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* add style transfer model * add examples for generation model * add unit test * add speed comparison * add speed comparison * add variable for constant * add preprocessor and postprocessor * add preprocessor and postprocessor * fix * fix according to review Co-authored-by: DefTruth <31974251+DefTruth@users.noreply.github.com>
81 lines
2.9 KiB
Python
81 lines
2.9 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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import fastdeploy as fd
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import cv2
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import os
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import numpy as np
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import pickle
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import runtime_config as rc
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def test_edvr():
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model_url = "https://bj.bcebos.com/paddlehub/fastdeploy/edvr.tgz"
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input_url = "https://bj.bcebos.com/paddlehub/fastdeploy/vsr_src.mp4"
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fd.download_and_decompress(model_url, "resources")
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fd.download(input_url, "resources")
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model_path = "resources/edvr/EDVR_M_wo_tsa_SRx4"
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# use default backend
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runtime_option = fd.RuntimeOption()
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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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model = fd.vision.sr.EDVR(
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model_file, params_file, runtime_option=rc.test_option)
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# 该处应该与你导出模型的第二个维度一致模型输入shape=[b,n,c,h,w]
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capture = cv2.VideoCapture("./resources/vsr_src.mp4")
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# Create VideoWriter for output
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frame_id = 0
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imgs = []
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t = 0
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while capture.isOpened():
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ret, frame = capture.read()
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if frame_id < 5 and frame is not None:
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imgs.append(frame)
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frame_id += 1
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continue
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# 始终保持imgs队列中具有frame_num帧
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imgs.pop(0)
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imgs.append(frame)
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frame_id += 1
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# 视频读取完毕退出
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if not ret:
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break
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results = model.predict(imgs)
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for item in results:
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if frame_id <= 4:
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continue
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if t < 10:
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ret = pickle.load(
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open("./resources/edvr/frame_" + str(t) + ".pkl", "rb"))
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mean_diff = np.fabs(ret["mean"] - item.mean())
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std_diff = np.fabs(ret["std"] - item.std())
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shape_diff = max(
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np.fabs(np.array(ret["shape"]) - np.array(item.shape)))
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thres = 1e-03
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assert mean_diff < thres, "The mean diff is %f, which is bigger than %f" % (
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mean_diff, thres)
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assert std_diff < thres, "The std diff is %f, which is bigger than %f" % (
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std_diff, thres)
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assert shape_diff <= 0, "The shape diff is %f, which is bigger than %f" % (
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shape_diff, 0)
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t = t + 1
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if t >= 10:
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break
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capture.release()
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if __name__ == "__main__":
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test_edvr()
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