def get_scores(checkpoint_dir):
saved_json_file = os.path.join(checkpoint_dir, 'opt.json')
train_opt = utils.load_opt_from_json(saved_json_file)
name = train_opt['name']
data_dir = '../../../data/ActiveVisionDataset/' + train_opt[
'data_dir'].split('/')[-1]
subsample_rate = train_opt['subsample_rate']
traj = train_opt['traj']
print(data_dir)
# load ground truth poses
dataset = AVD(data_dir, traj, subsample_rate)
gt_pose = dataset.gt
gt_location = gt_pose[:, :3]
pts = dataset.point_clouds #.numpy()
#np.save("local.npy",pts)
#np.save("pose.npy",gt_pose)
# load predicted poses
pred_file = os.path.join(opt.checkpoint_dir, 'pose_est.npy')
pred_pose = np.load(pred_file)
pred_location = pred_pose[:, :2] * dataset.depth_scale # denormalization
pred_location = add_y_coord_for_evaluation(pred_location)
#print(gt_pose)
print(pred_pose)
ate, aligned_location = utils.compute_ate(pred_location, gt_location)
print('{}, ate: {}'.format(name, ate))
gt_pose[:, :3] = gt_pose[:, :3] / dataset.depth_scale
gt_yaw = np.arccos(
gt_pose[:, 5] /
np.sqrt(gt_pose[:, 3] * gt_pose[:, 3] + gt_pose[:, 5] * gt_pose[:, 5]))
gt_pose_xzth = np.vstack((gt_pose[:, 0], gt_pose[:,
2], -gt_yaw)).transpose()
colors = np.array([[0, 1, 1], [0, 0, 0], [0, 0, 1], [1, 0, 1],
[0.5, 0.5, 0.5], [0, 0.5, 0], [0, 1, 0], [0.5, 0, 0],
[0, 0, 0.5], [0.5, 0.5, 0], [0.5, 0, 0.5], [1, 0, 0],
[0.75, 0.75, 0.75], [0, 0.5, 0.5], [1, 1, 1], [1, 1,
0]])
#path_or = '../../../bk_origin/DeepMapping/'
global_point_cloud_file = os.path.join(opt.checkpoint_dir,
'obs_global_est.npy')
#global_point_cloud_file_or = os.path.join(path_or,global_point_cloud_file[3:])
pcds_ours = utils.load_obs_global_est(global_point_cloud_file, colors)
#pcds_or = utils.load_obs_global_est(global_point_cloud_file_or,colors)
pts_gt = transform_to_global_AVD(gt_pose_xzth, pts).numpy()
#pts_or = np.load(global_point_cloud_file_or)
pts_ours = np.load(global_point_cloud_file)
pts_gt = pts_gt.reshape((16, -1, 3))
print(pt_diff(pts_gt, pts_ours))
print(pt_diff(pts_gt, pts_or))
parser.add_argument('--log_interval',
type=int,
default=10,
help='logging interval of saving results')
opt = parser.parse_args()
checkpoint_dir = os.path.join('../results/AVD', opt.name)
if not os.path.exists(checkpoint_dir):
os.makedirs(checkpoint_dir)
utils.save_opt(checkpoint_dir, opt)
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
print('loading dataset')
dataset = AVD(opt.data_dir, opt.traj, opt.subsample_rate)
loader = DataLoader(dataset, batch_size=opt.batch_size, shuffle=False)
loss_fn = eval('loss.' + opt.loss)
print('creating model')
model = DeepMapping_AVD(loss_fn=loss_fn, n_samples=opt.n_samples).to(device)
optimizer = optim.Adam(model.parameters(), lr=opt.lr)
latent_vecs = []
mask_vecs_pair = []
for i in range(len(dataset)):
vec = tini.xavier_normal_(torch.ones(latent_size)).to(device)
vec = torch.nn.Parameter(vec) #True
latent_vecs.append(vec)
y = np.zeros_like(x)
z = pred_pos_DM[:,1]
return np.stack((x,y,z),axis=-1)
parser = argparse.ArgumentParser()
parser.add_argument('-c','--checkpoint_dir',type=str,required=True,help='path to results folder')
opt = parser.parse_args()
saved_json_file = os.path.join(opt.checkpoint_dir,'opt.json')
train_opt = utils.load_opt_from_json(saved_json_file)
name = train_opt['name']
data_dir = train_opt['data_dir']
subsample_rate = train_opt['subsample_rate']
traj = train_opt['traj']
# load ground truth poses
dataset = AVD(data_dir,traj,subsample_rate)
gt_pose = dataset.gt
gt_location = gt_pose[:,:3]
# load predicted poses
pred_file = os.path.join(opt.checkpoint_dir,opt.loss+'_pose_est.npy')
pred_pose = np.load(pred_file)
pred_location = pred_pose[:,:2] * dataset.depth_scale # denormalization
pred_location = add_y_coord_for_evaluation(pred_location)
# compute absolute trajectory error (ATE)
ate,aligned_location = utils.compute_ate(pred_location,gt_location)
print('{}, ate: {}'.format(name,ate))
# vis results
global_point_cloud_file = os.path.join(opt.checkpoint_dir,opt.loss+'_obs_global_est.npy')