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from model import common
import torch.nn as nn
import torch.nn.init as init
url = {
'r20f64': ''
}
def make_model(args, parent=False):
return VDSR(args)
class VDSR(nn.Module):
def __init__(self, args, conv=common.default_conv):
super(VDSR, self).__init__()
n_resblocks = args.n_resblocks
n_feats = args.n_feats
kernel_size = 3
self.url = url['r{}f{}'.format(n_resblocks, n_feats)]
self.sub_mean = common.MeanShift(args.rgb_range)
self.add_mean = common.MeanShift(args.rgb_range, sign=1)
def basic_block(in_channels, out_channels, act):
return common.BasicBlock(
conv, in_channels, out_channels, kernel_size,
bias=True, bn=False, act=act
)
# define body module
m_body = []
m_body.append(basic_block(args.n_colors, n_feats, nn.ReLU(True)))
for _ in range(n_resblocks - 2):
m_body.append(basic_block(n_feats, n_feats, nn.ReLU(True)))
m_body.append(basic_block(n_feats, args.n_colors, None))
self.body = nn.Sequential(*m_body)
def forward(self, x):
x = self.sub_mean(x)
res = self.body(x)
res += x
x = self.add_mean(res)
return x
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