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)
def test_supervised_depth_problem(self):
dataset = GroundTruthDataset(length=260)
print (dataset._names)
lengths = (200, 30, 30)
dataset_manager = SupervisedDatasetManager(dataset, lengths=lengths, num_workers=WORKERS_COUNT)
model = DepthNet(max_depth=2., min_depth=1.0).cuda()
optimizer_manger = OptimizerManager()
criterion = SupervisedCriterion(0.01)
handler = TrainingProcessHandler(mlflow_tags={"name": "test"})
problem = SupervisedDepthProblem(model, criterion, optimizer_manger, dataset_manager, handler,
batch_size=1)
problem.train(1)
'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,
"lambda_disparity": args.lambda_disparity
})
optimizer_manager = OptimizerManager(lr=args.lr)
problem = UnsupervisedDepthProblem(model,
criterion,
optimizer_manager,
dataset_manager,
handler,
batch_size=args.batch,
name="undeepvo")
elif args.method == "supervised":
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,
"lambda_s": args.lambda_s,
"lambda_disparity": args.lambda_disparity,
"lr": args.lr,
"batch_size": args.batch_size,
"betta2": args.betta2,
"betta1": args.betta1,
"min_depth": args.min_depth,
"lambda_registration": args.lambda_registration},
enable_iteration_progress_bar=True)
optimizer_manager = OptimizerManager(lr=args.lr, betas=(args.betta1, args.betta2))
problem = UnsupervisedDepthProblem(model, criterion, optimizer_manager, dataset_manager, handler,
batch_size=args.batch_size, name="undeepvo", device=args.device)
elif args.method == "supervised":
dataset = GroundTruthDataset(length=lengths)
dataset_manager = SupervisedDatasetManager(dataset, lengths=lengths)
def test_training_process_handler(self):
handler = TrainingProcessHandler()
handler.setup_handler("test", None)
handler.start_callback(1, 1)
handler.epoch_callback({})
handler.finish_callback({})