mirror of
https://github.com/PaddlePaddle/FastDeploy.git
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51be3fea78
* first draft * add robx iou * add benchmark for ppyoloe_r * remove trash code * fix bugs * add pybind nms rotated option * add missing head file * fix bug * fix bug2 * fix shape bug --------- Co-authored-by: DefTruth <31974251+DefTruth@users.noreply.github.com>
838 lines
36 KiB
Python
Executable File
838 lines
36 KiB
Python
Executable File
# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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from __future__ import absolute_import
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from typing import Union, List
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import logging
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from .... import FastDeployModel, ModelFormat
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from .... import c_lib_wrap as C
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from ...common import ProcessorManager
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class PaddleDetPreprocessor(ProcessorManager):
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def __init__(self, config_file):
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"""Create a preprocessor for PaddleDetection Model from configuration file
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:param config_file: (str)Path of configuration file, e.g ppyoloe/infer_cfg.yml
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"""
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self._manager = C.vision.detection.PaddleDetPreprocessor(config_file)
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def disable_normalize(self):
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"""
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This function will disable normalize in preprocessing step.
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"""
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self._manager.disable_normalize()
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def disable_permute(self):
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"""
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This function will disable hwc2chw in preprocessing step.
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"""
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self._manager.disable_permute()
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class NMSOption:
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def __init__(self):
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self.nms_option = C.vision.detection.NMSOption()
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@property
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def background_label(self):
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return self.nms_option.background_label
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class NMSRotatedOption:
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def __init__(self):
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self.nms_rotated_option = C.vision.detection.NMSRotatedOption()
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@property
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def background_label(self):
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return self.nms_rotated_option.background_label
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class PaddleDetPostprocessor:
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def __init__(self):
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"""Create a postprocessor for PaddleDetection Model
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"""
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self._postprocessor = C.vision.detection.PaddleDetPostprocessor()
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def run(self, runtime_results):
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"""Postprocess the runtime results for PaddleDetection Model
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:param: runtime_results: (list of FDTensor)The output FDTensor results from runtime
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:return: list of ClassifyResult(If the runtime_results is predict by batched samples, the length of this list equals to the batch size)
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"""
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return self._postprocessor.run(runtime_results)
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def apply_nms(self):
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self._postprocessor.apply_nms()
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def set_nms_option(self, nms_option=None):
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"""This function will enable decode and nms in postprocess step.
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"""
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if nms_option is None:
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nms_option = NMSOption()
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self._postprocessor.set_nms_option(self, nms_option.nms_option)
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def set_nms_rotated_option(self, nms_rotated_option=None):
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"""This function will enable decode and rotated nms in postprocess step.
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"""
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if nms_rotated_option is None:
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nms_rotated_option = NMSRotatedOption()
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self._postprocessor.set_nms_rotated_option(
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self, nms_rotated_option.nms_rotated_option)
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class PPYOLOE(FastDeployModel):
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def __init__(self,
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model_file,
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params_file,
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config_file,
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runtime_option=None,
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model_format=ModelFormat.PADDLE):
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"""Load a PPYOLOE model exported by PaddleDetection.
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:param model_file: (str)Path of model file, e.g ppyoloe/model.pdmodel
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:param params_file: (str)Path of parameters file, e.g ppyoloe/model.pdiparams, if the model_fomat is ModelFormat.ONNX, this param will be ignored, can be set as empty string
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:param config_file: (str)Path of configuration file for deployment, e.g ppyoloe/infer_cfg.yml
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:param runtime_option: (fastdeploy.RuntimeOption)RuntimeOption for inference this model, if it's None, will use the default backend on CPU
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:param model_format: (fastdeploy.ModelForamt)Model format of the loaded model
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"""
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super(PPYOLOE, self).__init__(runtime_option)
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self._model = C.vision.detection.PPYOLOE(
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model_file, params_file, config_file, self._runtime_option,
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model_format)
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assert self.initialized, "PPYOLOE model initialize failed."
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def predict(self, im):
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"""Detect an input image
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:param im: (numpy.ndarray)The input image data, 3-D array with layout HWC, BGR format
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:return: DetectionResult
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"""
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assert im is not None, "The input image data is None."
