@@ -1,5 +1,5 @@
_base_ = [
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
'../_base_/schedules/schedule_1x.py', '../_base_/datasets/coco_detection.py', '../_base_/d efault_runtime.py'
]
norm_cfg = dict(type='BN', requires_grad=True)
model = dict(
@@ -61,71 +61,71 @@ model = dict(
nms=dict(type='nms', iou_threshold=0.6),
max_per_img=100))
dataset_type = 'CocoDataset'
classes = ('yiwei','loujian','celi','libei','fantie','lianxi','duojian','shunjian','shaoxi','jiahan','yiwu')
# dataset_type = 'CocoDataset'
# classes = ('yiwei','loujian','celi','libei','fantie','lianxi','duojian','shunjian','shaoxi','jiahan','yiwu')
img_norm_cfg = dict(
mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
# 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='Resize',
img_scale=[(512, 512), (640, 640)],
multiscale_mode='value',
keep_ratio=True),
dict(type='RandomFlip', flip_ratio=[0.2,0.2,0.2], direction=['horizontal', 'vertical', 'diagonal']),
dict(type='BrightnessTransform', level=5, prob=0.5),
dict(type='ContrastTransform', level=5, prob=0.5),
dict(type='RandomShift', shift_ratio=0.5),
dict(type='MinIoURandomCrop', min_ious=(0.5, 0.7, 0.9), min_crop_size=0.8),
dict(type='Normalize', **img_norm_cfg),
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=[(640, 640)],
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']),
])
]
# train_pipeline = [
# dict(type='LoadImageFromFile'),
# dict(type='LoadAnnotations', with_bbox=True),
# dict(
# type='Resize',
# img_scale=[(512, 512), (640, 640)],
# multiscale_mode='value',
# keep_ratio=True),
# dict(type='RandomFlip', flip_ratio=[0.2,0.2,0.2], direction=['horizontal', 'vertical', 'diagonal']),
# dict(type='BrightnessTransform', level=5, prob=0.5),
# dict(type='ContrastTransform', level=5, prob=0.5),
# dict(type='RandomShift', shift_ratio=0.5),
# dict(type='MinIoURandomCrop', min_ious=(0.5, 0.7, 0.9), min_crop_size=0.8),
# dict(type='Normalize', **img_norm_cfg),
# 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=[(640, 640)],
# 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=4,
workers_per_gpu=2,
train=dict(
type='AD_ClassBalancedDataset',
dataset=dict(
type=dataset_type,
img_prefix='data/coco/train2017/',
classes=classes,
ann_file='data/coco/annotations/instances_train2017.json',
pipeline=train_pipeline,
),
oversample_thr = 1.0),
val=dict(
type=dataset_type,
img_prefix='data/coco/train2017/',
classes=classes,
ann_file='data/coco/annotations/instances_train2017.json',
pipeline=test_pipeline),
test=dict(
type=dataset_type,
img_prefix='data/coco/train2017/',
classes=classes,
ann_file='data/coco/annotations/instances_train2017.json',
pipeline=test_pipeline))
# data = dict(
# samples_per_gpu=4,
# workers_per_gpu=2,
# train=dict(
# type='AD_ClassBalancedDataset',
# dataset=dict(
# type=dataset_type,
# img_prefix='data/coco/train2017/',
# classes=classes,
# ann_file='data/coco/annotations/instances_train2017.json',
# pipeline=train_pipeline,
# ),
# oversample_thr = 1.0),
# val=dict(
# type=dataset_type,
# img_prefix='data/coco/train2017/',
# classes=classes,
# ann_file='data/coco/annotations/instances_train2017.json',
# pipeline=test_pipeline),
# test=dict(
# type=dataset_type,
# img_prefix='data/coco/train2017/',
# classes=classes,
# ann_file='data/coco/annotations/instances_train2017.json',
# pipeline=test_pipeline))
# optimizer
optimizer = dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0001)
@@ -139,5 +139,5 @@ lr_config = dict(
min_lr_ratio=1e-5)
runner = dict(type='EpochBasedRunner', max_epochs=20)
evaluation = dict(interval=1, metric='bbox')
checkpoint_config = dict(interval=1 )
evaluation = dict(interval=10 , metric='bbox')
checkpoint_config = dict(interval=5 )