def init(sequence_length, eval_init_points_file_name, worlds):
"""Initializes the common operations between train and test."""
modality_types = create_modality_types()
logging.info('modality types: %r', modality_types)
# negative reward_goal_range prevents the env from terminating early when the
# agent is close to the goal. The policy should keep the agent until the end
# of the 100 steps either through chosing stop action or oscilating around
# the target.
env = active_vision_dataset_env.ActiveVisionDatasetEnv(
modality_types=modality_types +
[task_env.ModalityTypes.GOAL, task_env.ModalityTypes.PREV_ACTION],
reward_goal_range=-1,
eval_init_points_file_name=eval_init_points_file_name,
worlds=worlds,
output_size=FLAGS.obs_resolution,
)
config = create_task_io_config(
modality_types=modality_types,
goal_category_count=FLAGS.goal_category_count,
action_size=FLAGS.action_size,
sequence_length=sequence_length,
)
task = tasks.GotoStaticXNoExplorationTask(env=env, config=config)
embedders_dict = {mtype: map_to_embedder(mtype) for mtype in config.inputs}
policy_params = tf.contrib.training.HParams(
lstm_state_size=FLAGS.lstm_cell_size,
fc_channels=FLAGS.policy_fc_size,
weight_decay=FLAGS.weight_decay,
target_embedding_size=FLAGS.embedding_fc_size,
)
policy = policies.LSTMPolicy(
modality_names=config.inputs.keys(),
embedders_dict=embedders_dict,
action_size=FLAGS.action_size,
params=policy_params,
max_episode_length=sequence_length)
return env, config, task, policy
def main(_):
gin.parse_config_files_and_bindings(FLAGS.gin_config, FLAGS.gin_params)
print('********')
print(FLAGS.mode)
print(FLAGS.gin_config)
print(FLAGS.gin_params)
env = active_vision_dataset_env.ActiveVisionDatasetEnv(modality_types=[
task_env.ModalityTypes.IMAGE, task_env.ModalityTypes.
SEMANTIC_SEGMENTATION, task_env.ModalityTypes.OBJECT_DETECTION,
task_env.ModalityTypes.DEPTH, task_env.ModalityTypes.DISTANCE
])
if FLAGS.mode == BENCHMARK_MODE:
benchmark(env, env.possible_targets)
elif FLAGS.mode == GRAPH_MODE:
for loc in env.worlds:
env.check_scene_graph(loc, 'fridge')
elif FLAGS.mode == HUMAN_MODE:
human(env, env.possible_targets)
elif FLAGS.mode == VIS_MODE:
visualize_random_step_sequence(env)
elif FLAGS.mode == EVAL_MODE:
evaluate_folder(env, FLAGS.eval_folder)