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Initial media depth project backup

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2026-05-20 12:25:12 +08:00
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230
Depth-Anything-V1-main/semseg/config/depth_anything/depth_anything_large_mask2former_16xb1_160k_ade20k_896x896.py Normal file
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_base_ = [
'../_base_/default_runtime.py', '../_base_/datasets/ade20k_640x640.py'
]
crop_size = (896, 896)
data_preprocessor = dict(
type='SegDataPreProcessor',
mean=[123.675, 116.28, 103.53],
std=[58.395, 57.12, 57.375],
bgr_to_rgb=True,
pad_val=0,
seg_pad_val=255,
size=crop_size)
num_classes = 150
model = dict(
type='EncoderDecoder',
data_preprocessor=data_preprocessor,
backbone=dict(
type='DINOv2',
version='large',
freeze=False,
load_from='./checkpoints/depth_anything_vitl14.pth'),
neck=dict(type='Feature2Pyramid', embed_dim=1024, rescales=[4, 2, 1, 0.5]),
decode_head=dict(
type='Mask2FormerHead',
in_channels=[1024, 1024, 1024, 1024],
# strides=[4, 8, 16, 32],
feat_channels=1024,
out_channels=1024,
num_classes=num_classes,
num_queries=200,
num_transformer_feat_level=3,
align_corners=False,
pixel_decoder=dict(
type='mmdet.MSDeformAttnPixelDecoder',
num_outs=3,
norm_cfg=dict(type='GN', num_groups=32),
act_cfg=dict(type='ReLU'),
encoder=dict( # DeformableDetrTransformerEncoder
num_layers=6,
layer_cfg=dict( # DeformableDetrTransformerEncoderLayer
self_attn_cfg=dict( # MultiScaleDeformableAttention
embed_dims=1024,
num_heads=32,
num_levels=3,
num_points=4,
im2col_step=64,
dropout=0.0,
batch_first=True,
norm_cfg=None,
init_cfg=None),
ffn_cfg=dict(
embed_dims=1024,
feedforward_channels=4096,
num_fcs=2,
ffn_drop=0.0,
act_cfg=dict(type='ReLU', inplace=True))),
init_cfg=None),
positional_encoding=dict( # SinePositionalEncoding
num_feats=512, normalize=True),
init_cfg=None),
enforce_decoder_input_project=False,
positional_encoding=dict( # SinePositionalEncoding
num_feats=512, normalize=True),
transformer_decoder=dict( # Mask2FormerTransformerDecoder
return_intermediate=True,
num_layers=9,
layer_cfg=dict( # Mask2FormerTransformerDecoderLayer
self_attn_cfg=dict( # MultiheadAttention
embed_dims=1024,
num_heads=32,
attn_drop=0.0,
proj_drop=0.0,
dropout_layer=None,
batch_first=True),
cross_attn_cfg=dict( # MultiheadAttention
embed_dims=1024,
num_heads=32,
attn_drop=0.0,
proj_drop=0.0,
dropout_layer=None,
batch_first=True),
ffn_cfg=dict(
embed_dims=1024,
feedforward_channels=4096,
num_fcs=2,
act_cfg=dict(type='ReLU', inplace=True),
ffn_drop=0.0,
dropout_layer=None,
add_identity=True)),
init_cfg=None),
loss_cls=dict(
type='mmdet.CrossEntropyLoss',
use_sigmoid=False,
loss_weight=2.0,
reduction='mean',
class_weight=[1.0] * num_classes + [0.1]),
loss_mask=dict(
type='mmdet.CrossEntropyLoss',
use_sigmoid=True,
reduction='mean',
loss_weight=5.0),
loss_dice=dict(
type='mmdet.DiceLoss',
use_sigmoid=True,
activate=True,
reduction='mean',
naive_dice=True,
eps=1.0,
loss_weight=5.0),
train_cfg=dict(
num_points=12544,
oversample_ratio=3.0,
importance_sample_ratio=0.75,
assigner=dict(
type='mmdet.HungarianAssigner',
match_costs=[