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return self._model.predict(im)
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def batch_predict(self, images):
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"""Detect a batch of input image list
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:param im: (list of numpy.ndarray) The input image list, each element is a 3-D array with layout HWC, BGR format
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:return list of DetectionResult
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"""
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return self._model.batch_predict(images)
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def clone(self):
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"""Clone PPYOLOE object
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:return: a new PPYOLOE object
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"""
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class PPYOLOEClone(PPYOLOE):
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def __init__(self, model):
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self._model = model
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clone_model = PPYOLOEClone(self._model.clone())
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return clone_model
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@property
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def preprocessor(self):
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"""Get PaddleDetPreprocessor object of the loaded model
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:return PaddleDetPreprocessor
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"""
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return self._model.preprocessor
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@property
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def postprocessor(self):
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"""Get PaddleDetPostprocessor object of the loaded model
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:return PaddleDetPostprocessor
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"""
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return self._model.postprocessor
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class PPYOLO(PPYOLOE):
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def __init__(self,
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model_file,
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params_file,
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config_file,
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runtime_option=None,
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model_format=ModelFormat.PADDLE):
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"""Load a PPYOLO model exported by PaddleDetection.
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:param model_file: (str)Path of model file, e.g ppyolo/model.pdmodel
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:param params_file: (str)Path of parameters file, e.g ppyolo/model.pdiparams, if the model_fomat is ModelFormat.ONNX, this param will be ignored, can be set as empty string
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:param runtime_option: (fastdeploy.RuntimeOption)RuntimeOption for inference this model, if it's None, will use the default backend on CPU
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:param model_format: (fastdeploy.ModelForamt)Model format of the loaded model
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"""
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super(PPYOLOE, self).__init__(runtime_option)
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assert model_format == ModelFormat.PADDLE, "PPYOLO model only support model format of ModelFormat.Paddle now."
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self._model = C.vision.detection.PPYOLO(
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model_file, params_file, config_file, self._runtime_option,
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model_format)
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assert self.initialized, "PPYOLO model initialize failed."
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def clone(self):
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"""Clone PPYOLO object
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:return: a new PPYOLO object
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"""
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class PPYOLOClone(PPYOLO):
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def __init__(self, model):
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self._model = model
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clone_model = PPYOLOClone(self._model.clone())
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return clone_model
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class PaddleYOLOX(PPYOLOE):
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def __init__(self,
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model_file,
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params_file,
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config_file,
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runtime_option=None,
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model_format=ModelFormat.PADDLE):
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"""Load a YOLOX model exported by PaddleDetection.
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:param model_file: (str)Path of model file, e.g yolox/model.pdmodel
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: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
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:param config_file: (str)Path of configuration file for deployment, e.g ppyoloe/infer_cfg.yml
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:param runtime_option: (fastdeploy.RuntimeOption)RuntimeOption for inference this model, if it's None, will use the default backend on CPU
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:param model_format: (fastdeploy.ModelForamt)Model format of the loaded model
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"""
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super(PPYOLOE, self).__init__(runtime_option)
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assert model_format == ModelFormat.PADDLE, "PaddleYOLOX model only support model format of ModelFormat.Paddle now."
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self._model = C.vision.detection.PaddleYOLOX(
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model_file, params_file, config_file, self._runtime_option,
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model_format)
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assert self.initialized, "PaddleYOLOX model initialize failed."
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def clone(self):
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"""Clone PaddleYOLOX object
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:return: a new PaddleYOLOX object
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"""
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class PaddleYOLOXClone(PaddleYOLOX):
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def __init__(self, model):
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self._model = model
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clone_model = PaddleYOLOXClone(self._model.clone())
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return clone_model
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class PicoDet(PPYOLOE):
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def __init__(self,
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model_file,
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params_file,
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config_file,
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runtime_option=None,
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model_format=ModelFormat.PADDLE):
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"""Load a PicoDet model exported by PaddleDetection.
