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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
103 lines
3.0 KiB
C++
103 lines
3.0 KiB
C++
/**
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* Copyright (c) 2016-present, Facebook, Inc.
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*
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* Licensed under the Apache License, Version 2.0 (the "License");
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* you may not use this file except in compliance with the License.
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* You may obtain a copy of the License at
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*
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* http://www.apache.org/licenses/LICENSE-2.0
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*
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* Unless required by applicable law or agreed to in writing, software
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* distributed under the License is distributed on an "AS IS" BASIS,
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* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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* See the License for the specific language governing permissions and
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* limitations under the License.
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*/
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#include "caffe2/operators/stump_func_op.h"
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namespace caffe2 {
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template <>
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bool StumpFuncOp<float, float, CPUContext>::RunOnDevice() {
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auto& in = Input(0);
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const float* in_data = in.template data<float>();
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auto* out = Output(0, in.sizes(), at::dtype<float>());
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float* out_data = out->template mutable_data<float>();
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for (int i = 0; i < in.numel(); i++) {
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out_data[i] = (in_data[i] <= threshold_) ? low_value_ : high_value_;
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}
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return true;
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}
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template <>
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bool StumpFuncIndexOp<float, int64_t, CPUContext>::RunOnDevice() {
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auto& in = Input(0);
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const float* in_data = in.template data<float>();
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int lo_cnt = 0;
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for (int i = 0; i < in.numel(); i++) {
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lo_cnt += (in_data[i] <= threshold_);
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}
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auto* out_lo = Output(0, {lo_cnt}, at::dtype<int64_t>());
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auto* out_hi = Output(1, {in.numel() - lo_cnt}, at::dtype<int64_t>());
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int64_t* lo_data = out_lo->template mutable_data<int64_t>();
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int64_t* hi_data = out_hi->template mutable_data<int64_t>();
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int lidx = 0;
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int hidx = 0;
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for (int i = 0; i < in.numel(); i++) {
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if (in_data[i] <= threshold_) {
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lo_data[lidx++] = i;
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} else {
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hi_data[hidx++] = i;
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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(StumpFunc, StumpFuncOp<float, float, CPUContext>);
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OPERATOR_SCHEMA(StumpFunc)
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.NumInputs(1)
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.NumOutputs(1)
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.Input(0, "X", "tensor of float")
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.Output(0, "Y", "tensor of float")
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.TensorInferenceFunction([](const OperatorDef&,
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const vector<TensorShape>& input_types) {
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vector<TensorShape> out(1);
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out.at(0) = input_types.at(0);
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out.at(0).set_data_type(TensorProto_DataType::TensorProto_DataType_FLOAT);
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return out;
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})
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.SetDoc(R"DOC(
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Converts each input element into either high_ or low_value
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based on the given threshold.
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)DOC");
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NO_GRADIENT(StumpFunc);
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REGISTER_CPU_OPERATOR(
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StumpFuncIndex,
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StumpFuncIndexOp<float, int64_t, CPUContext>);
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OPERATOR_SCHEMA(StumpFuncIndex)
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.NumInputs(1)
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.NumOutputs(2)
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.Input(0, "X", "tensor of float")
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.Output(
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0,
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"Index_Low",
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"tensor of int64 indices for elements below/equal threshold")
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.Output(
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1,
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"Index_High",
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"tensor of int64 indices for elements above threshold")
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.SetDoc(R"DOC(
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Split the elements and return the indices based on the given threshold.
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)DOC");
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NO_GRADIENT(StumpFuncIndex);
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} // namespace caffe2
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