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feat: different SSPL(oracle, room sr)

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Ting-Jun Wang 2024-07-16 13:49:47 +08:00
parent 287a35965e
commit 59dfbf9c30
Signed by: snsd0805
GPG Key ID: 48D331A3D6160354
1 changed files with 31 additions and 3 deletions
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34
map_nav_src/reverie/env.py
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@ -8,12 +8,17 @@ import random
import networkx as nx
from collections import defaultdict
import copy
from glob import glob
import MatterSim
from utils.data import load_nav_graphs, new_simulator
from utils.data import angle_feature, get_all_point_angle_feature
with open('./node_region.json') as fp:
node_region = json.load(fp)
class EnvBatch(object):
''' A simple wrapper for a batch of MatterSim environments,
@ -360,6 +365,9 @@ class ReverieObjectNavBatch(object):
path = sum(pred_path, [])
assert gt_path[0] == path[0], 'Result trajectories should include the start position'
pred_stop_region = node_region[scan][path[-1]]
gt_stop_region = node_region[scan][gt_path[-1]]
scores['action_steps'] = len(pred_path) - 1
scores['trajectory_steps'] = len(path) - 1
scores['trajectory_lengths'] = np.sum([shortest_distances[a][b] for a, b in zip(path[:-1], path[1:])])
@ -369,15 +377,29 @@ class ReverieObjectNavBatch(object):
goal_viewpoints = set(self.obj2vps['%s_%s'%(scan, str(gt_objid))])
assert len(goal_viewpoints) > 0, '%s_%s'%(scan, str(gt_objid))
scores['found_success'] = float(pred_found == gt_found)
scores['success'] = float(path[-1] in goal_viewpoints)
scores['room_success'] = float(pred_stop_region == gt_stop_region)
scores['oracle_success'] = float(any(x in goal_viewpoints for x in path))
'''
if scores['success'] == 1.0:
scores['found_success'] = float(pred_found == gt_found)
else:
scores['found_success'] = 0.0
'''
scores['oracle_success'] = float(any(x in goal_viewpoints for x in path))
scores['spl'] = scores['success'] * gt_lengths / max(scores['trajectory_lengths'], gt_lengths, 0.01)
scores['sspl'] = scores['spl'] * scores['found_success']
scores['sspl_1'] = scores['success'] * gt_lengths / max(scores['trajectory_lengths'], gt_lengths, 0.01) * scores['found_success']
scores['sspl_2'] = scores['room_success'] * gt_lengths / max(scores['trajectory_lengths'], gt_lengths, 0.01) * scores['found_success']
scores['sspl_3'] = scores['oracle_success'] * gt_lengths / max(scores['trajectory_lengths'], gt_lengths, 0.01) * scores['found_success']
scores['ss_1'] = scores['success'] * scores['found_success']
scores['ss_2'] = scores['room_success'] * scores['found_success']
scores['ss_3'] = scores['oracle_success'] * scores['found_success']
# scores['sspl'] = scores['spl'] * scores['found_success']
scores['rgs'] = str(pred_objid) == str(gt_objid)
scores['rgspl'] = scores['rgs'] * gt_lengths / max(scores['trajectory_lengths'], gt_lengths, 0.01)
@ -419,8 +441,14 @@ class ReverieObjectNavBatch(object):
'spl': np.mean(metrics['spl']) * 100,
'rgs': np.mean(metrics['rgs']) * 100,
'rgspl': np.mean(metrics['rgspl']) * 100,
'sspl': np.mean(metrics['sspl']) * 100,
'sspl_1': np.mean(metrics['sspl_1']) * 100,
'sspl_2': np.mean(metrics['sspl_2']) * 100,
'sspl_3': np.mean(metrics['sspl_3']) * 100,
'ss_1': np.mean(metrics['ss_1']) * 100,
'ss_2': np.mean(metrics['ss_2']) * 100,
'ss_3': np.mean(metrics['ss_3']) * 100,
'found_sr': np.mean(metrics['found_success']) * 100,
'room_sr': np.mean(metrics['room_success']) * 100,
}
return avg_metrics, metrics