diff --git a/dynamics_learning/dynamics_learning/data_retrieval/__init__.py b/dynamics_learning/dynamics_learning/data_retrieval/__init__.py
index e01a8cf777e81b88e42740f1af6c1c247c5ad938..3cf38bfdff501772e99815bb6955f882c7f3a597 100644
--- a/dynamics_learning/dynamics_learning/data_retrieval/__init__.py
+++ b/dynamics_learning/dynamics_learning/data_retrieval/__init__.py
@@ -29,6 +29,17 @@ RESOURCE = PROJECT.resource(
) # Address the specific resource in the project
+def download_resource_content_into_uuid_folders():
+ files = RESOURCE.files(path="train", recursive=True, with_metadata=True)
+ for file in track(files):
+ if file.is_folder:
+ continue
+ logger.info(f"File: {file.name}")
+ robot_uuid = file.metadata_form()["Robot UUID"][0]
+ Path(f"./Trajectory Data/train/{robot_uuid}").mkdir(parents=True, exist_ok=True)
+ file.download(f"./Trajectory Data/train/{robot_uuid}/{file.name}")
+
+
def download_resource_content(resource: coscine.resource.Resource = RESOURCE) -> Path:
"""Downloads none-existend contents of the resource.
diff --git a/dynamics_learning/dynamics_learning/model_io/__init__.py b/dynamics_learning/dynamics_learning/model_io/__init__.py
index 55024f6f0fb89c9be218c6afca1d5ad4a0cd99d7..413f9bea0ae9bb5268fb103b780d2fc1a8c881d3 100644
--- a/dynamics_learning/dynamics_learning/model_io/__init__.py
+++ b/dynamics_learning/dynamics_learning/model_io/__init__.py
@@ -3,12 +3,25 @@ import io
import h5py
import tensorflow as tf
from pritty_logger import RichLogger
+from keras.models import Sequential
+import json
+import wandb
+from typing import Any
logger = RichLogger("dynamics_learning-model_io")
# Save model to a binary file
-def save_model_to_binary_file(model, file_path):
+def save_model_to_binary_file(model: Sequential, file_path: str) -> None:
+ """Save the model to a binary file serialized in HDF5 format. Can be used to upload the model to coscine.
+
+ Args:
+ model (Sequential): Model to be saved.
+ file_path (str): File path to save the model to. Ends with .h5.
+
+ Returns:
+ None
+ """
# Create an in-memory file
with io.BytesIO() as byte_io:
# Save the model to the in-memory file
@@ -20,10 +33,19 @@ def save_model_to_binary_file(model, file_path):
with open(file_path, "wb") as f:
f.write(byte_io.getvalue())
logger.log(f"Model saved to {file_path}")
+ return None
# Function to load the model from a binary file
-def load_model_from_binary_file(file_path):
+def load_model_from_binary_file(file_path: str) -> Sequential:
+ """Loads model from a binary file serialized in HDF5 format.
+
+ Args:
+ file_path (str): File path to load the model from. Ends with .h5.
+
+ Returns:
+ Sequential: loaded model
+ """
# Read the binary file content
with open(file_path, "rb") as f:
binary_data = f.read()
@@ -36,3 +58,38 @@ def load_model_from_binary_file(file_path):
logger.log("Model loaded from in-memory file")
return model
+
+
+def save_config(config: wandb.config, config_path: str) -> None:
+ """Save the config to a json file.
+
+ Args:
+ config (wandb.config): wandb.config object to be saved.
+ config_path (str): File path to save the config to. Ends with .json.
+
+ Returns:
+ None
+ """
+ config_data = json.dumps(dict(config.items()), indent=4)
+ config_bytes = config_data.encode("utf-8")
+ with open(config_path, "wb") as file:
+ file.write(config_bytes)
+ logger.log(f"Config saved to {config_path}")
+ return None
+
+
+def load_config(config_path: str) -> Any:
+ """Load the config from a json file.
+
+ Args:
+ config_path (str): File path to load the config from. Ends with .json.
+
+ Returns:
+ Any: python object of loaded config
+ """
+ with open(config_path, "rb") as file:
+ config_bytes = file.read()
+ config_data = json.loads(config_bytes.decode("utf-8"))
+ logger.log(f"Config loaded from {config_path}:")
+ logger.log(config_data)
+ return config_data
diff --git a/dynamics_learning/dynamics_learning/preprocessing/dataset_analysis.py b/dynamics_learning/dynamics_learning/preprocessing/dataset_analysis.py
index ce5ee90fbf7bc7865f8290e9b634d961fde4ce66..e26511710e5213a597eedec205a77d4a315e95a0 100644
--- a/dynamics_learning/dynamics_learning/preprocessing/dataset_analysis.py
+++ b/dynamics_learning/dynamics_learning/preprocessing/dataset_analysis.py
@@ -53,7 +53,7 @@ class CommandDataDict(TypedDict):
def analyze(directory: str) -> Tuple[AttainedDataDict, CommandDataDict]:
"""
- Analyzes the specified dataset in the given directory, generates statistics, creates plotsand concat all files into one in alphabetical order.