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:param model_file: (str)Path of model file, e.g picodet/model.pdmodel
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:param params_file: (str)Path of parameters file, e.g picodet/model.pdiparams, if the model_fomat is ModelFormat.ONNX, this param will be ignored, can be set as empty string
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:param config_file: (str)Path of configuration file for deployment, e.g ppyoloe/infer_cfg.yml
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:param runtime_option: (fastdeploy.RuntimeOption)RuntimeOption for inference this model, if it's None, will use the default backend on CPU
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:param model_format: (fastdeploy.ModelForamt)Model format of the loaded model
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"""
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super(PPYOLOE, self).__init__(runtime_option)
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self._model = C.vision.detection.PicoDet(
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model_file, params_file, config_file, self._runtime_option,
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model_format)
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assert self.initialized, "PicoDet model initialize failed."
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def clone(self):
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"""Clone PicoDet object
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:return: a new PicoDet object
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"""
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class PicoDetClone(PicoDet):
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def __init__(self, model):
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self._model = model
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clone_model = PicoDetClone(self._model.clone())
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return clone_model
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class FasterRCNN(PPYOLOE):
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def __init__(self,
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model_file,
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params_file,
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config_file,
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runtime_option=None,
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model_format=ModelFormat.PADDLE):
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"""Load a FasterRCNN model exported by PaddleDetection.
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:param model_file: (str)Path of model file, e.g fasterrcnn/model.pdmodel
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:param params_file: (str)Path of parameters file, e.g fasterrcnn/model.pdiparams, if the model_fomat is ModelFormat.ONNX, this param will be ignored, can be set as empty string
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:param config_file: (str)Path of configuration file for deployment, e.g ppyoloe/infer_cfg.yml
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:param runtime_option: (fastdeploy.RuntimeOption)RuntimeOption for inference this model, if it's None, will use the default backend on CPU
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:param model_format: (fastdeploy.ModelForamt)Model format of the loaded model
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"""
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super(PPYOLOE, self).__init__(runtime_option)
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assert model_format == ModelFormat.PADDLE, "FasterRCNN model only support model format of ModelFormat.Paddle now."
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self._model = C.vision.detection.FasterRCNN(
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model_file, params_file, config_file, self._runtime_option,
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model_format)
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assert self.initialized, "FasterRCNN model initialize failed."
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def clone(self):
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"""Clone FasterRCNN object
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:return: a new FasterRCNN object
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"""
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class FasterRCNNClone(FasterRCNN):
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def __init__(self, model):
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self._model = model
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clone_model = FasterRCNNClone(self._model.clone())
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return clone_model
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class YOLOv3(PPYOLOE):
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def __init__(self,
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model_file,
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params_file,
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config_file,
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runtime_option=None,
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model_format=ModelFormat.PADDLE):
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"""Load a YOLOv3 model exported by PaddleDetection.
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:param model_file: (str)Path of model file, e.g yolov3/model.pdmodel
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:param params_file: (str)Path of parameters file, e.g yolov3/model.pdiparams, if the model_fomat is ModelFormat.ONNX, this param will be ignored, can be set as empty string
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:param config_file: (str)Path of configuration file for deployment, e.g ppyoloe/infer_cfg.yml
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:param runtime_option: (fastdeploy.RuntimeOption)RuntimeOption for inference this model, if it's None, will use the default backend on CPU
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:param model_format: (fastdeploy.ModelForamt)Model format of the loaded model
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"""
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super(PPYOLOE, self).__init__(runtime_option)
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assert model_format == ModelFormat.PADDLE, "YOLOv3 model only support model format of ModelFormat.Paddle now."
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self._model = C.vision.detection.YOLOv3(
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model_file, params_file, config_file, self._runtime_option,
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model_format)
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assert self.initialized, "YOLOv3 model initialize failed."
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def clone(self):
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"""Clone YOLOv3 object
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:return: a new YOLOv3 object
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"""
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class YOLOv3Clone(YOLOv3):
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def __init__(self, model):
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self._model = model
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clone_model = YOLOv3Clone(self._model.clone())
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return clone_model
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class SOLOv2(PPYOLOE):
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def __init__(self,
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model_file,
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params_file,
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config_file,
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runtime_option=None,
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model_format=ModelFormat.PADDLE):
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"""Load a SOLOv2 model exported by PaddleDetection.
