mirror of
https://github.com/PaddlePaddle/FastDeploy.git
synced 2025-10-06 17:17:14 +08:00
[Other] Improve examples and readme for Ascend deployment (#1052)
* Add Huawei Ascend NPU deploy through PaddleLite CANN * Add NNAdapter interface for paddlelite * Modify Huawei Ascend Cmake * Update way for compiling Huawei Ascend NPU deployment * remove UseLiteBackend in UseCANN * Support compile python whlee * Change names of nnadapter API * Add nnadapter pybind and remove useless API * Support Python deployment on Huawei Ascend NPU * Add models suppor for ascend * Add PPOCR rec reszie for ascend * fix conflict for ascend * Rename CANN to Ascend * Rename CANN to Ascend * Improve ascend * fix ascend bug * improve ascend docs * improve ascend docs * improve ascend docs * Improve Ascend * Improve Ascend * Move ascend python demo * Imporve ascend * Improve ascend * Improve ascend * Improve ascend * Improve ascend * Imporve ascend * Imporve ascend * Improve ascend * acc eval script * acc eval * remove acc_eval from branch huawei * Add detection and segmentation examples for Ascend deployment * Add detection and segmentation examples for Ascend deployment * Add PPOCR example for ascend deploy * Imporve paddle lite compiliation * Add FlyCV doc * Add FlyCV doc * Add FlyCV doc * Imporve Ascend docs * Imporve Ascend docs * Improve PPOCR example
This commit is contained in:
yunyaoXYY
@@ -41,6 +41,9 @@ tar xvf ppyoloe_crn_l_300e_coco.tgz
|
||||
以上命令只适用于Linux或MacOS, Windows下SDK的使用方式请参考:
|
||||
- [如何在Windows中使用FastDeploy C++ SDK](../../../../../docs/cn/faq/use_sdk_on_windows.md)
|
||||
|
||||
如果用户使用华为昇腾NPU部署, 请参考以下方式在部署前初始化部署环境:
|
||||
- [如何使用华为昇腾NPU部署](../../../../../docs/cn/faq/use_sdk_on_ascend.md)
|
||||
|
||||
## PaddleDetection C++接口
|
||||
|
||||
### 模型类
|
||||
|
||||
@@ -55,6 +55,9 @@ wget https://gitee.com/paddlepaddle/PaddleDetection/raw/release/2.4/demo/0000000
|
||||
以上命令只适用于Linux或MacOS, Windows下SDK的使用方式请参考:
|
||||
- [如何在Windows中使用FastDeploy C++ SDK](../../../../../docs/cn/faq/use_sdk_on_windows.md)
|
||||
|
||||
如果用户使用华为昇腾NPU部署, 请参考以下方式在部署前初始化部署环境:
|
||||
- [如何使用华为昇腾NPU部署](../../../../../docs/cn/faq/use_sdk_on_ascend.md)
|
||||
|
||||
## YOLOv5 C++接口
|
||||
|
||||
### YOLOv5类
|
||||
|
||||
@@ -33,6 +33,10 @@ wget https://gitee.com/paddlepaddle/PaddleDetection/raw/release/2.4/demo/0000000
|
||||
./infer_paddle_demo yolov6s_infer 000000014439.jpg 3
|
||||
```
|
||||
|
||||
如果用户使用华为昇腾NPU部署, 请参考以下方式在部署前初始化部署环境:
|
||||
- [如何使用华为昇腾NPU部署](../../../../../docs/cn/faq/use_sdk_on_ascend.md)
|
||||
|
||||
|
||||
如果想要验证ONNX模型的推理，可以参考如下命令：
|
||||
```bash
|
||||
#下载官方转换好的YOLOv6 ONNX模型文件和测试图片
|
||||
|
||||
@@ -31,6 +31,10 @@ wget https://gitee.com/paddlepaddle/PaddleDetection/raw/release/2.4/demo/0000000
|
||||
# 华为昇腾推理
|
||||
./infer_paddle_model_demo yolov7_infer 000000014439.jpg 3
|
||||
```
|
||||
|
||||
如果用户使用华为昇腾NPU部署, 请参考以下方式在部署前初始化部署环境:
|
||||
- [如何使用华为昇腾NPU部署](../../../../../docs/cn/faq/use_sdk_on_ascend.md)
|
||||
|
||||
如果想要验证ONNX模型的推理，可以参考如下命令：
|
||||
```bash
|
||||
#下载官方转换好的yolov7 ONNX模型文件和测试图片
|
||||
|
||||
@@ -12,3 +12,7 @@ include_directories(${FASTDEPLOY_INCS})
|
||||
add_executable(infer_demo ${PROJECT_SOURCE_DIR}/infer.cc)
|
||||
# 添加FastDeploy库依赖
|
||||
target_link_libraries(infer_demo ${FASTDEPLOY_LIBS})
|
||||
|
||||
add_executable(infer_static_shape_demo ${PROJECT_SOURCE_DIR}/infer_static_shape.cc)
|
||||
# 添加FastDeploy库依赖
|
||||
target_link_libraries(infer_static_shape_demo ${FASTDEPLOY_LIBS})
|
||||
|
||||
@@ -43,13 +43,16 @@ wget https://gitee.com/paddlepaddle/PaddleOCR/raw/release/2.6/ppocr/utils/ppocr_
|
||||
./infer_demo ./ch_PP-OCRv2_det_infer ./ch_ppocr_mobile_v2.0_cls_infer ./ch_PP-OCRv2_rec_infer ./ppocr_keys_v1.txt ./12.jpg 3
|
||||
# 昆仑芯XPU推理
|
||||
./infer_demo ./ch_PP-OCRv2_det_infer ./ch_ppocr_mobile_v2.0_cls_infer ./ch_PP-OCRv2_rec_infer ./ppocr_keys_v1.txt ./12.jpg 4
|
||||
# 华为昇腾推理
|
||||
./infer_demo ./ch_PP-OCRv2_det_infer ./ch_ppocr_mobile_v2.0_cls_infer ./ch_PP-OCRv2_rec_infer ./ppocr_keys_v1.txt ./12.jpg 5
|
||||
# 华为昇腾推理, 需要使用静态shape的demo, 若用户需要连续地预测图片, 输入图片尺寸需要准备为统一尺寸
|
||||
./infer_static_shape_demo ./ch_PP-OCRv2_det_infer ./ch_ppocr_mobile_v2.0_cls_infer ./ch_PP-OCRv2_rec_infer ./ppocr_keys_v1.txt ./12.jpg 1
|
||||
```
|
||||
|
||||
以上命令只适用于Linux或MacOS, Windows下SDK的使用方式请参考:
|
||||
- [如何在Windows中使用FastDeploy C++ SDK](../../../../../docs/cn/faq/use_sdk_on_windows.md)
|
||||
|
||||
如果用户使用华为昇腾NPU部署, 请参考以下方式在部署前初始化部署环境:
|
||||
- [如何使用华为昇腾NPU部署](../../../../../docs/cn/faq/use_sdk_on_ascend.md)
|
||||
|
||||
运行完成可视化结果如下图所示
|
||||
|
||||
<img width="640" src="https://user-images.githubusercontent.com/109218879/185826024-f7593a0c-1bd2-4a60-b76c-15588484fa08.jpg">
|
||||
|
||||
@@ -55,10 +55,6 @@ void InitAndInfer(const std::string& det_model_dir, const std::string& cls_model
|
||||
auto cls_model = fastdeploy::vision::ocr::Classifier(cls_model_file, cls_params_file, cls_option);
|
||||
auto rec_model = fastdeploy::vision::ocr::Recognizer(rec_model_file, rec_params_file, rec_label_file, rec_option);
|
||||
|
||||
// Users could enable static shape infer for rec model when deploy PP-OCR on hardware
|
||||
// which can not support dynamic shape infer well, like Huawei Ascend series.
