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import tensorflow as tf
from data.utils import clean_task_instruction, quaternion_to_euler, euler_to_quaternion
def terminate_act_to_bool(terminate_act: tf.Tensor) -> tf.Tensor:
"""
Convert terminate action to a boolean, where True means terminate.
"""
return tf.where(tf.equal(terminate_act, tf.constant(0.0, dtype=tf.float32)),tf.constant(False),tf.constant(True))
def process_step(step: dict) -> dict:
"""
Unify the action format and clean the task instruction.
DO NOT use python list, use tf.TensorArray instead.
"""
# Convert raw action to our action
action = step['action']
action['terminate'] = terminate_act_to_bool(action['terminate_episode'])
eef_delta_pos = action['world_vector']
eef_ang = action['rotation_delta']
eef_ang = euler_to_quaternion(eef_ang)
# Ignore action['gripper_open']: 1 if close gripper, -1 if open gripper, 0 if no change.
# No base found
# Concatenate the action
arm_action = tf.concat([eef_delta_pos, eef_ang], axis=0)
action['arm_concat'] = arm_action
# Write the action format
action['format'] = tf.constant(
"eef_delta_pos_x,eef_delta_pos_y,eef_delta_pos_z,eef_delta_angle_x,eef_delta_angle_y,eef_delta_angle_z,eef_delta_angle_w")
# Convert raw state to our state
state = step['observation']
eef_pos = state['robot_state']
# Concatenate the state
state['arm_concat'] = eef_pos
# Write the state format
state['format'] = tf.constant(
"eef_pos_x,eef_pos_y")
# Clean the task instruction
# Define the replacements (old, new) as a dictionary
replacements = {
'_': ' ',
'1f': ' ',
'4f': ' ',
'-': ' ',
'50': ' ',
'55': ' ',
'56': ' ',
}
instr = step['observation']['natural_language_instruction']
instr = clean_task_instruction(instr, replacements)
step['observation']['natural_language_instruction'] = instr
return step
if __name__ == "__main__":
import tensorflow_datasets as tfds
from data.utils import dataset_to_path
DATASET_DIR = 'data/datasets/openx_embod'
DATASET_NAME = 'columbia_cairlab_pusht_real'
# Load the dataset
dataset = tfds.builder_from_directory(
builder_dir=dataset_to_path(
DATASET_NAME, DATASET_DIR))
dataset = dataset.as_dataset(split='all')
# Inspect the dataset
for episode in dataset:
for step in episode['steps']:
print(step)
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