"""
BSD 3-Clause License
Copyright (c) Soumith Chintala 2016,
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 the copyright holder 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 HOLDER 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.
Copyright 2020 Huawei Technologies Co., Ltd
Licensed under the BSD 3-Clause License (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
https://spdx.org/licenses/BSD-3-Clause.html
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.
"""
import torch
import torch.nn as nn
import torch.nn.functional as F
class SoftTargetCrossEntropy(nn.Module):
"""
The native CE loss with soft target
input: x is output of model, target is ground truth
return: loss
"""
def __init__(self):
super(SoftTargetCrossEntropy, self).__init__()
def forward(self, x, target):
N_rep = x.shape[0]
N = target.shape[0]
if not N == N_rep:
target = target.repeat(N_rep // N, 1)
loss = torch.sum(-target * F.log_softmax(x, dim=-1), dim=-1)
return loss.mean()
class TokenLabelGTCrossEntropy(nn.Module):
"""
Token labeling dense loss with ground gruth, see more from token labeling
input: x is output of model, target is ground truth
return: loss
"""
def __init__(self,
dense_weight=1.0,
cls_weight=1.0,
mixup_active=True,
smoothing=0.1,
classes=1000):
super(TokenLabelGTCrossEntropy, self).__init__()
self.CE = SoftTargetCrossEntropy()
self.dense_weight = dense_weight
self.smoothing = smoothing
self.mixup_active = mixup_active
self.classes = classes
self.cls_weight = cls_weight
assert dense_weight + cls_weight > 0
def forward(self, x, target):
output, aux_output, bb = x
bbx1, bby1, bbx2, bby2 = bb
B, N, C = aux_output.shape
if len(target.shape) == 2:
target_cls = target
target_aux = target.repeat(1, N).reshape(B * N, C)
else:
ground_truth = target[:, :, 0]
target_cls = target[:, :, 1]
ratio = (0.9 - 0.4 *
(ground_truth.max(-1)[1] == target_cls.max(-1)[1])
).unsqueeze(-1)
target_cls = target_cls * ratio + ground_truth * (1 - ratio)
target_aux = target[:, :, 2:]
target_aux = target_aux.transpose(1, 2).reshape(-1, C)
lam = 1 - ((bbx2 - bbx1) * (bby2 - bby1) / N)
if lam < 1:
target_cls = lam * target_cls + (1 - lam) * target_cls.flip(0)
aux_output = aux_output.reshape(-1, C)
loss_cls = self.CE(output, target_cls)
loss_aux = self.CE(aux_output, target_aux)
return self.cls_weight * loss_cls + self.dense_weight * loss_aux
class TokenLabelSoftTargetCrossEntropy(nn.Module):
"""
Token labeling dense loss with soft target, see more from token labeling
input: x is output of model, target is ground truth
return: loss
"""
def __init__(self):
super(TokenLabelSoftTargetCrossEntropy, self).__init__()
def forward(self, x, target):
N_rep = x.shape[0]
N = target.shape[0]
if not N == N_rep:
target = target.repeat(N_rep // N, 1)
if len(target.shape) == 3 and target.shape[-1] == 2:
target = target[:, :, 1]
loss = torch.sum(-target * F.log_softmax(x, dim=-1), dim=-1)
return loss.mean()
class TokenLabelCrossEntropy(nn.Module):
"""
Token labeling loss without ground truth
input: x is output of model, target is ground truth
return: loss
"""
def __init__(self,
dense_weight=1.0,
cls_weight=1.0,
mixup_active=True,
classes=1000):
"""
Constructor Token labeling loss.
"""
super(TokenLabelCrossEntropy, self).__init__()
self.CE = SoftTargetCrossEntropy()
self.dense_weight = dense_weight
self.mixup_active = mixup_active
self.classes = classes
self.cls_weight = cls_weight
assert dense_weight + cls_weight > 0
def forward(self, x, target):
output, aux_output, bb = x
bbx1, bby1, bbx2, bby2 = bb
B, N, C = aux_output.shape
if len(target.shape) == 2:
target_cls = target
target_aux = target.repeat(1, N).reshape(B * N, C)
else:
target_cls = target[:, :, 1]
target_aux = target[:, :, 2:]
target_aux = target_aux.transpose(1, 2).reshape(-1, C)
lam = 1 - ((bbx2 - bbx1) * (bby2 - bby1) / N)
if lam < 1:
target_cls = lam * target_cls + (1 - lam) * target_cls.flip(0)
aux_output = aux_output.reshape(-1, C)
loss_cls = self.CE(output, target_cls)
loss_aux = self.CE(aux_output, target_aux)
return self.cls_weight * loss_cls + self.dense_weight * loss_aux