* add softmax in cls postprocess
* minor
(cherry picked from commit bcb93ead58
)
pull/1884/head
parent
bace0a20d8
commit
c6cf781fa6
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@ -18,6 +18,7 @@ class LinearClsHead : public MMClassification {
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public:
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explicit LinearClsHead(const Value& cfg) : MMClassification(cfg) {
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if (cfg.contains("params")) {
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softmax_ = cfg["params"].value("softmax", false);
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topk_ = cfg["params"].value("topk", 1);
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if (topk_ <= 0) {
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MMDEPLOY_ERROR("'topk' should be greater than 0, but got '{}'", topk_);
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@ -47,16 +48,31 @@ class LinearClsHead : public MMClassification {
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private:
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Value GetLabels(const Tensor& scores, int class_num) const {
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auto scores_data = scores.data<float>();
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auto topk = std::min(topk_, class_num);
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Labels output;
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output.reserve(topk_);
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output.reserve(topk);
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std::vector<int> idx(class_num);
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iota(begin(idx), end(idx), 0);
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partial_sort(begin(idx), begin(idx) + topk_, end(idx),
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partial_sort(begin(idx), begin(idx) + topk, end(idx),
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[&](int i, int j) { return scores_data[i] > scores_data[j]; });
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for (int i = 0; i < topk_; ++i) {
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auto label = Label{idx[i], scores_data[idx[i]]};
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MMDEPLOY_DEBUG("label_id: {}, score: {}", label.label_id, label.score);
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output.push_back(label);
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auto sum_exp = 0.f;
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std::vector<float> exp_scores;
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if (softmax_) {
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exp_scores.reserve(class_num);
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auto max_val = scores_data[idx[0]];
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for (int i = 0; i < class_num; ++i) {
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sum_exp += exp_scores.emplace_back(std::exp(scores_data[i] - max_val));
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}
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}
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for (int i = 0; i < topk; ++i) {
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float score = 0.f;
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if (softmax_) {
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score = exp_scores[idx[i]] / sum_exp;
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} else {
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score = scores_data[idx[i]];
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}
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output.push_back({idx[i], score});
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}
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return to_value(std::move(output));
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}
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@ -64,6 +80,7 @@ class LinearClsHead : public MMClassification {
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private:
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static constexpr const auto kHost = Device{0};
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bool softmax_{false};
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int topk_{1};
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};
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