[Model] Refactor YOLOv7 module (#611)

* add paddle_trt in benchmark

* update benchmark in device

* update benchmark

* update result doc

* fixed for CI

* update python api_docs

* update index.rst

* add runtime cpp examples

* deal with comments

* Update infer_paddle_tensorrt.py

* Add runtime quick start

* deal with comments

* fixed reused_input_tensors&&reused_output_tensors

* fixed docs

* fixed headpose typo

* fixed typo

* refactor yolov5

* update model infer

* refactor pybind for yolov5

* rm origin yolov5

* fixed bugs

* rm cuda preprocess

* fixed bugs

* fixed bugs

* fixed bug

* fixed bug

* fix pybind

* rm useless code

* add convert_and_permute

* fixed bugs

* fixed im_info for bs_predict

* fixed bug

* add bs_predict for yolov5

* Add runtime test and batch eval

* deal with comments

* fixed bug

* update testcase

* fixed batch eval bug

* fixed preprocess bug

* refactor yolov7

* add yolov7 testcase

* rm resize_after_load and add is_scale_up

* fixed bug

* set multi_label true

Co-authored-by: Jason <928090362@qq.com>

python/fastdeploy/vision/detection/__init__.py

@@ -13,7 +13,7 @@
 # limitations under the License.
 
 from __future__ import absolute_import
-from .contrib.yolov7 import YOLOv7
+from .contrib.yolov7 import *
 from .contrib.yolor import YOLOR
 from .contrib.scaled_yolov4 import ScaledYOLOv4
 from .contrib.nanodet_plus import NanoDetPlus

python/fastdeploy/vision/detection/contrib/yolov5.py

@@ -41,9 +41,19 @@ class YOLOv5Preprocessor:
 
     @property
     def padding_value(self):
+        """
+        padding value for preprocessing, default [114.0, 114.0, 114.0]
+        """
         #  padding value, size should be the same as channels
         return self._preprocessor.padding_value
 
+    @property
+    def is_scale_up(self):
+        """
+        is_scale_up for preprocessing, the input image only can be zoom out, the maximum resize scale cannot exceed 1.0, default true
+        """
+        return self._preprocessor.is_scale_up
+
     @size.setter
     def size(self, wh):
         assert isinstance(wh, (list, tuple)),\
@@ -60,6 +70,13 @@ class YOLOv5Preprocessor:
             list), "The value to set `padding_value` must be type of list."
         self._preprocessor.padding_value = value
 
+    @is_scale_up.setter
+    def is_scale_up(self, value):
+        assert isinstance(
+            value,
+            bool), "The value to set `is_scale_up` must be type of bool."
+        self._preprocessor.is_scale_up = value
+
 
 class YOLOv5Postprocessor:
     def __init__(self):
@@ -93,7 +110,7 @@ class YOLOv5Postprocessor:
     @property
     def multi_label(self):
         """
-        multi_label for postprocessing, default is true
+        multi_label for postprocessing, set true for eval, default is True
         """
         return self._postprocessor.multi_label
 

python/fastdeploy/vision/detection/contrib/yolov7.py

@@ -18,6 +18,108 @@ from .... import FastDeployModel, ModelFormat
 from .... import c_lib_wrap as C
 
 
+class YOLOv7Preprocessor:
+    def __init__(self):
+        """Create a preprocessor for YOLOv7
+        """
+        self._preprocessor = C.vision.detection.YOLOv7Preprocessor()
+
+    def run(self, input_ims):
+        """Preprocess input images for YOLOv7
+
+        :param: input_ims: (list of numpy.ndarray)The input image
+        :return: list of FDTensor
+        """
+        return self._preprocessor.run(input_ims)
+
+    @property
+    def size(self):
+        """
+        Argument for image preprocessing step, the preprocess image size, tuple of (width, height), default size = [640, 640]
+        """
+        return self._preprocessor.size
+
+    @property
+    def padding_value(self):
+        """
+        padding value for preprocessing, default [114.0, 114.0, 114.0]
+        """
+        #  padding value, size should be the same as channels
+        return self._preprocessor.padding_value
+
+    @property
+    def is_scale_up(self):
+        """
+        is_scale_up for preprocessing, the input image only can be zoom out, the maximum resize scale cannot exceed 1.0, default true
+        """
+        return self._preprocessor.is_scale_up
+
+    @size.setter
+    def size(self, wh):
+        assert isinstance(wh, (list, tuple)),\
+            "The value to set `size` must be type of tuple or list."
+        assert len(wh) == 2,\
+            "The value to set `size` must contatins 2 elements means [width, height], but now it contains {} elements.".format(
+            len(wh))
+        self._preprocessor.size = wh
+
+    @padding_value.setter
+    def padding_value(self, value):
+        assert isinstance(
+            value,
+            list), "The value to set `padding_value` must be type of list."
+        self._preprocessor.padding_value = value
+
+    @is_scale_up.setter
+    def is_scale_up(self, value):
+        assert isinstance(
+            value,
+            bool), "The value to set `is_scale_up` must be type of bool."
+        self._preprocessor.is_scale_up = value
+
+
+class YOLOv7Postprocessor:
+    def __init__(self):
+        """Create a postprocessor for YOLOv7
+        """
+        self._postprocessor = C.vision.detection.YOLOv7Postprocessor()
+
+    def run(self, runtime_results, ims_info):
+        """Postprocess the runtime results for YOLOv7
+
+        :param: runtime_results: (list of FDTensor)The output FDTensor results from runtime
+        :param: ims_info: (list of dict)Record input_shape and output_shape
+        :return: list of DetectionResult(If the runtime_results is predict by batched samples, the length of this list equals to the batch size)
+        """
+        return self._postprocessor.run(runtime_results, ims_info)
+
+    @property
+    def conf_threshold(self):
+        """
+        confidence threshold for postprocessing, default is 0.25
+        """
+        return self._postprocessor.conf_threshold
+
+    @property
+    def nms_threshold(self):
+        """
+        nms threshold for postprocessing, default is 0.5
+        """
+        return self._postprocessor.nms_threshold
+
+    @conf_threshold.setter
+    def conf_threshold(self, conf_threshold):
+        assert isinstance(conf_threshold, float),\
+            "The value to set `conf_threshold` must be type of float."
+        self._postprocessor.conf_threshold = conf_threshold
+
+    @nms_threshold.setter
+    def nms_threshold(self, nms_threshold):
+        assert isinstance(nms_threshold, float),\
+            "The value to set `nms_threshold` must be type of float."
+        self._postprocessor.nms_threshold = nms_threshold
+
+
 class YOLOv7(FastDeployModel):
     def __init__(self,
                  model_file,
@@ -35,6 +137,7 @@ class YOLOv7(FastDeployModel):
         # 初始化后的option保存在self._runtime_option
         super(YOLOv7, self).__init__(runtime_option)
 
