INNER CODE UNIT · Python
model_baseline
adobe/antialiased-cnns · main.py:231
model_baseline = models.__dict__[args.arch[:-5]](pretrained=True)
antialiased_cnns.copy_params_buffers(model_baseline, model)
if args.weights is not None:
print("=> using saved weights [%s]"%args.weights)
weights = torch.load(args.weights)
model.load_state_dict(weights['state_dict'])
if args.distributed:
# For multiprocessing distributed, DistributedDataParallel constructor
# should always set the single device scope, otherwise,
# DistributedDataParallel will use all available devices.
if args.gpu is not None:
torch.cuda.set_device(args.gpu)
model.cuda(args.gpu)
# When using a single GPU per process and per
# DistributedDataParallel, we need to divide the batch size
# ourselves based on the total number of GPUs we have