fix conflicts for ascend

python/fastdeploy/runtime.py

@@ -245,6 +245,34 @@ class RuntimeOption:
             return
         return self._option.use_gpu(device_id)
 
+    def use_xpu(self,
+                device_id=0,
+                l3_workspace_size=16 * 1024 * 1024,
+                locked=False,
+                autotune=True,
+                autotune_file="",
+                precision="int16",
+                adaptive_seqlen=False,
+                enable_multi_stream=False):
+        """Inference with XPU
+
+        :param device_id: (int)The index of XPU will be used for inference, default 0
+        :param l3_workspace_size: (int)The size of the video memory allocated by the l3 cache, the maximum is 16M, default 16M
+        :param locked: (bool)Whether the allocated L3 cache can be locked. If false, it means that the L3 cache is not locked,
+                        and the allocated L3 cache can be shared by multiple models, and multiple models
+        :param autotune: (bool)Whether to autotune the conv operator in the model.
+                        If true, when the conv operator of a certain dimension is executed for the first time,
+                        it will automatically search for a better algorithm to improve the performance of subsequent conv operators of the same dimension.
+        :param autotune_file: (str)Specify the path of the autotune file. If autotune_file is specified,
+                        the algorithm specified in the file will be used and autotune will not be performed again.
+        :param precision: (str)Calculation accuracy of multi_encoder
+        :param adaptive_seqlen: (bool)adaptive_seqlen Is the input of multi_encoder variable length
+        :param enable_multi_stream: (bool)Whether to enable the multi stream of xpu.
+        """
+        return self._option.use_xpu(device_id, l3_workspace_size, locked,
+                                    autotune, autotune_file, precision,
+                                    adaptive_seqlen, enable_multi_stream)
+
     def use_cpu(self):
         """Inference with CPU
         """

python/fastdeploy/text/uie/__init__.py

@@ -86,7 +86,8 @@ class UIEModel(FastDeployModel):
         for result in results:
             uie_result = dict()
             for key, uie_results in result.items():
+                uie_result[key] = list()
                 for uie_res in uie_results:
-                    uie_result[key] = uie_res.get_dict()
+                    uie_result[key].append(uie_res.get_dict())
             new_results += [uie_result]
         return new_results

python/fastdeploy/vision/classification/ppcls/__init__.py

@@ -85,6 +85,19 @@ class PaddleClasModel(FastDeployModel):
             model_format)
         assert self.initialized, "PaddleClas model initialize failed."
 
+    def clone(self):
+        """Clone PaddleClasModel object
+
+        :return: a new PaddleClasModel object
+        """
+
+        class PaddleClasCloneModel(PaddleClasModel):
+            def __init__(self, model):
+                self._model = model
+
+        clone_model = PaddleClasCloneModel(self._model.clone())
+        return clone_model
+
     def predict(self, im, topk=1):
         """Classify an input image
 

python/fastdeploy/vision/detection/__init__.py

@@ -24,3 +24,4 @@ from .contrib.yolov6 import YOLOv6
 from .contrib.yolov7end2end_trt import YOLOv7End2EndTRT
 from .contrib.yolov7end2end_ort import YOLOv7End2EndORT
 from .ppdet import *
+from .contrib.rkyolo import *

python/fastdeploy/vision/detection/contrib/rkyolo/__init__.py

@@ -0,0 +1,16 @@
+# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
+#
+# 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.
+
+from __future__ import absolute_import
+from .rkyolov5 import *

