mirror of
https://github.com/PaddlePaddle/FastDeploy.git
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6a368f3448
* [FlyCV] Add global SetProcLibCpuNumThreads method * [Android] Support segmentation and facedet in Android * [Android] add JNI instance check to j_runtime_option_obj * [Model] fixed ppseg flycv resize error * [FlyCV] fix FlyCV resize flags * [cmake] remove un-need lite compile option * [Android] add PaddleSegModel JNI and fix some bugs * [Android] bind PaddleSegModel via JNI * [Android] bind VisSegmentation via JNI * [Android] bind YOLOv5Face and SCRFD via JNI * [Android] fix NewJavaFaceDetectionResultFromCxx error
465 lines
18 KiB
C++
Executable File
465 lines
18 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/yolov5lite.h"
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#include "fastdeploy/utils/perf.h"
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#include "fastdeploy/vision/utils/utils.h"
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#ifdef ENABLE_CUDA_PREPROCESS
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#include "fastdeploy/vision/utils/cuda_utils.h"
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#endif // ENABLE_CUDA_PREPROCESS
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namespace fastdeploy {
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namespace vision {
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namespace detection {
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void YOLOv5Lite::LetterBox(Mat* mat, const std::vector<int>& size,
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const std::vector<float>& color, bool _auto,
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bool scale_fill, bool scale_up, int stride) {
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float scale =
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std::min(size[1] * 1.0 / mat->Height(), size[0] * 1.0 / mat->Width());
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if (!scale_up) {
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scale = std::min(scale, 1.0f);
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}
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int resize_h = int(round(mat->Height() * scale));
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int resize_w = int(round(mat->Width() * scale));
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int pad_w = size[0] - resize_w;
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int pad_h = size[1] - resize_h;
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if (_auto) {
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pad_h = pad_h % stride;
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pad_w = pad_w % stride;
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} else if (scale_fill) {
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pad_h = 0;
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pad_w = 0;
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resize_h = size[1];
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resize_w = size[0];
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}
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if (resize_h != mat->Height() || resize_w != mat->Width()) {
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Resize::Run(mat, resize_w, resize_h);
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}
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if (pad_h > 0 || pad_w > 0) {
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float half_h = pad_h * 1.0 / 2;
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int top = int(round(half_h - 0.1));
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int bottom = int(round(half_h + 0.1));
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float half_w = pad_w * 1.0 / 2;
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int left = int(round(half_w - 0.1));
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int right = int(round(half_w + 0.1));
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Pad::Run(mat, top, bottom, left, right, color);
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}
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}
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void YOLOv5Lite::GenerateAnchors(const std::vector<int>& size,
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const std::vector<int>& downsample_strides,
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std::vector<Anchor>* anchors,
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int num_anchors) {
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// size: tuple of input (width, height)
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// downsample_strides: downsample strides in YOLOv5Lite, e.g (8,16,32)
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const int width = size[0];
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const int height = size[1];
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for (int i = 0; i < downsample_strides.size(); ++i) {
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const int ds = downsample_strides[i];
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int num_grid_w = width / ds;
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int num_grid_h = height / ds;
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for (int an = 0; an < num_anchors; ++an) {
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float anchor_w = anchor_config[i][an * 2];
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float anchor_h = anchor_config[i][an * 2 + 1];
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for (int g1 = 0; g1 < num_grid_h; ++g1) {
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for (int g0 = 0; g0 < num_grid_w; ++g0) {
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(*anchors).emplace_back(Anchor{g0, g1, ds, anchor_w, anchor_h});
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}
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}
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}
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}
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}
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YOLOv5Lite::YOLOv5Lite(const std::string& model_file,
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const std::string& params_file,
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const RuntimeOption& custom_option,
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const ModelFormat& model_format) {
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if (model_format == ModelFormat::ONNX) {
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valid_cpu_backends = {Backend::ORT};
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valid_gpu_backends = {Backend::ORT, Backend::TRT};
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} else {
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valid_cpu_backends = {Backend::PDINFER, Backend::ORT};
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valid_gpu_backends = {Backend::PDINFER, Backend::ORT, Backend::TRT};
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}
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runtime_option = custom_option;
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runtime_option.model_format = model_format;
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runtime_option.model_file = model_file;
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runtime_option.params_file = params_file;
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#ifdef ENABLE_CUDA_PREPROCESS
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cudaSetDevice(runtime_option.device_id);
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cudaStream_t stream;
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CUDA_CHECK(cudaStreamCreate(&stream));
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cuda_stream_ = reinterpret_cast<void*>(stream);
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runtime_option.SetExternalStream(cuda_stream_);
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#endif // ENABLE_CUDA_PREPROCESS
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initialized = Initialize();
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}
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bool YOLOv5Lite::Initialize() {
