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[Refactor] CFA & SASM #524

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Merged
RangiLyu merged 7 commits into from
Sep 30, 2022
Merged

[Refactor] CFA & SASM #524

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ef2e967
init
zytx121 Sep 27, 2022
26e8c1c
fix bug
zytx121 Sep 27, 2022
f37c1ca
add UT
zytx121 Sep 27, 2022
f184ee1
fix lint
zytx121 Sep 27, 2022
8866753
fix docstring
zytx121 Sep 27, 2022
affe568
update
zytx121 Sep 29, 2022
c93ab9b
fix
zytx121 Sep 30, 2022
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6 changes: 0 additions & 6 deletions configs/cfa/cfa_r50_fpn_1x_dota_le135.py
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6 changes: 0 additions & 6 deletions configs/cfa/cfa_r50_fpn_1x_dota_oc.py
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96 changes: 96 additions & 0 deletions configs/cfa/cfa_r50_fpn_1x_dota_qbox.py
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_base_ = [
'../_base_/datasets/dota.py', '../_base_/schedules/schedule_1x.py',
'../_base_/default_runtime.py'
]
norm_cfg = dict(type='GN', num_groups=32, requires_grad=True)

model = dict(
type='mmdet.RepPointsDetector',
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),
backbone=dict(
type='mmdet.ResNet',
depth=50,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
zero_init_residual=False,
norm_cfg=dict(type='BN', requires_grad=True),
norm_eval=True,
style='pytorch',
init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet50')),
neck=dict(
type='mmdet.FPN',
in_channels=[256, 512, 1024, 2048],
out_channels=256,
start_level=1,
add_extra_convs='on_input',
num_outs=5,
norm_cfg=norm_cfg),
bbox_head=dict(
type='CFAHead',
num_classes=15,
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='mmdet.FocalLoss',
use_sigmoid=True,
gamma=2.0,
alpha=0.25,
loss_weight=1.0),
loss_bbox_init=dict(type='ConvexGIoULoss', loss_weight=0.375),
loss_bbox_refine=dict(type='ConvexGIoULoss', loss_weight=1.0),
transform_method='rotrect',
topk=6,
anti_factor=0.75),
# training and testing settings
train_cfg=dict(
init=dict(
assigner=dict(type='ConvexAssigner', scale=4, pos_num=1),
allowed_border=-1,
pos_weight=-1,
debug=False),
refine=dict(
assigner=dict(
type='MaxConvexIoUAssigner',
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='nms_rotated', iou_threshold=0.4),
max_per_img=2000))

train_pipeline = [
dict(
type='mmdet.LoadImageFromFile',
file_client_args={{_base_.file_client_args}}),
dict(type='mmdet.LoadAnnotations', with_bbox=True, box_type='qbox'),
dict(type='mmdet.Resize', scale=(1024, 1024), keep_ratio=True),
dict(
type='mmdet.RandomFlip',
prob=0.75,
direction=['horizontal', 'vertical', 'diagonal']),
dict(type='mmdet.PackDetInputs')
]

train_dataloader = dict(dataset=dict(pipeline=train_pipeline))

optim_wrapper = dict(optimizer=dict(lr=0.008))
13 changes: 0 additions & 13 deletions configs/cfa/cfa_r50_fpn_40e_dota_oc.py
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94 changes: 94 additions & 0 deletions configs/cfa/cfa_r50_fpn_40e_dota_qbox.py
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_base_ = [
'../_base_/datasets/dota.py', '../_base_/schedules/schedule_40e.py',
'../_base_/default_runtime.py'
]
norm_cfg = dict(type='GN', num_groups=32, requires_grad=True)

