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Commit 4be2548b authored by Leon Michel Gorißen's avatar Leon Michel Gorißen
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chore:rename and delete uncommented code 

Renamed file paths so that they fit with data publication
Deleted uncommented and unnecessary code.
parent 57458092
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dynamics_learning/benchmark_number_of_runs.py
+ 5
− 218
View file @ 4be2548b
...@@ -161,15 +161,15 @@ if __name__ == "__main__": ...@@ -161,15 +161,15 @@ if __name__ == "__main__":
elif (count == 1): elif (count == 1):
continue continue
model = load_model_from_binary_file( 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) sweep_id, sweep_config = setup_sweep(create_sweep=True, from_model=True)
elif (count == 2): elif (count == 2):
model = load_model_from_binary_file( 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( 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}") logger.info(f"Setting model to \n{model}\nand config_data to \n{config_data}")
sweep_id, sweep_config = setup_sweep_from_hyperparameters( sweep_id, sweep_config = setup_sweep_from_hyperparameters(
...@@ -177,10 +177,10 @@ if __name__ == "__main__": ...@@ -177,10 +177,10 @@ if __name__ == "__main__":
) )
elif (count == 3): elif (count == 3):
model = load_model_from_binary_file( 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( 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( sweep_id, sweep_config = setup_sweep_from_hyperparameters(
config_data=config_data, create_sweep=True config_data=config_data, create_sweep=True
...@@ -222,216 +222,3 @@ if __name__ == "__main__": ...@@ -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}.") 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() 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}.
# """)
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