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checkpoint_config = dict(interval=9)
log_config = dict(interval=50, hooks=[dict(type='TextLoggerHook')])
custom_hooks = [dict(type='NumClassCheckHook')]
dist_params = dict(backend='nccl')
log_level = 'INFO'
load_from = None
resume_from = None
workflow = [('train', 1)]
optimizer = dict(
type='AdamW',
lr=2.5e-05,
betas=(0.9, 0.999),
weight_decay=0.05,
paramwise_cfg=dict(
custom_keys=dict(
absolute_pos_embed=dict(decay_mult=0.0),
relative_position_bias_table=dict(decay_mult=0.0),
norm=dict(decay_mult=0.0))))
optimizer_config = dict(grad_clip=None)
lr_config = dict(
policy='step',
warmup='linear',
warmup_iters=500,
warmup_ratio=0.3333333333333333,
step=[6])
runner = dict(type='EpochBasedRunner', max_epochs=9)
pretrained = 'https://github.com/SwinTransformer/storage/releases/download/v1.0.2/mask_rcnn_swin_tiny_patch4_window7.pth'
is_video_model = True
model = dict(
type='STPN',
detector=dict(
type='FasterRCNN',
backbone=dict(
type='STPNSwinTransformer',
embed_dims=96,
depths=[2, 2, 6, 2],
num_heads=[3, 6, 12, 24],
window_size=7,
mlp_ratio=4,
qkv_bias=True,
qk_scale=None,
drop_rate=0.0,
attn_drop_rate=0.0,
drop_path_rate=0.2,
patch_norm=True,
with_cp=False,
convert_weights=True,
init_cfg=dict(
type='Pretrained',
checkpoint=
'https://github.com/SwinTransformer/storage/releases/download/v1.0.2/mask_rcnn_swin_tiny_patch4_window7.pth'
),
prompt_cfg=dict(
num_tokens=5,
location='prepend',
deep=False,
dropout=0.0,
initiation='random')),
neck=dict(
type='FPN',
in_channels=[96, 192, 384, 768],
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='SmoothL1Loss', beta=0.1111111111111111,
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=30,
bbox_coder=dict(
type='DeltaXYWHBBoxCoder',
target_means=[0.0, 0.0, 0.0, 0.0],
target_stds=[0.2, 0.2, 0.2, 0.2]),
reg_class_agnostic=False,
loss_cls=dict(
type='CrossEntropyLoss',
use_sigmoid=False,
loss_weight=1.0),
loss_bbox=dict(
type='SmoothL1Loss',
beta=0.1111111111111111,
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=1000,
max_per_img=300,
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=True,
ignore_iof_thr=-1),
sampler=dict(
type='RandomSampler',
num=256,
pos_fraction=0.25,
neg_pos_ub=-1,
add_gt_as_proposals=True),
mask_size=28,
pos_weight=-1,
debug=False)),
test_cfg=dict(
rpn=dict(
nms_pre=1000,
max_per_img=300,
nms=dict(type='nms', iou_threshold=0.7),
min_bbox_size=0),
rcnn=dict(
score_thr=0.0001,
nms=dict(type='nms', iou_threshold=0.5),
max_per_img=100,
mask_thr_binary=0.5))))
dataset_type = 'ImagenetVIDDataset'
data_root = 'data/ILSVRC/'
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='LoadMultiImagesFromFile'),
dict(type='SeqLoadAnnotations', with_bbox=True, with_mask=False),
dict(type='SeqRandomFlip', share_params=True, flip_ratio=0.5),
dict(
type='AutoAugment',
policies=[[{
'type':
'SeqResize',
'img_scale': [(480, 1333), (512, 1333), (544, 1333), (576, 1333),
(608, 1333), (640, 1333), (672, 1333), (704, 1333),
(736, 1333), (768, 1333), (800, 1333)],
'multiscale_mode':
'value',
'keep_ratio':
True
