-
Notifications
You must be signed in to change notification settings - Fork 46
/
test_partseg.py
172 lines (149 loc) · 8.08 KB
/
test_partseg.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
165
166
167
168
169
170
171
"""
Author: Congyue Deng
Contact: congyue@stanford.edu
Date: April 2021
"""
import argparse
import os
from data_utils.ShapeNetDataLoader import PartNormalDataset
import torch
import logging
import sys
import importlib
from tqdm import tqdm
import numpy as np
from pytorch3d.transforms import RotateAxisAngle, Rotate, random_rotations
BASE_DIR = os.path.dirname(os.path.abspath(__file__))
ROOT_DIR = BASE_DIR
sys.path.append(os.path.join(ROOT_DIR, 'models'))
seg_classes = {'Earphone': [16, 17, 18], 'Motorbike': [30, 31, 32, 33, 34, 35], 'Rocket': [41, 42, 43], 'Car': [8, 9, 10, 11], 'Laptop': [28, 29], 'Cap': [6, 7], 'Skateboard': [44, 45, 46], 'Mug': [36, 37], 'Guitar': [19, 20, 21], 'Bag': [4, 5], 'Lamp': [24, 25, 26, 27], 'Table': [47, 48, 49], 'Airplane': [0, 1, 2, 3], 'Pistol': [38, 39, 40], 'Chair': [12, 13, 14, 15], 'Knife': [22, 23]}
seg_label_to_cat = {} # {0:Airplane, 1:Airplane, ...49:Table}
for cat in seg_classes.keys():
for label in seg_classes[cat]:
seg_label_to_cat[label] = cat
def to_categorical(y, num_classes):
""" 1-hot encodes a tensor """
new_y = torch.eye(num_classes)[y.cpu().data.numpy(),]
if (y.is_cuda):
return new_y.cuda()
return new_y
def parse_args():
'''PARAMETERS'''
parser = argparse.ArgumentParser('PointNet')
parser.add_argument('--batch_size', type=int, default=16, help='Batch size in testing [default: 24]')
parser.add_argument('--model', default='vn_dgcnn_partseg', help='Model name [default: None]',
choices = ['pointnet_cls', 'vn_pointnet_cls', 'dgcnn_cls', 'vn_dgcnn_cls'])
parser.add_argument('--gpu', type=str, default='0', help='Specify gpu device [default: 0]')
parser.add_argument('--num_point', type=int, default=2048, help='Point Number [default: 2048]')
parser.add_argument('--log_dir', type=str, default='vn_dgcnn/aligned', help='Experiment root [default: vn_dgcnn/aligned]')
parser.add_argument('--normal', action='store_true', default=False, help='Whether to use normal information [default: False]')
parser.add_argument('--num_votes', type=int, default=3, help='Aggregate segmentation scores with voting [default: 3]')
parser.add_argument('--rot', type=str, default='aligned', help='Rotation augmentation to input data [default: aligned]',
choices=['aligned', 'z', 'so3'])
parser.add_argument('--pooling', type=str, default='mean', help='VN networks only: pooling method [default: mean]',
choices=['mean', 'max'])
parser.add_argument('--n_knn', default=40, type=int, help='Number of nearest neighbors to use, not applicable to PointNet [default: 20]')
return parser.parse_args()
def main(args):
def log_string(str):
logger.info(str)
print(str)
'''HYPER PARAMETER'''
os.environ["CUDA_VISIBLE_DEVICES"] = args.gpu
experiment_dir = 'log/partseg/' + args.log_dir
'''LOG'''
args = parse_args()
logger = logging.getLogger("Model")
logger.setLevel(logging.INFO)
formatter = logging.Formatter('%(asctime)s - %(name)s - %(levelname)s - %(message)s')
file_handler = logging.FileHandler('%s/eval.txt' % experiment_dir)
file_handler.setLevel(logging.INFO)
file_handler.setFormatter(formatter)
logger.addHandler(file_handler)
log_string('PARAMETER ...')