+ Analyzes the specified dataset in the given directory, generates statistics, creates plots and concat all files into one in alphabetical order.
Args:
directory (str): The directory containing the dataset.
@@ -91,6 +91,7 @@ def analyze(directory: str) -> Tuple[AttainedDataDict, CommandDataDict]:
tau_meas = np.empty((0, 7), np.float32)
directory_in = f"{directory}"
+ logger.info(f"Analyzing dataset in {directory_in}")
# Return a list of all meas.csv files in the directory in alphabetical order
file_list = list(
diff --git a/dynamics_learning/dynamics_learning/sweep/setup.py b/dynamics_learning/dynamics_learning/sweep/setup.py
index 192059ee36887aad61745529031789ac2044089e..a72c7ad8a4cb78793319bcefc797780ff4774a69 100644
--- a/dynamics_learning/dynamics_learning/sweep/setup.py
+++ b/dynamics_learning/dynamics_learning/sweep/setup.py
@@ -4,26 +4,35 @@
# Released under MIT License
from pritty_logger import RichLogger
+from typing import Any
import wandb
from dynamics_learning.environment import (
- SWEEP_ID,
WANDB_ENTITY,
WANDB_NOTES,
WANDB_PROJECT,
)
-from dynamics_learning.sweep.sweep_config import sweep_config
+from dynamics_learning.sweep.sweep_config import (
+ sweep_config,
+ sweep_config_from_hyperparameters,
+)
# Create a logger named 'sweep_setup_logger'
-logger = RichLogger("sweep_setup_logger")
+logger = RichLogger("dynamics_learning-sweep_setup_logger")
+
+def setup_sweep(create_sweep: bool = False) -> tuple[str, dict[str, Any]]:
+ """Sets up a Sweep from the given sweep_config.
-def setup_sweep():
- """Sets up a Sweep.
+ Args:
+ create_sweep (bool, optional): If True a new sweep is initialized. Defaults to False.
- If SWEEP_ID is set in environment, this sweep will be used. Else
- a new sweep will be created using the sweep_config from sweep_config.py.
+ Returns:
+ tuple[str, dict[str, Any]]: sweep_id and sweep_config
"""
+ global SWEEP_ID
+ if create_sweep:
+ SWEEP_ID = None
if not SWEEP_ID:
logger.log("Running the sweep with this config:")
logger.log(sweep_config)
@@ -36,3 +45,71 @@ def setup_sweep():
sweep_config["notes"] = WANDB_NOTES
logger.log(f"Running sweep_id {sweep_id}")
return sweep_id, sweep_config
+
+
+def setup_sweep_from_hyperparameters(
+ config_data: Any, create_sweep: bool = False
+) -> tuple[str, dict[str, Any]]:
+ """Sets up a Sweep from given hyperparameters within a sain range.
+
+ Args:
+ config_data (Any): hyperparameters object.
+ create_sweep (bool, optional): If True, a new sweep is created. Defaults to False.
+
+ Returns:
+ tuple[str, dict[str, Any]]: sweep_id and sweep_config
+ """
+ global SWEEP_ID
+ parameters_dict_from_hyperparameters = {
+ "optimizer": config_data.optimizer,
+ "clipnorm": {
+ "distribution": "log_uniform_values",
+ "min": config_data.clipnorm * 0.5,
+ "max": config_data.clipnorm * 1.5,
+ },
+ "learning_rate": {
+ "distribution": "log_uniform_values",
+ "min": config_data.learning_rate * 0.5,
+ "max": min(config_data.learning_rate * 1.5, 0.9),
+ },
+ "window_size": {"value": config_data.window_size},
+ "batch_size": {"value": config_data.batch_size},
+ "units": {
+ "distribution": "int_uniform",
+ "min": max(1, config_data.units - 10),
+ "max": min(config_data.units + 10, 100),
+ },
+ "dropout": {
+ "distribution": "log_uniform_values",
+ "min": config_data.dropout * 0.5,
+ "max": min(config_data.dropout * 1.5, 1),
+ },
+ "layers": {
+ "distribution": "int_uniform",
