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mmpedestron_pretrained.py
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mmpedestron_pretrained.py
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# model settings
num_dec_layer = 6
lambda_2 = 2.0
pretrained = 'YOUR_DTAT_ROOT_PATH/mmpedestron_models/backbone/dualvit_s_384.pth.tar'
model = dict(
type='MMPedestron',
backbone=dict(
type='UNIXVit',
stem_width=32,
embed_dims=[64, 128, 320, 448],
num_heads=[2, 4, 10, 14],
mlp_ratios=[8, 8, 4, 3],
norm_layer='LN',
depths=[3, 4, 6, 3],
drop_path_rate=0.15,
score_embed_nums=3,
num_scores=2,
mod_nums=2,
with_cp=True,
pretrained=pretrained
),
neck=dict(
type='ChannelMapper',
in_channels=[64, 128, 320, 448],
out_channels=256,
norm_cfg=dict(type='GN', num_groups=32),
num_outs=5),
rpn_head=dict(
type='RPNHead',
in_channels=256,
feat_channels=256,
anchor_generator=dict(
type='AnchorGenerator',
octave_base_scale=4,
scales_per_octave=3,
ratios=[0.5, 1.0, 2.0],
strides=[4, 8, 16, 32, 64, 128]),
bbox_coder=dict(
type='DeltaXYWHBBoxCoder',
target_means=[.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 * num_dec_layer * lambda_2),
loss_bbox=dict(type='L1Loss', loss_weight=1.0 * num_dec_layer * lambda_2)),
query_head=dict(
type='CoDINOHead',
num_query=900,
num_classes=1,
num_feature_levels=5,
in_channels=2048,
sync_cls_avg_factor=True,
as_two_stage=True,
with_box_refine=True,
mixed_selection=True,
dn_cfg=dict(
type='CdnQueryGenerator',
noise_scale=dict(label=0.5, box=1.0), # 0.5, 0.4 for DN-DETR
group_cfg=dict(dynamic=True, num_groups=None, num_dn_queries=500)),
transformer=dict(
type='CoDinoTransformer',
with_pos_coord=True,
with_coord_feat=False,
num_co_heads=2,
num_feature_levels=5,
encoder=dict(
type='DetrTransformerEncoder_CP',
num_layers=6,
with_cp=6, # number of layers that use checkpoint
transformerlayers=dict(
type='BaseTransformerLayer',
attn_cfgs=dict(
type='MultiScaleDeformableAttention',
embed_dims=256, num_levels=5, dropout=0.0),
feedforward_channels=2048,
ffn_dropout=0.0,
operation_order=('self_attn', 'norm', 'ffn', 'norm'))),
decoder=dict(
type='DinoTransformerDecoder',
num_layers=6,
return_intermediate=True,
transformerlayers=dict(
type='DetrTransformerDecoderLayer',
attn_cfgs=[
dict(
type='MultiheadAttention',
embed_dims=256,
num_heads=8,
dropout=0.0),
dict(
type='MultiScaleDeformableAttention',
embed_dims=256,
num_levels=5,
dropout=0.0),
],
feedforward_channels=2048,
ffn_dropout=0.0,
operation_order=('self_attn', 'norm', 'cross_attn', 'norm',
'ffn', 'norm')))),
positional_encoding=dict(
type='SinePositionalEncoding',
num_feats=128,
temperature=20,
normalize=True),
loss_cls=dict(
type='QualityFocalLoss',
use_sigmoid=True,
beta=2.0,
loss_weight=1.0),
loss_bbox=dict(type='L1Loss', loss_weight=5.0),
loss_iou=dict(type='GIoULoss', loss_weight=2.0)),
roi_head=[dict(
type='CoStandardRoIHead',
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, 64],
finest_scale=56),
bbox_head=dict(
type='Shared2FCBBoxHead',
in_channels=256,
fc_out_channels=1024,
roi_feat_size=7,
num_classes=1,
bbox_coder=dict(
type='DeltaXYWHBBoxCoder',
target_means=[0., 0., 0., 0.],
target_stds=[0.1, 0.1, 0.2, 0.2]),
reg_class_agnostic=False,
reg_decoded_bbox=True,
loss_cls=dict(
type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0 * num_dec_layer * lambda_2),
loss_bbox=dict(type='GIoULoss', loss_weight=10.0 * num_dec_layer * lambda_2)))],
bbox_head=[dict(
type='CoATSSHead',
num_classes=1,
in_channels=256,
stacked_convs=1,
feat_channels=256,
anchor_generator=dict(
type='AnchorGenerator',
