mirror of
https://github.com/PaddlePaddle/FastDeploy.git
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beaa0fd190
* Add namespace for functions * Refactor PaddleDetection module * finish all the single image test * Update preprocessor.cc * fix some litte detail * add python api * Update postprocessor.cc
272 lines
12 KiB
Python
272 lines
12 KiB
Python
# 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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class PaddleDetPreprocessor:
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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._preprocessor = C.vision.detection.PaddleDetPreprocessor(
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config_file)
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def run(self, input_ims):
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"""Preprocess input images for PaddleDetection Model
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:param: input_ims: (list of numpy.ndarray)The input image
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:return: list of FDTensor, include image, scale_factor, im_shape
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"""
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return self._preprocessor.run(input_ims)
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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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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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assert model_format == ModelFormat.PADDLE, "PPYOLOE model only support model format of ModelFormat.Paddle now."
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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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@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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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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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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assert model_format == ModelFormat.PADDLE, "PicoDet model only support model format of ModelFormat.Paddle now."
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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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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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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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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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