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b557dbc2d8
* add yolov5cls * fixed bugs * fixed bugs * fixed preprocess bug * add yolov5cls readme * deal with comments * Add YOLOv5Cls Note * add yolov5cls test Co-authored-by: Jason <jiangjiajun@baidu.com>
70 lines
2.7 KiB
Python
70 lines
2.7 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 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 image classification model exported by YOLOv5.
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:param model_file: (str)Path of model file, e.g yolov5cls/yolov5n-cls.onnx
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:param params_file: (str)Path of parameters file, 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, default is ONNX
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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, topk=1):
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"""Classify 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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:param topk: (int)The topk result by the classify confidence score, default 1
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:return: ClassifyResult
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"""
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return self._model.predict(input_image, topk)
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@property
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def size(self):
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"""
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Returns the preprocess image size
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"""
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return self._model.size
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@size.setter
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def size(self, wh):
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"""
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Set the preprocess image size
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"""
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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._model.size = wh
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