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natanielruiz

2017-10-30 1480b09fa486a8f5252e9cd601bee165e9d0cd22

 code/test_preangles.py

@@ -36,6 +36,13 @@

    return args

def load_filtered_state_dict(model, snapshot):
    # By user apaszke from discuss.pytorch.org
    model_dict = model.state_dict()
    snapshot = {k: v for k, v in snapshot.items() if k in model_dict}
    model_dict.update(snapshot)
    model.load_state_dict(model_dict)

if __name__ == '__main__':
    args = parse_args()

@@ -50,6 +57,7 @@
    # Load snapshot
    saved_state_dict = torch.load(snapshot_path)
    model.load_state_dict(saved_state_dict)
    # load_filtered_state_dict(model, saved_state_dict)

    print 'Loading data.'

@@ -63,6 +71,8 @@
        pose_dataset = datasets.Pose_300W_LP_random_ds(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 == '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':
@@ -108,9 +118,13 @@
        _, roll_bpred = torch.max(roll.data, 1)

        # Continuous predictions
        yaw_predicted = angles[:,0].data.cpu()
        pitch_predicted = angles[:,1].data.cpu()
        roll_predicted = angles[:,2].data.cpu()
        yaw_predicted = utils.softmax_temperature(yaw.data, 1)
        pitch_predicted = utils.softmax_temperature(pitch.data, 1)
        roll_predicted = utils.softmax_temperature(roll.data, 1)

        yaw_predicted = torch.sum(yaw_predicted * idx_tensor, 1).cpu() * 3 - 99
        pitch_predicted = torch.sum(pitch_predicted * idx_tensor, 1).cpu() * 3 - 99
        roll_predicted = torch.sum(roll_predicted * idx_tensor, 1).cpu() * 3 - 99

        # Mean absolute error
        yaw_error += torch.sum(torch.abs(yaw_predicted - label_yaw))