from squid.loss import VGGLoss
from squid.net import DnCnn
target_net = DnCnn(layer_num=20)
target_net = nn.DataParallel(target_net).cuda()
model = SuperviseModel({
'net': target_net,
'optimizer': torch.optim.Adam([{'name':'net_params', 'params':target_net.parameters(), 'base_lr':1e-4}], betas=(0.9, 0.999), weight_decay=0.0005),
'lr_step_ratio': 0.5,
'lr_step_size': 2,
'supervise':{
'out': {'MSE_loss': {'obj': nn.MSELoss(size_average=True), 'factor':1.0, 'weight':1.0}},
},
'metrics': {}
})
train_dataset = Photo2PhotoData({
'data_root': DATASET_DIR_TRAIN,
'desc_file_path': os.path.join(DATASET_TXT_DIR_TRAIN, DATASET_ID, '9_train.txt'),
})
valid_dataset = Photo2PhotoData({
'data_root': DATASET_DIR_VAL,
'desc_file_path': os.path.join(DATASET_TXT_DIR_VAL, DATASET_ID, 'val.txt'),
})
5,
'supervise': {
'out': {
'MSE_loss': {
'obj': nn.MSELoss(size_average=True),
'factor': 0.1,
'weight': 1.0
}
},
},
'metrics': {}
})
train_dataset = RandomCropPhoto2PhotoData({
'crop_size':
hr_size,
'crop_stride':
2,
'data_root':
DATASET_DIR,
'desc_file_path':
os.path.join(DATASET_TXT_DIR, DATASET_ID, 'train.txt'),
})
valid_dataset = Photo2PhotoData({
'data_root':
DATASET_DIR,
'desc_file_path':
os.path.join(DATASET_TXT_DIR, DATASET_ID, 'val.txt'),
})