|
||||
// rec_model.GetPreprocessor().SetStaticShapeInfer(true);
|
||||
|
||||
assert(det_model.Initialized());
|
||||
assert(cls_model.Initialized());
|
||||
assert(rec_model.Initialized());
|
||||
@@ -70,9 +66,6 @@ void InitAndInfer(const std::string& det_model_dir, const std::string& cls_model
|
||||
// Set inference batch size for cls model and rec model, the value could be -1 and 1 to positive infinity.
|
||||
// When inference batch size is set to -1, it means that the inference batch size
|
||||
// of the cls and rec models will be the same as the number of boxes detected by the det model.
|
||||
// When users enable static shape infer for rec model, the batch size of cls and rec model needs to be set to 1.
|
||||
// ppocr_v2.SetClsBatchSize(1);
|
||||
// ppocr_v2.SetRecBatchSize(1);
|
||||
ppocr_v2.SetClsBatchSize(cls_batch_size);
|
||||
ppocr_v2.SetRecBatchSize(rec_batch_size);
|
||||
|
||||
@@ -129,8 +122,6 @@ int main(int argc, char* argv[]) {
|
||||
option.EnablePaddleToTrt();
|
||||
} else if (flag == 4) {
|
||||
option.UseKunlunXin();
|
||||
} else if (flag == 5) {
|
||||
option.UseAscend();
|
||||
}
|
||||
|
||||
std::string det_model_dir = argv[1];
|
||||
|
||||
107
examples/vision/ocr/PP-OCRv2/cpp/infer_static_shape.cc
Executable file
107
examples/vision/ocr/PP-OCRv2/cpp/infer_static_shape.cc
Executable file
@@ -0,0 +1,107 @@
|
||||
// Copyright (c) 2022 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 "fastdeploy/vision.h"
|
||||
#ifdef WIN32
|
||||
const char sep = '\\';
|
||||
#else
|
||||
const char sep = '/';
|
||||
#endif
|
||||
|
||||
void InitAndInfer(const std::string& det_model_dir, const std::string& cls_model_dir, const std::string& rec_model_dir, const std::string& rec_label_file, const std::string& image_file, const fastdeploy::RuntimeOption& option) {
|
||||
auto det_model_file = det_model_dir + sep + "inference.pdmodel";
|
||||
auto det_params_file = det_model_dir + sep + "inference.pdiparams";
|
||||
|
||||
auto cls_model_file = cls_model_dir + sep + "inference.pdmodel";
|
||||
auto cls_params_file = cls_model_dir + sep + "inference.pdiparams";
|
||||
|
||||
auto rec_model_file = rec_model_dir + sep + "inference.pdmodel";
|
||||
auto rec_params_file = rec_model_dir + sep + "inference.pdiparams";
|
||||
|
||||
auto det_option = option;
|
||||
auto cls_option = option;
|
||||
auto rec_option = option;
|
||||
|
||||
auto det_model = fastdeploy::vision::ocr::DBDetector(det_model_file, det_params_file, det_option);
|
||||
auto cls_model = fastdeploy::vision::ocr::Classifier(cls_model_file, cls_params_file, cls_option);
|
||||
auto rec_model = fastdeploy::vision::ocr::Recognizer(rec_model_file, rec_params_file, rec_label_file, rec_option);
|
||||
|
||||
// Users could enable static shape infer for rec model when deploy PP-OCR on hardware
|
||||
// which can not support dynamic shape infer well, like Huawei Ascend series.
|
||||
rec_model.GetPreprocessor().SetStaticShapeInfer(true);
|
||||
|
||||
assert(det_model.Initialized());
|
||||
assert(cls_model.Initialized());
|
||||
assert(rec_model.Initialized());
|
||||
|
||||
// The classification model is optional, so the PP-OCR can also be connected in series as follows
|
||||
// auto ppocr_v2 = fastdeploy::pipeline::PPOCRv2(&det_model, &rec_model);
|
||||
auto ppocr_v2 = fastdeploy::pipeline::PPOCRv2(&det_model, &cls_model, &rec_model);
|
||||
|
||||
// When users enable static shape infer for rec model, the batch size of cls and rec model must to be set to 1.
|
||||
ppocr_v2.SetClsBatchSize(1);
|
||||
ppocr_v2.SetRecBatchSize(1);
|
||||
|
||||
if(!ppocr_v2.Initialized()){
|
||||
std::cerr << "Failed to initialize PP-OCR." << std::endl;
|
||||
return;
|
||||
}
|
||||
|
||||
auto im = cv::imread(image_file);
|
||||
|
||||
fastdeploy::vision::OCRResult result;
|
||||
if (!ppocr_v2.Predict(im, &result)) {
|
||||
std::cerr << "Failed to predict." << std::endl;
|
||||
return;
|
||||
}
|
||||
|
||||
std::cout << result.Str() << std::endl;
|
||||
|
||||
auto vis_im = fastdeploy::vision::VisOcr(im, result);
|
||||
cv::imwrite("vis_result.jpg", vis_im);
|
||||
std::cout << "Visualized result saved in ./vis_result.jpg" << std::endl;
|
||||
}
|
||||
|
||||
int main(int argc, char* argv[]) {
|
||||
if (argc < 7) {
|
||||
std::cout << "Usage: infer_demo path/to/det_model path/to/cls_model "
|
||||
"path/to/rec_model path/to/rec_label_file path/to/image "
|
||||
"run_option, "
|
||||
"e.g ./infer_demo ./ch_PP-OCRv2_det_infer "
|
||||
"./ch_ppocr_mobile_v2.0_cls_infer ./ch_PP-OCRv2_rec_infer "
|
||||
"./ppocr_keys_v1.txt ./12.jpg 0"
|
||||
<< std::endl;
|
||||
std::cout << "The data type of run_option is int, 0: run with cpu; 1: run "
|
||||
"with ascend."
