-
Notifications
You must be signed in to change notification settings - Fork 10
/
main_part_seg.py
269 lines (241 loc) · 11.8 KB
/
main_part_seg.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
263
264
265
266
267
268
269
from __future__ import print_function
import os
import argparse
import torch
import torch.nn as nn
import torch.optim as optim
from torch.optim.lr_scheduler import CosineAnnealingLR, StepLR
from data import _ShapeNetDataset
from model.partpvt import pvt_partseg
import numpy as np
from torch.utils.data import DataLoader
from util import cal_loss, IOStream
import sklearn.metrics as metrics
seg_num = [4, 2, 2, 4, 4, 3, 3, 2, 4, 2, 6, 2, 3, 3, 3, 3]
index_start = [0, 4, 6, 8, 12, 16, 19, 22, 24, 28, 30, 36, 38, 41, 44, 47]
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_partseg.py checkpoints' + '/' + args.exp_name + '/' + 'main_partseg.py.backup')
os.system('cp model/partpvt.py checkpoints' + '/' + args.exp_name + '/' + 'partpvt.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_shape_IoU(pred_np, seg_np, label, class_choice):
label = label.squeeze()
shape_ious = []
for shape_idx in range(seg_np.shape[0]):
if not class_choice:
start_index = index_start[label[shape_idx]-1]
num = seg_num[label[shape_idx]-1]
parts = range(start_index, start_index + num)
else:
parts = range(seg_num[label[0]])
part_ious = []
for part in parts:
I = np.sum(np.logical_and(pred_np[shape_idx] == part, seg_np[shape_idx] == part))
U = np.sum(np.logical_or(pred_np[shape_idx] == part, seg_np[shape_idx] == part))
if U == 0:
iou = 1 # If the union of groundtruth and prediction points is empty, then count part IoU as 1
else:
iou = I / float(U)
part_ious.append(iou)
shape_ious.append(np.mean(part_ious))
return shape_ious
def train(args, io):
train_dataset = _ShapeNetDataset(num_points=args.num_points, partition='trainval')
if (len(train_dataset) < 100):
drop_last = False
else:
drop_last = True
train_loader = DataLoader(train_dataset, num_workers=16, batch_size=args.batch_size, shuffle=True,
drop_last=drop_last)
test_loader = DataLoader(_ShapeNetDataset(num_points=args.num_points, partition='test'),
num_workers=16, batch_size=args.test_batch_size, shuffle=False, drop_last=False)
device = torch.device("cuda" if args.cuda else "cpu")
# Try to load models
num_classes = train_loader.dataset.num_classes
num_shapes = train_loader.dataset.num_shapes
if args.model == 'pvt':
model = pvt_partseg(num_classes=num_classes, num_shapes=num_shapes).to(device)
else:
raise Exception("Not implemented")
# model = nn.DataParallel(model)
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, step_size=20, gamma=0.5)
criterion = cal_loss
best_test_iou = 0
for epoch in range(args.epochs):
####################
# Train
####################
model.train()
for data, seg, shape_label 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, num_classes), 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_loss = 0.0
count = 0.0
model.eval()
test_true_cls = []
test_pred_cls = []
test_true_seg = []
test_pred_seg = []
test_label_seg = []
for data, seg, shape_label 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, num_classes), 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)
test_label_seg.append(shape_label)
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_label_seg = np.concatenate(test_label_seg)
test_ious = calculate_shape_IoU(test_pred_seg, test_true_seg, test_label_seg, args.class_choice)
outstr = 'Test %d, loss: %.6f, test acc: %.6f, test avg acc: %.6f, test iou: %.6f' % (epoch,
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/partmodel.t7' % args.exp_name)
def test(args, io):
test_loader = DataLoader(_ShapeNetDataset(num_points=args.num_points, partition='test'),
batch_size=args.test_batch_size, shuffle=False, drop_last=False)
device = torch.device("cuda" if args.cuda else "cpu")
num_classes = test_loader.dataset.num_classes
num_shapes = test_loader.dataset.num_shapes
# Try to load models
if args.model == 'pvt':
model = pvt_partseg(num_classes=num_classes, num_shapes=num_shapes).to(device)
else:
raise Exception("Not implemented")
model.load_state_dict(torch.load(args.model_path))
model = model.eval()
test_true_cls = []
test_pred_cls = []
test_true_seg = []
test_pred_seg = []
test_label_seg = []
for data, seg, shape_label 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)
test_label_seg.append(shape_label)
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_label_seg = np.concatenate(test_label_seg)
test_ious = calculate_shape_IoU(test_pred_seg, test_true_seg, test_label_seg, args.class_choice)
outstr = 'Test :: test acc: %.6f, test avg acc: %.6f, test iou: %.6f' % (test_acc,
avg_per_class_acc,
np.mean(test_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='partseg', 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='shapenetpart', metavar='N',
choices=['shapenetpart'])
parser.add_argument('--class_choice', type=str, default=None, metavar='N',
choices=['airplane', 'bag', 'cap', 'car', 'chair',
'earphone', 'guitar', 'knife', 'lamp', 'laptop',
'motor', 'mug', 'pistol', 'rocket', 'skateboard', 'table'])
parser.add_argument('--batch_size', type=int, default=16, metavar='batch_size',
help='Size of batch)')
parser.add_argument('--test_batch_size', type=int, default=16, metavar='batch_size',
help='Size of batch)')
parser.add_argument('--epochs', type=int, default=250, 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=2048,
help='num of points to use')
parser.add_argument('--dropout', type=float, default=0.5,
help='dropout rate')
parser.add_argument('--model_path', type=str, default='checkpoints/partseg/partmodel.t7', metavar='N',
help='Pretrained model path')
args = parser.parse_args()
_init_()
io = IOStream('checkpoints/' + args.exp_name + '/partrun.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)