model = dict(
type='mmdet.RepPointsDetector',
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),
backbone=dict(
type='mmdet.ResNet',
depth=50,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
zero_init_residual=False,
norm_cfg=dict(type='BN', requires_grad=True),
norm_eval=True,
style='pytorch',
init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet50')),
neck=dict(
type='mmdet.FPN',
in_channels=[256, 512, 1024, 2048],
out_channels=256,
start_level=1,
add_extra_convs='on_input',
num_outs=5,
norm_cfg=norm_cfg),
bbox_head=dict(
type='CFAHead',
num_classes=15,
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='mmdet.FocalLoss',
use_sigmoid=True,
gamma=2.0,
alpha=0.25,
loss_weight=1.0),
loss_bbox_init=dict(type='ConvexGIoULoss', loss_weight=0.375),
loss_bbox_refine=dict(type='ConvexGIoULoss', loss_weight=1.0),
transform_method='rotrect'),
# training and testing settings
train_cfg=dict(
init=dict(
assigner=dict(type='ConvexAssigner', scale=4, pos_num=1),
allowed_border=-1,
pos_weight=-1,
debug=False),
refine=dict(
assigner=dict(
type='MaxConvexIoUAssigner',
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='nms_rotated', iou_threshold=0.4),
max_per_img=2000))

train_pipeline = [
dict(
type='mmdet.LoadImageFromFile',
file_client_args={{_base_.file_client_args}}),
dict(type='mmdet.LoadAnnotations', with_bbox=True, box_type='qbox'),
dict(type='mmdet.Resize', scale=(1024, 1024), keep_ratio=True),
dict(
type='mmdet.RandomFlip',
prob=0.75,
direction=['horizontal', 'vertical', 'diagonal']),
dict(type='mmdet.PackDetInputs')
]

train_dataloader = dict(dataset=dict(pipeline=train_pipeline))

optim_wrapper = dict(optimizer=dict(lr=0.008))
15 changes: 0 additions & 15 deletions configs/sasm_reppoints/sasm_reppoints_r50_fpn_1x_dota_oc.py
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89 changes: 89 additions & 0 deletions configs/sasm_reppoints/sasm_reppoints_r50_fpn_1x_dota_qbox.py
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_base_ = [
'../_base_/datasets/dota.py', '../_base_/schedules/schedule_1x.py',
'../_base_/default_runtime.py'
]
norm_cfg = dict(type='GN', num_groups=32, requires_grad=True)

model = dict(
type='mmdet.RepPointsDetector',
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),
backbone=dict(
type='mmdet.ResNet',
depth=50,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
zero_init_residual=False,
norm_cfg=dict(type='BN', requires_grad=True),
norm_eval=True,
style='pytorch',
init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet50')),
neck=dict(
type='mmdet.FPN',
in_channels=[256, 512, 1024, 2048],
out_channels=256,
start_level=1,
add_extra_convs='on_input',
num_outs=5,
norm_cfg=norm_cfg),
bbox_head=dict(
type='SAMRepPointsHead',
num_classes=15,
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='mmdet.FocalLoss',
use_sigmoid=True,
gamma=2.0,
alpha=0.25,
loss_weight=1.0),
loss_bbox_init=dict(type='BCConvexGIoULoss', loss_weight=0.375),
loss_bbox_refine=dict(type='ConvexGIoULoss', loss_weight=1.0),
transform_method='rotrect'),
# training and testing settings
train_cfg=dict(
init=dict(
assigner=dict(type='ConvexAssigner', scale=4, pos_num=1),
allowed_border=-1,
pos_weight=-1,
debug=False),
refine=dict(
assigner=dict(type='SASAssigner', topk=9),
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='nms_rotated', iou_threshold=0.4),
max_per_img=2000))

train_pipeline = [
dict(
type='mmdet.LoadImageFromFile',
file_client_args={{_base_.file_client_args}}),
dict(type='mmdet.LoadAnnotations', with_bbox=True, box_type='qbox'),
dict(type='mmdet.Resize', scale=(1024, 1024), keep_ratio=True),
dict(
type='mmdet.RandomFlip',
prob=0.75,
direction=['horizontal', 'vertical', 'diagonal']),
dict(type='mmdet.PackDetInputs')
]

train_dataloader = dict(dataset=dict(pipeline=train_pipeline))

optim_wrapper = dict(optimizer=dict(lr=0.008))
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