[cherry-pick]fix image_shape in export_model (#3094)

* fix image_shape in export_model

configs/slim/README.md

@@ -20,7 +20,7 @@
 **PaddleDetection、 PaddlePaddle与PaddleSlim 版本关系:**
 |  PaddleDetection版本  | PaddlePaddle版本  | PaddleSlim版本 |  备注    |
 | :------------------: | :---------------: | :-------: |:---------------: |
-| release/2.1       |       >= 2.1.0       | 2.1      |  --  |
+| release/2.1       |       >= 2.1.0       | 2.1      |  量化模型导出依赖最新Paddle develop分支，可在[PaddlePaddle每日版本](https://www.paddlepaddle.org.cn/documentation/docs/zh/install/Tables.html#whl-dev)中下载安装  |
 | release/2.0       |       >= 2.0.1       | 2.0      | 量化依赖Paddle 2.1及PaddleSlim 2.1 |
 
 
@@ -107,7 +107,7 @@ python tools/export_model.py -c configs/{MODEL.yml} --slim_config configs/slim/{
 #### COCO上benchmark
 | 模型         |  压缩策略 |     GFLOPs     |  模型体积(MB)   | 输入尺寸 | 预测时延(SD855) |   Box AP   |                           下载                          | 模型配置文件 | 压缩算法配置文件  |
 | :---------: | :-------: | :------------: |:-------------: | :------: | :-------------: | :------: | :-----------------------------------------------------: |:-------------: | :------: |
-| PP-YOLO-MobileNetV3_large      |  baseline | -- | 18.5      |   608    | 25.1ms | 24.3       |  [下载链接](https://paddledet.bj.bcebos.com/models/ppyolo_mbv3_large_coco.pdparams)  | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/ppyolo/ppyolo_mbv3_large_coco.yml)  |  -  |
+| PP-YOLO-MobileNetV3_large      |  baseline | -- | 18.5      |   608    | 25.1ms | 23.2       |  [下载链接](https://paddledet.bj.bcebos.com/models/ppyolo_mbv3_large_coco.pdparams)  | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/ppyolo/ppyolo_mbv3_large_coco.yml)  |  -  |
 | PP-YOLO-MobileNetV3_large      |  剪裁-FPGM | -37% | 12.6  |   608   | - | 22.3 | [下载链接](https://paddledet.bj.bcebos.com/models/slim/ppyolo_mbv3_large_prune_fpgm.pdparams) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/ppyolo/ppyolo_mbv3_large_coco.yml)  |  [slim配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/slim/prune/ppyolo_mbv3_large_prune_fpgm.yml)  |
 | YOLOv3-DarkNet53      |  baseline | -- | 238.2      |   608    | - | 39.0       |  [下载链接](https://paddledet.bj.bcebos.com/models/ppyolo_mbv3_large_coco.pdparams)  | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/yolov3/yolov3_darknet53_270e_coco.yml)  |  -  |
 | YOLOv3-DarkNet53      |  剪裁-FPGM | -24% | - |   608   | - | 37.6 | [下载链接](https://paddledet.bj.bcebos.com/models/slim/yolov3_darknet_prune_fpgm.pdparams) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/yolov3/yolov3_darknet53_270e_coco.yml)  |  [slim配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/slim/prune/yolov3_darknet_prune_fpgm.yml)  |

deploy/TENSOR_RT.md

@@ -8,7 +8,9 @@ TensorRT是NVIDIA提出的用于统一模型部署的加速库，可以应用于
 
 - 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。
+**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。
+- PaddleDetection中部署预测要求TensorRT版本 > 6.0。
 
