text: /**
   * 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 "softmax_focal_loss_op.h"
  #include "caffe2/operators/softmax_shared.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
  