def test_undeepvo(self):
model = UnDeepVO()
input_data = torch.rand(2, 3, 384, 128)
output = model.depth_net(input_data)
self.assertEqual(output.shape, torch.Size([2, 1, 384, 128]))
self.assertTrue(torch.all(output > 0))
input_data = torch.rand(2, 3, 384, 128)
rotation, translation = model.pose_net(input_data)
self.assertEqual(rotation.shape, torch.Size([2, 3]))
self.assertEqual(translation.shape, torch.Size([2, 3]))
def test_temporal_loss(self):
model = UnDeepVO().to(device)
left_current_img, right_current_img, left_next_img, right_next_img, \
left_current_depth, right_current_depth, left_next_depth, right_next_depth, \
left_current_rotation, left_current_position, right_current_rotation, right_current_position, \
left_next_rotation, left_next_position, right_next_rotation, right_next_position, \
src_trans_dst, left_camera_matrix, right_camera_matrix = self.prepare_data_for_tests(model)
camera_baseline = 0.54
src_trans_dst[0, 3] = camera_baseline
focal_length = left_camera_matrix[0, 0, 0]
transform_from_left_to_right = src_trans_dst
lambda_position, lambda_angle, lambda_s = 1e-3, 1e-3, 1e-2
temporal_losses = TemporalImageLosses(left_camera_matrix,
right_camera_matrix)
out, _ = temporal_losses(left_current_img, left_next_img,
left_current_depth, left_next_depth,
right_current_img, right_next_img,
right_current_depth, right_next_depth,
left_current_position, right_current_position,
left_current_rotation, right_current_rotation,
left_next_position, right_next_position,
left_next_rotation, right_next_rotation)
self.assertEqual(out.shape, torch.Size([]))
self.assertFalse(torch.isnan(out))
self.assertTrue(out > 0)
def test_unsupervised_depth_problem_cpu(self):
device = "cpu"
sequence_8 = Downloader('08')
if not os.path.exists("./dataset/poses"):
print("Download dataset")
sequence_8.download_sequence()
lengths = (200, 30, 30)
dataset = pykitti.odometry(sequence_8.main_dir,
sequence_8.sequence_id,
frames=range(0, 260, 1))
dataset_manager = DatasetManagerMock(dataset,
lenghts=lengths,
num_workers=WORKERS_COUNT)
model = UnDeepVO(max_depth=2., min_depth=1.0).to(device)
optimizer_manger = OptimizerManager()
criterion = UnsupervisedCriterion(
dataset_manager.get_cameras_calibration(device), 0.1, 1, 0.85)
handler = TrainingProcessHandler(mlflow_tags={"name": "test"})
problem = UnsupervisedDepthProblem(model,
criterion,
optimizer_manger,
dataset_manager,
handler,
batch_size=1,
device=device)
problem.train(1)
parser.add_argument('-supervised_lambda',
default=0.1,
type=float,
help='lambda os supervised method')
args = parser.parse_args()
MAIN_DIR = args.main_dir
lengths = args.split
if args.method == "unsupervised":
dataset = pykitti.odometry(MAIN_DIR,
'08',
frames=range(*args.frames_range))
dataset_manager = UnsupervisedDatasetManager(dataset, lenghts=lengths)
model = UnDeepVO(args.max_depth).cuda()
criterion = UnsupervisedCriterion(
dataset_manager.get_cameras_calibration("cuda:0"),
args.lambda_position, args.lambda_rotation, args.lambda_s,
args.lambda_disparity)
handler = TrainingProcessHandler(
enable_mlflow=True,
mlflow_tags={"name": args.mlflow_tags_name},
mlflow_parameters={
"image_step": args.frames_range[2],
"max_depth": args.max_depth,
"epoch": args.epoch,
"lambda_position": args.lambda_position,
"lambda_rotation": args.lambda_rotation,
"lambda_s": args.lambda_s,
help='whether to use resnet or not')
args = parser.parse_args()
MAIN_DIR = args.main_dir
lengths = args.split
problem = None
if args.method == "unsupervised":
sequence_8 = Downloader('08')
if not os.path.exists("./dataset/poses"):
print("Download dataset")
sequence_8.download_sequence()
dataset = pykitti.odometry(MAIN_DIR, '08', frames=range(*args.frames_range))
dataset_manager = UnsupervisedDatasetManager(dataset, lengths=lengths)
model = UnDeepVO(args.max_depth, args.min_depth, args.resnet).to(args.device)
if args.model_path != "":
model.load_state_dict(torch.load(args.model_path, map_location=args.device))
criterion = UnsupervisedCriterion(dataset_manager.get_cameras_calibration(args.device),
args.lambda_position,
args.lambda_rotation,
args.lambda_s,
args.lambda_disparity,
args.lambda_registration)
handler = TrainingProcessHandler(enable_mlflow=True, mlflow_tags={"name": args.mlflow_tags_name},
mlflow_parameters={"image_step": args.frames_range[2],
"max_depth": args.max_depth,
"epoch": args.epoch,
"lambda_position": args.lambda_position,
"lambda_rotation": args.lambda_rotation,