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[Feature] RTMDet Swin / ConvNeXt #11259

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[Feature] RTMDet Swin / ConvNeXt #11259

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19 changes: 11 additions & 8 deletions configs/rtmdet/README.md
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Expand Up @@ -20,14 +20,17 @@ In this paper, we aim to design an efficient real-time object detector that exce

### Object Detection

| Model | size | box AP | Params(M) | FLOPS(G) | TRT-FP16-Latency(ms)<br>RTX3090 | TRT-FP16-Latency(ms)<br>T4 | Config | Download |
| :---------: | :--: | :----: | :-------: | :------: | :-----------------------------: | :------------------------: | :----------------------------------------: | :----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: |
| RTMDet-tiny | 640 | 41.1 | 4.8 | 8.1 | 0.98 | 2.34 | [config](./rtmdet_tiny_8xb32-300e_coco.py) | [model](https://download.openmmlab.com/mmdetection/v3.0/rtmdet/rtmdet_tiny_8xb32-300e_coco/rtmdet_tiny_8xb32-300e_coco_20220902_112414-78e30dcc.pth) \| [log](https://download.openmmlab.com/mmdetection/v3.0/rtmdet/rtmdet_tiny_8xb32-300e_coco/rtmdet_tiny_8xb32-300e_coco_20220902_112414.log.json) |
| RTMDet-s | 640 | 44.6 | 8.89 | 14.8 | 1.22 | 2.96 | [config](./rtmdet_s_8xb32-300e_coco.py) | [model](https://download.openmmlab.com/mmdetection/v3.0/rtmdet/rtmdet_s_8xb32-300e_coco/rtmdet_s_8xb32-300e_coco_20220905_161602-387a891e.pth) \| [log](https://download.openmmlab.com/mmdetection/v3.0/rtmdet/rtmdet_s_8xb32-300e_coco/rtmdet_s_8xb32-300e_coco_20220905_161602.log.json) |
| RTMDet-m | 640 | 49.4 | 24.71 | 39.27 | 1.62 | 6.41 | [config](./rtmdet_m_8xb32-300e_coco.py) | [model](https://download.openmmlab.com/mmdetection/v3.0/rtmdet/rtmdet_m_8xb32-300e_coco/rtmdet_m_8xb32-300e_coco_20220719_112220-229f527c.pth) \| [log](https://download.openmmlab.com/mmdetection/v3.0/rtmdet/rtmdet_m_8xb32-300e_coco/rtmdet_m_8xb32-300e_coco_20220719_112220.log.json) |
| RTMDet-l | 640 | 51.5 | 52.3 | 80.23 | 2.44 | 10.32 | [config](./rtmdet_l_8xb32-300e_coco.py) | [model](https://download.openmmlab.com/mmdetection/v3.0/rtmdet/rtmdet_l_8xb32-300e_coco/rtmdet_l_8xb32-300e_coco_20220719_112030-5a0be7c4.pth) \| [log](https://download.openmmlab.com/mmdetection/v3.0/rtmdet/rtmdet_l_8xb32-300e_coco/rtmdet_l_8xb32-300e_coco_20220719_112030.log.json) |
| RTMDet-x | 640 | 52.8 | 94.86 | 141.67 | 3.10 | 18.80 | [config](./rtmdet_x_8xb32-300e_coco.py) | [model](https://download.openmmlab.com/mmdetection/v3.0/rtmdet/rtmdet_x_8xb32-300e_coco/rtmdet_x_8xb32-300e_coco_20220715_230555-cc79b9ae.pth) \| [log](https://download.openmmlab.com/mmdetection/v3.0/rtmdet/rtmdet_x_8xb32-300e_coco/rtmdet_x_8xb32-300e_coco_20220715_230555.log.json) |
| RTMDet-x-P6 | 1280 | 54.9 | | | | | [config](./rtmdet_x_p6_4xb8-300e_coco.py) | [model](https://github.com/orange0-jp/orange-weights/releases/download/v0.1.0rtmdet-p6/rtmdet_x_p6_4xb8-300e_coco-bf32be58.pth) |
| Model | size | box AP | Params(M) | FLOPS(G) | TRT-FP16-Latency(ms)<br>RTX3090 | TRT-FP16-Latency(ms)<br>T4 | Config | Download |