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:param model_file: (str)Path of model file, e.g solov2/model.pdmodel
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:param params_file: (str)Path of parameters file, e.g solov2/model.pdiparams, if the model_fomat is ModelFormat.ONNX, this param will be ignored, can be set as empty string
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:param config_file: (str)Path of configuration file for deployment, e.g solov2/infer_cfg.yml
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:param runtime_option: (fastdeploy.RuntimeOption)RuntimeOption for inference this model, if it's None, will use the default backend on CPU
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:param model_format: (fastdeploy.ModelForamt)Model format of the loaded model
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"""
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super(PPYOLOE, self).__init__(runtime_option)
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assert model_format == ModelFormat.PADDLE, "SOLOv2 model only support model format of ModelFormat.Paddle now."
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self._model = C.vision.detection.SOLOv2(
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model_file, params_file, config_file, self._runtime_option,
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model_format)
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assert self.initialized, "SOLOv2 model initialize failed."
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def clone(self):
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"""Clone SOLOv2 object
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:return: a new SOLOv2 object
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"""
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class SOLOv2Clone(SOLOv2):
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def __init__(self, model):
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self._model = model
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clone_model = SOLOv2Clone(self._model.clone())
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return clone_model
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class MaskRCNN(PPYOLOE):
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def __init__(self,
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model_file,
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params_file,
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config_file,
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runtime_option=None,
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model_format=ModelFormat.PADDLE):
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"""Load a MaskRCNN model exported by PaddleDetection.
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:param model_file: (str)Path of model file, e.g fasterrcnn/model.pdmodel
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:param params_file: (str)Path of parameters file, e.g fasterrcnn/model.pdiparams, if the model_fomat is ModelFormat.ONNX, this param will be ignored, can be set as empty string
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:param config_file: (str)Path of configuration file for deployment, e.g ppyoloe/infer_cfg.yml
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:param runtime_option: (fastdeploy.RuntimeOption)RuntimeOption for inference this model, if it's None, will use the default backend on CPU
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:param model_format: (fastdeploy.ModelForamt)Model format of the loaded model
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"""
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super(PPYOLOE, self).__init__(runtime_option)
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assert model_format == ModelFormat.PADDLE, "MaskRCNN model only support model format of ModelFormat.Paddle now."
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self._model = C.vision.detection.MaskRCNN(
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model_file, params_file, config_file, self._runtime_option,
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model_format)
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assert self.initialized, "MaskRCNN model initialize failed."
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def batch_predict(self, images):
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"""Detect a batch of input image list, batch_predict is not supported for maskrcnn now.
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:param im: (list of numpy.ndarray) The input image list, each element is a 3-D array with layout HWC, BGR format
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:return list of DetectionResult
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"""
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raise Exception(
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"batch_predict is not supported for MaskRCNN model now.")
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def clone(self):
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"""Clone MaskRCNN object
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:return: a new MaskRCNN object
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"""
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class MaskRCNNClone(MaskRCNN):
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def __init__(self, model):
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self._model = model
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clone_model = MaskRCNNClone(self._model.clone())
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return clone_model
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class SSD(PPYOLOE):
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def __init__(self,
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model_file,
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params_file,
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config_file,
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runtime_option=None,
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model_format=ModelFormat.PADDLE):
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"""Load a SSD model exported by PaddleDetection.
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:param model_file: (str)Path of model file, e.g ssd/model.pdmodel
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:param params_file: (str)Path of parameters file, e.g ssd/model.pdiparams, if the model_fomat is ModelFormat.ONNX, this param will be ignored, can be set as empty string
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:param config_file: (str)Path of configuration file for deployment, e.g ppyoloe/infer_cfg.yml
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:param runtime_option: (fastdeploy.RuntimeOption)RuntimeOption for inference this model, if it's None, will use the default backend on CPU
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:param model_format: (fastdeploy.ModelForamt)Model format of the loaded model
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"""
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super(PPYOLOE, self).__init__(runtime_option)
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assert model_format == ModelFormat.PADDLE, "SSD model only support model format of ModelFormat.Paddle now."
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self._model = C.vision.detection.SSD(model_file, params_file,
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config_file, self._runtime_option,
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model_format)
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assert self.initialized, "SSD model initialize failed."
