| | |
|---|
| | | default='', type=str) |
|---|
| | | parser.add_argument('--filename_list', dest='filename_list', help='Path to text file containing relative paths for every example.', |
|---|
| | | default='', type=str) |
|---|
| | | parser.add_argument('--snapshot', dest='snapshot', help='Name of model snapshot.', |
|---|
| | | parser.add_argument('--snapshot', dest='snapshot', help='Path of model snapshot.', |
|---|
| | | default='', type=str) |
|---|
| | | parser.add_argument('--batch_size', dest='batch_size', help='Batch size.', |
|---|
| | | default=1, type=int) |
|---|
| | | parser.add_argument('--save_viz', dest='save_viz', help='Save images with pose cube.', |
|---|
| | | default=False, type=bool) |
|---|
| | | parser.add_argument('--iter_ref', dest='iter_ref', default=1, type=int) |
|---|
| | | |
|---|
| | | args = parser.parse_args() |
|---|
| | | |
|---|
| | |
|---|
| | | |
|---|
| | | cudnn.enabled = True |
|---|
| | | gpu = args.gpu_id |
|---|
| | | snapshot_path = os.path.join('output/snapshots', args.snapshot + '.pkl') |
|---|
| | | snapshot_path = args.snapshot |
|---|
| | | |
|---|
| | | # ResNet101 with 3 outputs. |
|---|
| | | # model = hopenet.Hopenet(torchvision.models.resnet.Bottleneck, [3, 4, 23, 3], 66) |
|---|
| | | # ResNet50 |
|---|
| | | model = hopenet.Hopenet(torchvision.models.resnet.Bottleneck, [3, 4, 6, 3], 66) |
|---|
| | | model = hopenet.Hopenet(torchvision.models.resnet.Bottleneck, [3, 4, 6, 3], 66, args.iter_ref) |
|---|
| | | # ResNet18 |
|---|
| | | # model = hopenet.Hopenet(torchvision.models.resnet.BasicBlock, [2, 2, 2, 2], 66) |
|---|
| | | |
|---|
| | |
|---|
| | | model.load_state_dict(saved_state_dict) |
|---|
| | | |
|---|
| | | print 'Loading data.' |
|---|
| | | |
|---|
| | | # transformations = transforms.Compose([transforms.Scale(224), |
|---|
| | | # transforms.RandomCrop(224), transforms.ToTensor()]) |
|---|
| | | |
|---|
| | | transformations = transforms.Compose([transforms.Scale(224), |
|---|
| | | transforms.RandomCrop(224), transforms.ToTensor(), |
|---|
| | |
|---|
| | | label_roll = labels[:,2].float() |
|---|
| | | |
|---|
| | | pre_yaw, pre_pitch, pre_roll, angles = model(images) |
|---|
| | | yaw = angles[:,0].cpu().data |
|---|
| | | pitch = angles[:,1].cpu().data |
|---|
| | | roll = angles[:,2].cpu().data |
|---|
| | | yaw = angles[args.iter_ref-1][:,0].cpu().data |
|---|
| | | pitch = angles[args.iter_ref-1][:,1].cpu().data |
|---|
| | | roll = angles[args.iter_ref-1][:,2].cpu().data |
|---|
| | | |
|---|
| | | # Mean absolute error |
|---|
| | | yaw_error += torch.sum(torch.abs(yaw - label_yaw) * 3) |
|---|
