chore: import upstream snapshot with attribution

2026-07-13 13:33:03 +08:00
commit 5b57521aa1
8226 changed files with 3425766 additions and 0 deletions
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+//
+//  Loss.cpp
+//  MNN
+//
+//  Created by MNN on 2019/11/29.
+//  Copyright © 2018, Alibaba Group Holding Limited
+//
+
+#include "Loss.hpp"
+
+using namespace MNN::Express;
+
+namespace MNN {
+namespace Train {
+
+Express::VARP _CrossEntropy(Express::VARP predicts, Express::VARP oneHotTargets) {
+    MNN_ASSERT(predicts->getInfo()->dim.size() == 2);
+    MNN_ASSERT(predicts->getInfo()->dim == oneHotTargets->getInfo()->dim);
+    auto loss = _Negative(_ReduceMean(_ReduceSum(_Log(predicts) * oneHotTargets, {1}), {}));
+    return loss;
+}
+
+Express::VARP _KLDivergence(Express::VARP predicts, Express::VARP oneHotTargets) {
+    MNN_ASSERT(predicts->getInfo()->dim.size() == 2);
+    MNN_ASSERT(predicts->getInfo()->dim == oneHotTargets->getInfo()->dim);
+    auto loss = _ReduceMean(_ReduceSum(_Multiply(predicts, _Log(predicts) - _Log(oneHotTargets)), {1}), {});
+    return loss;
+}
+
+Express::VARP _MSE(Express::VARP predicts, Express::VARP oneHotTargets) {
+    MNN_ASSERT(predicts->getInfo()->dim.size() == 2);
+    MNN_ASSERT(predicts->getInfo()->dim == oneHotTargets->getInfo()->dim);
+    auto loss = _ReduceMean(_ReduceSum(_Square(predicts - oneHotTargets), {1}), {});
+    return loss;
+}
+
+Express::VARP _MAE(Express::VARP predicts, Express::VARP oneHotTargets) {
+    MNN_ASSERT(predicts->getInfo()->dim.size() == 2);
+    MNN_ASSERT(predicts->getInfo()->dim == oneHotTargets->getInfo()->dim);
+    auto loss = _ReduceMean(_ReduceSum(_Abs(predicts - oneHotTargets), {1}), {});
+    return loss;
+}
+
+Express::VARP _Hinge(Express::VARP predicts, Express::VARP oneHotTargets) {
+    MNN_ASSERT(predicts->getInfo()->dim.size() == 2);
+    MNN_ASSERT(predicts->getInfo()->dim == oneHotTargets->getInfo()->dim);
+    auto loss = _ReduceMean(_ReduceSum(_Maximum(_Const(0.), _Const(1.) - predicts * oneHotTargets), {1}), {});
+    return loss;
+}
+
+Express::VARP _DistillLoss(Express::VARP studentLogits, Express::VARP teacherLogits, Express::VARP oneHotTargets, const float temperature, const float alpha) {
+    auto info = teacherLogits->getInfo();
+    if (info->order == NC4HW4) {
+        teacherLogits = _Convert(teacherLogits, NCHW);
+        studentLogits = _Convert(studentLogits, NCHW);
+    }
+    MNN_ASSERT(studentLogits->getInfo()->dim.size() == 2);
+    MNN_ASSERT(studentLogits->getInfo()->dim == teacherLogits->getInfo()->dim);
+    MNN_ASSERT(studentLogits->getInfo()->dim == oneHotTargets->getInfo()->dim);
+    MNN_ASSERT(alpha >= 0 && alpha <= 1);
+    auto softTargets = _Softmax(teacherLogits * _Scalar(1 / temperature));
+    auto studentPredict = _Softmax(studentLogits * _Scalar(1 / temperature));
+    auto loss1 = _Scalar(temperature * temperature) * _KLDivergence(studentPredict, softTargets);
+    auto loss2 = _CrossEntropy(_Softmax(studentLogits), oneHotTargets);
+    auto loss = _Scalar(alpha) * loss1 + _Scalar(1 - alpha) * loss2;
+    return loss;
+}
+
+} // namespace Train
+} // namespace MNN