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import tensorflow as tf
from data.utils import clean_task_instruction, euler_to_quaternion, euler_to_rotation_matrix, \
rotation_matrix_to_ortho6d
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
origin_action = step['action']
step['action']={}
action=step['action']
action['terminate'] = step['is_terminal']
eef_pos=tf.cast(origin_action, dtype=tf.float32)
eef_ang=tf.cast(step['action_angle'][2:3], dtype=tf.float32)
eef_ang = euler_to_quaternion(tf.stack([0,0,eef_ang[0]], axis=0))
# No base found
# Concatenate the action
action['arm_concat'] = tf.concat([eef_pos,eef_ang],axis=0)
# Write the action format
action['format'] = tf.constant(
"eef_delta_pos_x,eef_delta_pos_y,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']
# Concatenate the state
eef_pos=tf.cast(state['position'],dtype=tf.float32)
eef_ang=tf.cast(state['yaw'],dtype=tf.float32)
eef_ang = euler_to_rotation_matrix(tf.stack([0,0,eef_ang[0]],axis=0))
eef_ang = rotation_matrix_to_ortho6d(eef_ang)
state['arm_concat'] = tf.concat([eef_pos/100,eef_ang],axis=0)
# Write the state format
state['format'] = tf.constant(
"eef_pos_x,eef_pos_y,eef_angle_0,eef_angle_1,eef_angle_2,eef_angle_3,eef_angle_4,eef_angle_5")
# Clean the task instruction
# Define the replacements (old, new) as a dictionary
replacements = {
'_': ' ',
'1f': ' ',
'4f': ' ',
'-': ' ',
'50': ' ',
'55': ' ',
'56': ' ',
}
instr = step['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 = 'berkeley_gnm_sac_son'
# 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['action'][6:7])
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