-
Notifications
You must be signed in to change notification settings - Fork 7
/
test_seg_waymo.py
164 lines (141 loc) · 7.61 KB
/
test_seg_waymo.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
import os
import os.path as osp
import tqdm
import yaml
import argparse
import numpy as np
import torch
from torch.utils.data import DataLoader
from metrics.seg_metric_ignmask import accumulate_eval_results, calculate_AP, calculate_PQ_F1, ClusteringMetrics
from utils.pytorch_util import AverageMeter
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('config', type=str, help='Config files')
parser.add_argument('--split', type=str, help='Dataset split')
parser.add_argument('--round', type=int, default=0, help='Trained segmentation model of which round')
parser.add_argument('--visualize', dest='visualize', default=False, action='store_true', help='Qualitative / Quantitative evaluation mode')
parser.add_argument('--test_batch_size', type=int, default=64, help='Batch size in testing')
parser.add_argument('--save', dest='save', default=False, action='store_true', help='Save segmentation predictions or not')
# Read parameters
args = parser.parse_args()
with open(args.config) as f:
configs = yaml.load(f, Loader=yaml.FullLoader)
for ckey, cvalue in configs.items():
args.__dict__[ckey] = cvalue
# Configuration for different dataset
data_root = args.data['root']
from models.segnet_kitti import MaskFormer3D
from datasets.dataset_waymo_singleframe import WaymoOpenDataset as TestDataset
if args.split == 'val':
mapping_path = 'data_prepare/waymo/splits/val.txt'
else:
mapping_path = 'data_prepare/waymo/splits/train.txt'
# Setup the network
segnet = MaskFormer3D(n_slot=args.segnet['n_slot'],
n_point=args.segnet['n_point'],
use_xyz=args.segnet['use_xyz'],
n_transformer_layer=args.segnet['n_transformer_layer'],
transformer_embed_dim=args.segnet['transformer_embed_dim'],
transformer_input_pos_enc=args.segnet['transformer_input_pos_enc']).cuda()
# Load the trained model weights
# if args.round > 0:
# weight_path = osp.join(args.save_path + '_R%d'%(args.round), 'current.pth.tar')
# else:
# weight_path = osp.join(args.save_path, 'current.pth.tar')
if args.round > 0:
weight_path = osp.join(args.save_path + '_R%d'%(args.round), 'best.pth.tar')
else:
weight_path = osp.join(args.save_path, 'best.pth.tar')
segnet.load_state_dict(torch.load(weight_path)['model_state'])
segnet.cuda().eval()
print('Loaded weights from', weight_path)
# Setup the dataset
n_frame = 1
ignore_class_ids = [2, 3]
ignore_npoint_thresh = 50
test_set = TestDataset(data_root=data_root,
mapping_path=mapping_path,
downsampled=True,
sampled_interval=1,
decentralize=args.data['decentralize'],
ignore_class_ids=ignore_class_ids,
ignore_npoint_thresh=ignore_npoint_thresh)
batch_size = args.test_batch_size
# Qualitative evaluation mode
if args.visualize:
import open3d as o3d
from utils.visual_util import build_pointcloud
test_loader = DataLoader(test_set, batch_size=1, shuffle=True, pin_memory=True, num_workers=1)
# test_loader = DataLoader(test_set, batch_size=1, shuffle=False, pin_memory=True, num_workers=1)
w_interval = 60
with_background = True
with tqdm.tqdm(enumerate(test_loader, 0), total=len(test_loader), desc='test') as tbar:
for i, batch in tbar:
pcs, segms, _ = batch
pc = pcs[:, 0].contiguous().cuda()
segm = segms[:, 0].contiguous() # Groundtruth segmentation
# Forward inference
mask = segnet(pc, pc)
mask = mask.detach().cpu().numpy()
segm_pred = mask.argmax(2)
# Visualize
pc = pc.detach().cpu().numpy()
segm = segm.numpy()
pcds = []
pcds.append(build_pointcloud(pc[0], segm[0], with_background=with_background).translate([0.0, 0.0, 0.0]))
pcds.append(build_pointcloud(pc[0], segm_pred[0], with_background=with_background).translate([w_interval, 0.0, 0.0]))
o3d.visualization.draw_geometries(pcds)
# Quantitative evaluation mode
else:
assert batch_size % n_frame == 0, \
'Frames of one scene should be in the same batch, otherwise very inconvenient for evaluation!'