}],
[{
'type': 'SeqResize',
'img_scale': [(400, 1333), (500, 1333), (600, 1333)],
'multiscale_mode': 'value',
'keep_ratio': True
}, {
'type': 'SeqRandomCrop',
'crop_type': 'absolute_range',
'crop_size': (384, 600),
'allow_negative_crop': True
}, {
'type': 'SeqMaxSizePad'
}, {
'type':
'SeqResize2',
'img_scale': [(480, 1333), (512, 1333), (544, 1333),
(576, 1333), (608, 1333), (640, 1333),
(672, 1333), (704, 1333), (736, 1333),
(768, 1333), (800, 1333)],
'multiscale_mode':
'value',
'keep_ratio':
True
}]]),
dict(
type='SeqNormalize',
mean=[123.675, 116.28, 103.53],
std=[58.395, 57.12, 57.375],
to_rgb=True),
dict(type='SeqPad', size_divisor=16),
dict(type='VideoCollect', keys=['img', 'gt_bboxes', 'gt_labels']),
dict(type='ConcatVideoReferences'),
dict(type='SeqDefaultFormatBundle', ref_prefix='ref')
]
test_pipeline = [
dict(type='LoadMultiImagesFromFile'),
dict(type='SeqResize', img_scale=(1000, 600), keep_ratio=True),
dict(type='SeqRandomFlip', share_params=True, flip_ratio=0.0),
dict(
type='SeqNormalize',
mean=[123.675, 116.28, 103.53],
std=[58.395, 57.12, 57.375],
to_rgb=True),
dict(type='SeqPad', size_divisor=16),
dict(
type='VideoCollect',
keys=['img'],
meta_keys=('num_left_ref_imgs', 'frame_stride')),
dict(type='ConcatVideoReferences'),
dict(type='MultiImagesToTensor', ref_prefix='ref'),
dict(type='ToList')
]
data = dict(
samples_per_gpu=1,
workers_per_gpu=4,
train=[
dict(
type='ImagenetVIDDataset',
ann_file='data/ILSVRC/annotations/imagenet_vid_train.json',
img_prefix='data/ILSVRC/Data/VID',
ref_img_sampler=dict(
num_ref_imgs=2,
frame_range=9,
filter_key_img=True,
method='bilateral_uniform'),
pipeline=[
dict(type='LoadMultiImagesFromFile'),
dict(
type='SeqLoadAnnotations', with_bbox=True,
with_mask=False),
dict(type='SeqRandomFlip', share_params=True, flip_ratio=0.5),
dict(
type='AutoAugment',
policies=[[{
'type':
'SeqResize',
'img_scale': [(480, 1333), (512, 1333), (544, 1333),
(576, 1333), (608, 1333), (640, 1333),
(672, 1333), (704, 1333), (736, 1333),
(768, 1333), (800, 1333)],
'multiscale_mode':
'value',
'keep_ratio':
True
}],
[{
'type':
'SeqResize',
'img_scale': [(400, 1333), (500, 1333),
(600, 1333)],
'multiscale_mode':
'value',
'keep_ratio':
True
}, {
'type': 'SeqRandomCrop',
'crop_type': 'absolute_range',
'crop_size': (384, 600),
'allow_negative_crop': True
}, {
'type': 'SeqMaxSizePad'
}, {
'type':
'SeqResize2',
'img_scale': [(480, 1333), (512, 1333),
(544, 1333), (576, 1333),
(608, 1333), (640, 1333),
(672, 1333), (704, 1333),
(736, 1333), (768, 1333),
(800, 1333)],
'multiscale_mode':
'value',
'keep_ratio':
True
}]]),
dict(
type='SeqNormalize',
mean=[123.675, 116.28, 103.53],
std=[58.395, 57.12, 57.375],
to_rgb=True),
dict(type='SeqPad', size_divisor=16),
dict(
type='VideoCollect',
keys=['img', 'gt_bboxes', 'gt_labels']),
dict(type='ConcatVideoReferences'),
dict(type='SeqDefaultFormatBundle', ref_prefix='ref')
]),
dict(
type='ImagenetVIDDataset',
load_as_video=False,
ann_file='data/ILSVRC/annotations/imagenet_det_30plus1cls.json',
img_prefix='data/ILSVRC/Data/DET',
ref_img_sampler=dict(
