def define_F(opt, use_bn=False):
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
# PyTorch pretrained VGG19-54, before ReLU.
if use_bn:
feature_layer = 49
else:
feature_layer = 34
netF = SRGAN_arch.VGGFeatureExtractor(feature_layer=feature_layer, use_bn=use_bn,
use_input_norm=True, device=device)
netF.eval() # No need to train
return netF
def define_F(opt, use_bn=False):
gpu_ids = opt['gpu_ids']
device = torch.device('cuda' if gpu_ids else 'cpu')
if use_bn:
feature_layer = 49
else:
feature_layer = 34
netF = SRGAN_arch.VGGFeatureExtractor(feature_layer=feature_layer,
use_bn=use_bn,
use_input_norm=True,
device=device)
netF.eval()
return netF
def define_F(opt, use_bn=False):
gpu_ids = opt["gpu_ids"]
device = torch.device("cuda" if gpu_ids else "cpu")
# PyTorch pretrained VGG19-54, before ReLU.
if use_bn:
feature_layer = 49
else:
feature_layer = 34
netF = SRGAN_arch.VGGFeatureExtractor(
feature_layer=feature_layer, use_bn=use_bn, use_input_norm=True, device=device
)
netF.eval() # No need to train
return netF