|
||||
<< std::endl;
|
||||
return -1;
|
||||
}
|
||||
|
||||
fastdeploy::RuntimeOption option;
|
||||
int flag = std::atoi(argv[6]);
|
||||
|
||||
if (flag == 0) {
|
||||
option.UseCpu();
|
||||
} else if (flag == 1) {
|
||||
option.UseAscend();
|
||||
}
|
||||
|
||||
std::string det_model_dir = argv[1];
|
||||
std::string cls_model_dir = argv[2];
|
||||
std::string rec_model_dir = argv[3];
|
||||
std::string rec_label_file = argv[4];
|
||||
std::string test_image = argv[5];
|
||||
InitAndInfer(det_model_dir, cls_model_dir, rec_model_dir, rec_label_file, test_image, option);
|
||||
return 0;
|
||||
}
|
||||
@@ -36,8 +36,8 @@ python infer.py --det_model ch_PP-OCRv2_det_infer --cls_model ch_ppocr_mobile_v2
|
||||
python infer.py --det_model ch_PP-OCRv2_det_infer --cls_model ch_ppocr_mobile_v2.0_cls_infer --rec_model ch_PP-OCRv2_rec_infer --rec_label_file ppocr_keys_v1.txt --image 12.jpg --device gpu --backend trt
|
||||
# 昆仑芯XPU推理
|
||||
python infer.py --det_model ch_PP-OCRv2_det_infer --cls_model ch_ppocr_mobile_v2.0_cls_infer --rec_model ch_PP-OCRv2_rec_infer --rec_label_file ppocr_keys_v1.txt --image 12.jpg --device kunlunxin
|
||||
# 华为昇腾推理
|
||||
python infer.py --det_model ch_PP-OCRv2_det_infer --cls_model ch_ppocr_mobile_v2.0_cls_infer --rec_model ch_PP-OCRv2_rec_infer --rec_label_file ppocr_keys_v1.txt --image 12.jpg --device ascend
|
||||
# 华为昇腾推理,需要使用静态shape脚本, 若用户需要连续地预测图片, 输入图片尺寸需要准备为统一尺寸
|
||||
python infer_static_shape.py --det_model ch_PP-OCRv2_det_infer --cls_model ch_ppocr_mobile_v2.0_cls_infer --rec_model ch_PP-OCRv2_rec_infer --rec_label_file ppocr_keys_v1.txt --image 12.jpg --device ascend
|
||||
```
|
||||
|
||||
运行完成可视化结果如下图所示
|
||||
|
||||
@@ -58,43 +58,113 @@ def parse_arguments():
|
||||
type=int,
|
||||
default=9,
|
||||
help="Number of threads while inference on CPU.")
|
||||
parser.add_argument(
|
||||
"--cls_bs",
|
||||
type=int,
|
||||
default=1,
|
||||
help="Classification model inference batch size.")
|
||||
parser.add_argument(
|
||||
"--rec_bs",
|
||||
type=int,
|
||||
default=6,
|
||||
help="Recognition model inference batch size")
|
||||
return parser.parse_args()
|
||||
|
||||
|
||||
def build_option(args):
|
||||
option = fd.RuntimeOption()
|
||||
if args.device.lower() == "gpu":
|
||||
option.use_gpu(0)
|
||||
|
||||
option.set_cpu_thread_num(args.cpu_thread_num)
|
||||
det_option = fd.RuntimeOption()
|
||||
cls_option = fd.RuntimeOption()
|
||||
rec_option = fd.RuntimeOption()
|
||||
|
||||
det_option.set_cpu_thread_num(args.cpu_thread_num)
|
||||
cls_option.set_cpu_thread_num(args.cpu_thread_num)
|
||||
rec_option.set_cpu_thread_num(args.cpu_thread_num)
|
||||
|
||||
if args.device.lower() == "gpu":
|
||||
det_option.use_gpu(args.device_id)
|
||||
cls_option.use_gpu(args.device_id)
|
||||
rec_option.use_gpu(args.device_id)
|
||||
|
||||
if args.device.lower() == "kunlunxin":
|
||||
option.use_kunlunxin()
|
||||
return option
|
||||
det_option.use_kunlunxin()
|
||||
cls_option.use_kunlunxin()
|
||||
rec_option.use_kunlunxin()
|
||||
|
||||
if args.device.lower() == "ascend":
|
||||
option.use_ascend()
|
||||
return option
|
||||
return det_option, cls_option, rec_option
|
||||
|
||||
if args.backend.lower() == "trt":
|
||||
assert args.device.lower(
|
||||
) == "gpu", "TensorRT backend require inference on device GPU."
|
||||
option.use_trt_backend()
|
||||
det_option.use_trt_backend()
|
||||
cls_option.use_trt_backend()
|
||||
rec_option.use_trt_backend()
|
||||
|
||||
# 设置trt input shape
|
||||
# 如果用户想要自己改动检测模型的输入shape, 我们建议用户把检测模型的长和高设置为32的倍数.
|
||||
det_option.set_trt_input_shape("x", [1, 3, 64, 64], [1, 3, 640, 640],
|
||||
[1, 3, 960, 960])
|
||||
cls_option.set_trt_input_shape("x", [1, 3, 48, 10],
|
||||
[args.cls_bs, 3, 48, 320],
|
||||
[args.cls_bs, 3, 48, 1024])
|
||||
rec_option.set_trt_input_shape("x", [1, 3, 32, 10],
|
||||
[args.rec_bs, 3, 32, 320],
|
||||
[args.rec_bs, 3, 32, 2304])
|
||||
|
||||
# 用户可以把TRT引擎文件保存至本地
|
||||
det_option.set_trt_cache_file(args.det_model + "/det_trt_cache.trt")
|
||||
cls_option.set_trt_cache_file(args.cls_model + "/cls_trt_cache.trt")
|
||||
rec_option.set_trt_cache_file(args.rec_model + "/rec_trt_cache.trt")
|
||||
|
||||
elif args.backend.lower() == "pptrt":
|
||||
assert args.device.lower(
|
||||
) == "gpu", "Paddle-TensorRT backend require inference on device GPU."
|
||||
option.use_trt_backend()
|
||||
option.enable_paddle_trt_collect_shape()
|
||||
option.enable_paddle_to_trt()
|
||||
det_option.use_trt_backend()
|
||||
det_option.enable_paddle_trt_collect_shape()
|
||||
det_option.enable_paddle_to_trt()
|
||||
|
||||
cls_option.use_trt_backend()
|
||||
cls_option.enable_paddle_trt_collect_shape()
|
||||
cls_option.enable_paddle_to_trt()
|
||||
|
||||
rec_option.use_trt_backend()
|
||||
rec_option.enable_paddle_trt_collect_shape()
|
||||
rec_option.enable_paddle_to_trt()
|
||||
|
||||
# 设置trt input shape
|
||||
# 如果用户想要自己改动检测模型的输入shape, 我们建议用户把检测模型的长和高设置为32的倍数.
|
||||
det_option.set_trt_input_shape("x", [1, 3, 64, 64], [1, 3, 640, 640],
|
||||
[1, 3, 960, 960])
|
||||
cls_option.set_trt_input_shape("x", [1, 3, 48, 10],
|
||||
[args.cls_bs, 3, 48, 320],
|
||||
[args.cls_bs, 3, 48, 1024])
|
||||
rec_option.set_trt_input_shape("x", [1, 3, 32, 10],
|
||||
[args.rec_bs, 3, 32, 320],
|
||||
[args.rec_bs, 3, 32, 2304])
|
||||
|
||||
# 用户可以把TRT引擎文件保存至本地
|
||||
det_option.set_trt_cache_file(args.det_model)
|
||||
cls_option.set_trt_cache_file(args.cls_model)
|
||||
rec_option.set_trt_cache_file(args.rec_model)
|
||||
|
||||
elif args.backend.lower() == "ort":
|
||||
option.use_ort_backend()
|
||||
det_option.use_ort_backend()
|
||||
cls_option.use_ort_backend()
|
||||
rec_option.use_ort_backend()
|
||||
|
||||
elif args.backend.lower() == "paddle":
|
||||
option.use_paddle_infer_backend()
|
||||
det_option.use_paddle_infer_backend()
|
||||
cls_option.use_paddle_infer_backend()
|
||||
rec_option.use_paddle_infer_backend()
|
||||
|
||||
elif args.backend.lower() == "openvino":
|
||||
assert args.device.lower(
|
||||
) == "cpu", "OpenVINO backend require inference on device CPU."
