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// This source file has been modified by The TinyMS Authors.

// Copyright 2021 Huawei Technologies Co., Ltd

//

// Copyright 2015, Google Inc.

// All rights reserved.

//

// Redistribution and use in source and binary forms, with or without

// modification, are permitted provided that the following conditions are

// met:

//

// * Redistributions of source code must retain the above copyright

// notice, this list of conditions and the following disclaimer.

// * Redistributions in binary form must reproduce the above

// copyright notice, this list of conditions and the following disclaimer

// in the documentation and/or other materials provided with the

// distribution.

// * Neither the name of Google Inc. nor the names of its

// contributors may be used to endorse or promote products derived from

// this software without specific prior written permission.

//

// THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS

// "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT

// LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR

// A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT

// OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,

// SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT

// LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,

// DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY

// THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT

// (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE

// OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.

syntax = "proto3";

package losses;

// CrossEntropyWith LabelSmooth.

message CrossEntropyWithLabelSmooth {

OneHot onehot = 1;

Tensor on_value =2;

Tensor off_value = 3;

SoftmaxCrossEntropyWithLogits ce = 4;

ReduceMean mean = 5;

Cast cast = 6;

}

// Provide SSD300 training loss through network.

message SSD300WithLoss {

Layer network = 1;

Less less = 2;

Tile tile = 3;

ReduceSum reduce_sum = 4;

ReduceMean reduce_mean = 5;

ExpandDims expand_dims = 6;

SigmoidFocalClassificationLoss class_loss = 7;

SmoothL1Loss loc_loss = 8;

}

// Cycle GAN loss factory.

message GANLoss {

MSELoss loss = 1;

OnesLike ones = 2;

}

// Cycle GAN generator loss.

message CycleGANGeneratorLoss {

float lambda_A = 1;

float lambda_B = 2;

float lambda_idt = 3;

bool use_identity = 4;

Layer generator = 5;

GANLoss dis_loss = 6;

L1Loss rec_loss = 7;

Layer D_A = 8;

Layer D_B = 9;

Tensor true = 10;

}

// Cycle GAN discriminator loss.

message CycleGANDiscriminatorLoss {

Layer D_A = 1;

Layer D_B = 2;

Tensor false = 3;

Tensor true = 4;

GANLoss dis_loss = 5;

L1Loss rec_loss = 6;

string reduction = 7;

}