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[Feature] Release dyhead big model. (open-mmlab#7733)
* Release dyhead with big model * Update new config * Update config * Fix lint * Update * Update
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configs/dyhead/atss_swin-l-p4-w12_fpn_dyhead_mstrain_2x_coco.py
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_base_ = '../_base_/default_runtime.py' | ||
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pretrained = 'https://github.com/SwinTransformer/storage/releases/download/v1.0.0/swin_large_patch4_window12_384_22k.pth' # noqa | ||
model = dict( | ||
type='ATSS', | ||
backbone=dict( | ||
type='SwinTransformer', | ||
pretrain_img_size=384, | ||
embed_dims=192, | ||
depths=[2, 2, 18, 2], | ||
num_heads=[6, 12, 24, 48], | ||
window_size=12, | ||
mlp_ratio=4, | ||
qkv_bias=True, | ||
qk_scale=None, | ||
drop_rate=0., | ||
attn_drop_rate=0., | ||
drop_path_rate=0.2, | ||
patch_norm=True, | ||
out_indices=(1, 2, 3), | ||
# Please only add indices that would be used | ||
# in FPN, otherwise some parameter will not be used | ||
with_cp=False, | ||
convert_weights=True, | ||
init_cfg=dict(type='Pretrained', checkpoint=pretrained)), | ||
neck=[ | ||
dict( | ||
type='FPN', | ||
in_channels=[384, 768, 1536], | ||
out_channels=256, | ||
start_level=0, | ||
add_extra_convs='on_output', | ||
num_outs=5), | ||
dict( | ||
type='DyHead', | ||
in_channels=256, | ||
out_channels=256, | ||
num_blocks=6, | ||
# disable zero_init_offset to follow official implementation | ||
zero_init_offset=False) | ||
], | ||
bbox_head=dict( | ||
type='ATSSHead', | ||
num_classes=80, | ||
in_channels=256, | ||
pred_kernel_size=1, # follow DyHead official implementation | ||
stacked_convs=0, | ||
feat_channels=256, | ||
anchor_generator=dict( | ||
type='AnchorGenerator', | ||
ratios=[1.0], | ||
octave_base_scale=8, | ||
scales_per_octave=1, | ||
strides=[8, 16, 32, 64, 128], | ||
center_offset=0.5), # follow DyHead official implementation | ||
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), | ||
loss_bbox=dict(type='GIoULoss', loss_weight=2.0), | ||
loss_centerness=dict( | ||
type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0)), | ||
# training and testing settings | ||
train_cfg=dict( | ||
assigner=dict(type='ATSSAssigner', topk=9), | ||
allowed_border=-1, | ||
pos_weight=-1, | ||
debug=False), | ||
test_cfg=dict( | ||
nms_pre=1000, | ||
min_bbox_size=0, | ||
score_thr=0.05, | ||
nms=dict(type='nms', iou_threshold=0.6), | ||
max_per_img=100)) | ||
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# dataset settings | ||
dataset_type = 'CocoDataset' | ||
data_root = 'data/coco/' | ||
img_norm_cfg = dict( | ||
mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True) | ||
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train_pipeline = [ | ||
dict(type='LoadImageFromFile'), | ||
dict(type='LoadAnnotations', with_bbox=True), | ||
dict( | ||
type='Resize', | ||
img_scale=[(2000, 480), (2000, 1200)], | ||
multiscale_mode='range', | ||
keep_ratio=True, | ||
backend='pillow'), | ||
dict(type='RandomFlip', flip_ratio=0.5), | ||
dict(type='Normalize', **img_norm_cfg), | ||
dict(type='Pad', size_divisor=128), | ||
dict(type='DefaultFormatBundle'), | ||
dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']), | ||
] | ||
test_pipeline = [ | ||
dict(type='LoadImageFromFile'), | ||
dict( | ||
type='MultiScaleFlipAug', | ||
img_scale=(2000, 1200), | ||
flip=False, | ||
transforms=[ | ||
dict(type='Resize', keep_ratio=True, backend='pillow'), | ||
dict(type='RandomFlip'), | ||
dict(type='Normalize', **img_norm_cfg), | ||
dict(type='Pad', size_divisor=128), | ||
dict(type='ImageToTensor', keys=['img']), | ||
dict(type='Collect', keys=['img']), | ||
]) | ||
] | ||
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# Use RepeatDataset to speed up training | ||
data = dict( | ||
samples_per_gpu=2, | ||
workers_per_gpu=2, | ||
train=dict( | ||
type='RepeatDataset', | ||
times=2, | ||
dataset=dict( | ||
type=dataset_type, | ||
ann_file=data_root + 'annotations/instances_train2017.json', | ||
img_prefix=data_root + 'train2017/', | ||
pipeline=train_pipeline)), | ||
val=dict( | ||
type=dataset_type, | ||
ann_file=data_root + 'annotations/instances_val2017.json', | ||
img_prefix=data_root + 'val2017/', | ||
pipeline=test_pipeline), | ||
test=dict( | ||
type=dataset_type, | ||
ann_file=data_root + 'annotations/instances_val2017.json', | ||
img_prefix=data_root + 'val2017/', | ||
pipeline=test_pipeline)) | ||
evaluation = dict(interval=1, metric='bbox') | ||
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# optimizer | ||
optimizer_config = dict(grad_clip=None) | ||
optimizer = dict( | ||
type='AdamW', | ||
lr=0.00005, | ||
betas=(0.9, 0.999), | ||
weight_decay=0.05, | ||
paramwise_cfg=dict( | ||
custom_keys={ | ||
'absolute_pos_embed': dict(decay_mult=0.), | ||
'relative_position_bias_table': dict(decay_mult=0.), | ||
'norm': dict(decay_mult=0.) | ||
})) | ||
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# learning policy | ||
lr_config = dict( | ||
policy='step', | ||
warmup='linear', | ||
warmup_iters=500, | ||
warmup_ratio=0.001, | ||
step=[8, 11]) | ||
runner = dict(type='EpochBasedRunner', max_epochs=12) |
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