191 lines
6.1 KiB
C++
191 lines
6.1 KiB
C++
// Copyright (c) OpenMMLab. All rights reserved.
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#include "pose_detector.h"
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#include <numeric>
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#include "codebase/mmpose/mmpose.h"
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#include "core/device.h"
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#include "core/graph.h"
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#include "core/mat.h"
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#include "core/tensor.h"
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#include "core/utils/formatter.h"
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#include "handle.h"
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using namespace std;
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using namespace mmdeploy;
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namespace {
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const Value& config_template() {
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// clang-format off
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static Value v {
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{
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"pipeline", {
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{"input", {"img_with_boxes"}},
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{"output", {"key_points_unflat"}},
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{
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"tasks", {
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{
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{"name", "flatten"},
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{"type", "Flatten"},
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{"input", {"img_with_boxes"}},
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{"output", {"patch_flat", "patch_index"}},
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},
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{
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{"name", "pose-detector"},
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{"type", "Inference"},
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{"params", {{"model", "TBD"},{"batch_size", 1}}},
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{"input", {"patch_flat"}},
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{"output", {"key_points"}}
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},
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{
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{"name", "unflatten"},
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{"type", "Unflatten"},
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{"input", {"key_points", "patch_index"}},
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{"output", {"key_points_unflat"}},
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}
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}
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}
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}
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}
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};
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// clang-format on
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return v;
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}
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template <class ModelType>
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int mmdeploy_pose_detector_create_impl(ModelType&& m, const char* device_name, int device_id,
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mm_handle_t* handle) {
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try {
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auto value = config_template();
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value["pipeline"]["tasks"][1]["params"]["model"] = std::forward<ModelType>(m);
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auto pose_estimator = std::make_unique<Handle>(device_name, device_id, std::move(value));
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*handle = pose_estimator.release();
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return MM_SUCCESS;
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} catch (const std::exception& e) {
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MMDEPLOY_ERROR("exception caught: {}", e.what());
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} catch (...) {
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MMDEPLOY_ERROR("unknown exception caught");
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}
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return MM_E_FAIL;
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}
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} // namespace
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int mmdeploy_pose_detector_create(mm_model_t model, const char* device_name, int device_id,
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mm_handle_t* handle) {
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return mmdeploy_pose_detector_create_impl(*static_cast<Model*>(model), device_name, device_id,
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handle);
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}
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int mmdeploy_pose_detector_create_by_path(const char* model_path, const char* device_name,
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int device_id, mm_handle_t* handle) {
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return mmdeploy_pose_detector_create_impl(model_path, device_name, device_id, handle);
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}
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int mmdeploy_pose_detector_apply(mm_handle_t handle, const mm_mat_t* mats, int mat_count,
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mm_pose_detect_t** results) {
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return mmdeploy_pose_detector_apply_bbox(handle, mats, mat_count, nullptr, nullptr, results);
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}
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int mmdeploy_pose_detector_apply_bbox(mm_handle_t handle, const mm_mat_t* mats, int mat_count,
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const mm_rect_t* bboxes, const int* bbox_count,
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mm_pose_detect_t** results) {
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if (handle == nullptr || mats == nullptr || mat_count == 0 || results == nullptr) {
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return MM_E_INVALID_ARG;
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}
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try {
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auto pose_detector = static_cast<Handle*>(handle);
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Value input{Value::kArray};
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auto result_count = 0;
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for (int i = 0; i < mat_count; ++i) {
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mmdeploy::Mat _mat{mats[i].height, mats[i].width, PixelFormat(mats[i].format),
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DataType(mats[i].type), mats[i].data, Device{"cpu"}};
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Value img_with_boxes;
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if (bboxes && bbox_count) {
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for (int j = 0; j < bbox_count[i]; ++j) {
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Value obj;
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obj["ori_img"] = _mat;
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float width = bboxes[j].right - bboxes[j].left + 1;
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float height = bboxes[j].bottom - bboxes[j].top + 1;
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obj["box"] = {bboxes[j].left, bboxes[j].top, width, height, 1.0};
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obj["rotation"] = 0.f;
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img_with_boxes.push_back(obj);
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}
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bboxes += bbox_count[i];
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result_count += bbox_count[i];
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} else {
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// inference whole image
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Value obj;
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obj["ori_img"] = _mat;
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obj["box"] = {0, 0, _mat.width(), _mat.height(), 1.0};
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obj["rotation"] = 0.f;
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img_with_boxes.push_back(obj);
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result_count += 1;
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}
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input.front().push_back(img_with_boxes);
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}
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auto output = pose_detector->Run(std::move(input)).value().front();
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auto pose_outputs = from_value<vector<vector<mmpose::PoseDetectorOutput>>>(output);
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std::vector<int> counts;
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if (bboxes && bbox_count) {
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counts = std::vector<int>(bbox_count, bbox_count + mat_count);
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} else {
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counts.resize(mat_count, 1);
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}
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std::vector<int> offsets{0};
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std::partial_sum(begin(counts), end(counts), back_inserter(offsets));
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auto deleter = [&](mm_pose_detect_t* p) {
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mmdeploy_pose_detector_release_result(p, offsets.back());
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};
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std::unique_ptr<mm_pose_detect_t[], decltype(deleter)> _results(
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new mm_pose_detect_t[result_count]{}, deleter);
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for (int i = 0; i < mat_count; ++i) {
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auto& pose_output = pose_outputs[i];
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for (int j = 0; j < pose_output.size(); ++j) {
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auto& res = _results[offsets[i] + j];
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auto& box_result = pose_output[j];
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int sz = box_result.key_points.size();
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res.point = new mm_pointf_t[sz];
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res.score = new float[sz];
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res.length = sz;
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for (int k = 0; k < sz; k++) {
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res.point[k].x = box_result.key_points[k].bbox[0];
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res.point[k].y = box_result.key_points[k].bbox[1];
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res.score[k] = box_result.key_points[k].score;
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}
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}
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}
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*results = _results.release();
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return MM_SUCCESS;
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} catch (const std::exception& e) {
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MMDEPLOY_ERROR("exception caught: {}", e.what());
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} catch (...) {
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MMDEPLOY_ERROR("unknown exception caught");
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}
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return MM_E_FAIL;
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}
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void mmdeploy_pose_detector_release_result(mm_pose_detect_t* results, int count) {
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for (int i = 0; i < count; ++i) {
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delete[] results[i].point;
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delete[] results[i].score;
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}
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delete[] results;
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}
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void mmdeploy_pose_detector_destroy(mm_handle_t handle) { delete static_cast<Handle*>(handle); }
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