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Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/13856 Codemod generated with clangr shard mode, 25 files per diff, motivation: https://github.com/pytorch/pytorch/pull/12407 Reviewed By: smessmer Differential Revision: D13007310 fbshipit-source-id: 941f064ef8934bb17fbfb706e6ed3db173b5d268
70 lines
2.2 KiB
C++
70 lines
2.2 KiB
C++
#include "caffe2/operators/summarize_op.h"
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namespace caffe2 {
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template <>
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bool SummarizeOp<float, CPUContext>::RunOnDevice() {
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auto& X = Input(0);
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const auto N = X.numel();
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CAFFE_ENFORCE_GT(N, 0);
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const float* Xdata = X.data<float>();
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double mean = 0;
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float max = Xdata[0];
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float min = Xdata[0];
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for (auto i = 0; i < N; ++i) {
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mean += static_cast<double>(Xdata[i]) / N;
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max = std::max(max, Xdata[i]);
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min = std::min(min, Xdata[i]);
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}
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// We will simply do a two-pass. More efficient solutions can be written but
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// I'll keep code simple for now.
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double standard_deviation = 0;
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for (auto i = 0; i < N; ++i) {
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double diff = Xdata[i] - mean;
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standard_deviation += diff * diff;
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}
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// Unbiased or biased? Let's do unbiased now.
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standard_deviation = N == 1 ? 0 : std::sqrt(standard_deviation / (N - 1));
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if (to_file_) {
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(*log_file_) << min << " " << max << " " << mean << " "
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<< standard_deviation << std::endl;
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}
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if (OutputSize()) {
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auto* Y = Output(0, {NUM_STATS}, at::dtype<float>());
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float* Ydata = Y->template mutable_data<float>();
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Ydata[MIN_IDX] = min;
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Ydata[MAX_IDX] = max;
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Ydata[MEAN_IDX] = static_cast<float>(mean);
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Ydata[STD_IDX] = static_cast<float>(standard_deviation);
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}
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return true;
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}
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REGISTER_CPU_OPERATOR(Summarize, SummarizeOp<float, CPUContext>);
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// Input: X; output: if set, a summarized Tensor of shape 4, with the values
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// being min, max, mean and std respectively.
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OPERATOR_SCHEMA(Summarize)
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.NumInputs(1)
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.NumOutputs(0, 1)
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.SetDoc(R"DOC(
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Summarize computes four statistics of the input tensor (Tensor)- min,
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max, mean and standard deviation. The output will be written to a 1-D tensor of
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size 4 if an output tensor is provided. Else, if the argument 'to_file' is
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greater than 0, the values are written to a log file in the root folder.
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)DOC")
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.Arg(
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"to_file",
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"(int, default 0) flag to indicate if the summarized "
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"statistics have to be written to a log file.")
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.Input(0, "data", "The input data as Tensor.")
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.Output(
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0,
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"output",
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"1-D tensor (Tensor) of size 4 containing min, "
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"max, mean and standard deviation");
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SHOULD_NOT_DO_GRADIENT(Summarize);
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} // namespace caffe2
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