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import torch.nn as nn
import torch
from . import common
from lambda_networks import LambdaLayer
def build_model(args):
return ResNet(args)
class ConvGRU(nn.Module):
def __init__(self, hidden_dim=128, input_dim=192+128):
super(ConvGRU, self).__init__()
self.convz = nn.Conv2d(hidden_dim+input_dim, hidden_dim, 3, padding=1)
self.convr = nn.Conv2d(hidden_dim+input_dim, hidden_dim, 3, padding=1)
self.convq = nn.Conv2d(hidden_dim+input_dim, hidden_dim, 3, padding=1)
def forward(self, h, x):
hx = torch.cat([h, x], dim=1)
z = torch.sigmoid(self.convz(hx))
r = torch.sigmoid(self.convr(hx))
q = torch.tanh(self.convq(torch.cat([r*h, x], dim=1)))
# h = (1-z) * h + z * q
# return h
return (1-z) * h + z * q
class SepConvGRU(nn.Module):
def __init__(self, hidden_dim=128, input_dim=192+128):
super(SepConvGRU, self).__init__()
self.convz1 = nn.Conv2d(hidden_dim+input_dim, hidden_dim, (1,5), padding=(0,2))
self.convr1 = nn.Conv2d(hidden_dim+input_dim, hidden_dim, (1,5), padding=(0,2))
self.convq1 = nn.Conv2d(hidden_dim+input_dim, hidden_dim, (1,5), padding=(0,2))
self.convz2 = nn.Conv2d(hidden_dim+input_dim, hidden_dim, (5,1), padding=(2,0))
self.convr2 = nn.Conv2d(hidden_dim+input_dim, hidden_dim, (5,1), padding=(2,0))
self.convq2 = nn.Conv2d(hidden_dim+input_dim, hidden_dim, (5,1), padding=(2,0))
def forward(self, h, x):
# horizontal
hx = torch.cat([h, x], dim=1)
z = torch.sigmoid(self.convz1(hx))
r = torch.sigmoid(self.convr1(hx))
q = torch.tanh(self.convq1(torch.cat([r*h, x], dim=1)))
h = (1-z) * h + z * q
# vertical
hx = torch.cat([h, x], dim=1)
z = torch.sigmoid(self.convz2(hx))
r = torch.sigmoid(self.convr2(hx))
q = torch.tanh(self.convq2(torch.cat([r*h, x], dim=1)))
h = (1-z) * h + z * q
return h
class ResNet(nn.Module):
def __init__(
self,
args
):
super(ResNet, self).__init__()
self.in_channels = 3
self.out_channels = 3
self.rgb_range = args.rgb_range
self.mean = self.rgb_range / 2
self.n_feats = args.n_feats
self.kernel_size = args.kernel_size
self.n_resblocks = args.n_resblocks
self.recurrence = args.n_scales
modules = []
m_head=[common.default_conv(self.in_channels, self.n_feats, self.kernel_size)]
for i in range(3):
m_head.append(common.ResBlock(self.n_feats, self.kernel_size))
for _ in range(self.n_resblocks // 2):
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=4
)
)
for _ in range(self.n_resblocks // 2):
modules.append(common.ResBlock(self.n_feats, self.kernel_size))
m_tail=[]
for i in range(3):
m_tail.append(common.ResBlock(self.n_feats, self.kernel_size))
m_tail.append(
common.default_conv(self.n_feats, self.out_channels, self.kernel_size)
)
self.head=nn.Sequential(*m_head)
self.body = nn.Sequential(*modules)
self.tail=nn.Sequential(*m_tail)
self.gru=SepConvGRU(hidden_dim=self.n_feats,input_dim=self.n_feats)
def forward(self, input):
input = input[0] - self.mean
input=self.head(input)
hidden=input.clone()
output_lst=[None]*self.recurrence
for i in range(self.recurrence):
gru_out=self.gru(hidden,input)
res=self.body(gru_out)
gru_out=res+gru_out
hidden=gru_out
tail_out=self.tail(gru_out)
output_lst[i] = self.tail(gru_out) + self.mean
return output_lst
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