# Copyright 2022 Cerebras Systems.
#
# 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.
import torch.nn as nn
[docs]class GroupInstanceNorm(nn.Module):
"""
Uses torch.nn.GroupNorm to emulate InstanceNorm by setting number of groups
equal to the number of channels.
Args:
num_channels (int): number of channels. `C` from an expected input of size (N, C, H, W).
"""
[docs] def __init__(
self, num_channels, eps=1e-5, affine=True, device=None, dtype=None,
):
super(GroupInstanceNorm, self).__init__()
self.instancenorm = nn.GroupNorm(
num_channels,
num_channels,
eps=eps,
affine=affine,
device=device,
dtype=dtype,
)
def forward(self, input):
out = self.instancenorm(input)
return out