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# Copyright (c) 2023-2024, Zexin He
#
# 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
#
# https://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.
import torch
import torch.nn as nn
__all__ = ['TVLoss']
class TVLoss(nn.Module):
"""
Total variance loss.
"""
def __init__(self):
super().__init__()
def numel_excluding_first_dim(self, x):
return x.numel() // x.shape[0]
@torch.compile
def forward(self, x):
"""
Assume batched and channel first with inner sizes.
Args:
x: [N, M, C, H, W]
Returns:
Mean-reduced TV loss with element-level scaling.
"""
N, M, C, H, W = x.shape
x = x.reshape(N*M, C, H, W)
diff_i = x[..., 1:, :] - x[..., :-1, :]
diff_j = x[..., :, 1:] - x[..., :, :-1]
div_i = self.numel_excluding_first_dim(diff_i)
div_j = self.numel_excluding_first_dim(diff_j)
tv_i = diff_i.pow(2).sum(dim=[1,2,3]) / div_i
tv_j = diff_j.pow(2).sum(dim=[1,2,3]) / div_j
tv = tv_i + tv_j
batch_tv = tv.reshape(N, M).mean(dim=1)
all_tv = batch_tv.mean()
return all_tv
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