mirror of
https://github.com/PaddlePaddle/FastDeploy.git
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a36f5d3396
* cuda normalize and permute, cuda concat * add use cuda option for preprocessor * ppyoloe use cuda normalize * ppseg use cuda normalize * add proclib cuda in processor base * ppcls add use cuda preprocess api * ppcls preprocessor set gpu id * fix pybind * refine ppcls preprocessing use gpu logic * fdtensor device id is -1 by default * refine assert message Co-authored-by: heliqi <1101791222@qq.com>
123 lines
4.5 KiB
Python
123 lines
4.5 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 PaddleClasPreprocessor:
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def __init__(self, config_file):
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"""Create a preprocessor for PaddleClasModel from configuration file
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:param config_file: (str)Path of configuration file, e.g resnet50/inference_cls.yaml
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"""
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self._preprocessor = C.vision.classification.PaddleClasPreprocessor(
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config_file)
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def run(self, input_ims):
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"""Preprocess input images for PaddleClasModel
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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)
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def use_gpu(self, gpu_id=-1):
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"""Use CUDA preprocessors
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:param: gpu_id: GPU device id
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"""
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return self._preprocessor.use_gpu(gpu_id)
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class PaddleClasPostprocessor:
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def __init__(self, topk=1):
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"""Create a postprocessor for PaddleClasModel
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:param topk: (int)Filter the top k classify label
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"""
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self._postprocessor = C.vision.classification.PaddleClasPostprocessor(
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topk)
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def run(self, runtime_results):
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"""Postprocess the runtime results for PaddleClasModel
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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 PaddleClasModel(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 image classification model exported by PaddleClas.
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:param model_file: (str)Path of model file, e.g resnet50/inference.pdmodel
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:param params_file: (str)Path of parameters file, e.g resnet50/inference.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 deploy, e.g resnet50/inference_cls.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(PaddleClasModel, self).__init__(runtime_option)
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assert model_format == ModelFormat.PADDLE, "PaddleClasModel only support model format of ModelFormat.PADDLE now."
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self._model = C.vision.classification.PaddleClasModel(
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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, "PaddleClas model initialize failed."
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def predict(self, im, topk=1):
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"""Classify an input image
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:param im: (numpy.ndarray) The input image data, a 3-D array with layout HWC, BGR format
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:param topk: (int) Filter the topk classify result, default 1
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:return: ClassifyResult
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"""
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self.postprocessor.topk = topk
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return self._model.predict(im)
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def batch_predict(self, images):
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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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:return list of ClassifyResult
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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 PaddleClasPreprocessor object of the loaded model
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:return PaddleClasPreprocessor
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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 PaddleClasPostprocessor object of the loaded model
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:return PaddleClasPostprocessor
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"""
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return self._model.postprocessor
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