python/fastdeploy/vision/detection/contrib/rkyolo/rkyolov5.py

@@ -0,0 +1,195 @@
+# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
+#
+# 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.
+
+from __future__ import absolute_import
+import logging
+from ..... import FastDeployModel, ModelFormat
+from ..... import c_lib_wrap as C
+
+
+class RKYOLOPreprocessor:
+    def __init__(self):
+        """Create a preprocessor for RKYOLOV5
+        """
+        self._preprocessor = C.vision.detection.RKYOLOPreprocessor()
+
+    def run(self, input_ims):
+        """Preprocess input images for RKYOLOV5
+
+        :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 RKYOLOPostprocessor:
+    def __init__(self):
+        """Create a postprocessor for RKYOLOV5
+        """
+        self._postprocessor = C.vision.detection.RKYOLOPostprocessor()
+
+    def run(self, runtime_results):
+        """Postprocess the runtime results for RKYOLOV5
+
+        :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)
+
+    @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
+
+    @property
+    def multi_label(self):
+        """
+        multi_label for postprocessing, set true for eval, default is True
+        """
+        return self._postprocessor.multi_label
+
+    @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
+
+    @multi_label.setter
+    def multi_label(self, value):
+        assert isinstance(
+            value,
+            bool), "The value to set `multi_label` must be type of bool."
+        self._postprocessor.multi_label = value
+
+
+class RKYOLOV5(FastDeployModel):
+    def __init__(self,
+                 model_file,
+                 params_file="",
+                 runtime_option=None,
+                 model_format=ModelFormat.ONNX):
+        """Load a RKYOLOV5 model exported by RKYOLOV5.
+
+        :param model_file: (str)Path of model file, e.g ./yolov5.onnx
+        :param params_file: (str)Path of parameters file, e.g yolox/model.pdiparams, if the model_fomat is ModelFormat.ONNX, this param will be ignored, can be set as empty string
+        :param runtime_option: (fastdeploy.RuntimeOption)RuntimeOption for inference this model, if it's None, will use the default backend on CPU
+        :param model_format: (fastdeploy.ModelForamt)Model format of the loaded model
+        """
+        # 调用基函数进行backend_option的初始化
+        # 初始化后的option保存在self._runtime_option
+        super(RKYOLOV5, self).__init__(runtime_option)
+
+        self._model = C.vision.detection.RKYOLOV5(
+            model_file, self._runtime_option, model_format)
+        # 通过self.initialized判断整个模型的初始化是否成功
+        assert self.initialized, "RKYOLOV5 initialize failed."
+
+    def predict(self, input_image, conf_threshold=0.25, nms_iou_threshold=0.5):
+        """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 threshold for postprocessing, default is 0.25
+        :param nms_iou_threshold: iou threshold for NMS, default is 0.5
+        :return: DetectionResult
+        """
+
+        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
+        """
+
+        return self._model.batch_predict(images)
+
+    @property
+    def preprocessor(self):
+        """Get RKYOLOV5Preprocessor object of the loaded model
+
+        :return RKYOLOV5Preprocessor
+        """
+        return self._model.preprocessor
+
+    @property
+    def postprocessor(self):
+        """Get RKYOLOV5Postprocessor object of the loaded model
+
+        :return RKYOLOV5Postprocessor
+        """
+        return self._model.postprocessor

python/fastdeploy/vision/detection/ppdet/__init__.py

@@ -99,6 +99,19 @@ class PPYOLOE(FastDeployModel):
 
         return self._model.batch_predict(images)
 
+    def clone(self):
+        """Clone PPYOLOE object
+
+        :return: a new PPYOLOE object
+        """
+
+        class PPYOLOEClone(PPYOLOE):
+            def __init__(self, model):
+                self._model = model
+
+        clone_model = PPYOLOEClone(self._model.clone())
+        return clone_model
+
     @property
     def preprocessor(self):
         """Get PaddleDetPreprocessor object of the loaded model
@@ -139,6 +152,19 @@ class PPYOLO(PPYOLOE):
             model_format)
         assert self.initialized, "PPYOLO model initialize failed."
 
+    def clone(self):
+        """Clone PPYOLO object
+
+        :return: a new PPYOLO object
+        """
+
+        class PPYOLOClone(PPYOLO):
+            def __init__(self, model):
+                self._model = model
+
+        clone_model = PPYOLOClone(self._model.clone())
+        return clone_model
+
 
 class PaddleYOLOX(PPYOLOE):
     def __init__(self,
@@ -164,6 +190,19 @@ class PaddleYOLOX(PPYOLOE):
             model_format)
         assert self.initialized, "PaddleYOLOX model initialize failed."
 
+    def clone(self):
+        """Clone PaddleYOLOX object
+
+        :return: a new PaddleYOLOX object
+        """
+
+        class PaddleYOLOXClone(PaddleYOLOX):
+            def __init__(self, model):
+                self._model = model
+
+        clone_model = PaddleYOLOXClone(self._model.clone())
+        return clone_model
+
 
 class PicoDet(PPYOLOE):
     def __init__(self,
@@ -188,6 +227,19 @@ class PicoDet(PPYOLOE):
             model_format)
         assert self.initialized, "PicoDet model initialize failed."
 
