Configuration for the
Opened this issue · 3 comments
xavibou commented
Hello,
I was able to train and test the H2RBox-v2 model using the provided config h2rbox_v2p_r50_fpn_1x_dota_le90.py. However, this accounts for the FCOS-based model with about 71.5 mAP in the DOTAv1 test set. I was wondering which config should be used in order to use Multi-scale (MS) and random rotation (RR) and achieve the higher results reported in the Table 2 of the article.
I am providing the exact config file i am using to achieve a performance of about 71.5 mAP:
dataset_type = 'DOTADataset'
data_root = 'data/DOTA/split_ss_dota1_0/'
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=64),
dict(type='DefaultFormatBundle'),
dict(type='Collect', keys=['img'])
])
]
data = dict(
samples_per_gpu=2,
workers_per_gpu=16,
train=dict(
type='DOTAWSOODDataset',
ann_file='data/DOTA/split_ss_dota1_0/trainval/annfiles/',
img_prefix='data/DOTA/split_ss_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='DOTAWSOODDataset',
ann_file='data/DOTA/split_ss_dota1_0/trainval/annfiles/',
img_prefix='data/DOTA/split_ss_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=64),
dict(type='DefaultFormatBundle'),
dict(type='Collect', keys=['img'])
])
],
version='le90'),
test=dict(
type='DOTAWSOODDataset',
ann_file='data/DOTA/split_ss_dota1_0/test/images/',
img_prefix='data/DOTA/split_ss_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=64),
dict(type='DefaultFormatBundle'),
dict(type='Collect', keys=['img'])
])
],
version='le90'))
evaluation = dict(interval=1, metric='mAP')
optimizer = dict(type='AdamW', lr=5e-05, betas=(0.9, 0.999), weight_decay=0.05)
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='H2RBoxV2PDetector',
backbone=dict(
type='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='FPN',
in_channels=[256, 512, 1024, 2048],
out_channels=256,
start_level=1,
add_extra_convs='on_output',
num_outs=5,
relu_before_extra_convs=True),
bbox_head=dict(
type='H2RBoxV2PHead',
num_classes=15,
in_channels=256,
stacked_convs=4,
feat_channels=256,
strides=[8, 16, 32, 64, 128],
center_sampling=True,
center_sample_radius=1.5,
norm_on_bbox=True,
centerness_on_reg=True,
square_cls=[1, 9, 11],
resize_cls=[1],
scale_angle=False,
bbox_coder=dict(type='DistanceAnglePointCoder', angle_version='le90'),
loss_cls=dict(
type='FocalLoss',
use_sigmoid=True,
gamma=2.0,
alpha=0.25,
loss_weight=1.0),
loss_bbox=dict(type='IoULoss', loss_weight=1.0),
loss_centerness=dict(
type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0),
loss_ss_symmetry=dict(type='SmoothL1Loss', loss_weight=0.2, beta=0.1)),
train_cfg=None,
test_cfg=dict(
nms_pre=2000,
min_bbox_size=0,
score_thr=0.05,
nms=dict(iou_thr=0.01),
max_per_img=2000))
work_dir = './work_dirs/h2rbox_v2p_r50_fpn_1x_dota_le90'
auto_resume = False
gpu_ids = range(0, 1)Tanks in advance!
yangxue0827 commented
ms means offline multi-scale image cropping, so you need to get a multiple scale dataset by running the following command before training.
python tools/data/dota/split/img_split.py --base-json \
tools/data/dota/split/split_configs/ms_trainval.json
python tools/data/dota/split/img_split.py --base-json \
tools/data/dota/split/split_configs/ms_test.json
yangxue0827 commented
As for RR, please refer to
yangxue0827 commented
Actually, the original code of h2rbox-v2 is based on mmrotate1.x, while this code is based on mmrotate0.3.4. There are big differences between the two versions, so I cannot guarantee that you can reproduce it perfectly. It is recommended to use the original implementation code.