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model = dict(
detector=dict(
type='FasterRCNN',
backbone=dict(
type='ResNet',
depth=101,
num_stages=4,
out_indices=(3, ),
strides=(1, 2, 2, 1),
dilations=(1, 1, 1, 2),
frozen_stages=1,
norm_cfg=dict(type='BN', requires_grad=True),
norm_eval=True,
style='pytorch',
init_cfg=dict(
type='Pretrained', checkpoint='torchvision://resnet101')),
neck=dict(
type='ChannelMapper',
in_channels=[2048],
out_channels=512,
kernel_size=3),
rpn_head=dict(
type='RPNHead',
in_channels=512,
feat_channels=512,
anchor_generator=dict(
type='AnchorGenerator',
scales=[4, 8, 16, 32],
ratios=[0.5, 1.0, 2.0],
strides=[16]),
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='MambaRoIHead',
bbox_roi_extractor=dict(
type='SingleRoIExtractor',
roi_layer=dict(
type='RoIAlign', output_size=7, sampling_ratio=2),
out_channels=512,
featmap_strides=[16]),
bbox_head=dict(
type='MambaBBoxHead',
in_channels=512,
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=1.0, loss_weight=1.0),
num_shared_fcs=2,
topk=75,
aggregator=dict(
type='MambaAggregator',
in_channels=1024,
num_attention_blocks=16))),
train_cfg=dict(
rpn=dict(
assigner=dict(
type='MaxIoUAssigner',
pos_iou_thr=0.7,
neg_iou_thr=0.3,
min_pos_iou=0.3,
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=0,
pos_weight=-1,
debug=False),
rpn_proposal=dict(
nms_pre=6000,
max_per_img=600,
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,
ignore_iof_thr=-1),
sampler=dict(
type='RandomSampler',
num=256,
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=6000,
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))),
type='MAMBA')
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_track=True),
dict(type='SeqResize', img_scale=(1000, 600), keep_ratio=True),
dict(type='SeqRandomFlip', share_params=True, flip_ratio=0.5),
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', 'gt_instance_ids']),
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=1000,
filter_key_img=True,
method='bilateral_uniform'),
pipeline=[
dict(type='LoadMultiImagesFromFile'),
dict(
type='SeqLoadAnnotations', with_bbox=True,
with_track=True),
dict(type='SeqResize', img_scale=(1000, 600), keep_ratio=True),
dict(type='SeqRandomFlip', share_params=True, flip_ratio=0.5),
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', 'gt_instance_ids']),
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_track=True),
dict(type='SeqResize', img_scale=(1000, 600), keep_ratio=True),
dict(type='SeqRandomFlip', share_params=True, flip_ratio=0.5),
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', 'gt_instance_ids']),
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],
stride=1,
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,
shuffle_video_frames=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],
stride=1,
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,
shuffle_video_frames=True))
checkpoint_config = dict(interval=3)
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 = 'work_dirs/mamba_r101_dc5_6x/epoch_3.pth'
workflow = [('train', 1)]
optimizer = dict(type='SGD', lr=0.001, momentum=0.9, weight_decay=0.0001)
optimizer_config = dict(grad_clip=dict(max_norm=35, norm_type=2))
lr_config = dict(
policy='step',
warmup='linear',
warmup_iters=500,
warmup_ratio=0.3333333333333333,
step=[4])
runner = dict(type='EpochBasedRunner', max_epochs=6)
is_video_model = True
total_epochs = 6
evaluation = dict(metric=['bbox'], vid_style=True, interval=1)
work_dir = './work_dirs/mamba_r101_dc5_6x'
gpu_ids = range(0, 8)
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