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https://github.com/PaddlePaddle/FastDeploy.git
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8dd3e64227
* add paddle_trt in benchmark * update benchmark in device * update benchmark * update result doc * fixed for CI * update python api_docs * update index.rst * add runtime cpp examples * deal with comments * Update infer_paddle_tensorrt.py * Add runtime quick start * deal with comments * fixed reused_input_tensors&&reused_output_tensors * fixed docs * fixed headpose typo * fixed typo * refactor yolov5 * update model infer * refactor pybind for yolov5 * rm origin yolov5 * fixed bugs * rm cuda preprocess * fixed bugs * fixed bugs * fixed bug * fixed bug * fix pybind * rm useless code * add convert_and_permute * fixed bugs * fixed im_info for bs_predict * fixed bug * add bs_predict for yolov5 * Add runtime test and batch eval * deal with comments * fixed bug * update testcase * fixed batch eval bug * fixed preprocess bug * refactor yolov7 * add yolov7 testcase * rm resize_after_load and add is_scale_up * fixed bug * set multi_label true Co-authored-by: Jason <928090362@qq.com>
104 lines
4.5 KiB
C++
Executable File
104 lines
4.5 KiB
C++
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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#include "fastdeploy/vision/detection/contrib/yolov7/postprocessor.h"
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#include "fastdeploy/vision/utils/utils.h"
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namespace fastdeploy {
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namespace vision {
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namespace detection {
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YOLOv7Postprocessor::YOLOv7Postprocessor() {
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conf_threshold_ = 0.25;
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nms_threshold_ = 0.5;
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max_wh_ = 7680.0;
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}
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bool YOLOv7Postprocessor::Run(const std::vector<FDTensor>& tensors, std::vector<DetectionResult>* results,
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const std::vector<std::map<std::string, std::array<float, 2>>>& ims_info) {
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int batch = tensors[0].shape[0];
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results->resize(batch);
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for (size_t bs = 0; bs < batch; ++bs) {
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(*results)[bs].Clear();
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(*results)[bs].Reserve(tensors[0].shape[1]);
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if (tensors[0].dtype != FDDataType::FP32) {
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FDERROR << "Only support post process with float32 data." << std::endl;
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return false;
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}
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const float* data = reinterpret_cast<const float*>(tensors[0].Data()) + bs * tensors[0].shape[1] * tensors[0].shape[2];
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for (size_t i = 0; i < tensors[0].shape[1]; ++i) {
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int s = i * tensors[0].shape[2];
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float confidence = data[s + 4];
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const float* max_class_score =
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std::max_element(data + s + 5, data + s + tensors[0].shape[2]);
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confidence *= (*max_class_score);
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// filter boxes by conf_threshold
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if (confidence <= conf_threshold_) {
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continue;
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}
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int32_t label_id = std::distance(data + s + 5, max_class_score);
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// convert from [x, y, w, h] to [x1, y1, x2, y2]
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(*results)[bs].boxes.emplace_back(std::array<float, 4>{
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data[s] - data[s + 2] / 2.0f + label_id * max_wh_,
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data[s + 1] - data[s + 3] / 2.0f + label_id * max_wh_,
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data[s + 0] + data[s + 2] / 2.0f + label_id * max_wh_,
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data[s + 1] + data[s + 3] / 2.0f + label_id * max_wh_});
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(*results)[bs].label_ids.push_back(label_id);
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(*results)[bs].scores.push_back(confidence);
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}
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if ((*results)[bs].boxes.size() == 0) {
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return true;
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}
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utils::NMS(&((*results)[bs]), nms_threshold_);
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// scale the boxes to the origin image shape
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auto iter_out = ims_info[bs].find("output_shape");
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auto iter_ipt = ims_info[bs].find("input_shape");
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FDASSERT(iter_out != ims_info[bs].end() && iter_ipt != ims_info[bs].end(),
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"Cannot find input_shape or output_shape from im_info.");
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float out_h = iter_out->second[0];
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float out_w = iter_out->second[1];
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float ipt_h = iter_ipt->second[0];
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float ipt_w = iter_ipt->second[1];
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float scale = std::min(out_h / ipt_h, out_w / ipt_w);
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float pad_h = (out_h - ipt_h * scale) / 2;
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float pad_w = (out_w - ipt_w * scale) / 2;
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for (size_t i = 0; i < (*results)[bs].boxes.size(); ++i) {
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int32_t label_id = ((*results)[bs].label_ids)[i];
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// clip box
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(*results)[bs].boxes[i][0] = (*results)[bs].boxes[i][0] - max_wh_ * label_id;
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(*results)[bs].boxes[i][1] = (*results)[bs].boxes[i][1] - max_wh_ * label_id;
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(*results)[bs].boxes[i][2] = (*results)[bs].boxes[i][2] - max_wh_ * label_id;
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(*results)[bs].boxes[i][3] = (*results)[bs].boxes[i][3] - max_wh_ * label_id;
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(*results)[bs].boxes[i][0] = std::max(((*results)[bs].boxes[i][0] - pad_w) / scale, 0.0f);
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(*results)[bs].boxes[i][1] = std::max(((*results)[bs].boxes[i][1] - pad_h) / scale, 0.0f);
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(*results)[bs].boxes[i][2] = std::max(((*results)[bs].boxes[i][2] - pad_w) / scale, 0.0f);
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(*results)[bs].boxes[i][3] = std::max(((*results)[bs].boxes[i][3] - pad_h) / scale, 0.0f);
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(*results)[bs].boxes[i][0] = std::min((*results)[bs].boxes[i][0], ipt_w - 1.0f);
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(*results)[bs].boxes[i][1] = std::min((*results)[bs].boxes[i][1], ipt_h - 1.0f);
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(*results)[bs].boxes[i][2] = std::min((*results)[bs].boxes[i][2], ipt_w - 1.0f);
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(*results)[bs].boxes[i][3] = std::min((*results)[bs].boxes[i][3], ipt_h - 1.0f);
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}
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}
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return true;
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}
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} // namespace detection
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} // namespace vision
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} // namespace fastdeploy
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