/
orientedrepoints_swin_tiny_demo.py
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/
orientedrepoints_swin_tiny_demo.py
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# model settings
norm_cfg = dict(type='GN', num_groups=32, requires_grad=True)
model = dict(
type='OrientedRepPointsDetector',
# pretrained = 'https://github.com/SwinTransformer/storage/releases/download/v1.0.0/swin_tiny_patch4_window7_224.pth',
pretrained='/work_dirs/pretrained_models/swin_tiny_patch4_window7_224_torch1_4.pth',
backbone=dict(
type='SwinTransformer',
embed_dim=96,
depths=[2, 2, 6, 2],
num_heads=[3, 6, 12, 24],
window_size=7,
mlp_ratio=4.,
qkv_bias=True,
qk_scale=None,
drop_rate=0.,
attn_drop_rate=0.,
drop_path_rate=0.2,
ape=False,
patch_norm=True,
out_indices=(1, 2, 3),
use_checkpoint=False),
neck=dict(
type='FPN',
in_channels=[192, 384, 768],
out_channels=256,
num_outs=5,
norm_cfg=norm_cfg),
bbox_head=dict(
type='OrientedRepPointsHead',
num_classes=16,
in_channels=256,
feat_channels=256,
point_feat_channels=256,
stacked_convs=3,
num_points=9,
gradient_mul=0.3,
point_strides=[8, 16, 32, 64, 128],
point_base_scale=2,
norm_cfg=norm_cfg,
loss_cls=dict(type='FocalLoss', use_sigmoid=True, gamma=2.0, alpha=0.25, loss_weight=1.0),
loss_rbox_init=dict(type='GIoULoss', loss_weight=0.375),
loss_rbox_refine=dict(type='GIoULoss', loss_weight=1.0),
loss_spatial_init=dict(type='SpatialBorderLoss', loss_weight=0.05),
loss_spatial_refine=dict(type='SpatialBorderLoss', loss_weight=0.1),
top_ratio=0.4,))
# training and testing settings
train_cfg = dict(
init=dict(
assigner=dict(type='PointAssigner', scale=4, pos_num=1),
allowed_border=-1,
pos_weight=-1,
debug=False),
refine=dict(
assigner=dict(
type='MaxIoUAssigner', #pre-assign
pos_iou_thr=0.1,
neg_iou_thr=0.1,
min_pos_iou=0,
ignore_iof_thr=-1),
allowed_border=-1,
pos_weight=-1,
debug=False))
test_cfg = dict(
nms_pre=2000,
min_bbox_size=0,
score_thr=0.05,
nms=dict(type='rnms', iou_thr=0.4),
max_per_img=2000)
# dataset settings
dataset_type = 'DotaDataset'
data_root = 'data/dota_1024/'
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', with_bbox=True),
dict(type='CorrectBox', correct_rbbox=True, refine_rbbox=True),
dict(type='PolyResize',
img_scale=[(1333, 768), (1333, 1280)],
keep_ratio=True,
multiscale_mode='range',
clamp_rbbox=False),
dict(type='PolyRandomFlip', flip_ratio=0.5),
dict(type='HSVAugment', hgain=0.015, sgain=0.7, vgain=0.4),
dict(type='PolyRandomRotate', rotate_ratio=0.5, angles_range=180, auto_bound=False),
dict(type='Pad', size_divisor=32),
dict(type='Normalize', **img_norm_cfg),
dict(type='DefaultFormatBundle'),
dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels'])]
test_pipeline = [
dict(type='LoadImageFromFile'),
dict(
type='MultiScaleFlipAug',
img_scale=(1333, 960),
flip=False,
transforms=[
dict(type='RotateResize', keep_ratio=True),
dict(type='RotateRandomFlip'),
dict(type='Normalize', **img_norm_cfg),
dict(type='Pad', size_divisor=32),
dict(type='ImageToTensor', keys=['img']),
dict(type='Collect', keys=['img']),
])
]
data = dict(
imgs_per_gpu=2,
workers_per_gpu=2,
train=dict(
type=dataset_type,
ann_file=data_root + 'trainval_split/trainval_dota.json',
img_prefix=data_root + 'trainval_split/images/',
pipeline=train_pipeline),
val=dict(
type=dataset_type,
ann_file=data_root + 'test_split/test_dota.json',
img_prefix=data_root + 'test_split/images/',
pipeline=test_pipeline),
test=dict(
type=dataset_type,
ann_file=data_root + 'test_split/test_dota.json',
img_prefix=data_root + 'test_split/images/',
pipeline=test_pipeline))
evaluation = dict(interval=1, metric='bbox')
# optimizer
optimizer = dict(type='AdamW', lr=0.0001, betas=(0.9, 0.999), weight_decay=0.05,
paramwise_cfg=dict(custom_keys={'absolute_pos_embed': dict(decay_mult=0.),
'relative_position_bias_table': dict(decay_mult=0.),
'norm': dict(decay_mult=0.)}))
# learning policy
lr_config = dict(
policy='step',
warmup='linear',
warmup_iters=500,
warmup_ratio=0.001,
step=[24, 32, 38])
# runtime settings
runner = dict(type='EpochBasedRunnerAmp', max_epochs=40)
total_epochs = 40
checkpoint_config = dict(interval=1)
# yapf:disable
log_config = dict(
interval=50,
hooks=[
dict(type='TextLoggerHook'),
])
dist_params = dict(backend='nccl')
log_level = 'INFO'
load_from = None
resume_from = None
workflow = [('train', 1)]
fp16 = None
optimizer_config = dict(grad_clip=dict(max_norm=35, norm_type=2))
work_dir = 'work_dirs/orientedreppoints_swin-tiny_demo/'