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norm_cfg = dict(type='SyncBN', requires_grad=True)
model = dict(
type='EncoderDecoderFreeze',
pretrained=
'pretrained/deeplabv3plus_r101-d8_512x1024_80k_cityscapes_20200606_114143-068fcfe9.pth',
backbone=dict(
type='ResNetV1cCustomInitWeights',
depth=101,
num_stages=4,
out_indices=(0, 1, 2, 3),
dilations=(1, 1, 2, 4),
strides=(1, 2, 1, 1),
norm_cfg=dict(type='SyncBN', requires_grad=True),
norm_eval=False,
style='pytorch',
contract_dilation=True),
decode_head=dict(
type='DepthwiseSeparableASPPHeadUnetFCHeadSingleStep',
pretrained=
'pretrained/deeplabv3plus_r101-d8_512x1024_80k_cityscapes_20200606_114143-068fcfe9.pth',
dim=256,
out_dim=256,
unet_channels=528,
dim_mults=[1, 1, 1],
cat_embedding_dim=16,
ignore_index=0,
in_channels=2048,
in_index=3,
channels=512,
dilations=(1, 12, 24, 36),
c1_in_channels=256,
c1_channels=48,
dropout_ratio=0.1,
num_classes=20,
norm_cfg=dict(type='SyncBN', requires_grad=True),
align_corners=False,
loss_decode=dict(
type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0)),
auxiliary_head=None,
train_cfg=dict(),
test_cfg=dict(mode='whole'),
freeze_parameters=['backbone', 'decode_head'])
dataset_type = 'Cityscapes20Dataset'
data_root = 'data/cityscapes/'
img_norm_cfg = dict(
mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
crop_size = (512, 1024)
train_pipeline = [
dict(type='LoadImageFromFile'),
dict(type='LoadAnnotationsCityscapes20'),
dict(type='Resize', img_scale=(2048, 1024), ratio_range=(0.5, 2.0)),
dict(type='RandomCrop', crop_size=(512, 1024), cat_max_ratio=0.75),
dict(type='RandomFlip', prob=0.5),
dict(type='PhotoMetricDistortion'),
dict(
type='Normalize',
mean=[123.675, 116.28, 103.53],
std=[58.395, 57.12, 57.375],
to_rgb=True),
dict(type='Pad', size=(512, 1024), pad_val=0, seg_pad_val=0),
dict(type='DefaultFormatBundle'),
dict(type='Collect', keys=['img', 'gt_semantic_seg'])
]
test_pipeline = [
dict(type='LoadImageFromFile'),
dict(
type='MultiScaleFlipAug',
img_scale=(2048, 1024),
flip=False,
transforms=[
dict(type='Resize', keep_ratio=True),
dict(type='RandomFlip'),
dict(
type='Normalize',
mean=[123.675, 116.28, 103.53],
std=[58.395, 57.12, 57.375],
to_rgb=True),
dict(type='ImageToTensor', keys=['img']),
dict(type='Collect', keys=['img'])
])
]
data = dict(
samples_per_gpu=2,
workers_per_gpu=2,
train=dict(
type='Cityscapes20Dataset',
data_root='data/cityscapes/',
img_dir='leftImg8bit/train',
ann_dir='gtFine/train',
pipeline=[
dict(type='LoadImageFromFile'),
dict(type='LoadAnnotationsCityscapes20'),
dict(
type='Resize', img_scale=(2048, 1024), ratio_range=(0.5, 2.0)),
dict(type='RandomCrop', crop_size=(512, 1024), cat_max_ratio=0.75),
dict(type='RandomFlip', prob=0.5),
dict(type='PhotoMetricDistortion'),
dict(
type='Normalize',
mean=[123.675, 116.28, 103.53],
std=[58.395, 57.12, 57.375],
to_rgb=True),
dict(type='Pad', size=(512, 1024), pad_val=0, seg_pad_val=0),
dict(type='DefaultFormatBundle'),
dict(type='Collect', keys=['img', 'gt_semantic_seg'])
]),
val=dict(
type='Cityscapes20Dataset',
data_root='data/cityscapes/',
img_dir='leftImg8bit/val',
ann_dir='gtFine/val',
pipeline=[
dict(type='LoadImageFromFile'),
dict(
type='MultiScaleFlipAug',
img_scale=(2048, 1024),
flip=False,
transforms=[
dict(type='Resize', keep_ratio=True),
dict(type='RandomFlip'),
dict(
type='Normalize',
mean=[123.675, 116.28, 103.53],
std=[58.395, 57.12, 57.375],
to_rgb=True),
dict(type='ImageToTensor', keys=['img']),
dict(type='Collect', keys=['img'])
])
]),
test=dict(
type='Cityscapes20Dataset',
data_root='data/cityscapes/',
img_dir='leftImg8bit/val',
ann_dir='gtFine/val',
pipeline=[
dict(type='LoadImageFromFile'),
dict(
type='MultiScaleFlipAug',
img_scale=(2048, 1024),
flip=False,
transforms=[
dict(type='Resize', keep_ratio=True),
dict(type='RandomFlip'),
dict(
type='Normalize',
mean=[123.675, 116.28, 103.53],
std=[58.395, 57.12, 57.375],
to_rgb=True),
dict(type='ImageToTensor', keys=['img']),
dict(type='Collect', keys=['img'])
])
]))
log_config = dict(
interval=50, hooks=[dict(type='TextLoggerHook', by_epoch=False)])
dist_params = dict(backend='nccl')
log_level = 'INFO'
load_from = None
resume_from = None
workflow = [('train', 1)]
cudnn_benchmark = True
optimizer = dict(
type='AdamW', lr=0.00015, betas=[0.9, 0.96], weight_decay=0.045)
optimizer_config = dict()
lr_config = dict(
policy='step',
warmup='linear',
warmup_iters=1000,
warmup_ratio=1e-06,
step=10000,
gamma=0.5,
min_lr=1e-06,
by_epoch=False)
runner = dict(type='IterBasedRunner', max_iters=80000)
checkpoint_config = dict(by_epoch=False, interval=8000, max_keep_ckpts=1)
evaluation = dict(
interval=8000, metric='mIoU', pre_eval=True, save_best='mIoU')
checkpoint = 'pretrained/deeplabv3plus_r101-d8_512x1024_80k_cityscapes_20200606_114143-068fcfe9.pth'
work_dir = './work_dirs2/deeplabv3plus_r101-d8_aspp_head_unet_fc_small_single_step_cityscapes_pretrained_freeze_embed_80k_cityscapes20'
gpu_ids = range(0, 8)
auto_resume = True
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