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mask_rcnn_internimage_t_fpn_1x_coco.py
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auto_scale_lr = dict(base_batch_size=16, enable=False)
backend_args = None
checkpoint_config = dict(interval=1, max_keep_ckpts=3, save_last=True)
data = dict(samples_per_gpu=2)
data_root = 'data/coco/'
dataset_type = 'CocoDataset'
default_hooks = dict(
checkpoint=dict(interval=1, type='CheckpointHook'),
logger=dict(interval=10, type='LoggerHook'),
param_scheduler=dict(type='ParamSchedulerHook'),
sampler_seed=dict(type='DistSamplerSeedHook'),
timer=dict(type='IterTimerHook'),
visualization=dict(type='DetVisualizationHook'))
default_scope = 'mmdet'
env_cfg = dict(
cudnn_benchmark=False,
dist_cfg=dict(backend='nccl'),
mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0))
evaluation = dict(save_best='auto')
fp16 = dict(loss_scale=dict(init_scale=512))
load_from = None
log_level = 'INFO'
log_processor = dict(by_epoch=True, type='LogProcessor', window_size=50)
model = dict(
backbone=dict(
channels=64,
core_op='DCNv3',
depths=[
4,
4,
18,
4,
],
drop_path_rate=0.2,
groups=[
4,
8,
16,
32,
],
init_cfg=dict(
checkpoint='mask_rcnn_internimage_t_fpn_1x_coco.pth',
type='Pretrained'),
layer_scale=1.0,
mlp_ratio=4.0,
norm_layer='LN',
offset_scale=1.0,
out_indices=(
0,
1,
2,
3,
),
post_norm=False,
type='InternImage',
with_cp=False),
data_preprocessor=dict(
bgr_to_rgb=True,
mean=[
123.675,
116.28,
103.53,
],
pad_mask=True,
pad_size_divisor=32,
std=[
58.395,
57.12,
57.375,
],
type='DetDataPreprocessor'),
neck=dict(
in_channels=[
64,
128,
256,
512,
],
num_outs=5,
out_channels=256,
type='FPN'),
roi_head=dict(
bbox_head=dict(
bbox_coder=dict(
target_means=[
0.0,
0.0,
0.0,
0.0,
],
target_stds=[
0.1,
0.1,
0.2,
0.2,
],
type='DeltaXYWHBBoxCoder'),
fc_out_channels=1024,
in_channels=256,
loss_bbox=dict(loss_weight=1.0, type='L1Loss'),
loss_cls=dict(
loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False),
num_classes=80,
reg_class_agnostic=False,
roi_feat_size=7,
type='Shared2FCBBoxHead'),
bbox_roi_extractor=dict(
featmap_strides=[
4,
8,
16,
32,
],
out_channels=256,
roi_layer=dict(output_size=7, sampling_ratio=0, type='RoIAlign'),
type='SingleRoIExtractor'),
mask_head=dict(
conv_out_channels=256,
in_channels=256,
loss_mask=dict(
loss_weight=1.0, type='CrossEntropyLoss', use_mask=True),
num_classes=80,
num_convs=4,
type='FCNMaskHead'),
mask_roi_extractor=dict(
featmap_strides=[
4,
8,
16,
32,
],
out_channels=256,
roi_layer=dict(output_size=14, sampling_ratio=0, type='RoIAlign'),
type='SingleRoIExtractor'),
type='StandardRoIHead'),
rpn_head=dict(
anchor_generator=dict(
ratios=[
0.5,
1.0,
2.0,
],
scales=[
8,
],
strides=[
4,
8,
16,
32,
64,
],
type='AnchorGenerator'),
bbox_coder=dict(
target_means=[
0.0,
0.0,
0.0,
0.0,
],
target_stds=[
1.0,
1.0,
1.0,
1.0,
],
type='DeltaXYWHBBoxCoder'),
feat_channels=256,
in_channels=256,
loss_bbox=dict(loss_weight=1.0, type='L1Loss'),
loss_cls=dict(
loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=True),
type='RPNHead'),
test_cfg=dict(
rcnn=dict(
mask_thr_binary=0.5,
max_per_img=100,
nms=dict(iou_threshold=0.5, type='nms'),
score_thr=0.05),
rpn=dict(
max_per_img=1000,
min_bbox_size=0,
nms=dict(iou_threshold=0.7, type='nms'),
nms_pre=1000)),
train_cfg=dict(
rcnn=dict(
assigner=dict(
ignore_iof_thr=-1,
match_low_quality=True,
min_pos_iou=0.5,
neg_iou_thr=0.5,
pos_iou_thr=0.5,
type='MaxIoUAssigner'),
debug=False,
mask_size=28,
pos_weight=-1,
sampler=dict(
add_gt_as_proposals=True,
neg_pos_ub=-1,
num=512,
pos_fraction=0.25,
type='RandomSampler')),
rpn=dict(
allowed_border=-1,
assigner=dict(
ignore_iof_thr=-1,
match_low_quality=True,
min_pos_iou=0.3,
neg_iou_thr=0.3,
pos_iou_thr=0.7,
type='MaxIoUAssigner'),
debug=False,
pos_weight=-1,
sampler=dict(
