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pwcnet_ft_4x1_300k_sintel_final_384x768.py
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pwcnet_ft_4x1_300k_sintel_final_384x768.py
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dataset_type = 'Sintel'
data_root = 'data/Sintel'
img_norm_cfg = dict(
mean=[0.0, 0.0, 0.0], std=[255.0, 255.0, 255.0], to_rgb=False)
crop_size = (384, 768)
global_transform = dict(
translates=(0.05, 0.05),
zoom=(1.0, 1.5),
shear=(0.86, 1.16),
rotate=(-10.0, 10.0))
relative_transform = dict(
translates=(0.00375, 0.00375),
zoom=(0.985, 1.015),
shear=(1.0, 1.0),
rotate=(-1.0, 1.0))
train_pipeline = [
dict(type='LoadImageFromFile'),
dict(type='LoadAnnotations', with_occ=True),
dict(
type='ColorJitter',
brightness=0.5,
contrast=0.5,
saturation=0.5,
hue=0.5),
dict(type='RandomGamma', gamma_range=(0.7, 1.5)),
dict(
type='Normalize',
mean=[0.0, 0.0, 0.0],
std=[255.0, 255.0, 255.0],
to_rgb=False),
dict(type='RandomFlip', prob=0.5, direction='horizontal'),
dict(type='RandomFlip', prob=0.5, direction='vertical'),
dict(
type='RandomAffine',
global_transform=dict(
translates=(0.05, 0.05),
zoom=(1.0, 1.5),
shear=(0.86, 1.16),
rotate=(-10.0, 10.0)),
relative_transform=dict(
translates=(0.00375, 0.00375),
zoom=(0.985, 1.015),
shear=(1.0, 1.0),
rotate=(-1.0, 1.0))),
dict(type='RandomCrop', crop_size=(384, 768)),
dict(type='DefaultFormatBundle'),
dict(
type='Collect',
keys=['imgs', 'flow_gt'],
meta_keys=[
'img_fields', 'ann_fields', 'filename1', 'filename2',
'ori_filename1', 'ori_filename2', 'filename_flow',
'ori_filename_flow', 'ori_shape', 'img_shape', 'img_norm_cfg'
])
]
test_pipeline = [
dict(type='LoadImageFromFile'),
dict(type='LoadAnnotations'),
dict(type='InputResize', exponent=6),
dict(
type='Normalize',
mean=[0.0, 0.0, 0.0],
std=[255.0, 255.0, 255.0],
to_rgb=False),
dict(type='TestFormatBundle'),
dict(
type='Collect',
keys=['imgs'],
meta_keys=[
'flow_gt', 'filename1', 'filename2', 'ori_filename1',
'ori_filename2', 'ori_shape', 'img_shape', 'img_norm_cfg',
'scale_factor', 'pad_shape'
])
]
sintel_final_train = dict(
type='Sintel',
pipeline=[
dict(type='LoadImageFromFile'),
dict(type='LoadAnnotations', with_occ=True),
dict(
type='ColorJitter',
brightness=0.5,
contrast=0.5,
saturation=0.5,
hue=0.5),
dict(type='RandomGamma', gamma_range=(0.7, 1.5)),
dict(
type='Normalize',
mean=[0.0, 0.0, 0.0],
std=[255.0, 255.0, 255.0],
to_rgb=False),
dict(type='RandomFlip', prob=0.5, direction='horizontal'),
dict(type='RandomFlip', prob=0.5, direction='vertical'),
dict(
type='RandomAffine',
global_transform=dict(
translates=(0.05, 0.05),
zoom=(1.0, 1.5),
shear=(0.86, 1.16),
rotate=(-10.0, 10.0)),
relative_transform=dict(
translates=(0.00375, 0.00375),
zoom=(0.985, 1.015),
shear=(1.0, 1.0),
rotate=(-1.0, 1.0))),
dict(type='RandomCrop', crop_size=(384, 768)),
dict(type='DefaultFormatBundle'),
dict(
type='Collect',
keys=['imgs', 'flow_gt'],
meta_keys=[
'img_fields', 'ann_fields', 'filename1', 'filename2',
'ori_filename1', 'ori_filename2', 'filename_flow',
'ori_filename_flow', 'ori_shape', 'img_shape', 'img_norm_cfg'
])
],
data_root='data/Sintel',
test_mode=False,
pass_style='final')
sintel_clean_test = dict(
type='Sintel',
pipeline=[
dict(type='LoadImageFromFile'),
