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pix2pix_vanilla-unet-bn_1xb1-80kiters_facades.py
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pix2pix_vanilla-unet-bn_1xb1-80kiters_facades.py
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_base_ = [
'../_base_/models/base_pix2pix.py',
'../_base_/datasets/paired_imgs_256x256_crop.py',
'../_base_/gen_default_runtime.py'
]
# deterministic training can improve the performance of Pix2Pix
randomness = dict(deterministic=True)
source_domain = domain_b = 'mask'
target_domain = domain_a = 'photo'
# model settings
model = dict(
default_domain=target_domain,
reachable_domains=[target_domain],
related_domains=[target_domain, source_domain],
data_preprocessor=dict(data_keys=[f'img_{domain_a}', f'img_{domain_b}']))
train_cfg = dict(max_iters=80000)
# dataset settings
dataroot = 'data/pix2pix/facades'
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
# optimizer
optim_wrapper = dict(
generators=dict(
type='OptimWrapper',
optimizer=dict(type='Adam', lr=2e-4, betas=(0.5, 0.999))),
discriminators=dict(
type='OptimWrapper',
optimizer=dict(type='Adam', lr=2e-4, betas=(0.5, 0.999))))
custom_hooks = [
dict(
type='VisualizationHook',
interval=5000,
fixed_input=True,
vis_kwargs_list=[
dict(type='Translation', name='trans'),
dict(type='TranslationVal', name='trans_val')
])
]
# save multi best checkpoints
default_hooks = dict(checkpoint=dict(save_best='FID-Full/fid', rule='less'))
fake_nums = 106
metrics = [
dict(
type='TransIS',
prefix='IS-Full',
fake_nums=fake_nums,
inception_style='PyTorch',
fake_key=f'fake_{target_domain}',
sample_model='orig'),
dict(
type='TransFID',
prefix='FID-Full',
fake_nums=fake_nums,
inception_style='PyTorch',
real_key=f'img_{target_domain}',
fake_key=f'fake_{target_domain}',
sample_model='orig')
]
val_evaluator = dict(metrics=metrics)
test_evaluator = dict(metrics=metrics)