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// parameters for preprocess
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size = {640, 640};
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padding_value = {114.0, 114.0, 114.0};
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downsample_strides = {8, 16, 32};
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is_mini_pad = false;
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is_no_pad = false;
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is_scale_up = false;
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stride = 32;
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max_wh = 7680.0;
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is_decode_exported = false;
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anchor_config = {{10.0, 13.0, 16.0, 30.0, 33.0, 23.0},
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{30.0, 61.0, 62.0, 45.0, 59.0, 119.0},
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{116.0, 90.0, 156.0, 198.0, 373.0, 326.0}};
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reused_input_tensors_.resize(1);
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if (!InitRuntime()) {
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FDERROR << "Failed to initialize fastdeploy backend." << std::endl;
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return false;
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}
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// Check if the input shape is dynamic after Runtime already initialized,
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// Note that, We need to force is_mini_pad 'false' to keep static
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// shape after padding (LetterBox) when the is_dynamic_shape is 'false'.
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is_dynamic_input_ = false;
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auto shape = InputInfoOfRuntime(0).shape;
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for (int i = 0; i < shape.size(); ++i) {
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// if height or width is dynamic
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if (i >= 2 && shape[i] <= 0) {
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is_dynamic_input_ = true;
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break;
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}
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}
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if (!is_dynamic_input_) {
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is_mini_pad = false;
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}
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return true;
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}
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YOLOv5Lite::~YOLOv5Lite() {
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#ifdef ENABLE_CUDA_PREPROCESS
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if (use_cuda_preprocessing_) {
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CUDA_CHECK(cudaFreeHost(input_img_cuda_buffer_host_));
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CUDA_CHECK(cudaFree(input_img_cuda_buffer_device_));
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CUDA_CHECK(cudaFree(input_tensor_cuda_buffer_device_));
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CUDA_CHECK(cudaStreamDestroy(reinterpret_cast<cudaStream_t>(cuda_stream_)));
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}
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#endif // ENABLE_CUDA_PREPROCESS
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}
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bool YOLOv5Lite::Preprocess(
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Mat* mat, FDTensor* output,
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std::map<std::string, std::array<float, 2>>* im_info) {
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// process after image load
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float ratio = std::min(size[1] * 1.0f / static_cast<float>(mat->Height()),
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size[0] * 1.0f / static_cast<float>(mat->Width()));
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if (std::fabs(ratio - 1.0f) > 1e-06) {
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int interp = cv::INTER_AREA;
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if (ratio > 1.0) {
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interp = cv::INTER_LINEAR;
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}
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int resize_h = int(mat->Height() * ratio);
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int resize_w = int(mat->Width() * ratio);
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Resize::Run(mat, resize_w, resize_h, -1, -1, interp);
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}
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// yolov5lite's preprocess steps
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// 1. letterbox
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// 2. BGR->RGB
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// 3. HWC->CHW
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YOLOv5Lite::LetterBox(mat, size, padding_value, is_mini_pad, is_no_pad,
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is_scale_up, stride);
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BGR2RGB::Run(mat);
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// Normalize::Run(mat, std::vector<float>(mat->Channels(), 0.0),
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// std::vector<float>(mat->Channels(), 1.0));
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// Compute `result = mat * alpha + beta` directly by channel
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std::vector<float> alpha = {1.0f / 255.0f, 1.0f / 255.0f, 1.0f / 255.0f};
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std::vector<float> beta = {0.0f, 0.0f, 0.0f};
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Convert::Run(mat, alpha, beta);
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// Record output shape of preprocessed image
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(*im_info)["output_shape"] = {static_cast<float>(mat->Height()),
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static_cast<float>(mat->Width())};
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HWC2CHW::Run(mat);
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Cast::Run(mat, "float");
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mat->ShareWithTensor(output);
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output->shape.insert(output->shape.begin(), 1); // reshape to n, h, w, c
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return true;
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}
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void YOLOv5Lite::UseCudaPreprocessing(int max_image_size) {
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#ifdef ENABLE_CUDA_PREPROCESS
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use_cuda_preprocessing_ = true;
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is_scale_up = true;
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if (input_img_cuda_buffer_host_ == nullptr) {
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// prepare input data cache in GPU pinned memory
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CUDA_CHECK(cudaMallocHost((void**)&input_img_cuda_buffer_host_, max_image_size * 3));
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// prepare input data cache in GPU device memory
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CUDA_CHECK(cudaMalloc((void**)&input_img_cuda_buffer_device_, max_image_size * 3));
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CUDA_CHECK(cudaMalloc((void**)&input_tensor_cuda_buffer_device_, 3 * size[0] * size[1] * sizeof(float)));
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}
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#else
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FDWARNING << "The FastDeploy didn't compile with BUILD_CUDA_SRC=ON."
