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/*
// Copyright (C) 2020-2022 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/classification_model.h"
#include <algorithm>
#include <fstream>
#include <iterator>
#include <map>
#include <stdexcept>
#include <string>
#include <utility>
#include <vector>
#include <openvino/op/softmax.hpp>
#include <openvino/op/topk.hpp>
#include <openvino/openvino.hpp>
#include <utils/slog.hpp>
#include "models/results.h"
ClassificationModel::ClassificationModel(const std::string& modelFileName,
size_t nTop,
bool useAutoResize,
const std::vector<std::string>& labels,
const std::string& layout)
: ImageModel(modelFileName, useAutoResize, layout),
nTop(nTop),
labels(labels) {}
std::unique_ptr<ResultBase> ClassificationModel::postprocess(InferenceResult& infResult) {
const ov::Tensor& indicesTensor = infResult.outputsData.find(outputsNames[0])->second;
const int* indicesPtr = indicesTensor.data<int>();
const ov::Tensor& scoresTensor = infResult.outputsData.find(outputsNames[1])->second;
const float* scoresPtr = scoresTensor.data<float>();
ClassificationResult* result = new ClassificationResult(infResult.frameId, infResult.metaData);
auto retVal = std::unique_ptr<ResultBase>(result);
result->topLabels.reserve(scoresTensor.get_size());
for (size_t i = 0; i < scoresTensor.get_size(); ++i) {
int ind = indicesPtr[i];
if (ind < 0 || ind >= static_cast<int>(labels.size())) {
throw std::runtime_error("Invalid index for the class label is found during postprocessing");
}
result->topLabels.emplace_back(ind, labels[ind], scoresPtr[i]);
}
return retVal;
}
std::vector<std::string> ClassificationModel::loadLabels(const std::string& labelFilename) {
std::vector<std::string> labels;
/* Read labels */
std::ifstream inputFile(labelFilename);
if (!inputFile.is_open())
throw std::runtime_error("Can't open the labels file: " + labelFilename);
std::string labelsLine;
while (std::getline(inputFile, labelsLine)) {
size_t labelBeginIdx = labelsLine.find(' ');
size_t labelEndIdx = labelsLine.find(','); // can be npos when class has only one label
if (labelBeginIdx == std::string::npos) {
throw std::runtime_error("The labels file has incorrect format.");
}
labels.push_back(labelsLine.substr(labelBeginIdx + 1, labelEndIdx - (labelBeginIdx + 1)));
}
if (labels.empty())
throw std::logic_error("File is empty: " + labelFilename);
return labels;
}
void ClassificationModel::prepareInputsOutputs(std::shared_ptr<ov::Model>& model) {
// --------------------------- Configure input & output -------------------------------------------------
// --------------------------- Prepare input ------------------------------------------------------
if (model->inputs().size() != 1) {
throw std::logic_error("Classification model wrapper supports topologies with only 1 input");
}
const auto& input = model->input();
inputsNames.push_back(input.get_any_name());
const ov::Shape& inputShape = input.get_shape();
const ov::Layout& inputLayout = getInputLayout(input);
if (inputShape.size() != 4 || inputShape[ov::layout::channels_idx(inputLayout)] != 3) {
throw std::logic_error("3-channel 4-dimensional model's input is expected");
}
const auto width = inputShape[ov::layout::width_idx(inputLayout)];
const auto height = inputShape[ov::layout::height_idx(inputLayout)];
if (height != width) {
throw std::logic_error("Model input has incorrect image shape. Must be NxN square."
" Got " +
std::to_string(height) + "x" + std::to_string(width) + ".");
}
ov::preprocess::PrePostProcessor ppp(model);
ppp.input().tensor().set_element_type(ov::element::u8).set_layout({"NHWC"});
if (useAutoResize) {
ppp.input().tensor().set_spatial_dynamic_shape();
ppp.input()
.preprocess()
.convert_element_type(ov::element::f32)
.resize(ov::preprocess::ResizeAlgorithm::RESIZE_LINEAR);
}
ppp.input().model().set_layout(inputLayout);
// --------------------------- Prepare output -----------------------------------------------------
if (model->outputs().size() != 1) {
throw std::logic_error("Classification model wrapper supports topologies with only 1 output");
}
const ov::Shape& outputShape = model->output().get_shape();
if (outputShape.size() != 2 && outputShape.size() != 4) {
throw std::logic_error("Classification model wrapper supports topologies only with"
" 2-dimensional or 4-dimensional output");
}
const ov::Layout outputLayout("NCHW");
if (outputShape.size() == 4 && (outputShape[ov::layout::height_idx(outputLayout)] != 1 ||
outputShape[ov::layout::width_idx(outputLayout)] != 1)) {
throw std::logic_error("Classification model wrapper supports topologies only"
" with 4-dimensional output which has last two dimensions of size 1");
}
size_t classesNum = outputShape[ov::layout::channels_idx(outputLayout)];
if (nTop > classesNum) {
throw std::logic_error("The model provides " + std::to_string(classesNum) + " classes, but " +
std::to_string(nTop) + " labels are requested to be predicted");
}
if (classesNum == labels.size() + 1) {
labels.insert(labels.begin(), "other");
slog::warn << "Inserted 'other' label as first." << slog::endl;
} else if (classesNum != labels.size()) {
throw std::logic_error("Model's number of classes and parsed labels must match (" +
std::to_string(outputShape[1]) + " and " + std::to_string(labels.size()) + ')');
}
ppp.output().tensor().set_element_type(ov::element::f32);
model = ppp.build();
// --------------------------- Adding softmax and topK output ---------------------------
auto nodes = model->get_ops();
auto softmaxNodeIt = std::find_if(std::begin(nodes), std::end(nodes), [](const std::shared_ptr<ov::Node>& op) {
return std::string(op->get_type_name()) == "Softmax";
});
std::shared_ptr<ov::Node> softmaxNode;
if (softmaxNodeIt == nodes.end()) {
auto logitsNode = model->get_output_op(0)->input(0).get_source_output().get_node();
softmaxNode = std::make_shared<ov::op::v1::Softmax>(logitsNode->output(0), 1);
} else {
softmaxNode = *softmaxNodeIt;
}
const auto k = std::make_shared<ov::op::v0::Constant>(ov::element::i32, ov::Shape{}, std::vector<size_t>{nTop});
std::shared_ptr<ov::Node> topkNode = std::make_shared<ov::op::v3::TopK>(softmaxNode,
k,
1,
ov::op::v3::TopK::Mode::MAX,
ov::op::v3::TopK::SortType::SORT_VALUES);
auto indices = std::make_shared<ov::op::v0::Result>(topkNode->output(0));
auto scores = std::make_shared<ov::op::v0::Result>(topkNode->output(1));
ov::ResultVector res({scores, indices});
model = std::make_shared<ov::Model>(res, model->get_parameters(), "classification");
// manually set output tensors name for created topK node
model->outputs()[0].set_names({"indices"});
outputsNames.push_back("indices");
model->outputs()[1].set_names({"scores"});
outputsNames.push_back("scores");
// set output precisions
ppp = ov::preprocess::PrePostProcessor(model);
ppp.output("indices").tensor().set_element_type(ov::element::i32);
ppp.output("scores").tensor().set_element_type(ov::element::f32);
model = ppp.build();
}
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