/
cyclegan_lsgan-id0-resnet-in_1xb1-250kiters_summer2winter.py
109 lines (99 loc) 路 3.09 KB
/
cyclegan_lsgan-id0-resnet-in_1xb1-250kiters_summer2winter.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
_base_ = [
'../_base_/models/base_cyclegan.py',
'../_base_/datasets/unpaired_imgs_256x256.py',
'../_base_/gen_default_runtime.py'
]
domain_a = 'summer'
domain_b = 'winter'
train_cfg = dict(max_iters=250000)
model = dict(
loss_config=dict(cycle_loss_weight=10., id_loss_weight=0.),
default_domain=domain_b,
reachable_domains=[domain_a, domain_b],
related_domains=[domain_a, domain_b],
data_preprocessor=dict(data_keys=[f'img_{domain_a}', f'img_{domain_b}']))
dataroot = './data/cyclegan/summer2winter_yosemite'
train_pipeline = _base_.train_dataloader.dataset.pipeline
val_pipeline = _base_.val_dataloader.dataset.pipeline
test_pipeline = _base_.test_dataloader.dataset.pipeline
key_mapping = dict(
type='KeyMapper',
mapping={
f'img_{domain_a}': 'img_A',
f'img_{domain_b}': 'img_B'
},
remapping={
f'img_{domain_a}': f'img_{domain_a}',
f'img_{domain_b}': f'img_{domain_b}'
})
pack_input = dict(
type='PackInputs',
keys=[f'img_{domain_a}', f'img_{domain_b}'],
data_keys=[f'img_{domain_a}', f'img_{domain_b}'])
train_pipeline += [key_mapping, pack_input]
val_pipeline += [key_mapping, pack_input]
test_pipeline += [key_mapping, pack_input]
train_dataloader = dict(dataset=dict(data_root=dataroot))
val_dataloader = dict(dataset=dict(data_root=dataroot, test_mode=True))
test_dataloader = val_dataloader
optim_wrapper = dict(
generators=dict(
optimizer=dict(type='Adam', lr=0.0002, betas=(0.5, 0.999))),
discriminators=dict(
optimizer=dict(type='Adam', lr=0.0002, betas=(0.5, 0.999))))
# learning policy
param_scheduler = dict(
type='LinearLrInterval',
interval=1250,
by_epoch=False,
start_factor=0.0002,
end_factor=0,
begin=125000,
end=250000)
custom_hooks = [
dict(
type='VisualizationHook',
interval=5000,
fixed_input=True,
vis_kwargs_list=[
dict(type='Translation', name='trans'),
dict(type='TranslationVal', name='trans_val')
])
]
# testA: 309, testB:238
num_images_a = 309
num_images_b = 238
metrics = [
dict(
type='TransIS',
prefix=f'IS-{domain_a}-to-{domain_b}',
fake_nums=num_images_b,
fake_key=f'fake_{domain_b}',
use_pillow_resize=False,
resize_method='bilinear',
inception_style='PyTorch'),
dict(
type='TransIS',
prefix=f'IS-{domain_b}-to-{domain_a}',
fake_nums=num_images_a,
fake_key=f'fake_{domain_a}',
use_pillow_resize=False,
resize_method='bilinear',
inception_style='PyTorch'),
dict(
type='TransFID',
prefix=f'FID-{domain_a}-to-{domain_b}',
fake_nums=num_images_b,
inception_style='PyTorch',
real_key=f'img_{domain_b}',
fake_key=f'fake_{domain_b}'),
dict(
type='TransFID',
prefix=f'FID-{domain_b}-to-{domain_a}',
fake_nums=num_images_a,
inception_style='PyTorch',
real_key=f'img_{domain_a}',
fake_key=f'fake_{domain_a}')
]
val_evaluator = dict(metrics=metrics)
test_evaluator = dict(metrics=metrics)