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4f34cd6d1e
Avoid exposing defines that conflict with google logging, since this blocks external usage of libtorch in certain cases. All the 'interesting' changes should be in these two files, and the rest should just be mechanical changes via sed. c10/util/logging_is_not_google_glog.h c10/util/logging_is_google_glog.h Fixes https://github.com/pytorch/pytorch/issues/81415 cc @miladm @malfet Pull Request resolved: https://github.com/pytorch/pytorch/pull/82032 Approved by: https://github.com/soumith, https://github.com/miladm
40 lines
1.2 KiB
C++
40 lines
1.2 KiB
C++
#include "caffe2/operators/perplexity_op.h"
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namespace caffe2 {
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template <>
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bool PerplexityOp<float, CPUContext>::RunOnDevice() {
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auto& X = Input(0);
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TORCH_DCHECK_EQ(X.dim(), 1);
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int N = X.dim32(0);
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auto* Y = Output(0, vector<int64_t>(), at::dtype<float>());
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const auto* Xdata = X.data<float>();
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float perplexity = 1.0;
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for (int i = 0; i < N; ++i) {
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perplexity *= pow(Xdata[i], -1.0/N);
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}
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*(Y->template mutable_data<float>()) = perplexity;
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return true;
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}
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REGISTER_CPU_OPERATOR(Perplexity, PerplexityOp<float, CPUContext>);
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OPERATOR_SCHEMA(Perplexity).NumInputs(1).NumOutputs(1)
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.SetDoc(R"DOC(
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Perplexity calculates how well a probability distribution predicts a sample.
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Perplexity takes a 1-D tensor containing a batch of probabilities. Each value
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in the tensor belongs to a different sample and represents the probability of
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the model predicting the true label for that sample. The operator returns a
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single (float) perplexity value for the batch.
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)DOC")
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.Input(0, "probabilities", "The input data as Tensor. It contains a batch of"
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"true label or target probabilities")
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.Output(0, "output", "The output- a single (float) perplexity value for the "
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"batch");
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SHOULD_NOT_DO_GRADIENT(Perplexity);
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} // namespace caffe2
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