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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
84 lines
2.8 KiB
C++
84 lines
2.8 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 "modules/detectron/group_spatial_softmax_op.h"
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#include "caffe2/operators/softmax_utils.h"
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namespace caffe2 {
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REGISTER_CPU_OPERATOR(
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GroupSpatialSoftmax,
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GroupSpatialSoftmaxOp<float, CPUContext>);
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REGISTER_CPU_OPERATOR(
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GroupSpatialSoftmaxGradient,
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GroupSpatialSoftmaxGradientOp<float, CPUContext>);
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OPERATOR_SCHEMA(GroupSpatialSoftmax)
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.NumInputs(1)
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.NumOutputs(1)
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.SetDoc(R"DOC(
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RetinaNet specific form of spatial softmax.
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The input is assumed to be unnormalized scores (sometimes called 'logits')
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arranged in a 4D tensor with shape (N, C, H, W), where N is the number of
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elements in the batch, H and W are the height and width, and C = num_anchors *
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num_classes defines num_anchors 'groups' of softmax inputs, each of length
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num_classes. The softmax is applied to each group independently.
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See: https://arxiv.org/abs/1708.02002 for details.
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)DOC")
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.Arg(
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"num_classes",
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"(int) default 81; number of classes in each softmax group.")
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.Input(
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0,
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"scores",
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"4D tensor of softmax inputs (called 'scores' or 'logits') with shape "
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"(N, C, H, W), where C = num_anchors * num_classes defines num_anchors "
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"groups of contiguous num_classes softmax inputs.")
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.Output(
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0,
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"probabilities",
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"4D tensor of softmax probabilities with shape (N, C, H, W), where "
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"C = num_anchors * num_classes, and softmax was applied to each of the "
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"num_anchors groups; within a group the num_classes values sum to 1.");
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OPERATOR_SCHEMA(GroupSpatialSoftmaxGradient)
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.NumInputs(2)
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.NumOutputs(1)
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.Input(0, "scores", "See GroupSpatialSoftmax")
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.Input(
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1,
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"d_probabilities",
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"Gradient of forward output 0 (probabilities).")
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.Output(0, "d_scores", "Gradient of forward input 0 (scores).");
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class GetGroupSpatialSoftmaxGradient : public GradientMakerBase {
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using GradientMakerBase::GradientMakerBase;
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vector<OperatorDef> GetGradientDefs() override {
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return SingleGradientDef(
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"GroupSpatialSoftmaxGradient",
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"",
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vector<string>{O(0), GO(0)},
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vector<string>{GI(0)});
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}
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};
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REGISTER_GRADIENT(GroupSpatialSoftmax, GetGroupSpatialSoftmaxGradient);
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} // namespace caffe2
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