dict(type='mmdet.ClassificationCost', weight=2.0),
dict(
type='mmdet.CrossEntropyLossCost',
weight=5.0,
use_sigmoid=True),
dict(
type='mmdet.DiceCost',
weight=5.0,
pred_act=True,
eps=1.0)
]),
sampler=dict(type='mmdet.MaskPseudoSampler'))),
train_cfg=dict(),
test_cfg=dict(mode='slide', crop_size=crop_size, stride=(426, 426)))
# dataset config
train_pipeline = [
dict(type='LoadImageFromFile'),
dict(type='LoadAnnotations', reduce_zero_label=True),
dict(
type='RandomChoiceResize',
scales=[int(x * 0.1 * 896) for x in range(5, 21)],
resize_type='ResizeShortestEdge',
max_size=3584),
dict(type='RandomCrop', crop_size=crop_size, cat_max_ratio=0.75),
dict(type='RandomFlip', prob=0.5),
dict(type='PhotoMetricDistortion'),
dict(type='PackSegInputs')
]
test_pipeline = [
dict(type='LoadImageFromFile'),
dict(type='Resize', scale=(3584, 896), keep_ratio=True),
# add loading annotation after ``Resize`` because ground truth
# does not need to do resize data transform
dict(type='LoadAnnotations', reduce_zero_label=True),
dict(type='PackSegInputs')
]
train_dataloader = dict(batch_size=1, dataset=dict(pipeline=train_pipeline))
val_dataloader = dict(dataset=dict(pipeline=test_pipeline))
test_dataloader = val_dataloader
# optim_wrapper = dict(
# _delete_=True,
# type='OptimWrapper',
# optimizer=dict(
# type='AdamW', lr=3e-5, betas=(0.9, 0.999), weight_decay=0.05),
# constructor='LayerDecayOptimizerConstructor',
# paramwise_cfg=dict(num_layers=12, layer_decay_rate=0.9))
# set all layers in backbone to lr_mult=0.1
# set all norm layers, position_embeding,
# query_embeding, level_embeding to decay_multi=0.0
backbone_norm_multi = dict(lr_mult=0.1, decay_mult=0.0)
backbone_embed_multi = dict(lr_mult=0.1, decay_mult=0.0)
embed_multi = dict(lr_mult=1.0, decay_mult=0.0)
custom_keys = {
'backbone.dinov2': dict(lr_mult=0.1, decay_mult=1.0),
'backbone.dinov2.norm': backbone_norm_multi,
'pos_embed': backbone_embed_multi,
'query_embed': embed_multi,
'query_feat': embed_multi,
'level_embed': embed_multi
}
custom_keys.update({
f'backbone.dinov2.blocks.{block_id}.norm': backbone_norm_multi
for block_id in range(24)
})
# optimizer
optimizer = dict(
type='AdamW', lr=0.00003, weight_decay=0.05, eps=1e-8, betas=(0.9, 0.999))
optim_wrapper = dict(
type='OptimWrapper',
optimizer=optimizer,
clip_grad=dict(max_norm=0.01, norm_type=2),
paramwise_cfg=dict(custom_keys=custom_keys, norm_decay_mult=0.0))
find_unused_parameters=True
param_scheduler = [
dict(
type='LinearLR', start_factor=1e-6, by_epoch=False, begin=0, end=1500),
dict(
type='PolyLR',
power=1.0,
begin=1500,
end=160000,
eta_min=0.0,
by_epoch=False,
)
]
# training schedule for 160k
train_cfg = dict(
type='IterBasedTrainLoop', max_iters=160000, val_interval=5000)
val_cfg = dict(type='ValLoop')
test_cfg = dict(type='TestLoop')
default_hooks = dict(
timer=dict(type='IterTimerHook'),
logger=dict(type='LoggerHook', interval=50, log_metric_by_epoch=False),
param_scheduler=dict(type='ParamSchedulerHook'),
checkpoint=dict(
type='CheckpointHook', by_epoch=False, interval=5000, save_best='mIoU', max_keep_ckpts=1),
sampler_seed=dict(type='DistSamplerSeedHook'),
visualization=dict(type='SegVisualizationHook'))
