1, 24, 20, 20}},
{"nearest_interp_v2_4.tmp_0", {1, 24, 20, 20}},
{"nearest_interp_v2_5.tmp_0", {1, 24, 20, 20}},
{"elementwise_add_7", {1, 56, 2, 2}},
{"nearest_interp_v2_0.tmp_0", {1, 96, 2, 2}}};
std::map<std::string, std::vector<int>> max_input_shape = {
{"x", {1, 3, this->max_side_len_, this->max_side_len_}},
{"conv2d_92.tmp_0", {1, 96, 400, 400}},
{"conv2d_91.tmp_0", {1, 96, 200, 200}},
{"nearest_interp_v2_1.tmp_0", {1, 96, 200, 200}},
{"nearest_interp_v2_2.tmp_0", {1, 96, 400, 400}},
{"nearest_interp_v2_3.tmp_0", {1, 24, 400, 400}},
{"nearest_interp_v2_4.tmp_0", {1, 24, 400, 400}},
{"nearest_interp_v2_5.tmp_0", {1, 24, 400, 400}},
{"elementwise_add_7", {1, 56, 400, 400}},
{"nearest_interp_v2_0.tmp_0", {1, 96, 400, 400}}};
std::map<std::string, std::vector<int>> opt_input_shape = {
{"x", {1, 3, 640, 640}},
{"conv2d_92.tmp_0", {1, 96, 160, 160}},
{"conv2d_91.tmp_0", {1, 96, 80, 80}},
{"nearest_interp_v2_1.tmp_0", {1, 96, 80, 80}},
{"nearest_interp_v2_2.tmp_0", {1, 96, 160, 160}},
{"nearest_interp_v2_3.tmp_0", {1, 24, 160, 160}},
{"nearest_interp_v2_4.tmp_0", {1, 24, 160, 160}},
{"nearest_interp_v2_5.tmp_0", {1, 24, 160, 160}},
{"elementwise_add_7", {1, 56, 40, 40}},
{"nearest_interp_v2_0.tmp_0", {1, 96, 40, 40}}};
config.SetTRTDynamicShapeInfo(min_input_shape, max_input_shape,
opt_input_shape);
}
} else {
config.DisableGpu();
@ -48,7 +84,7 @@ void DBDetector::LoadModel(const std::string &model_dir) {
config.SwitchIrOptim(true);
config.EnableMemoryOptim();
config.DisableGlogInfo();
// config.DisableGlogInfo();
this->predictor_ = CreatePredictor(config);
}

9
deploy/cpp_infer/src/ocr_rec.cpp
View File

@ -106,6 +106,15 @@ void CRNNRecognizer::LoadModel(const std::string &model_dir) {
this->use_fp16_ ? paddle_infer::Config::Precision::kHalf
: paddle_infer::Config::Precision::kFloat32,
false, false);
std::map<std::string, std::vector<int>> min_input_shape = {
{"x", {1, 3, 32, 10}}};
std::map<std::string, std::vector<int>> max_input_shape = {
{"x", {1, 3, 32, 2000}}};
std::map<std::string, std::vector<int>> opt_input_shape = {
{"x", {1, 3, 32, 320}}};
config.SetTRTDynamicShapeInfo(min_input_shape, max_input_shape,
opt_input_shape);
}
} else {
config.DisableGpu();

