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https://github.com/PaddlePaddle/FastDeploy.git
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3589c0fa94
* Refactor the PaddleClas module * fix bug * remove debug code * clean unused code * support pybind * Update fd_tensor.h * Update fd_tensor.cc * temporary revert python api * fix ci error * fix code style problem
102 lines
4.0 KiB
Python
Executable File
102 lines
4.0 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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import runtime_config as rc
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def test_classification_mobilenetv2():
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model_url = "https://bj.bcebos.com/paddlehub/fastdeploy/MobileNetV1_x0_25_infer.tgz"
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input_url1 = "https://gitee.com/paddlepaddle/PaddleClas/raw/release/2.4/deploy/images/ImageNet/ILSVRC2012_val_00000010.jpeg"
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input_url2 = "https://gitee.com/paddlepaddle/PaddleClas/raw/release/2.4/deploy/images/ImageNet/ILSVRC2012_val_00030010.jpeg"
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fd.download_and_decompress(model_url, "resources")
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fd.download(input_url1, "resources")
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fd.download(input_url2, "resources")
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model_path = "resources/MobileNetV1_x0_25_infer"
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model_file = "resources/MobileNetV1_x0_25_infer/inference.pdmodel"
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params_file = "resources/MobileNetV1_x0_25_infer/inference.pdiparams"
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config_file = "resources/MobileNetV1_x0_25_infer/inference_cls.yaml"
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model = fd.vision.classification.PaddleClasModel(
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model_file, params_file, config_file, runtime_option=rc.test_option)
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expected_label_ids_1 = [153, 333, 259, 338, 265, 154]
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expected_scores_1 = [
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0.221088, 0.109457, 0.078668, 0.076814, 0.052401, 0.048206
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]
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expected_label_ids_2 = [80, 23, 93, 99, 143, 7]
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expected_scores_2 = [
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0.975599, 0.014083, 0.003821, 0.001571, 0.001233, 0.000924
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]
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# compare diff
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im1 = cv2.imread("./resources/ILSVRC2012_val_00000010.jpeg")
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im2 = cv2.imread("./resources/ILSVRC2012_val_00030010.jpeg")
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# for i in range(3000000):
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while True:
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# test single predict
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model.postprocessor.topk = 6
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result1 = model.predict(im1)
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result2 = model.predict(im2)
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diff_label_1 = np.fabs(
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np.array(result1.label_ids) - np.array(expected_label_ids_1))
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diff_label_2 = np.fabs(
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np.array(result2.label_ids) - np.array(expected_label_ids_2))
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diff_scores_1 = np.fabs(
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np.array(result1.scores) - np.array(expected_scores_1))
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diff_scores_2 = np.fabs(
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np.array(result2.scores) - np.array(expected_scores_2))
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assert diff_label_1.max(
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) < 1e-06, "There's difference in classify label 1."
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assert diff_scores_1.max(
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) < 1e-05, "There's difference in classify score 1."
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assert diff_label_2.max(
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) < 1e-06, "There's difference in classify label 2."
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assert diff_scores_2.max(
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) < 1e-05, "There's difference in classify score 2."
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# test batch predict
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results = model.batch_predict([im1, im2])
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result1 = results[0]
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result2 = results[1]
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diff_label_1 = np.fabs(
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np.array(result1.label_ids) - np.array(expected_label_ids_1))
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diff_label_2 = np.fabs(
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np.array(result2.label_ids) - np.array(expected_label_ids_2))
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diff_scores_1 = np.fabs(
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np.array(result1.scores) - np.array(expected_scores_1))
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diff_scores_2 = np.fabs(
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np.array(result2.scores) - np.array(expected_scores_2))
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assert diff_label_1.max(
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) < 1e-06, "There's difference in classify label 1."
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assert diff_scores_1.max(
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) < 1e-05, "There's difference in classify score 1."
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assert diff_label_2.max(
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) < 1e-06, "There's difference in classify label 2."
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assert diff_scores_2.max(
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) < 1e-05, "There's difference in classify score 2."
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if __name__ == "__main__":
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test_classification_mobilenetv2()
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