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https://github.com/PaddlePaddle/FastDeploy.git
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beaa0fd190
* Add namespace for functions * Refactor PaddleDetection module * finish all the single image test * Update preprocessor.cc * fix some litte detail * add python api * Update postprocessor.cc
70 lines
2.7 KiB
Python
Executable File
70 lines
2.7 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_detection_yolov3():
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model_url = "https://bj.bcebos.com/paddlehub/fastdeploy/yolov3_darknet53_270e_coco.tgz"
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input_url1 = "https://gitee.com/paddlepaddle/PaddleDetection/raw/release/2.4/demo/000000014439.jpg"
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result_url = "https://bj.bcebos.com/fastdeploy/tests/data/yolov3_baseline.pkl"
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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(result_url, "resources")
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model_path = "resources/yolov3_darknet53_270e_coco"
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model_file = os.path.join(model_path, "model.pdmodel")
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params_file = os.path.join(model_path, "model.pdiparams")
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config_file = os.path.join(model_path, "infer_cfg.yml")
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rc.test_option.use_ort_backend()
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model = fd.vision.detection.YOLOv3(
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model_file, params_file, config_file, runtime_option=rc.test_option)
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# compare diff
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im1 = cv2.imread("./resources/000000014439.jpg")
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for i in range(2):
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result = model.predict(im1)
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with open("resources/yolov3_baseline.pkl", "rb") as f:
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boxes, scores, label_ids = pickle.load(f)
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pred_boxes = np.array(result.boxes)
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pred_scores = np.array(result.scores)
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pred_label_ids = np.array(result.label_ids)
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diff_boxes = np.fabs(boxes - pred_boxes)
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diff_scores = np.fabs(scores - pred_scores)
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diff_label_ids = np.fabs(label_ids - pred_label_ids)
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print(diff_boxes.max(), diff_scores.max(), diff_label_ids.max())
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score_threshold = 0.1
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assert diff_boxes[scores > score_threshold].max(
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) < 1e-04, "There's diff in boxes."
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assert diff_scores[scores > score_threshold].max(
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) < 1e-04, "There's diff in scores."
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assert diff_label_ids[scores > score_threshold].max(
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) < 1e-04, "There's diff in label_ids."
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# result = model.predict(im1)
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# with open("yolov3_baseline.pkl", "wb") as f:
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# pickle.dump([np.array(result.boxes), np.array(result.scores), np.array(result.label_ids)], f)
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if __name__ == "__main__":
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test_detection_yolov3()
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