# Save segmentation predictions
if args.save:
# Path to save segmentation predictions
SAVE_DIR = osp.join(data_root, 'segm_preds/OGC' + '_R%d'%(args.round))
os.makedirs(SAVE_DIR, exist_ok=True)
# Iterate over the dataset
mbs_eval = ClusteringMetrics(spec=[ClusteringMetrics.IOU, ClusteringMetrics.RI])
eval_meter = AverageMeter()
ap_eval_meter = {'Pred_IoU': [], 'Pred_Matched': [], 'Confidence': [], 'N_GT_Inst': []}
test_loader = DataLoader(test_set, batch_size=batch_size, shuffle=False, pin_memory=True, num_workers=4)
with tqdm.tqdm(enumerate(test_loader, 0), total=len(test_loader), desc='test') as tbar:
for i, batch in tbar:
pcs, segms, valids = batch
ignores = 1 - valids
pc = pcs[:, 0].contiguous().cuda()
segm = segms[:, 0].contiguous() # Groundtruth segmentation
ignore = ignores[:, 0].contiguous()
# Forward inference
mask = segnet(pc, pc)
# Accumulate for AP, PQ, F1, Pre, Rec
Pred_IoU, Pred_Matched, Confidence, N_GT_Inst = accumulate_eval_results(segm, mask, ignore)
ap_eval_meter['Pred_IoU'].append(Pred_IoU)
ap_eval_meter['Pred_Matched'].append(Pred_Matched)
ap_eval_meter['Confidence'].append(Confidence)
ap_eval_meter['N_GT_Inst'].append(N_GT_Inst)
# mIoU & RI metrics
for sid in range(segm.shape[0] // n_frame):
all_mask = mask[(n_frame * sid):(n_frame * (sid + 1))]
all_segm = segm[(n_frame * sid):(n_frame * (sid + 1))].long()
all_ignore = ignore[(n_frame * sid):(n_frame * (sid + 1))]
per_scan_mbs = mbs_eval(all_mask, all_segm, all_ignore)
eval_meter.append_loss({'per_scan_iou_avg': np.mean(per_scan_mbs['iou']),
'per_scan_iou_std': np.std(per_scan_mbs['iou']),
'per_scan_ri_avg': np.mean(per_scan_mbs['ri']),
'per_scan_ri_std': np.std(per_scan_mbs['ri'])})
# Save
if args.save:
test_set._save_predsegm(mask, save_root=SAVE_DIR, batch_size=batch_size, n_frame=n_frame, offset=i)
# Evaluate
print('Evaluation on %s-%s:'%(args.dataset, args.split))
Pred_IoU = np.concatenate(ap_eval_meter['Pred_IoU'])
Pred_Matched = np.concatenate(ap_eval_meter['Pred_Matched'])
Confidence = np.concatenate(ap_eval_meter['Confidence'])
N_GT_Inst = np.sum(ap_eval_meter['N_GT_Inst'])
AP = calculate_AP(Pred_Matched, Confidence, N_GT_Inst, plot='True')
print('AveragePrecision@50:', AP)
PQ, F1, Pre, Rec = calculate_PQ_F1(Pred_IoU, Pred_Matched, N_GT_Inst)
print('PanopticQuality@50:', PQ, 'F1-score@50:', F1, 'Prec@50:', Pre, 'Recall@50:', Rec)
eval_avg = eval_meter.get_mean_loss_dict()
print(eval_avg)