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#include "caffe2/operators/perplexity_op.h"
namespace caffe2 {
template <>
bool PerplexityOp<float, CPUContext>::RunOnDevice() {
auto& X = Input(0);
TORCH_DCHECK_EQ(X.dim(), 1);
int N = X.dim32(0);
auto* Y = Output(0, vector<int64_t>(), at::dtype<float>());
const auto* Xdata = X.data<float>();
float perplexity = 1.0;
for (int i = 0; i < N; ++i) {
perplexity *= pow(Xdata[i], -1.0/N);
}
*(Y->template mutable_data<float>()) = perplexity;
return true;
}
REGISTER_CPU_OPERATOR(Perplexity, PerplexityOp<float, CPUContext>);
OPERATOR_SCHEMA(Perplexity).NumInputs(1).NumOutputs(1)
.SetDoc(R"DOC(
Perplexity calculates how well a probability distribution predicts a sample.
Perplexity takes a 1-D tensor containing a batch of probabilities. Each value
in the tensor belongs to a different sample and represents the probability of
the model predicting the true label for that sample. The operator returns a
single (float) perplexity value for the batch.
)DOC")
.Input(0, "probabilities", "The input data as Tensor. It contains a batch of"
"true label or target probabilities")
.Output(0, "output", "The output- a single (float) perplexity value for the "
"batch");
SHOULD_NOT_DO_GRADIENT(Perplexity);
} // namespace caffe2
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