Automatically set use_dynamic_shape (#3116)

* Automatically set use_dynamic_shape

deploy/TENSOR_RT.md

@@ -34,31 +34,33 @@ config->EnableTensorRtEngine(1 << 20             /*workspace_size*/,
 
 ### 3.2 TensorRT固定尺寸预测
 
-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) 。
-
-`TestReader.inputs_def.image_shape`设置的是输入TensorRT引擎的数据尺寸（在像FasterRCNN中，`TestReader.inputs_def.image_shape`指定的是在`Pad`操作之前的图像数据尺寸）。
+例如在模型Reader配置文件中设置：
+```yaml
+TestReader:
+  inputs_def:
+    image_shape: [3,608,608]
+  ...
+```
+或者在导出模型时设置`-o TestReader.inputs_def.image_shape=[3,608,608]`，模型将会进行固定尺寸预测，具体请参考[PaddleDetection模型导出教程](../EXPORT_MODEL.md) 。
 
 可以通过[visualdl](https://www.paddlepaddle.org.cn/paddle/visualdl/demo/graph) 打开`model.pdmodel`文件，查看输入的第一个Tensor尺寸是否是固定的，如果不指定，尺寸会用`？`表示，如下图所示：
 ![img](../docs/images/input_shape.png)
 
-同时需要将图像预处理后的尺寸与设置车模型输入尺寸保持一致，需要设置`infer_cfg.yml`配置文件中`Resize OP`的`target_size`参数和`keep_ratio`参数。
 
-注意：由于TesnorRT不支持在batch维度进行slice操作，Faster RCNN 和 Mask RCNN使用固定尺寸输入预测会报错，这两个模型请使用动态尺寸输入。
+注意：由于TesnorRT不支持在batch维度进行slice操作，Faster RCNN 和 Mask RCNN不能使用固定尺寸输入预测，所以不能设置`TestReader.inputs_def.image_shape`字段。
 
-以`YOLOv3`为例，使用动态尺寸输入预测：
+以`YOLOv3`为例，使用固定尺寸输入预测：
 ```
-python python/infer.py --model_dir=../inference_model/yolov3_darknet53_270e_coco/ --image_file=../demo/000000014439_640x640.jpg --use_gpu=True --run_mode=trt_fp32 --run_benchmark=True
+python python/infer.py --model_dir=../inference_model/yolov3_darknet53_270e_coco/ --image_file=../demo/000000014439.jpg --use_gpu=True --run_mode=trt_fp32 --run_benchmark=True
 ```
 
 ### 3.3 TensorRT动态尺寸预测
 
-TensorRT版本>=6时，使用TensorRT预测时，可以支持动态尺寸输入。
+TensorRT版本>=6时，使用TensorRT预测时，可以支持动态尺寸输入。如果模型Reader配置文件中没有设置例如`TestReader.inputs_def.image_shape=[3,608,608]`的字段，或者`image_shape=[3.-1,-1]`，导出模型将以动态尺寸进行预测。一般RCNN系列模型使用动态图尺寸预测。
 Paddle预测库关于动态尺寸输入请查看[Paddle CPP预测](https://www.paddlepaddle.org.cn/documentation/docs/zh/guides/05_inference_deployment/inference/native_infer.html) 的`SetTRTDynamicShapeInfo`函数说明。
 
 `python/infer.py`设置动态尺寸输入参数说明：
 
-- use_dynamic_shape 用于设定TensorRT的输入尺寸是否是动态尺寸，默认值：False
-
 - trt_min_shape 用于设定TensorRT的输入图像height、width中的最小尺寸，默认值：1
 
 - trt_max_shape 用于设定TensorRT的输入图像height、width中的最大尺寸，默认值：1280
@@ -69,7 +71,7 @@ Paddle预测库关于动态尺寸输入请查看[Paddle CPP预测](https://www.p
 
 以`Faster RCNN`为例，使用动态尺寸输入预测：
 ```
-python python/infer.py --model_dir=../inference_model/faster_rcnn_r50_fpn_1x_coco/ --image_file=../demo/000000014439.jpg --use_gpu=True --run_mode=trt_fp16 --run_benchmark=True --use_dynamic_shape=True --trt_max_shape=1280 --trt_min_shape=800 --trt_opt_shape=960
+python python/infer.py --model_dir=../inference_model/faster_rcnn_r50_fpn_1x_coco/ --image_file=../demo/000000014439.jpg --use_gpu=True --run_mode=trt_fp16 --run_benchmark=True --trt_max_shape=1280 --trt_min_shape=800 --trt_opt_shape=960
 ```
 