ratios=[1.0],
octave_base_scale=8,
scales_per_octave=1,
strides=[4, 8, 16, 32, 64, 128]),
bbox_coder=dict(
type='DeltaXYWHBBoxCoder',
target_means=[.0, .0, .0, .0],
target_stds=[0.1, 0.1, 0.2, 0.2]),
loss_cls=dict(
type='FocalLoss',
use_sigmoid=True,
gamma=2.0,
alpha=0.25,
loss_weight=1.0 * num_dec_layer * lambda_2),
loss_bbox=dict(
type='GIoULoss',
loss_weight=2.0 *
num_dec_layer *
lambda_2),
loss_centerness=dict(
type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0 * num_dec_layer * lambda_2))],
# model training and testing settings
train_cfg=[
dict(
assigner=dict(
type='HungarianAssigner',
cls_cost=dict(type='FocalLossCost', weight=2.0),
reg_cost=dict(
type='BBoxL1Cost',
weight=5.0,
box_format='xywh'),
iou_cost=dict(type='IoUCost', iou_mode='giou', weight=2.0))),
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=4000,
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)),
dict(
assigner=dict(type='ATSSAssigner', topk=9),
allowed_border=-1,
pos_weight=-1,
debug=False)],
test_cfg=[
dict(
max_per_img=1000,
nms=dict(type='soft_nms', iou_threshold=0.8)),
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=0.0,
nms=dict(type='nms', iou_threshold=0.5),
max_per_img=1000)),
dict(
nms_pre=1000,
min_bbox_size=0,
score_thr=0.0,
nms=dict(type='nms', iou_threshold=0.6),
max_per_img=1000),
# soft-nms is also supported for rcnn testing
# e.g., nms=dict(type='soft_nms', iou_threshold=0.5, min_score=0.05)
])
img_norm_cfg = dict(
mean=[123.675, 116.28, 103.53],
std=[58.395, 57.12, 57.375],
to_rgb=True)
# train_pipeline, NOTE the img_scale and the Pad's size_divisor is different
# from the default setting in mmdet.
#
train_pipeline = [
dict(type='LoadMultiModalitiesImages',
mod_path_mapping_dict={
'canwangmscoco': {
'img': {
'org_key': 'canwangmscoco/train2017',
'target_key': 'canwangmscoco/train2017'
},
'extra_mod_img': {
'org_key': 'canwangmscoco/train2017',
'target_key': 'canwangmscoco/train2017'
}
},
'add_yingda_objects365': {
'img': {
'org_key': 'add_yingda_objects365/Objects365',
'target_key': 'add_yingda_objects365/Objects365'
},
'extra_mod_img': {
'org_key': 'add_yingda_objects365/Objects365',
'target_key': 'add_yingda_objects365/Objects365'
}
},
'add_yingda_crowd': {
'img': {
'org_key': 'add_yingda_crowd/CrowdHuman',
'target_key': 'add_yingda_crowd/CrowdHuman'
},
'extra_mod_img': {
'org_key': 'add_yingda_crowd/CrowdHuman',
'target_key': 'add_yingda_crowd/CrowdHuman'
}
}
},
mod_list=['img',
'extra_mod_img'],
file_client_args=dict(backend='petrel')),
dict(type='LoadAnnotations', with_bbox=True),
dict(type='RandomFlip', flip_ratio=0.5),
dict(
type='AutoAugment',
policies=[
[
dict(
type='Resize',
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='Resize',
# The radio of all image in train dataset < 7
# follow the original impl
img_scale=[(400, 1333), (500, 1333), (600, 1333)],
multiscale_mode='value',
keep_ratio=True),
dict(
type='RandomCrop',
crop_type='absolute_range',
crop_size=(384, 600),
allow_negative_crop=True),
dict(
type='Resize',
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',
override=True,
keep_ratio=True)
]
]),
dict(type='Normalize', **img_norm_cfg),
dict(type='Pad', size_divisor=32),
dict(type='RandomDropImages', drop_p=0.3),
dict(type='MulitAllImageFormatBundle',
extra_image_list=['extra_mod_img']),
dict(type='Collect', keys=['img',
'extra_mod_img',
'gt_bboxes',
'gt_labels',
'gt_bboxes_ignore'], meta_keys=('filename', 'ori_filename', 'ori_shape',
'img_shape', 'pad_shape', 'scale_factor', 'flip',
'flip_direction', 'img_norm_cfg', 'valid_img_fields'))
]