# Default setting for scaling LR automatically
# - `enable` means enable scaling LR automatically
# or not by default.
# - `base_batch_size` = (8 GPUs) x (2 samples per GPU).
auto_scale_lr = dict(enable=False, base_batch_size=16)
work_dir = './work_dirs/depth_anything_large_mask2former_16xb1_160k_ade20k_896x896'

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Depth-Anything-V1-main/semseg/config/depth_anything/depth_anything_large_mask2former_16xb1_80k_cityscapes_896x896.py Normal file
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_base_ = [
'../_base_/default_runtime.py', '../_base_/datasets/cityscapes.py'
]
crop_size = (896, 896)
data_preprocessor = dict(
type='SegDataPreProcessor',
mean=[123.675, 116.28, 103.53],
std=[58.395, 57.12, 57.375],
bgr_to_rgb=True,
pad_val=0,
seg_pad_val=255,
size=crop_size)
num_classes = 19
model = dict(
type='EncoderDecoder',
data_preprocessor=data_preprocessor,
backbone=dict(
type='DINOv2',
version='large',
freeze=False,
load_from='./checkpoints/depth_anything_vitl14.pth'),
neck=dict(type='Feature2Pyramid', embed_dim=1024, rescales=[4, 2, 1, 0.5]),
decode_head=dict(
type='Mask2FormerHead',
in_channels=[1024, 1024, 1024, 1024],
# strides=[4, 8, 16, 32],
feat_channels=1024,
out_channels=1024,
num_classes=num_classes,
num_queries=200,
num_transformer_feat_level=3,
align_corners=False,
pixel_decoder=dict(
type='mmdet.MSDeformAttnPixelDecoder',
num_outs=3,
norm_cfg=dict(type='GN', num_groups=32),
act_cfg=dict(type='ReLU'),
encoder=dict( # DeformableDetrTransformerEncoder
num_layers=6,
layer_cfg=dict( # DeformableDetrTransformerEncoderLayer
self_attn_cfg=dict( # MultiScaleDeformableAttention
embed_dims=1024,
num_heads=32,
num_levels=3,
num_points=4,
im2col_step=64,
dropout=0.0,
batch_first=True,
norm_cfg=None,
init_cfg=None),
ffn_cfg=dict(
embed_dims=1024,
feedforward_channels=4096,
num_fcs=2,
ffn_drop=0.0,
act_cfg=dict(type='ReLU', inplace=True))),
init_cfg=None),
positional_encoding=dict( # SinePositionalEncoding
num_feats=512, normalize=True),
init_cfg=None),
enforce_decoder_input_project=False,
positional_encoding=dict( # SinePositionalEncoding
num_feats=512, normalize=True),
transformer_decoder=dict( # Mask2FormerTransformerDecoder
return_intermediate=True,
num_layers=9,
layer_cfg=dict( # Mask2FormerTransformerDecoderLayer
self_attn_cfg=dict( # MultiheadAttention
embed_dims=1024,
num_heads=32,
attn_drop=0.0,
proj_drop=0.0,
dropout_layer=None,
batch_first=True),
cross_attn_cfg=dict( # MultiheadAttention
embed_dims=1024,
num_heads=32,
attn_drop=0.0,
proj_drop=0.0,
dropout_layer=None,
batch_first=True),
ffn_cfg=dict(
embed_dims=1024,
feedforward_channels=4096,
num_fcs=2,
act_cfg=dict(type='ReLU', inplace=True),
ffn_drop=0.0,
dropout_layer=None,
add_identity=True)),
init_cfg=None),
loss_cls=dict(
type='mmdet.CrossEntropyLoss',
use_sigmoid=False,
loss_weight=2.0,
reduction='mean',
class_weight=[1.0] * num_classes + [0.1]),
loss_mask=dict(
type='mmdet.CrossEntropyLoss',
use_sigmoid=True,
reduction='mean',
loss_weight=5.0),
loss_dice=dict(
type='mmdet.DiceLoss',
use_sigmoid=True,
activate=True,
