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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 import networkx as nx
from collections import defaultdict from collections import defaultdict
import copy import copy
from glob import glob
import MatterSim import MatterSim
from utils.data import load_nav_graphs, new_simulator from utils.data import load_nav_graphs, new_simulator
from utils.data import angle_feature, get_all_point_angle_feature 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): class EnvBatch(object):
''' A simple wrapper for a batch of MatterSim environments, ''' A simple wrapper for a batch of MatterSim environments,
@ -360,6 +365,9 @@ class ReverieObjectNavBatch(object):
path = sum(pred_path, []) path = sum(pred_path, [])
assert gt_path[0] == path[0], 'Result trajectories should include the start position' 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['action_steps'] = len(pred_path) - 1
scores['trajectory_steps'] = len(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:])]) 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))]) goal_viewpoints = set(self.obj2vps['%s_%s'%(scan, str(gt_objid))])
assert len(goal_viewpoints) > 0, '%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['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: if scores['success'] == 1.0:
scores['found_success'] = float(pred_found == gt_found) scores['found_success'] = float(pred_found == gt_found)
else: else:
scores['found_success'] = 0.0 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['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['rgs'] = str(pred_objid) == str(gt_objid)
scores['rgspl'] = scores['rgs'] * gt_lengths / max(scores['trajectory_lengths'], gt_lengths, 0.01) 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, 'spl': np.mean(metrics['spl']) * 100,
'rgs': np.mean(metrics['rgs']) * 100, 'rgs': np.mean(metrics['rgs']) * 100,
'rgspl': np.mean(metrics['rgspl']) * 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, 'found_sr': np.mean(metrics['found_success']) * 100,
'room_sr': np.mean(metrics['room_success']) * 100,
} }
return avg_metrics, metrics return avg_metrics, metrics