diff --git a/dynamics_learning/benchmark_number_of_runs.py b/dynamics_learning/benchmark_number_of_runs.py
index 58717ba6e2e6908d8079b9a396adfd324d99e204..ff3bf193559776917c507057c5742a5de0e21fec 100644
--- a/dynamics_learning/benchmark_number_of_runs.py
+++ b/dynamics_learning/benchmark_number_of_runs.py
@@ -161,15 +161,15 @@ if __name__ == "__main__":
elif (count == 1):
continue
model = load_model_from_binary_file(
- "/app/dynamics_learning/Foundation_Model/models/Instance_model_ITA_2024-11-05_09-30-54_0.2551353871822357.h5"
+ "/app/dynamics_learning/Benchmark-models/pretrained/Instance_model_ITA.h5"
)
sweep_id, sweep_config = setup_sweep(create_sweep=True, from_model=True)
elif (count == 2):
model = load_model_from_binary_file(
- "/app/dynamics_learning/Foundation_Model/models/Instance_model_ITA_2024-11-05_09-30-54_0.2551353871822357.h5"
+ "/app/dynamics_learning/Benchmark-model/pretrained/Instance_model_ITA.h5"
)
config_data = load_config(
- "/app/dynamics_learning/Foundation_Model/hyperparameters/hyperparameters_Instance_model_ITA_2024-11-05_09-30-54_0.2551353871822357.json"
+ "/app/dynamics_learning/Benchmark-models/pretrained/hyperparameters_instance_model_ITA.json"
)
logger.info(f"Setting model to \n{model}\nand config_data to \n{config_data}")
sweep_id, sweep_config = setup_sweep_from_hyperparameters(
@@ -177,10 +177,10 @@ if __name__ == "__main__":
)
elif (count == 3):
model = load_model_from_binary_file(
- "/app/dynamics_learning/Foundation_Model/models/Foundation_model_2024-11-04_01-12-00_2.492475748062134.h5"
+ "/app/dynamics_learning/Benchmark-models/pretrained/Foundation_model.h5"
)
config_data = load_config(
- "/app/dynamics_learning/Foundation_Model/hyperparameters/hyperparameters_Foundation_model_2024-11-04_01-12-00_2.492475748062134.json"
+ "/app/dynamics_learning/Benchmark-models/pretrained/hyperparameters_foundation_model.json"
)
sweep_id, sweep_config = setup_sweep_from_hyperparameters(
config_data=config_data, create_sweep=True
@@ -222,216 +222,3 @@ if __name__ == "__main__":
logger.info(f"The fourth model using sweep {sweep_id4} was trained for {runs_model4} runs and reached a validation loss of {val_loss_model4}.")
wandb.finish()
-
-
-
-
-
-# ###############################################
-# ####################Model 1####################
-# ###############################################
-# if model1:
-# # LLT instance model trained from scratch
-# robot_uuid = LLT_ROBOT_UUID
-# directory = Path(
-# f"/app/dynamics_learning/benchmark_trajectory_data/{robot_uuid}"
-# )
-# # Interpolate Training Data in UUID folders
-# (
-# attained_data,
-# command_data,
-# interpolated_command_data,
-# q_qd_qdd_interpolated_command_input,
-# tau_attained_input,
-# ) = prepare_data(directory)
-
-# # ensure that the sweep id is set correctly: g5qxvipa
-# # assert SWEEP_ID == "g5qxvipa", "Sweep ID is not set correctly. Ensure that the sweep id is set to g5qxvipa"
-# assert (
-# robot_uuid == LLT_ROBOT_UUID
-# ), "Robot UUID is not set correctly. Ensure that the robot uuid is set to LLT_ROBOT_UUID"
-# sweep_id, sweep_config = setup_sweep(create_sweep=True)
-
-# # reset runs counter
-# runs = 0
-# val_loss = 1000
-# # Train the model until the threshold validation loss is reached
-# train_until_threshold_val_loss(
-# sweep_id=sweep_id,
-# robot_uuid=robot_uuid,
-# q_qd_qdd_interpolated_command_input=q_qd_qdd_interpolated_command_input,
-# tau_attained_input=tau_attained_input,
-# model=None,
-# notes="Sweep to train model from scratch. 100 Trajectories are avaiulable for training. Training ist stoped when the validation loss is below 50.",
-# )
-
-# runs_model1 = runs
-# val_loss_model1 = val_loss
-# sweep_id1 = sweep_id
-
-# ###############################################
-# ####################Model 2####################
-# ###############################################
-# if model2:
-# # LLT model based on ITA model without known hyperparameters
-# robot_uuid = LLT_ROBOT_UUID
-# directory = Path(
-# f"/app/dynamics_learning/benchmark_trajectory_data/{robot_uuid}"
-# )
-# # Interpolate Training Data in UUID folders
-# (
-# attained_data,
-# command_data,
-# interpolated_command_data,
-# q_qd_qdd_interpolated_command_input,
-# tau_attained_input,
-# ) = prepare_data(directory)
-# # assert SWEEP_ID == "42d8t40t", "Sweep ID is not set correctly. Ensure that the sweep id is set to 42d8t40t"
-# assert (
-# robot_uuid == LLT_ROBOT_UUID
-# ), "Robot UUID is not set correctly. Ensure that the robot uuid is set to LLT_ROBOT_UUID"
-
-# sweep_id, sweep_config = setup_sweep(create_sweep=True)
-
-# # reset runs counter
-# runs = 0
-# val_loss = 1000
-
-# model = load_model_from_binary_file(
-# "/app/dynamics_learning/models/99.99706268310547.h5"
-# )
-
-# # Train the model until the threshold validation loss is reached
-# train_until_threshold_val_loss(
-# sweep_id=sweep_id,
-# robot_uuid=robot_uuid,
-# q_qd_qdd_interpolated_command_input=q_qd_qdd_interpolated_command_input,