# test_pipeline, NOTE the Pad's size_divisor is different from the default
# setting (size_divisor=32). While there is little effect on the performance
# whether we use the default setting or use size_divisor=1.
test_pipeline = [
dict(type='LoadMultiModalitiesImages',
mod_path_mapping_dict={
'canwangmscoco': {
'img': {
'org_key': 'canwangmscoco/val2017',
'target_key': 'canwangmscoco/val2017'
},
'extra_mod_img': {
'org_key': 'canwangmscoco/val2017',
'target_key': 'canwangmscoco/val2017'
}
},
'add_yingda_objects365': {
'img': {
'org_key': 'add_yingda_objects365/Objects365',
'target_key': 'add_yingda_objects365/Objects365'
},
'extra_mod_img': {
'org_key': 'add_yingda_objects365/Objects365',
'target_key': 'add_yingda_objects365/Objects365'
}
},
'add_yingda_crowd': {
'img': {
'org_key': 'add_yingda_crowd/CrowdHuman',
'target_key': 'add_yingda_crowd/CrowdHuman'
},
'extra_mod_img': {
'org_key': 'add_yingda_crowd/CrowdHuman',
'target_key': 'add_yingda_crowd/CrowdHuman'
}
}
},
mod_list=['img',
'extra_mod_img', ],
file_client_args=dict(backend='petrel')),
dict(
type='MultiScaleFlipAug',
img_scale=(1333, 800),
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='MaskImages', masked_mod_list=['extra_mod_img']),
dict(type='ImageToTensor', keys=['img',
'extra_mod_img',
]),
dict(type='Collect', keys=['img',
'extra_mod_img',
]),
])
]
ann_prefix = 'YOUR_DTAT_ROOT_PATH/mmpedestron_datasets_ann/pretrained_ann/'
train_ann_list = [
ann_prefix + 'coco_train2017.json',
ann_prefix + 'objects365_train.json',
ann_prefix + 'crowdhuman_annotation_train.json',
]
img_prefix_list = [
'YOUR_DTAT_ROOT_PATH/mmpedestron_images/COCO/train2017',
'YOUR_DTAT_ROOT_PATH/mmpedestron_images//Objects365',
'YOUR_DTAT_ROOT_PATH/mmpedestron_images/CrowdHuman',
]
val_list = [
'YOUR_DTAT_ROOT_PATH/mmpedestron_datasets_ann/pretrained_ann/instances_val2017.json',
'YOUR_DTAT_ROOT_PATH/mmpedestron_datasets_ann/pretrained_ann/crowdhuman_annotation_val.json',
]
val_img_prefix_list = [
'YOUR_DTAT_ROOT_PATH/mmpedestron_images/COCO/val2017',
'YOUR_DTAT_ROOT_PATH/mmpedestron_images/CrowdHuman/',
]
dataset_type = 'CocoPedestronDataset'
data = dict(
samples_per_gpu=1,
workers_per_gpu=2,
train=dict(
type=dataset_type,
filter_empty_gt=False,
ann_file=train_ann_list,
img_prefix=img_prefix_list,
classes=('person', ),
pipeline=train_pipeline),
val=dict(
type=dataset_type,
ann_file=val_list,
img_prefix=val_img_prefix_list,
classes=('person', ),
pipeline=test_pipeline),
test=dict(
type=dataset_type,
ann_file=val_list,
img_prefix=val_img_prefix_list,
classes=('person', ),
pipeline=test_pipeline))
evaluation = dict(
interval=1, metric='bbox',
crowdhuman_odgt_val_gt_path='YOUR_DTAT_ROOT_PATH/mmpedestron_datasets_ann/crowdhuman_raw/annotation_val.odgt'
)
optimizer = dict(
type='AdamW',
lr=0.0001,
weight_decay=0.0001,
paramwise_cfg=dict(custom_keys={'backbone': dict(lr_mult=0.1)})
)
optimizer_config = dict(type='GC_OptimizerHook',
grad_clip=dict(max_norm=0.1, norm_type=2))
# learning policy
lr_config = dict(
policy='step',
warmup='linear',
warmup_iters=500,
warmup_ratio=0.001,
step=[7, 10])
runner = dict(type='EpochBasedRunner', max_epochs=12)
# NOTE: `auto_scale_lr` is for automatically scaling LR,
# USER SHOULD NOT CHANGE ITS VALUES.
# base_batch_size = (8 GPUs) x (2 samples per GPU)
auto_scale_lr = dict(base_batch_size=16)
checkpoint_config = dict(interval=1)
# yapf:disable
log_config = dict(
interval=500,
hooks=[
dict(type='TextLoggerHook'),
])
# yapf:enable
custom_hooks = [dict(type='NumClassCheckHook')]
dist_params = dict(backend='nccl',
# port=36666
)
log_level = 'INFO'
load_from = None
resume_from = None
workflow = [('train', 1)]
# disable opencv multithreading to avoid system being overloaded
opencv_num_threads = 0
# set multi-process start method as `fork` to speed up the training
mp_start_method = 'fork'