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/**
* Copyright (c) 2016-present, Facebook, Inc.
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
#include "select_smooth_l1_loss_op.h"
namespace caffe2 {
REGISTER_CPU_OPERATOR(
SelectSmoothL1Loss,
SelectSmoothL1LossOp<float, CPUContext>);
REGISTER_CPU_OPERATOR(
SelectSmoothL1LossGradient,
SelectSmoothL1LossGradientOp<float, CPUContext>);
OPERATOR_SCHEMA(SelectSmoothL1Loss)
.NumInputs(4)
.NumOutputs(1)
.SetDoc(R"DOC(
RetinaNet specific op for computing Smooth L1 Loss at select locations in a 4D
tensor that encodes bounding box regression predictions.
)DOC")
.Arg(
"beta",
"(float) default 1.0; L2 to L1 transition point.")
.Arg(
"scale",
"(float) default 1.0; multiply the loss by this scale factor.")
.Input(
0,
"Y_hat",
"4D tensor of bounding box regression predictions with shape "
"(N, 4 * num_bbox_classes * num_anchors, H, W).")
.Input(
1,
"Y",
"2D tensor of labels shape (M, 4) for 4 contiguous channels starting "
"at each of the M locations selected by the locations input.")
.Input(
2,
"locations",
"2D tensor of shape (M, 4) that identifies M 'select' locations "
"encoded by the four columns: (n, c, y, x). The loss is computed on the "
"four contiguous channel locations [c, c + 3] (inclusive).")
.Input(
3,
"normalizer",
"Scalar; the loss is divided by max(1, normalizer).")
.Output(
0,
"loss",
"Scalar loss.");
OPERATOR_SCHEMA(SelectSmoothL1LossGradient)
.NumInputs(5)
.NumOutputs(1)
.Input(
0,
"Y_hat",
"See SelectSmoothL1Loss.")
.Input(
1,
"Y",
"See SelectSmoothL1Loss.")
.Input(
2,
"locations",
"See SelectSmoothL1Loss.")
.Input(
3,
"normalizer",
"See SelectSmoothL1Loss.")
.Input(
4,
"d_loss",
"Gradient of forward output 0 (loss).")
.Output(
0,
"d_Y_hat",
"Gradient of forward input 0 (Y_hat).");
class GetSelectSmoothL1LossGradient : public GradientMakerBase {
using GradientMakerBase::GradientMakerBase;
vector<OperatorDef> GetGradientDefs() override {
return SingleGradientDef(
"SelectSmoothL1LossGradient",
"",
vector<string>{I(0), I(1), I(2), I(3), GO(0)},
vector<string>{GI(0)});
}
};
REGISTER_GRADIENT(SelectSmoothL1Loss, GetSelectSmoothL1LossGradient);
} // namespace caffe2
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