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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_yolov3_onnx.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"
ModelYoloV3ONNX::ModelYoloV3ONNX(const std::string& modelFileName,
float confidenceThreshold,
const std::vector<std::string>& labels,
const std::string& layout)
: DetectionModel(modelFileName, confidenceThreshold, false, labels, layout) {
interpolationMode = cv::INTER_CUBIC;
resizeMode = RESIZE_KEEP_ASPECT_LETTERBOX;
}
void ModelYoloV3ONNX::prepareInputsOutputs(std::shared_ptr<ov::Model>& model) {
// --------------------------- Configure input & output -------------------------------------------------
// --------------------------- Prepare inputs ------------------------------------------------------
const ov::OutputVector& inputs = model->inputs();
if (inputs.size() != 2) {
throw std::logic_error("YoloV3ONNX model wrapper expects models that have 2 inputs");
}
ov::preprocess::PrePostProcessor ppp(model);
inputsNames.reserve(inputs.size());
for (auto& input : inputs) {
const ov::Shape& currentShape = input.get_shape();
std::string currentName = input.get_any_name();
const ov::Layout& currentLayout = getInputLayout(input);
if (currentShape.size() == 4) {
if (currentShape[ov::layout::channels_idx(currentLayout)] != 3) {
throw std::logic_error("Expected 4D image input with 3 channels");
}
inputsNames[0] = currentName;
netInputWidth = currentShape[ov::layout::width_idx(currentLayout)];
netInputHeight = currentShape[ov::layout::height_idx(currentLayout)];
ppp.input(currentName).tensor().set_element_type(ov::element::u8).set_layout({"NHWC"});
} else if (currentShape.size() == 2) {
if (currentShape[ov::layout::channels_idx(currentLayout)] != 2) {
throw std::logic_error("Expected 2D image info input with 2 channels");
}
inputsNames[1] = currentName;
ppp.input(currentName).tensor().set_element_type(ov::element::i32);
}
ppp.input(currentName).model().set_layout(currentLayout);
}
// --------------------------- Prepare outputs -----------------------------------------------------
const ov::OutputVector& outputs = model->outputs();
if (outputs.size() != 3) {
throw std::logic_error("YoloV3ONNX model wrapper expects models that have 3 outputs");
}
for (auto& output : outputs) {
const ov::Shape& currentShape = output.get_partial_shape().get_max_shape();
std::string currentName = output.get_any_name();
if (currentShape.back() == 3) {
indicesOutputName = currentName;
ppp.output(currentName).tensor().set_element_type(ov::element::i32);
} else if (currentShape[2] == 4) {
boxesOutputName = currentName;
ppp.output(currentName).tensor().set_element_type(ov::element::f32);
} else if (currentShape[1] == numberOfClasses) {
scoresOutputName = currentName;
ppp.output(currentName).tensor().set_element_type(ov::element::f32);
} else {
throw std::logic_error("Expected shapes [:,:,4], [:,"
+ std::to_string(numberOfClasses) + ",:] and [:,3] for outputs");
}
outputsNames.push_back(currentName);
}
model = ppp.build();
}
std::shared_ptr<InternalModelData> ModelYoloV3ONNX::preprocess(const InputData& inputData,
ov::InferRequest& request) {
const auto& origImg = inputData.asRef<ImageInputData>().inputImage;
cv::Mat info(cv::Size(1, 2), CV_32SC1);
info.at<int>(0, 0) = origImg.rows;
info.at<int>(0, 1) = origImg.cols;
auto allocator = std::make_shared<SharedTensorAllocator>(info);
ov::Tensor infoInput = ov::Tensor(ov::element::i32, ov::Shape({1, 2}), ov::Allocator(allocator));
request.set_tensor(inputsNames[1], infoInput);
return ImageModel::preprocess(inputData, request);
}
namespace {
float getScore(const ov::Tensor& scoresTensor, size_t classInd, size_t boxInd) {
const float* scoresPtr = scoresTensor.data<float>();
const auto shape = scoresTensor.get_shape();
int N = shape[2];
return scoresPtr[classInd * N + boxInd];
}
}
std::unique_ptr<ResultBase> ModelYoloV3ONNX::postprocess(InferenceResult& infResult) {
// Get info about input image
const auto imgWidth = infResult.internalModelData->asRef<InternalImageModelData>().inputImgWidth;
const auto imgHeight = infResult.internalModelData->asRef<InternalImageModelData>().inputImgHeight;
// Get outputs tensors
const ov::Tensor& boxes = infResult.outputsData[boxesOutputName];
const float* boxesPtr = boxes.data<float>();
const ov::Tensor& scores = infResult.outputsData[scoresOutputName];
const ov::Tensor& indices = infResult.outputsData[indicesOutputName];
const int* indicesData = indices.data<int>();
const auto indicesShape = indices.get_shape();
const auto boxShape = boxes.get_shape();
// Generate detection results
DetectionResult* result = new DetectionResult(infResult.frameId, infResult.metaData);
size_t numberOfBoxes = indicesShape.size() == 3 ? indicesShape[1] : indicesShape[0];
int indicesStride = indicesShape.size() == 3 ? indicesShape[2] : indicesShape[1];
for (size_t i = 0; i < numberOfBoxes; ++i) {
int batchInd = indicesData[i * indicesStride];
int classInd = indicesData[i * indicesStride + 1];
int boxInd = indicesData[i * indicesStride + 2];
if (batchInd == -1) {
break;
}
float score = getScore(scores, classInd, boxInd);
if (score > confidenceThreshold) {
DetectedObject obj;
size_t startPos = boxShape[2] * boxInd;
auto x = boxesPtr[startPos + 1];
auto y = boxesPtr[startPos];
auto width = boxesPtr[startPos + 3] - x;
auto height = boxesPtr[startPos + 2] - y;
// Create new detected box
obj.x = clamp(x, 0.f, static_cast<float>(imgWidth));
obj.y = clamp(y, 0.f, static_cast<float>(imgHeight));
obj.height = clamp(height, 0.f, static_cast<float>(imgHeight));
obj.width = clamp(width, 0.f, static_cast<float>(imgWidth));
obj.confidence = score;
obj.labelID = classInd;
obj.label = getLabelName(classInd);
result->objects.push_back(obj);
}
}
return std::unique_ptr<ResultBase>(result);
}
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