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def clone(self):
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"""Clone SSD object
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:return: a new SSD object
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"""
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class SSDClone(SSD):
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def __init__(self, model):
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self._model = model
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clone_model = SSDClone(self._model.clone())
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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, "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 PaddleYOLOv8(PPYOLOE):
|
|
def __init__(self,
|
|
model_file,
|
|
params_file,
|
|
config_file,
|
|
runtime_option=None,
|
|
model_format=ModelFormat.PADDLE):
|
|
"""Load a YOLOv8 model exported by PaddleDetection.
|
|
|
|
:param model_file: (str)Path of model file, e.g yolov8/model.pdmodel
|
|
:param params_file: (str)Path of parameters file, e.g yolov8/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 yolov8/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)
|
|
|
|
self._model = C.vision.detection.PaddleYOLOv8(
|
|
model_file, params_file, config_file, self._runtime_option,
|
|
model_format)
|
|
assert self.initialized, "PaddleYOLOv8 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."
|
|
|
|
|
|
class CascadeRCNN(PPYOLOE):
|
|
def __init__(self,
|
|
model_file,
|
|
params_file,
|
|
config_file,
|
|
runtime_option=None,
|
|
model_format=ModelFormat.PADDLE):
|
|
"""Load a CascadeRCNN model exported by PaddleDetection.
|
|
|
|
:param model_file: (str)Path of model file, e.g cascadercnn/model.pdmodel
|
|
:param params_file: (str)Path of parameters file, e.g cascadercnn/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, "CascadeRCNN model only support model format of ModelFormat.Paddle now."
|
|
self._model = C.vision.detection.CascadeRCNN(
|
|
model_file, params_file, config_file, self._runtime_option,
|
|
model_format)
|
|
assert self.initialized, "CascadeRCNN model initialize failed."
|
|
|
|
|
|
class PSSDet(PPYOLOE):
|
|
def __init__(self,
|
|
model_file,
|
|
params_file,
|
|
config_file,
|
|
runtime_option=None,
|
|
model_format=ModelFormat.PADDLE):
|
|
"""Load a PSSDet model exported by PaddleDetection.
|
|
|
|
:param model_file: (str)Path of model file, e.g pssdet/model.pdmodel
|
|
:param params_file: (str)Path of parameters file, e.g pssdet/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, "PSSDet model only support model format of ModelFormat.Paddle now."
|
|
self._model = C.vision.detection.PSSDet(
|
|
model_file, params_file, config_file, self._runtime_option,
|
|
model_format)
|
|
assert self.initialized, "PSSDet model initialize failed."
|
|
|
|
|
|
class RetinaNet(PPYOLOE):
|
|
def __init__(self,
|
|
model_file,
|
|
params_file,
|
|
config_file,
|
|
runtime_option=None,
|
|
model_format=ModelFormat.PADDLE):
|
|
"""Load a RetinaNet model exported by PaddleDetection.
|
|
|
|
:param model_file: (str)Path of model file, e.g retinanet/model.pdmodel
|
|
:param params_file: (str)Path of parameters file, e.g retinanet/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, "RetinaNet model only support model format of ModelFormat.Paddle now."
|
|
self._model = C.vision.detection.RetinaNet(
|
|
model_file, params_file, config_file, self._runtime_option,
|
|
model_format)
|
|
assert self.initialized, "RetinaNet model initialize failed."
|
|
|
|
|
|
class PPYOLOESOD(PPYOLOE):
|
|
def __init__(self,
|
|
model_file,
|
|
params_file,
|
|
config_file,
|
|
runtime_option=None,
|
|
model_format=ModelFormat.PADDLE):
|
|
"""Load a PPYOLOESOD model exported by PaddleDetection.
|
|
|
|
:param model_file: (str)Path of model file, e.g ppyoloesod/model.pdmodel
|
|
:param params_file: (str)Path of parameters file, e.g ppyoloesod/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, "PPYOLOESOD model only support model format of ModelFormat.Paddle now."
|
|
self._model = C.vision.detection.PPYOLOESOD(
|
|
model_file, params_file, config_file, self._runtime_option,
|
|
model_format)
|
|
assert self.initialized, "PPYOLOESOD model initialize failed."
