###LOSS
MSE_loss = nn.MSELoss()
BCE_loss = nn.BCELoss()
print('---------- Generator architecture -------------')
utils.print_network(model)
print('---------- Discriminator architecture ---------')
utils.print_network(D)
print('-----------------------------------------------')
if opt.load_pretrained:
model_name = os.path.join(opt.save_folder + opt.pretrained_sr)
if os.path.exists(model_name):
#model= torch.load(model_name, map_location=lambda storage, loc: storage)
model.load_state_dict(
torch.load(model_name, map_location=lambda storage, loc: storage))
print('Pre-trained SR model is loaded.')
if opt.load_pretrained_D:
D_name = os.path.join(opt.save_folder + opt.pretrained_D)
if os.path.exists(D_name):
#model= torch.load(model_name, map_location=lambda storage, loc: storage)
D.load_state_dict(
torch.load(D_name, map_location=lambda storage, loc: storage))
print('Pre-trained Discriminator model is loaded.')
if cuda:
model = model.cuda(gpus_list[0])
D = D.cuda(gpus_list[0])
feature_extractor = feature_extractor.cuda(gpus_list[0])
MSE_loss = MSE_loss.cuda(gpus_list[0])
model = DBPNITER(num_channels=3,
base_filter=64,
feat=256,
num_stages=3,
scale_factor=opt.upscale_factor) ###D-DBPN
else:
model = DBPN(num_channels=3,
base_filter=64,
feat=256,
num_stages=7,
scale_factor=opt.upscale_factor) ###D-DBPN
if cuda:
model = torch.nn.DataParallel(model, device_ids=gpus_list)
model.load_state_dict(
torch.load(opt.model, map_location=lambda storage, loc: storage))
print('Pre-trained SR model is loaded.')
if cuda:
model = model.cuda(gpus_list[0])
def eval():
model.eval()
for batch in testing_data_loader:
with torch.no_grad():
input, bicubic, name = Variable(batch[0]), Variable(
batch[1]), batch[2]
if cuda:
input = input.cuda(gpus_list[0])
bicubic = bicubic.cuda(gpus_list[0])
feat=256,
num_stages=7,
scale_factor=opt.upscale_factor) ###D-DBPN
if cuda:
model = torch.nn.DataParallel(model, device_ids=gpus_list)
#여기까진 일단됨.
from collections import OrderedDict
new_state_dict = OrderedDict()
state_dict = torch.load(opt.model, map_location=lambda storage, loc: storage)
for k, v in state_dict.items():
name = k[7:] # remove `module.`
new_state_dict[name] = v
model.load_state_dict(new_state_dict)
print('Pre-trained SR model is loaded.')
if cuda:
model = model.cuda(gpus_list[0])
def eval():
model.eval()
for batch in testing_data_loader:
with torch.no_grad():
input, bicubic, name = Variable(batch[0]), Variable(
batch[1]), batch[2]
if cuda:
input = input.cuda(gpus_list[0])