|
|
|
model = dict(
|
|
type='FasterRCNN',
|
|
data_preprocessor=dict(
|
|
type='DetDataPreprocessor',
|
|
mean=[103.53, 116.28, 123.675],
|
|
std=[1.0, 1.0, 1.0],
|
|
bgr_to_rgb=False,
|
|
pad_size_divisor=32),
|
|
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=False),
|
|
norm_eval=True,
|
|
style='caffe',
|
|
init_cfg=dict(
|
|
type='Pretrained',
|
|
checkpoint='open-mmlab://detectron2/resnet50_caffe')),
|
|
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, 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='L1Loss', loss_weight=1.0)),
|
|
roi_head=dict(
|
|
type='StandardRoIHead',
|
|
bbox_roi_extractor=dict(
|
|
type='SingleRoIExtractor',
|
|
roi_layer=dict(type='RoIAlign', output_size=7, sampling_ratio=0),
|
|
out_channels=256,
|
|
featmap_strides=[4, 8, 16, 32]),
|
|
bbox_head=dict(
|
|
type='Shared2FCBBoxHead',
|
|
in_channels=256,
|
|
fc_out_channels=1024,
|
|
roi_feat_size=7,
|
|
num_classes=80,
|
|
bbox_coder=dict(
|
|
type='DeltaXYWHBBoxCoder',
|
|
target_means=[0.0, 0.0, 0.0, 0.0],
|
|
target_stds=[0.1, 0.1, 0.2, 0.2]),
|
|
reg_class_agnostic=False,
|
|
loss_cls=dict(
|
|
type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0),
|
|
loss_bbox=dict(type='L1Loss', 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,
|
|
match_low_quality=True,
|
|
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=-1,
|
|
pos_weight=-1,
|
|
debug=False),
|
|
rpn_proposal=dict(
|
|
nms_pre=2000,
|
|
max_per_img=1000,
|
|
nms=dict(type='nms', iou_threshold=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),
|
|
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_pre=1000,
|
|
max_per_img=1000,
|
|
nms=dict(type='nms', iou_threshold=0.7),
|
|
min_bbox_size=0),
|
|
rcnn=dict(
|
|
score_thr=0.05,
|
|
nms=dict(type='nms', iou_threshold=0.5),
|
|
max_per_img=100)))
|
|
dataset_type = 'CocoDataset'
|
|
data_root = 'data/coco/'
|
|
backend_args = None
|
|
train_pipeline = [
|
|
dict(type='LoadImageFromFile', backend_args=None),
|
|
dict(type='LoadAnnotations', with_bbox=True),
|
|
dict(
|
|
type='RandomChoiceResize',
|
|
scales=[(1333, 640), (1333, 672), (1333, 704), (1333, 736),
|
|
(1333, 768), (1333, 800)],
|
|
keep_ratio=True),
|
|
dict(type='RandomFlip', prob=0.5),
|
|
dict(type='PackDetInputs')
|
|
]
|
|
test_pipeline = [
|
|
dict(type='LoadImageFromFile', backend_args=None),
|
|
dict(type='Resize', scale=(1333, 800), keep_ratio=True),
|
|
dict(type='LoadAnnotations', with_bbox=True),
|
|
dict(
|
|
type='PackDetInputs',
|
|
meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape',
|
|
'scale_factor'))
|
|
]
|
|
train_dataloader = dict(
|
|
batch_size=2,
|
|
num_workers=2,
|
|
persistent_workers=True,
|
|
sampler=dict(type='DefaultSampler', shuffle=True),
|
|
batch_sampler=dict(type='AspectRatioBatchSampler'),
|
|
dataset=dict(
|
|
type='CocoDataset',
|
|
data_root='data/coco/',
|
|
ann_file='annotations/instances_train2017.json',
|
|
data_prefix=dict(img='train2017/'),
|
|
filter_cfg=dict(filter_empty_gt=True, min_size=32),
|
|
pipeline=[
|
|
dict(type='LoadImageFromFile', backend_args=None),
|
|
dict(type='LoadAnnotations', with_bbox=True),
|
|
dict(
|
|
type='RandomChoiceResize',
|
|
scales=[(1333, 640), (1333, 672), (1333, 704), (1333, 736),
|
|
(1333, 768), (1333, 800)],
