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* Update README.md * Update README.md * Update README.md * Create README.md * Update README.md * Update README.md * Update README.md * Update README.md * Add evaluation calculate time and fix some bugs * Update classification __init__ * Move to ppseg Co-authored-by: Jason <jiangjiajun@baidu.com>
60 lines
2.2 KiB
C++
60 lines
2.2 KiB
C++
// 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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#include "fastdeploy/vision/segmentation/ppseg/model.h"
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namespace fastdeploy {
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namespace vision {
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namespace segmentation {
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void FDTensor2FP32CVMat(cv::Mat& mat, FDTensor& infer_result,
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bool contain_score_map) {
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// output with argmax channel is 1
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int channel = 1;
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int height = infer_result.shape[1];
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int width = infer_result.shape[2];
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if (contain_score_map) {
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// output without argmax and convent to NHWC
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channel = infer_result.shape[3];
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}
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// create FP32 cvmat
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if (infer_result.dtype == FDDataType::INT64) {
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FDWARNING << "The PaddleSeg model is exported with argmax. Inference "
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"result type is " +
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Str(infer_result.dtype) +
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". If you want the edge of segmentation image more "
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"smoother. Please export model with --without_argmax "
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"--with_softmax."
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<< std::endl;
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int64_t chw = channel * height * width;
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int64_t* infer_result_buffer = static_cast<int64_t*>(infer_result.Data());
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std::vector<float_t> float_result_buffer(chw);
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mat = cv::Mat(height, width, CV_32FC(channel));
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int index = 0;
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for (int i = 0; i < height; i++) {
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for (int j = 0; j < width; j++) {
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mat.at<float_t>(i, j) =
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static_cast<float_t>(infer_result_buffer[index++]);
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}
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}
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} else if (infer_result.dtype == FDDataType::FP32) {
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mat = cv::Mat(height, width, CV_32FC(channel), infer_result.Data());
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}
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}
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} // namespace segmentation
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} // namespace vision
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} // namespace fastdeploy
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