 ## 4、常见问题QA

deploy/cpp/include/config_parser.h

@@ -91,6 +91,14 @@ class ConfigPaser {
       return false;
     }
 
+    // Get use_dynamic_shape for TensorRT
+    if (config["use_dynamic_shape"].IsDefined()) {
+      use_dynamic_shape_ = config["use_dynamic_shape"].as<bool>();
+    } else {
+      std::cerr << "Please set use_dynamic_shape." << std::endl;
+      return false;
+    }
+
     return true;
   }
   std::string mode_;
@@ -99,6 +107,7 @@ class ConfigPaser {
   int min_subgraph_size_;
   YAML::Node preprocess_info_;
   std::vector<std::string> label_list_;
+  bool use_dynamic_shape_;
 };
 
 }  // namespace PaddleDetection

deploy/cpp/include/object_detector.h

@@ -64,7 +64,6 @@ class ObjectDetector {
                           const std::string& run_mode="fluid",
                           const int batch_size=1,
                           const int gpu_id=0,
-                          bool use_dynamic_shape=false,
                           const int trt_min_shape=1,
                           const int trt_max_shape=1280,
                           const int trt_opt_shape=640,
@@ -74,12 +73,12 @@ class ObjectDetector {
     this->cpu_math_library_num_threads_ = cpu_threads;
     this->use_mkldnn_ = use_mkldnn;
 
-    this->use_dynamic_shape_ = use_dynamic_shape;
     this->trt_min_shape_ = trt_min_shape;
     this->trt_max_shape_ = trt_max_shape;
     this->trt_opt_shape_ = trt_opt_shape;
     this->trt_calib_mode_ = trt_calib_mode;
     config_.load_config(model_dir);
+    this->use_dynamic_shape_ = config_.use_dynamic_shape_;
     this->min_subgraph_size_ = config_.min_subgraph_size_;
     threshold_ = config_.draw_threshold_;
     preprocessor_.Init(config_.preprocess_info_);

deploy/cpp/src/main.cc

@@ -49,7 +49,6 @@ DEFINE_int32(gpu_id, 0, "Device id of GPU to execute");
 DEFINE_bool(run_benchmark, false, "Whether to predict a image_file repeatedly for benchmark");
 DEFINE_bool(use_mkldnn, false, "Whether use mkldnn with CPU");
 DEFINE_int32(cpu_threads, 1, "Num of threads with CPU");
-DEFINE_bool(use_dynamic_shape, false, "Trt use dynamic shape or not");
 DEFINE_int32(trt_min_shape, 1, "Min shape of TRT DynamicShapeI");
 DEFINE_int32(trt_max_shape, 1280, "Max shape of TRT DynamicShapeI");
 DEFINE_int32(trt_opt_shape, 640, "Opt shape of TRT DynamicShapeI");
@@ -361,8 +360,9 @@ int main(int argc, char** argv) {
   }
   // Load model and create a object detector
   PaddleDetection::ObjectDetector det(FLAGS_model_dir, FLAGS_use_gpu, FLAGS_use_mkldnn,
-                        FLAGS_cpu_threads, FLAGS_run_mode, FLAGS_batch_size,FLAGS_gpu_id, FLAGS_use_dynamic_shape,
-                        FLAGS_trt_min_shape, FLAGS_trt_max_shape, FLAGS_trt_opt_shape, FLAGS_trt_calib_mode);
+                        FLAGS_cpu_threads, FLAGS_run_mode, FLAGS_batch_size,FLAGS_gpu_id,
+                        FLAGS_trt_min_shape, FLAGS_trt_max_shape, FLAGS_trt_opt_shape,
+			FLAGS_trt_calib_mode);
   // Do inference on input video or image
   if (!FLAGS_video_file.empty() || FLAGS_camera_id != -1) {
     PredictVideo(FLAGS_video_file, &det);
@@ -374,13 +374,14 @@ int main(int argc, char** argv) {
     if (!FLAGS_image_file.empty()) {
       all_imgs.push_back(FLAGS_image_file);
       if (FLAGS_batch_size > 1) {
-        std::cout << "batch_size should be 1, when image_file is not None" << std::endl;
-        FLAGS_batch_size = 1;
+        std::cout << "batch_size should be 1, when set `image_file`." << std::endl;
+	return -1;
       }
     } else {
       GetAllFiles((char *)FLAGS_image_dir.c_str(), all_imgs);
     }
-    PredictImage(all_imgs, FLAGS_batch_size, FLAGS_threshold, FLAGS_run_benchmark, &det, FLAGS_output_dir);
+    PredictImage(all_imgs, FLAGS_batch_size, FLAGS_threshold,
+		 FLAGS_run_benchmark, &det, FLAGS_output_dir);
   }
   return 0;
 }