add_gt_as_proposals=False,
neg_pos_ub=-1,
num=256,
pos_fraction=0.5,
type='RandomSampler')),
rpn_proposal=dict(
max_per_img=1000,
min_bbox_size=0,
nms=dict(iou_threshold=0.7, type='nms'),
nms_pre=2000)),
type='MaskRCNN')
optim_wrapper = dict(
optimizer=dict(lr=0.02, momentum=0.9, type='SGD', weight_decay=0.0001),
type='OptimWrapper')
optimizer = dict(
_delete_=True,
constructor='CustomLayerDecayOptimizerConstructor',
lr=0.0001,
paramwise_cfg=dict(
depths=[
4,
4,
18,
4,
], layer_decay_rate=1.0, num_layers=30),
type='AdamW',
weight_decay=0.05)
optimizer_config = dict(grad_clip=None)
param_scheduler = [
dict(
begin=0, by_epoch=False, end=500, start_factor=0.001, type='LinearLR'),
dict(
begin=0,
by_epoch=True,
end=12,
gamma=0.1,
milestones=[
8,
11,
],
type='MultiStepLR'),
]
pretrained = 'mask_rcnn_internimage_t_fpn_1x_coco.pth'
resume = False
test_cfg = dict(type='TestLoop')
test_dataloader = dict(
dataset=dict(
ann_file='annotations/image_info_test-dev2017.json',
data_prefix=dict(img='test2017/'),
data_root='data/coco/',
pipeline=[
dict(backend_args=None, type='LoadImageFromFile'),
dict(type='LoadAnnotations', with_bbox=True, with_mask=True),
],
test_mode=True,
type='CocoDataset'),
drop_last=False,
num_workers=16,
persistent_workers=True,
sampler=dict(shuffle=False, type='DefaultSampler'))
test_evaluator = dict(
ann_file='data/coco/annotations/image_info_test-dev2017.json',
format_only=False,
metric=[
'bbox',
'segm',
],
type='CocoMetric')
test_pipeline = [
dict(backend_args=None, type='LoadImageFromFile'),
dict(keep_ratio=True, scale=(
700,
400,
), type='Resize'),
dict(type='LoadAnnotations', with_bbox=True, with_mask=True),
dict(
meta_keys=(
'img_id',
'img_path',
'ori_shape',
'img_shape',
'scale_factor',
),
type='PackDetInputs'),
]
train_cfg = dict(max_epochs=12, type='EpochBasedTrainLoop', val_interval=1)
train_dataloader = dict(
batch_sampler=dict(type='AspectRatioBatchSampler'),
batch_size=8,
dataset=dict(
ann_file='annotations/instances_train2017.json',
backend_args=None,
data_prefix=dict(img='train2017/'),
data_root='data/coco/',
filter_cfg=dict(filter_empty_gt=True, min_size=32),
indices=100,
pipeline=[
dict(backend_args=None, type='LoadImageFromFile'),
dict(type='LoadAnnotations', with_bbox=True, with_mask=True),
dict(keep_ratio=True, scale=(
700,
400,
), type='Resize'),
dict(prob=0.5, type='RandomFlip'),
dict(type='PackDetInputs'),
],
type='CocoDataset'),
num_workers=20,
persistent_workers=True,
sampler=dict(shuffle=True, type='DefaultSampler'))
train_pipeline = [
dict(backend_args=None, type='LoadImageFromFile'),
dict(type='LoadAnnotations', with_bbox=True, with_mask=True),
dict(keep_ratio=True, scale=(
700,
400,
), type='Resize'),
dict(prob=0.5, type='RandomFlip'),
dict(type='PackDetInputs'),
]
val_cfg = dict(type='ValLoop')
val_dataloader = dict(
batch_size=8,
dataset=dict(
ann_file='annotations/instances_val2017.json',
backend_args=None,
data_prefix=dict(img='val2017/'),
data_root='data/coco/',
indices=500,
pipeline=[
dict(backend_args=None, type='LoadImageFromFile'),
dict(keep_ratio=True, scale=(
700,
400,
), type='Resize'),
dict(type='LoadAnnotations', with_bbox=True, with_mask=True),
dict(
meta_keys=(
'img_id',
'img_path',
'ori_shape',
'img_shape',
'scale_factor',
),
type='PackDetInputs'),
],
test_mode=True,
type='CocoDataset'),
drop_last=False,
num_workers=20,
persistent_workers=True,
sampler=dict(shuffle=False, type='DefaultSampler'))
val_evaluator = dict(
ann_file='data/coco/annotations/instances_val2017.json',
backend_args=None,
format_only=False,
metric=[
'bbox',
'segm',
],
type='CocoMetric')
vis_backends = [
dict(type='LocalVisBackend'),
]
visualizer = dict(
name='visualizer',
type='DetLocalVisualizer',
vis_backends=[
dict(type='LocalVisBackend'),
])
work_dir = 'results'