def __init__(self, data_dir, transformation, device, num_bin):
args.data_dir = data_dir
args.filename_list = data_dir
args.dataset = 'AFLW2000'
self.transformations = transformation
self.device = device
self.num_bin = num_bin
idx_tensor = [idx for idx in xrange(67)]
self.idx_tensor = torch.FloatTensor(idx_tensor).to(self.device)
if args.dataset == 'Pose_300W_LP':
pose_dataset = datasets.Pose_300W_LP(args.data_dir,
args.filename_list,
self.transformations)
elif args.dataset == 'Pose_300W_LP_random_ds':
pose_dataset = datasets.Pose_300W_LP_random_ds(
args.data_dir, args.filename_list, self.transformations)
elif args.dataset == 'AFLW2000':
pose_dataset = datasets.AFLW2000(args.data_dir, args.filename_list,
self.transformations)
elif args.dataset == 'AFLW2000_ds':
pose_dataset = datasets.AFLW2000_ds(args.data_dir,
args.filename_list,
self.transformations)
elif args.dataset == 'BIWI':
pose_dataset = datasets.BIWI(args.data_dir, args.filename_list,
self.transformations)
elif args.dataset == 'AFLW':
pose_dataset = datasets.AFLW(args.data_dir, args.filename_list,
self.transformations)
elif args.dataset == 'AFLW_aug':
pose_dataset = datasets.AFLW_aug(args.data_dir, args.filename_list,
self.transformations)
elif args.dataset == 'AFW':
pose_dataset = datasets.AFW(args.data_dir, args.filename_list,
self.transformations)
else:
print 'Error: not a valid dataset name'
sys.exit()
self.test_loader = torch.utils.data.DataLoader(
dataset=pose_dataset, batch_size=args.batch_size, num_workers=2)
pose_dataset = datasets.AFLW2000(args.data_dir, args.filename_list,
transformations)
elif args.dataset == 'AFLW2000_ds':
pose_dataset = datasets.AFLW2000_ds(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()
test_loader = torch.utils.data.DataLoader(dataset=pose_dataset,
batch_size=args.batch_size,
num_workers=2)
model.cuda(gpu)
print('Ready to test network.')
# Test the Model
model.eval() # Change model to 'eval' mode (BN uses moving mean/var).
total = 0
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,
batch_size=batch_size,
shuffle=True,
num_workers=2)
model.cuda(gpu)
criterion = nn.CrossEntropyLoss().cuda(gpu)
reg_criterion = nn.MSELoss().cuda(gpu)
# Regression loss coefficient
alpha = args.alpha