log_string(args)
root = 'data/shapenetcore_partanno_segmentation_benchmark_v0_normal/'
TEST_DATASET = PartNormalDataset(root = root, npoints=args.num_point, split='test', normal_channel=args.normal)
testDataLoader = torch.utils.data.DataLoader(TEST_DATASET, batch_size=args.batch_size,shuffle=False, num_workers=4)
log_string("The number of test data is: %d" % len(TEST_DATASET))
num_classes = 16
num_part = 50
'''MODEL LOADING'''
MODEL = importlib.import_module(args.model)
classifier = MODEL.get_model(args, num_part, normal_channel=args.normal).cuda()
checkpoint = torch.load(str(experiment_dir) + '/checkpoints/best_model.pth')
classifier.load_state_dict(checkpoint['model_state_dict'])
with torch.no_grad():
test_metrics = {}
total_correct = 0
total_seen = 0
total_seen_class = [0 for _ in range(num_part)]
total_correct_class = [0 for _ in range(num_part)]
shape_ious = {cat: [] for cat in seg_classes.keys()}
seg_label_to_cat = {} # {0:Airplane, 1:Airplane, ...49:Table}
for cat in seg_classes.keys():
for label in seg_classes[cat]:
seg_label_to_cat[label] = cat
for batch_id, (points, label, target) in tqdm(enumerate(testDataLoader), total=len(testDataLoader), smoothing=0.9):
batchsize, num_point, _ = points.size()
cur_batch_size, NUM_POINT, _ = points.size()
if args.rot == 'z':
trot = RotateAxisAngle(angle=torch.rand(points.shape[0])*360, axis="Z", degrees=True)
elif args.rot == 'so3':
trot = Rotate(R=random_rotations(points.shape[0]))
points = trot.transform_points(points)
points, label, target = points.float().cuda(), label.long().cuda(), target.long().cuda()
points = points.transpose(2, 1)
classifier = classifier.eval()
vote_pool = torch.zeros(target.size()[0], target.size()[1], num_part).cuda()
for _ in range(args.num_votes):
seg_pred, _ = classifier(points, to_categorical(label, num_classes))
vote_pool += seg_pred
seg_pred = vote_pool / args.num_votes
cur_pred_val = seg_pred.cpu().data.numpy()
cur_pred_val_logits = cur_pred_val
cur_pred_val = np.zeros((cur_batch_size, NUM_POINT)).astype(np.int32)
target = target.cpu().data.numpy()
for i in range(cur_batch_size):
cat = seg_label_to_cat[target[i, 0]]
logits = cur_pred_val_logits[i, :, :]
cur_pred_val[i, :] = np.argmax(logits[:, seg_classes[cat]], 1) + seg_classes[cat][0]
correct = np.sum(cur_pred_val == target)
total_correct += correct
total_seen += (cur_batch_size * NUM_POINT)
for l in range(num_part):
total_seen_class[l] += np.sum(target == l)
total_correct_class[l] += (np.sum((cur_pred_val == l) & (target == l)))
for i in range(cur_batch_size):
segp = cur_pred_val[i, :]
segl = target[i, :]
cat = seg_label_to_cat[segl[0]]
part_ious = [0.0 for _ in range(len(seg_classes[cat]))]
for l in seg_classes[cat]:
if (np.sum(segl == l) == 0) and (
np.sum(segp == l) == 0): # part is not present, no prediction as well
part_ious[l - seg_classes[cat][0]] = 1.0
else:
part_ious[l - seg_classes[cat][0]] = np.sum((segl == l) & (segp == l)) / float(
np.sum((segl == l) | (segp == l)))
shape_ious[cat].append(np.mean(part_ious))
all_shape_ious = []
for cat in shape_ious.keys():
for iou in shape_ious[cat]:
all_shape_ious.append(iou)
shape_ious[cat] = np.mean(shape_ious[cat])
mean_shape_ious = np.mean(list(shape_ious.values()))
test_metrics['accuracy'] = total_correct / float(total_seen)
test_metrics['class_avg_accuracy'] = np.mean(
np.array(total_correct_class) / np.array(total_seen_class, dtype=np.float))
for cat in sorted(shape_ious.keys()):
log_string('eval mIoU of %s %f' % (cat + ' ' * (14 - len(cat)), shape_ious[cat]))
test_metrics['class_avg_iou'] = mean_shape_ious
test_metrics['inctance_avg_iou'] = np.mean(all_shape_ious)
log_string('Accuracy is: %.5f'%test_metrics['accuracy'])
log_string('Class avg accuracy is: %.5f'%test_metrics['class_avg_accuracy'])
log_string('Class avg mIOU is: %.5f'%test_metrics['class_avg_iou'])
log_string('Inctance avg mIOU is: %.5f'%test_metrics['inctance_avg_iou'])
if __name__ == '__main__':
args = parse_args()
main(args)