+    def clone(self):
+        """Clone PicoDet object
+
+        :return: a new PicoDet object
+        """
+
+        class PicoDetClone(PicoDet):
+            def __init__(self, model):
+                self._model = model
+
+        clone_model = PicoDetClone(self._model.clone())
+        return clone_model
+
 
 class FasterRCNN(PPYOLOE):
     def __init__(self,
@@ -213,6 +265,19 @@ class FasterRCNN(PPYOLOE):
             model_format)
         assert self.initialized, "FasterRCNN model initialize failed."
 
+    def clone(self):
+        """Clone FasterRCNN object
+
+        :return: a new FasterRCNN object
+        """
+
+        class FasterRCNNClone(FasterRCNN):
+            def __init__(self, model):
+                self._model = model
+
+        clone_model = FasterRCNNClone(self._model.clone())
+        return clone_model
+
 
 class YOLOv3(PPYOLOE):
     def __init__(self,
@@ -238,6 +303,19 @@ class YOLOv3(PPYOLOE):
             model_format)
         assert self.initialized, "YOLOv3 model initialize failed."
 
+    def clone(self):
+        """Clone YOLOv3 object
+
+        :return: a new YOLOv3 object
+        """
+
+        class YOLOv3Clone(YOLOv3):
+            def __init__(self, model):
+                self._model = model
+
+        clone_model = YOLOv3Clone(self._model.clone())
+        return clone_model
+
 
 class MaskRCNN(PPYOLOE):
     def __init__(self,
@@ -273,6 +351,19 @@ class MaskRCNN(PPYOLOE):
         raise Exception(
             "batch_predict is not supported for MaskRCNN model now.")
 
+    def clone(self):
+        """Clone MaskRCNN object
+
+        :return: a new MaskRCNN object
+        """
+
+        class MaskRCNNClone(MaskRCNN):
+            def __init__(self, model):
+                self._model = model
+
+        clone_model = MaskRCNNClone(self._model.clone())
+        return clone_model
+
 
 class SSD(PPYOLOE):
     def __init__(self,
@@ -293,7 +384,120 @@ class SSD(PPYOLOE):
         super(PPYOLOE, self).__init__(runtime_option)
 
