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import torch.nn as nn
from . import common
from lambda_networks import LambdaLayer
def build_model(args):
return ResNet(args)
class ResNet(nn.Module):
def __init__(
self,
args,
in_channels=3,
out_channels=3,
n_feats=None,
kernel_size=None,
n_resblocks=None,
mean_shift=True,
):
super(ResNet, self).__init__()
self.in_channels = in_channels
self.out_channels = out_channels
self.n_feats = args.n_feats if n_feats is None else n_feats
self.kernel_size = args.kernel_size if kernel_size is None else kernel_size
self.n_resblocks = args.n_resblocks if n_resblocks is None else n_resblocks
self.mean_shift = mean_shift
self.rgb_range = args.rgb_range
self.mean = self.rgb_range / 2
modules = []
modules.append(
common.default_conv(self.in_channels, self.n_feats, self.kernel_size)
)
for _ in range(self.n_resblocks // 3):
modules.append(common.ResBlock(self.n_feats, self.kernel_size))
modules.append(
LambdaLayer(
dim=self.n_feats, dim_out=self.n_feats, r=23, dim_k=16, heads=4, dim_u=1
)
)
for _ in range(self.n_resblocks // 3):
modules.append(common.ResBlock(self.n_feats, self.kernel_size))
modules.append(
LambdaLayer(
dim=self.n_feats, dim_out=self.n_feats, r=7, dim_k=16, heads=4, dim_u=4
)
)
for _ in range(self.n_resblocks // 3):
modules.append(common.ResBlock(self.n_feats, self.kernel_size))
modules.append(
common.default_conv(self.n_feats, self.n_feats, self.kernel_size)
)
modules.append(common.default_conv(self.n_feats, self.out_channels, 1))
self.body = nn.Sequential(*modules)
def forward(self, input):
if self.mean_shift:
input = input - self.mean
output = self.body(input)
if self.mean_shift:
output = output + self.mean
return output
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