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https://github.com/PaddlePaddle/FastDeploy.git
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718698a32a
* add yolov5cls * fixed bugs * fixed bugs * fixed preprocess bug * add yolov5cls readme * deal with comments * Add YOLOv5Cls Note * add yolov5cls test * add rvm support * support rvm model * add rvm demo * fixed bugs * add rvm readme * add TRT support * add trt support * add rvm test * add EXPORT.md * rename export.md * rm poros doxyen * deal with comments * deal with comments * add rvm video_mode note Co-authored-by: Jason <jiangjiajun@baidu.com> Co-authored-by: DefTruth <31974251+DefTruth@users.noreply.github.com>
102 lines
3.7 KiB
Python
102 lines
3.7 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 pickle
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import numpy as np
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def test_matting_rvm_cpu():
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model_url = "https://bj.bcebos.com/paddlehub/fastdeploy/rvm.tgz"
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input_url = "https://bj.bcebos.com/paddlehub/fastdeploy/video.mp4"
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fd.download_and_decompress(model_url, ".")
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fd.download(input_url, ".")
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model_path = "rvm/rvm_mobilenetv3_fp32.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.matting.RobustVideoMatting(
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model_path, runtime_option=runtime_option)
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cap = cv2.VideoCapture(input_url)
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frame_id = 0
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while True:
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_, frame = cap.read()
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if frame is None:
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break
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result = model.predict(frame)
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# compare diff
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expect_alpha = np.load("rvm/result_alpha_" + str(frame_id) + ".npy")
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result_alpha = np.array(result.alpha).reshape(1920, 1080)
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diff = np.fabs(expect_alpha - result_alpha)
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thres = 1e-05
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assert diff.max(
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) < thres, "The label diff is %f, which is bigger than %f" % (
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diff.max(), thres)
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frame_id = frame_id + 1
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cv2.waitKey(30)
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if frame_id >= 10:
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cap.release()
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cv2.destroyAllWindows()
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break
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def test_matting_rvm_gpu_trt():
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model_url = "https://bj.bcebos.com/paddlehub/fastdeploy/rvm.tgz"
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input_url = "https://bj.bcebos.com/paddlehub/fastdeploy/video.mp4"
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fd.download_and_decompress(model_url, ".")
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fd.download(input_url, ".")
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model_path = "rvm/rvm_mobilenetv3_trt.onnx"
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# use TRT
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runtime_option = fd.RuntimeOption()
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runtime_option.use_gpu()
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runtime_option.use_trt_backend()
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runtime_option.set_trt_input_shape("src", [1, 3, 1920, 1080])
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runtime_option.set_trt_input_shape("r1i", [1, 1, 1, 1], [1, 16, 240, 135],
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[1, 16, 240, 135])
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runtime_option.set_trt_input_shape("r2i", [1, 1, 1, 1], [1, 20, 120, 68],
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[1, 20, 120, 68])
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runtime_option.set_trt_input_shape("r3i", [1, 1, 1, 1], [1, 40, 60, 34],
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[1, 40, 60, 34])
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runtime_option.set_trt_input_shape("r4i", [1, 1, 1, 1], [1, 64, 30, 17],
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[1, 64, 30, 17])
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model = fd.vision.matting.RobustVideoMatting(
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model_path, runtime_option=runtime_option)
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cap = cv2.VideoCapture("./video.mp4")
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frame_id = 0
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while True:
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_, frame = cap.read()
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if frame is None:
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break
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result = model.predict(frame)
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# compare diff
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expect_alpha = np.load("rvm/result_alpha_" + str(frame_id) + ".npy")
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result_alpha = np.array(result.alpha).reshape(1920, 1080)
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diff = np.fabs(expect_alpha - result_alpha)
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thres = 1e-04
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assert diff.max(
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) < thres, "The label diff is %f, which is bigger than %f" % (
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diff.max(), thres)
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frame_id = frame_id + 1
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cv2.waitKey(30)
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if frame_id >= 10:
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cap.release()
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cv2.destroyAllWindows()
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break
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