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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
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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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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