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auto_scale_lr = dict(base_batch_size=16, enable=False)
backbone_embed_multi = dict(decay_mult=0.0, lr_mult=0.1)
backbone_norm_multi = dict(decay_mult=0.0, lr_mult=0.1)
crop_size = (
256,
512,
)
custom_keys = dict({
'absolute_pos_embed':
dict(decay_mult=0.0, lr_mult=0.1),
'backbone':
dict(decay_mult=1.0, lr_mult=0.1),
'backbone.norm':
dict(decay_mult=0.0, lr_mult=0.1),
'backbone.patch_embed.norm':
dict(decay_mult=0.0, lr_mult=0.1),
'backbone.stages.0.blocks.0.norm':
dict(decay_mult=0.0, lr_mult=0.1),
'backbone.stages.0.blocks.1.norm':
dict(decay_mult=0.0, lr_mult=0.1),
'backbone.stages.0.downsample.norm':
dict(decay_mult=0.0, lr_mult=0.1),
'backbone.stages.1.blocks.0.norm':
dict(decay_mult=0.0, lr_mult=0.1),
'backbone.stages.1.blocks.1.norm':
dict(decay_mult=0.0, lr_mult=0.1),
'backbone.stages.1.downsample.norm':
dict(decay_mult=0.0, lr_mult=0.1),
'backbone.stages.2.blocks.0.norm':
dict(decay_mult=0.0, lr_mult=0.1),
'backbone.stages.2.blocks.1.norm':
dict(decay_mult=0.0, lr_mult=0.1),
'backbone.stages.2.blocks.2.norm':
dict(decay_mult=0.0, lr_mult=0.1),
'backbone.stages.2.blocks.3.norm':
dict(decay_mult=0.0, lr_mult=0.1),
'backbone.stages.2.blocks.4.norm':
dict(decay_mult=0.0, lr_mult=0.1),
'backbone.stages.2.blocks.5.norm':
dict(decay_mult=0.0, lr_mult=0.1),
'backbone.stages.2.downsample.norm':
dict(decay_mult=0.0, lr_mult=0.1),
'backbone.stages.3.blocks.0.norm':
dict(decay_mult=0.0, lr_mult=0.1),
'backbone.stages.3.blocks.1.norm':
dict(decay_mult=0.0, lr_mult=0.1),
'level_embed':
dict(decay_mult=0.0, lr_mult=1.0),
'query_embed':
dict(decay_mult=0.0, lr_mult=1.0),
'query_feat':
dict(decay_mult=0.0, lr_mult=1.0),
'relative_position_bias_table':
dict(decay_mult=0.0, lr_mult=0.1)
})
data_preprocessor = dict(
bgr_to_rgb=True,
mean=[
123.675,
116.28,
103.53,
],
pad_val=0,
seg_pad_val=255,
size=(
256,
512,
),
std=[
58.395,
57.12,
57.375,
],
test_cfg=dict(size_divisor=32),
type='SegDataPreProcessor')
data_root = '/dataset/cityscapes/'
dataset_type = 'CityscapesDataset'
default_hooks = dict(
checkpoint=dict(
by_epoch=False, interval=5000, save_best='mIoU',
type='CheckpointHook'),
logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'),
param_scheduler=dict(type='ParamSchedulerHook'),
sampler_seed=dict(type='DistSamplerSeedHook'),
timer=dict(type='IterTimerHook'),
visualization=dict(type='SegVisualizationHook'))
default_scope = 'mmseg'
depths = [
2,
2,
6,
2,
]
embed_multi = dict(decay_mult=0.0, lr_mult=1.0)
env_cfg = dict(
cudnn_benchmark=True,
dist_cfg=dict(backend='nccl'),
mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0))
img_ratios = [
0.5,
0.75,
1.0,
1.25,
1.5,
1.75,
]
launcher = 'pytorch'
load_from = 'work_dirs/mask2former-swint-8xb2-512x1024-90k/mask2former-swint-8xb2-512x1024-90k_ckpt.pth'
log_level = 'INFO'
log_processor = dict(by_epoch=False)