|
||||
option.use_openvino_backend()
|
||||
return option
|
||||
det_option.use_openvino_backend()
|
||||
cls_option.use_openvino_backend()
|
||||
rec_option.use_openvino_backend()
|
||||
|
||||
return det_option, cls_option, rec_option
|
||||
|
||||
|
||||
args = parse_arguments()
|
||||
@@ -111,49 +181,18 @@ rec_params_file = os.path.join(args.rec_model, "inference.pdiparams")
|
||||
rec_label_file = args.rec_label_file
|
||||
|
||||
# 对于三个模型，均采用同样的部署配置
|
||||
# 用户也可根据自行需求分别配置
|
||||
runtime_option = build_option(args)
|
||||
# 用户也可根据自己的需求,个性化配置
|
||||
det_option, cls_option, rec_option = build_option(args)
|
||||
|
||||
# PPOCR的cls和rec模型现在已经支持推理一个Batch的数据
|
||||
# 定义下面两个变量后, 可用于设置trt输入shape, 并在PPOCR模型初始化后, 完成Batch推理设置
|
||||
# 当用户要把PP-OCR部署在对动态shape推理支持有限的设备上时,(例如华为昇腾)
|
||||
# 需要把cls_batch_size和rec_batch_size都设置为1.
|
||||
cls_batch_size = 1
|
||||
rec_batch_size = 6
|
||||
|
||||
# 当使用TRT时，分别给三个模型的runtime设置动态shape,并完成模型的创建.
|
||||
# 注意: 需要在检测模型创建完成后，再设置分类模型的动态输入并创建分类模型, 识别模型同理.
|
||||
# 如果用户想要自己改动检测模型的输入shape, 我们建议用户把检测模型的长和高设置为32的倍数.
|
||||
det_option = runtime_option
|
||||
det_option.set_trt_input_shape("x", [1, 3, 64, 64], [1, 3, 640, 640],
|
||||
[1, 3, 960, 960])
|
||||
# 用户可以把TRT引擎文件保存至本地
|
||||
# det_option.set_trt_cache_file(args.det_model + "/det_trt_cache.trt")
|
||||
det_model = fd.vision.ocr.DBDetector(
|
||||
det_model_file, det_params_file, runtime_option=det_option)
|
||||
|
||||
cls_option = runtime_option
|
||||
cls_option.set_trt_input_shape("x", [1, 3, 48, 10],
|
||||
[cls_batch_size, 3, 48, 320],
|
||||
[cls_batch_size, 3, 48, 1024])
|
||||
# 用户可以把TRT引擎文件保存至本地
|
||||
# cls_option.set_trt_cache_file(args.cls_model + "/cls_trt_cache.trt")
|
||||
cls_model = fd.vision.ocr.Classifier(
|
||||
cls_model_file, cls_params_file, runtime_option=cls_option)
|
||||
|
||||
rec_option = runtime_option
|
||||
rec_option.set_trt_input_shape("x", [1, 3, 32, 10],
|
||||
[rec_batch_size, 3, 32, 320],
|
||||
[rec_batch_size, 3, 32, 2304])
|
||||
# 用户可以把TRT引擎文件保存至本地
|
||||
# rec_option.set_trt_cache_file(args.rec_model + "/rec_trt_cache.trt")
|
||||
rec_model = fd.vision.ocr.Recognizer(
|
||||
rec_model_file, rec_params_file, rec_label_file, runtime_option=rec_option)
|
||||
|
||||
# 当用户要把PP-OCR部署在对动态shape推理支持有限的设备上时,(例如华为昇腾)
|
||||
# 需要使用下行代码, 来启用rec模型的静态shape推理.
|
||||
# rec_model.preprocessor.static_shape_infer = True
|
||||
|
||||
# 创建PP-OCR，串联3个模型，其中cls_model可选，如无需求，可设置为None
|
||||
ppocr_v2 = fd.vision.ocr.PPOCRv2(
|
||||
det_model=det_model, cls_model=cls_model, rec_model=rec_model)
|
||||
@@ -161,8 +200,8 @@ ppocr_v2 = fd.vision.ocr.PPOCRv2(
|
||||
# 给cls和rec模型设置推理时的batch size
|
||||
# 此值能为-1, 和1到正无穷
|
||||
# 当此值为-1时, cls和rec模型的batch size将默认和det模型检测出的框的数量相同
|
||||
ppocr_v2.cls_batch_size = cls_batch_size
|
||||
ppocr_v2.rec_batch_size = rec_batch_size
|
||||
ppocr_v2.cls_batch_size = args.cls_bs
|
||||
ppocr_v2.rec_batch_size = args.rec_bs
|
||||
|
||||
# 预测图片准备
|
||||
im = cv2.imread(args.image)
|
||||
|
||||
114
examples/vision/ocr/PP-OCRv2/python/infer_static_shape.py
Executable file
114
examples/vision/ocr/PP-OCRv2/python/infer_static_shape.py
Executable file
@@ -0,0 +1,114 @@
|
||||
# Copyright (c) 2022 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 fastdeploy as fd
|
||||
import cv2
|
||||
import os
|
||||
|
||||
|
||||
def parse_arguments():
|
||||
import argparse
|
||||
import ast
|
||||
parser = argparse.ArgumentParser()
|
||||
parser.add_argument(
|
||||
"--det_model", required=True, help="Path of Detection model of PPOCR.")
|
||||
parser.add_argument(
|
||||
"--cls_model",
|
||||
required=True,
|
||||
help="Path of Classification model of PPOCR.")
|
||||
parser.add_argument(
|
||||
"--rec_model",
|
||||
required=True,
|
||||
help="Path of Recognization model of PPOCR.")
|
||||
parser.add_argument(
|
||||
"--rec_label_file",
|
||||
required=True,
|
||||
help="Path of Recognization model of PPOCR.")
|
||||
parser.add_argument(
|
||||
"--image", type=str, required=True, help="Path of test image file.")
|
||||
parser.add_argument(
|
||||
"--device",
|
||||
type=str,
|
||||
default='cpu',
|
||||
help="Type of inference device, support 'cpu', 'kunlunxin' or 'gpu'.")
|
||||
parser.add_argument(
|
||||
"--cpu_thread_num",
|
||||
type=int,
|
||||
default=9,
|
||||
help="Number of threads while inference on CPU.")
|
||||
return parser.parse_args()
|
||||
|
||||
|
||||
def build_option(args):
|
||||
|
||||
det_option = fd.RuntimeOption()
|
||||
cls_option = fd.RuntimeOption()
|
||||
rec_option = fd.RuntimeOption()
