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rtmdet-ins_x_8xb16-300e_coco_few_shot.py
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default_scope = 'mmdet'
custom_imports = dict(
# import custom made modules
imports=[
'configs/FewShotRTMDetInsSepBNHead',
'losses/FewShotDiceLoss',
'datasets/FishDataset'
],
allow_failed_imports=False
)
default_hooks = dict(
timer=dict(type='IterTimerHook'),
logger=dict(type='LoggerHook', interval=50),
param_scheduler=dict(type='ParamSchedulerHook'),
checkpoint=dict(type='CheckpointHook', interval=10, max_keep_ckpts=10),
sampler_seed=dict(type='DistSamplerSeedHook'),
visualization=dict(type='DetVisualizationHook'))
env_cfg = dict(
cudnn_benchmark=False,
mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0),
dist_cfg=dict(backend='nccl'))
vis_backends = [dict(type='LocalVisBackend')]
visualizer = dict(
type='DetLocalVisualizer',
vis_backends=[
dict(type='LocalVisBackend'),
dict(type='TensorboardVisBackend')
],
name='visualizer')
log_processor = dict(type='LogProcessor', window_size=50, by_epoch=True)
log_level = 'INFO'
load_from = None
resume = False
train_cfg = dict(
type='EpochBasedTrainLoop',
max_epochs=100,
val_interval=1,
dynamic_intervals=[(280, 1)])
val_cfg = dict(type='ValLoop')
test_cfg = dict(type='TestLoop')
param_scheduler = [
dict(
type='LinearLR', start_factor=1e-05, by_epoch=False, begin=0,
end=1000),
dict(
type='CosineAnnealingLR',
eta_min=0.0001,
begin=150,
end=300,
T_max=150,
by_epoch=True,
convert_to_iter_based=True)
]
optim_wrapper = dict(
type='OptimWrapper',
optimizer=dict(type='AdamW', lr=0.002, weight_decay=0.05),
paramwise_cfg=dict(
norm_decay_mult=0, bias_decay_mult=0, bypass_duplicate=True))
auto_scale_lr = dict(enable=False, base_batch_size=16)
dataset_type = 'FishDataset'
data_root = 'data/FishDataset'
file_client_args = dict(backend='disk')
train_pipeline = [
dict(type='LoadImageFromFile', file_client_args=dict(backend='disk')),
dict(
type='LoadAnnotations',
with_bbox=True,
with_mask=True,
poly2mask=False),
dict(type='CachedMosaic', img_scale=(640, 640), pad_val=114.0),
dict(
type='RandomResize',
scale=(1280, 1280),
ratio_range=(0.1, 2.0),
keep_ratio=True),
dict(
type='RandomCrop',
crop_size=(640, 640),
recompute_bbox=True,
allow_negative_crop=True),
dict(type='YOLOXHSVRandomAug'),
dict(type='RandomFlip', prob=0.5),
dict(type='Pad', size=(640, 640), pad_val=dict(img=(114, 114, 114))),
dict(
type='CachedMixUp',
img_scale=(640, 640),
ratio_range=(1.0, 1.0),
max_cached_images=20,
pad_val=(114, 114, 114)),
dict(type='FilterAnnotations', min_gt_bbox_wh=(1, 1)),
dict(type='PackDetInputs')
]
test_pipeline = [
dict(type='LoadImageFromFile', file_client_args=dict(backend='disk')),
dict(type='Resize', scale=(640, 640), keep_ratio=True),
dict(type='Pad', size=(640, 640), pad_val=dict(img=(114, 114, 114))),
dict(
type='PackDetInputs',
meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape',
'scale_factor'))
]
train_dataloader = dict(
batch_size=2,
num_workers=8,
persistent_workers=True,
sampler=dict(type='DefaultSampler', shuffle=True),
batch_sampler=None,
dataset=dict(
type='FishDataset',
data_root='data/FishDataset',
ann_file='annotations/MAIS2K_train.json',
data_prefix=dict(img='train_MAIS2K/raw/'),
filter_cfg=dict(filter_empty_gt=True, min_size=32),
pipeline=[
dict(
type='LoadImageFromFile',
file_client_args=dict(backend='disk')),
dict(
type='LoadAnnotations',
with_bbox=True,
with_mask=True,
poly2mask=False),