dict(type='LoadAnnotations'),
dict(type='InputResize', exponent=6),
dict(
type='Normalize',
mean=[0.0, 0.0, 0.0],
std=[255.0, 255.0, 255.0],
to_rgb=False),
dict(type='TestFormatBundle'),
dict(
type='Collect',
keys=['imgs'],
meta_keys=[
'flow_gt', 'filename1', 'filename2', 'ori_filename1',
'ori_filename2', 'ori_shape', 'img_shape', 'img_norm_cfg',
'scale_factor', 'pad_shape'
])
],
data_root='data/Sintel',
test_mode=True,
pass_style='clean')
sintel_final_test = dict(
type='Sintel',
pipeline=[
dict(type='LoadImageFromFile'),
dict(type='LoadAnnotations'),
dict(type='InputResize', exponent=6),
dict(
type='Normalize',
mean=[0.0, 0.0, 0.0],
std=[255.0, 255.0, 255.0],
to_rgb=False),
dict(type='TestFormatBundle'),
dict(
type='Collect',
keys=['imgs'],
meta_keys=[
'flow_gt', 'filename1', 'filename2', 'ori_filename1',
'ori_filename2', 'ori_shape', 'img_shape', 'img_norm_cfg',
'scale_factor', 'pad_shape'
])
],
data_root='data/Sintel',
test_mode=True,
pass_style='final')
data = dict(
train_dataloader=dict(
samples_per_gpu=1,
workers_per_gpu=5,
drop_last=True,
persistent_workers=True),
val_dataloader=dict(
samples_per_gpu=1,
workers_per_gpu=5,
shuffle=False,
persistent_workers=True),
test_dataloader=dict(samples_per_gpu=1, workers_per_gpu=5, shuffle=False),
train=[
dict(
type='Sintel',
pipeline=[
dict(type='LoadImageFromFile'),
dict(type='LoadAnnotations', with_occ=True),
dict(
type='ColorJitter',
brightness=0.5,
contrast=0.5,
saturation=0.5,
hue=0.5),
dict(type='RandomGamma', gamma_range=(0.7, 1.5)),
dict(
type='Normalize',
mean=[0.0, 0.0, 0.0],
std=[255.0, 255.0, 255.0],
to_rgb=False),
dict(type='RandomFlip', prob=0.5, direction='horizontal'),
dict(type='RandomFlip', prob=0.5, direction='vertical'),
dict(
type='RandomAffine',
global_transform=dict(
translates=(0.05, 0.05),
zoom=(1.0, 1.5),
shear=(0.86, 1.16),
rotate=(-10.0, 10.0)),
relative_transform=dict(
translates=(0.00375, 0.00375),
zoom=(0.985, 1.015),
shear=(1.0, 1.0),
rotate=(-1.0, 1.0))),
dict(type='RandomCrop', crop_size=(384, 768)),
dict(type='DefaultFormatBundle'),
dict(
type='Collect',
keys=['imgs', 'flow_gt'],
meta_keys=[
'img_fields', 'ann_fields', 'filename1', 'filename2',
'ori_filename1', 'ori_filename2', 'filename_flow',
'ori_filename_flow', 'ori_shape', 'img_shape',
'img_norm_cfg'
])
],
data_root='data/Sintel',
test_mode=False,
pass_style='final')
],
val=dict(
type='ConcatDataset',
datasets=[
dict(
type='Sintel',
pipeline=[
dict(type='LoadImageFromFile'),
dict(type='LoadAnnotations'),
dict(type='InputResize', exponent=6),
dict(
type='Normalize',
mean=[0.0, 0.0, 0.0],
std=[255.0, 255.0, 255.0],
to_rgb=False),
dict(type='TestFormatBundle'),
dict(
type='Collect',
keys=['imgs'],
meta_keys=[
'flow_gt', 'filename1', 'filename2',
'ori_filename1', 'ori_filename2', 'ori_shape',
'img_shape', 'img_norm_cfg', 'scale_factor',
'pad_shape'
])
],
data_root='data/Sintel',
test_mode=True,
pass_style='clean'),
dict(
type='Sintel',
pipeline=[
dict(type='LoadImageFromFile'),
dict(type='LoadAnnotations'),
dict(type='InputResize', exponent=6),
dict(
type='Normalize',
mean=[0.0, 0.0, 0.0],
std=[255.0, 255.0, 255.0],
to_rgb=False),
dict(type='TestFormatBundle'),
dict(
type='Collect',
keys=['imgs'],
meta_keys=[
