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* 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>
50 lines
1.9 KiB
Python
Executable File
50 lines
1.9 KiB
Python
Executable File
# 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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import fastdeploy as fd
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import cv2
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import os
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import pickle
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import numpy as np
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def test_classification_yolov5cls():
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model_url = "https://bj.bcebos.com/paddlehub/fastdeploy/yolov5n-cls.tgz"
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input_url = "https://gitee.com/paddlepaddle/PaddleClas/raw/release/2.4/deploy/images/ImageNet/ILSVRC2012_val_00000010.jpeg"
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fd.download_and_decompress(model_url, ".")
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fd.download(input_url, ".")
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model_path = "yolov5n-cls/yolov5n-cls.onnx"
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# use ORT
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runtime_option = fd.RuntimeOption()
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runtime_option.use_ort_backend()
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model = fd.vision.classification.YOLOv5Cls(
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model_path, runtime_option=runtime_option)
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# compare diff
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im = cv2.imread("./ILSVRC2012_val_00000010.jpeg")
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result = model.predict(im.copy(), topk=5)
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with open("yolov5n-cls/result.pkl", "rb") as f:
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expect = pickle.load(f)
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diff_label = np.fabs(
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np.array(result.label_ids) - np.array(expect["labels"]))
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diff_score = np.fabs(np.array(result.scores) - np.array(expect["scores"]))
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thres = 1e-05
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assert diff_label.max(
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) < thres, "The label diff is %f, which is bigger than %f" % (
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diff_label.max(), thres)
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assert diff_score.max(
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) < thres, "The score diff is %f, which is bigger than %f" % (
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diff_score.max(), thres)
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