|
||||
|
||||
# 当前需要对PP-OCR启用静态shape推理的硬件只有昇腾.
|
||||
if args.device.lower() == "ascend":
|
||||
det_option.use_ascend()
|
||||
cls_option.use_ascend()
|
||||
rec_option.use_ascend()
|
||||
|
||||
return det_option, cls_option, rec_option
|
||||
|
||||
|
||||
args = parse_arguments()
|
||||
|
||||
# Detection模型, 检测文字框
|
||||
det_model_file = os.path.join(args.det_model, "inference.pdmodel")
|
||||
det_params_file = os.path.join(args.det_model, "inference.pdiparams")
|
||||
# Classification模型，方向分类，可选
|
||||
cls_model_file = os.path.join(args.cls_model, "inference.pdmodel")
|
||||
cls_params_file = os.path.join(args.cls_model, "inference.pdiparams")
|
||||
# Recognition模型，文字识别模型
|
||||
rec_model_file = os.path.join(args.rec_model, "inference.pdmodel")
|
||||
rec_params_file = os.path.join(args.rec_model, "inference.pdiparams")
|
||||
rec_label_file = args.rec_label_file
|
||||
|
||||
det_option, cls_option, rec_option = build_option(args)
|
||||
|
||||
det_model = fd.vision.ocr.DBDetector(
|
||||
det_model_file, det_params_file, runtime_option=det_option)
|
||||
|
||||
cls_model = fd.vision.ocr.Classifier(
|
||||
cls_model_file, cls_params_file, runtime_option=cls_option)
|
||||
|
||||
rec_model = fd.vision.ocr.Recognizer(
|
||||
rec_model_file, rec_params_file, rec_label_file, runtime_option=rec_option)
|
||||
|
||||
# Rec模型启用静态shape推理
|
||||
rec_model.preprocessor.static_shape_infer = True
|
||||
|
||||
# 创建PP-OCR，串联3个模型，其中cls_model可选，如无需求，可设置为None
|
||||
ppocr_v2 = fd.vision.ocr.PPOCRv2(
|
||||
det_model=det_model, cls_model=cls_model, rec_model=rec_model)
|
||||
|
||||
# Cls模型和Rec模型的batch size 必须设置为1, 开启静态shape推理
|
||||
ppocr_v2.cls_batch_size = 1
|
||||
ppocr_v2.rec_batch_size = 1
|
||||
|
||||
# 预测图片准备
|
||||
im = cv2.imread(args.image)
|
||||
|
||||
#预测并打印结果
|
||||
result = ppocr_v2.predict(im)
|
||||
|
||||
print(result)
|
||||
|
||||
# 可视化结果
|
||||
vis_im = fd.vision.vis_ppocr(im, result)
|
||||
cv2.imwrite("visualized_result.jpg", vis_im)
|
||||
print("Visualized result save in ./visualized_result.jpg")
|
||||
@@ -12,3 +12,7 @@ include_directories(${FASTDEPLOY_INCS})
|
||||
add_executable(infer_demo ${PROJECT_SOURCE_DIR}/infer.cc)
|
||||
# 添加FastDeploy库依赖
|
||||
target_link_libraries(infer_demo ${FASTDEPLOY_LIBS})
|
||||
|
||||
add_executable(infer_static_shape_demo ${PROJECT_SOURCE_DIR}/infer_static_shape.cc)
|
||||
# 添加FastDeploy库依赖
|
||||
target_link_libraries(infer_static_shape_demo ${FASTDEPLOY_LIBS})
|
||||
|
||||
@@ -43,13 +43,16 @@ wget https://gitee.com/paddlepaddle/PaddleOCR/raw/release/2.6/ppocr/utils/ppocr_
|
||||
./infer_demo ./ch_PP-OCRv3_det_infer ./ch_ppocr_mobile_v2.0_cls_infer ./ch_PP-OCRv3_rec_infer ./ppocr_keys_v1.txt ./12.jpg 3
|
||||
# 昆仑芯XPU推理
|
||||
./infer_demo ./ch_PP-OCRv3_det_infer ./ch_ppocr_mobile_v2.0_cls_infer ./ch_PP-OCRv3_rec_infer ./ppocr_keys_v1.txt ./12.jpg 4
|
||||
# 华为昇腾推理, 请用户在代码里正确开启Rec模型的静态shape推理，并设置分类模型和识别模型的推理batch size为1.
|
||||
./infer_demo ./ch_PP-OCRv3_det_infer ./ch_ppocr_mobile_v2.0_cls_infer ./ch_PP-OCRv3_rec_infer ./ppocr_keys_v1.txt ./12.jpg 5
|
||||
# 华为昇腾推理,需要使用静态shape的demo, 若用户需要连续地预测图片, 输入图片尺寸需要准备为统一尺寸
|
||||
./infer_static_shape_demo ./ch_PP-OCRv3_det_infer ./ch_ppocr_mobile_v2.0_cls_infer ./ch_PP-OCRv3_rec_infer ./ppocr_keys_v1.txt ./12.jpg 1
|
||||
```
|
||||
|
||||
以上命令只适用于Linux或MacOS, Windows下SDK的使用方式请参考:
|
||||
- [如何在Windows中使用FastDeploy C++ SDK](../../../../../docs/cn/faq/use_sdk_on_windows.md)
|
||||
|
||||
如果用户使用华为昇腾NPU部署, 请参考以下方式在部署前初始化部署环境:
|
||||
- [如何使用华为昇腾NPU部署](../../../../../docs/cn/faq/use_sdk_on_ascend.md)
|
||||
|
||||
运行完成可视化结果如下图所示
|
||||
|
||||
<img width="640" src="https://user-images.githubusercontent.com/109218879/185826024-f7593a0c-1bd2-4a60-b76c-15588484fa08.jpg">
|
||||
|
||||
@@ -56,10 +56,6 @@ void InitAndInfer(const std::string& det_model_dir, const std::string& cls_model
|
||||
auto cls_model = fastdeploy::vision::ocr::Classifier(cls_model_file, cls_params_file, cls_option);
|
||||
auto rec_model = fastdeploy::vision::ocr::Recognizer(rec_model_file, rec_params_file, rec_label_file, rec_option);
|
||||
|
||||
// Users could enable static shape infer for rec model when deploy PP-OCR on hardware
|
||||
// which can not support dynamic shape infer well, like Huawei Ascend series.
|
||||
// rec_model.GetPreprocessor().SetStaticShapeInfer(true);
|
||||
|
||||
assert(det_model.Initialized());
|
||||
assert(cls_model.Initialized());
|
||||
assert(rec_model.Initialized());
|
||||
@@ -71,9 +67,6 @@ void InitAndInfer(const std::string& det_model_dir, const std::string& cls_model
|
||||
// Set inference batch size for cls model and rec model, the value could be -1 and 1 to positive infinity.
|
||||
// When inference batch size is set to -1, it means that the inference batch size
|
||||
// of the cls and rec models will be the same as the number of boxes detected by the det model.
|
||||
// When users enable static shape infer for rec model, the batch size of cls and rec model needs to be set to 1.
|
||||
// ppocr_v3.SetClsBatchSize(1);
|
||||
// ppocr_v3.SetRecBatchSize(1);
|
||||
ppocr_v3.SetClsBatchSize(cls_batch_size);
|
||||
ppocr_v3.SetRecBatchSize(rec_batch_size);
|
||||
|
||||
@@ -130,8 +123,6 @@ int main(int argc, char* argv[]) {
|
||||
option.EnablePaddleToTrt();
|
||||
} else if (flag == 4) {
|
||||
option.UseKunlunXin();
|
||||
} else if (flag == 5) {
|
||||
option.UseAscend();
|
||||
}
|
||||
|
||||
std::string det_model_dir = argv[1];
|
||||
|
||||
107
examples/vision/ocr/PP-OCRv3/cpp/infer_static_shape.cc
Executable file
107
examples/vision/ocr/PP-OCRv3/cpp/infer_static_shape.cc
Executable file
@@ -0,0 +1,107 @@
|
||||
// Copyright (c) 2022 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 "fastdeploy/vision.h"
|
||||
#ifdef WIN32
|
||||
const char sep = '\\';
|
||||
#else
|
||||
const char sep = '/';
|
||||
#endif
|
||||
|
||||
void InitAndInfer(const std::string& det_model_dir, const std::string& cls_model_dir, const std::string& rec_model_dir, const std::string& rec_label_file, const std::string& image_file, const fastdeploy::RuntimeOption& option) {
|
||||
auto det_model_file = det_model_dir + sep + "inference.pdmodel";
|
||||
auto det_params_file = det_model_dir + sep + "inference.pdiparams";
|
||||
|
||||
auto cls_model_file = cls_model_dir + sep + "inference.pdmodel";
|
||||
auto cls_params_file = cls_model_dir + sep + "inference.pdiparams";
|
||||
|
||||
auto rec_model_file = rec_model_dir + sep + "inference.pdmodel";
|
||||
auto rec_params_file = rec_model_dir + sep + "inference.pdiparams";
|
||||
|
||||
auto det_option = option;
|
||||
auto cls_option = option;
|
||||
auto rec_option = option;
|
||||
|
||||
auto det_model = fastdeploy::vision::ocr::DBDetector(det_model_file, det_params_file, det_option);
|
||||
auto cls_model = fastdeploy::vision::ocr::Classifier(cls_model_file, cls_params_file, cls_option);
|
||||
auto rec_model = fastdeploy::vision::ocr::Recognizer(rec_model_file, rec_params_file, rec_label_file, rec_option);
|
||||
|
||||
// Users could enable static shape infer for rec model when deploy PP-OCR on hardware
|
||||
// which can not support dynamic shape infer well, like Huawei Ascend series.