+        assert model_format == ModelFormat.ONNX, "YOLOv7 only support model format of ModelFormat.ONNX now."
         self._model = C.vision.detection.YOLOv7(
             model_file, params_file, self._runtime_option, model_format)
         # 通过self.initialized判断整个模型的初始化是否成功
@@ -44,96 +147,36 @@ class YOLOv7(FastDeployModel):
         """Detect an input image
 
         :param input_image: (numpy.ndarray)The input image data, 3-D array with layout HWC, BGR format
-        :param conf_threshold: confidence threashold for postprocessing, default is 0.25
-        :param nms_iou_threshold: iou threashold for NMS, default is 0.5
+        :param conf_threshold: confidence threshold for postprocessing, default is 0.25
+        :param nms_iou_threshold: iou threshold for NMS, default is 0.5
         :return: DetectionResult
         """
-        return self._model.predict(input_image, conf_threshold,
-                                   nms_iou_threshold)
 
-    # 一些跟YOLOv7模型有关的属性封装
-    # 多数是预处理相关，可通过修改如model.size = [1280, 1280]改变预处理时resize的大小（前提是模型支持）
-    @property
-    def size(self):
+        self.postprocessor.conf_threshold = conf_threshold
+        self.postprocessor.nms_threshold = nms_iou_threshold
+        return self._model.predict(input_image)
+
+    def batch_predict(self, images):
+        """Classify a batch of input image
+
+        :param im: (list of numpy.ndarray) The input image list, each element is a 3-D array with layout HWC, BGR format
+        :return list of DetectionResult
         """
-        Argument for image preprocessing step, the preprocess image size, tuple of (width, height), default size = [640, 640]
+
+        return self._model.batch_predict(images)
+
+    @property
+    def preprocessor(self):
+        """Get YOLOv7Preprocessor object of the loaded model
+
+        :return YOLOv7Preprocessor
         """
-        return self._model.size
+        return self._model.preprocessor
 
     @property
-    def padding_value(self):
-        #  padding value, size should be the same as channels
-        return self._model.padding_value
+    def postprocessor(self):
+        """Get YOLOv7Postprocessor object of the loaded model
 
-    @property
-    def is_no_pad(self):
-        # while is_mini_pad = false and is_no_pad = true, will resize the image to the set size
-        return self._model.is_no_pad
-
-    @property
-    def is_mini_pad(self):
-        # only pad to the minimum rectange which height and width is times of stride
-        return self._model.is_mini_pad
-
-    @property
-    def is_scale_up(self):
-        # if is_scale_up is false, the input image only can be zoom out, the maximum resize scale cannot exceed 1.0
-        return self._model.is_scale_up
-
-    @property
-    def stride(self):
-        # padding stride, for is_mini_pad
-        return self._model.stride
-
-    @property
-    def max_wh(self):
-        # for offseting the boxes by classes when using NMS
-        return self._model.max_wh
-
-    @size.setter
-    def size(self, wh):
-        assert isinstance(wh, (list, tuple)),\
-            "The value to set `size` must be type of tuple or list."
-        assert len(wh) == 2,\
-            "The value to set `size` must contatins 2 elements means [width, height], but now it contains {} elements.".format(
-            len(wh))
-        self._model.size = wh
-
-    @padding_value.setter
-    def padding_value(self, value):
-        assert isinstance(
-            value,
-            list), "The value to set `padding_value` must be type of list."
-        self._model.padding_value = value
-
-    @is_no_pad.setter
-    def is_no_pad(self, value):
-        assert isinstance(
-            value, bool), "The value to set `is_no_pad` must be type of bool."
-        self._model.is_no_pad = value
-
-    @is_mini_pad.setter
-    def is_mini_pad(self, value):
-        assert isinstance(
-            value,
-            bool), "The value to set `is_mini_pad` must be type of bool."
-        self._model.is_mini_pad = value
-
-    @is_scale_up.setter
-    def is_scale_up(self, value):
-        assert isinstance(
-            value,
-            bool), "The value to set `is_scale_up` must be type of bool."
-        self._model.is_scale_up = value
-
-    @stride.setter
-    def stride(self, value):
-        assert isinstance(
-            value, int), "The value to set `stride` must be type of int."
-        self._model.stride = value
-
-    @max_wh.setter
-    def max_wh(self, value):
-        assert isinstance(
-            value, float), "The value to set `max_wh` must be type of float."
-        self._model.max_wh = value
+        :return YOLOv7Postprocessor
+        """
+        return self._model.postprocessor