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swinv2b_640reso_joint.py
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swinv2b_640reso_joint.py
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checkpoint_config = dict(interval=12115)
log_config = dict(
interval=20,
hooks=[
dict(type='TextLoggerHook'),
dict(type='TensorboardLoggerHook')
])
custom_hooks = [dict(type='NumClassCheckHook')]
dist_params = dict(backend='nccl')
log_level = 'INFO'
resume_from = None
workflow = [('train', 1)]
num_bins = 2000
num_classes = 80
num_embeddings = 128
num_embeddings_depth = 128
num_embeddings_others = 128 # other tasks token
num_vocal = num_bins+1 + num_classes + 2 + num_embeddings + \
num_embeddings_depth + num_embeddings_others
model = dict(
type='AiT',
padto=640,
backbone=dict(
type="SwinV2TransformerRPE2FC",
pretrain_img_size=192,
embed_dim=128,
depths=[2, 2, 18, 2],
num_heads=[4, 8, 16, 32],
window_size=[30, 30, 30, 15],
use_shift=[True, True, True, True],
pretrain_window_size=[12, 12, 12, 6],
ape=False,
drop_path_rate=0.3,
patch_norm=True,
use_checkpoint=False,
out_indices=(3,),
init_cfg=dict(type='Pretrained',
checkpoint='swin_v2_base_densesimmim.pth'),
),
transformer=dict(
type='ARTransformer',
in_chans=1024,
d_model=256,
drop_path=0.1,
drop_out=0.1,
nhead=8,
dim_feedforward=1024,
num_encoder_layers=6,
num_decoder_layers=6,
num_vocal=num_vocal,
num_bins=num_bins,
num_classes=num_classes,
num_embeddings=num_embeddings,
num_embeddings_depth=num_embeddings_depth,
dec_length=2100,
n_rows=20,
n_cols=20,
pos_enc='sine',
pred_eos=False,
soft_vae=True,
soft_transformer=True,
top_p=0.3
),
task_heads=dict(
insseg=dict(
type='InsSegHead',
task_id=1,
loss_weight=1.,
num_classes=num_classes,
num_bins=num_bins,
coord_norm='abs', # abs or rel
norm_val=640,
sync_cls_avg_factor=True,
vae_cfg=dict(
type='VQVAE',
token_length=16,
mask_size=64,
embedding_dim=512,
hidden_dim=128,
num_resnet_blocks=2,
num_embeddings=num_embeddings,
pretrained='vqvae_insseg.pt',
freeze=True
),
mask_weight=0.2,
decoder_loss_weight=5.0,
max_obj_decoderloss=100,
seq_aug=True),
depth=dict(
type='DepthHead',
task_id=2,
loss_weight=0.2,
depth_token_offset=num_bins+1 + num_classes + 2 + num_embeddings,
vae_cfg=dict(
type='VQVAE',
use_norm=False,
token_length=15*15,
mask_size=480,
embedding_dim=512,
hidden_dim=256,
num_resnet_blocks=2,
num_embeddings=num_embeddings_depth,
pretrained='vqvae_depth.pt',
freeze=True
),
decoder_loss_weight=1.0,
soft_vae=True
),
))
img_norm_cfg = dict(
mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
# large scale jitter pipeline from configs/common/lsj_100e_coco_instance.py
image_size = (640, 640)
file_client_args = dict(backend='disk')
runner = dict(type='IterBasedRunnerMultitask', max_iters=12115 * 25)
evaluation = dict(interval=12115 * 25)
# learning policy
lr_config = dict(
policy='LinearAnnealing',
by_epoch=False,
min_lr_ratio=0.01,
warmup='linear',
warmup_by_epoch=False,
warmup_iters=12115,
)
# optimizer
optimizer = dict(
type='AdamW',
lr=8e-4,
weight_decay=0.05,
constructor='SwinLayerDecayOptimizerConstructor',
paramwise_cfg=dict(
num_layers=[2, 2, 18, 2], layer_decay_rate=0.85,
no_decay_names=['relative_position_bias_table', 'rpe_mlp', 'logit_scale',
'det_embed', 'voc_embed', 'enc_embed', 'dec_embed', 'mask_embed'],
))
optimizer_config = dict(grad_clip={'max_norm': 10, 'norm_type': 2})
insseg_train_pipeline = [
dict(type='LoadImageFromFile', file_client_args=file_client_args),
dict(type='LoadAnnotations', with_bbox=True, with_mask=True),
dict(
type='Resize',
img_scale=image_size,
ratio_range=(0.1, 3.0),
multiscale_mode='range',
keep_ratio=True),
dict(
type='RandomCrop',
crop_type='absolute',
crop_size=image_size,
recompute_bbox=False,
allow_negative_crop=True),
dict(type='FilterAnnotations', min_gt_bbox_wh=(1e-2, 1e-2)),
dict(type='RandomFlip', flip_ratio=0.5),
dict(type='Normalize', **img_norm_cfg),
dict(type='Pad', size=image_size),
dict(type='DefaultFormatBundle'),
dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels', 'gt_masks']),
dict(type='AddKey', kv={'task_type': 'insseg'})
]
insseg_test_pipeline = [
dict(type='LoadImageFromFile', file_client_args=file_client_args),
dict(
type='MultiScaleFlipAug',
img_scale=(640, 640),
flip=False,
transforms=[
dict(type='Resize', keep_ratio=True),
dict(type='RandomFlip'),
dict(type='Normalize', **img_norm_cfg),
dict(type='Pad', size=image_size),
dict(type='ImageToTensor', keys=['img']),
dict(type='Collect', keys=['img']),
])
]
det_dataset_type = 'CocoDataset'
task = dict(
insseg=dict( # len=117266
times=1,
data=dict(
train=dict(type=det_dataset_type, ann_file='data/coco/annotations/instances_train2017.json',
img_prefix='data/coco/train2017/', pipeline=insseg_train_pipeline, samples_total_gpu=60),
val=dict(type=det_dataset_type, ann_file='data/coco/annotations/instances_val2017.json', img_prefix='data/coco/val2017/', pipeline=insseg_test_pipeline, samples_per_gpu=8, workers_per_gpu=4),)
),
depth=dict( # len=24231
times=1,
data=dict(
train=dict(type='nyudepthv2', data_path='data', filenames_path='code/dataset/depth/filenames/',
is_train=True, crop_size=(480, 480), samples_total_gpu=4),
val=dict(type='nyudepthv2', data_path='data', filenames_path='code/dataset/depth/filenames/',
is_train=False, crop_size=(480, 480), samples_per_gpu=2, workers_per_gpu=8),
)
)
)
# enable fp16
fp16 = dict(loss_scale='dynamic')
load_from = 'ait_det_swinv2b_wodec.pth'