|
||||
rec_model.GetPreprocessor().SetStaticShapeInfer(true);
|
||||
|
||||
assert(det_model.Initialized());
|
||||
assert(cls_model.Initialized());
|
||||
assert(rec_model.Initialized());
|
||||
|
||||
// The classification model is optional, so the PP-OCR can also be connected in series as follows
|
||||
// auto ppocr_v3 = fastdeploy::pipeline::PPOCRv3(&det_model, &rec_model);
|
||||
auto ppocr_v3 = fastdeploy::pipeline::PPOCRv3(&det_model, &cls_model, &rec_model);
|
||||
|
||||
// When users enable static shape infer for rec model, the batch size of cls and rec model must to be set to 1.
|
||||
ppocr_v3.SetClsBatchSize(1);
|
||||
ppocr_v3.SetRecBatchSize(1);
|
||||
|
||||
if(!ppocr_v3.Initialized()){
|
||||
std::cerr << "Failed to initialize PP-OCR." << std::endl;
|
||||
return;
|
||||
}
|
||||
|
||||
auto im = cv::imread(image_file);
|
||||
|
||||
fastdeploy::vision::OCRResult result;
|
||||
if (!ppocr_v3.Predict(im, &result)) {
|
||||
std::cerr << "Failed to predict." << std::endl;
|
||||
return;
|
||||
}
|
||||
|
||||
std::cout << result.Str() << std::endl;
|
||||
|
||||
auto vis_im = fastdeploy::vision::VisOcr(im, result);
|
||||
cv::imwrite("vis_result.jpg", vis_im);
|
||||
std::cout << "Visualized result saved in ./vis_result.jpg" << std::endl;
|
||||
}
|
||||
|
||||
int main(int argc, char* argv[]) {
|
||||
if (argc < 7) {
|
||||
std::cout << "Usage: infer_demo path/to/det_model path/to/cls_model "
|
||||
"path/to/rec_model path/to/rec_label_file path/to/image "
|
||||
"run_option, "
|
||||
"e.g ./infer_demo ./ch_PP-OCRv3_det_infer "
|
||||
"./ch_ppocr_mobile_v2.0_cls_infer ./ch_PP-OCRv3_rec_infer "
|
||||
"./ppocr_keys_v1.txt ./12.jpg 0"
|
||||
<< std::endl;
|
||||
std::cout << "The data type of run_option is int, 0: run with cpu; 1: run "
|
||||
"with ascend."
|
||||
<< std::endl;
|
||||
return -1;
|
||||
}
|
||||
|
||||
fastdeploy::RuntimeOption option;
|
||||
int flag = std::atoi(argv[6]);
|
||||
|
||||
if (flag == 0) {
|
||||
option.UseCpu();
|
||||
} else if (flag == 1) {
|
||||
option.UseAscend();
|
||||
}
|
||||
|
||||
std::string det_model_dir = argv[1];
|
||||
std::string cls_model_dir = argv[2];
|
||||
std::string rec_model_dir = argv[3];
|
||||
std::string rec_label_file = argv[4];
|
||||
std::string test_image = argv[5];
|
||||
InitAndInfer(det_model_dir, cls_model_dir, rec_model_dir, rec_label_file, test_image, option);
|
||||
return 0;
|
||||
}
|
||||
@@ -35,8 +35,8 @@ python infer.py --det_model ch_PP-OCRv3_det_infer --cls_model ch_ppocr_mobile_v2
|
||||
python infer.py --det_model ch_PP-OCRv3_det_infer --cls_model ch_ppocr_mobile_v2.0_cls_infer --rec_model ch_PP-OCRv3_rec_infer --rec_label_file ppocr_keys_v1.txt --image 12.jpg --device gpu --backend trt
|
||||
# 昆仑芯XPU推理
|
||||
python infer.py --det_model ch_PP-OCRv3_det_infer --cls_model ch_ppocr_mobile_v2.0_cls_infer --rec_model ch_PP-OCRv3_rec_infer --rec_label_file ppocr_keys_v1.txt --image 12.jpg --device kunlunxin
|
||||
# 华为昇腾推理
|
||||
python infer.py --det_model ch_PP-OCRv3_det_infer --cls_model ch_ppocr_mobile_v2.0_cls_infer --rec_model ch_PP-OCRv3_rec_infer --rec_label_file ppocr_keys_v1.txt --image 12.jpg --device ascend
|
||||
# 华为昇腾推理,需要使用静态shape脚本, 若用户需要连续地预测图片, 输入图片尺寸需要准备为统一尺寸
|
||||
python infer_static_shape.py --det_model ch_PP-OCRv3_det_infer --cls_model ch_ppocr_mobile_v2.0_cls_infer --rec_model ch_PP-OCRv3_rec_infer --rec_label_file ppocr_keys_v1.txt --image 12.jpg --device ascend
|
||||
```
|
||||
|
||||
运行完成可视化结果如下图所示
|
||||
|
||||
@@ -58,43 +58,113 @@ def parse_arguments():
|
||||
type=int,
|
||||
default=9,
|
||||
help="Number of threads while inference on CPU.")
|
||||
parser.add_argument(
|
||||
"--cls_bs",
|
||||
type=int,
|
||||
default=1,
|
||||
help="Classification model inference batch size.")
|
||||
parser.add_argument(
|
||||
"--rec_bs",
|
||||
type=int,
|
||||
default=6,
|
||||
help="Recognition model inference batch size")
|
||||
return parser.parse_args()
|
||||
|
||||
|
||||
def build_option(args):
|
||||
option = fd.RuntimeOption()
|
||||
if args.device.lower() == "gpu":
|
||||
option.use_gpu(0)
|
||||
|
||||
option.set_cpu_thread_num(args.cpu_thread_num)
|
||||
det_option = fd.RuntimeOption()
|
||||
cls_option = fd.RuntimeOption()
|
||||
rec_option = fd.RuntimeOption()
|
||||
|
||||
det_option.set_cpu_thread_num(args.cpu_thread_num)
|
||||
cls_option.set_cpu_thread_num(args.cpu_thread_num)
|
||||
rec_option.set_cpu_thread_num(args.cpu_thread_num)
|
||||
|
||||
if args.device.lower() == "gpu":
|
||||
det_option.use_gpu(args.device_id)
|
||||
cls_option.use_gpu(args.device_id)
|
||||
rec_option.use_gpu(args.device_id)
|
||||
|
||||
if args.device.lower() == "kunlunxin":
|
||||
option.use_kunlunxin()
|
||||
return option
|
||||
det_option.use_kunlunxin()
|
||||
cls_option.use_kunlunxin()
|
||||
rec_option.use_kunlunxin()
|
||||
|
||||
if args.device.lower() == "ascend":
|
||||
option.use_ascend()
|
||||
return option
|
||||
return det_option, cls_option, rec_option
|
||||
|
||||
if args.backend.lower() == "trt":
|
||||
assert args.device.lower(
|
||||
) == "gpu", "TensorRT backend require inference on device GPU."