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<< std::endl;
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use_cuda_preprocessing_ = false;
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#endif
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}
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bool YOLOv5Lite::CudaPreprocess(Mat* mat, FDTensor* output,
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std::map<std::string, std::array<float, 2>>* im_info) {
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#ifdef ENABLE_CUDA_PREPROCESS
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if (is_mini_pad != false || is_no_pad != false || is_scale_up != true) {
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FDERROR << "Preprocessing with CUDA is only available when the arguments satisfy (is_mini_pad=false, is_no_pad=false, is_scale_up=true)." << std::endl;
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return false;
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}
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// Record the shape of image and the shape of preprocessed image
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(*im_info)["input_shape"] = {static_cast<float>(mat->Height()),
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static_cast<float>(mat->Width())};
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(*im_info)["output_shape"] = {static_cast<float>(mat->Height()),
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static_cast<float>(mat->Width())};
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cudaStream_t stream = reinterpret_cast<cudaStream_t>(cuda_stream_);
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int src_img_buf_size = mat->Height() * mat->Width() * mat->Channels();
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memcpy(input_img_cuda_buffer_host_, mat->Data(), src_img_buf_size);
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CUDA_CHECK(cudaMemcpyAsync(input_img_cuda_buffer_device_,
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input_img_cuda_buffer_host_,
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src_img_buf_size, cudaMemcpyHostToDevice, stream));
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utils::CudaYoloPreprocess(input_img_cuda_buffer_device_, mat->Width(),
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mat->Height(), input_tensor_cuda_buffer_device_,
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size[0], size[1], padding_value, stream);
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// Record output shape of preprocessed image
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(*im_info)["output_shape"] = {static_cast<float>(size[0]), static_cast<float>(size[1])};
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output->SetExternalData({mat->Channels(), size[0], size[1]}, FDDataType::FP32,
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input_tensor_cuda_buffer_device_);
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output->device = Device::GPU;
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output->shape.insert(output->shape.begin(), 1); // reshape to n, h, w, c
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return true;
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#else
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FDERROR << "CUDA src code was not enabled." << std::endl;
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return false;
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#endif // ENABLE_CUDA_PREPROCESS
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}
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bool YOLOv5Lite::PostprocessWithDecode(
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FDTensor& infer_result, DetectionResult* result,
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const std::map<std::string, std::array<float, 2>>& im_info,
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float conf_threshold, float nms_iou_threshold) {
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FDASSERT(infer_result.shape[0] == 1, "Only support batch =1 now.");
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result->Clear();
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result->Reserve(infer_result.shape[1]);
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if (infer_result.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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// generate anchors with dowmsample strides
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std::vector<YOLOv5Lite::Anchor> anchors;
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int num_anchors = anchor_config[0].size() / 2;
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GenerateAnchors(size, downsample_strides, &anchors, num_anchors);
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// infer_result shape might look like (1,n,85=5+80)
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float* data = static_cast<float*>(infer_result.Data());
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for (size_t i = 0; i < infer_result.shape[1]; ++i) {
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int s = i * infer_result.shape[2];
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float confidence = data[s + 4];
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float* max_class_score =
