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pkinet-s_fpn_o-rcnn-dotav15-ss_le90.py
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pkinet-s_fpn_o-rcnn-dotav15-ss_le90.py
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_base_ = [
'../_base_/default_runtime.py', '../_base_/schedules/schedule_30e.py',
'../_base_/datasets/dotav15_ss1024.py'
]
project_dir = '{{fileBasenameNoExtension}}' # TODO change
work_dir = f'./work_dirs/{project_dir}'
num_classes = 16
bs = 2 # batch_size
num_workers = bs * 4
base_lr = 0.0002
save_interval = 1
val_interval = 3
max_keep_ckpts = 3 # TODO change
# base_batch_size = (4 GPUs) x (2 samples per GPU)
auto_scale_lr = dict(base_batch_size=8, enable=False)
checkpoint = '' # pretrain # noqa
# load_from = '' # resume # noqa
# model settings
angle_version = 'le90'
model = dict(
type='OrientedRCNN',
backbone=dict(
type='PKINet',
arch='S',
drop_path_rate=0.1,
out_indices=(1, 2, 3, 4),
norm_cfg=dict(type='SyncBN', requires_grad=True),
act_cfg=dict(type='SiLU'),
init_cfg=dict(type='Pretrained', prefix='backbone.', checkpoint=checkpoint),
),
neck=dict(
type='FPN',
in_channels=[64, 128, 256, 512],
out_channels=256,
num_outs=5),
rpn_head=dict(
type='OrientedRPNHead',
in_channels=256,
feat_channels=256,
version=angle_version,
anchor_generator=dict(
type='AnchorGenerator',
scales=[8],
ratios=[0.5, 1.0, 2.0],
strides=[4, 8, 16, 32, 64]),
bbox_coder=dict(
type='MidpointOffsetCoder',
angle_range=angle_version,
target_means=[0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
target_stds=[1.0, 1.0, 1.0, 1.0, 0.5, 0.5]),
loss_cls=dict(type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0),
loss_bbox=dict(type='SmoothL1Loss', beta=0.1111111111111111, loss_weight=1.0)),
roi_head=dict(
type='OrientedStandardRoIHead',
bbox_roi_extractor=dict(
type='RotatedSingleRoIExtractor',
roi_layer=dict(
type='RoIAlignRotated',
out_size=7,
sample_num=2,
clockwise=True),
out_channels=256,
featmap_strides=[4, 8, 16, 32]),
bbox_head=dict(
type='RotatedShared2FCBBoxHead',
in_channels=256,
fc_out_channels=1024,
roi_feat_size=7,
num_classes=num_classes,
bbox_coder=dict(
type='DeltaXYWHAOBBoxCoder',
angle_range=angle_version,
norm_factor=None,
edge_swap=True,
proj_xy=True,
target_means=(.0, .0, .0, .0, .0),
target_stds=(0.1, 0.1, 0.2, 0.2, 0.1)),
reg_class_agnostic=True,
loss_cls=dict(type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0),
loss_bbox=dict(type='SmoothL1Loss', beta=1.0, loss_weight=1.0))),
train_cfg=dict(
rpn=dict(
assigner=dict(
type='MaxIoUAssigner',
pos_iou_thr=0.7,
neg_iou_thr=0.3,
min_pos_iou=0.3,
match_low_quality=True,
gpu_assign_thr=800,
ignore_iof_thr=-1),
sampler=dict(
type='RandomSampler',
num=256,
pos_fraction=0.5,
neg_pos_ub=-1,
add_gt_as_proposals=False),
allowed_border=0,
pos_weight=-1,
debug=False),
rpn_proposal=dict(
nms_pre=2000,
max_per_img=2000,
nms=dict(type='nms', iou_threshold=0.8),
min_bbox_size=0),
rcnn=dict(
assigner=dict(
type='MaxIoUAssigner',
pos_iou_thr=0.5,
neg_iou_thr=0.5,
min_pos_iou=0.5,
match_low_quality=False,
iou_calculator=dict(type='RBboxOverlaps2D'),
gpu_assign_thr=800,
ignore_iof_thr=-1),
sampler=dict(
type='RRandomSampler',
num=512,
pos_fraction=0.25,
neg_pos_ub=-1,
add_gt_as_proposals=True),
pos_weight=-1,
debug=False)),
test_cfg=dict(
rpn=dict(
nms_pre=2000,
max_per_img=2000,
nms=dict(type='nms', iou_threshold=0.8),
min_bbox_size=0),
rcnn=dict(
nms_pre=2000,
min_bbox_size=0,
score_thr=0.05,
nms=dict(iou_thr=0.1),
max_per_img=2000)))
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='RResize', img_scale=(1024, 1024)),
dict(
type='RRandomFlip',
flip_ratio=[0.25, 0.25, 0.25],
direction=['horizontal', 'vertical', 'diagonal'],
version=angle_version),
dict(type='Normalize', **img_norm_cfg),
dict(type='Pad', size_divisor=32),
dict(type='DefaultFormatBundle'),
dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels'])
]
# batch_size = (4 GPUs) x (2 samples per GPU) = 8
data = dict(
samples_per_gpu=bs,
workers_per_gpu=num_workers,
train=dict(pipeline=train_pipeline, version=angle_version),
val=dict(version=angle_version),
test=dict(version=angle_version))
optimizer = dict(
_delete_=True,
type='AdamW',
lr=base_lr,
betas=(0.9, 0.999),
weight_decay=0.05,
paramwise_cfg=dict(norm_decay_mult=0, bias_decay_mult=0, bypass_duplicate=True),
)
# evaluation
evaluation = dict(interval=val_interval, metric='mAP', save_best='mAP')
runner = dict(type='EpochBasedRunner', max_epochs=30)
checkpoint_config = dict(interval=save_interval, max_keep_ckpts=max_keep_ckpts)