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dataset_type = 'DOTADataset' |
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data_root = 'data/split_1024_dota1_0/' |
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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='le90'), |
|
dict( |
|
type='Normalize', |
|
mean=[123.675, 116.28, 103.53], |
|
std=[58.395, 57.12, 57.375], |
|
to_rgb=True), |
|
dict(type='Pad', size_divisor=32), |
|
dict(type='DefaultFormatBundle'), |
|
dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']) |
|
] |
|
test_pipeline = [ |
|
dict(type='LoadImageFromFile'), |
|
dict( |
|
type='MultiScaleFlipAug', |
|
img_scale=(1024, 1024), |
|
flip=False, |
|
transforms=[ |
|
dict(type='RResize'), |
|
dict( |
|
type='Normalize', |
|
mean=[123.675, 116.28, 103.53], |
|
std=[58.395, 57.12, 57.375], |
|
to_rgb=True), |
|
dict(type='Pad', size_divisor=32), |
|
dict(type='DefaultFormatBundle'), |
|
dict(type='Collect', keys=['img']) |
|
]) |
|
] |
|
data = dict( |
|
samples_per_gpu=2, |
|
workers_per_gpu=2, |
|
train=dict( |
|
type='DOTADataset', |
|
ann_file='data/split_1024_dota1_0/trainval/annfiles/', |
|
img_prefix='data/split_1024_dota1_0/trainval/images/', |
|
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='le90'), |
|
dict( |
|
type='Normalize', |
|
mean=[123.675, 116.28, 103.53], |
|
std=[58.395, 57.12, 57.375], |
|
to_rgb=True), |
|
dict(type='Pad', size_divisor=32), |
|
dict(type='DefaultFormatBundle'), |
|
dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']) |
|
], |
|
version='le90'), |
|
val=dict( |
|
type='DOTADataset', |
|
ann_file='data/split_1024_dota1_0/trainval/annfiles/', |
|
img_prefix='data/split_1024_dota1_0/trainval/images/', |
|
pipeline=[ |
|
dict(type='LoadImageFromFile'), |
|
dict( |
|
type='MultiScaleFlipAug', |
|
img_scale=(1024, 1024), |
|
flip=False, |
|
transforms=[ |
|
dict(type='RResize'), |
|
dict( |
|
type='Normalize', |
|
mean=[123.675, 116.28, 103.53], |
|
std=[58.395, 57.12, 57.375], |
|
to_rgb=True), |
|
dict(type='Pad', size_divisor=32), |
|
dict(type='DefaultFormatBundle'), |
|
dict(type='Collect', keys=['img']) |
|
]) |
|
], |
|
version='le90'), |
|
test=dict( |
|
type='DOTADataset', |
|
ann_file='data/split_1024_dota1_0/test/images/', |
|
img_prefix='data/split_1024_dota1_0/test/images/', |
|
pipeline=[ |
|
dict(type='LoadImageFromFile'), |
|
dict( |
|
type='MultiScaleFlipAug', |
|
img_scale=(1024, 1024), |
|
flip=False, |
|
transforms=[ |
|
dict(type='RResize'), |
|
dict( |
|
type='Normalize', |
|
mean=[123.675, 116.28, 103.53], |
|
std=[58.395, 57.12, 57.375], |
|
to_rgb=True), |
|
dict(type='Pad', size_divisor=32), |
|
dict(type='DefaultFormatBundle'), |
|
dict(type='Collect', keys=['img']) |
|
]) |
|
], |
|
version='le90')) |
|
evaluation = dict(interval=1, metric='mAP') |
|
optimizer = dict(type='SGD', lr=0.005, momentum=0.9, weight_decay=0.0001) |
|
optimizer_config = dict(grad_clip=dict(max_norm=35, norm_type=2)) |
|
lr_config = dict( |
|
policy='step', |
|
warmup='linear', |
|
warmup_iters=500, |
|
warmup_ratio=0.3333333333333333, |
|
step=[8, 11]) |
|
runner = dict(type='EpochBasedRunner', max_epochs=12) |
|
checkpoint_config = dict(interval=1) |
|
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)] |
|
opencv_num_threads = 0 |
|
mp_start_method = 'fork' |
|
angle_version = 'le90' |
|
model = dict( |
|
type='OrientedRCNN', |
|
backbone=dict( |
|
type='ResNet', |
|
depth=50, |
|
num_stages=4, |
|
out_indices=(0, 1, 2, 3), |
|
frozen_stages=1, |
|
norm_cfg=dict(type='BN', requires_grad=True), |
|
norm_eval=True, |
|
style='pytorch', |
|
init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet50')), |
|
neck=dict( |
|
type='FPN', |
|
in_channels=[256, 512, 1024, 2048], |
|
out_channels=256, |
|
num_outs=5), |
|
rpn_head=dict( |
|
type='OrientedRPNHead', |
|
in_channels=256, |
|
feat_channels=256, |
|
version='le90', |
|
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='le90', |
|
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=15, |
|
bbox_coder=dict( |
|
type='DeltaXYWHAOBBoxCoder', |
|
angle_range='le90', |
|
norm_factor=None, |
|
edge_swap=True, |
|
proj_xy=True, |
|
target_means=(0.0, 0.0, 0.0, 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, |
|
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'), |
|
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))) |
|
|