'flow_gt', 'filename1', 'filename2',
'ori_filename1', 'ori_filename2', 'ori_shape',
'img_shape', 'img_norm_cfg', 'scale_factor',
'pad_shape'
])
],
data_root='data/Sintel',
test_mode=True,
pass_style='final')
],
separate_eval=True),
test=dict(
type='ConcatDataset',
datasets=[
dict(
type='Sintel',
pipeline=[
dict(type='LoadImageFromFile'),
dict(type='LoadAnnotations'),
dict(type='InputResize', exponent=6),
dict(
type='Normalize',
mean=[0.0, 0.0, 0.0],
std=[255.0, 255.0, 255.0],
to_rgb=False),
dict(type='TestFormatBundle'),
dict(
type='Collect',
keys=['imgs'],
meta_keys=[
'flow_gt', 'filename1', 'filename2',
'ori_filename1', 'ori_filename2', 'ori_shape',
'img_shape', 'img_norm_cfg', 'scale_factor',
'pad_shape'
])
],
data_root='data/Sintel',
test_mode=True,
pass_style='clean'),
dict(
type='Sintel',
pipeline=[
dict(type='LoadImageFromFile'),
dict(type='LoadAnnotations'),
dict(type='InputResize', exponent=6),
dict(
type='Normalize',
mean=[0.0, 0.0, 0.0],
std=[255.0, 255.0, 255.0],
to_rgb=False),
dict(type='TestFormatBundle'),
dict(
type='Collect',
keys=['imgs'],
meta_keys=[
'flow_gt', 'filename1', 'filename2',
'ori_filename1', 'ori_filename2', 'ori_shape',
'img_shape', 'img_norm_cfg', 'scale_factor',
'pad_shape'
])
],
data_root='data/Sintel',
test_mode=True,
pass_style='final')
],
separate_eval=True))
optimizer = dict(
type='Adam', lr=3e-05, weight_decay=0.0004, betas=(0.9, 0.999))
optimizer_config = dict(grad_clip=None)
lr_config = dict(
policy='MultiStage',
by_epoch=False,
gammas=[0.5, 0.5],
milestone_lrs=[3e-05, 2e-05],
milestone_iters=[0, 150000],
steps=[[
45000, 65000, 85000, 95000, 97500, 100000, 110000, 120000, 130000,
140000
],
[
195000, 215000, 235000, 245000, 247500, 250000, 260000, 270000,
280000, 290000
]])
runner = dict(type='IterBasedRunner', max_iters=300000)
checkpoint_config = dict(by_epoch=False, interval=50000)
evaluation = dict(interval=50000, metric='EPE')
log_config = dict(
interval=50,
hooks=[dict(type='TextLoggerHook'),
dict(type='TensorboardLoggerHook')])
dist_params = dict(backend='nccl')
log_level = 'INFO'
load_from = 'https://download.openmmlab.com/mmflow/pwcnet/pwcnet_ft_4x1_300k_sintel_384x768.pth'
resume_from = None
workflow = [('train', 1)]
model = dict(
type='PWCNet',
encoder=dict(
type='PWCNetEncoder',
in_channels=3,
net_type='Basic',
pyramid_levels=[
'level1', 'level2', 'level3', 'level4', 'level5', 'level6'
],
out_channels=(16, 32, 64, 96, 128, 196),
strides=(2, 2, 2, 2, 2, 2),
dilations=(1, 1, 1, 1, 1, 1),
act_cfg=dict(type='LeakyReLU', negative_slope=0.1)),
decoder=dict(
type='PWCNetDecoder',
in_channels=dict(
level6=81, level5=213, level4=181, level3=149, level2=117),
flow_div=20.0,
corr_cfg=dict(type='Correlation', max_displacement=4, padding=0),
warp_cfg=dict(type='Warp', align_corners=True, use_mask=True),
act_cfg=dict(type='LeakyReLU', negative_slope=0.1),
scaled=False,
post_processor=dict(type='ContextNet', in_channels=565),
flow_loss=dict(
type='MultiLevelEPE',
p=1,
q=0.4,
eps=0.01,
reduction='sum',
weights=dict(
level2=0.005,
level3=0.01,
level4=0.02,
level5=0.08,
level6=0.32))),
train_cfg=dict(),
test_cfg=dict(),
init_cfg=dict(
type='Kaiming',
nonlinearity='leaky_relu',
layer=['Conv2d', 'ConvTranspose2d'],
mode='fan_in',
bias=0))