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pointobb_r50_fpn_2x_dior.py
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_base_ = [
'../../configs/_base_/default_runtime.py'
]
norm_cfg = dict(type='GN', num_groups=32, requires_grad=True) # add
debug = False
num_stages = 2
model = dict(
type='PointOBB',
pretrained='torchvision://resnet50',
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'),
neck=dict(
type='FPN',
in_channels=[256, 512, 1024, 2048],
out_channels=256,
start_level=0,
add_extra_convs='on_input',
num_outs=4,
norm_cfg=norm_cfg
),
loss_diff_view=dict(type='SmoothL1Loss', beta=1.0, loss_weight=1.0), # SSC loss
crop_size = (800, 800),
construct_view = True, # rot/flp view
construct_resize = True, # resized view
roi_head=dict(
type='PointOBBHead',
num_stages=num_stages,
top_k=7,
with_atten=False,
loss_symmetry_ss=dict(
type='SmoothL1Loss', loss_weight=0.5, beta=0.1),
angle_coder=dict(
type='PSCCoder',
angle_version='le90',
dual_freq=False,
num_step=3,
thr_mod=0),
angle_version = 'le90',
rotation_agnostic_classes=[5, 9, 15, 19],
agnostic_resize_classes = [13, 18],
use_angle_loss = False,
add_angle_pred_begin = False,
not_use_rot_mil = False,
detach_angle_head = False,
stacked_convs = 2,
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='Shared2FCInstanceMILHead',
num_stages=num_stages,
with_loss_pseudo=False,
in_channels=256,
fc_out_channels=1024,
roi_feat_size=7,
num_classes=20,
num_ref_fcs=0,
bbox_coder=dict(
type='DeltaXYWHBBoxCoder',
target_means=[0., 0., 0., 0.],
target_stds=[0.1, 0.1, 0.2, 0.2]),
reg_class_agnostic=True,
loss_type='MIL',
loss_mil1=dict(
type='MILLoss',
binary_ins=False,
loss_weight=0.25,
loss_type='binary_cross_entropy'),
loss_mil2=dict(
type='MILLoss',
binary_ins=False,
loss_weight=0.25,
loss_type='gfocal_loss'),),
),
# model training and testing settings
train_cfg=dict(
base_proposal=dict(
base_scales=[4, 8, 16, 24, 32, 48, 64, 72, 80, 96],
base_ratios=[1 / 3, 1 / 2, 1 / 1.5, 1.0, 1.5, 2.0, 3.0],
shake_ratio=None,
cut_mode='symmetry',
gen_num_neg=0),
fine_proposal=dict(
gen_proposal_mode='fix_gen',
cut_mode=None,
shake_ratio=[0.1],
base_ratios=[1, 1.2, 1.3, 0.8, 0.7],
iou_thr=0.3,
gen_num_neg=500,
),
rcnn=None,
iter_count = 0,
burn_in_steps1 = 16000,
burn_in_steps2 = 22000
),
test_cfg=dict(
rpn=None,
rcnn=None,
))
# dataset settings
dataset_type = 'CocoFmtObbDataset'
angle_version = 'le90'
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='Resize', img_scale=(800, 800), keep_ratio=True),
dict(type='RandomFlip', flip_ratio=0.5,version=angle_version) if not debug else dict(type='RandomFlip', flip_ratio=0.),
# dict(
# type='RandomFlip',
# 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', 'gt_bboxes_ignore', 'gt_true_bboxes']),
]
test_pipeline = [
dict(type='LoadImageFromFile'),
dict(type='LoadAnnotations', with_bbox=True),
dict(
type='MultiScaleFlipAug',
img_scale=(800, 800),
flip=False,
transforms=[
dict(type='Resize', img_scale=(800, 800) , keep_ratio=True),
dict(type='Normalize', **img_norm_cfg),
dict(type='Pad', size_divisor=32),
dict(type='DefaultFormatBundle'),
dict(type='Collect',
keys=['img', 'gt_bboxes', 'gt_labels', 'gt_bboxes_ignore', 'gt_anns_id', 'gt_true_bboxes']),
])
]
data_root_trainval = '../Dataset/DIOR/'
data_root_test = '../Dataset/DIOR/'
data = dict(
samples_per_gpu=2,
workers_per_gpu=2,
shuffle=False if debug else None,
train=dict(
type=dataset_type,
version=angle_version,
ann_file = data_root_trainval + "Annotations/trainval_rbox_pt_P2Bfmt.json",
img_prefix = data_root_trainval + 'JPEGImages-trainval/',
pipeline=train_pipeline,
filter_empty_gt=True
),
val=dict(
samples_per_gpu=2,
type=dataset_type,
ann_file = data_root_trainval + "Annotations/trainval_rbox_pt_P2Bfmt.json",
img_prefix = data_root_trainval + 'JPEGImages-trainval/',
pipeline=test_pipeline,
test_mode=False,
),
test=dict(
type=dataset_type,
img_prefix=data_root_test + 'JPEGImages-testfordebug/',
ann_file=data_root_test + "Annotations/testfordebug_rbox_pt_P2Bfmt.json",
pipeline=test_pipeline))
check = dict(stop_while_nan=False)
# optimizer
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))
# learning policy
training_time = 2
lr_config = dict(
policy='step',
warmup='linear',
warmup_iters=500,
warmup_ratio=0.001,
step=[8*training_time, 11*training_time])
runner = dict(type='EpochBasedRunner', max_epochs=12*training_time)
work_dir = 'xxx/work_dir/dior/'
evaluation = dict(
interval=1, metric='bbox',
save_result_file=work_dir + 'pseudo_obb_result.json',
do_first_eval=False, # test
do_final_eval=True,
)
# Inference
# load_from = 'xxx/work_dir/epoch_12.pth'
# evaluation = dict(
# save_result_file='xxx/work_dir/test/test_debug_result.json',
# do_first_eval=True
# )
# runner = dict(type='EpochBasedRunner',max_epochs=0)