-
Notifications
You must be signed in to change notification settings - Fork 7
/
train_events.py
206 lines (152 loc) · 8.39 KB
/
train_events.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
import argparse
import collections
import numpy as np
import time
import math
import torch
import torch.optim as optim
from torchvision import transforms
from retinanet import model
from retinanet.dataloader import CSVDataset_event, collater, Resizer, AspectRatioBasedSampler, \
Augmenter, \
Normalizer
from torch.utils.data import DataLoader
# from retinanet import coco_eval
from retinanet import csv_eval
assert torch.__version__.split('.')[0] == '1'
print('CUDA available: {}'.format(torch.cuda.is_available()))
def time_since(since):
now = time.time()
s = now - since
m = math.floor(s / 60)
s -= m * 60
return '%dm %ds' % (m, s)
def main(args=None):
base_dir = '/home/abhishek/connect'
parser = argparse.ArgumentParser(description='Simple training script for training a RetinaNet network.')
parser.add_argument('--dataset', default='csv', help='Dataset type, must be one of csv or coco.')
parser.add_argument('--coco_path', help='Path to COCO directory')
parser.add_argument('--csv_train', default=f'./DSEC_detection_labels/labels_filtered_train.csv',
help='Path to file containing training annotations (see readme)')
parser.add_argument('--csv_classes', default=f'./DSEC_detection_labels/labels_filtered_map.csv',
help='Path to file containing class list (see readme)')
parser.add_argument('--csv_val', help='Path to file containing validation annotations (optional, see readme)')
parser.add_argument('--root_img',default=f'{base_dir}/DSEC/train/transformed_images',help='dir to root rgb images')
parser.add_argument('--root_event', default=f'{base_dir}/DSEC_events_img',help='dir to toot event files in dsec directory structure')
parser.add_argument('--fusion', help='Type of fusion:1)early_fusion, fpn_fusion, multi-level', type=str, default='fpn_fusion')
parser.add_argument('--depth', help='Resnet depth, must be one of 18, 34, 50, 101, 152', type=int, default=50)
parser.add_argument('--epochs', help='Number of epochs', type=int, default=60)
parser.add_argument('--continue_training', help='load a pretrained file', default=False)
parser.add_argument('--checkpoint', help='location of pretrained file', default='./csv_dropout_retinanet_63.pt')
parser = parser.parse_args(args)
if parser.dataset == 'csv':
if parser.csv_train is None:
raise ValueError('Must provide --csv_train when training on COCO,')
if parser.csv_classes is None:
raise ValueError('Must provide --csv_classes when training on COCO,')
dataset_train = CSVDataset_event(train_file=parser.csv_train, class_list=parser.csv_classes,root_event_dir=parser.root_event,root_img_dir=parser.root_img,
transform=transforms.Compose([Normalizer(), Resizer()]))
if parser.csv_val is None:
dataset_val = None
print('No validation annotations provided.')
else:
dataset_val = CSVDataset(train_file=parser.csv_val, class_list=parser.csv_classes,
transform=transforms.Compose([Normalizer(), Resizer()]))
else:
raise ValueError('Dataset type not understood (must be csv or coco), exiting.')
