else:
saved_state_dict = torch.load(args.snapshot)
model.load_state_dict(saved_state_dict)
print 'Loading data.'
transformations = transforms.Compose([transforms.Scale(240),
transforms.RandomCrop(224), transforms.ToTensor(),
transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])])
if args.dataset == 'Pose_300W_LP':
pose_dataset = datasets.Pose_300W_LP(args.data_dir, args.filename_list, transformations)
elif args.dataset == 'Pose_300W_LP_random_ds':
pose_dataset = datasets.Pose_300W_LP_random_ds(args.data_dir, args.filename_list, transformations)
elif args.dataset == 'Synhead':
pose_dataset = datasets.Synhead(args.data_dir, args.filename_list, transformations)
elif args.dataset == 'AFLW2000':
pose_dataset = datasets.AFLW2000(args.data_dir, args.filename_list, transformations)
elif args.dataset == 'BIWI':
pose_dataset = datasets.BIWI(args.data_dir, args.filename_list, transformations)
elif args.dataset == 'AFLW':
pose_dataset = datasets.AFLW(args.data_dir, args.filename_list, transformations)
elif args.dataset == 'AFLW_aug':
pose_dataset = datasets.AFLW_aug(args.data_dir, args.filename_list, transformations)
elif args.dataset == 'AFW':
pose_dataset = datasets.AFW(args.data_dir, args.filename_list, transformations)
else:
print 'Error: not a valid dataset name'
sys.exit()
train_loader = torch.utils.data.DataLoader(dataset=pose_dataset,
print "Loading from snapshot"
saved_state_dict = torch.load(args.snapshot)
load_filtered_state_dict(model, saved_state_dict)
print 'Loading data.'
transformations = transforms.Compose([transforms.Scale(240),
transforms.RandomCrop(224), transforms.ToTensor(),
transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])])
if args.dataset == 'Pose_300W_LP':
pose_dataset = datasets.Pose_300W_LP(args.data_dir, args.filename_list, transformations, bin_width_degrees)
elif args.dataset == 'Pose_300W_LP_random_ds':
pose_dataset = datasets.Pose_300W_LP_random_ds(args.data_dir, args.filename_list, transformations, bin_width_degrees)
elif args.dataset == 'Synhead':
pose_dataset = datasets.Synhead(args.data_dir, args.filename_list, transformations, bin_width_degrees)
elif args.dataset == 'AFLW2000':
pose_dataset = datasets.AFLW2000(args.data_dir, args.filename_list, transformations, bin_width_degrees)
elif args.dataset == 'BIWI':
pose_dataset = datasets.BIWI(args.data_dir, args.filename_list, transformations, bin_width_degrees)
elif args.dataset == 'AFLW':
pose_dataset = datasets.AFLW(args.data_dir, args.filename_list, transformations, bin_width_degrees)
elif args.dataset == 'AFLW_aug':
pose_dataset = datasets.AFLW_aug(args.data_dir, args.filename_list, transformations, bin_width_degrees)
elif args.dataset == 'AFW':
pose_dataset = datasets.AFW(args.data_dir, args.filename_list, transformations, bin_width_degrees)
else:
print 'Error: not a valid dataset name'
sys.exit()
train_loader = torch.utils.data.DataLoader(dataset=pose_dataset,