         assert model_format == ModelFormat.PADDLE, "SSD model only support model format of ModelFormat.Paddle now."
-        self._model = C.vision.detection.SSD(
+        self._model = C.vision.detection.SSD(model_file, params_file,
+                                             config_file, self._runtime_option,
+                                             model_format)
+        assert self.initialized, "SSD model initialize failed."
+
+    def clone(self):
+        """Clone SSD object
+
+        :return: a new SSD object
+        """
+
+        class SSDClone(SSD):
+            def __init__(self, model):
+                self._model = model
+
+        clone_model = SSDClone(self._model.clone())
+        return clone_model
+
+
+class PaddleYOLOv5(PPYOLOE):
+    def __init__(self,
+                 model_file,
+                 params_file,
+                 config_file,
+                 runtime_option=None,
+                 model_format=ModelFormat.PADDLE):
+        """Load a YOLOv5 model exported by PaddleDetection.
+
+        :param model_file: (str)Path of model file, e.g yolov5/model.pdmodel
+        :param params_file: (str)Path of parameters file, e.g yolov5/model.pdiparams, if the model_fomat is ModelFormat.ONNX, this param will be ignored, can be set as empty string
+        :param config_file: (str)Path of configuration file for deployment, e.g ppyoloe/infer_cfg.yml
+        :param runtime_option: (fastdeploy.RuntimeOption)RuntimeOption for inference this model, if it's None, will use the default backend on CPU
+        :param model_format: (fastdeploy.ModelForamt)Model format of the loaded model
+        """
+
+        super(PPYOLOE, self).__init__(runtime_option)
+
+        assert model_format == ModelFormat.PADDLE, "PaddleYOLOv5 model only support model format of ModelFormat.Paddle now."
+        self._model = C.vision.detection.PaddleYOLOv5(
             model_file, params_file, config_file, self._runtime_option,
             model_format)
-        assert self.initialized, "SSD model initialize failed."
+        assert self.initialized, "PaddleYOLOv5 model initialize failed."
+
+
+class PaddleYOLOv6(PPYOLOE):
+    def __init__(self,
+                 model_file,
+                 params_file,
+                 config_file,
+                 runtime_option=None,
+                 model_format=ModelFormat.PADDLE):
+        """Load a YOLOv6 model exported by PaddleDetection.
+
+        :param model_file: (str)Path of model file, e.g yolov6/model.pdmodel
+        :param params_file: (str)Path of parameters file, e.g yolov6/model.pdiparams, if the model_fomat is ModelFormat.ONNX, this param will be ignored, can be set as empty string
+        :param config_file: (str)Path of configuration file for deployment, e.g ppyoloe/infer_cfg.yml
+        :param runtime_option: (fastdeploy.RuntimeOption)RuntimeOption for inference this model, if it's None, will use the default backend on CPU
+        :param model_format: (fastdeploy.ModelForamt)Model format of the loaded model
+        """
+
+        super(PPYOLOE, self).__init__(runtime_option)
+
+        assert model_format == ModelFormat.PADDLE, "PaddleYOLOv6 model only support model format of ModelFormat.Paddle now."
+        self._model = C.vision.detection.PaddleYOLOv6(
+            model_file, params_file, config_file, self._runtime_option,
+            model_format)
+        assert self.initialized, "PaddleYOLOv6 model initialize failed."
+
+
+class PaddleYOLOv7(PPYOLOE):
+    def __init__(self,
+                 model_file,
+                 params_file,
+                 config_file,
+                 runtime_option=None,
+                 model_format=ModelFormat.PADDLE):
+        """Load a YOLOv7 model exported by PaddleDetection.
+
+        :param model_file: (str)Path of model file, e.g yolov7/model.pdmodel
+        :param params_file: (str)Path of parameters file, e.g yolov7/model.pdiparams, if the model_fomat is ModelFormat.ONNX, this param will be ignored, can be set as empty string
+        :param config_file: (str)Path of configuration file for deployment, e.g ppyoloe/infer_cfg.yml
+        :param runtime_option: (fastdeploy.RuntimeOption)RuntimeOption for inference this model, if it's None, will use the default backend on CPU
+        :param model_format: (fastdeploy.ModelForamt)Model format of the loaded model
+        """
+
+        super(PPYOLOE, self).__init__(runtime_option)
+
+        assert model_format == ModelFormat.PADDLE, "PaddleYOLOv7 model only support model format of ModelFormat.Paddle now."
+        self._model = C.vision.detection.PaddleYOLOv7(
+            model_file, params_file, config_file, self._runtime_option,
+            model_format)
+        assert self.initialized, "PaddleYOLOv7 model initialize failed."
+
+
+class RTMDet(PPYOLOE):
+    def __init__(self,
+                 model_file,
+                 params_file,
+                 config_file,
+                 runtime_option=None,
+                 model_format=ModelFormat.PADDLE):
+        """Load a RTMDet model exported by PaddleDetection.
+
+        :param model_file: (str)Path of model file, e.g rtmdet/model.pdmodel
+        :param params_file: (str)Path of parameters file, e.g rtmdet/model.pdiparams, if the model_fomat is ModelFormat.ONNX, this param will be ignored, can be set as empty string
+        :param config_file: (str)Path of configuration file for deployment, e.g ppyoloe/infer_cfg.yml
+        :param runtime_option: (fastdeploy.RuntimeOption)RuntimeOption for inference this model, if it's None, will use the default backend on CPU
+        :param model_format: (fastdeploy.ModelForamt)Model format of the loaded model
+        """
+
+        super(PPYOLOE, self).__init__(runtime_option)
+
+        assert model_format == ModelFormat.PADDLE, "RTMDet model only support model format of ModelFormat.Paddle now."
+        self._model = C.vision.detection.RTMDet(
+            model_file, params_file, config_file, self._runtime_option,
+            model_format)
+        assert self.initialized, "RTMDet model initialize failed."