num_ref_imgs=2,
frame_range=0,
filter_key_img=False,
method='bilateral_uniform'),
pipeline=[
dict(type='LoadMultiImagesFromFile'),
dict(
type='SeqLoadAnnotations', with_bbox=True,
with_mask=False),
dict(type='SeqRandomFlip', share_params=True, flip_ratio=0.5),
dict(
type='AutoAugment',
policies=[[{
'type':
'SeqResize',
'img_scale': [(480, 1333), (512, 1333), (544, 1333),
(576, 1333), (608, 1333), (640, 1333),
(672, 1333), (704, 1333), (736, 1333),
(768, 1333), (800, 1333)],
'multiscale_mode':
'value',
'keep_ratio':
True
}],
[{
'type':
'SeqResize',
'img_scale': [(400, 1333), (500, 1333),
(600, 1333)],
'multiscale_mode':
'value',
'keep_ratio':
True
}, {
'type': 'SeqRandomCrop',
'crop_type': 'absolute_range',
'crop_size': (384, 600),
'allow_negative_crop': True
}, {
'type': 'SeqMaxSizePad'
}, {
'type':
'SeqResize2',
'img_scale': [(480, 1333), (512, 1333),
(544, 1333), (576, 1333),
(608, 1333), (640, 1333),
(672, 1333), (704, 1333),
(736, 1333), (768, 1333),
(800, 1333)],
'multiscale_mode':
'value',
'keep_ratio':
True
}]]),
dict(
type='SeqNormalize',
mean=[123.675, 116.28, 103.53],
std=[58.395, 57.12, 57.375],
to_rgb=True),
dict(type='SeqPad', size_divisor=16),
dict(
type='VideoCollect',
keys=['img', 'gt_bboxes', 'gt_labels']),
dict(type='ConcatVideoReferences'),
dict(type='SeqDefaultFormatBundle', ref_prefix='ref')
])
],
val=dict(
type='ImagenetVIDDataset',
ann_file='data/ILSVRC/annotations/imagenet_vid_val.json',
img_prefix='data/ILSVRC/Data/VID',
ref_img_sampler=dict(
num_ref_imgs=14,
frame_range=[-7, 7],
method='test_with_adaptive_stride'),
pipeline=[
dict(type='LoadMultiImagesFromFile'),
dict(type='SeqResize', img_scale=(1000, 600), keep_ratio=True),
dict(type='SeqRandomFlip', share_params=True, flip_ratio=0.0),
dict(
type='SeqNormalize',
mean=[123.675, 116.28, 103.53],
std=[58.395, 57.12, 57.375],
to_rgb=True),
dict(type='SeqPad', size_divisor=16),
dict(
type='VideoCollect',
keys=['img'],
meta_keys=('num_left_ref_imgs', 'frame_stride')),
dict(type='ConcatVideoReferences'),
dict(type='MultiImagesToTensor', ref_prefix='ref'),
dict(type='ToList')
],
test_mode=True),
test=dict(
type='ImagenetVIDDataset',
ann_file='data/ILSVRC/annotations/imagenet_vid_val.json',
img_prefix='data/ILSVRC/Data/VID',
ref_img_sampler=dict(
num_ref_imgs=14,
frame_range=[-7, 7],
method='test_with_adaptive_stride'),
pipeline=[
dict(type='LoadMultiImagesFromFile'),
dict(type='SeqResize', img_scale=(1000, 600), keep_ratio=True),
dict(type='SeqRandomFlip', share_params=True, flip_ratio=0.0),
dict(
type='SeqNormalize',
mean=[123.675, 116.28, 103.53],
std=[58.395, 57.12, 57.375],
to_rgb=True),
dict(type='SeqPad', size_divisor=16),
dict(
type='VideoCollect',
keys=['img'],
meta_keys=('num_left_ref_imgs', 'frame_stride')),
dict(type='ConcatVideoReferences'),
dict(type='MultiImagesToTensor', ref_prefix='ref'),
dict(type='ToList')
],
test_mode=True))
total_epochs = 9
evaluation = dict(metric=['bbox'], vid_style=True, interval=9)
work_dir = './work_dirs/stpn_swint_adam_9x'
gpu_ids = range(0, 8)
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