+ "min": max(1, config_data.layers - 10),
+ "max": min(config_data.layers + 10, 100),
+ }, # {"value": 10},
+ "epochs": {"value": config_data.epochs},
+ }
+ sweep_config_from_hyperparameters["parameters"] = (
+ parameters_dict_from_hyperparameters
+ )
+
+ if create_sweep:
+ SWEEP_ID = None
+ if not SWEEP_ID:
+ logger.log("Running the sweep with this config:")
+ logger.log(sweep_config_from_hyperparameters)
+ sweep_id = wandb.sweep(
+ sweep_config_from_hyperparameters,
+ project=WANDB_PROJECT,
+ entity=WANDB_ENTITY,
+ )
+ else:
+ sweep_id = SWEEP_ID
+ sweep_config_from_hyperparameters["sweep_id"] = sweep_id
+ sweep_config_from_hyperparameters["project"] = WANDB_PROJECT
+ sweep_config_from_hyperparameters["entity"] = WANDB_ENTITY
+ sweep_config_from_hyperparameters["notes"] = WANDB_NOTES
+ logger.log(f"Running sweep_id {sweep_id}")
+ return sweep_id, sweep_config_from_hyperparameters
diff --git a/dynamics_learning/dynamics_learning/sweep/sweep_config.py b/dynamics_learning/dynamics_learning/sweep/sweep_config.py
index 64f472afa22675c30bdc1860859e2ca5a8c94e1c..b913243f20b3388f11ad60069e3ba69a79f533ed 100644
--- a/dynamics_learning/dynamics_learning/sweep/sweep_config.py
+++ b/dynamics_learning/dynamics_learning/sweep/sweep_config.py
@@ -27,7 +27,11 @@ parameters_dict = {
"units": {"distribution": "int_uniform", "min": 1, "max": 100},
"dropout": {"distribution": "log_uniform_values", "min": 1e-5, "max": 1},
"layers": {"distribution": "int_uniform", "min": 1, "max": 100}, # {"value": 10},
- "epochs": {"value": 100},
+ "epochs": {"value": 2},
}
sweep_config["parameters"] = parameters_dict
+
+sweep_config_from_hyperparameters = {"method": "bayes"}
+sweep_config_from_hyperparameters["metric"] = metric
+sweep_config_from_hyperparameters["early_terminate"] = early_terminate
diff --git a/dynamics_learning/dynamics_learning/training/__init__.py b/dynamics_learning/dynamics_learning/training/__init__.py
index 9c51d7423df4b5c79efb2b2b60dcc092e3cd3fc5..18505eb88b520125e82889840175e30b46a50d02 100644
--- a/dynamics_learning/dynamics_learning/training/__init__.py
+++ b/dynamics_learning/dynamics_learning/training/__init__.py
@@ -2,7 +2,7 @@
# -*- coding:utf-8 -*-
# Copyright Leon Gorissen
# Released under MIT License
-import json
+# import json
from pathlib import Path
from typing import Dict, Tuple
@@ -15,6 +15,7 @@ from keras.metrics import Accuracy, KLDivergence, MeanAbsolutePercentageError
from keras.models import Sequential
from pritty_logger import RichLogger
from tensorflow.keras.callbacks import History
+from tensorflow.keras.models import Model # , Sequential
import wandb
from dynamics_learning.data_retrieval import (
@@ -23,7 +24,7 @@ from dynamics_learning.data_retrieval import (
update_existing_file,
)
from dynamics_learning.environment import WANDB_ENTITY, WANDB_NOTES, WANDB_PROJECT
-from dynamics_learning.model_io import save_model_to_binary_file
+from dynamics_learning.model_io import save_model_to_binary_file, save_config
# Create a logger named 'sweep_setup_logger'
logger = RichLogger("dynamics_learning-model_training")
@@ -92,7 +93,9 @@ class Metrics(Callback):
def train(
- q_qd_qdd_interpolated_command_input: tf.Tensor, tau_attained_input: tf.Tensor
+ q_qd_qdd_interpolated_command_input: tf.Tensor,
+ tau_attained_input: tf.Tensor,
+ model: Sequential = None,
) -> Tuple[Sequential, History]:
"""Train the model.