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std::max_element(data + s + 5, data + s + infer_result.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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// fetch i-th anchor
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float grid0 = static_cast<float>(anchors.at(i).grid0);
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float grid1 = static_cast<float>(anchors.at(i).grid1);
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float downsample_stride = static_cast<float>(anchors.at(i).stride);
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float anchor_w = static_cast<float>(anchors.at(i).anchor_w);
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float anchor_h = static_cast<float>(anchors.at(i).anchor_h);
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// convert from offsets to [x, y, w, h]
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float dx = data[s];
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float dy = data[s + 1];
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float dw = data[s + 2];
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float dh = data[s + 3];
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float x = (dx * 2.0f - 0.5f + grid0) * downsample_stride;
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float y = (dy * 2.0f - 0.5f + grid1) * downsample_stride;
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float w = std::pow(dw * 2.0f, 2.0f) * anchor_w;
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float h = std::pow(dh * 2.0f, 2.0f) * anchor_h;
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// convert from [x, y, w, h] to [x1, y1, x2, y2]
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result->boxes.emplace_back(std::array<float, 4>{
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x - w / 2.0f + label_id * max_wh, y - h / 2.0f + label_id * max_wh,
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x + w / 2.0f + label_id * max_wh, y + h / 2.0f + label_id * max_wh});
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// label_id * max_wh for multi classes NMS
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result->label_ids.push_back(label_id);
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result->scores.push_back(confidence);
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}
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utils::NMS(result, nms_iou_threshold);
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// scale the boxes to the origin image shape
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auto iter_out = im_info.find("output_shape");
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auto iter_ipt = im_info.find("input_shape");
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FDASSERT(iter_out != im_info.end() && iter_ipt != im_info.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) / 2.0f;
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float pad_w = (out_w - ipt_w * scale) / 2.0f;
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if (is_mini_pad) {
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pad_h = static_cast<float>(static_cast<int>(pad_h) % stride);
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pad_w = static_cast<float>(static_cast<int>(pad_w) % stride);
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}
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for (size_t i = 0; i < result->boxes.size(); ++i) {
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int32_t label_id = (result->label_ids)[i];
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// clip box
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result->boxes[i][0] = result->boxes[i][0] - max_wh * label_id;
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result->boxes[i][1] = result->boxes[i][1] - max_wh * label_id;
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result->boxes[i][2] = result->boxes[i][2] - max_wh * label_id;
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result->boxes[i][3] = result->boxes[i][3] - max_wh * label_id;
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result->boxes[i][0] = std::max((result->boxes[i][0] - pad_w) / scale, 0.0f);
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result->boxes[i][1] = std::max((result->boxes[i][1] - pad_h) / scale, 0.0f);
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result->boxes[i][2] = std::max((result->boxes[i][2] - pad_w) / scale, 0.0f);
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result->boxes[i][3] = std::max((result->boxes[i][3] - pad_h) / scale, 0.0f);
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result->boxes[i][0] = std::min(result->boxes[i][0], ipt_w - 1.0f);
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result->boxes[i][1] = std::min(result->boxes[i][1], ipt_h - 1.0f);
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result->boxes[i][2] = std::min(result->boxes[i][2], ipt_w - 1.0f);
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result->boxes[i][3] = std::min(result->boxes[i][3], ipt_h - 1.0f);
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}
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return true;
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}
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bool YOLOv5Lite::Postprocess(