dict(type='CachedMosaic', img_scale=(640, 640), pad_val=114.0),
dict(
type='RandomResize',
scale=(1280, 1280),
ratio_range=(0.1, 2.0),
keep_ratio=True),
dict(
type='RandomCrop',
crop_size=(640, 640),
recompute_bbox=True,
allow_negative_crop=True),
dict(type='YOLOXHSVRandomAug'),
dict(type='RandomFlip', prob=0.5),
dict(
type='Pad', size=(640, 640),
pad_val=dict(img=(114, 114, 114))),
dict(
type='CachedMixUp',
img_scale=(640, 640),
ratio_range=(1.0, 1.0),
max_cached_images=20,
pad_val=(114, 114, 114)),
dict(type='FilterAnnotations', min_gt_bbox_wh=(1, 1)),
dict(type='PackDetInputs')
],
metainfo=dict(
CLASSES=('clupeid', 'cod', 'dab', 'dogfish', 'haddock', 'herring',
'plaice', 'prawn', 'sole', 'sprat', 'squid', 'unk fish',
'unk flatfish', 'unk gadoid', 'unk organism',
'unk round fish', 'whiting'),
PALETTE=[(220, 20, 60), (119, 11, 32), (0, 0, 142), (0, 0, 230),
(106, 0, 228), (0, 60, 100), (0, 80, 100), (0, 0, 70),
(0, 0, 192),
(250, 170, 30), (100, 170, 30), (220, 220, 0),
(175, 116, 175), (250, 0, 30), (165, 42, 42),
(255, 77, 255), (0, 226, 252)])),
pin_memory=True)
val_dataloader = dict(
batch_size=2,
num_workers=8,
persistent_workers=True,
drop_last=False,
sampler=dict(type='DefaultSampler', shuffle=False),
dataset=dict(
type='FishDataset',
data_root='data/FishDataset',
ann_file='annotations/MAIS2K_test.json',
data_prefix=dict(img='val_MAIS2K/raw/'),
test_mode=True,
pipeline=[
dict(
type='LoadImageFromFile',
file_client_args=dict(backend='disk')),
dict(type='Resize', scale=(640, 640), keep_ratio=True),
dict(
type='Pad', size=(640, 640),
pad_val=dict(img=(114, 114, 114))),
dict(
type='PackDetInputs',
meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape',
'scale_factor'))
],
metainfo=dict(
CLASSES=('clupeid', 'cod', 'dab', 'dogfish', 'haddock', 'herring',
'plaice', 'prawn', 'sole', 'sprat', 'squid', 'unk fish',
'unk flatfish', 'unk gadoid', 'unk organism',
'unk round fish', 'whiting'),
PALETTE=[(220, 20, 60), (119, 11, 32), (0, 0, 142), (0, 0, 230),
(106, 0, 228), (0, 60, 100), (0, 80, 100), (0, 0, 70),
(0, 0, 192),
(250, 170, 30), (100, 170, 30), (220, 220, 0),
(175, 116, 175), (250, 0, 30), (165, 42, 42),
(255, 77, 255), (0, 226, 252)])))
test_dataloader = dict(
batch_size=2,
num_workers=8,
persistent_workers=True,
drop_last=False,
sampler=dict(type='DefaultSampler', shuffle=False),
dataset=dict(
type='FishDataset',
data_root='data/FishDataset',
ann_file='annotations/MAIS2K_test.json',
data_prefix=dict(img='train_MAIS2K/raw/'),
test_mode=True,
pipeline=[
dict(
type='LoadImageFromFile',
file_client_args=dict(backend='disk')),
dict(type='Resize', scale=(640, 640), keep_ratio=True),
dict(
type='Pad', size=(640, 640),
pad_val=dict(img=(114, 114, 114))),
dict(
type='PackDetInputs',
meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape',
'scale_factor'))
],
metainfo=dict(
classes=('clupeid', 'cod', 'dab', 'dogfish', 'haddock', 'herring',
'plaice', 'prawn', 'sole', 'sprat', 'squid', 'unk fish',
'unk flatfish', 'unk gadoid', 'unk organism',
'unk round fish', 'whiting'),
palette=[(220, 20, 60), (119, 11, 32), (0, 0, 142), (0, 0, 230),
(106, 0, 228), (0, 60, 100), (0, 80, 100), (0, 0, 70),
(0, 0, 192),
(250, 170, 30), (100, 170, 30), (220, 220, 0),
(175, 116, 175), (250, 0, 30), (165, 42, 42),
(255, 77, 255), (0, 226, 252)])))
val_evaluator = dict(
type='CocoMetric',
ann_file='data/FishDataset/annotations/MAIS2K_test.json',
metric=['bbox', 'segm'],
format_only=False,
proposal_nums=(100, 1, 10))
test_evaluator = dict(
type='CocoMetric',