|
|
keep_ratio=True),
|
|
dict(type='RandomFlip', prob=0.5),
|
|
dict(type='PackDetInputs')
|
|
],
|
|
backend_args=None))
|
|
val_dataloader = dict(
|
|
batch_size=1,
|
|
num_workers=2,
|
|
persistent_workers=True,
|
|
drop_last=False,
|
|
sampler=dict(type='DefaultSampler', shuffle=False),
|
|
dataset=dict(
|
|
type='CocoDataset',
|
|
data_root='data/coco/',
|
|
ann_file='annotations/instances_val2017.json',
|
|
data_prefix=dict(img='val2017/'),
|
|
test_mode=True,
|
|
pipeline=[
|
|
dict(type='LoadImageFromFile', backend_args=None),
|
|
dict(type='Resize', scale=(1333, 800), keep_ratio=True),
|
|
dict(type='LoadAnnotations', with_bbox=True),
|
|
dict(
|
|
type='PackDetInputs',
|
|
meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape',
|
|
'scale_factor'))
|
|
],
|
|
backend_args=None))
|
|
test_dataloader = dict(
|
|
batch_size=1,
|
|
num_workers=2,
|
|
persistent_workers=True,
|
|
drop_last=False,
|
|
sampler=dict(type='DefaultSampler', shuffle=False),
|
|
dataset=dict(
|
|
type='CocoDataset',
|
|
data_root='data/coco/',
|
|
ann_file='annotations/instances_val2017.json',
|
|
data_prefix=dict(img='val2017/'),
|
|
test_mode=True,
|
|
pipeline=[
|
|
dict(type='LoadImageFromFile', backend_args=None),
|
|
dict(type='Resize', scale=(1333, 800), keep_ratio=True),
|
|
dict(type='LoadAnnotations', with_bbox=True),
|
|
dict(
|
|
type='PackDetInputs',
|
|
meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape',
|
|
'scale_factor'))
|
|
],
|
|
backend_args=None))
|
|
val_evaluator = dict(
|
|
type='CocoMetric',
|
|
ann_file='data/coco/annotations/instances_val2017.json',
|
|
metric='bbox',
|
|
format_only=False,
|
|
backend_args=None)
|
|
test_evaluator = dict(
|
|
type='CocoMetric',
|
|
ann_file='data/coco/annotations/instances_val2017.json',
|
|
metric='bbox',
|
|
format_only=False,
|
|
backend_args=None)
|
|
train_cfg = dict(type='EpochBasedTrainLoop', max_epochs=12, val_interval=1)
|
|
val_cfg = dict(type='ValLoop')
|
|
test_cfg = dict(type='TestLoop')
|
|
param_scheduler = [
|
|
dict(
|
|
type='LinearLR', start_factor=0.001, by_epoch=False, begin=0, end=500),
|
|
dict(
|
|
type='MultiStepLR',
|
|
begin=0,
|
|
end=12,
|
|
by_epoch=True,
|
|
milestones=[8, 11],
|
|
gamma=0.1)
|
|
]
|
|
optim_wrapper = dict(
|
|
type='OptimWrapper',
|
|
optimizer=dict(type='SGD', lr=0.02, momentum=0.9, weight_decay=0.0001))
|
|
auto_scale_lr = dict(enable=False, base_batch_size=16)
|
|
default_scope = 'mmdet'
|
|
default_hooks = dict(
|
|
timer=dict(type='IterTimerHook'),
|
|
logger=dict(type='LoggerHook', interval=50),
|
|
param_scheduler=dict(type='ParamSchedulerHook'),
|
|
checkpoint=dict(type='CheckpointHook', interval=1),
|
|
sampler_seed=dict(type='DistSamplerSeedHook'),
|
|
visualization=dict(type='DetVisualizationHook'))
|
|
env_cfg = dict(
|
|
cudnn_benchmark=False,
|
|
mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0),
|
|
dist_cfg=dict(backend='nccl'))
|
|
vis_backends = [dict(type='LocalVisBackend')]
|
|
visualizer = dict(
|
|
type='DetLocalVisualizer',
|
|
vis_backends=[dict(type='LocalVisBackend')],
|
|
name='visualizer')
|
|
log_processor = dict(type='LogProcessor', window_size=50, by_epoch=True)
|
|
log_level = 'INFO'
|
|
load_from = None
|
|
resume = False
|
|
|