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ReDet_re50_refpn_1x_dota1.py
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ReDet_re50_refpn_1x_dota1.py
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# model settings
model = dict(
type='ReDet',
backbone=dict(
type='ReResNet',
pretrained='work_dirs/ReResNet_pretrain/re_resnet50_c8_batch256-25b16846.pkl',
depth=50,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
style='jittor'),
neck=dict(
type='ReFPN',
in_channels=[256, 512, 1024, 2048],
out_channels=256,
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=True,
with_module=False,
loss_cls=dict(
type='CrossEntropyLossForRcnn', use_sigmoid=False, loss_weight=1.0),
loss_bbox=dict(type='SmoothL1Loss', beta=1.0, loss_weight=1.0)),
rbbox_roi_extractor=dict(
type='RboxSingleRoIExtractor',
roi_layer=dict(type='RiRoIAlign', output_size=7, sample_num=2),
out_channels=256,
featmap_strides=[4, 8, 16, 32]),
rbbox_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=[0.05, 0.05, 0.1, 0.1, 0.05],
reg_class_agnostic=False,
loss_cls=dict(
type='CrossEntropyLossForRcnn', 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,
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),
dict(
assigner=dict(
type='MaxIoUAssignerRbbox',
pos_iou_thr=0.5,
neg_iou_thr=0.5,
min_pos_iou=0.5,
ignore_iof_thr=-1,
iou_calculator=dict(type='BboxOverlaps2D_rotated')),
sampler=dict(
type='RandomSamplerRotated',
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(
# TODO: test nms 2000
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='py_cpu_nms_poly_fast', iou_thr=0.1), max_per_img = 2000)
),
)
# model training and testing settings
# dataset settings
dataset_type = 'DOTADataset'
dataset = dict(
train=dict(
type=dataset_type,
images_dir='/mnt/disk/flowey/dataset/DOTA_1024/trainval_split/images/',
annotations_file='/mnt/disk/flowey/dataset/DOTA_1024/trainval_split/trainval1024.pkl',
version='1',
filter_min_size=32,
transforms=[
dict(
type="RotatedResize",
min_size=1024,
max_size=1024
),
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=False),
],
batch_size=2,
shuffle=True,
),
val=dict(
type=dataset_type,
images_dir='/mnt/disk/flowey/dataset/DOTA_1024/trainval_split/images/',
annotations_file='/mnt/disk/flowey/dataset/DOTA_1024/trainval_split/trainval1024.pkl',
version='1',
filter_min_size=32,
transforms=[
dict(
type="RotatedResize",
min_size=1024,
max_size=1024
),
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=False),
],
),
test=dict(
type="ImageDataset",
images_dir='/mnt/disk/flowey/dataset/DOTA_1024/test_split/images/',
transforms=[
dict(
type="RotatedResize",
min_size=1024,
max_size=1024
),
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=False),
],
batch_size=1,
shuffle=False,
)
)
# 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=[8, 11])
logger = dict(
type="RunLogger")
max_epoch = 12
eval_interval = 1
checkpoint_interval = 1
log_interval = 20
# pretrained_weights = '/mnt/disk2/flowey/JDet/work_dirs/ReDet_re50_refpn_1x_dota1/epoch_12.pkl'
# map: 0.7623183203127312
# classaps: plane:0.8912137088763317, baseball-diamond:0.8227656853636833, bridge:0.5438046929395232,
# ground-track-field:0.741438878333126, small-vehicle:0.7792042787717315, large-vehicle:0.8293264252845306,
# ship:0.8816971776117367, tennis-court:0.9082554204483129, basketball-court:0.8708274706937482,
# storage-tank:0.8588086316992405, soccer-ball-field:0.5960834417486613, roundabout:0.6243493262883512,
# harbor:0.7679488008566184, swimming-pool:0.6758004726927681, helicopter:0.6432503930826027