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https://github.com/zebrajr/pytorch.git
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a9b0a921d5
Summary: As GoogleTest `TEST` macro is non-compliant with it as well as `DEFINE_DISPATCH` All changes but the ones to `.clang-tidy` are generated using following script: ``` for i in `find . -type f -iname "*.c*" -or -iname "*.h"|xargs grep cppcoreguidelines-avoid-non-const-global-variables|cut -f1 -d:|sort|uniq`; do sed -i "/\/\/ NOLINTNEXTLINE(cppcoreguidelines-avoid-non-const-global-variables)/d" $i; done ``` Pull Request resolved: https://github.com/pytorch/pytorch/pull/62008 Reviewed By: driazati, r-barnes Differential Revision: D29838584 Pulled By: malfet fbshipit-source-id: 1b2f8602c945bd4ce50a9bfdd204755556e31d13
66 lines
2.1 KiB
C++
66 lines
2.1 KiB
C++
#include "caffe2/operators/conditional_op.h"
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#include "caffe2/core/operator.h"
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#include "caffe2/core/tensor.h"
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namespace caffe2 {
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template <>
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bool ConditionalOp<CPUContext>::RunOnDevice() {
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auto& condition = Input(0);
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auto& dataT = Input(1);
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auto& dataF = Input(2);
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// verify the inputs shape
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CAFFE_ENFORCE_EQ(condition.dim(), 1);
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CAFFE_ENFORCE(dataT.dim() >= 1);
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CAFFE_ENFORCE(dataT.sizes()[0] == condition.sizes()[0]);
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CAFFE_ENFORCE_EQ(dataT.dim(), dataF.dim());
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for (size_t i = 0; i < dataT.sizes().size(); i++) {
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CAFFE_ENFORCE(dataT.sizes().at(i) == dataF.sizes().at(i));
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}
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const auto innerSize = dataT.size_from_dim(1);
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const auto innerSizeBytes = innerSize * dataT.dtype().itemsize();
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CAFFE_ENFORCE(innerSize * dataF.dtype().itemsize() == innerSizeBytes);
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// initialize output shape
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auto* dataOut = Output(0);
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const auto* condPtr = condition.template data<bool>();
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dataOut->ResizeLike(dataT);
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auto* outPtr = (char*)dataOut->raw_mutable_data(dataT.dtype());
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// perform conditional op along first dimension
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const auto* ptrT = (char*)dataT.raw_data();
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const auto* ptrF = (char*)dataF.raw_data();
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for (int64_t i = 0; i < condition.numel(); i++) {
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auto* dst = outPtr + i * innerSizeBytes;
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if (condPtr[i]) {
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context_.CopyItemsSameDevice(
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dataT.dtype(), innerSize, ptrT + i * innerSizeBytes, dst);
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} else {
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context_.CopyItemsSameDevice(
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dataF.dtype(), innerSize, ptrF + i * innerSizeBytes, dst);
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}
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}
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return true;
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}
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REGISTER_CPU_OPERATOR(Conditional, ConditionalOp<CPUContext>);
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OPERATOR_SCHEMA(Conditional)
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.NumInputs(3)
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.NumOutputs(1)
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.SetDoc(R"DOC(
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Given a 1-D tensor of boolean values, apply conditional operator along the first
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dimension of DataT and DataF and return DataO. Note, DataT and DataF must
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have the exact same shape and type.
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)DOC")
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.Input(0, "Condition", "Boolean tensor to select DataT or DataF")
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.Input(1, "DataT", "Data to use when True")
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.Input(2, "DataF", "Data to use when False")
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.Output(0, "DataO", "Output data after applying ConditionalOp")
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.IdenticalTypeAndShapeOfInput(1);
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NO_GRADIENT(Conditional);
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} // caffe2
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