model = dict(
backbone=dict(
attn_drop_rate=0.0,
depths=[
2,
2,
6,
2,
],
drop_path_rate=0.3,
drop_rate=0.0,
embed_dims=96,
frozen_stages=-1,
init_cfg=dict(
checkpoint=
'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/swin/swin_tiny_patch4_window7_224_20220317-1cdeb081.pth',
type='Pretrained'),
mlp_ratio=4,
num_heads=[
3,
6,
12,
24,
],
out_indices=(
0,
1,
2,
3,
),
patch_norm=True,
qk_scale=None,
qkv_bias=True,
type='SwinTransformer',
window_size=7,
with_cp=False),
data_preprocessor=dict(
bgr_to_rgb=True,
mean=[
123.675,
116.28,
103.53,
],
pad_val=0,
seg_pad_val=255,
size=(
256,
512,
),
std=[
58.395,
57.12,
57.375,
],
test_cfg=dict(size_divisor=32),
type='SegDataPreProcessor'),
decode_head=dict(
align_corners=False,
enforce_decoder_input_project=False,
feat_channels=256,
in_channels=[
96,
192,
384,
768,
],
loss_cls=dict(
class_weight=[
1.0,
1.0,
1.0,
1.0,
1.0,
1.0,
1.0,
1.0,
1.0,
1.0,
1.0,
1.0,
1.0,
1.0,
1.0,
1.0,
1.0,
1.0,
1.0,
0.1,
],
loss_weight=2.0,
reduction='mean',
type='mmdet.CrossEntropyLoss',
use_sigmoid=False),
loss_dice=dict(
activate=True,
eps=1.0,
loss_weight=5.0,
naive_dice=True,
reduction='mean',
type='mmdet.DiceLoss',
use_sigmoid=True),
loss_mask=dict(
loss_weight=5.0,
reduction='mean',
type='mmdet.CrossEntropyLoss',
use_sigmoid=True),
num_classes=19,
num_queries=100,
num_transformer_feat_level=3,
out_channels=256,
pixel_decoder=dict(
act_cfg=dict(type='ReLU'),
encoder=dict(
init_cfg=None,
layer_cfg=dict(
ffn_cfg=dict(
act_cfg=dict(inplace=True, type='ReLU'),
embed_dims=256,
feedforward_channels=1024,
ffn_drop=0.0,
num_fcs=2),
self_attn_cfg=dict(
batch_first=True,
dropout=0.0,
embed_dims=256,
im2col_step=64,
init_cfg=None,
norm_cfg=None,
num_heads=8,
num_levels=3,
num_points=4)),
num_layers=6),
init_cfg=None,
norm_cfg=dict(num_groups=32, type='GN'),
num_outs=3,
positional_encoding=dict(normalize=True, num_feats=128),
type='mmdet.MSDeformAttnPixelDecoder'),
positional_encoding=dict(normalize=True, num_feats=128),
strides=[
4,
8,
16,
32,
],
train_cfg=dict(
assigner=dict(
match_costs=[
dict(type='mmdet.ClassificationCost', weight=2.0),
dict(
type='mmdet.CrossEntropyLossCost',
use_sigmoid=True,
weight=5.0),
dict(
eps=1.0,
pred_act=True,
type='mmdet.DiceCost',
weight=5.0),
],
type='mmdet.HungarianAssigner'),
importance_sample_ratio=0.75,
num_points=12544,
oversample_ratio=3.0,
sampler=dict(type='mmdet.MaskPseudoSampler')),
transformer_decoder=dict(
init_cfg=None,
layer_cfg=dict(
cross_attn_cfg=dict(
attn_drop=0.0,
batch_first=True,
dropout_layer=None,
embed_dims=256,
num_heads=8,
proj_drop=0.0),
ffn_cfg=dict(
act_cfg=dict(inplace=True, type='ReLU'),
add_identity=True,
dropout_layer=None,
embed_dims=256,
feedforward_channels=2048,
ffn_drop=0.0,
num_fcs=2),
self_attn_cfg=dict(
attn_drop=0.0,
batch_first=True,
dropout_layer=None,
embed_dims=256,
num_heads=8,
