def __init__(self, args, gpu_id, strict_done=False):
super(BasicEpisode, self).__init__()
self._env = None
self.gpu_id = gpu_id
self.strict_done = strict_done
self.task_data = None
self.glove_embedding = None
self.actions = get_actions(args)
self.done_count = 0
self.duplicate_count = 0
self.failed_action_count = 0
self._last_action_embedding_idx = 0
self.target_object = None
self.prev_frame = None
self.current_frame = None
self.det_frame = None
self.last_det = False
self.current_det = False
self.det_gt = None
self.optimal_actions = None
self.scene_states = []
self.detections = []
if args.eval:
random.seed(args.seed)
def __init__(self, args, gpu_id, strict_done=False):
super(BasicEpisode, self).__init__()
self._env = None
self.gpu_id = gpu_id
self.strict_done = strict_done
self.task_data = None
self.glove_embedding = None
self.prototype = None
self.actions = get_actions(args)
self.done_count = 0
self.duplicate_count = 0
self.failed_action_count = 0
self._last_action_embedding_idx = 0
self.target_object = None
self.prev_frame = None
self.current_frame = None
self.grid_size = args.grid_size
self.goal_success_reward = args.goal_success_reward
self.step_penalty = args.step_penalty
self.step_penalty_table = []
self.episode_id = ""
step_penalty = args.step_penalty
for _ in range(0, args.max_ep, args.num_ep_per_stage):
self.step_penalty_table.append(step_penalty)
step_penalty = step_penalty * args.penalty_decay
self.scene_states = []
self.episode_trajectories = []
self.actions_taken = []
if args.eval:
random.seed(args.seed)
def __init__(self, args, gpu_id, strict_done=False):
super(BasicEpisode, self).__init__()
self._env = None
self.gpu_id = gpu_id
self.strict_done = strict_done
self.task_data = None
self.glove_embedding = None
self.actions = get_actions(args)
self.done_count = 0
self.duplicate_count = 0
self.failed_action_count = 0
self._last_action_embedding_idx = 0
self.target_object = None
self.prev_frame = None
self.current_frame = None
self.scene = None
self.scene_states = []
if args.eval:
random.seed(args.seed)
self._episode_times = 0
self.seen_percentage = 0
self.state_reps = []
self.state_memory = []
self.action_memory = []
self.obs_reps = []
self.episode_length = 0
self.target_object_detected = False
# tools
self.states = []
self.actions_record = []
self.action_outputs = []
self.detection_results = []
# imitation learning
self.imitation_learning = args.imitation_learning
self.action_failed_il = False
self.action_probs = []
self.meta_learning = args.update_meta_network
self.meta_predictions = []
self.visual_infos = {}
self.match_score = []
self.indices_topk = []