51
deploy/cpp_infer/src/preprocess_op.cpp Executable file → Normal file
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@ -77,19 +77,13 @@ void ResizeImgType0::Run(const cv::Mat &img, cv::Mat &resize_img,
int resize_h = int(float(h) * ratio);
int resize_w = int(float(w) * ratio);
resize_h = max(int(round(float(resize_h) / 32) * 32), 32);
resize_w = max(int(round(float(resize_w) / 32) * 32), 32);
if (!use_tensorrt) {
cv::resize(img, resize_img, cv::Size(resize_w, resize_h));
ratio_h = float(resize_h) / float(h);
ratio_w = float(resize_w) / float(w);
} else {
cv::resize(img, resize_img, cv::Size(640, 640));
ratio_h = float(640) / float(h);
ratio_w = float(640) / float(w);
}
cv::resize(img, resize_img, cv::Size(resize_w, resize_h));
ratio_h = float(resize_h) / float(h);
ratio_w = float(resize_w) / float(w);
}
void CrnnResizeImg::Run(const cv::Mat &img, cv::Mat &resize_img, float wh_ratio,
@ -108,23 +102,12 @@ void CrnnResizeImg::Run(const cv::Mat &img, cv::Mat &resize_img, float wh_ratio,
resize_w = imgW;
else
resize_w = int(ceilf(imgH * ratio));
if (!use_tensorrt) {
cv::resize(img, resize_img, cv::Size(resize_w, imgH), 0.f, 0.f,
cv::INTER_LINEAR);
cv::copyMakeBorder(resize_img, resize_img, 0, 0, 0,
int(imgW - resize_img.cols), cv::BORDER_CONSTANT,
{127, 127, 127});
} else {
int k = int(img.cols * 32 / img.rows);
if (k >= 100) {
cv::resize(img, resize_img, cv::Size(100, 32), 0.f, 0.f,
cv::INTER_LINEAR);
} else {
cv::resize(img, resize_img, cv::Size(k, 32), 0.f, 0.f, cv::INTER_LINEAR);
cv::copyMakeBorder(resize_img, resize_img, 0, 0, 0, int(100 - k),
cv::BORDER_CONSTANT, {127, 127, 127});
}
}
cv::resize(img, resize_img, cv::Size(resize_w, imgH), 0.f, 0.f,
cv::INTER_LINEAR);
cv::copyMakeBorder(resize_img, resize_img, 0, 0, 0,
int(imgW - resize_img.cols), cv::BORDER_CONSTANT,
{127, 127, 127});
}
void ClsResizeImg::Run(const cv::Mat &img, cv::Mat &resize_img,
@ -142,15 +125,11 @@ void ClsResizeImg::Run(const cv::Mat &img, cv::Mat &resize_img,
else
resize_w = int(ceilf(imgH * ratio));
if (!use_tensorrt) {
cv::resize(img, resize_img, cv::Size(resize_w, imgH), 0.f, 0.f,
cv::INTER_LINEAR);
if (resize_w < imgW) {
cv::copyMakeBorder(resize_img, resize_img, 0, 0, 0, imgW - resize_w,
cv::BORDER_CONSTANT, cv::Scalar(0, 0, 0));
}
} else {
cv::resize(img, resize_img, cv::Size(100, 32), 0.f, 0.f, cv::INTER_LINEAR);
cv::resize(img, resize_img, cv::Size(resize_w, imgH), 0.f, 0.f,
cv::INTER_LINEAR);
if (resize_w < imgW) {
cv::copyMakeBorder(resize_img, resize_img, 0, 0, 0, imgW - resize_w,
cv::BORDER_CONSTANT, cv::Scalar(0, 0, 0));
}
}

2
deploy/cpp_infer/tools/build.sh
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@ -12,7 +12,7 @@ cmake .. \
-DWITH_MKL=ON \
-DWITH_GPU=OFF \
-DWITH_STATIC_LIB=OFF \
-DUSE_TENSORRT=OFF \
-DWITH_TENSORRT=OFF \
-DOPENCV_DIR=${OPENCV_DIR} \
-DCUDNN_LIB=${CUDNN_LIB_DIR} \
-DCUDA_LIB=${CUDA_LIB_DIR} \

