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https://github.com/PaddlePaddle/FastDeploy.git
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9cd00ad4c5
* Refactoring code of YOLOv5Cls with new model type * fix reviewed problem * Normalize&HWC2CHW -> NormalizeAndPermute * remove cast()
136 lines
4.8 KiB
Python
136 lines
4.8 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 YOLOv5ClsPreprocessor:
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def __init__(self):
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"""Create a preprocessor for YOLOv5Cls
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"""
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self._preprocessor = C.vision.classification.YOLOv5ClsPreprocessor()
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def run(self, input_ims):
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"""Preprocess input images for YOLOv5Cls
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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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@property
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def size(self):
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"""
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Argument for image preprocessing step, the preprocess image size, tuple of (width, height), default size = [224, 224]
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"""
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return self._preprocessor.size
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@size.setter
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def size(self, wh):
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assert isinstance(wh, (list, tuple)),\
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"The value to set `size` must be type of tuple or list."
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assert len(wh) == 2,\
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"The value to set `size` must contatins 2 elements means [width, height], but now it contains {} elements.".format(
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len(wh))
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self._preprocessor.size = wh
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class YOLOv5ClsPostprocessor:
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def __init__(self):
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"""Create a postprocessor for YOLOv5Cls
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"""
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self._postprocessor = C.vision.classification.YOLOv5ClsPostprocessor()
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def run(self, runtime_results, ims_info):
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"""Postprocess the runtime results for YOLOv5Cls
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:param: runtime_results: (list of FDTensor)The output FDTensor results from runtime
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:param: ims_info: (list of dict)Record input_shape and output_shape
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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, ims_info)
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@property
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def topk(self):
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"""
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topk for postprocessing, default is 1
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"""
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return self._postprocessor.topk
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@topk.setter
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def topk(self, topk):
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assert isinstance(topk, int),\
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"The value to set `top k` must be type of int."
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self._postprocessor.topk = topk
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class YOLOv5Cls(FastDeployModel):
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def __init__(self,
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model_file,
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params_file="",
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runtime_option=None,
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model_format=ModelFormat.ONNX):
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"""Load a YOLOv5Cls model exported by YOLOv5Cls.
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:param model_file: (str)Path of model file, e.g ./YOLOv5Cls.onnx
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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 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(YOLOv5Cls, self).__init__(runtime_option)
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assert model_format == ModelFormat.ONNX, "YOLOv5Cls only support model format of ModelFormat.ONNX now."
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self._model = C.vision.classification.YOLOv5Cls(
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model_file, params_file, self._runtime_option, model_format)
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assert self.initialized, "YOLOv5Cls initialize failed."
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def predict(self, input_image):
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"""Classify 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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:return: ClassifyResult
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"""
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assert input_image is not None, "Input image is None."
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return self._model.predict(input_image)
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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 YOLOv5ClsPreprocessor object of the loaded model
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:return YOLOv5ClsPreprocessor
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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 YOLOv5ClsPostprocessor object of the loaded model
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:return YOLOv5ClsPostprocessor
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"""
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return self._model.postprocessor
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