deploy/python/infer.py

@@ -51,7 +51,6 @@ class Detector(object):
         use_gpu (bool): whether use gpu
         run_mode (str): mode of running(fluid/trt_fp32/trt_fp16)
         batch_size (int): size of pre batch in inference
-        use_dynamic_shape (bool): use dynamic shape or not
         trt_min_shape (int): min shape for dynamic shape in trt
         trt_max_shape (int): max shape for dynamic shape in trt
         trt_opt_shape (int): opt shape for dynamic shape in trt
@@ -65,7 +64,6 @@ class Detector(object):
                  use_gpu=False,
                  run_mode='fluid',
                  batch_size=1,
-                 use_dynamic_shape=False,
                  trt_min_shape=1,
                  trt_max_shape=1280,
                  trt_opt_shape=640,
@@ -79,7 +77,7 @@ class Detector(object):
             batch_size=batch_size,
             min_subgraph_size=self.pred_config.min_subgraph_size,
             use_gpu=use_gpu,
-            use_dynamic_shape=use_dynamic_shape,
+            use_dynamic_shape=self.pred_config.use_dynamic_shape,
             trt_min_shape=trt_min_shape,
             trt_max_shape=trt_max_shape,
             trt_opt_shape=trt_opt_shape,
@@ -189,7 +187,6 @@ class DetectorSOLOv2(Detector):
         use_gpu (bool): whether use gpu
         run_mode (str): mode of running(fluid/trt_fp32/trt_fp16)
         batch_size (int): size of pre batch in inference
-        use_dynamic_shape (bool): use dynamic shape or not
         trt_min_shape (int): min shape for dynamic shape in trt
         trt_max_shape (int): max shape for dynamic shape in trt
         trt_opt_shape (int): opt shape for dynamic shape in trt
@@ -202,7 +199,6 @@ class DetectorSOLOv2(Detector):
                  use_gpu=False,
                  run_mode='fluid',
                  batch_size=1,
-                 use_dynamic_shape=False,
                  trt_min_shape=1,
                  trt_max_shape=1280,
                  trt_opt_shape=640,
@@ -216,7 +212,7 @@ class DetectorSOLOv2(Detector):
             batch_size=batch_size,
             min_subgraph_size=self.pred_config.min_subgraph_size,
             use_gpu=use_gpu,
-            use_dynamic_shape=use_dynamic_shape,
+            use_dynamic_shape=self.pred_config.use_dynamic_shape,
             trt_min_shape=trt_min_shape,
             trt_max_shape=trt_max_shape,
             trt_opt_shape=trt_opt_shape,
@@ -328,6 +324,7 @@ class PredictConfig():
         self.min_subgraph_size = yml_conf['min_subgraph_size']
         self.labels = yml_conf['label_list']
         self.mask = False
+        self.use_dynamic_shape = yml_conf['use_dynamic_shape']
         if 'mask' in yml_conf:
             self.mask = yml_conf['mask']
         self.print_config()
@@ -573,7 +570,6 @@ def main():
         use_gpu=FLAGS.use_gpu,
         run_mode=FLAGS.run_mode,
         batch_size=FLAGS.batch_size,
-        use_dynamic_shape=FLAGS.use_dynamic_shape,
         trt_min_shape=FLAGS.trt_min_shape,
         trt_max_shape=FLAGS.trt_max_shape,
         trt_opt_shape=FLAGS.trt_opt_shape,
@@ -587,7 +583,6 @@ def main():
             use_gpu=FLAGS.use_gpu,
             run_mode=FLAGS.run_mode,
             batch_size=FLAGS.batch_size,
-            use_dynamic_shape=FLAGS.use_dynamic_shape,
             trt_min_shape=FLAGS.trt_min_shape,
             trt_max_shape=FLAGS.trt_max_shape,
             trt_opt_shape=FLAGS.trt_opt_shape,