118
tools/infer/utility.py
View File

@ -21,6 +21,9 @@ import json
from PIL import Image, ImageDraw, ImageFont
import math
from paddle import inference
import time
from ppocr.utils.logging import get_logger
logger = get_logger()
def parse_args():
@ -98,6 +101,7 @@ def parse_args():
parser.add_argument("--cls_thresh", type=float, default=0.9)
parser.add_argument("--enable_mkldnn", type=str2bool, default=False)
parser.add_argument("--cpu_threads", type=int, default=10)
parser.add_argument("--use_pdserving", type=str2bool, default=False)
parser.add_argument("--use_mp", type=str2bool, default=False)
@ -135,19 +139,97 @@ def create_predictor(args, mode, logger):
config.enable_use_gpu(args.gpu_mem, 0)
if args.use_tensorrt:
config.enable_tensorrt_engine(
precision_mode=inference.PrecisionType.Half
if args.use_fp16 else inference.PrecisionType.Float32,
max_batch_size=args.max_batch_size)
precision_mode=inference.PrecisionType.Float32,
max_batch_size=args.max_batch_size,
min_subgraph_size=3) # skip the minmum trt subgraph
if mode == "det" and "mobile" in model_file_path:
min_input_shape = {
"x": [1, 3, 50, 50],
"conv2d_92.tmp_0": [1, 96, 20, 20],
"conv2d_91.tmp_0": [1, 96, 10, 10],
"nearest_interp_v2_1.tmp_0": [1, 96, 10, 10],
"nearest_interp_v2_2.tmp_0": [1, 96, 20, 20],
"nearest_interp_v2_3.tmp_0": [1, 24, 20, 20],
"nearest_interp_v2_4.tmp_0": [1, 24, 20, 20],
"nearest_interp_v2_5.tmp_0": [1, 24, 20, 20],
"elementwise_add_7": [1, 56, 2, 2],
"nearest_interp_v2_0.tmp_0": [1, 96, 2, 2]
}
max_input_shape = {
"x": [1, 3, 2000, 2000],
"conv2d_92.tmp_0": [1, 96, 400, 400],
"conv2d_91.tmp_0": [1, 96, 200, 200],
"nearest_interp_v2_1.tmp_0": [1, 96, 200, 200],
"nearest_interp_v2_2.tmp_0": [1, 96, 400, 400],
"nearest_interp_v2_3.tmp_0": [1, 24, 400, 400],
"nearest_interp_v2_4.tmp_0": [1, 24, 400, 400],
"nearest_interp_v2_5.tmp_0": [1, 24, 400, 400],
"elementwise_add_7": [1, 56, 400, 400],
"nearest_interp_v2_0.tmp_0": [1, 96, 400, 400]
}
opt_input_shape = {
"x": [1, 3, 640, 640],
"conv2d_92.tmp_0": [1, 96, 160, 160],
"conv2d_91.tmp_0": [1, 96, 80, 80],
"nearest_interp_v2_1.tmp_0": [1, 96, 80, 80],
"nearest_interp_v2_2.tmp_0": [1, 96, 160, 160],
"nearest_interp_v2_3.tmp_0": [1, 24, 160, 160],
"nearest_interp_v2_4.tmp_0": [1, 24, 160, 160],
"nearest_interp_v2_5.tmp_0": [1, 24, 160, 160],
"elementwise_add_7": [1, 56, 40, 40],
"nearest_interp_v2_0.tmp_0": [1, 96, 40, 40]
}
if mode == "det" and "server" in model_file_path:
min_input_shape = {
"x": [1, 3, 50, 50],
"conv2d_59.tmp_0": [1, 96, 20, 20],
"nearest_interp_v2_2.tmp_0": [1, 96, 20, 20],
"nearest_interp_v2_3.tmp_0": [1, 24, 20, 20],
"nearest_interp_v2_4.tmp_0": [1, 24, 20, 20],
"nearest_interp_v2_5.tmp_0": [1, 24, 20, 20]
}
max_input_shape = {
"x": [1, 3, 2000, 2000],
"conv2d_59.tmp_0": [1, 96, 400, 400],
"nearest_interp_v2_2.tmp_0": [1, 96, 400, 400],
"nearest_interp_v2_3.tmp_0": [1, 24, 400, 400],
"nearest_interp_v2_4.tmp_0": [1, 24, 400, 400],
"nearest_interp_v2_5.tmp_0": [1, 24, 400, 400]
}
opt_input_shape = {
"x": [1, 3, 640, 640],
"conv2d_59.tmp_0": [1, 96, 160, 160],
"nearest_interp_v2_2.tmp_0": [1, 96, 160, 160],
"nearest_interp_v2_3.tmp_0": [1, 24, 160, 160],
"nearest_interp_v2_4.tmp_0": [1, 24, 160, 160],
"nearest_interp_v2_5.tmp_0": [1, 24, 160, 160]
}
elif mode == "rec":
min_input_shape = {"x": [args.rec_batch_num, 3, 32, 10]}
max_input_shape = {"x": [args.rec_batch_num, 3, 32, 2000]}
opt_input_shape = {"x": [args.rec_batch_num, 3, 32, 320]}
elif mode == "cls":
min_input_shape = {"x": [args.rec_batch_num, 3, 48, 10]}
max_input_shape = {"x": [args.rec_batch_num, 3, 48, 2000]}
opt_input_shape = {"x": [args.rec_batch_num, 3, 48, 320]}
else:
min_input_shape = {"x": [1, 3, 10, 10]}
max_input_shape = {"x": [1, 3, 1000, 1000]}
opt_input_shape = {"x": [1, 3, 500, 500]}
config.set_trt_dynamic_shape_info(min_input_shape, max_input_shape,
opt_input_shape)
else:
config.disable_gpu()
config.set_cpu_math_library_num_threads(6)
if hasattr(args, "cpu_threads"):
config.set_cpu_math_library_num_threads(args.cpu_threads)
else:
# default cpu threads as 10
config.set_cpu_math_library_num_threads(10)
if args.enable_mkldnn:
# cache 10 different shapes for mkldnn to avoid memory leak
config.set_mkldnn_cache_capacity(10)
config.enable_mkldnn()
# TODO LDOUBLEV: fix mkldnn bug when bach_size > 1
#config.set_mkldnn_op({'conv2d', 'depthwise_conv2d', 'pool2d', 'batch_norm'})
args.rec_batch_num = 1
# enable memory optim
config.enable_memory_optim()
@ -210,7 +292,7 @@ def draw_ocr(image,
txts=None,
scores=None,
drop_score=0.5,
font_path="./doc/simfang.ttf"):
font_path="./doc/fonts/simfang.ttf"):
"""
Visualize the results of OCR detection and recognition
args:
@ -418,22 +500,4 @@ def draw_boxes(image, boxes, scores=None, drop_score=0.5):
if __name__ == '__main__':
test_img = "./doc/test_v2"
predict_txt = "./doc/predict.txt"
f = open(predict_txt, 'r')
data = f.readlines()
img_path, anno = data[0].strip().split('\t')
img_name = os.path.basename(img_path)
img_path = os.path.join(test_img, img_name)
image = Image.open(img_path)
data = json.loads(anno)
boxes, txts, scores = [], [], []
for dic in data:
boxes.append(dic['points'])
txts.append(dic['transcription'])
scores.append(round(dic['scores'], 3))
new_img = draw_ocr(image, boxes, txts, scores)
cv2.imwrite(img_name, new_img)
pass