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from . import common
import torch
import torch.nn as nn
from models import register
import torch.nn.functional as F
from argparse import Namespace
def make_model(args, parent=False):
return VDSR(args)
@register('VDSR')
def VDSR(scale_ratio, rgb_range=1):
args = Namespace()
args.scale = [scale_ratio]
args.n_colors = 3
args.rgb_range = rgb_range
return VDSR(args)
class VDSR(nn.Module):
def __init__(self, args, conv=common.default_conv):
super(VDSR, self).__init__()
n_feats = 64
kernel_size = 3
m_head = [common.BasicBlock(conv, args.n_colors, n_feats, kernel_size, bias=True, bn=True)]
layer_nums = 18
m_body = [
common.BasicBlock(conv, n_feats, n_feats, kernel_size, bias=True, bn=True)
for _ in range(layer_nums)
]
m_tail = [conv(n_feats, args.n_colors, kernel_size, bias=True)]
self.head = nn.Sequential(*m_head)
self.body = nn.Sequential(*m_body)
self.tail = nn.Sequential(*m_tail)
def forward(self, x, out_size):
x = F.interpolate(x, size=out_size, mode='bicubic')
residual = x
x = self.head(x)
x = self.body(x)
x = self.tail(x)
out = x + residual
return out
def load_state_dict(self, state_dict, strict=False):
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') >= 0:
print('Replace pre-trained upsampler to new one...')
else:
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))
if strict:
missing = set(own_state.keys()) - set(state_dict.keys())
if len(missing) > 0:
raise KeyError('missing keys in state_dict: "{}"'.format(missing)) |