| :-----------------: | :--: | :----: | :-------: | :------: | :-----------------------------: | :------------------------: | :------------------------------------------------: | :----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: |
| RTMDet-tiny | 640 | 41.1 | 4.8 | 8.1 | 0.98 | 2.34 | [config](./rtmdet_tiny_8xb32-300e_coco.py) | [model](https://download.openmmlab.com/mmdetection/v3.0/rtmdet/rtmdet_tiny_8xb32-300e_coco/rtmdet_tiny_8xb32-300e_coco_20220902_112414-78e30dcc.pth) \| [log](https://download.openmmlab.com/mmdetection/v3.0/rtmdet/rtmdet_tiny_8xb32-300e_coco/rtmdet_tiny_8xb32-300e_coco_20220902_112414.log.json) |
| RTMDet-s | 640 | 44.6 | 8.89 | 14.8 | 1.22 | 2.96 | [config](./rtmdet_s_8xb32-300e_coco.py) | [model](https://download.openmmlab.com/mmdetection/v3.0/rtmdet/rtmdet_s_8xb32-300e_coco/rtmdet_s_8xb32-300e_coco_20220905_161602-387a891e.pth) \| [log](https://download.openmmlab.com/mmdetection/v3.0/rtmdet/rtmdet_s_8xb32-300e_coco/rtmdet_s_8xb32-300e_coco_20220905_161602.log.json) |
| RTMDet-m | 640 | 49.4 | 24.71 | 39.27 | 1.62 | 6.41 | [config](./rtmdet_m_8xb32-300e_coco.py) | [model](https://download.openmmlab.com/mmdetection/v3.0/rtmdet/rtmdet_m_8xb32-300e_coco/rtmdet_m_8xb32-300e_coco_20220719_112220-229f527c.pth) \| [log](https://download.openmmlab.com/mmdetection/v3.0/rtmdet/rtmdet_m_8xb32-300e_coco/rtmdet_m_8xb32-300e_coco_20220719_112220.log.json) |
| RTMDet-l | 640 | 51.5 | 52.3 | 80.23 | 2.44 | 10.32 | [config](./rtmdet_l_8xb32-300e_coco.py) | [model](https://download.openmmlab.com/mmdetection/v3.0/rtmdet/rtmdet_l_8xb32-300e_coco/rtmdet_l_8xb32-300e_coco_20220719_112030-5a0be7c4.pth) \| [log](https://download.openmmlab.com/mmdetection/v3.0/rtmdet/rtmdet_l_8xb32-300e_coco/rtmdet_l_8xb32-300e_coco_20220719_112030.log.json) |
| RTMDet-x | 640 | 52.8 | 94.86 | 141.67 | 3.10 | 18.80 | [config](./rtmdet_x_8xb32-300e_coco.py) | [model](https://download.openmmlab.com/mmdetection/v3.0/rtmdet/rtmdet_x_8xb32-300e_coco/rtmdet_x_8xb32-300e_coco_20220715_230555-cc79b9ae.pth) \| [log](https://download.openmmlab.com/mmdetection/v3.0/rtmdet/rtmdet_x_8xb32-300e_coco/rtmdet_x_8xb32-300e_coco_20220715_230555.log.json) |
| RTMDet-x-P6 | 1280 | 54.9 | | | | | [config](./rtmdet_x_p6_4xb8-300e_coco.py) | [model](https://github.com/orange0-jp/orange-weights/releases/download/v0.1.0rtmdet-p6/rtmdet_x_p6_4xb8-300e_coco-bf32be58.pth) |
| RTMDet-l-ConvNeXt-B | 640 | 53.1 | | | | | [config](./rtmdet_l_convnext_b_4xb32-100e_coco.py) | [model](https://github.com/orange0-jp/orange-weights/releases/download/v0.1.0rtmdet-swin-convnext/rtmdet_l_convnext_b_4xb32-100e_coco-d4731b3d.pth) |
| RTMDet-l-Swin-B | 640 | 52.4 | | | | | [config](./rtmdet_l_swin_b_4xb32-100e_coco.py) | [model](https://github.com/orange0-jp/orange-weights/releases/download/v0.1.0rtmdet-swin-convnext/rtmdet_l_swin_b_4xb32-100e_coco-0828ce5d.pth) |
| RTMDet-l-Swin-B-P6 | 1280 | 56.4 | | | | | [config](./rtmdet_l_swin_b_p6_4xb16-100e_coco.py) | [model](https://github.com/orange0-jp/orange-weights/releases/download/v0.1.0rtmdet-swin-convnext/rtmdet_l_swin_b_p6_4xb16-100e_coco-a1486b6f.pth) |