|
|
|
|
|
|
class FCOS(PPYOLOE):
|
|
def __init__(self,
|
|
model_file,
|
|
params_file,
|
|
config_file,
|
|
runtime_option=None,
|
|
model_format=ModelFormat.PADDLE):
|
|
"""Load a FCOS model exported by PaddleDetection.
|
|
|
|
:param model_file: (str)Path of model file, e.g fcos/model.pdmodel
|
|
:param params_file: (str)Path of parameters file, e.g fcos/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, "FCOS model only support model format of ModelFormat.Paddle now."
|
|
self._model = C.vision.detection.FCOS(
|
|
model_file, params_file, config_file, self._runtime_option,
|
|
model_format)
|
|
assert self.initialized, "FCOS model initialize failed."
|
|
|
|
|
|
class TTFNet(PPYOLOE):
|
|
def __init__(self,
|
|
model_file,
|
|
params_file,
|
|
config_file,
|
|
runtime_option=None,
|
|
model_format=ModelFormat.PADDLE):
|
|
"""Load a TTFNet model exported by PaddleDetection.
|
|
|
|
:param model_file: (str)Path of model file, e.g ttfnet/model.pdmodel
|
|
:param params_file: (str)Path of parameters file, e.g ttfnet/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, "TTFNet model only support model format of ModelFormat.Paddle now."
|
|
self._model = C.vision.detection.TTFNet(
|
|
model_file, params_file, config_file, self._runtime_option,
|
|
model_format)
|
|
assert self.initialized, "TTFNet model initialize failed."
|
|
|
|
|
|
class TOOD(PPYOLOE):
|
|
def __init__(self,
|
|
model_file,
|
|
params_file,
|
|
config_file,
|
|
runtime_option=None,
|
|
model_format=ModelFormat.PADDLE):
|
|
"""Load a TOOD model exported by PaddleDetection.
|
|
|
|
:param model_file: (str)Path of model file, e.g tood/model.pdmodel
|
|
:param params_file: (str)Path of parameters file, e.g tood/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, "TOOD model only support model format of ModelFormat.Paddle now."
|
|
self._model = C.vision.detection.TOOD(
|
|
model_file, params_file, config_file, self._runtime_option,
|
|
model_format)
|
|
assert self.initialized, "TOOD model initialize failed."
|
|
|
|
|
|
class GFL(PPYOLOE):
|
|
def __init__(self,
|
|
model_file,
|
|
params_file,
|
|
config_file,
|
|
runtime_option=None,
|
|
model_format=ModelFormat.PADDLE):
|
|
"""Load a GFL model exported by PaddleDetection.
|
|
|
|
:param model_file: (str)Path of model file, e.g gfl/model.pdmodel
|
|
:param params_file: (str)Path of parameters file, e.g gfl/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, "GFL model only support model format of ModelFormat.Paddle now."
|
|
self._model = C.vision.detection.GFL(model_file, params_file,
|
|
config_file, self._runtime_option,
|
|
model_format)
|
|
assert self.initialized, "GFL model initialize failed."
|
|
|
|
|
|
class PPYOLOER(PPYOLOE):
|
|
def __init__(self,
|
|
model_file,
|
|
params_file,
|
|
config_file,
|
|
runtime_option=None,
|
|
model_format=ModelFormat.PADDLE):
|
|
"""Load a PPYOLOER model exported by PaddleDetection.
|
|
|
|
:param model_file: (str)Path of model file, e.g ppyoloe_r/model.pdmodel
|
|
:param params_file: (str)Path of parameters file, e.g ppyoloe_r/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_r/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)
|
|
|
|
self._model = C.vision.detection.PPYOLOER(
|
|
model_file, params_file, config_file, self._runtime_option,
|
|
model_format)
|
|
assert self.initialized, "PicoDet model initialize failed."
|
|
|
|
def clone(self):
|
|
"""Clone PPYOLOER object
|
|
|
|
:return: a new PPYOLOER object
|
|
"""
|
|
|
|
class PPYOLOERClone(PPYOLOER):
|
|
def __init__(self, model):
|
|
self._model = model
|
|
|
|
clone_model = PPYOLOERClone(self._model.clone())
|
|
return clone_model
|