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FDTensor& infer_result, DetectionResult* result,
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const std::map<std::string, std::array<float, 2>>& im_info,
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float conf_threshold, float nms_iou_threshold) {
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FDASSERT(infer_result.shape[0] == 1, "Only support batch =1 now.");
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result->Clear();
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result->Reserve(infer_result.shape[1]);
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if (infer_result.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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float* data = static_cast<float*>(infer_result.Data());
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for (size_t i = 0; i < infer_result.shape[1]; ++i) {
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int s = i * infer_result.shape[2];
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float confidence = data[s + 4];
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float* max_class_score =
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std::max_element(data + s + 5, data + s + infer_result.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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result->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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result->label_ids.push_back(label_id);
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result->scores.push_back(confidence);
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}
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utils::NMS(result, nms_iou_threshold);
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// scale the boxes to the origin image shape
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auto iter_out = im_info.find("output_shape");
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auto iter_ipt = im_info.find("input_shape");
|
|
FDASSERT(iter_out != im_info.end() && iter_ipt != im_info.end(),
|
|
"Cannot find input_shape or output_shape from im_info.");
|
|
float out_h = iter_out->second[0];
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|
float out_w = iter_out->second[1];
|
|
float ipt_h = iter_ipt->second[0];
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|
float ipt_w = iter_ipt->second[1];
|
|
float scale = std::min(out_h / ipt_h, out_w / ipt_w);
|
|
float pad_h = (out_h - ipt_h * scale) / 2.0f;
|
|
float pad_w = (out_w - ipt_w * scale) / 2.0f;
|
|
if (is_mini_pad) {
|
|
pad_h = static_cast<float>(static_cast<int>(pad_h) % stride);
|
|
pad_w = static_cast<float>(static_cast<int>(pad_w) % stride);
|
|
}
|
|
for (size_t i = 0; i < result->boxes.size(); ++i) {
|
|
int32_t label_id = (result->label_ids)[i];
|
|
// clip box
|
|
result->boxes[i][0] = result->boxes[i][0] - max_wh * label_id;
|
|
result->boxes[i][1] = result->boxes[i][1] - max_wh * label_id;
|
|
result->boxes[i][2] = result->boxes[i][2] - max_wh * label_id;
|
|
result->boxes[i][3] = result->boxes[i][3] - max_wh * label_id;
|
|
result->boxes[i][0] = std::max((result->boxes[i][0] - pad_w) / scale, 0.0f);
|
|
result->boxes[i][1] = std::max((result->boxes[i][1] - pad_h) / scale, 0.0f);
|
|
result->boxes[i][2] = std::max((result->boxes[i][2] - pad_w) / scale, 0.0f);
|
|
result->boxes[i][3] = std::max((result->boxes[i][3] - pad_h) / scale, 0.0f);
|
|
result->boxes[i][0] = std::min(result->boxes[i][0], ipt_w - 1.0f);
|
|
result->boxes[i][1] = std::min(result->boxes[i][1], ipt_h - 1.0f);
|
|
result->boxes[i][2] = std::min(result->boxes[i][2], ipt_w - 1.0f);
|
|
result->boxes[i][3] = std::min(result->boxes[i][3], ipt_h - 1.0f);
|
|
}
|
|
return true;
|
|
}
|
|
|
|
bool YOLOv5Lite::Predict(cv::Mat* im, DetectionResult* result,
|
|
float conf_threshold, float nms_iou_threshold) {
|
|
Mat mat(*im);
|
|
|
|
std::map<std::string, std::array<float, 2>> im_info;
|
|
|
|
// Record the shape of image and the shape of preprocessed image
|
|
im_info["input_shape"] = {static_cast<float>(mat.Height()),
|
|
static_cast<float>(mat.Width())};
|
|
im_info["output_shape"] = {static_cast<float>(mat.Height()),
|
|
static_cast<float>(mat.Width())};
|
|
|
|
if (use_cuda_preprocessing_) {
|
|
if (!CudaPreprocess(&mat, &reused_input_tensors_[0], &im_info)) {
|
|
FDERROR << "Failed to preprocess input image." << std::endl;
|
|
return false;
|
|
}
|
|
} else {
|
|
if (!Preprocess(&mat, &reused_input_tensors_[0], &im_info)) {
|
|
FDERROR << "Failed to preprocess input image." << std::endl;
|
|
return false;
|
|
}
|
|
}
|
|
|
|
reused_input_tensors_[0].name = InputInfoOfRuntime(0).name;
|
|
if (!Infer()) {
|
|
FDERROR << "Failed to inference." << std::endl;
|
|
return false;
|
|
}
|
|
|
|
if (is_decode_exported) {
|
|
if (!Postprocess(reused_output_tensors_[0], result, im_info, conf_threshold,
|
|
nms_iou_threshold)) {
|
|
FDERROR << "Failed to post process." << std::endl;
|
|
return false;
|
|
}
|
|
} else {
|
|
if (!PostprocessWithDecode(reused_output_tensors_[0], result, im_info,
|
|
conf_threshold, nms_iou_threshold)) {
|
|
FDERROR << "Failed to post process." << std::endl;
|
|
return false;
|
|
}
|
|
}
|
|
return true;
|
|
}
|
|
|
|
} // namespace detection
|
|
} // namespace vision
|
|
} // namespace fastdeploy
|