deploy/python/keypoint_det_unite_infer.py

@@ -158,7 +158,6 @@ def main():
         FLAGS.det_model_dir,
         use_gpu=FLAGS.use_gpu,
         run_mode=FLAGS.run_mode,
-        use_dynamic_shape=FLAGS.use_dynamic_shape,
         trt_min_shape=FLAGS.trt_min_shape,
         trt_max_shape=FLAGS.trt_max_shape,
         trt_opt_shape=FLAGS.trt_opt_shape,
@@ -172,7 +171,6 @@ def main():
         FLAGS.keypoint_model_dir,
         use_gpu=FLAGS.use_gpu,
         run_mode=FLAGS.run_mode,
-        use_dynamic_shape=FLAGS.use_dynamic_shape,
         trt_min_shape=FLAGS.trt_min_shape,
         trt_max_shape=FLAGS.trt_max_shape,
         trt_opt_shape=FLAGS.trt_opt_shape,

deploy/python/keypoint_infer.py

@@ -46,7 +46,6 @@ class KeyPoint_Detector(object):
         model_dir (str): root path of model.pdiparams, model.pdmodel and infer_cfg.yml
         use_gpu (bool): whether use gpu
         run_mode (str): mode of running(fluid/trt_fp32/trt_fp16)
-        use_dynamic_shape (bool): use dynamic shape or not
         trt_min_shape (int): min shape for dynamic shape in trt
         trt_max_shape (int): max shape for dynamic shape in trt
         trt_opt_shape (int): opt shape for dynamic shape in trt
@@ -59,7 +58,6 @@ class KeyPoint_Detector(object):
                  model_dir,
                  use_gpu=False,
                  run_mode='fluid',
-                 use_dynamic_shape=False,
                  trt_min_shape=1,
                  trt_max_shape=1280,
                  trt_opt_shape=640,
@@ -72,7 +70,7 @@ class KeyPoint_Detector(object):
             run_mode=run_mode,
             min_subgraph_size=self.pred_config.min_subgraph_size,
             use_gpu=use_gpu,
-            use_dynamic_shape=use_dynamic_shape,
+            use_dynamic_shape=self.pred_config.use_dynamic_shape,
             trt_min_shape=trt_min_shape,
             trt_max_shape=trt_max_shape,
             trt_opt_shape=trt_opt_shape,
@@ -210,6 +208,7 @@ class PredictConfig_KeyPoint():
         self.min_subgraph_size = yml_conf['min_subgraph_size']
         self.labels = yml_conf['label_list']
         self.tagmap = False
+        self.use_dynamic_shape = yml_conf['use_dynamic_shape']
         if 'keypoint_bottomup' == self.archcls:
             self.tagmap = True
         self.print_config()
@@ -384,7 +383,6 @@ def main():
         FLAGS.model_dir,
         use_gpu=FLAGS.use_gpu,
         run_mode=FLAGS.run_mode,
-        use_dynamic_shape=FLAGS.use_dynamic_shape,
         trt_min_shape=FLAGS.trt_min_shape,
         trt_max_shape=FLAGS.trt_max_shape,
         trt_opt_shape=FLAGS.trt_opt_shape,

deploy/python/topdown_unite_utils.py

@@ -84,11 +84,6 @@ def argsparser():
         help="Whether use mkldnn with CPU.")
     parser.add_argument(
         "--cpu_threads", type=int, default=1, help="Num of threads with CPU.")
-    parser.add_argument(
-        "--use_dynamic_shape",
-        type=ast.literal_eval,
-        default=False,
-        help="Dynamic_shape for TensorRT.")
     parser.add_argument(
         "--trt_min_shape", type=int, default=1, help="min_shape for TensorRT.")
     parser.add_argument(

deploy/python/utils.py

@@ -76,11 +76,6 @@ def argsparser():
         help="Whether use mkldnn with CPU.")
     parser.add_argument(
         "--cpu_threads", type=int, default=1, help="Num of threads with CPU.")
-    parser.add_argument(
-        "--use_dynamic_shape",
-        type=ast.literal_eval,
-        default=False,
-        help="Dynamic_shape for TensorRT.")
     parser.add_argument(
         "--trt_min_shape", type=int, default=1, help="min_shape for TensorRT.")
     parser.add_argument(

ppdet/engine/export_utils.py

@@ -81,10 +81,12 @@ def _dump_infer_config(config, path, image_shape, model):
     arch_state = False
     from ppdet.core.config.yaml_helpers import setup_orderdict
     setup_orderdict()
+    use_dynamic_shape = True if image_shape[1] == -1 else False
     infer_cfg = OrderedDict({
         'mode': 'fluid',
         'draw_threshold': 0.5,
         'metric': config['metric'],
+        'use_dynamic_shape': use_dynamic_shape
     })
     infer_arch = config['architecture']