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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 "modules/detectron/softmax_focal_loss_op.h"
#include "caffe2/operators/softmax_utils.h"
namespace caffe2 {
REGISTER_CPU_OPERATOR(SoftmaxFocalLoss, SoftmaxFocalLossOp<float, CPUContext>);
REGISTER_CPU_OPERATOR(
SoftmaxFocalLossGradient,
SoftmaxFocalLossGradientOp<float, CPUContext>);
OPERATOR_SCHEMA(SoftmaxFocalLoss)
.NumInputs(3)
.NumOutputs(2)
.SetDoc(R"DOC(
A multiclass form of Focal Loss designed for use in RetinaNet-like models.
The input is assumed to be unnormalized scores (sometimes called 'logits')
arranged in a 4D tensor with shape (N, C, H, W), where N is the number of
elements in the batch, H and W are the height and width, and C = num_anchors *
num_classes. The softmax is applied num_anchors times along the C axis.
The softmax version of focal loss is:
FL(p_t) = -alpha * (1 - p_t)**gamma * log(p_t),
where p_i = exp(s_i) / sum_j exp(s_j), t is the target (ground truth) class, and
s_j is the unnormalized score for class j.
See: https://arxiv.org/abs/1708.02002 for details.
)DOC")
.Arg(
"scale",
"(float) default 1.0; multiply the loss by this scale factor.")
.Arg("alpha", "(float) default 0.25; Focal Loss's alpha hyper-parameter.")
.Arg("gamma", "(float) default 1.0; Focal Loss's gamma hyper-parameter.")
.Arg(
"num_classes",
"(int) default 81; number of classes in each softmax group.")
.Input(
0,
"scores",
"4D tensor of softmax inputs (called 'scores' or 'logits') with shape "
"(N, C, H, W), where C = num_anchors * num_classes defines num_anchors "
"groups of contiguous num_classes softmax inputs.")
.Input(
1,
"labels",
"4D tensor of labels with shape (N, num_anchors, H, W). Each entry is "
"a class label in [0, num_classes - 1] (inclusive).")
.Input(
2,
"normalizer",
"Scalar; the loss is normalized by 1 / max(1, normalizer).")
.Output(0, "loss", "Scalar loss.")
.Output(
1,
"probabilities",
"4D tensor of softmax probabilities with shape (N, C, H, W), where "
"C = num_anchors * num_classes, and softmax was applied to each of the "
"num_anchors groups; within a group the num_classes values sum to 1.");
OPERATOR_SCHEMA(SoftmaxFocalLossGradient)
.NumInputs(5)
.NumOutputs(1)
.Input(0, "scores", "See SoftmaxFocalLoss.")
.Input(1, "labels", "See SoftmaxFocalLoss.")
.Input(2, "normalizer", "See SoftmaxFocalLoss.")
.Input(
3,
"probabilities",
"Output 1 from SoftmaxFocalLoss; See SoftmaxFocalLoss.")
.Input(4, "d_loss", "Gradient of forward output 0 (loss)")
.Output(0, "d_scores", "Gradient of forward input 0 (scores)");
class GetSoftmaxFocalLossGradient : public GradientMakerBase {
using GradientMakerBase::GradientMakerBase;
vector<OperatorDef> GetGradientDefs() override {
return SingleGradientDef(
"SoftmaxFocalLossGradient",
"",
vector<string>{I(0), I(1), I(2), O(1), GO(0)},
vector<string>{GI(0)});
}
};
REGISTER_GRADIENT(SoftmaxFocalLoss, GetSoftmaxFocalLossGradient);
} // namespace caffe2
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