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faster_rcnn_obb_r50_fpn_1x_dota.py
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faster_rcnn_obb_r50_fpn_1x_dota.py
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# model settings
model = dict(
type='FasterRCNNOBB',
pretrained='modelzoo://resnet50',
backbone=dict(
type='Resnet50',
frozen_stages=1,
return_stages=["layer1","layer2","layer3","layer4"],
pretrained=True),
neck=dict(
type='FPN',
in_channels=[256, 512, 1024, 2048],
out_channels=256,
start_level=0,
add_extra_convs=False,
num_outs=5),
rpn_head=dict(
type='FasterrcnnHead',
in_channels=256,
feat_channels=256,
anchor_scales=[8],
anchor_ratios=[0.5, 1.0, 2.0],
anchor_strides=[4, 8, 16, 32, 64],
target_means=[.0, .0, .0, .0],
target_stds=[1.0, 1.0, 1.0, 1.0],
loss_cls=dict(
type='CrossEntropyLossForRcnn', use_sigmoid=True, loss_weight=1.0),
loss_bbox=dict(type='SmoothL1Loss', beta=1.0 / 9.0, loss_weight=1.0)),
bbox_roi_extractor=dict(
type='SingleRoIExtractor',
roi_layer=dict(type='ROIAlign', output_size=7, sampling_ratio=2, version=1),
out_channels=256,
featmap_strides=[4, 8, 16, 32]),
bbox_head=dict(
type='SharedFCBBoxHeadRbbox',
num_fcs=2,
in_channels=256,
fc_out_channels=1024,
roi_feat_size=7,
num_classes=16,
target_means=[0., 0., 0., 0., 0.],
target_stds=[0.1, 0.1, 0.2, 0.2, 0.1],
reg_class_agnostic=False,
with_module=False,
hbb_trans='hbbpolyobb',
loss_cls=dict(
type='CrossEntropyLossForRcnn', use_sigmoid=False, loss_weight=1.0),
loss_bbox=dict(type='SmoothL1Loss', beta=1.0, loss_weight=1.0)),
# 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,
ignore_iof_thr=-1,
iou_calculator=dict(type='BboxOverlaps2D_v1')),
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,
ignore_iof_thr=-1,
iou_calculator=dict(type='BboxOverlaps2D_v1')),
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='nms', iou_thr=0.5), max_per_img=1000)
score_thr = 0.05, nms = dict(type='py_cpu_nms_poly_fast', iou_thr=0.1), max_per_img = 2000)
# soft-nms is also supported for rcnn testing
# e.g., nms=dict(type='soft_nms', iou_thr=0.5, min_score=0.05)
)
)
# dataset settings
dataset_type = 'DOTADataset'
dataset = dict(
imgs_per_gpu=2,
workers_per_gpu=2,
train=dict(
type=dataset_type,
dataset_dir='/home/cxjyxx_me/workspace/JAD/datasets/processed_DOTA/trainval_1024_200_1.0',
version='1',
filter_min_size=32,
transforms=[
dict(
type = "RotatedRandomFlip",
prob = 0.5,
direction="horizontal",
),
dict(
type = "Pad",
size_divisor=32),
dict(
type = "Normalize",
mean = [123.675, 116.28, 103.53],
std = [58.395, 57.12, 57.375],
to_bgr=True),
],
batch_size=2,
shuffle=True,
),
val=dict(
type=dataset_type,
dataset_dir='/home/cxjyxx_me/workspace/JAD/datasets/processed_DOTA/trainval_1024_200_1.0',
version='1',
filter_min_size=32,
transforms=[
dict(
type = "Pad",
size_divisor=32),
dict(
type = "Normalize",
mean = [123.675, 116.28, 103.53],
std = [58.395, 57.12, 57.375],
to_bgr=True),
],
),
test=dict(
type="ImageDataset",
images_dir='/home/cxjyxx_me/workspace/JAD/datasets/processed_DOTA/test_1024_200_1.0/images',
transforms=[
dict(
type = "Pad",
size_divisor=32),
dict(
type = "Normalize",
mean = [123.675, 116.28, 103.53],
std = [58.395, 57.12, 57.375],
to_bgr=True),
],
)
)
# optimizer
optimizer = dict(
type='SGD',
lr=0.01,
momentum=0.9,
weight_decay=0.0001,
grad_clip=dict(
max_norm=35,
norm_type=2))
# learning policy
scheduler = dict(
type='StepLR',
warmup='linear',
warmup_iters=500,
warmup_ratio=1.0 / 3,
milestones=[7, 10])
# yapf:disable
logger = dict(
type="RunLogger")
# yapf:enable
# runtime settings
max_epoch = 12
eval_interval = 6
checkpoint_interval = 1
log_interval = 20