-
Notifications
You must be signed in to change notification settings - Fork 102
/
train_partseg.py
240 lines (203 loc) · 10.5 KB
/
train_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
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
"""
Author: Benny
Date: Nov 2019
"""
import argparse
import os
import torch
import datetime
import logging
import sys
import importlib
import shutil
import provider
import numpy as np
from pathlib import Path
from tqdm import tqdm
from dataset import PartNormalDataset
import hydra
import omegaconf
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 inplace_relu(m):
classname = m.__class__.__name__
if classname.find('ReLU') != -1:
m.inplace=True
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
@hydra.main(config_path='config', config_name='partseg')
def main(args):
omegaconf.OmegaConf.set_struct(args, False)
'''HYPER PARAMETER'''
os.environ["CUDA_VISIBLE_DEVICES"] = str(args.gpu)
logger = logging.getLogger(__name__)
root = hydra.utils.to_absolute_path('data/shapenetcore_partanno_segmentation_benchmark_v0_normal/')
TRAIN_DATASET = PartNormalDataset(root=root, npoints=args.num_point, split='trainval', normal_channel=args.normal)
trainDataLoader = torch.utils.data.DataLoader(TRAIN_DATASET, batch_size=args.batch_size, shuffle=True, num_workers=10, drop_last=True)
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=10)
'''MODEL LOADING'''
args.input_dim = (6 if args.normal else 3) + 16
args.num_class = 50
num_category = 16
num_part = args.num_class
shutil.copy(hydra.utils.to_absolute_path('models/{}/model.py'.format(args.model.name)), '.')
classifier = getattr(importlib.import_module('models.{}.model'.format(args.model.name)), 'PointTransformerSeg')(args).cuda()
criterion = torch.nn.CrossEntropyLoss()
try:
checkpoint = torch.load('best_model.pth')
start_epoch = checkpoint['epoch']
classifier.load_state_dict(checkpoint['model_state_dict'])
logger.info('Use pretrain model')
except:
logger.info('No existing model, starting training from scratch...')
start_epoch = 0
if args.optimizer == 'Adam':
optimizer = torch.optim.Adam(
classifier.parameters(),
lr=args.learning_rate,
betas=(0.9, 0.999),
eps=1e-08,
weight_decay=args.weight_decay
)
else:
optimizer = torch.optim.SGD(classifier.parameters(), lr=args.learning_rate, momentum=0.9)
def bn_momentum_adjust(m, momentum):
if isinstance(m, torch.nn.BatchNorm2d) or isinstance(m, torch.nn.BatchNorm1d):
m.momentum = momentum
LEARNING_RATE_CLIP = 1e-5
MOMENTUM_ORIGINAL = 0.1
MOMENTUM_DECCAY = 0.5
MOMENTUM_DECCAY_STEP = args.step_size
best_acc = 0
global_epoch = 0
best_class_avg_iou = 0
best_inctance_avg_iou = 0
for epoch in range(start_epoch, args.epoch):
mean_correct = []
logger.info('Epoch %d (%d/%s):' % (global_epoch + 1, epoch + 1, args.epoch))
'''Adjust learning rate and BN momentum'''
lr = max(args.learning_rate * (args.lr_decay ** (epoch // args.step_size)), LEARNING_RATE_CLIP)
logger.info('Learning rate:%f' % lr)
for param_group in optimizer.param_groups:
param_group['lr'] = lr
momentum = MOMENTUM_ORIGINAL * (MOMENTUM_DECCAY ** (epoch // MOMENTUM_DECCAY_STEP))
if momentum < 0.01:
momentum = 0.01
print('BN momentum updated to: %f' % momentum)
classifier = classifier.apply(lambda x: bn_momentum_adjust(x, momentum))
classifier = classifier.train()
'''learning one epoch'''
for i, (points, label, target) in tqdm(enumerate(trainDataLoader), total=len(trainDataLoader), smoothing=0.9):
points = points.data.numpy()
points[:, :, 0:3] = provider.random_scale_point_cloud(points[:, :, 0:3])
points[:, :, 0:3] = provider.shift_point_cloud(points[:, :, 0:3])
points = torch.Tensor(points)
points, label, target = points.float().cuda(), label.long().cuda(), target.long().cuda()
optimizer.zero_grad()
seg_pred = classifier(torch.cat([points, to_categorical(label, num_category).repeat(1, points.shape[1], 1)], -1))
seg_pred = seg_pred.contiguous().view(-1, num_part)
target = target.view(-1, 1)[:, 0]
pred_choice = seg_pred.data.max(1)[1]
correct = pred_choice.eq(target.data).cpu().sum()
mean_correct.append(correct.item() / (args.batch_size * args.num_point))
loss = criterion(seg_pred, target)
loss.backward()
optimizer.step()
train_instance_acc = np.mean(mean_correct)
logger.info('Train accuracy is: %.5f' % train_instance_acc)
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
classifier = classifier.eval()
for batch_id, (points, label, target) in tqdm(enumerate(testDataLoader), total=len(testDataLoader), smoothing=0.9):
cur_batch_size, NUM_POINT, _ = points.size()
points, label, target = points.float().cuda(), label.long().cuda(), target.long().cuda()
seg_pred = classifier(torch.cat([points, to_categorical(label, num_category).repeat(1, points.shape[1], 1)], -1))
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()):
logger.info('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)
logger.info('Epoch %d test Accuracy: %f Class avg mIOU: %f Inctance avg mIOU: %f' % (
epoch + 1, test_metrics['accuracy'], test_metrics['class_avg_iou'], test_metrics['inctance_avg_iou']))
if (test_metrics['inctance_avg_iou'] >= best_inctance_avg_iou):
logger.info('Save model...')
savepath = 'best_model.pth'
logger.info('Saving at %s' % savepath)
state = {
'epoch': epoch,
'train_acc': train_instance_acc,
'test_acc': test_metrics['accuracy'],
'class_avg_iou': test_metrics['class_avg_iou'],
'inctance_avg_iou': test_metrics['inctance_avg_iou'],
'model_state_dict': classifier.state_dict(),
'optimizer_state_dict': optimizer.state_dict(),
}
torch.save(state, savepath)
logger.info('Saving model....')
if test_metrics['accuracy'] > best_acc:
best_acc = test_metrics['accuracy']
if test_metrics['class_avg_iou'] > best_class_avg_iou:
best_class_avg_iou = test_metrics['class_avg_iou']
if test_metrics['inctance_avg_iou'] > best_inctance_avg_iou:
best_inctance_avg_iou = test_metrics['inctance_avg_iou']
logger.info('Best accuracy is: %.5f' % best_acc)
logger.info('Best class avg mIOU is: %.5f' % best_class_avg_iou)
logger.info('Best inctance avg mIOU is: %.5f' % best_inctance_avg_iou)
global_epoch += 1
if __name__ == '__main__':
main()