|
||||
option.use_trt_backend()
|
||||
det_option.use_trt_backend()
|
||||
cls_option.use_trt_backend()
|
||||
rec_option.use_trt_backend()
|
||||
|
||||
# 设置trt input shape
|
||||
# 如果用户想要自己改动检测模型的输入shape, 我们建议用户把检测模型的长和高设置为32的倍数.
|
||||
det_option.set_trt_input_shape("x", [1, 3, 64, 64], [1, 3, 640, 640],
|
||||
[1, 3, 960, 960])
|
||||
cls_option.set_trt_input_shape("x", [1, 3, 48, 10],
|
||||
[args.cls_bs, 3, 48, 320],
|
||||
[args.cls_bs, 3, 48, 1024])
|
||||
rec_option.set_trt_input_shape("x", [1, 3, 48, 10],
|
||||
[args.rec_bs, 3, 48, 320],
|
||||
[args.rec_bs, 3, 48, 2304])
|
||||
|
||||
# 用户可以把TRT引擎文件保存至本地
|
||||
det_option.set_trt_cache_file(args.det_model + "/det_trt_cache.trt")
|
||||
cls_option.set_trt_cache_file(args.cls_model + "/cls_trt_cache.trt")
|
||||
rec_option.set_trt_cache_file(args.rec_model + "/rec_trt_cache.trt")
|
||||
|
||||
elif args.backend.lower() == "pptrt":
|
||||
assert args.device.lower(
|
||||
) == "gpu", "Paddle-TensorRT backend require inference on device GPU."
|
||||
option.use_trt_backend()
|
||||
option.enable_paddle_trt_collect_shape()
|
||||
option.enable_paddle_to_trt()
|
||||
det_option.use_trt_backend()
|
||||
det_option.enable_paddle_trt_collect_shape()
|
||||
det_option.enable_paddle_to_trt()
|
||||
|
||||
cls_option.use_trt_backend()
|
||||
cls_option.enable_paddle_trt_collect_shape()
|
||||
cls_option.enable_paddle_to_trt()
|
||||
|
||||
rec_option.use_trt_backend()
|
||||
rec_option.enable_paddle_trt_collect_shape()
|
||||
rec_option.enable_paddle_to_trt()
|
||||
|
||||
# 设置trt input shape
|
||||
# 如果用户想要自己改动检测模型的输入shape, 我们建议用户把检测模型的长和高设置为32的倍数.
|
||||
det_option.set_trt_input_shape("x", [1, 3, 64, 64], [1, 3, 640, 640],
|
||||
[1, 3, 960, 960])
|
||||
cls_option.set_trt_input_shape("x", [1, 3, 48, 10],
|
||||
[args.cls_bs, 3, 48, 320],
|
||||
[args.cls_bs, 3, 48, 1024])
|
||||
rec_option.set_trt_input_shape("x", [1, 3, 48, 10],
|
||||
[args.rec_bs, 3, 48, 320],
|
||||
[args.rec_bs, 3, 48, 2304])
|
||||
|
||||
# 用户可以把TRT引擎文件保存至本地
|
||||
det_option.set_trt_cache_file(args.det_model)
|
||||
cls_option.set_trt_cache_file(args.cls_model)
|
||||
rec_option.set_trt_cache_file(args.rec_model)
|
||||
|
||||
elif args.backend.lower() == "ort":
|
||||
option.use_ort_backend()
|
||||
det_option.use_ort_backend()
|
||||
cls_option.use_ort_backend()
|
||||
rec_option.use_ort_backend()
|
||||
|
||||
elif args.backend.lower() == "paddle":
|
||||
option.use_paddle_infer_backend()
|
||||
det_option.use_paddle_infer_backend()
|
||||
cls_option.use_paddle_infer_backend()
|
||||
rec_option.use_paddle_infer_backend()
|
||||
|
||||
elif args.backend.lower() == "openvino":
|
||||
assert args.device.lower(
|
||||
) == "cpu", "OpenVINO backend require inference on device CPU."
|
||||
option.use_openvino_backend()
|
||||
return option
|
||||
det_option.use_openvino_backend()
|
||||
cls_option.use_openvino_backend()
|
||||
rec_option.use_openvino_backend()
|
||||
|
||||
return det_option, cls_option, rec_option
|
||||
|
||||
|
||||
args = parse_arguments()
|
||||
@@ -111,49 +181,18 @@ rec_params_file = os.path.join(args.rec_model, "inference.pdiparams")
|
||||
rec_label_file = args.rec_label_file
|
||||
|
||||
# 对于三个模型，均采用同样的部署配置
|
||||
# 用户也可根据自行需求分别配置
|
||||
runtime_option = build_option(args)
|
||||
# 用户也可根据自己的需求,个性化配置
|
||||
det_option, cls_option, rec_option = build_option(args)
|
||||
|
||||
# PPOCR的cls和rec模型现在已经支持推理一个Batch的数据
|
||||
# 定义下面两个变量后, 可用于设置trt输入shape, 并在PPOCR模型初始化后, 完成Batch推理设置
|
||||
# 当用户要把PP-OCR部署在对动态shape推理支持有限的设备上时,(例如华为昇腾)
|
||||
# 需要把cls_batch_size和rec_batch_size都设置为1.
|
||||
cls_batch_size = 1
|
||||
rec_batch_size = 6
|
||||
|
||||
# 当使用TRT时，分别给三个模型的runtime设置动态shape,并完成模型的创建.
|
||||
# 注意: 需要在检测模型创建完成后，再设置分类模型的动态输入并创建分类模型, 识别模型同理.
|
||||
# 如果用户想要自己改动检测模型的输入shape, 我们建议用户把检测模型的长和高设置为32的倍数.
|
||||
det_option = runtime_option
|
||||
det_option.set_trt_input_shape("x", [1, 3, 64, 64], [1, 3, 640, 640],
|
||||
[1, 3, 960, 960])
|
||||
# 用户可以把TRT引擎文件保存至本地
|
||||
# det_option.set_trt_cache_file(args.det_model + "/det_trt_cache.trt")
|
||||
det_model = fd.vision.ocr.DBDetector(
|
||||
det_model_file, det_params_file, runtime_option=det_option)
|
||||
|
||||
cls_option = runtime_option
|
||||
cls_option.set_trt_input_shape("x", [1, 3, 48, 10],
|
||||
[cls_batch_size, 3, 48, 320],
|
||||
[cls_batch_size, 3, 48, 1024])
|
||||
# 用户可以把TRT引擎文件保存至本地
|
||||
# cls_option.set_trt_cache_file(args.cls_model + "/cls_trt_cache.trt")
|
||||
cls_model = fd.vision.ocr.Classifier(
|
||||
cls_model_file, cls_params_file, runtime_option=cls_option)
|
||||
|
||||
rec_option = runtime_option
|
||||
rec_option.set_trt_input_shape("x", [1, 3, 48, 10],
|
||||
[rec_batch_size, 3, 48, 320],
|
||||
[rec_batch_size, 3, 48, 2304])
|
||||
# 用户可以把TRT引擎文件保存至本地
|
||||
# rec_option.set_trt_cache_file(args.rec_model + "/rec_trt_cache.trt")
|
||||
rec_model = fd.vision.ocr.Recognizer(
|
||||
rec_model_file, rec_params_file, rec_label_file, runtime_option=rec_option)