-# tau_attained_input=tau_attained_input,
-# model=model,
-# notes="Sweep to train model based on ITA model. 100 Trajectories are avaiulable for training. Training is stoped when the validation loss is below 50.",
-# )
-
-# runs_model2 = runs
-# val_loss_model2 = val_loss
-# sweep_id2 = sweep_id
-
-# ###############################################
-# ####################Model 3####################
-# ###############################################
-# if model3:
-# # LLT model based on ITA model with known hyperparameters
-# robot_uuid = LLT_ROBOT_UUID
-# directory = Path(f"/app/dynamics_learning/Trajectory Data/train/{robot_uuid}")
-# # Interpolate Training Data in UUID folders
-# (
-# attained_data,
-# command_data,
-# interpolated_command_data,
-# q_qd_qdd_interpolated_command_input,
-# tau_attained_input,
-# ) = prepare_data(directory)
-# # assert SWEEP_ID == "42d8t40t", "Sweep ID is not set correctly. Ensure that the sweep id is set to 42d8t40t"
-# assert (
-# robot_uuid == LLT_ROBOT_UUID
-# ), "Robot UUID is not set correctly. Ensure that the robot uuid is set to LLT_ROBOT_UUID"
-
-# config_data = load_config(
-# "/app/dynamics_learning/Foundation_Model/models/hyperparameters.json"
-# )
-
-# sweep_id, sweep_config = setup_sweep_from_hyperparameters(
-# config_data=config_data, create_sweep=True
-# )
-
-# # reset runs counter
-# runs = 0
-# val_loss = 1000
-
-# model = load_model_from_binary_file(
-# "/app/dynamics_learning/models/99.99706268310547.h5"
-# )
-
-# # Train the model until the threshold validation loss is reached
-# train_until_threshold_val_loss(
-# sweep_id=sweep_id,
-# robot_uuid=robot_uuid,
-# q_qd_qdd_interpolated_command_input=q_qd_qdd_interpolated_command_input,
-# tau_attained_input=tau_attained_input,
-# model=model,
-# notes="Sweep to train model based on ITA model with known hyperparameters. 50 Trajectories are avaiulable for training. Training ist stoped when the validation loss is below 50.",
-# )
-
-# runs_model3 = runs
-# val_loss_model3 = val_loss
-# sweep_id3 = sweep_id
-
-# # assert (
-# # SWEEP_ID == "fe3gjovo"
-# # ), "Sweep ID is not set correctly. Ensure that the sweep id is set to fe3gjovo"
-# # assert (
-# # robot_uuid == LLT_ROBOT_UUID
-# # ), "Robot UUID is not set correctly. Ensure that the robot uuid is set to LLT_ROBOT"
-
-# ###############################################
-# ####################Model 4####################
-# ###############################################
-# if model4:
-# # LLT model based on foundation model
-# # assert (
-# # SWEEP_ID == "7tglijx8"
-# # ), "Sweep ID is not set correctly. Ensure that the sweep id is set to 7tglijx8"
-# # assert (
-# # robot_uuid == LLT_ROBOT_UUID
-# # ), "Robot UUID is not set correctly. Ensure that the robot uuid is set to LLT_ROBOT"
-
-# robot_uuid = LLT_ROBOT_UUID
-# directory = Path(f"/app/dynamics_learning/Trajectory Data/train/{robot_uuid}")
-# # Interpolate Training Data in UUID folders
-# (
-# attained_data,
-# command_data,
-# interpolated_command_data,
-# q_qd_qdd_interpolated_command_input,
-# tau_attained_input,
-# ) = prepare_data(directory)
-# # assert SWEEP_ID == "42d8t40t", "Sweep ID is not set correctly. Ensure that the sweep id is set to 42d8t40t"
-# assert (
-# robot_uuid == LLT_ROBOT_UUID
-# ), "Robot UUID is not set correctly. Ensure that the robot uuid is set to LLT_ROBOT_UUID"
-
-# config_data = load_config(
-# "/app/dynamics_learning/Foundation_Model/models/hyperparameters.json"
-# )
-
-# sweep_id, sweep_config = setup_sweep_from_hyperparameters(
-# config_data=config_data, create_sweep=True
-# )
-
-# # reset runs counter
-# runs = 0
-# val_loss = 1000
-
-# model = load_model_from_binary_file(
-# "/app/dynamics_learning/Foundation_Model/models/Foundation_model.h5"
-# )
-
-# # Train the model until the threshold validation loss is reached
-# train_until_threshold_val_loss(
-# sweep_id=sweep_id,
-# robot_uuid=robot_uuid,
-# q_qd_qdd_interpolated_command_input=q_qd_qdd_interpolated_command_input,
-# tau_attained_input=tau_attained_input,
-# model=model,
-# notes="Sweep to train model based on foundation model with known hyperparameters. 50 Trajectories are avaiulable for training. Training ist stoped when the validation loss is below 50.",
-# )
-
-# runs_model4 = runs
-# val_loss_model4 = val_loss
-# sweep_id4 = sweep_id
-
-# logger.info(f"""Training concluded.
-# The first model using sweep {sweep_id1} was trained for {runs_model1} runs and reached a validation loss of {val_loss_model1}.
-# The second model using sweep {sweep_id2} was trained for {runs_model2} runs and reached a validation loss of {val_loss_model2}.
-# The third model using sweep {sweep_id3} was trained for {runs_model3} runs and reached a validation loss of {val_loss_model3}.
-# The fourth model using sweep {sweep_id4} was trained for {runs_model4} runs and reached a validation loss of {val_loss_model4}.
-# """)