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from model import common
# from model import attention
import torch.nn as nn
from lambda_networks import LambdaLayer
def make_model(args, parent=False):
if args.dilation:
from model import dilated
return LAMBDAEDSR(args, dilated.dilated_conv)
else:
return LAMBDAEDSR(args)
class MCheck(nn.Module):
def __init__(self):
super(MCheck, self).__init__()
self.kill = False
self.shape = None
def forward(self, x):
if self.kill:
raise NotImplementedError("Shape checked here", self.shape, x.shape)
self.kill = True
self.shape = x.shape
return x
class LAMBDAEDSR(nn.Module):
def __init__(self, args, conv=common.default_conv):
super(LAMBDAEDSR, self).__init__()
n_resblock = args.n_resblocks
n_feats = args.n_feats
kernel_size = 3
scale = args.scale[0]
act = nn.ReLU(True)
rgb_mean = (0.4488, 0.4371, 0.4040)
rgb_std = (1.0, 1.0, 1.0)
self.sub_mean = common.MeanShift(args.rgb_range, rgb_mean, rgb_std)
# self.msa = attention.PyramidAttention(channel=256, reduction=8,res_scale=args.res_scale);
self.lambda_layer = LambdaLayer(dim=256, dim_out=256, r=25, dim_k=16, heads=4, dim_u=4)
# define head module
m_head = [conv(args.n_colors, n_feats, kernel_size)]
# define body module
m_body = [
common.ResBlock(
conv, n_feats, kernel_size, act=act, res_scale=args.res_scale
) for _ in range(n_resblock // 2)
]
# mcheck = MCheck()
# m_body.append(mcheck)
m_body.append(self.lambda_layer)
# m_body.append(mcheck)
for _ in range(n_resblock // 2):
m_body.append(common.ResBlock(
conv, n_feats, kernel_size, act=act, res_scale=args.res_scale
))
m_body.append(conv(n_feats, n_feats, kernel_size))
# define tail module
m_tail = [
common.Upsampler(conv, scale, n_feats, act=False),
nn.Conv2d(
n_feats, args.n_colors, kernel_size,
padding=(kernel_size // 2)
)
]
self.add_mean = common.MeanShift(args.rgb_range, rgb_mean, rgb_std, 1)
self.head = nn.Sequential(*m_head)
self.body = nn.Sequential(*m_body)
self.tail = nn.Sequential(*m_tail)
def forward(self, x):
x = self.sub_mean(x)
x = self.head(x)
res = self.body(x)
res += x
x = self.tail(res)
x = self.add_mean(x)
return x
def load_state_dict(self, state_dict, strict=True):
own_state = self.state_dict()
for name, param in state_dict.items():
if name in own_state:
if isinstance(param, nn.Parameter):
param = param.data
try:
own_state[name].copy_(param)
except Exception:
if name.find('tail') == -1:
raise RuntimeError('While copying the parameter named {}, '
'whose dimensions in the model are {} and '
'whose dimensions in the checkpoint are {}.'
.format(name, own_state[name].size(), param.size()))
elif strict:
if name.find('tail') == -1:
raise KeyError('unexpected key "{}" in state_dict'
.format(name))
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