# sampler = AspectRatioBasedSampler(dataset_train, batch_size=2, drop_last=False)
dataloader_train = DataLoader(dataset_train, batch_size=8, num_workers=6, shuffle=True,collate_fn=collater)
dataset_val1 = CSVDataset_event(train_file=f'{base_dir}/DSEC_detection_labels/events/labels_filtered_test.csv', class_list=parser.csv_classes,
root_event_dir=parser.root_event,root_img_dir=parser.root_img, transform=transforms.Compose([Normalizer(), Resizer()]))
dataloader_val1 = DataLoader(dataset_val1 , batch_size=1, num_workers=6, shuffle=True,collate_fn=collater)
if dataset_val is not None:
sampler_val = AspectRatioBasedSampler(dataset_val, batch_size=1, drop_last=False)
dataloader_val = DataLoader(dataset_val, num_workers=1, collate_fn=collater, batch_sampler=sampler_val)
# Create the model
list_models = ['early_fusion','fpn_fusion', 'event', 'rgb']
if parser.fusion in list_models:
if parser.depth == 50:
retinanet = model.resnet50(num_classes=dataset_train.num_classes(),fusion_model=parser.fusion,pretrained=False)
if parser.depth == 101:
retinanet = model.resnet101(num_classes=dataset_train.num_classes(),fusion_model=parser.fusion,pretrained=False)
else:
raise ValueError('Unsupported model fusion')
use_gpu = True
if parser.continue_training:
checkpoint = torch.load(parser.checkpoint)
retinanet.load_state_dict(checkpoint['model_state_dict'])
epoch_loss_all = checkpoint['loss']
epoch_total = checkpoint['epoch']
print('training sensor fusion model')
retinanet.eval()
else:
epoch_total = 0
epoch_loss_all =[]
if use_gpu:
if torch.cuda.is_available():
retinanet = retinanet.cuda()
if torch.cuda.is_available():
retinanet = torch.nn.DataParallel(retinanet).cuda()
else:
retinanet = torch.nn.DataParallel(retinanet)
retinanet.training = True
optimizer = optim.Adam(retinanet.parameters(), lr=1e-4)
scheduler = optim.lr_scheduler.ReduceLROnPlateau(optimizer, patience=3, verbose=True)
loss_hist = collections.deque(maxlen=100)
retinanet.train()
retinanet.module.freeze_bn()
print('Num training images: {}'.format(len(dataset_train)))
num_batches = 0
start = time.time()
# print('sensor fusion, impulse_noise images')
# mAP = csv_eval.evaluate(dataset_val1, retinanet)
print(time_since(start))
epoch_loss = []
for epoch_num in range(parser.epochs):
retinanet.train()
retinanet.module.freeze_bn()
epoch_total += 1
for iter_num, data in enumerate(dataloader_train):
try:
classification_loss, regression_loss = retinanet([data['img_rgb'],data['img'].cuda().float(),data['annot']])
classification_loss = classification_loss.mean()
regression_loss = regression_loss.mean()
loss = classification_loss + regression_loss
if bool(loss == 0):
continue
loss.backward()
torch.nn.utils.clip_grad_norm_(retinanet.parameters(), 0.1)
num_batches += 1
if num_batches == 8: # optimize every 5 mini-batches
optimizer.step()
optimizer.zero_grad()
num_batches = 0
loss_hist.append(float(loss))
if iter_num % 500 ==0:
print(
'[sensor fusion homographic] [{}], Epoch: {} | Iteration: {} | Classification loss: {:1.5f} | Regression loss: {:1.5f} | Running loss: {:1.5f}'.format(
time_since(start), epoch_num, iter_num, float(classification_loss), float(regression_loss), np.mean(loss_hist)))
epoch_loss.append(np.mean(loss_hist))
del classification_loss
del regression_loss
except Exception as e:
print(e)
continue
if parser.dataset == 'coco':
print('Evaluating dataset')
coco_eval.evaluate_coco(dataset_val, retinanet)
elif parser.dataset == 'csv' and parser.csv_val is not None:
print('Evaluating dataset')
mAP = csv_eval.evaluate(dataset_val, retinanet)
scheduler.step(np.mean(epoch_loss))
# torch.save(retinanet.module, '{}_retinanet_{}.pt'.format(parser.dataset, epoch_num))
if epoch_num % 5 ==0:
torch.save({'epoch': epoch_total, 'model_state_dict': retinanet.module.state_dict(),
'optimizer_state_dict': optimizer.state_dict(),
'loss': np.append(epoch_loss_all,epoch_loss)}, f'{parser.dataset}_fpn_homographic_retinanet_retinanet101_{epoch_total}.pt')
# retinanet.eval()
torch.save({'epoch': epoch_total, 'model_state_dict': retinanet.module.state_dict(),
'optimizer_state_dict': optimizer.state_dict(),
'loss': np.append(epoch_loss_all,epoch_loss)}, f'{parser.dataset}_fpn_homographic_retinanet_retinanet101_{epoch_total}.pt')
if __name__ == '__main__':
main()