def get_norm_dtw(action_list, env_output_list, environment):
"""Returns normalized DTW.
"Effective and General Evaluation for Instruction Conditioned Navigation using
Dynamic Time Warping" 2019 Magalhaes et al. https://arxiv.org/abs/1907.05446
Args:
action_list: List of actions.
env_output_list: List of observations in the trajectories.
environment: Testing environment.
Returns:
Value of normalized DTW.
"""
invalid_panos = [constants.STOP_NODE_ID, constants.INVALID_NODE_ID]
golden_panos = env_output_list[0].observation[constants.GOLDEN_PATH]
golden_panos = [pano for pano in golden_panos if pano not in invalid_panos]
obs_panos = [e.observation[constants.PANO_ID] for e in env_output_list[:-1]]
obs_panos.append(action_list[-1])
obs_panos = [pano for pano in obs_panos if pano not in invalid_panos]
scan_id = env_output_list[0].observation[constants.SCAN_ID]
dtw_matrix = base_eval_metric.get_dtw_matrix(
obs_panos,
golden_panos,
lambda pano1, pano2: environment.get_distance(pano1, pano2, scan_id))
dtw = dtw_matrix[len(obs_panos)][len(golden_panos)]
return np.exp(-1. * dtw / (SUCCESS_THRESHOLD * len(golden_panos)))
def dense_dtw(path_history, next_pano, golden_path, end_of_episode, scan_info):
"""Rewards an agent based on the difference in DTW after going to nex_pano.
Args:
path_history: See above.
next_pano: See above.
golden_path: See above.
end_of_episode: See above.
scan_info: See above.
Returns:
A scalar float immediate reward for the transition
current_pano --> next_pano.
"""
del end_of_episode
if next_pano in [constants.STOP_NODE_ID, constants.INVALID_NODE_ID]:
return 0.0
observed_pano_ids = path_history + [next_pano]
observed_pano_names = [
scan_info.pano_id_to_name[pano] for pano in observed_pano_ids
]
dtw_matrix = eval_metric.get_dtw_matrix(observed_pano_names, golden_path,
scan_info.graph.get_distance)
num_obs_panos = len(observed_pano_names)
num_golden_panos = len(golden_path)
previous_dtw = dtw_matrix[num_obs_panos - 1][num_golden_panos]
current_dtw = dtw_matrix[num_obs_panos][num_golden_panos]
return previous_dtw - current_dtw
def get_dtw(action_list, env_output_list, environment):
"""Dynamic Time Warping (DTW).
Muller, Meinard. "Dynamic time warping."
Information retrieval for music and motion (2007): 69-84.
Dynamic Programming implementation, O(NM) time and memory complexity.
Args:
action_list: List of actions.
env_output_list: List of observations in the trajectories.
environment: Testing environment.
Returns:
The DTW score.
"""
invalid_panos = [constants.STOP_NODE_ID, constants.INVALID_NODE_ID]
obs_panos = _get_predicted_path(action_list, env_output_list)
obs_panos = [pano for pano in obs_panos if pano not in invalid_panos]
golden_panos = env_output_list[0].observation[constants.GOLDEN_PATH]
golden_panos = [pano for pano in golden_panos if pano not in invalid_panos]
scan_id = env_output_list[0].observation[constants.SCAN_ID]
dtw_matrix = base_eval_metric.get_dtw_matrix(
obs_panos,
golden_panos,
lambda pano1, pano2: environment.get_distance(pano1, pano2, scan_id))
golden_path_length = _get_path_length(golden_panos, scan_id, environment)
# Note: We normalize DTW (which is sum of distances in the graph) by
# golden_path_length.
return dtw_matrix[len(obs_panos)][len(golden_panos)] / golden_path_length
def _get_dtw_score(self, success_rate, golden_path, agent_path):
distance_fn = self._env.shortest_path_length
dtw_matrix = base_eval_metric.get_dtw_matrix(agent_path, golden_path,
distance_fn)
dtw = dtw_matrix[len(agent_path)][len(golden_path)]
pln_dtw = dtw / len(golden_path)
ndtw = tf.math.exp(-1. * dtw / (_SUCCESS_THRESHOLD * len(golden_path)))
sdtw = ndtw if success_rate else 0.
return pln_dtw, ndtw, sdtw