reduction='mean',
naive_dice=True,
eps=1.0,
loss_weight=5.0),
train_cfg=dict(
num_points=12544,
oversample_ratio=3.0,
importance_sample_ratio=0.75,
assigner=dict(
type='mmdet.HungarianAssigner',
match_costs=[
dict(type='mmdet.ClassificationCost', weight=2.0),
dict(
type='mmdet.CrossEntropyLossCost',
weight=5.0,
use_sigmoid=True),
dict(
type='mmdet.DiceCost',
weight=5.0,
pred_act=True,
eps=1.0)
]),
sampler=dict(type='mmdet.MaskPseudoSampler'))),
train_cfg=dict(),
test_cfg=dict(mode='slide', crop_size=crop_size, stride=(518, 518)))
# dataset config
train_pipeline = [
dict(type='LoadImageFromFile'),
dict(type='LoadAnnotations'),
dict(
type='RandomChoiceResize',
scales=[int(x * 0.1 * 896) for x in range(5, 21)],
resize_type='ResizeShortestEdge',
max_size=896 * 4),
dict(type='RandomCrop', crop_size=crop_size, cat_max_ratio=0.75),
dict(type='RandomFlip', prob=0.5),
dict(type='PhotoMetricDistortion'),
dict(type='PackSegInputs')
]
test_pipeline = [
dict(type='LoadImageFromFile'),
dict(type='Resize', scale=(896 * 4, 896), keep_ratio=True),
# add loading annotation after ``Resize`` because ground truth
# does not need to do resize data transform
dict(type='LoadAnnotations'),
dict(type='PackSegInputs')
]
train_dataloader = dict(batch_size=1, dataset=dict(pipeline=train_pipeline))
val_dataloader = dict(dataset=dict(pipeline=test_pipeline))
test_dataloader = val_dataloader
# set all layers in backbone to lr_mult=0.1
# set all norm layers, position_embeding,
# query_embeding, level_embeding to decay_multi=0.0
backbone_norm_multi = dict(lr_mult=0.1, decay_mult=0.0)
backbone_embed_multi = dict(lr_mult=0.1, decay_mult=0.0)
embed_multi = dict(lr_mult=1.0, decay_mult=0.0)
custom_keys = {
'backbone.dinov2': dict(lr_mult=0.1, decay_mult=1.0),
'backbone.dinov2.norm': backbone_norm_multi,
'pos_embed': backbone_embed_multi,
'query_embed': embed_multi,
'query_feat': embed_multi,
'level_embed': embed_multi
}
custom_keys.update({
f'backbone.dinov2.blocks.{block_id}.norm': backbone_norm_multi
for block_id in range(24)
})
# optimizer
optimizer = dict(
type='AdamW', lr=0.00003, weight_decay=0.05, eps=1e-8, betas=(0.9, 0.999))
optim_wrapper = dict(
type='OptimWrapper',
optimizer=optimizer,
clip_grad=dict(max_norm=0.01, norm_type=2),
paramwise_cfg=dict(custom_keys=custom_keys, norm_decay_mult=0.0))
find_unused_parameters=True
param_scheduler = [
dict(
type='LinearLR', start_factor=1e-6, by_epoch=False, begin=0, end=1500),
dict(
type='PolyLR',
power=1.0,
begin=1500,
end=80000,
eta_min=0.0,
by_epoch=False,
)
]
# training schedule for 160k
train_cfg = dict(
type='IterBasedTrainLoop', max_iters=80000, val_interval=5000)
val_cfg = dict(type='ValLoop')
test_cfg = dict(type='TestLoop')
default_hooks = dict(
timer=dict(type='IterTimerHook'),
logger=dict(type='LoggerHook', interval=50, log_metric_by_epoch=False),
param_scheduler=dict(type='ParamSchedulerHook'),
checkpoint=dict(
type='CheckpointHook', by_epoch=False, interval=5000, save_best='mIoU', max_keep_ckpts=1),
sampler_seed=dict(type='DistSamplerSeedHook'),
visualization=dict(type='SegVisualizationHook'))
