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import tensorflow as tf
from data.utils import clean_task_instruction, euler_to_quaternion, quaternion_to_rotation_matrix,\
rotation_matrix_to_ortho6d
def terminate_act_to_bool(terminate_act: tf.Tensor) -> tf.Tensor:
"""
Convert terminate action to a boolean, where True means terminate.
"""
return tf.reduce_all(tf.equal(terminate_act, tf.constant([1, 0, 0], dtype=tf.int32)))
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)
grip_open = 1 - (action['gripper_closedness_action'] + 1) / 2
# Multiplied by 3 Hz to get units m/s and rad/s
base_delta_pos = action['base_displacement_vector'] * 3
base_delta_ang = action['base_displacement_vertical_rotation'] * 3
# Concatenate the action
arm_action = tf.concat([eef_delta_pos, eef_ang, grip_open], axis=0)
action['arm_concat'] = arm_action
base_action = tf.concat([base_delta_pos, base_delta_ang], axis=0)
action['base_concat'] = base_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,gripper_open,base_vel_x,base_vel_y,base_angular_vel")
# Convert raw state to our state
state = step['observation']
eef_pos = state['base_pose_tool_reached'][:3]
# eef_ang = quaternion_to_euler(state['base_pose_tool_reached'][3:])
eef_ang = quaternion_to_rotation_matrix(state['base_pose_tool_reached'][3:])
eef_ang = rotation_matrix_to_ortho6d(eef_ang)
grip_open = 1 - state['gripper_closed']
# Concatenate the state
state['arm_concat'] = tf.concat([eef_pos, eef_ang, grip_open], axis=0)
# Write the state format
state['format'] = tf.constant(
"eef_pos_x,eef_pos_y,eef_pos_z,eef_angle_0,eef_angle_1,eef_angle_2,eef_angle_3,eef_angle_4,eef_angle_5,gripper_open")
# 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 = 'fractal20220817_data'
# 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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