-
Notifications
You must be signed in to change notification settings - Fork 10
/
main_semseg.py
262 lines (237 loc) · 12.2 KB
/
main_semseg.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
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
from __future__ import print_function
import os
import argparse
import torch
import torch.optim as optim
from torch.optim.lr_scheduler import CosineAnnealingLR, StepLR
from data import S3DIS
from model.sempvt import pvt_semseg
import numpy as np
from torch.utils.data import DataLoader
from util import cal_loss, IOStream
import sklearn.metrics as metrics
import time
def _init_():
if not os.path.exists('checkpoints'):
os.makedirs('checkpoints')
if not os.path.exists('checkpoints/'+args.exp_name):
os.makedirs('checkpoints/'+args.exp_name)
os.system('cp main_semseg.py checkpoints'+'/'+args.exp_name+'/'+'main_semseg.py.backup')
os.system('cp model/sempvt.py checkpoints' + '/' + args.exp_name + '/' + 'sempvt.py.backup')
os.system('cp util.py checkpoints' + '/' + args.exp_name + '/' + 'util.py.backup')
os.system('cp data.py checkpoints' + '/' + args.exp_name + '/' + 'data.py.backup')
def calculate_sem_IoU(pred_np, seg_np):
I_all = np.zeros(13)
U_all = np.zeros(13)
for sem_idx in range(seg_np.shape[0]):
for sem in range(13):
I = np.sum(np.logical_and(pred_np[sem_idx] == sem, seg_np[sem_idx] == sem))
U = np.sum(np.logical_or(pred_np[sem_idx] == sem, seg_np[sem_idx] == sem))
I_all[sem] += I
U_all[sem] += U
return I_all / U_all
def train(args, io):
train_loader = DataLoader(S3DIS(partition='train', num_points=args.num_points, test_area=args.test_area),
num_workers=8, batch_size=args.batch_size, shuffle=True, drop_last=True)
test_loader = DataLoader(S3DIS(partition='test', num_points=args.num_points, test_area=args.test_area),
num_workers=8, batch_size=args.test_batch_size, shuffle=False, drop_last=False)
device = torch.device("cuda" if args.cuda else "cpu")
# Try to load models
if args.model == 'pvt':
model = pvt_semseg(args).to(device)
else:
raise Exception("Not implemented")
print("Let's use", torch.cuda.device_count(), "GPUs!")
if args.use_sgd:
print("Use SGD")
opt = optim.SGD(model.parameters(), lr=args.lr * 100, momentum=args.momentum, weight_decay=1e-4)
else:
print("Use Adam")
opt = optim.Adam(model.parameters(), lr=args.lr, weight_decay=1e-4)
if args.scheduler == 'cos':
scheduler = CosineAnnealingLR(opt, args.epochs, eta_min=1e-3)
elif args.scheduler == 'step':
scheduler = StepLR(opt, 20, 0.5, args.epochs)
criterion = cal_loss
best_test_iou = 0
for epoch in range(args.epochs):
####################
# Train
####################
model.train()
for data, seg in train_loader:
data, seg = data.to(device), seg.to(device)
opt.zero_grad()
seg_pred = model(data)
seg_pred = seg_pred.permute(0, 2, 1).contiguous()
loss = criterion(seg_pred.view(-1, 13), seg.view(-1, 1).squeeze())
loss.backward()
opt.step()
if args.scheduler == 'cos':
scheduler.step()
elif args.scheduler == 'step':
if opt.param_groups[0]['lr'] > 1e-5:
scheduler.step()
if opt.param_groups[0]['lr'] < 1e-5:
for param_group in opt.param_groups:
param_group['lr'] = 1e-5
####################
# Test
####################
test_area = args.test_area
test_loss = 0.0
count = 0.0
model.eval()
test_true_cls = []
test_pred_cls = []
test_true_seg = []
test_pred_seg = []
for data, seg in test_loader:
data, seg = data.to(device), seg.to(device)
batch_size = data.size()[0]
seg_pred = model(data)
seg_pred = seg_pred.permute(0, 2, 1).contiguous()
loss = criterion(seg_pred.view(-1, 13), seg.view(-1, 1).squeeze())
pred = seg_pred.max(dim=2)[1]
count += batch_size
test_loss += loss.item() * batch_size
seg_np = seg.cpu().numpy()
pred_np = pred.detach().cpu().numpy()
test_true_cls.append(seg_np.reshape(-1))
test_pred_cls.append(pred_np.reshape(-1))
test_true_seg.append(seg_np)
test_pred_seg.append(pred_np)
# sys.exit(0)
test_true_cls = np.concatenate(test_true_cls)
test_pred_cls = np.concatenate(test_pred_cls)
test_acc = metrics.accuracy_score(test_true_cls, test_pred_cls)
avg_per_class_acc = metrics.balanced_accuracy_score(test_true_cls, test_pred_cls)
test_true_seg = np.concatenate(test_true_seg, axis=0)
test_pred_seg = np.concatenate(test_pred_seg, axis=0)
test_ious = calculate_sem_IoU(test_pred_seg, test_true_seg)
outstr = 'test area: %s, loss: %.6f, test acc: %.6f, test avg acc: %.6f, test iou: %.6f' % (test_area,
test_loss * 1.0 / count,
test_acc,
avg_per_class_acc,
np.mean(test_ious))
io.cprint(outstr)
if np.mean(test_ious) >= best_test_iou:
best_test_iou = np.mean(test_ious)
torch.save(model.state_dict(), 'checkpoints/%s/model_seg_%s.t7' % (args.exp_name, args.test_area))
def test(args, io):
all_true_cls = []
all_pred_cls = []
