def setUp(self) -> None:
current_folder = os.path.dirname(os.path.abspath(__file__))
dataset_folder = os.path.join(
os.path.dirname(current_folder), "datasets", "tum_rgbd",
"rgbd_dataset_freiburg3_large_cabinet_validation")
data_module_factory = TumVideoDataModuleFactory(dataset_folder)
self._data_module = data_module_factory.make_data_module(
final_image_size=(128, 384),
transform_manager_parameters={"filters": True},
batch_size=1,
num_workers=WORKERS_COUNT,
split=(0.8, 0.1, 0.1),
device="cuda:0")
self._data_module = DataModuleMock(self._data_module)
pose_net = PoseNetResNet()
depth_net = DepthNetResNet()
criterion = MonoUnsupervisedCriterion(
self._data_module.get_cameras_calibration(), 1, 1)
params = AttributeDict(lr=1e-3, beta1=0.99, beta2=0.9)
self._model = UnsupervisedDepthModel(params,
pose_net,
depth_net,
criterion,
stereo=False,
mono=True).cuda()
def test_unfreeze_last_layer(self):
params = AttributeDict(
lr=1e-4,
beta1=0.9,
beta2=0.99,
lambda_position=0.01,
lambda_rotation=0.1,
batch_size=8,
)
pose_net = PoseNetResNet()
depth_net = DepthNetResNet()
params.update(scale_lr=5e-1,
initial_log_scale=4.59,
initial_log_min_depth=0.)
model = ScaledUnsupervisedDepthModel(params,
pose_net,
depth_net,
criterion=None)
freeze_feature_extractor(model)
unfreeze_last_layer(model)
self.assertTrue(model._depth_net._last_conv.requires_grad)
self.assertTrue(model._pose_net.rot3.requires_grad)
self.assertTrue(model._pose_net.transl3.requires_grad)
def setUp(self) -> None:
current_folder = os.path.dirname(os.path.abspath(__file__))
dataset_folder = os.path.join(os.path.dirname(current_folder),
"datasets", "kitti")
data_module_factory = KittiDataModuleFactory(range(0, 301, 1),
directory=dataset_folder)
self._data_module = data_module_factory.make_dataset_manager(
final_image_size=(128, 384),
transform_manager_parameters={"filters": True},
batch_size=1,
num_workers=WORKERS_COUNT,
split=(0.8, 0.1, 0.1))
self._data_module = DataModuleMock(self._data_module)
pose_net = PoseNetResNet()
depth_net = DepthNetResNet()
criterion = UnsupervisedCriterion(
self._data_module.get_cameras_calibration(), 1, 1)
params = AttributeDict(
lr=1e-3,
beta1=0.99,
beta2=0.9,
scale_lr=1e-3,
initial_log_scale=0.,
initial_log_min_depth=0.,
initial_log_pose_scale=0.,
)
self._model = ScaledUnsupervisedDepthModel(params, pose_net, depth_net,
criterion).cuda()
def test_load_undeepvo_checkpoint(self):
filename = 'checkpoint_undeepvo.pth'
# subprocess.run('checkpoint_download.sh')
params = AttributeDict(
lr=1e-4,
beta1=0.9,
beta2=0.99,
lambda_position=0.01,
lambda_rotation=0.1,
batch_size=8,
)
pose_net = PoseNetResNet()
depth_net = DepthNetResNet()
params.update(scale_lr=5e-1,
initial_log_scale=4.59,
initial_log_min_depth=0.)
model = ScaledUnsupervisedDepthModel(params,
pose_net,
depth_net,
criterion=None)
model_before = copy.deepcopy(model)
load_undeepvo_checkpoint(model, filename)
self.assertTrue(
torch.any(model_before._pose_net._first_layer.weight !=
model._pose_net._first_layer.weight))
self.assertTrue(
torch.any(model_before._depth_net.skip_zero.weight !=
model._depth_net.skip_zero.weight))
def setUp(self) -> None:
current_folder = os.path.dirname(os.path.abspath(__file__))
dataset_folder = os.path.join(os.path.dirname(current_folder),
"datasets", "kitti")
data_module_factory = KittiDataModuleFactory(range(0, 301, 1),
directory=dataset_folder)
self._data_module = data_module_factory.make_dataset_manager(
final_image_size=(128, 384),
transform_manager_parameters={"filters": True},
batch_size=1,
num_workers=WORKERS_COUNT,
split=(0.8, 0.1, 0.1))
self._data_module = DataModuleMock(self._data_module)
pose_net = PoseNetResNet()
depth_net = DepthNetResNet()
criterion = UnsupervisedCriterion(
self._data_module.get_cameras_calibration(), 1, 1)
result_visualizer = ResultVisualizer(
cameras_calibration=self._data_module.get_cameras_calibration())
params = AttributeDict(lr=1e-3, beta1=0.99, beta2=0.9)
self._model = UnsupervisedDepthModel(
params,
pose_net,
depth_net,
criterion,
result_visualizer=result_visualizer).cuda()
self._tb_logger = TensorBoardLogger('logs/')
self._second_tb_logger = TensorBoardLogger('logs1/')
self._double_tb_logger = LoggerCollection(
[self._tb_logger, self._second_tb_logger])
os.environ[
"MLFLOW_S3_ENDPOINT_URL"] = "http://ec2-3-134-104-174.us-east-2.compute.amazonaws.com:9000"
os.environ["AWS_ACCESS_KEY_ID"] = "depth"
os.environ["AWS_SECRET_ACCESS_KEY"] = "depth123"
self._mlflow_logger = MLFlowLogger(
experiment_name="test",
tracking_uri=
"http://ec2-3-134-104-174.us-east-2.compute.amazonaws.com:5001")