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custom_imports = dict(
imports=['mmseg.datasets', 'mmseg.models', 'mmdet.models'],
allow_failed_imports=False)
sub_model_train = [
'panoptic_head',
'data_preprocessor'
]
sub_model_optim = {
'panoptic_head': {'lr_mult': 1},
}
max_epochs = 1200
optimizer = dict(type='AdamW', lr=0.0005, weight_decay=0.0001)
param_scheduler = [
dict(
type='LinearLR',
start_factor=0.0005,
by_epoch=True,
begin=0,
end=1,
convert_to_iter_based=True),
dict(type='CosineAnnealingLR', T_max=120, by_epoch=True, begin=1, end=120)
]
param_scheduler_callback = dict(type='ParamSchedulerHook')
evaluator_ = dict(type='MeanAveragePrecision', iou_type='segm')
evaluator = dict(
val_evaluator=dict(type='MeanAveragePrecision', iou_type='segm'))
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 = 10
num_stuff_classes = 0
num_classes = num_things_classes + num_stuff_classes
prompt_shape = (60, 4)
model_cfg = dict(
type='SegSAMAnchorPLer',
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',
),
panoptic_head=dict(
type='SAMAnchorInstanceHead',
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,
),
rpn_head=dict(
type='mmdet.RPNHead',
in_channels=256,
feat_channels=256,
anchor_generator=dict(
type='mmdet.AnchorGenerator',
scales=[2, 4, 8, 16, 32, 64],
ratios=[0.5, 1.0, 2.0],
strides=[8, 16, 32]),
bbox_coder=dict(
type='mmdet.DeltaXYWHBBoxCoder',
target_means=[.0, .0, .0, .0],
target_stds=[1.0, 1.0, 1.0, 1.0]),
loss_cls=dict(
type='mmdet.CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0),
loss_bbox=dict(type='mmdet.SmoothL1Loss', loss_weight=1.0)),
roi_head=dict(
type='SAMAnchorPromptRoIHead',
bbox_roi_extractor=dict(
type='mmdet.SingleRoIExtractor',
roi_layer=dict(type='RoIAlign', output_size=7, sampling_ratio=0),
out_channels=256,
featmap_strides=[8, 16, 32]),
bbox_head=dict(
type='mmdet.Shared2FCBBoxHead',
in_channels=256,
fc_out_channels=1024,
roi_feat_size=7,
num_classes=num_classes,
bbox_coder=dict(
type='mmdet.DeltaXYWHBBoxCoder',
target_means=[0., 0., 0., 0.],
target_stds=[0.1, 0.1, 0.2, 0.2]),
reg_class_agnostic=False,
loss_cls=dict(
type='mmdet.CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0),
loss_bbox=dict(type='mmdet.SmoothL1Loss', loss_weight=1.0)),
mask_roi_extractor=dict(
type='mmdet.SingleRoIExtractor',
roi_layer=dict(type='RoIAlign', output_size=14, sampling_ratio=0),
out_channels=256,
featmap_strides=[8, 16, 32]),
mask_head=dict(
type='SAMPromptMaskHead',
per_query_point=prompt_shape[1],
with_sincos=True,
class_agnostic=True,
loss_mask=dict(
type='mmdet.CrossEntropyLoss', use_mask=True, loss_weight=1.0))),
# model training and testing settings
train_cfg=dict(
rpn=dict(
assigner=dict(
type='mmdet.MaxIoUAssigner',
pos_iou_thr=0.7,
neg_iou_thr=0.3,
min_pos_iou=0.3,
match_low_quality=True,
ignore_iof_thr=-1),
sampler=dict(
type='mmdet.RandomSampler',
num=512,
pos_fraction=0.5,
neg_pos_ub=-1,
add_gt_as_proposals=False),
allowed_border=-1,
pos_weight=-1,
debug=False),
rpn_proposal=dict(
nms_pre=2000,
max_per_img=1000,
nms=dict(type='nms', iou_threshold=0.7),
min_bbox_size=0),
rcnn=dict(
assigner=dict(
type='mmdet.MaxIoUAssigner',
pos_iou_thr=0.5,
neg_iou_thr=0.5,
min_pos_iou=0.5,
match_low_quality=True,
ignore_iof_thr=-1),
sampler=dict(
type='mmdet.RandomSampler',
num=256,
pos_fraction=0.25,
neg_pos_ub=-1,
add_gt_as_proposals=True),
mask_size=1024,
pos_weight=-1,
debug=False)),
test_cfg=dict(
rpn=dict(
nms_pre=1000,
max_per_img=1000,
nms=dict(type='nms', iou_threshold=0.7),
min_bbox_size=0),
rcnn=dict(
score_thr=0.05,
nms=dict(type='nms', iou_threshold=0.5),
max_per_img=100,
mask_thr_binary=0.5)
)
)
)
task_name = 'nwpu_ins'
exp_name = 'rsprompter_anchor_E20230601_0'
callbacks = [
dict(
type='DetVisualizationHook',
draw=True,
interval=1,
score_thr=0.1,
show=False,
wait_time=1.,
test_out_dir='visualization',
)
]
vis_backends = [dict(type='mmdet.LocalVisBackend')]
visualizer = dict(
type='mmdet.DetLocalVisualizer',
vis_backends=vis_backends,
name='visualizer',
fig_save_cfg=dict(
frameon=False,
figsize=(40, 20),
# dpi=300,
),
line_width=2,
alpha=0.8
)
trainer_cfg = dict(
compiled_model=False,
accelerator='auto',
strategy='auto',
devices=[0],
default_root_dir=f'results/{task_name}/{exp_name}',
max_epochs=120,
logger=None,
callbacks=callbacks,
log_every_n_steps=20,
check_val_every_n_epoch=10,
benchmark=True,
use_distributed_sampler=True)
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 = 8
train_num_workers = 4
test_batch_size_per_gpu = 2
test_num_workers = 0
persistent_workers = False
data_parent = '/mnt/search01/dataset/cky_data/NWPU10'
train_data_prefix = ''
val_data_prefix = ''
dataset_type = 'NWPUInsSegDataset'
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='NWPU_instances_val.json',
data_prefix=dict(img_path='positive image set'),
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',
predict_loader=val_loader,
)
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