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maet_yolo_exdark.py
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maet_yolo_exdark.py
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_base_ = '../_base_/default_runtime.py'
load_from = '/home/czt/mmdetection_v1/work_dirs/maet_yolo_coco_ort/latest.pth'
# model settings
model = dict(
type='YOLOV3',
pretrained='open-mmlab://darknet53',
backbone=dict(type='Darknet', depth=53, out_indices=(3, 4, 5)),
neck=dict(
type='YOLOV3Neck',
num_scales=3,
in_channels=[1024, 512, 256],
out_channels=[512, 256, 128]),
bbox_head=dict(
type='YOLOV3Head',
num_classes=12,
in_channels=[512, 256, 128],
out_channels=[1024, 512, 256],
anchor_generator=dict(
type='YOLOAnchorGenerator',
base_sizes=[[(116, 90), (156, 198), (373, 326)],
[(30, 61), (62, 45), (59, 119)],
[(10, 13), (16, 30), (33, 23)]],
strides=[32, 16, 8]),
bbox_coder=dict(type='YOLOBBoxCoder'),
featmap_strides=[32, 16, 8],
loss_cls=dict(
type='CrossEntropyLoss',
use_sigmoid=True,
loss_weight=1.0,
reduction='sum'),
loss_conf=dict(
type='CrossEntropyLoss',
use_sigmoid=True,
loss_weight=1.0,
reduction='sum'),
loss_xy=dict(
type='CrossEntropyLoss',
use_sigmoid=True,
loss_weight=2.0,
reduction='sum'),
loss_wh=dict(type='MSELoss', loss_weight=2.0, reduction='sum')))
# training and testing settings
train_cfg = dict(
assigner=dict(
type='GridAssigner', pos_iou_thr=0.5, neg_iou_thr=0.5, min_pos_iou=0))
test_cfg = dict(
nms_pre=1000,
min_bbox_size=0,
score_thr=0.05,
conf_thr=0.005,
nms=dict(type='nms', iou_thr=0.45),
max_per_img=100)
# dataset settings
dataset_type = 'ExdarkDataset'
data_root = 'data/Exdark/'
img_norm_cfg = dict(mean=[0, 0, 0], std=[255., 255., 255.], to_rgb=True)
train_pipeline = [
dict(type='LoadImageFromFile', to_float32=True),
dict(type='LoadAnnotations', with_bbox=True),
dict(
type='Expand',
mean=img_norm_cfg['mean'],
to_rgb=img_norm_cfg['to_rgb'],
ratio_range=(1, 2)),
dict(
type='MinIoURandomCrop',
min_ious=(0.4, 0.5, 0.6, 0.7, 0.8, 0.9),
min_crop_size=0.3),
dict(type='Resize', img_scale=[(320, 320), (608, 608)], keep_ratio=True),
dict(type='RandomFlip', flip_ratio=0.5),
dict(type='Normalize', **img_norm_cfg), #this step change the images from bgr2rgb
dict(type='Pad', size_divisor=32),
dict(type='DefaultFormatBundle'),
#return img gt_bboxer gt_labels
dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels'])
]
test_pipeline = [
dict(type='LoadImageFromFile'),
dict(
type='MultiScaleFlipAug',
img_scale=(608, 608),
flip=False,
transforms=[
dict(type='Resize', keep_ratio=True),
dict(type='RandomFlip'),
dict(type='Normalize', **img_norm_cfg),
dict(type='Pad', size_divisor=32),
dict(type='ImageToTensor', keys=['img']),
dict(type='Collect', keys=['img'])
])
]
data = dict(
samples_per_gpu=8,
workers_per_gpu=4,
train=dict(
type=dataset_type,
ann_file=data_root + 'main/train.txt',
img_prefix=data_root,
pipeline=train_pipeline),
val=dict(
type=dataset_type,
ann_file = data_root + 'main/val.txt',
img_prefix=data_root,
pipeline=test_pipeline),
test=dict(
type=dataset_type,
ann_file = data_root + 'main/val.txt',
img_prefix=data_root,
pipeline=test_pipeline))
# optimizer
optimizer = dict(type='SGD', lr=0.001, momentum=0.9, weight_decay=0.0005)
optimizer_config = dict(grad_clip=dict(max_norm=35, norm_type=2))
# learning policy
lr_config = dict(
policy='step',
warmup='linear',
warmup_iters=2000, # same as burn-in in darknet
warmup_ratio=0.1,
step=[18, 23])
# runtime settings
total_epochs = 25
evaluation = dict(interval=1, metric=['mAP'])