# Default setting for scaling LR automatically
# - `enable` means enable scaling LR automatically
# or not by default.
# - `base_batch_size` = (8 GPUs) x (2 samples per GPU).
auto_scale_lr = dict(enable=False, base_batch_size=16)
work_dir = './work_dirs/depth_anything_large_mask2former_16xb1_80k_cityscapes_896x896'

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Depth-Anything-V1-main/semseg/config/depth_anything/depth_anything_large_mask2former_16xb1_80k_cityscapes_896x896_ms.py Normal file
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_base_ = [
'../_base_/default_runtime.py', '../_base_/datasets/cityscapes.py'
]
crop_size = (896, 896)
data_preprocessor = dict(
type='SegDataPreProcessor',
mean=[123.675, 116.28, 103.53],
std=[58.395, 57.12, 57.375],
bgr_to_rgb=True,
pad_val=0,
seg_pad_val=255,
size=crop_size,
test_cfg=dict(size_divisor=14))
num_classes = 19
model = dict(
type='EncoderDecoder',
data_preprocessor=data_preprocessor,
backbone=dict(
type='DINOv2',
version='large',
freeze=False,
load_from='./checkpoints/depth_anything_vitl14.pth'),
neck=dict(type='Feature2Pyramid', embed_dim=1024, rescales=[4, 2, 1, 0.5]),
decode_head=dict(
type='Mask2FormerHead',
in_channels=[1024, 1024, 1024, 1024],
# strides=[4, 8, 16, 32],
feat_channels=1024,
out_channels=1024,
num_classes=num_classes,
num_queries=200,
num_transformer_feat_level=3,
align_corners=False,
pixel_decoder=dict(
type='mmdet.MSDeformAttnPixelDecoder',
num_outs=3,
norm_cfg=dict(type='GN', num_groups=32),
act_cfg=dict(type='ReLU'),
encoder=dict( # DeformableDetrTransformerEncoder
num_layers=6,
layer_cfg=dict( # DeformableDetrTransformerEncoderLayer
self_attn_cfg=dict( # MultiScaleDeformableAttention
embed_dims=1024,
num_heads=32,
num_levels=3,
num_points=4,
im2col_step=64,
dropout=0.0,
batch_first=True,
norm_cfg=None,
init_cfg=None),
ffn_cfg=dict(
embed_dims=1024,
feedforward_channels=4096,
num_fcs=2,
ffn_drop=0.0,
act_cfg=dict(type='ReLU', inplace=True))),
init_cfg=None),
positional_encoding=dict( # SinePositionalEncoding
num_feats=512, normalize=True),
init_cfg=None),
enforce_decoder_input_project=False,
positional_encoding=dict( # SinePositionalEncoding
num_feats=512, normalize=True),
transformer_decoder=dict( # Mask2FormerTransformerDecoder
return_intermediate=True,
num_layers=9,
layer_cfg=dict( # Mask2FormerTransformerDecoderLayer
self_attn_cfg=dict( # MultiheadAttention
embed_dims=1024,
num_heads=32,
attn_drop=0.0,
proj_drop=0.0,
dropout_layer=None,
batch_first=True),
cross_attn_cfg=dict( # MultiheadAttention
embed_dims=1024,
num_heads=32,
attn_drop=0.0,
proj_drop=0.0,
dropout_layer=None,
batch_first=True),
ffn_cfg=dict(
embed_dims=1024,
feedforward_channels=4096,
num_fcs=2,
act_cfg=dict(type='ReLU', inplace=True),
ffn_drop=0.0,
dropout_layer=None,
add_identity=True)),
init_cfg=None),
loss_cls=dict(
type='mmdet.CrossEntropyLoss',
use_sigmoid=False,
loss_weight=2.0,
reduction='mean',
class_weight=[1.0] * num_classes + [0.1]),
loss_mask=dict(
type='mmdet.CrossEntropyLoss',
use_sigmoid=True,
reduction='mean',
loss_weight=5.0),
loss_dice=dict(
type='mmdet.DiceLoss',
use_sigmoid=True,
activate=True,
reduction='mean',
naive_dice=True,
eps=1.0,
loss_weight=5.0),
train_cfg=dict(
num_points=12544,
oversample_ratio=3.0,
importance_sample_ratio=0.75,
assigner=dict(
type='mmdet.HungarianAssigner',
match_costs=[
dict(type='mmdet.ClassificationCost', weight=2.0),
dict(
type='mmdet.CrossEntropyLossCost',
weight=5.0,
use_sigmoid=True),
dict(
type='mmdet.DiceCost',
weight=5.0,
pred_act=True,
eps=1.0)