all_true_seg = []
all_pred_seg = []
for test_area in range(1,7):
test_area = str(test_area)
if (args.test_area == 'all') or (test_area == args.test_area):
test_loader = DataLoader(S3DIS(partition='test', num_points=args.num_points, test_area=test_area),
batch_size=args.test_batch_size, shuffle=False, drop_last=False)
device = torch.device("cuda" if args.cuda else "cpu")
#Try to load models
if args.model == 'pvt':
model = pvt_semseg(args).to(device)
else:
raise Exception("Not implemented")
model.load_state_dict(torch.load(os.path.join(args.model_root, 'model_seg_%s.t7' % test_area)))
model = model.eval()
test_true_cls = []
test_pred_cls = []
test_true_seg = []
test_pred_seg = []
for data, seg in test_loader:
data, seg = data.to(device), seg.to(device)
seg_pred = model(data)
seg_pred = seg_pred.permute(0, 2, 1).contiguous()
pred = seg_pred.max(dim=2)[1]
seg_np = seg.cpu().numpy()
pred_np = pred.detach().cpu().numpy()
test_true_cls.append(seg_np.reshape(-1))
test_pred_cls.append(pred_np.reshape(-1))
test_true_seg.append(seg_np)
test_pred_seg.append(pred_np)
# sys.exit(0)
test_true_cls = np.concatenate(test_true_cls)
test_pred_cls = np.concatenate(test_pred_cls)
test_acc = metrics.accuracy_score(test_true_cls, test_pred_cls)
avg_per_class_acc = metrics.balanced_accuracy_score(test_true_cls, test_pred_cls)
test_true_seg = np.concatenate(test_true_seg, axis=0)
test_pred_seg = np.concatenate(test_pred_seg, axis=0)
test_ious = calculate_sem_IoU(test_pred_seg, test_true_seg)
outstr = 'Test :: test area: %s, test acc: %.6f, test avg acc: %.6f, test iou: %.6f' % (test_area,
test_acc,
avg_per_class_acc,
np.mean(test_ious))
io.cprint(outstr)
all_true_cls.append(test_true_cls)
all_pred_cls.append(test_pred_cls)
all_true_seg.append(test_true_seg)
all_pred_seg.append(test_pred_seg)
if args.test_area == 'all':
all_true_cls = np.concatenate(all_true_cls)
all_pred_cls = np.concatenate(all_pred_cls)
all_acc = metrics.accuracy_score(all_true_cls, all_pred_cls)
avg_per_class_acc = metrics.balanced_accuracy_score(all_true_cls, all_pred_cls)
all_true_seg = np.concatenate(all_true_seg, axis=0)
all_pred_seg = np.concatenate(all_pred_seg, axis=0)
all_ious = calculate_sem_IoU(all_pred_seg, all_true_seg)
outstr = 'Overall Test :: test acc: %.6f, test avg acc: %.6f, test iou: %.6f' % (all_acc,
avg_per_class_acc,
np.mean(all_ious))
io.cprint(outstr)
if __name__ == "__main__":
# Training settings
parser = argparse.ArgumentParser(description='Point Cloud Part Segmentation')
parser.add_argument('--exp_name', type=str, default='semseg', metavar='N',
help='Name of the experiment')
parser.add_argument('--model', type=str, default='pvt', metavar='N',
choices=['pvt'],
help='Model to use, [pvt]')
parser.add_argument('--dataset', type=str, default='S3DIS', metavar='N',
choices=['S3DIS'])
parser.add_argument('--test_area', type=str, default='5', metavar='N',
choices=['1', '2', '3', '4', '5', '6', 'all'])
parser.add_argument('--batch_size', type=int, default=8, metavar='batch_size',
help='Size of batch)')
parser.add_argument('--test_batch_size', type=int, default=8, metavar='batch_size',
help='Size of batch)')
parser.add_argument('--epochs', type=int, default=50, metavar='N',
help='number of episode to train ')
parser.add_argument('--use_sgd', type=bool, default=True,
help='Use SGD')
parser.add_argument('--lr', type=float, default=0.001, metavar='LR',
help='learning rate (default: 0.001, 0.1 if using sgd)')
parser.add_argument('--momentum', type=float, default=0.9, metavar='M',
help='SGD momentum (default: 0.9)')
parser.add_argument('--scheduler', type=str, default='cos', metavar='N',
choices=['cos', 'step'],
help='Scheduler to use, [cos, step]')
parser.add_argument('--no_cuda', type=bool, default=False,
help='enables CUDA training')
parser.add_argument('--seed', type=int, default=1, metavar='S',
help='random seed (default: 1)')
parser.add_argument('--eval', type=bool, default=False,
help='evaluate the model')
parser.add_argument('--num_points', type=int, default=4096,
help='num of points to use')
parser.add_argument('--dropout', type=float, default=0.5,
help='dropout rate')
parser.add_argument('--model_root', type=str, default='checkpoints/semseg', metavar='N',
help='Pretrained model root')
args = parser.parse_args()
_init_()
io = IOStream('checkpoints/' + args.exp_name + '/semrun.log')
io.cprint(str(args))
args.cuda = not args.no_cuda and torch.cuda.is_available()
torch.manual_seed(args.seed)
if args.cuda:
io.cprint(
'Using GPU : ' + str(torch.cuda.current_device()) + ' from ' + str(torch.cuda.device_count()) + ' devices')
torch.cuda.manual_seed(args.seed)
else:
io.cprint('Using CPU')
if not args.eval:
train(args, io)
else:
test(args, io)