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/*
// Copyright (C) 2022 Intel Corporation
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
// http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.
*/
#include "models/detection_model_yolox.h"
#include <algorithm>
#include <cmath>
#include <cstdint>
#include <stdexcept>
#include <string>
#include <utility>
#include <vector>
#include <openvino/openvino.hpp>
#include <utils/common.hpp>
#include <utils/slog.hpp>
#include "models/input_data.h"
#include "models/internal_model_data.h"
#include "models/results.h"
#include "utils/image_utils.h"
#include "utils/nms.hpp"
ModelYoloX::ModelYoloX(const std::string& modelFileName,
float confidenceThreshold,
float boxIOUThreshold,
const std::vector<std::string>& labels,
const std::string& layout)
: DetectionModel(modelFileName, confidenceThreshold, false, labels, layout),
boxIOUThreshold(boxIOUThreshold) {
resizeMode = RESIZE_KEEP_ASPECT;
}
void ModelYoloX::prepareInputsOutputs(std::shared_ptr<ov::Model>& model) {
// --------------------------- Configure input & output -------------------------------------------------
// --------------------------- Prepare input ------------------------------------------------------
const ov::OutputVector& inputs = model->inputs();
if (inputs.size() != 1) {
throw std::logic_error("YOLOX model wrapper accepts models that have only 1 input");
}
//--- Check image input
const auto& input = model->input();
const ov::Shape& inputShape = model->input().get_shape();
ov::Layout inputLayout = getInputLayout(input);
if (inputShape.size() != 4 && inputShape[ov::layout::channels_idx(inputLayout)] != 3) {
throw std::logic_error("Expected 4D image input with 3 channels");
}
ov::preprocess::PrePostProcessor ppp(model);
ppp.input().tensor().set_element_type(ov::element::u8).set_layout({"NHWC"});
ppp.input().model().set_layout(inputLayout);
//--- Reading image input parameters
inputsNames.push_back(input.get_any_name());
netInputWidth = inputShape[ov::layout::width_idx(inputLayout)];
netInputHeight = inputShape[ov::layout::height_idx(inputLayout)];
setStridesGrids();
// --------------------------- Prepare output -----------------------------------------------------
if (model->outputs().size() != 1) {
throw std::logic_error("YoloX model wrapper expects models that have only 1 output");
}
const auto& output = model->output();
outputsNames.push_back(output.get_any_name());
const ov::Shape& shape = output.get_shape();
if (shape.size() != 3) {
throw std::logic_error("YOLOX single output must have 3 dimensions, but had " + std::to_string(shape.size()));
}
ppp.output().tensor().set_element_type(ov::element::f32);
model = ppp.build();
}
void ModelYoloX::setStridesGrids() {
std::vector<size_t> strides = {8, 16, 32};
std::vector<size_t> hsizes(3);
std::vector<size_t> wsizes(3);
for (size_t i = 0; i < strides.size(); ++i) {
hsizes[i] = netInputHeight / strides[i];
wsizes[i] = netInputWidth / strides[i];
}
for (size_t size_index = 0; size_index < hsizes.size(); ++size_index) {
for (size_t h_index = 0; h_index < hsizes[size_index]; ++h_index) {
for (size_t w_index = 0; w_index < wsizes[size_index]; ++w_index) {
grids.emplace_back(w_index, h_index);
expandedStrides.push_back(strides[size_index]);
}
}
}
}
std::shared_ptr<InternalModelData> ModelYoloX::preprocess(const InputData& inputData,
ov::InferRequest& request) {
const auto& origImg = inputData.asRef<ImageInputData>().inputImage;
double scale = std::min(static_cast<double>(netInputWidth) / origImg.cols,
static_cast<double>(netInputHeight) / origImg.rows);
cv::Mat resizedImage = resizeImageExt(origImg, netInputWidth, netInputHeight, resizeMode,
interpolationMode, nullptr, cv::Scalar(114, 114, 114));
request.set_input_tensor(wrapMat2Tensor(resizedImage));
return std::make_shared<InternalScaleData>(origImg.cols, origImg.rows, scale, scale);
}
std::unique_ptr<ResultBase> ModelYoloX::postprocess(InferenceResult& infResult) {
// Get metadata about input image shape and scale
const auto& scale = infResult.internalModelData->asRef<InternalScaleData>();
// Get output tensor
const ov::Tensor& output = infResult.outputsData[outputsNames[0]];
const auto& outputShape = output.get_shape();
float* outputPtr = output.data<float>();
// Generate detection results
DetectionResult* result = new DetectionResult(infResult.frameId, infResult.metaData);
// Update coordinates according to strides
for (size_t box_index = 0; box_index < expandedStrides.size(); ++box_index) {
size_t startPos = outputShape[2] * box_index;
outputPtr[startPos] = (outputPtr[startPos] + grids[box_index].first) * expandedStrides[box_index];
outputPtr[startPos + 1] = (outputPtr[startPos + 1] + grids[box_index].second) * expandedStrides[box_index];
outputPtr[startPos + 2] = std::exp(outputPtr[startPos + 2]) * expandedStrides[box_index];
outputPtr[startPos + 3] = std::exp(outputPtr[startPos + 3]) * expandedStrides[box_index];
}
// Filter predictions
std::vector<Anchor> validBoxes;
std::vector<float> scores;
std::vector<size_t> classes;
for (size_t box_index = 0; box_index < expandedStrides.size(); ++box_index) {
size_t startPos = outputShape[2] * box_index;
float score = outputPtr[startPos + 4];
if (score < confidenceThreshold)
continue;
float maxClassScore = -1;
size_t mainClass = 0;
for (size_t class_index = 0; class_index < numberOfClasses; ++class_index) {
if (outputPtr[startPos + 5 + class_index] > maxClassScore) {
maxClassScore = outputPtr[startPos + 5 + class_index];
mainClass = class_index;
}
}
// Filter by score
score *= maxClassScore;
if (score < confidenceThreshold)
continue;
// Add successful boxes
scores.push_back(score);
classes.push_back(mainClass);
Anchor trueBox = {outputPtr[startPos + 0] - outputPtr[startPos + 2] / 2, outputPtr[startPos + 1] - outputPtr[startPos + 3] / 2,
outputPtr[startPos + 0] + outputPtr[startPos + 2] / 2, outputPtr[startPos + 1] + outputPtr[startPos + 3] / 2};
validBoxes.push_back(Anchor({trueBox.left / scale.scaleX, trueBox.top / scale.scaleY,
trueBox.right / scale.scaleX, trueBox.bottom / scale.scaleY}));
}
// NMS for valid boxes
std::vector<int> keep = nms(validBoxes, scores, boxIOUThreshold, true);
for (auto& index: keep) {
// Create new detected box
DetectedObject obj;
obj.x = clamp(validBoxes[index].left, 0.f, static_cast<float>(scale.inputImgWidth));
obj.y = clamp(validBoxes[index].top, 0.f, static_cast<float>(scale.inputImgHeight));
obj.height = clamp(validBoxes[index].bottom - validBoxes[index].top, 0.f, static_cast<float>(scale.inputImgHeight));
obj.width = clamp(validBoxes[index].right - validBoxes[index].left, 0.f, static_cast<float>(scale.inputImgWidth));
obj.confidence = scores[index];
obj.labelID = classes[index];
obj.label = getLabelName(classes[index]);
result->objects.push_back(obj);
}
return std::unique_ptr<ResultBase>(result);
}
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