]),
sampler=dict(type='mmdet.MaskPseudoSampler'))),
train_cfg=dict(),
test_cfg=dict(mode='slide', crop_size=crop_size, stride=(518, 518)))
# dataset config
img_norm_cfg = dict(
mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
train_pipeline = [
dict(type='LoadImageFromFile'),
dict(type='LoadAnnotations'),
dict(
type='RandomChoiceResize',
scales=[int(x * 0.1 * 896) for x in range(5, 21)],
resize_type='ResizeShortestEdge',
max_size=896 * 4),
dict(type='RandomCrop', crop_size=crop_size, cat_max_ratio=0.75),
dict(type='RandomFlip', prob=0.5),
dict(type='PhotoMetricDistortion'),
dict(type='PackSegInputs')
]
test_pipeline = [
dict(type='LoadImageFromFile'),
dict(type='Resize', scale=(896 * 4, 896), keep_ratio=True),
# add loading annotation after ``Resize`` because ground truth
# does not need to do resize data transform
dict(type='LoadAnnotations'),
dict(type='PackSegInputs')
]
img_ratios = [0.5, 0.75, 1.0, 1.25, 1.5, 1.75, 2.0]
# img_ratios = [1.0]
tta_pipeline = [
dict(type='LoadImageFromFile', backend_args=None),
dict(
type='TestTimeAug',
transforms=[
[
dict(type='Resize', scale_factor=r, keep_ratio=True)
for r in img_ratios
],
[
dict(type='RandomFlip', prob=0., direction='horizontal'),
dict(type='RandomFlip', prob=1., direction='horizontal')
], [dict(type='LoadAnnotations')], [dict(type='PackSegInputs')]
])
]
train_dataloader = dict(batch_size=1, dataset=dict(pipeline=train_pipeline))
val_dataloader = dict(dataset=dict(pipeline=test_pipeline))
test_dataloader = val_dataloader
# set all layers in backbone to lr_mult=0.1
# set all norm layers, position_embeding,
# query_embeding, level_embeding to decay_multi=0.0
backbone_norm_multi = dict(lr_mult=0.1, decay_mult=0.0)
backbone_embed_multi = dict(lr_mult=0.1, decay_mult=0.0)
embed_multi = dict(lr_mult=1.0, decay_mult=0.0)
custom_keys = {
'backbone.dinov2': dict(lr_mult=0.1, decay_mult=1.0),
'backbone.dinov2.norm': backbone_norm_multi,
'pos_embed': backbone_embed_multi,
'query_embed': embed_multi,
'query_feat': embed_multi,
'level_embed': embed_multi
}
custom_keys.update({
f'backbone.dinov2.blocks.{block_id}.norm': backbone_norm_multi
for block_id in range(24)
})
# optimizer
optimizer = dict(
type='AdamW', lr=0.00003, weight_decay=0.05, eps=1e-8, betas=(0.9, 0.999))
optim_wrapper = dict(
type='OptimWrapper',
optimizer=optimizer,
clip_grad=dict(max_norm=0.01, norm_type=2),
paramwise_cfg=dict(custom_keys=custom_keys, norm_decay_mult=0.0))
find_unused_parameters=True
param_scheduler = [
dict(
type='LinearLR', start_factor=1e-6, by_epoch=False, begin=0, end=1500),
dict(
type='PolyLR',
power=1.0,
begin=1500,
end=80000,
eta_min=0.0,
by_epoch=False,
)
]
# training schedule for 160k
train_cfg = dict(
type='IterBasedTrainLoop', max_iters=80000, val_interval=5000)
val_cfg = dict(type='ValLoop')
test_cfg = dict(type='TestLoop')
default_hooks = dict(
timer=dict(type='IterTimerHook'),
logger=dict(type='LoggerHook', interval=1, log_metric_by_epoch=False),
param_scheduler=dict(type='ParamSchedulerHook'),
checkpoint=dict(
type='CheckpointHook', by_epoch=False, interval=5000, save_best='mIoU', max_keep_ckpts=1),
sampler_seed=dict(type='DistSamplerSeedHook'),
visualization=dict(type='SegVisualizationHook'))
# Default setting for scaling LR automatically
# - `enable` means enable scaling LR automatically
# or not by default.
# - `base_batch_size` = (8 GPUs) x (2 samples per GPU).
auto_scale_lr = dict(enable=False, base_batch_size=16)
work_dir = './work_dirs/depth_anything_large_mask2former_16xb1_80k_cityscapes_896x896_ms'