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custom_imports = dict(imports=['mmseg.datasets', 'mmseg.models'], allow_failed_imports=False)
sub_model_train = [
'panoptic_head',
'sam_neck',
'data_preprocessor'
]
sub_model_optim = {
'sam_neck': {'lr_mult': 1},
'panoptic_head': {'lr_mult': 1},
}
max_epochs = 600
optimizer = dict(
type='AdamW',
sub_model=sub_model_optim,
lr=0.0005,
weight_decay=1e-3
)
param_scheduler = [
# warm up learning rate scheduler
dict(
type='LinearLR',
start_factor=5e-4,
by_epoch=True,
begin=0,
end=1,
# update by iter
convert_to_iter_based=True),
# main learning rate scheduler
dict(
type='CosineAnnealingLR',
T_max=max_epochs,
by_epoch=True,
begin=1,
end=max_epochs,
),
]
param_scheduler_callback = dict(
type='ParamSchedulerHook'
)
evaluator_ = dict(
type='CocoPLMetric',
metric=['bbox', 'segm'],
proposal_nums=[1, 10, 100]
)
evaluator = dict(
val_evaluator=evaluator_,
)
image_size = (1024, 1024)
data_preprocessor = dict(
type='mmdet.DetDataPreprocessor',
mean=[123.675, 116.28, 103.53],
std=[58.395, 57.12, 57.375],
bgr_to_rgb=True,
pad_size_divisor=32,
pad_mask=True,
mask_pad_value=0,
)
num_things_classes = 1
num_stuff_classes = 0
num_classes = num_things_classes + num_stuff_classes
num_queries = 30
model_cfg = dict(
type='SegSAMPLer',
hyperparameters=dict(
optimizer=optimizer,
param_scheduler=param_scheduler,
evaluator=evaluator,
),
need_train_names=sub_model_train,
data_preprocessor=data_preprocessor,
backbone=dict(
type='vit_h',
checkpoint='pretrain/sam/sam_vit_h_4b8939.pth',
# type='vit_b',
# checkpoint='pretrain/sam/sam_vit_b_01ec64.pth',
),
sam_neck=dict(
type='SAMAggregatorNeck',
in_channels=[1280] * 32,
# in_channels=[768] * 12,
inner_channels=32,
selected_channels=range(4, 32, 2),
# selected_channels=range(4, 12, 2),
out_channels=256,
up_sample_scale=4,
),
panoptic_head=dict(
type='mmdet.Mask2FormerHead',
in_channels=[256, 256, 256], # pass to pixel_decoder inside
strides=[8, 16, 32],
feat_channels=256,
out_channels=256,
num_things_classes=num_things_classes,
num_stuff_classes=num_stuff_classes,
num_queries=num_queries,
num_transformer_feat_level=3,
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,
num_layers=2,
layer_cfg=dict( # DeformableDetrTransformerEncoderLayer
self_attn_cfg=dict( # MultiScaleDeformableAttention
embed_dims=256,
num_heads=8,
num_levels=3,
num_points=4,
dropout=0.1,
batch_first=True),
ffn_cfg=dict(
embed_dims=256,
feedforward_channels=1024,
num_fcs=2,
ffn_drop=0.1,
act_cfg=dict(type='ReLU', inplace=True)))),
positional_encoding=dict(num_feats=128, normalize=True)),
enforce_decoder_input_project=False,
positional_encoding=dict(num_feats=128, normalize=True),
transformer_decoder=dict( # Mask2FormerTransformerDecoder
return_intermediate=True,
# num_layers=9,
num_layers=3,
layer_cfg=dict( # Mask2FormerTransformerDecoderLayer
self_attn_cfg=dict( # MultiheadAttention
embed_dims=256,
num_heads=8,
dropout=0.1,
batch_first=True),
cross_attn_cfg=dict( # MultiheadAttention
embed_dims=256,
num_heads=8,
dropout=0.1,
batch_first=True),
ffn_cfg=dict(
embed_dims=256,
feedforward_channels=2048,
num_fcs=2,
ffn_drop=0.1,
act_cfg=dict(type='ReLU', inplace=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)),
