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gliding_vertex_r50_fpn_3x_hrsc.py
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_base_ = [
'../_base_/schedules/schedule_3x.py',
'../../_base_/default_runtime.py'
]
# default runtime
checkpoint_config = dict(interval=3)
model = dict(
type='GlidingVertex',
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,
num_outs=5),
rpn_head=dict(
type='RPNHead',
in_channels=256,
feat_channels=256,
anchor_generator=dict(
type='AnchorGenerator',
scales=[8],
ratios=[0.5, 1.0, 2.0],
strides=[4, 8, 16, 32, 64]),
bbox_coder=dict(
type='DeltaXYWHBBoxCoder',
target_means=[.0, .0, .0, .0],
target_stds=[1.0, 1.0, 1.0, 1.0]),
loss_cls=dict(
type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0),
loss_bbox=dict(type='SmoothL1Loss', beta=1.0 / 9.0, loss_weight=1.0)),
roi_head=dict(
type='GVRatioRoIHead',
bbox_roi_extractor=dict(
type='SingleRoIExtractor',
roi_layer=dict(type='RoIAlign', out_size=7, sample_num=2),
out_channels=256,
featmap_strides=[4, 8, 16, 32]),
bbox_head=dict(
type='GVBBoxHead',
num_shared_fcs=2,
roi_feat_size=7,
in_channels=256,
fc_out_channels=1024,
num_classes=1,
ratio_thr=0.8,
bbox_coder=dict(
type='DeltaXYWHBBoxCoder',
target_means=[0., 0., 0., 0.],
target_stds=[0.1, 0.1, 0.2, 0.2]),
fix_coder=dict(type='GVFixCoder'),
ratio_coder=dict(type='GVRatioCoder'),
loss_cls=dict(
type='CrossEntropyLoss',
use_sigmoid=False,
loss_weight=1.0),
loss_bbox=dict(
type='SmoothL1Loss', beta=1., loss_weight=1.0),
loss_fix=dict(
type='SmoothL1Loss', beta=1./3., loss_weight=1.0),
loss_ratio=dict(
type='SmoothL1Loss', beta=1./3., loss_weight=16.0))))
# model training and testing settings
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=200,
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_across_levels=False,
nms_pre=2000,
nms_post=2000,
max_num=2000,
nms_thr=0.7,
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,
ignore_iof_thr=-1,
iou_calculator=dict(type='BboxOverlaps2D')),
sampler=dict(
type='RandomSampler',
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_across_levels=False,
nms_pre=2000,
nms_post=2000,
max_num=2000,
nms_thr=0.7,
min_bbox_size=0),
rcnn=dict(
score_thr=0.05, nms=dict(type='poly_nms', iou_thr=0.1), max_per_img=2000))
# Dataset
dataset_type = 'HRSCDataset'
data_root = 'data/hrsc/'
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='LoadOBBAnnotations', with_bbox=True,
with_label=True, obb_as_mask=True),
dict(type='Resize', img_scale=(1333, 800), keep_ratio=True),
dict(type='OBBRandomFlip', h_flip_ratio=0.5, v_flip_ratio=0.5),
dict(type='Normalize', **img_norm_cfg),
dict(type='RandomOBBRotate', rotate_after_flip=True,
angles=(0, 0), vert_rate=0.5),
dict(type='Pad', size_divisor=32),
dict(type='Mask2OBB', obb_type='poly'),
dict(type='OBBDefaultFormatBundle'),
dict(type='OBBCollect', keys=['img', 'gt_bboxes', 'gt_obboxes', 'gt_labels'])
]
test_pipeline = [
dict(type='LoadImageFromFile'),
dict(
type='MultiScaleFlipRotateAug',
img_scale=[(1333, 800)],
h_flip=False,
v_flip=False,
rotate=False,
transforms=[
dict(type='Resize', keep_ratio=True),
dict(type='OBBRandomFlip'),
dict(type='Normalize', **img_norm_cfg),
dict(type='RandomOBBRotate', rotate_after_flip=True),
dict(type='Pad', size_divisor=32),
dict(type='ImageToTensor', keys=['img']),
dict(type='OBBCollect', keys=['img']),
])
]
# does evaluation while training
# uncomments it when you need evaluate every epoch
# data = dict(
# samples_per_gpu=2,
# workers_per_gpu=4,
# train=dict(
# type=dataset_type,
# imgset=data_root + 'ImageSets/train.txt',
# ann_file=data_root + 'FullDataSet/Annotations/',
# img_prefix=data_root + 'FullDataSet/AllImages/',
# pipeline=train_pipeline),
# val=dict(
# type=dataset_type,
# imgset=data_root + 'ImageSets/val.txt',
# ann_file=data_root + 'FullDataSet/Annotations/',
# img_prefix=data_root + 'FullDataSet/AllImages/',
# pipeline=test_pipeline),
# test=dict(
# type=dataset_type,
# imgset=data_root + 'ImageSets/val.txt',
# ann_file=data_root + 'FullDataSet/Annotations/',
# img_prefix=data_root + 'FullDataSet/AllImages/',
# pipeline=test_pipeline))
# evaluation = dict(metric='mAP')
# disable evluation, only need train and test
# uncomments it when use trainval as train
data = dict(
samples_per_gpu=2,
workers_per_gpu=4,
train=dict(
type=dataset_type,
imgset=data_root + 'ImageSets/trainval.txt',
ann_file=data_root + 'FullDataSet/Annotations/',
img_prefix=data_root + 'FullDataSet/AllImages/',
pipeline=train_pipeline),
test=dict(
type=dataset_type,
imgset=data_root + 'ImageSets/test.txt',
ann_file=data_root + 'FullDataSet/Annotations/',
img_prefix=data_root + 'FullDataSet/AllImages/',
pipeline=test_pipeline))
evaluation = None