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Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/10974 Pull Request resolved: https://github.com/pytorch/pytorch/pull/10291 This new operator will do the following: Given a LENGTHS vector and n_splits, output a "split" LENGTHS vector where: 1. Each length in input vector is split into n_splits values (thus output vector should have LENGTHS.size(0) * n_splits elements) 2. The new lengths in output should be evenly split, and if the length is not divisible by n_splits, then order new values in descending order. (e.g. n_splits = 3, length = 5 -> 2 2 1) 3. If n_splits > some element in the array, its split elements will contain 0s. (e.g. n_splits = 3, length = 2 - > 1 1 0) Reviewed By: bddppq, chocjy Differential Revision: D9013119 fbshipit-source-id: 82bf3371ec08c41fc3379177f0007afc142e0d84
38 lines
1.2 KiB
C++
38 lines
1.2 KiB
C++
#include "caffe2/operators/length_split_op.h"
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namespace caffe2 {
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REGISTER_CPU_OPERATOR(LengthsSplit, LengthsSplitOp<CPUContext>);
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OPERATOR_SCHEMA(LengthsSplit)
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.NumInputs(1, 2)
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.NumOutputs(1)
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.ScalarType(TensorProto::INT32)
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.SetDoc(R"DOC(
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Given input vector LENGTHS, and input n_split, LengthsSplit returns
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a single output vector. It "splits" each length into n_split values which add
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up to the original length. It will attempt to do equal splits, and if not possible,
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it orders larger values first. If the n_split is larger than the length, zero
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padding will be applied.
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e.g. LENGTHS = [9 4 5]
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n_split = 3
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Y = [3 3 3 2 1 1 2 2 1]
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e.g. LENGTHS = [2, 1, 2]
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n_split = 3
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Y = [1 1 0 1 0 0 1 1 0]
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)DOC")
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.Arg("n_split", "Number of splits for each element in LENGTHS")
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.Input(0, "LENGTHS", "Mx1 Input tensor denoting INT32 lengths")
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.Input(
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1,
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"n_split",
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"(Optional) Number of splits for each element in LENGTHS (overrides argument)")
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.Output(0, "Y", "(M*n_split)x1 Output vector denoting split lengths");
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// TODO: Write gradient for this when needed
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GRADIENT_NOT_IMPLEMENTED_YET(LengthsSplit);
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} // namespace caffe2
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