 ## 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)。
@@ -31,7 +33,6 @@ config->EnableTensorRtEngine(1 << 20             /*workspace_size*/,
 ```
 
 ### 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) 。
 

deploy/cpp/include/config_parser.h

@@ -91,13 +91,6 @@ class ConfigPaser {
       return false;
     }
 
-    if (config["image_shape"].IsDefined()) {
-      image_shape_ = config["image_shape"].as<std::vector<int>>();
-    } else {
-      std::cerr << "Please set image_shape." << std::endl;
-      return false;
-    }
-
     return true;
   }
   std::string mode_;
@@ -106,7 +99,6 @@ class ConfigPaser {
   int min_subgraph_size_;
   YAML::Node preprocess_info_;
   std::vector<std::string> label_list_;
-  std::vector<int> image_shape_;
 };
 
 }  // namespace PaddleDetection

deploy/cpp/include/object_detector.h

@@ -82,8 +82,7 @@ class ObjectDetector {
     config_.load_config(model_dir);
     this->min_subgraph_size_ = config_.min_subgraph_size_;
     threshold_ = config_.draw_threshold_;
-    image_shape_ = config_.image_shape_;
-    preprocessor_.Init(config_.preprocess_info_, image_shape_);
+    preprocessor_.Init(config_.preprocess_info_);
     LoadModel(model_dir, batch_size, run_mode);
   }
 
@@ -134,7 +133,6 @@ class ObjectDetector {
   std::vector<int> out_bbox_num_data_;
   float threshold_;
   ConfigPaser config_;
-  std::vector<int> image_shape_;
 };
 
 }  // namespace PaddleDetection

deploy/cpp/include/preprocess_op.h

@@ -48,19 +48,19 @@ class ImageBlob {
 // Abstraction of preprocessing opration class
 class PreprocessOp {
  public:
-  virtual void Init(const YAML::Node& item, const std::vector<int> image_shape) = 0;
+  virtual void Init(const YAML::Node& item) = 0;
   virtual void Run(cv::Mat* im, ImageBlob* data) = 0;
 };
 
 class InitInfo : public PreprocessOp{
  public:
-  virtual void Init(const YAML::Node& item, const std::vector<int> image_shape) {}
+  virtual void Init(const YAML::Node& item) {}
   virtual void Run(cv::Mat* im, ImageBlob* data);
 };
 
 class NormalizeImage : public PreprocessOp {
  public:
-  virtual void Init(const YAML::Node& item, const std::vector<int> image_shape) {
+  virtual void Init(const YAML::Node& item) {
     mean_ = item["mean"].as<std::vector<float>>();
     scale_ = item["std"].as<std::vector<float>>();
     is_scale_ = item["is_scale"].as<bool>();
@@ -77,21 +77,18 @@ class NormalizeImage : public PreprocessOp {
 
 class Permute : public PreprocessOp {
  public:
-  virtual void Init(const YAML::Node& item, const std::vector<int> image_shape) {}
+  virtual void Init(const YAML::Node& item) {}
   virtual void Run(cv::Mat* im, ImageBlob* data);
 
 };
 
 class Resize : public PreprocessOp {
  public:
-  virtual void Init(const YAML::Node& item, const std::vector<int> image_shape) {
+  virtual void Init(const YAML::Node& item) {
     interp_ = item["interp"].as<int>();
     //max_size_ = item["target_size"].as<int>();
     keep_ratio_ = item["keep_ratio"].as<bool>();
     target_size_ = item["target_size"].as<std::vector<int>>();
-    if (item["keep_ratio"]) {
-      in_net_shape_ = image_shape;
-    }
  }
 
   // Compute best resize scale for x-dimension, y-dimension
@@ -109,7 +106,7 @@ class Resize : public PreprocessOp {
 // Models with FPN need input shape % stride == 0
 class PadStride : public PreprocessOp {
  public:
-  virtual void Init(const YAML::Node& item, const std::vector<int> image_shape) {
+  virtual void Init(const YAML::Node& item) {
     stride_ = item["stride"].as<int>();
   }
 
@@ -121,14 +118,14 @@ class PadStride : public PreprocessOp {
 
 class Preprocessor {
  public:
-  void Init(const YAML::Node& config_node, const std::vector<int> image_shape) {
+  void Init(const YAML::Node& config_node) {
     // initialize image info at first
     ops_["InitInfo"] = std::make_shared<InitInfo>();
     for (const auto& item : config_node) {
       auto op_name = item["type"].as<std::string>();
 
       ops_[op_name] = CreateOp(op_name);
-      ops_[op_name]->Init(item, image_shape);
+      ops_[op_name]->Init(item);
     }
   }
 

deploy/python/infer.py

@@ -99,8 +99,7 @@ class Detector(object):
         input_im_lst = []
         input_im_info_lst = []
         for im_path in image_list:
-            im, im_info = preprocess(im_path, preprocess_ops,
-                                     self.pred_config.input_shape)
+            im, im_info = preprocess(im_path, preprocess_ops)
             input_im_lst.append(im)
             input_im_info_lst.append(im_info)
         inputs = create_inputs(input_im_lst, input_im_info_lst)
@@ -141,12 +140,12 @@ class Detector(object):
         '''
         self.det_times.preprocess_time_s.start()
         inputs = self.preprocess(image_list)
+        self.det_times.preprocess_time_s.end()
         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]])
-        self.det_times.preprocess_time_s.end()
         for i in range(warmup):
             self.predictor.run()
             output_names = self.predictor.get_output_names()
