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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
118 lines
3.3 KiB
C++
118 lines
3.3 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 "smooth_l1_loss_op.h"
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namespace caffe2 {
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REGISTER_CPU_OPERATOR(SmoothL1Loss, SmoothL1LossOp<float, CPUContext>);
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REGISTER_CPU_OPERATOR(
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SmoothL1LossGradient,
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SmoothL1LossGradientOp<float, CPUContext>);
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OPERATOR_SCHEMA(SmoothL1Loss)
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.NumInputs(4)
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.NumOutputs(1)
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.SetDoc(R"DOC(
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Smooth L1 Loss is a minor variation of Huber loss in which the point of
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transition between L2 loss and L1 loss is adjustable by a hyper-parameter beta:
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SmoothL1(x) = 0.5 * x^2 / beta if |x| < beta
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|x| - 0.5 * beta otherwise.
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SmoothL1 is used in Fast R-CNN and descendants as the loss function for bounding
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box regression.
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The loss computed by this op has a flexible form:
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scale / N * sum_i alpha_out[i] * SmoothL1(alpha_in[i] * (y_hat[i] - y[i])).
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The weights alpha_in and alpha_out are called the "inside" and "outside"
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weights, respectively. The inside weights are typically set to either 0 or 1 to
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implement ignoring (when 0) certain samples. The outside weights can be used
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to implement a per-sample loss weight. The overall loss is scaled by scale / N,
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where N is the number of batch elements in the input predictions.
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)DOC")
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.Arg(
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"beta",
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"(float) default 1.0; L2 to L1 transition point.")
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.Arg(
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"scale",
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"(float) default 1.0; multiply the loss by this scale factor.")
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.Input(
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0,
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"Y_hat",
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"Tensor of predictions (at least 1D).")
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.Input(
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1,
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"Y",
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"Tensor of labels with the same shape as Y_hat.")
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.Input(
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2,
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"alpha_in",
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"Tensor of inside weights with the same shape as Y.")
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.Input(
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3,
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"alpha_out",
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"Tensor of outside weights with the same shape as Y.")
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.Output(
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0,
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"loss",
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"Scalar loss.");
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OPERATOR_SCHEMA(SmoothL1LossGradient)
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.NumInputs(5)
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.NumOutputs(1)
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.Input(
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0,
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"Y_hat",
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"See SmoothL1Loss.")
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.Input(
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1,
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"Y",
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"See SmoothL1Loss.")
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.Input(
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2,
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"alpha_in",
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"See SmoothL1Loss.")
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.Input(
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3,
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"alpha_out",
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"See SmoothL1Loss.")
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.Input(
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4,
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"d_loss",
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"Gradient of forward output 0 (loss).")
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.Output(
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0,
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"d_Y_hat",
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"Gradient of forward input 0 (Y_hat).");
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class GetSmoothL1LossGradient : 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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"SmoothL1LossGradient",
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"",
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vector<string>{I(0), I(1), I(2), I(3), GO(0)},
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vector<string>{GI(0)});
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}
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};
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REGISTER_GRADIENT(SmoothL1Loss, GetSmoothL1LossGradient);
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} // namespace caffe2
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