proj_drop=0.0)),
num_layers=9,
return_intermediate=True),
type='Mask2FormerHead'),
test_cfg=dict(mode='whole'),
train_cfg=dict(),
type='EncoderDecoder')
num_classes = 19
optim_wrapper = dict(
clip_grad=dict(max_norm=0.01, norm_type=2),
optimizer=dict(
betas=(
0.9,
0.999,
),
eps=1e-08,
lr=0.0001,
type='AdamW',
weight_decay=0.05),
paramwise_cfg=dict(
custom_keys=dict({
'absolute_pos_embed':
dict(decay_mult=0.0, lr_mult=0.1),
'backbone':
dict(decay_mult=1.0, lr_mult=0.1),
'backbone.norm':
dict(decay_mult=0.0, lr_mult=0.1),
'backbone.patch_embed.norm':
dict(decay_mult=0.0, lr_mult=0.1),
'backbone.stages.0.blocks.0.norm':
dict(decay_mult=0.0, lr_mult=0.1),
'backbone.stages.0.blocks.1.norm':
dict(decay_mult=0.0, lr_mult=0.1),
'backbone.stages.0.downsample.norm':
dict(decay_mult=0.0, lr_mult=0.1),
'backbone.stages.1.blocks.0.norm':
dict(decay_mult=0.0, lr_mult=0.1),
'backbone.stages.1.blocks.1.norm':
dict(decay_mult=0.0, lr_mult=0.1),
'backbone.stages.1.downsample.norm':
dict(decay_mult=0.0, lr_mult=0.1),
'backbone.stages.2.blocks.0.norm':
dict(decay_mult=0.0, lr_mult=0.1),
'backbone.stages.2.blocks.1.norm':
dict(decay_mult=0.0, lr_mult=0.1),
'backbone.stages.2.blocks.2.norm':
dict(decay_mult=0.0, lr_mult=0.1),
'backbone.stages.2.blocks.3.norm':
dict(decay_mult=0.0, lr_mult=0.1),
'backbone.stages.2.blocks.4.norm':
dict(decay_mult=0.0, lr_mult=0.1),
'backbone.stages.2.blocks.5.norm':
dict(decay_mult=0.0, lr_mult=0.1),
'backbone.stages.2.downsample.norm':
dict(decay_mult=0.0, lr_mult=0.1),
'backbone.stages.3.blocks.0.norm':
dict(decay_mult=0.0, lr_mult=0.1),
'backbone.stages.3.blocks.1.norm':
dict(decay_mult=0.0, lr_mult=0.1),
'level_embed':
dict(decay_mult=0.0, lr_mult=1.0),
'query_embed':
dict(decay_mult=0.0, lr_mult=1.0),
'query_feat':
dict(decay_mult=0.0, lr_mult=1.0),
'relative_position_bias_table':
dict(decay_mult=0.0, lr_mult=0.1)
}),
norm_decay_mult=0.0),
type='OptimWrapper')
optimizer = dict(
betas=(
0.9,
0.999,
),
eps=1e-08,
lr=0.0001,
type='AdamW',
weight_decay=0.05)
param_scheduler = [
dict(
begin=0,
by_epoch=False,
end=90000,
eta_min=0,
power=0.9,
type='PolyLR'),
]
pretrained = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/swin/swin_tiny_patch4_window7_224_20220317-1cdeb081.pth'
resume = False
test_cfg = dict(type='TestLoop')
test_dataloader = dict(
batch_size=1,
dataset=dict(
data_prefix=dict(
img_path='leftImg8bit/val', seg_map_path='gtFine/val'),
data_root='/dataset/cityscapes/',
pipeline=[
dict(type='LoadImageFromFile'),
dict(keep_ratio=True, scale=(
2048,
1024,
), type='Resize'),
dict(type='LoadAnnotations'),
dict(type='PackSegInputs'),
],
type='CityscapesDataset'),
num_workers=4,
persistent_workers=True,
sampler=dict(shuffle=False, type='DefaultSampler'))
test_evaluator = dict(
iou_metrics=[
'mIoU',
], type='IoUMetric')
test_pipeline = [
dict(type='LoadImageFromFile'),
