diff --git a/deploy/openvino/python/convertor.py b/deploy/openvino/python/convertor.py index f04720374b933f4472125a754a800ada1c48cae2..79979817b62ad3b1bd8c1e895c3c2b207d42d298 100644 --- a/deploy/openvino/python/convertor.py +++ b/deploy/openvino/python/convertor.py @@ -16,9 +16,9 @@ import os from six import text_type as _text_type import argparse import sys -from utils import logging import paddlex as pdx + def arg_parser(): parser = argparse.ArgumentParser() parser.add_argument( @@ -46,41 +46,39 @@ def arg_parser(): return parser - - - def export_openvino_model(model, args): if model.model_type == "detector" or model.__class__.__name__ == "FastSCNN": - logging.error( - "Only image classifier models and semantic segmentation models(except FastSCNN) are supported to export to openvino") + print( + "Only image classifier models and semantic segmentation models(except FastSCNN) are supported to export to openvino" + ) try: import x2paddle if x2paddle.__version__ < '0.7.4': logging.error("You need to upgrade x2paddle >= 0.7.4") except: - logging.error( + print( "You need to install x2paddle first, pip install x2paddle>=0.7.4") - + import x2paddle.convert as x2pc x2pc.paddle2onnx(args.model_dir, args.save_dir) import mo.main as mo from mo.utils.cli_parser import get_onnx_cli_parser onnx_parser = get_onnx_cli_parser() - onnx_parser.add_argument("--model_dir",type=_text_type) - onnx_parser.add_argument("--save_dir",type=_text_type) + onnx_parser.add_argument("--model_dir", type=_text_type) + onnx_parser.add_argument("--save_dir", type=_text_type) onnx_parser.add_argument("--fixed_input_shape") onnx_input = os.path.join(args.save_dir, 'x2paddle_model.onnx') onnx_parser.set_defaults(input_model=onnx_input) onnx_parser.set_defaults(output_dir=args.save_dir) - shape = '[1,3,' - shape = shape + args.fixed_input_shape[1:] + shape_list = args.fixed_input_shape[1:-1].split(',') + shape = '[1,3,' + shape_list[1] + ',' + shape_list[0] + ']' if model.__class__.__name__ == "YOLOV3": shape = shape + ",[1,2]" inputs = "image,im_size" - onnx_parser.set_defaults(input = inputs) - onnx_parser.set_defaults(input_shape = shape) - mo.main(onnx_parser,'onnx') + onnx_parser.set_defaults(input=inputs) + onnx_parser.set_defaults(input_shape=shape) + mo.main(onnx_parser, 'onnx') def main(): @@ -90,12 +88,11 @@ def main(): assert args.save_dir is not None, "--save_dir should be defined to create openvino model" model = pdx.load_model(args.model_dir) if model.status == "Normal" or model.status == "Prune": - logging.error( + print( "Only support inference model, try to export model first as below,", exit=False) export_openvino_model(model, args) -if __name__ == "__main__": - main() - +if __name__ == "__main__": + main() diff --git a/deploy/raspberry/python/deploy.py b/deploy/raspberry/python/deploy.py new file mode 100644 index 0000000000000000000000000000000000000000..73da8ad0071c9f5b77bd9920deff2c9d48c9d1e1 --- /dev/null +++ b/deploy/raspberry/python/deploy.py @@ -0,0 +1,216 @@ +# copyright (c) 2020 PaddlePaddle Authors. All Rights Reserve. +# +# 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 sys +import os +import os.path as osp +import time +import cv2 +import numpy as np +import yaml +from six import text_type as _text_type +from paddlelite.lite import * + + +class Predictor: + def __init__(self, model_nb, model_yaml, thread_num, shape): + if not osp.exists(model_nb): + print("model nb file is not exists in {}".format(model_xml)) + self.model_nb = model_nb + self.shape = shape + config = MobileConfig() + config.set_model_from_file(model_nb) + config.set_threads(thread_num) + if not osp.exists(model_yaml): + print("model yaml file is not exists in {}".format(model_yaml)) + with open(model_yaml) as f: + self.info = yaml.load(f.read(), Loader=yaml.Loader) + self.model_type = self.info['_Attributes']['model_type'] + self.model_name = self.info['Model'] + self.num_classes = self.info['_Attributes']['num_classes'] + self.labels = self.info['_Attributes']['labels'] + if self.info['Model'] == 'MaskRCNN': + if self.info['_init_params']['with_fpn']: + self.mask_head_resolution = 28 + else: + self.mask_head_resolution = 14 + transforms_mode = self.info.get('TransformsMode', 'RGB') + if transforms_mode == 'RGB': + to_rgb = True + else: + to_rgb = False + self.transforms = self.build_transforms(self.info['Transforms'], + to_rgb) + self.predictor = create_paddle_predictor(config) + self.total_time = 0 + self.count_num = 0 + + def build_transforms(self, transforms_info, to_rgb=True): + if self.model_type == "classifier": + import transforms.cls_transforms as transforms + elif self.model_type == "detector": + import transforms.det_transforms as transforms + elif self.model_type == "segmenter": + import transforms.seg_transforms as transforms + op_list = list() + for op_info in transforms_info: + op_name = list(op_info.keys())[0] + op_attr = op_info[op_name] + if not hasattr(transforms, op_name): + raise Exception( + "There's no operator named '{}' in transforms of {}". + format(op_name, self.model_type)) + op_list.append(getattr(transforms, op_name)(**op_attr)) + eval_transforms = transforms.Compose(op_list) + if hasattr(eval_transforms, 'to_rgb'): + eval_transforms.to_rgb = to_rgb + self.arrange_transforms(eval_transforms) + return eval_transforms + + def arrange_transforms(self, eval_transforms): + if self.model_type == 'classifier': + import transforms.cls_transforms as transforms + arrange_transform = transforms.ArrangeClassifier + elif self.model_type == 'segmenter': + import transforms.seg_transforms as transforms + arrange_transform = transforms.ArrangeSegmenter + elif self.model_type == 'detector': + import transforms.det_transforms as transforms + arrange_name = 'Arrange{}'.format(self.model_name) + arrange_transform = getattr(transforms, arrange_name) + else: + raise Exception("Unrecognized model type: {}".format( + self.model_type)) + if type(eval_transforms.transforms[-1]).__name__.startswith('Arrange'): + eval_transforms.transforms[-1] = arrange_transform(mode='test') + else: + eval_transforms.transforms.append(arrange_transform(mode='test')) + + def raw_predict(self, preprocessed_input): + self.count_num += 1 + input_tensor = self.predictor.get_input(0) + input_tensor.resize(self.shape) + input_tensor.set_float_data(preprocessed_input['image']) + if self.model_name == "YOLOv3": + input_size_tensor = self.predictor.get_input(1) + input_size_tensor.resize([1, 2]) + input_size_tensor.set_float_data(preprocessed_input['im_size']) + #Start