@@ -110,6 +113,21 @@ def train(
) as run:
config = wandb.config
+ if model:
+ # Take window size from the loaded model's input shape
+ model_input_shape = model.layers[0].input_shape
+ window_size = model_input_shape[1]
+ batch_size = model_input_shape[0]
+ else:
+ # Use the window size defined in config if no model is loaded
+ model_input_shape = (
+ None,
+ config.window_size,
+ q_qd_qdd_interpolated_command_input.shape[-1],
+ )
+ window_size = config.window_size
+ batch_size = config.batch_size
+
# Split Dataset to train and validation
train_size = int(0.8 * q_qd_qdd_interpolated_command_input.shape[0])
val_size = q_qd_qdd_interpolated_command_input.shape[0] - train_size
@@ -120,10 +138,10 @@ def train(
_, y_train, _, y_val = tf.split(
tau_attained_input,
[
- config.window_size - 1,
- train_size - config.window_size + 1,
- config.window_size - 1,
- val_size - config.window_size + 1,
+ window_size - 1,
+ train_size - window_size + 1,
+ window_size - 1,
+ val_size - window_size + 1,
],
0,
)
@@ -131,10 +149,88 @@ def train(
# Sliding Window for input interpolated command joint data
import tensorflow_text as tf_text
- x_train = tf_text.sliding_window(data=x_train, width=config.window_size, axis=0)
- x_val = tf_text.sliding_window(data=x_val, width=config.window_size, axis=0)
+ x_train = tf_text.sliding_window(data=x_train, width=window_size, axis=0)
+ x_val = tf_text.sliding_window(data=x_val, width=window_size, axis=0)
+
+ # if a model is passed, use it, else create a new model
+ if model:
+ model = model
+ logger.info(f"model shape is {model.layers[0].input_shape}")
+ # Define how many layers you want to keep (e.g., exclude last 2 layers)
+ # Get the number of layers in the model
+ total_layers = len(model.layers)
+
+ # Print the total number of layers
+ logger.info(f"Total layers in the model: {total_layers}")
+
+ # Define how many layers you want to remove
+ layers_to_remove = 5
+
+ # Check if you can remove five layers; if not, adjust to delete fewer layers
+ layers_to_remove = min(layers_to_remove, total_layers - 2)
+
+ # If it's a Sequential model
+ if isinstance(model, Sequential):
+ # Remove the specified number of layers
+ for _ in range(layers_to_remove):
+ model.pop()
+ new_model = model
+ else:
+ # If it's a Functional model
+ # Truncate the model to remove the specified number of layers
+ new_model = Model(
+ inputs=model.input,
+ outputs=model.layers[-(layers_to_remove + 1)].output,
+ )
+
+ for layer in new_model.layers:
+ layer.trainable = False
+
+ # Add three LSTM layers, a dropout layer, and a dense layer
+ model.add(
+ LSTM(
+ units=config.units,
+ return_sequences=True,
+ input_shape=(x_train.shape[1], x_train.shape[2]),
+ activation="tanh",
+ recurrent_activation="sigmoid",
+ recurrent_dropout=0,
+ unroll=False,
+ use_bias=True,
+ name="LSTM_1",
+ )
+ )
+ model.add(
+ LSTM(
+ units=config.units,
+ return_sequences=True,
+ input_shape=(x_train.shape[1], x_train.shape[2]),
+ activation="tanh",
+ recurrent_activation="sigmoid",
+ recurrent_dropout=0,
+ unroll=False,
+ use_bias=True,
+ name="LSTM_2",
+ )
+ )
+ model.add(LSTM(units=config.units, return_sequences=False, name="LSTM_3"))
+ model.add(Dropout(config.dropout))
+ model.add(Dense(units=y_train.shape[1]))
+
+ # Compile the model
+ opt = optimizers.Adam(
+ learning_rate=config.learning_rate, clipvalue=config.clipnorm
+ )
+ model.compile(
+ optimizer=opt,
+ loss=MeanSquaredError(),
+ metrics=[Accuracy(), KLDivergence(), MeanAbsolutePercentageError()],
+ run_eagerly=True,
+ )
+ model.build(x_train.shape)
- model = make_model(config, x_train, y_train)
+ else:
+ model = make_model(config, x_train, y_train)
from wandb.integration.keras import WandbCallback
@@ -143,7 +239,7 @@ def train(
y_train,
epochs=config.epochs,
shuffle=True,
- batch_size=config.batch_size,
+ batch_size=batch_size,
verbose=2,
validation_data=(x_val, y_val),
callbacks=[
@@ -172,7 +268,7 @@ def upload(model: Sequential, history: History, config: Dict):
resource = PROJECT.resource("Foundation_Model")
metadataform = resource.metadata_form()
metadataform["Title"] = (
- f"{decision_metric}_val_loss" # TODO implement custom coscine application profile for the models
+ f"{decision_metric}_val_loss" # TODO implement custom coscine application profile for the models based on MITM base profile
)
logger.info(f"The following meta data is saved\n{metadataform}")
_resource_files, resource_objects = get_resource_content(resource, path="models/")
@@ -189,10 +285,7 @@ def upload(model: Sequential, history: History, config: Dict):
config_local_file_name = (
"/app/dynamics_learning/Foundation_Model/hyperparameters/hyperparameters.json"
)
- config_data = json.dumps(dict(config.items()), indent=4)
- config_bytes = config_data.encode("utf-8")
- with open(config_local_file_name, "wb") as file:
- file.write(config_bytes)
+ save_config(config, config_local_file_name)
logger.info("Checking if a model exists in the resource.")
# if no model exits, upload the current model to coscine