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8aa5174106
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/13944 Pull Request resolved: https://github.com/pytorch/pytorch/pull/13854 Codemod generated with clangr shard mode, 25 files per diff, motivation: https://github.com/pytorch/pytorch/pull/12407 Reviewed By: ezyang Differential Revision: D13054836 fbshipit-source-id: 5de07a156687f1ee607d0450410881d9176a87a7
85 lines
2.2 KiB
C++
85 lines
2.2 KiB
C++
#include "caffe2/operators/multi_class_accuracy_op.h"
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namespace caffe2 {
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template <>
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bool MultiClassAccuracyOp<float, CPUContext>::RunOnDevice() {
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auto& X = Input(PREDICTION);
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auto& label = Input(LABEL);
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DCHECK_EQ(X.dim(), 2);
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// amount, number of instances
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int N = X.dim32(0);
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// dimension, number of classes
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int D = X.dim32(1);
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DCHECK_EQ(label.dim(), 1);
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DCHECK_EQ(label.dim32(0), N);
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auto* Y0 = Output(0, {D}, at::dtype<float>());
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auto* Y1 = Output(1, {D}, at::dtype<int>());
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const auto* Xdata = X.data<float>();
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const auto* labeldata = label.data<int>();
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auto* accuracies = Y0->template mutable_data<float>();
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auto* amounts = Y1->template mutable_data<int>();
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std::fill(accuracies, accuracies + D, 0);
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std::fill(amounts, amounts + D, 0);
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for (int i = 0; i < N; ++i) {
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float maxval = std::numeric_limits<float>::lowest();
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int maxid = 0;
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for (int j = 0; j < D; ++j) {
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if (Xdata[i * D + j] > maxval) {
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maxval = Xdata[i * D + j];
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maxid = j;
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}
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}
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int labelid = labeldata[i];
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DCHECK_LT(labelid, D);
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if (maxid == labelid) {
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accuracies[labelid]++;
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}
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amounts[labelid]++;
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}
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for (int i = 0; i < D; ++i) {
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int amount = amounts[i];
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if (amount) {
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accuracies[i] /= amount;
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}
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}
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return true;
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}
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REGISTER_CPU_OPERATOR(
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MultiClassAccuracy, MultiClassAccuracyOp<float, CPUContext>);
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OPERATOR_SCHEMA(MultiClassAccuracy)
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.NumInputs(2)
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.NumOutputs(2)
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.SetDoc(R"DOC(
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Respectively compute accuracy score for each class given a number of instances
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and predicted scores of each class for each instance.
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)DOC")
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.Input(
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0,
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"prediction",
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"2-D float tensor (N,D,) of predicted scores of each class for "
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"each data. N is the number of instances, i.e., batch size. D is number of "
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"possible classes/labels.")
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.Input(
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1,
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"labels",
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"1-D int tensor (N,) of labels for each instance.")
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.Output(
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0,
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"accuracies",
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"1-D float tensor (D,) of accuracy for each class. If a class has no "
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"instance in the batch, its accuracy score is set to zero.")
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.Output(
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1,
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"amounts",
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"1-D int tensor (D,) of number of instances for each class in the batch.");
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SHOULD_NOT_DO_GRADIENT(MultiClassAccuracy);
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} // namespace caffe2
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