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,