fix some bug

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 +46,45 @@ def arg_parser():
     return parser
 
 
-    
+def reverse_input(shape):
+    shape_list = shape[1:-1].split(',')
+    shape = '[1,3,' + shape_list[1] + ',' + shape_list[0] + ']'
+    return shape
 
 
 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 +94,11 @@ 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()

deploy/raspberry/python/deploy.py

+# 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)

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。

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 |

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