ann_file='data/FishDataset/annotations/MAIS2K_test.json',
metric=['bbox', 'segm'],
format_only=False,
proposal_nums=(100, 1, 10))
model = dict(
type='RTMDet',
data_preprocessor=dict(
type='DetDataPreprocessor',
mean=[103.53, 116.28, 123.675],
std=[57.375, 57.12, 58.395],
bgr_to_rgb=False,
batch_augments=None),
backbone=dict(
type='CSPNeXt',
arch='P5',
expand_ratio=0.5,
deepen_factor=1.33,
widen_factor=1.25,
channel_attention=True,
norm_cfg=dict(type='SyncBN'),
act_cfg=dict(type='SiLU', inplace=True)),
neck=dict(
type='CSPNeXtPAFPN',
in_channels=[320, 640, 1280],
out_channels=320,
num_csp_blocks=4,
expand_ratio=0.5,
norm_cfg=dict(type='SyncBN'),
act_cfg=dict(type='SiLU', inplace=True)),
bbox_head=dict(
type='FewShotRTMDetInsSepBNHead',
num_classes=17,
in_channels=320,
stacked_convs=2,
share_conv=True,
pred_kernel_size=1,
feat_channels=320,
act_cfg=dict(type='SiLU', inplace=True),
norm_cfg=dict(type='SyncBN', requires_grad=True),
anchor_generator=dict(
type='MlvlPointGenerator', offset=0, strides=[8, 16, 32]),
bbox_coder=dict(type='DistancePointBBoxCoder'),
loss_cls=dict(
type='QualityFocalLoss',
use_sigmoid=True,
beta=2.0,
loss_weight=1.0),
loss_bbox=dict(type='GIoULoss', loss_weight=2.0),
loss_mask=dict(
type='FewShotDiceLoss', loss_weight=2.0, eps=5e-06, reduction='mean')),
train_cfg=dict(
assigner=dict(type='DynamicSoftLabelAssigner', topk=13),
allowed_border=-1,
pos_weight=-1,
debug=False),
test_cfg=dict(
nms_pre=1000,
min_bbox_size=0,
score_thr=0.05,
nms=dict(type='nms', iou_threshold=0.6),
max_per_img=100,
mask_thr_binary=0.5))
train_pipeline_stage2 = [
dict(type='LoadImageFromFile', file_client_args=dict(backend='disk')),
dict(
type='LoadAnnotations',
with_bbox=True,
with_mask=True,
poly2mask=False),
dict(
type='RandomResize',
scale=(640, 640),
ratio_range=(0.1, 2.0),
keep_ratio=True),
dict(
type='RandomCrop',
crop_size=(640, 640),
recompute_bbox=True,
allow_negative_crop=True),
dict(type='FilterAnnotations', min_gt_bbox_wh=(1, 1)),
dict(type='YOLOXHSVRandomAug'),
dict(type='RandomFlip', prob=0.5),
dict(type='Pad', size=(640, 640), pad_val=dict(img=(114, 114, 114))),
dict(type='PackDetInputs')
]
max_epochs = 300
stage2_num_epochs = 20
base_lr = 0.002
interval = 10
custom_hooks = [
dict(
type='EMAHook',
ema_type='ExpMomentumEMA',
momentum=0.0002,
update_buffers=True,
priority=49),
dict(
type='PipelineSwitchHook',
switch_epoch=280,
switch_pipeline=[
dict(
type='LoadImageFromFile',
file_client_args=dict(backend='disk')),
dict(
type='LoadAnnotations',
with_bbox=True,
with_mask=True,
poly2mask=False),
dict(
type='RandomResize',
scale=(640, 640),
ratio_range=(0.1, 2.0),
keep_ratio=True),
dict(
type='RandomCrop',
crop_size=(640, 640),
recompute_bbox=True,
allow_negative_crop=True),
dict(type='FilterAnnotations', min_gt_bbox_wh=(1, 1)),
dict(type='YOLOXHSVRandomAug'),
dict(type='RandomFlip', prob=0.5),
dict(
type='Pad', size=(640, 640),
pad_val=dict(img=(114, 114, 114))),
dict(type='PackDetInputs')
])
]
metainfo = dict(
CLASSES=('clupeid', 'cod', 'dab', 'dogfish', 'haddock', 'herring',
'plaice', 'prawn', 'sole', 'sprat', 'squid', 'unk fish',
'unk flatfish', 'unk gadoid', 'unk organism', 'unk round fish',
'whiting'),
PALETTE=[(220, 20, 60), (119, 11, 32), (0, 0, 142), (0, 0, 230),
(106, 0, 228), (0, 60, 100), (0, 80, 100), (0, 0, 70),
(0, 0, 192), (250, 170, 30), (100, 170, 30), (220, 220, 0),
(175, 116, 175), (250, 0, 30), (165, 42, 42), (255, 77, 255),
(0, 226, 252)])
work_dir = './outputs/feb_10'
seed = 0