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#include "caffe2/operators/margin_ranking_criterion_op.h"
#include <algorithm>
#include "caffe2/utils/math.h"
namespace caffe2 {
template <>
bool MarginRankingCriterionOp<CPUContext>::RunOnDevice() {
auto& X1 = Input(0);
auto& X2 = Input(1);
auto& Y = Input(2);
CAFFE_ENFORCE_EQ(
X1.numel(),
X2.numel(),
"The two inputs for computing ranking loss should have the same size.");
CAFFE_ENFORCE_EQ(
X1.numel(), Y.numel(), "The input and label should have the same size.");
auto* loss = Output(0, X1.sizes(), at::dtype<float>());
const float* X1data = X1.data<float>();
const float* X2data = X2.data<float>();
const int* Ydata = Y.data<int>();
float* output = loss->template mutable_data<float>();
for (int i = 0; i < X1.numel(); ++i) {
// NOLINTNEXTLINE(bugprone-narrowing-conversions,cppcoreguidelines-narrowing-conversions)
output[i] = std::max(-Ydata[i] * (X1data[i] - X2data[i]) + margin_, 0.f);
}
return true;
}
template <>
bool MarginRankingCriterionGradientOp<CPUContext>::RunOnDevice() {
auto& X1 = Input(0);
auto& X2 = Input(1);
auto& Y = Input(2);
auto& dLoss = Input(3);
auto* dX1 = Output(0, X1.sizes(), at::dtype<float>());
auto* dX2 = Output(1, X2.sizes(), at::dtype<float>());
const float* X1data = X1.data<float>();
const float* X2data = X2.data<float>();
const int* Ydata = Y.data<int>();
const float* dLoss_data = dLoss.data<float>();
float* dX1_data = dX1->template mutable_data<float>();
float* dX2_data = dX2->template mutable_data<float>();
for (int i = 0; i < X1.numel(); ++i) {
// NOLINTNEXTLINE(bugprone-narrowing-conversions,cppcoreguidelines-narrowing-conversions)
auto dist = -Ydata[i] * (X1data[i] - X2data[i]) + margin_;
if (dist < 0.f) {
dX1_data[i] = dX2_data[i] = 0.f;
} else {
// NOLINTNEXTLINE(bugprone-narrowing-conversions,cppcoreguidelines-narrowing-conversions)
dX1_data[i] = -Ydata[i] * dLoss_data[i];
// NOLINTNEXTLINE(bugprone-narrowing-conversions,cppcoreguidelines-narrowing-conversions)
dX2_data[i] = Ydata[i] * dLoss_data[i];
}
}
return true;
}
REGISTER_CPU_OPERATOR(
MarginRankingCriterion,
MarginRankingCriterionOp<CPUContext>);
REGISTER_CPU_OPERATOR(
MarginRankingCriterionGradient,
MarginRankingCriterionGradientOp<CPUContext>);
OPERATOR_SCHEMA(MarginRankingCriterion)
.NumInputs(3)
.NumOutputs(1)
.SetDoc(R"DOC(
MarginRankingCriterion takes two input data X1 (Tensor),
X2 (Tensor), and label Y (Tensor) to produce the
loss (Tensor) where the loss function,
loss(X1, X2, Y) = max(0, -Y * (X1 - X2) + margin), is applied to
the tensor elementwise.
If y == 1 then it assumed the first input should be ranked higher
(have a larger value) than the second input, and vice-versa for
y == -1.
)DOC")
.Arg("margin", "The margin value as a float. Default is 1.0.")
.Input(0, "X1", "The left input vector as a 1-dim TensorCPU.")
.Input(1, "X2", "The right input vector as a 1-dim TensorCPU.")
.Input(2, "Y", "The label as a 1-dim TensorCPU with int value of 1 or -1.")
.Output(0, "loss", "The output loss with the same dimensionality as X1.");
OPERATOR_SCHEMA(MarginRankingCriterionGradient)
.NumInputs(4)
.NumOutputs(2)
.SetDoc(R"DOC(
MarginRankingCriterionGradient takes both X1, X2, Y and dY and
uses them to update dX1, and dX2 according to the chain rule
and derivatives of the loss function.
)DOC");
class GetMarginRankingCriterionGradient : public GradientMakerBase {
using GradientMakerBase::GradientMakerBase;
vector<OperatorDef> GetGradientDefs() override {
return SingleGradientDef(
"MarginRankingCriterionGradient",
"",
vector<string>{I(0), I(1), I(2), GO(0)},
vector<string>{GI(0), GI(1)});
}
};
REGISTER_GRADIENT(MarginRankingCriterion, GetMarginRankingCriterionGradient);
} // namespace caffe2
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