inference + start_time = time.time() + self.predictor.run() + time_use = time.time() - start_time + if (self.count_num >= 20): + self.total_time += time_use + if (self.count_num >= 120): + print("avgtime:", self.total_time * 10) + + #Processing output blob + print("Processing output blob") + return + + def preprocess(self, image): + res = dict() + if self.model_type == "classifier": + im, = self.transforms(image) + im = np.expand_dims(im, axis=0).copy() + im = im.flatten() + res['image'] = im + elif self.model_type == "detector": + if self.model_name == "YOLOv3": + im, im_shape = self.transforms(image) + im = np.expand_dims(im, axis=0).copy() + im_shape = np.expand_dims(im_shape, axis=0).copy() + res['image'] = im + res['im_size'] = im_shape + if self.model_name.count('RCNN') > 0: + im, im_resize_info, im_shape = self.transforms(image) + im = np.expand_dims(im, axis=0).copy() + im_resize_info = np.expand_dims(im_resize_info, axis=0).copy() + im_shape = np.expand_dims(im_shape, axis=0).copy() + res['image'] = im + res['im_info'] = im_resize_info + res['im_shape'] = im_shape + elif self.model_type == "segmenter": + im, im_info = self.transforms(image) + im = np.expand_dims(im, axis=0).copy() + #np.savetxt('./input_data.txt',im.flatten()) + res['image'] = im + res['im_info'] = im_info + return res + + def classifier_postprocess(self, topk=1): + output_tensor = self.predictor.get_output(0) + output_data = output_tensor.float_data() + true_topk = min(self.num_classes, topk) + pred_label = np.argsort(-np.array(output_data))[:true_topk] + result = [{ + 'category_id': l, + 'category': self.labels[l], + 'score': output_data[l], + } for l in pred_label] + print(result) + return result + + def segmenter_postprocess(self, preprocessed_inputs): + out_label_tensor = self.predictor.get_output(0) + out_label = out_label_tensor.float_data() + label_shape = tuple(out_label_tensor.shape()) + label_map = np.array(out_label).astype('uint8') + label_map = label_map.reshap(label_shape) + label_map = np.squeeze(label_map) + + out_score_tensor = self.predictor.get_output(1) + out_score = out_score_tensor.float_data() + score_shape = tuple(out_score_tensor.shape()) + score_map = np.array(out_score) + score_map = score_map.reshap(score_shape) + score_map = np.transpose(score_map, (1, 2, 0)) + + im_info = preprocessed_inputs['im_info'] + for info in im_info[::-1]: + if info[0] == 'resize': + w, h = info[1][1], info[1][0] + label_map = cv2.resize(label_map, (w, h), cv2.INTER_NEAREST) + score_map = cv2.resize(score_map, (w, h), cv2.INTER_LINEAR) + elif info[0] == 'padding': + w, h = info[1][1], info[1][0] + label_map = label_map[0:h, 0:w] + score_map = score_map[0:h, 0:w, :] + else: + raise Exception("Unexpected info '{}' in im_info".format(info[ + 0])) + return {'label_map': label_map, 'score_map': score_map} + + def detector_postprocess(self, preprocessed_inputs): + out_tensor = self.predictor.get_output(0) + out_data = out_tensor.float_data() + out_shape = tuple(out_tensor.shape()) + out_data = np.array(out_data) + outputs = label_data.reshap(out_shape) + + result = [] + for out in outputs: + result.append(out.tolist()) + return result + + def predict(self, image, topk=1, threshold=0.5): + preprocessed_input = self.preprocess(image) + self.raw_predict(preprocessed_input) + if self.model_type == "classifier": + results = self.classifier_postprocess(topk) + elif self.model_type == "detector": + results = self.detector_postprocess(preprocessed_input) + elif self.model_type == "segmenter": + pass + results = self.segmenter_postprocess(preprocessed_input) diff --git a/docs/deploy/openvino/export_openvino_model.md b/docs/deploy/openvino/export_openvino_model.md index 1d3ddc44378bba5ab061862914f94a22b8721016..4e1ee3082a573ef68d5a981b29ba0b0edf20035e 100644 --- a/docs/deploy/openvino/export_openvino_model.md +++ b/docs/deploy/openvino/export_openvino_model.md @@ -33,6 +33,7 @@ python convertor.py --model_dir /path/to/inference_model --save_dir /path/to/ope | --save_dir | OpenVINO模型保存路径 | | --fixed_input_shape | 模型输入的[W,H] | | --data type(option) | FP32、FP16，默认为FP32，VPU下的IR需要为FP16 | + **注意**： - 由于OpenVINO不支持ONNX的resize-11 OP的原因，目前还不支持Paddle的分割模型 - YOLOv3在通过OpenVINO部署时，由于OpenVINO对ONNX OP的支持限制，我们在将YOLOv3的Paddle模型导出时，对最后一层multiclass_nms进行了特殊处理，导出的ONNX模型，最终输出的Box结果包括背景类别（而Paddle模型不包含），此处在OpenVINO的部署代码中，我们通过后处理过滤了背景类别。 diff --git a/docs/deploy/openvino/windows.md b/docs/deploy/openvino/windows.md index a65312561f9628e2d99e16241e79b0a8acd0e83e..bef7c933e98cbc22c983df4314683e7b75faeb4b 100644 --- a/docs/deploy/openvino/windows.md +++ b/docs/deploy/openvino/windows.md @@ -14,8 +14,11 @@ Windows 平台下，我们使用`Visual Studio 2019 Community` 进行了测试 - 我的电脑->属性->高级系统设置->环境变量 - 在系统变量中找到Path（如没有，自行创建），并双击编辑 - 新建，分别将OpenVINO以下路径填入并保存: + `C:\Program File (x86)\IntelSWTools\openvino\inference_engine\bin\intel64\Release` + `C:\Program File (x86)\IntelSWTools\openvino\inference_engine\external\tbb\bin` + `C:\Program File (x86)\IntelSWTools\openvino\deployment_tools\ngraph\lib` 请确保系统已经安装好上述基本软件，并配置好相应环境，**下面所有示例以工作目录为 `D:\projects`演示。** @@ -50,13 +53,13 @@ git clone https://github.com/PaddlePaddle/PaddleX.git ### Step3: 使用Visual Studio 2019直接编译CMake 1. 打开Visual Studio 2019 Community，点击`继续但无需代码` -2. 点击： `文件`->`打开`->`CMake` 选择C++预测代码所在路径（例如`D:\projects\PaddleX\deploy\openvino`），并打开`CMakeList.txt`： +2. 点击： `文件`->`打开`->`CMake` 选择C++预测代码所在路径（例如`D:\projects\PaddleX\deploy\openvino`），并打开`CMakeList.txt` 3. 点击：`项目`->`CMake设置` 4. 点击`浏览`，分别设置编译选项指定`OpenVINO`、`Gflags`、`GLOG`、`NGRAPH`、`OPENCV`的路径 | 参数名 | 含义 | | ---- | ---- | -| OPENCV_DIR | opencv库路径 | +| OPENCV_DIR | OpenCV库路径 | | OPENVINO_DIR | OpenVINO推理库路径，在OpenVINO安装目录下的deployment/inference_engine目录，若未修改OpenVINO默认安装目录可以不用修改 | | NGRAPH_LIB | OpenVINO的ngraph库路径，在OpenVINO安装目录下的deployment/ngraph/lib目录，若未修改OpenVINO默认安装目录可以不用修改 | | GFLAGS_DIR | gflags库路径 | diff --git a/docs/deploy/raspberry/Raspberry.md b/docs/deploy/raspberry/Raspberry.md index a49b9605e9956a288162e4d6fc6bd969f0103d90..0ab0d97df5218b070c6693df1e731400c6ddb626 100644 --- a/docs/deploy/raspberry/Raspberry.md +++ b/docs/deploy/raspberry/Raspberry.md @@ -125,7 +125,8 @@ OPENCV_DIR=$(pwd)/deps/opencv/ 系统：raspbian OS 软件：paddle-lite 2.6.1 ### 测试结果 -单位ms，num表示paddle-lite下使用的线程数 +单位ms，num表示paddle-lite下使用的线程数 + |模型|lite(num=4)|输入图片大小| | ----| ---- | ----| |mobilenet-v2|136.19|224*224| @@ -148,6 +149,7 @@ OPENCV_DIR=$(pwd)/deps/opencv/ |Xception41|1418.29|224*224| |Xception65|2094.7|224*224| + 从测试结果看建议用户在树莓派上使用MobileNetV1-V3,ShuffleNetV2这类型的小型网络 ## NCS2部署