@@ -236,14 +235,14 @@ class DetectorSOLOv2(Detector):
                             'cate_label': label of segm, shape:[N]
                             'cate_score': confidence score of segm, shape:[N]
         '''
-        self.det_times.postprocess_time_s.start()
+        self.det_times.preprocess_time_s.start()
         inputs = self.preprocess(image)
+        self.det_times.preprocess_time_s.end()
         np_label, np_score, np_segms = None, 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]])
-        self.det_times.postprocess_time_s.end()
         for i in range(warmup):
             self.predictor.run()
             output_names = self.predictor.get_output_names()
@@ -331,7 +330,6 @@ class PredictConfig():
         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):

deploy/python/keypoint_infer.py

@@ -88,8 +88,7 @@ class KeyPoint_Detector(object):
             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)
+        im, im_info = preprocess(im, preprocess_ops)
         inputs = create_inputs(im, im_info)
         return inputs
 
@@ -213,7 +212,6 @@ class PredictConfig_KeyPoint():
         self.tagmap = False
         if 'keypoint_bottomup' == self.archcls:
             self.tagmap = True
-        self.input_shape = yml_conf['image_shape']
         self.print_config()
 
     def check_model(self, yml_conf):

deploy/python/preprocess.py

@@ -47,11 +47,7 @@ class Resize(object):
         interp (int): method of resize
     """
 
-    def __init__(
-            self,
-            target_size,
-            keep_ratio=True,
-            interp=cv2.INTER_LINEAR, ):
+    def __init__(self, target_size, keep_ratio=True, interp=cv2.INTER_LINEAR):
         if isinstance(target_size, int):
             target_size = [target_size, target_size]
         self.target_size = target_size
@@ -81,14 +77,6 @@ class Resize(object):
         im_info['im_shape'] = np.array(im.shape[:2]).astype('float32')
         im_info['scale_factor'] = np.array(
             [im_scale_y, im_scale_x]).astype('float32')
-        # padding im when image_shape fixed by infer_cfg.yml
-        if self.keep_ratio and im_info['input_shape'][1] != -1:
-            max_size = im_info['input_shape'][1]
-            padding_im = np.zeros(
-                (max_size, max_size, im_channel), dtype=np.float32)
-            im_h, im_w = im.shape[:2]
-            padding_im[:im_h, :im_w, :] = im
-            im = padding_im
         return im, im_info
 
     def generate_scale(self, im):
@@ -205,13 +193,12 @@ class PadStride(object):
         return padding_im, im_info
 
 
-def preprocess(im, preprocess_ops, input_shape):
+def preprocess(im, preprocess_ops):
     # process image by preprocess_ops
     im_info = {
         'scale_factor': np.array(
             [1., 1.], dtype=np.float32),
         'im_shape': None,
-        'input_shape': input_shape,
     }
     im, im_info = decode_image(im, im_info)
     for operator in preprocess_ops:

ppdet/engine/export_utils.py

@@ -58,9 +58,7 @@ def _parse_reader(reader_cfg, dataset_cfg, metric, arch, image_shape):
         for key, value in st.items():
             p = {'type': key}
             if key == 'Resize':
-                if value.get('keep_ratio', False) and int(image_shape[1]) != -1:
-                    max_size = max(image_shape[1:])
-                    image_shape = [3, max_size, max_size]
+                if int(image_shape[1]) != -1:
                     value['target_size'] = image_shape[1:]
             p.update(value)
             preprocess_list.append(p)
@@ -76,7 +74,7 @@ def _parse_reader(reader_cfg, dataset_cfg, metric, arch, image_shape):
                     })
                     break
 
-    return preprocess_list, label_list, image_shape
+    return preprocess_list, label_list
 
 
 def _dump_infer_config(config, path, image_shape, model):
@@ -87,7 +85,6 @@ def _dump_infer_config(config, path, image_shape, model):
         'mode': 'fluid',
         'draw_threshold': 0.5,
         'metric': config['metric'],
-        'image_shape': image_shape
     })
     infer_arch = config['architecture']
 
@@ -107,10 +104,9 @@ def _dump_infer_config(config, path, image_shape, model):
     label_arch = 'detection_arch'
     if infer_arch in KEYPOINT_ARCH:
         label_arch = 'keypoint_arch'
-    infer_cfg['Preprocess'], infer_cfg[
-        'label_list'], image_shape = _parse_reader(
-            config['TestReader'], config['TestDataset'], config['metric'],
-            label_arch, image_shape)
+    infer_cfg['Preprocess'], infer_cfg['label_list'] = _parse_reader(
+        config['TestReader'], config['TestDataset'], config['metric'],
+        label_arch, image_shape)
 
     if infer_arch == 'S2ANet':
         # TODO: move background to num_classes
@@ -119,4 +115,3 @@ def _dump_infer_config(config, path, image_shape, model):
 
     yaml.dump(infer_cfg, open(path, 'w'))
     logger.info("Export inference config file to {}".format(os.path.join(path)))
-    return image_shape