dict(keep_ratio=True, scale=(
2048,
1024,
), type='Resize'),
dict(type='LoadAnnotations'),
dict(type='PackSegInputs'),
]
train_cfg = dict(max_iters=90000, type='IterBasedTrainLoop', val_interval=5000)
train_dataloader = dict(
batch_size=2,
dataset=dict(
data_prefix=dict(
img_path='leftImg8bit/train', seg_map_path='gtFine/train'),
data_root='/dataset/cityscapes/',
pipeline=[
dict(type='LoadImageFromFile'),
dict(type='LoadAnnotations'),
dict(
max_size=4096,
resize_type='ResizeShortestEdge',
scales=[
512,
614,
716,
819,
921,
1024,
1126,
1228,
1331,
1433,
1536,
1638,
1740,
1843,
1945,
2048,
],
type='RandomChoiceResize'),
dict(
cat_max_ratio=0.75, crop_size=(
256,
512,
), type='RandomCrop'),
dict(prob=0.5, type='RandomFlip'),
dict(type='PhotoMetricDistortion'),
dict(type='PackSegInputs'),
],
type='CityscapesDataset'),
num_workers=2,
persistent_workers=True,
sampler=dict(shuffle=True, type='InfiniteSampler'))
train_pipeline = [
dict(type='LoadImageFromFile'),
dict(type='LoadAnnotations'),
dict(
max_size=4096,
resize_type='ResizeShortestEdge',
scales=[
512,
614,
716,
819,
921,
1024,
1126,
1228,
1331,
1433,
1536,
1638,
1740,
1843,
1945,
2048,
],
type='RandomChoiceResize'),
dict(cat_max_ratio=0.75, crop_size=(
256,
512,
), type='RandomCrop'),
dict(prob=0.5, type='RandomFlip'),
dict(type='PhotoMetricDistortion'),
dict(type='PackSegInputs'),
]
tta_model = dict(type='SegTTAModel')
tta_pipeline = [
dict(backend_args=None, type='LoadImageFromFile'),
dict(
transforms=[
[
dict(keep_ratio=True, scale_factor=0.5, type='Resize'),
dict(keep_ratio=True, scale_factor=0.75, type='Resize'),
dict(keep_ratio=True, scale_factor=1.0, type='Resize'),
dict(keep_ratio=True, scale_factor=1.25, type='Resize'),
dict(keep_ratio=True, scale_factor=1.5, type='Resize'),
dict(keep_ratio=True, scale_factor=1.75, type='Resize'),
],
[
dict(direction='horizontal', prob=0.0, type='RandomFlip'),
dict(direction='horizontal', prob=1.0, type='RandomFlip'),
],
[
dict(type='LoadAnnotations'),
],
[
dict(type='PackSegInputs'),
],
],
type='TestTimeAug'),
]
val_cfg = dict(type='ValLoop')
val_dataloader = dict(
batch_size=1,
dataset=dict(
data_prefix=dict(
img_path='leftImg8bit/val', seg_map_path='gtFine/val'),
data_root='/dataset/cityscapes/',
pipeline=[
dict(type='LoadImageFromFile'),
dict(keep_ratio=True, scale=(
2048,
1024,
), type='Resize'),
dict(type='LoadAnnotations'),
dict(type='PackSegInputs'),
],
type='CityscapesDataset'),
num_workers=4,
persistent_workers=True,
sampler=dict(shuffle=False, type='DefaultSampler'))
val_evaluator = dict(
iou_metrics=[
'mIoU',
], type='IoUMetric')
vis_backends = [
dict(type='LocalVisBackend'),
]
visualizer = dict(
name='visualizer',
type='SegLocalVisualizer',
vis_backends=[
dict(type='LocalVisBackend'),
])
work_dir = './work_dirs/mask2former-swint-8xb2-512x1024-90k'
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