-
Notifications
You must be signed in to change notification settings - Fork 2
/
Copy pathtrain.py
153 lines (147 loc) · 7.34 KB
/
train.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
import string
import torch
from net import RINet, RINet_attention
from database import evalDataset_kitti360, SigmoidDataset_kitti360, SigmoidDataset_train, SigmoidDataset_eval
import numpy as np
from torch.utils.data import DataLoader
from tqdm import tqdm
from sklearn import metrics
import os
import argparse
# from tensorboardX import SummaryWriter
from torch.utils.tensorboard.writer import SummaryWriter
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
def train(cfg):
writer = SummaryWriter()
net = RINet_attention()
net.to(device=device)
print(net)
sequs = cfg.all_seqs
sequs.remove(cfg.seq)
train_dataset = SigmoidDataset_train(sequs=sequs, neg_ratio=cfg.neg_ratio,
eva_ratio=cfg.eval_ratio, desc_folder=cfg.desc_folder, gt_folder=cfg.gt_folder)
test_dataset = SigmoidDataset_eval(sequs=sequs, neg_ratio=cfg.neg_ratio,
eva_ratio=cfg.eval_ratio, desc_folder=cfg.desc_folder, gt_folder=cfg.gt_folder)
# train_dataset=SigmoidDataset_kitti360(['0009','0003','0007','0002','0004','0006','0010'],1)
# test_dataset=evalDataset_kitti360('0005')
batch_size = cfg.batch_size
train_loader = DataLoader(
dataset=train_dataset, batch_size=batch_size, shuffle=True, num_workers=6)
test_loader = DataLoader(
dataset=test_dataset, batch_size=batch_size, shuffle=False, num_workers=6)
optimizer = torch.optim.Adam(filter(lambda p: p.requires_grad, net.parameters(
)), lr=cfg.learning_rate, weight_decay=1e-6)
epoch = cfg.max_epoch
starting_epoch = 0
batch_num = 0
if not cfg.model == "":
checkpoint = torch.load(cfg.model)
starting_epoch = checkpoint['epoch']
batch_num = checkpoint['batch_num']
net.load_state_dict(checkpoint['state_dict'])
optimizer.load_state_dict(checkpoint['optimizer'])
for i in range(starting_epoch, epoch):
net.train()
pred = []
gt = []
for i_batch, sample_batch in tqdm(enumerate(train_loader), total=len(train_loader), desc='Train epoch '+str(i), leave=False):
optimizer.zero_grad()
out, diff = net(sample_batch["desc1"].to(
device=device), sample_batch["desc2"].to(device=device))
labels = sample_batch["label"].to(device=device)
loss1 = torch.nn.functional.binary_cross_entropy_with_logits(
out, labels)
loss2 = labels*diff*diff+(1-labels)*torch.nn.functional.relu(
cfg.margin-diff)*torch.nn.functional.relu(cfg.margin-diff)
loss2 = torch.mean(loss2)
loss = loss1+loss2
loss.backward()
optimizer.step()
with torch.no_grad():
writer.add_scalar(
'total loss', loss.cpu().item(), global_step=batch_num)
writer.add_scalar('loss1', loss1.cpu().item(),
global_step=batch_num)
writer.add_scalar('loss2', loss2.cpu().item(),
global_step=batch_num)
batch_num += 1
outlabel = out.cpu().numpy()
label = sample_batch['label'].cpu().numpy()
mask = (label > 0.9906840407) | (label < 0.0012710163)
label = label[mask]
label[label < 0.5] = 0
label[label > 0.5] = 1
pred.extend(outlabel[mask].tolist())
gt.extend(label.tolist())
pred = np.array(pred, dtype='float32')
pred = np.nan_to_num(pred)
gt = np.array(gt, dtype='float32')
precision, recall, _ = metrics.precision_recall_curve(gt, pred)
F1_score = 2 * precision * recall / (precision + recall)
F1_score = np.nan_to_num(F1_score)
trainaccur = np.max(F1_score)
print('Train F1:', trainaccur)
writer.add_scalar('train f1', trainaccur, global_step=i)
lastaccur = test(net=net, dataloader=test_loader)
writer.add_scalar('eval f1', lastaccur, global_step=i)
print('Eval F1:', lastaccur)
torch.save({'epoch': i, 'state_dict': net.state_dict(), 'optimizer': optimizer.state_dict(
), 'batch_num': batch_num}, os.path.join(cfg.log_dir, cfg.seq, str(i)+'.ckpt'))
def test(net, dataloader):
net.eval()
pred = []
gt = []
with torch.no_grad():
for i_batch, sample_batch in tqdm(enumerate(dataloader), total=len(dataloader), desc="Eval", leave=False):
out, _ = net(sample_batch["desc1"].to(
device=device), sample_batch["desc2"].to(device=device))
out = out.cpu()
outlabel = out
label = sample_batch['label']
mask = (label > 0.9906840407) | (label < 0.0012710163)
label = label[mask]
label[label < 0.5] = 0
label[label > 0.5] = 1
pred.extend(outlabel[mask])
gt.extend(label)
pred = np.array(pred, dtype='float32')
gt = np.array(gt, dtype='float32')
pred = np.nan_to_num(pred)
precision, recall, pr_thresholds = metrics.precision_recall_curve(
gt, pred)
F1_score = 2 * precision * recall / (precision + recall)
F1_score = np.nan_to_num(F1_score)
testaccur = np.max(F1_score)
return testaccur
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('--log_dir', default='log/',
help='Log dir. [default: log]')
parser.add_argument('--seq', default='00',
help='Sequence to test. [default: 00]')
parser.add_argument('--all_seqs', type=list, default=['00', '01', '02', '03', '04', '05', '06', '07', '08',
'09', '10'], help="All sequence. [default: ['00','01','02','03','04','05','06','07','08','09','10'] ]")
parser.add_argument('--neg_ratio', type=float, default=1,
help='The proportion of negative samples used during training. [default: 1]')
parser.add_argument('--eval_ratio', type=float, default=0.1,
help='Proportion of samples used for validation. [default: 0.1]')
parser.add_argument('--desc_folder', default="./data/desc_kitti",
help='Folder containing descriptors. [default: ./data/desc_kitti]')
parser.add_argument('--gt_folder', default="./data/gt_kitti",
help='Folder containing gt files. [default: ./data/gt_kitti]')
parser.add_argument('--model', default="",
help='Pretrained model. [default: ""]')
parser.add_argument('--max_epoch', type=int, default=20,
help='Epoch to run. [default: 20]')
parser.add_argument('--batch_size', type=int, default=1024,
help='Batch Size during training. [default: 1024]')
parser.add_argument('--learning_rate', type=float, default=0.02,
help='Initial learning rate. [default: 0.02]')
parser.add_argument('--weight_decay', type=float,
default=1e-6, help='Weight decay. [default: 1e-6]')
parser.add_argument('--margin', type=float, default=0.2,
help='Margin used in contrastive loss. [default: 0.2]')
cfg = parser.parse_args()
if(not os.path.exists(os.path.join(cfg.log_dir, cfg.seq))):
os.makedirs(os.path.join(cfg.log_dir, cfg.seq))
train(cfg)