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mixup.py
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mixup.py
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# Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
#
# 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://opensource.org/licenses/BSD-3-Clause
#
# 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 numpy as np
def mixup(alpha, data, target):
with torch.no_grad():
bs = data.size(0)
c = np.random.beta(alpha, alpha)
perm = torch.randperm(bs).cuda()
md = c * data + (1 - c) * data[perm, :]
mt = c * target + (1 - c) * target[perm, :]
return md, mt
class MixUpWrapper(object):
def __init__(self, alpha, dataloader):
self.alpha = alpha
self.dataloader = dataloader
def mixup_loader(self, loader):
for input, target in loader:
i, t = mixup(self.alpha, input, target)
yield i, t
def __iter__(self):
return self.mixup_loader(self.dataloader)
def __len__(self):
return len(self.dataloader)
class NLLMultiLabelSmooth(nn.Module):
def __init__(self, smoothing=0.0):
super(NLLMultiLabelSmooth, self).__init__()
self.confidence = 1.0 - smoothing
self.smoothing = smoothing
def forward(self, x, target):
if self.training:
x = x.float()
target = target.float()
logprobs = torch.nn.functional.log_softmax(x, dim=-1)
nll_loss = -logprobs * target
nll_loss = nll_loss.sum(-1)
smooth_loss = -logprobs.mean(dim=-1)
loss = self.confidence * nll_loss + self.smoothing * smooth_loss
return loss.mean()
else:
return torch.nn.functional.cross_entropy(x, target)