panoptic_fusion_head=dict(
type='mmdet.MaskFormerFusionHead',
num_things_classes=num_things_classes,
num_stuff_classes=num_stuff_classes,
loss_panoptic=None,
init_cfg=None),
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')),
test_cfg=dict(
panoptic_on=False,
# For now, the dataset does not support
# evaluating semantic segmentation metric.
semantic_on=False,
instance_on=True,
# max_per_image is for instance segmentation.
max_per_image=num_queries,
iou_thr=0.8,
# In Mask2Former's panoptic postprocessing,
# it will filter mask area where score is less than 0.5 .
filter_low_score=True),
init_cfg=None)
task_name = 'ssdd_ins'
exp_name = 'E20230531_1'
logger = dict(
type='WandbLogger',
project=task_name,
group='samcls-mask2former',
name=exp_name
)
# logger = None
callbacks = [
param_scheduler_callback,
dict(
type='ModelCheckpoint',
dirpath=f'results/{task_name}/{exp_name}/checkpoints',
save_last=True,
mode='max',
monitor='valsegm_map_0',
save_top_k=2,
filename='epoch_{epoch}-map_{valsegm_map_0:.4f}'
),
dict(
type='LearningRateMonitor',
logging_interval='step'
)
]
trainer_cfg = dict(
compiled_model=False,
accelerator="auto",
strategy="auto",
# strategy="ddp",
# strategy='ddp_find_unused_parameters_true',
# precision='32',
# precision='16-mixed',
devices=8,
default_root_dir=f'results/{task_name}/{exp_name}',
# default_root_dir='results/tmp',
max_epochs=max_epochs,
logger=logger,
callbacks=callbacks,
log_every_n_steps=5,
check_val_every_n_epoch=5,
benchmark=True,
# sync_batchnorm=True,
# fast_dev_run=True,
# limit_train_batches=1,
# limit_val_batches=0,
# limit_test_batches=None,
# limit_predict_batches=None,
# overfit_batches=0.0,
# val_check_interval=None,
# num_sanity_val_steps=0,
# enable_checkpointing=None,
# enable_progress_bar=None,
# enable_model_summary=None,
# accumulate_grad_batches=32,
# gradient_clip_val=15,
# gradient_clip_algorithm='norm',
# deterministic=None,
# inference_mode: bool=True,
use_distributed_sampler=True,
# profiler="simple",
# detect_anomaly=False,
# barebones=False,
# plugins=None,
# reload_dataloaders_every_n_epochs=0,
)
backend_args = None
train_pipeline = [
dict(type='mmdet.LoadImageFromFile'),
dict(type='mmdet.LoadAnnotations', with_bbox=True, with_mask=True),
dict(type='mmdet.Resize', scale=image_size),
dict(type='mmdet.RandomFlip', prob=0.5),
dict(type='mmdet.PackDetInputs')
]
test_pipeline = [
dict(type='mmdet.LoadImageFromFile', backend_args=backend_args),
dict(type='mmdet.Resize', scale=image_size),
# If you don't have a gt annotation, delete the pipeline
dict(type='mmdet.LoadAnnotations', with_bbox=True, with_mask=True),
dict(
type='mmdet.PackDetInputs',
meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape',
'scale_factor'))
]
train_batch_size_per_gpu = 6
train_num_workers = 4
test_batch_size_per_gpu = 6
test_num_workers = 4
persistent_workers = True
data_parent = '/mnt/search01/dataset/cky_data/SSDD'
dataset_type = 'SSDDInsSegDataset'
val_loader = dict(
batch_size=test_batch_size_per_gpu,
num_workers=test_num_workers,
persistent_workers=persistent_workers,
pin_memory=True,
dataset=dict(
type=dataset_type,
data_root=data_parent,
ann_file='annotations/SSDD_instances_val.json',
data_prefix=dict(img_path='imgs'),
test_mode=True,
filter_cfg=dict(filter_empty_gt=True, min_size=32),
pipeline=test_pipeline,
backend_args=backend_args))
datamodule_cfg = dict(
type='PLDataModule',
train_loader=dict(
batch_size=train_batch_size_per_gpu,
num_workers=train_num_workers,
persistent_workers=persistent_workers,
pin_memory=True,
dataset=dict(
type=dataset_type,
data_root=data_parent,
ann_file='annotations/SSDD_instances_train.json',
data_prefix=dict(img_path='imgs'),
filter_cfg=dict(filter_empty_gt=True, min_size=32),
pipeline=train_pipeline,
backend_args=backend_args)
),
val_loader=val_loader,
# test_loader=val_loader
predict_loader=val_loader
) |