forked from hehao13/EBLNet
-
Notifications
You must be signed in to change notification settings - Fork 0
/
Copy path__init__.py
205 lines (180 loc) · 7.57 KB
/
__init__.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
"""
Dataset setup and loaders
This file including the different datasets processing pipelines
"""
from datasets import MSD
from datasets import GDD
from datasets import Trans10k
import torchvision.transforms as standard_transforms
import transforms.joint_transforms as joint_transforms
import transforms.transforms as extended_transforms
from torch.utils.data import DataLoader
def setup_loaders(args):
"""
Setup Data Loaders[Currently supports Cityscapes, Mapillary and ADE20kin]
input: argument passed by the user
return: training data loader, validation data loader loader, train_set
"""
if args.dataset == 'MSD':
args.dataset_cls = MSD
args.train_batch_size = args.bs_mult * args.ngpu
if args.bs_mult_val > 0:
args.val_batch_size = args.bs_mult_val * args.ngpu
else:
args.val_batch_size = args.bs_mutl * args.ngpu
elif args.dataset == 'GDD':
args.dataset_cls = GDD
args.train_batch_size = args.bs_mult * args.ngpu
if args.bs_mult_val > 0:
args.val_batch_size = args.bs_mult_val * args.ngpu
else:
args.val_batch_size = args.bs_mutl * args.ngpu
elif args.dataset == 'Trans10k':
args.dataset_cls = Trans10k
args.train_batch_size = args.bs_mult * args.ngpu
if args.bs_mult_val > 0:
args.val_batch_size = args.bs_mult_val * args.ngpu
else:
args.val_batch_size = args.bs_mult * args.ngpu
else:
raise Exception('Dataset {} is not supported'.format(args.dataset))
# Readjust batch size to mini-batch size for apex
if args.apex:
args.train_batch_size = args.bs_mult
args.val_batch_size = args.bs_mult_val
args.num_workers = 4 * args.ngpu
if args.test_mode:
args.num_workers = 1
mean_std = ([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
# Geometric image transformations
if args.dataset == 'GDD':
train_joint_transform_list = [
joint_transforms.Resize(args.crop_size),
joint_transforms.RandomHorizontallyFlip()]
val_joint_transform_list = [
joint_transforms.Resize(args.crop_size)]
if args.dataset == 'MSD':
train_joint_transform_list = [
joint_transforms.Resize(args.crop_size),
joint_transforms.RandomHorizontallyFlip()]
val_joint_transform_list = [
joint_transforms.Resize(args.crop_size)]
if args.dataset == 'Trans10k':
train_joint_transform_list = [
joint_transforms.Resize(args.crop_size)]
val_joint_transform_list = [
joint_transforms.Resize(args.crop_size)]
# Image appearance transformations
train_input_transform = []
if args.color_aug:
train_input_transform += [extended_transforms.ColorJitter(
brightness=args.color_aug,
contrast=args.color_aug,
saturation=args.color_aug,
hue=args.color_aug)]
if args.bblur:
train_input_transform += [extended_transforms.RandomBilateralBlur()]
elif args.gblur:
train_input_transform += [extended_transforms.RandomGaussianBlur()]
else:
pass
train_input_transform += [standard_transforms.ToTensor(),
standard_transforms.Normalize(*mean_std)]
train_input_transform = standard_transforms.Compose(train_input_transform)
val_input_transform = standard_transforms.Compose([
standard_transforms.ToTensor(),
standard_transforms.Normalize(*mean_std)
])
target_transform = extended_transforms.MaskToTensor()
## relax the segmentation border
if args.jointwtborder:
target_train_transform = extended_transforms.RelaxedBoundaryLossToTensor(args.dataset_cls.ignore_label,
args.dataset_cls.num_classes)
else:
target_train_transform = extended_transforms.MaskToTensor()
edge_map = args.joint_edge_loss_light_cascade
if args.dataset == 'MSD':
train_set = args.dataset_cls.MSDDateset(
'semantic', 'train', args.maxSkip,
joint_transform_list=train_joint_transform_list,
transform=train_input_transform,
target_transform=target_train_transform,
dump_images=args.dump_augmentation_images,
class_uniform_pct=args.class_uniform_pct,
class_uniform_title=args.class_uniform_tile,
test=args.test_mode,
cv_split=args.cv,
scf=args.scf,
hardnm=args.hardnm,
edge_map=edge_map,
thicky=args.thicky
)
val_set = args.dataset_cls.MSDDateset(
'semantic', 'test', 0,
joint_transform_list=val_joint_transform_list,
transform=val_input_transform,
target_transform=target_transform,
test=False,
cv_split=args.cv,
scf=None)
elif args.dataset == 'GDD':
train_set = args.dataset_cls.GDDDateset(
'semantic', 'train', args.maxSkip,
joint_transform_list=train_joint_transform_list,
transform=train_input_transform,
target_transform=target_train_transform,
dump_images=args.dump_augmentation_images,
class_uniform_pct=args.class_uniform_pct,
class_uniform_title=args.class_uniform_tile,
test=args.test_mode,
cv_split=args.cv,
scf=args.scf,
hardnm=args.hardnm,
edge_map=edge_map,
thicky=args.thicky
)
val_set = args.dataset_cls.GDDDateset(
'semantic', 'test', 0,
joint_transform_list=val_joint_transform_list,
transform=val_input_transform,
target_transform=target_transform,
test=False,
cv_split=args.cv,
scf=None)
elif args.dataset == 'Trans10k':
train_set = args.dataset_cls.Trains10kDataset(
'semantic', 'train', args.maxSkip,
joint_transform_list=train_joint_transform_list,
transform=train_input_transform,
target_transform=target_train_transform,
dump_images=args.dump_augmentation_images,
class_uniform_pct=args.class_uniform_pct,
class_uniform_title=args.class_uniform_tile,
test=args.test_mode,
cv_split=args.cv,
scf=args.scf,
hardnm=args.hardnm,
edge_map=edge_map,
thicky=args.thicky)
val_set = args.dataset_cls.Trains10kDataset(
'semantic', 'validation', 0,
joint_transform_list=val_joint_transform_list,
transform=val_input_transform,
target_transform=target_transform,
test=False,
cv_split=args.cv,
scf=None)
else:
raise Exception('Dataset {} is not supported'.format(args.dataset))
if args.apex:
from datasets.sampler import DistributedSampler
train_sampler = DistributedSampler(train_set, pad=True, permutation=True, consecutive_sample=False)
val_sampler = DistributedSampler(val_set, pad=False, permutation=False, consecutive_sample=False)
else:
train_sampler = None
val_sampler = None
train_loader = DataLoader(train_set, batch_size=args.train_batch_size,
num_workers=args.num_workers, shuffle=(train_sampler is None), drop_last=True, sampler=train_sampler)
val_loader = DataLoader(val_set, batch_size=args.val_batch_size,
num_workers=args.num_workers // 2, shuffle=False, drop_last=False, sampler=val_sampler)
return train_loader, val_loader, train_set