python/fastdeploy/vision/facedet/__init__.py

@@ -14,6 +14,7 @@
 
 from __future__ import absolute_import
 from .contrib.yolov5face import YOLOv5Face
+from .contrib.yolov7face import *
 from .contrib.retinaface import RetinaFace
 from .contrib.scrfd import SCRFD
 from .contrib.ultraface import UltraFace

python/fastdeploy/vision/facedet/contrib/yolov7face.py

@@ -0,0 +1,166 @@
+# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
+#
+# 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.
+
+from __future__ import absolute_import
+import logging
+from .... import FastDeployModel, ModelFormat
+from .... import c_lib_wrap as C
+
+
+class Yolov7FacePreprocessor:
+    def __init__(self):
+        """Create a preprocessor for Yolov7Face
+        """
+        self._preprocessor = C.vision.facedet.Yolov7Preprocessor()
+
+    def run(self, input_ims):
+        """Preprocess input images for Yolov7Face
+
+        :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_color_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_color_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_color_value.setter
+    def padding_color_value(self, value):
+        assert isinstance(
+            value, list
+        ), "The value to set `padding_color_value` must be type of list."
+        self._preprocessor.padding_color_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 Yolov7FacePostprocessor:
+    def __init__(self):
+        """Create a postprocessor for Yolov7Face
+        """
+        self._postprocessor = C.vision.facedet.Yolov7FacePostprocessor()
+
+    def run(self, runtime_results, ims_info):
+        """Postprocess the runtime results for Yolov7Face
+
+        :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 0.5
+        """
+        return self._postprocessor.conf_threshold
+
+    @property
+    def nms_threshold(self):
+        """
+        nms threshold for postprocessing, default is 0.45
+        """
+        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 YOLOv7Face(FastDeployModel):
+    def __init__(self,
+                 model_file,
+                 params_file="",
+                 runtime_option=None,
+                 model_format=ModelFormat.ONNX):
+        """Load a YOLOv7Face model exported by YOLOv7Face.
+
+        :param model_file: (str)Path of model file, e.g ./yolov7face.onnx
+        :param params_file: (str)Path of parameters file, e.g yolox/model.pdiparams, if the model_fomat is ModelFormat.ONNX, this param will be ignored, can be set as empty string
+        :param runtime_option: (fastdeploy.RuntimeOption)RuntimeOption for inference this model, if it's None, will use the default backend on CPU
+        :param model_format: (fastdeploy.ModelForamt)Model format of the loaded model
+        """
+        super(YOLOv7Face, self).__init__(runtime_option)
+
+        self._model = C.vision.facedet.YOLOv7Face(
+            model_file, params_file, self._runtime_option, model_format)
+
+        assert self.initialized, "YOLOv7Face initialize failed."
+
+    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
+        """
+
+        return self._model.batch_predict(images)
+
+    @property
+    def preprocessor(self):
+        """Get YOLOv7Preprocessor object of the loaded model
+
+        :return YOLOv7Preprocessor
+        """
+        return self._model.preprocessor
+
+    @property
+    def postprocessor(self):
+        """Get YOLOv7Postprocessor object of the loaded model
+
+        :return YOLOv7Postprocessor
+        """
+        return self._model.postprocessor

python/fastdeploy/vision/keypointdetection/pptinypose/__init__.py

@@ -41,14 +41,18 @@ class PPTinyPose(FastDeployModel):
             model_format)
         assert self.initialized, "PPTinyPose model initialize failed."
 
-    def predict(self, input_image):
+    def predict(self, input_image, detection_result=None):
         """Detect keypoints in an input image
 
         :param im: (numpy.ndarray)The input image data, 3-D array with layout HWC, BGR format
+        :param detection_result: (DetectionResult)Pre-detected boxes result, default is None
         :return: KeyPointDetectionResult
         """
         assert input_image is not None, "The input image data is None."
-        return self._model.predict(input_image)
+        if detection_result:
+            return self._model.predict(input_image, detection_result)
+        else:
+            return self._model.predict(input_image)
 
     @property
     def use_dark(self):

python/fastdeploy/vision/segmentation/ppseg/__init__.py

@@ -57,6 +57,19 @@ class PaddleSegModel(FastDeployModel):
         """
         return self._model.batch_predict(image_list)
 
+    def clone(self):
+        """Clone PaddleSegModel object
+
+        :return: a new PaddleSegModel object
+        """
+
+        class PaddleSegCloneModel(PaddleSegModel):
+            def __init__(self, model):
+                self._model = model
+
+        clone_model = PaddleSegCloneModel(self._model.clone())
+        return clone_model
+
     @property
     def preprocessor(self):
         """Get PaddleSegPreprocessor object of the loaded model