mirror of
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109 lines
4.0 KiB
Python
109 lines
4.0 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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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 CaddnPreprocessor:
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def __init__(self, config_file):
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"""Create a preprocessor for Caddn
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"""
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self._preprocessor = C.vision.perception.CaddnPreprocessor(config_file)
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def run(self, input_ims, cam_data, lidar_data):
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"""Preprocess input images for Caddn
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:param: input_ims: (list of numpy.ndarray)The input image
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:return: list of FDTensor
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"""
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return self._preprocessor.run(input_ims, cam_data, lidar_data)
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class CaddnPostprocessor:
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def __init__(self):
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"""Create a postprocessor for Caddn
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"""
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self._postprocessor = C.vision.perception.CaddnPostprocessor()
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def run(self, runtime_results):
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"""Postprocess the runtime results for Caddn
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:param: runtime_results: (list of FDTensor)The output FDTensor results from runtime
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:return: list of PerceptionResult(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 Caddn(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 Caddn model exported by Caddn.
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:param model_file: (str)Path of model file, e.g ./Caddn.pdmodel
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:param params_file: (str)Path of parameters file, e.g ./Caddn.pdiparams
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:param config_file: (str)Path of config file, e.g ./infer_cfg.yaml
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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(Caddn, self).__init__(runtime_option)
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self._model = C.vision.perception.Caddn(
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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, "Caddn initialize failed."
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def predict(self, input_image, cam_data, lidar_data):
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"""Detect an input image
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:param input_image: (numpy.ndarray)The input image data, 3-D array with layout HWC, BGR format
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:param: cam_data: (list)The input camera data
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:param: lidar_data: (list)The input lidar data
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:return: PerceptionResult
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"""
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return self._model.predict(input_image, cam_data, lidar_data)
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def batch_predict(self, images, cam_data, lidar_data):
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"""Classify a batch of input image
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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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:param: cam_data: (list)The input camera data
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:param: lidar_data: (list)The input lidar data
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:return list of PerceptionResult
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"""
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return self._model.batch_predict(images, cam_data, lidar_data)
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@property
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def preprocessor(self):
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"""Get CaddnPreprocessor object of the loaded model
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:return CaddnPreprocessor
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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 CaddnPostprocessor object of the loaded model
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:return CaddnPostprocessor
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"""
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return self._model.postprocessor
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