mmdeploy/csrc/codebase/mmcls/linear_cls.cpp

71 lines
2.0 KiB
C++

// Copyright (c) OpenMMLab. All rights reserved.
#include <numeric>
#include "codebase/mmcls/mmcls.h"
#include "core/tensor.h"
#include "core/utils/device_utils.h"
#include "core/utils/formatter.h"
#include "experimental/module_adapter.h"
using std::vector;
namespace mmdeploy::mmcls {
class LinearClsHead : public MMClassification {
public:
explicit LinearClsHead(const Value& cfg) : MMClassification(cfg) {
if (cfg.contains("params")) {
topk_ = cfg["params"].value("topk", 1);
if (topk_ <= 0) {
ERROR("'topk' should be greater than 0, but got '{}'", topk_);
throw_exception(eInvalidArgument);
}
}
}
Result<Value> operator()(const Value& infer_res) {
DEBUG("infer_res: {}", infer_res);
auto output = infer_res["output"].get<Tensor>();
if (!(output.shape().size() >= 2 && output.data_type() == DataType::kFLOAT)) {
ERROR("unsupported `output` tensor, shape: {}, dtype: {}", output.shape(),
(int)output.data_type());
return Status(eNotSupported);
}
auto class_num = (int)output.shape(1);
OUTCOME_TRY(auto _scores, MakeAvailableOnDevice(output, kHost, stream()));
OUTCOME_TRY(stream().Wait());
return GetLabels(_scores, class_num);
}
private:
Value GetLabels(const Tensor& scores, int class_num) const {
auto scores_data = scores.data<float>();
ClassifyOutput output;
output.labels.reserve(topk_);
std::vector<int> idx(class_num);
iota(begin(idx), end(idx), 0);
partial_sort(begin(idx), begin(idx) + topk_, end(idx),
[&](int i, int j) { return scores_data[i] > scores_data[j]; });
for (int i = 0; i < topk_; ++i) {
auto label = ClassifyOutput::Label{idx[i], scores_data[idx[i]]};
DEBUG("label_id: {}, score: {}", label.label_id, label.score);
output.labels.push_back(label);
}
return to_value(std::move(output));
}
private:
static constexpr const auto kHost = Device{0};
int topk_{1};
};
REGISTER_CODEBASE_COMPONENT(MMClassification, LinearClsHead);
} // namespace mmdeploy::mmcls