|
||||
|
||||
# 当用户要把PP-OCR部署在对动态shape推理支持有限的设备上时,(例如华为昇腾)
|
||||
# 需要使用下行代码, 来启用rec模型的静态shape推理.
|
||||
# rec_model.preprocessor.static_shape_infer = True
|
||||
|
||||
# 创建PP-OCR，串联3个模型，其中cls_model可选，如无需求，可设置为None
|
||||
ppocr_v3 = fd.vision.ocr.PPOCRv3(
|
||||
det_model=det_model, cls_model=cls_model, rec_model=rec_model)
|
||||
@@ -161,8 +200,8 @@ ppocr_v3 = fd.vision.ocr.PPOCRv3(
|
||||
# 给cls和rec模型设置推理时的batch size
|
||||
# 此值能为-1, 和1到正无穷
|
||||
# 当此值为-1时, cls和rec模型的batch size将默认和det模型检测出的框的数量相同
|
||||
ppocr_v3.cls_batch_size = cls_batch_size
|
||||
ppocr_v3.rec_batch_size = rec_batch_size
|
||||
ppocr_v3.cls_batch_size = args.cls_bs
|
||||
ppocr_v3.rec_batch_size = args.rec_bs
|
||||
|
||||
# 预测图片准备
|
||||
im = cv2.imread(args.image)
|
||||
|
||||
114
examples/vision/ocr/PP-OCRv3/python/infer_static_shape.py
Executable file
114
examples/vision/ocr/PP-OCRv3/python/infer_static_shape.py
Executable file
@@ -0,0 +1,114 @@
|
||||
# Copyright (c) 2022 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 fastdeploy as fd
|
||||
import cv2
|
||||
import os
|
||||
|
||||
|
||||
def parse_arguments():
|
||||
import argparse
|
||||
import ast
|
||||
parser = argparse.ArgumentParser()
|
||||
parser.add_argument(
|
||||
"--det_model", required=True, help="Path of Detection model of PPOCR.")
|
||||
parser.add_argument(
|
||||
"--cls_model",
|
||||
required=True,
|
||||
help="Path of Classification model of PPOCR.")
|
||||
parser.add_argument(
|
||||
"--rec_model",
|
||||
required=True,
|
||||
help="Path of Recognization model of PPOCR.")
|
||||
parser.add_argument(
|
||||
"--rec_label_file",
|
||||
required=True,
|
||||
help="Path of Recognization model of PPOCR.")
|
||||
parser.add_argument(
|
||||
"--image", type=str, required=True, help="Path of test image file.")
|
||||
parser.add_argument(
|
||||
"--device",
|
||||
type=str,
|
||||
default='cpu',
|
||||
help="Type of inference device, support 'cpu', 'kunlunxin' or 'gpu'.")
|
||||
parser.add_argument(
|
||||
"--cpu_thread_num",
|
||||
type=int,
|
||||
default=9,
|
||||
help="Number of threads while inference on CPU.")
|
||||
return parser.parse_args()
|
||||
|
||||
|
||||
def build_option(args):
|
||||
|
||||
det_option = fd.RuntimeOption()
|
||||
cls_option = fd.RuntimeOption()
|
||||
rec_option = fd.RuntimeOption()
|
||||
|
||||
# 当前需要对PP-OCR启用静态shape推理的硬件只有昇腾.
|
||||
if args.device.lower() == "ascend":
|
||||
det_option.use_ascend()
|
||||
cls_option.use_ascend()
|
||||
rec_option.use_ascend()
|
||||
|
||||
return det_option, cls_option, rec_option
|
||||
|
||||
|
||||
args = parse_arguments()
|
||||
|
||||
# Detection模型, 检测文字框
|
||||
det_model_file = os.path.join(args.det_model, "inference.pdmodel")
|
||||
det_params_file = os.path.join(args.det_model, "inference.pdiparams")
|
||||
# Classification模型，方向分类，可选
|
||||
cls_model_file = os.path.join(args.cls_model, "inference.pdmodel")
|
||||
cls_params_file = os.path.join(args.cls_model, "inference.pdiparams")
|
||||
# Recognition模型，文字识别模型
|
||||
rec_model_file = os.path.join(args.rec_model, "inference.pdmodel")
|
||||
rec_params_file = os.path.join(args.rec_model, "inference.pdiparams")
|
||||
rec_label_file = args.rec_label_file
|
||||
|
||||
det_option, cls_option, rec_option = build_option(args)
|
||||
|
||||
det_model = fd.vision.ocr.DBDetector(
|
||||
det_model_file, det_params_file, runtime_option=det_option)
|
||||
|
||||
cls_model = fd.vision.ocr.Classifier(
|
||||
cls_model_file, cls_params_file, runtime_option=cls_option)
|
||||
|
||||
rec_model = fd.vision.ocr.Recognizer(
|
||||
rec_model_file, rec_params_file, rec_label_file, runtime_option=rec_option)
|
||||
|
||||
# Rec模型启用静态shape推理
|
||||
rec_model.preprocessor.static_shape_infer = True
|
||||
|
||||
# 创建PP-OCR，串联3个模型，其中cls_model可选，如无需求，可设置为None
|
||||
ppocr_v3 = fd.vision.ocr.PPOCRv3(
|
||||
det_model=det_model, cls_model=cls_model, rec_model=rec_model)
|
||||
|
||||
# Cls模型和Rec模型的batch size 必须设置为1, 开启静态shape推理
|
||||
ppocr_v3.cls_batch_size = 1
|
||||
ppocr_v3.rec_batch_size = 1
|
||||
|
||||
# 预测图片准备
|
||||
im = cv2.imread(args.image)
|
||||
|
||||
#预测并打印结果
|
||||
result = ppocr_v3.predict(im)
|
||||
|
||||
print(result)
|
||||
|
||||
# 可视化结果
|
||||
vis_im = fd.vision.vis_ppocr(im, result)
|
||||
cv2.imwrite("visualized_result.jpg", vis_im)
|
||||
print("Visualized result save in ./visualized_result.jpg")
|
||||
@@ -46,6 +46,9 @@ wget https://paddleseg.bj.bcebos.com/dygraph/demo/cityscapes_demo.png
|
||||
以上命令只适用于Linux或MacOS, Windows下SDK的使用方式请参考:
|
||||
- [如何在Windows中使用FastDeploy C++ SDK](../../../../../docs/cn/faq/use_sdk_on_windows.md)
|
||||
|
||||
如果用户使用华为昇腾NPU部署, 请参考以下方式在部署前初始化部署环境:
|
||||
- [如何使用华为昇腾NPU部署](../../../../../docs/cn/faq/use_sdk_on_ascend.md)
|
||||
|
||||
## PaddleSeg C++接口
|
||||
|
||||
### PaddleSeg类
|
||||
|
||||
Reference in New Issue
Block a user