-
Notifications
You must be signed in to change notification settings - Fork 7
/
Copy pathmain_m40.py
204 lines (174 loc) · 9 KB
/
main_m40.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
import os
import argparse
import torch
import torch.nn as nn
import torch.optim as optim
import numpy as np
from model_cls import Model, transformer
from torch.optim.lr_scheduler import CosineAnnealingLR, StepLR
from torch.utils.data import DataLoader
from sklearn.metrics import accuracy_score, balanced_accuracy_score
from tqdm import tqdm
from visualdl import LogWriter
from util import IOStream, cal_loss
from modelnet40_dataset import ModelNet40
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)
if args.mode == 'train':
if not os.path.exists('checkpoints/' + args.exp_name + '/' + 'models'):
os.makedirs('checkpoints/' + args.exp_name + '/' + 'models')
os.system('cp model_cls.py checkpoints' + '/' + args.exp_name + '/' + 'model.py')
os.system('cp util.py checkpoints' + '/' + args.exp_name + '/' + 'util.py')
os.system('cp modelnet40_dataset.py checkpoints' + '/' + args.exp_name + '/' + 'data.py')
os.system('cp main_m40.py checkpoints' + '/' + args.exp_name + '/' + 'main.py')
def train(args, io, num_class=40):
train_loader = DataLoader(ModelNet40(args.num_points, partition='train'),
num_workers=8, batch_size=args.batch_size, shuffle=True, drop_last=True)
test_loader = DataLoader(ModelNet40(args.num_points, partition='test'),
num_workers=8, batch_size=args.test_batch_size, shuffle=True, drop_last=False)
device = torch.device("cuda" if args.cuda else "cpu")
args.device = device
# Try to load models
model = Model(args, transformer, num_class).to(device)
# if use multiple GPUs
if args.use_gpus:
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=args.lr / 100)
elif args.scheduler == 'step':
scheduler = StepLR(opt, step_size=20, gamma=0.5)
best_test_acc = 0.0
best_test_bal_acc = 0.0
with LogWriter(logdir='checkpoints/%s/log/train' % args.exp_name) as writer:
for epoch in range(args.epochs):
train_loss = 0.0
count = 0.0
model.train()
train_pred = []
train_true = []
for batch_data in tqdm(train_loader, total=len(train_loader)):
data, label = batch_data
data, label = data.to(device), label.to(device).squeeze()
batch_size = data.shape[0]
# start training the model
opt.zero_grad()
logits = model(data)
loss = cal_loss(logits, label)
loss.backward()
opt.step()
preds = logits.max(dim=1)[1]
train_true.append(label.detach().cpu().numpy())
train_pred.append(preds.detach().cpu().numpy())
count += batch_size
train_loss += loss.item() * batch_size
train_true = np.concatenate(train_true)
train_pred = np.concatenate(train_pred)
epoch_loss = train_loss * 1.0 / count
train_acc = accuracy_score(train_true, train_pred)
train_bal_acc = balanced_accuracy_score(train_true, train_pred)
io.cprint('[Train %d, loss: %.6f, train acc: %.3f, balanced train acc: %.3f]' % (epoch,
epoch_loss,
train_acc,
train_bal_acc))
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
# add to logger
writer.add_scalar(tag='train_loss', step=epoch, value=epoch_loss)
writer.add_scalar(tag='train_acc', step=epoch, value=train_acc)
writer.add_scalar(tag='train_bal_acc', step=epoch, value=train_bal_acc)
test_pred = []
test_true = []
model.eval()
with torch.no_grad():
for batch_data in tqdm(test_loader, total=len(test_loader)):
data, label = batch_data
data, label = data.to(device), label.to(device).squeeze()
logits = model(data)
preds = logits.max(dim=1)[1]
test_true.append(label.cpu().numpy())
test_pred.append(preds.detach().cpu().numpy())
test_true = np.concatenate(test_true)
test_pred = np.concatenate(test_pred)
test_acc = accuracy_score(test_true, test_pred)
avg_per_class_acc = balanced_accuracy_score(test_true, test_pred)
if avg_per_class_acc >= best_test_bal_acc:
best_test_bal_acc = avg_per_class_acc
if test_acc >= best_test_acc:
best_test_acc = test_acc
torch.save(model.state_dict(), 'checkpoints/%s/models/model.pth' % args.exp_name)
io.cprint('[Test %d, test acc: %.3f, test avg acc: %.3f]' % (epoch, test_acc, avg_per_class_acc))
# once the epochs are completed
io.cprint('Best test acc:: %.3f | Best test avg acc:: %.3f' % (best_test_acc, best_test_bal_acc))
if __name__ == "__main__":
# Training settings
parser = argparse.ArgumentParser(description='Point Cloud Recognition')
parser.add_argument('--exp_name', type=str, default='m40_cls', metavar='N',
help='Name of the experiment')
parser.add_argument('--mode', type=str, default='train', metavar='N', choices=['train', 'test'],
help='model mode')
parser.add_argument('--batch_size', type=int, default=32, 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('--seed', type=int, default=1, metavar='S',
help='random seed (default: 1)')
parser.add_argument('--lr', type=float, default=0.001, metavar='LR',
help='learning rate')
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('--use_sgd', type=bool, default=False,
help='Use SGD')
parser.add_argument('--use_cuda', type=bool, default=False,
help='enables CUDA training')
parser.add_argument('--use_gpus', type=bool, default=False,
help='evaluate the model')
parser.add_argument('--use_norm', type=bool, default=False,
help='Whether to use norm')
parser.add_argument('--num_points', type=int, default=1024,
help='num of points to use')
parser.add_argument('--num_K', nargs='+', type=int,
help='list of num of neighbors')
parser.add_argument('--dropout', type=float, default=0.5,
help='initial dropout rate')
parser.add_argument('--emb_dims', type=int, default=1024, metavar='N',
help='Dimension of embeddings')
parser.add_argument('--head', type=int, default=8, metavar='N',
help='Dimension of heads')
parser.add_argument('--dim_k', type=int, default=32, metavar='N',
help='Dimension of key/query tensors')
args = parser.parse_args()
_init_()
np.random.seed(args.seed)
torch.manual_seed(args.seed)
io = IOStream('checkpoints/' + args.exp_name + '/run.log')
io.cprint(str(args))
args.cuda = not args.use_cuda and torch.cuda.is_available()
if args.cuda:
torch.cuda.manual_seed(args.seed)
# torch.backends.cudnn.benchmark = True
torch.backends.cudnn.deterministic = True
io.cprint(
'Using GPU : ' + str(torch.cuda.current_device()) + ' from ' + str(torch.cuda.device_count()) + ' devices')
else:
io.cprint('Using CPU')
train(args, io)