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from argparse import Namespace
import torch.nn as nn
from models import register
import torch.nn.functional as F
def make_model(args, parent=False):
return SRCNN(args)
@register('SRCNN')
def SRCNN(scale_ratio=1, rgb_range=1):
args = Namespace()
args.scale = scale_ratio
args.rgb_range = rgb_range
args.n_colors = 3
return SRCNN(args)
class SRCNN(nn.Module):
def __init__(self, args):
super(SRCNN, self).__init__()
self.conv1 = nn.Conv2d(args.n_colors, 64, kernel_size=9, padding=9 // 2)
self.conv2 = nn.Conv2d(64, 32, kernel_size=5, padding=5 // 2)
self.conv3 = nn.Conv2d(32, args.n_colors, kernel_size=5, padding=5 // 2)
self.relu = nn.ReLU(inplace=True)
self.scale = args.scale
def forward(self, x, out_size):
x = F.interpolate(x, out_size, mode='bicubic')
x = self.relu(self.conv1(x))
x = self.relu(self.conv2(x))
x = self.conv3(x)
return x
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)) |