mirror of
https://github.com/PaddlePaddle/FastDeploy.git
synced 2025-10-06 00:57:33 +08:00
866d044898
* model done, CLA fix * remove letter_box and ConvertAndPermute, use resize hwc2chw and convert in preprocess * remove useless values in preprocess * remove useless values in preprocess * fix reviewed problem * fix reviewed problem pybind * fix reviewed problem pybind * postprocess fix * add test_fastestdet.py, coco_val2017_500 fixed done, ready to review * fix reviewed problem * python/.../fastestdet.py * fix infer.cc, preprocess, python/fastestdet.py * fix examples/python/infer.py
111 lines
4.2 KiB
Python
111 lines
4.2 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 fastdeploy import ModelFormat
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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_fastestdet():
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model_url = "https://bj.bcebos.com/paddlehub/fastdeploy/FastestDet.onnx"
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input_url1 = "https://gitee.com/paddlepaddle/PaddleDetection/raw/release/2.4/demo/000000014439.jpg"
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input_url2 = "https://gitee.com/paddlepaddle/PaddleDetection/raw/release/2.4/demo/000000570688.jpg"
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result_url1 = "https://bj.bcebos.com/paddlehub/fastdeploy/fastestdet_result1.pkl"
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fd.download(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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fd.download(result_url1, "resources")
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model_file = "resources/FastestDet.onnx"
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model = fd.vision.detection.FastestDet(
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model_file, runtime_option=rc.test_option)
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with open("resources/fastestdet_result1.pkl", "rb") as f:
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expect1 = pickle.load(f)
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# compare diff
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im1 = cv2.imread("./resources/000000014439.jpg")
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print(expect1)
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for i in range(3):
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# test single predict
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result1 = model.predict(im1)
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diff_boxes_1 = np.fabs(
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np.array(result1.boxes) - np.array(expect1["boxes"]))
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diff_label_1 = np.fabs(
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np.array(result1.label_ids) - np.array(expect1["label_ids"]))
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diff_scores_1 = np.fabs(
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np.array(result1.scores) - np.array(expect1["scores"]))
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print(diff_boxes_1.max(), diff_boxes_1.mean())
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assert diff_boxes_1.max(
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) < 1e-04, "There's difference in detection boxes 1."
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assert diff_label_1.max(
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) < 1e-04, "There's difference in detection label 1."
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assert diff_scores_1.max(
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) < 1e-05, "There's difference in detection score 1."
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def test_detection_fastestdet_runtime():
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model_url = "https://bj.bcebos.com/paddlehub/fastdeploy/FastestDet.onnx"
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input_url1 = "https://gitee.com/paddlepaddle/PaddleDetection/raw/release/2.4/demo/000000014439.jpg"
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result_url1 = "https://bj.bcebos.com/paddlehub/fastdeploy/fastestdet_result1.pkl"
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fd.download(model_url, "resources")
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fd.download(input_url1, "resources")
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fd.download(result_url1, "resources")
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model_file = "resources/FastestDet.onnx"
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preprocessor = fd.vision.detection.FastestDetPreprocessor()
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postprocessor = fd.vision.detection.FastestDetPostprocessor()
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rc.test_option.set_model_path(model_file, model_format=ModelFormat.ONNX)
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rc.test_option.use_openvino_backend()
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runtime = fd.Runtime(rc.test_option)
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with open("resources/fastestdet_result1.pkl", "rb") as f:
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expect1 = pickle.load(f)
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# compare diff
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im1 = cv2.imread("./resources/000000014439.jpg")
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for i in range(3):
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# test runtime
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input_tensors, ims_info = preprocessor.run([im1.copy()])
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output_tensors = runtime.infer({"input.1": input_tensors[0]})
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results = postprocessor.run(output_tensors, ims_info)
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result1 = results[0]
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diff_boxes_1 = np.fabs(
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np.array(result1.boxes) - np.array(expect1["boxes"]))
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diff_label_1 = np.fabs(
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np.array(result1.label_ids) - np.array(expect1["label_ids"]))
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diff_scores_1 = np.fabs(
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np.array(result1.scores) - np.array(expect1["scores"]))
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assert diff_boxes_1.max(
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) < 1e-04, "There's difference in detection boxes 1."
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assert diff_label_1.max(
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) < 1e-04, "There's difference in detection label 1."
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assert diff_scores_1.max(
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) < 1e-05, "There's difference in detection score 1."
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if __name__ == "__main__":
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test_detection_fastestdet()
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test_detection_fastestdet_runtime() |