**Note**:

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42 changes: 42 additions & 0 deletions configs/rtmdet/metafile.yml
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Expand Up @@ -104,6 +104,48 @@ Models:
box AP: 54.9
Weights: https://github.com/orange0-jp/orange-weights/releases/download/v0.1.0rtmdet-p6/rtmdet_x_p6_4xb8-300e_coco-bf32be58.pth

- Name: rtmdet_l_convnext_b_4xb32-100e_coco
Alias:
- rtmdet-l_convnext_b
In Collection: RTMDet
Config: configs/rtmdet/rtmdet_l_convnext_b_4xb32-100e_coco.py
Metadata:
Epochs: 100
Results:
- Task: Object Detection
Dataset: COCO
Metrics:
box AP: 53.1
Weights: https://github.com/orange0-jp/orange-weights/releases/download/v0.1.0rtmdet-swin-convnext/rtmdet_l_convnext_b_4xb32-100e_coco-d4731b3d.pth

- Name: rtmdet_l_swin_b_4xb32-100e_coco
Alias:
- rtmdet-l_swin_b
In Collection: RTMDet
Config: configs/rtmdet/rtmdet_l_swin_b_4xb32-100e_coco.py
Metadata:
Epochs: 100
Results:
- Task: Object Detection
Dataset: COCO
Metrics:
box AP: 52.4
Weights: https://github.com/orange0-jp/orange-weights/releases/download/v0.1.0rtmdet-swin-convnext/rtmdet_l_swin_b_4xb32-100e_coco-0828ce5d.pth

- Name: rtmdet_l_swin_b_p6_4xb16-100e_coco
Alias:
- rtmdet-l_swin_b_p6
In Collection: RTMDet
Config: configs/rtmdet/rtmdet_l_swin_b_p6_4xb16-100e_coco.py
Metadata:
Epochs: 100
Results:
- Task: Object Detection
Dataset: COCO
Metrics:
box AP: 56.4
Weights: https://github.com/orange0-jp/orange-weights/releases/download/v0.1.0rtmdet-swin-convnext/rtmdet_l_swin_b_p6_4xb16-100e_coco-a1486b6f.pth

- Name: rtmdet-ins_tiny_8xb32-300e_coco
Alias:
- rtmdet-ins-t
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81 changes: 81 additions & 0 deletions configs/rtmdet/rtmdet_l_convnext_b_4xb32-100e_coco.py
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_base_ = './rtmdet_l_8xb32-300e_coco.py'

custom_imports = dict(
imports=['mmpretrain.models'], allow_failed_imports=False)

norm_cfg = dict(type='GN', num_groups=32)
checkpoint_file = 'https://download.openmmlab.com/mmclassification/v0/convnext/convnext-base_in21k-pre-3rdparty_in1k-384px_20221219-4570f792.pth' # noqa
model = dict(
type='RTMDet',
data_preprocessor=dict(
_delete_=True,
type='DetDataPreprocessor',
mean=[123.675, 116.28, 103.53],
std=[58.395, 57.12, 57.375],
bgr_to_rgb=True,
batch_augments=None),
backbone=dict(
_delete_=True,
type='mmpretrain.ConvNeXt',
arch='base',
out_indices=[1, 2, 3],
drop_path_rate=0.7,
layer_scale_init_value=1.0,
gap_before_final_norm=False,
with_cp=True,
init_cfg=dict(
type='Pretrained', checkpoint=checkpoint_file,
prefix='backbone.')),
neck=dict(in_channels=[256, 512, 1024], norm_cfg=norm_cfg),
bbox_head=dict(norm_cfg=norm_cfg))

max_epochs = 100
stage2_num_epochs = 10
interval = 10
base_lr = 0.001

train_cfg = dict(
max_epochs=max_epochs,
val_interval=interval,
dynamic_intervals=[(max_epochs - stage2_num_epochs, 1)])

optim_wrapper = dict(
constructor='LearningRateDecayOptimizerConstructor',
paramwise_cfg={
'decay_rate': 0.8,
'decay_type': 'layer_wise',
'num_layers': 12
},
optimizer=dict(lr=base_lr))

# learning rate
param_scheduler = [
dict(
type='LinearLR',
start_factor=1.0e-5,
by_epoch=False,
begin=0,
end=1000),
dict(
# use cosine lr from 50 to 100 epoch
type='CosineAnnealingLR',
eta_min=base_lr * 0.05,
begin=max_epochs // 2,
end=max_epochs,
T_max=max_epochs // 2,
by_epoch=True,
convert_to_iter_based=True),
]

custom_hooks = [
dict(
type='EMAHook',
ema_type='ExpMomentumEMA',
momentum=0.0002,
update_buffers=True,
priority=49),
dict(
type='PipelineSwitchHook',
switch_epoch=max_epochs - stage2_num_epochs,
switch_pipeline={{_base_.train_pipeline_stage2}})
]
78 changes: 78 additions & 0 deletions configs/rtmdet/rtmdet_l_swin_b_4xb32-100e_coco.py
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_base_ = './rtmdet_l_8xb32-300e_coco.py'

norm_cfg = dict(type='GN', num_groups=32)
checkpoint = 'https://github.com/SwinTransformer/storage/releases/download/v1.0.0/swin_base_patch4_window12_384_22k.pth' # noqa
model = dict(
type='RTMDet',
data_preprocessor=dict(
_delete_=True,
type='DetDataPreprocessor',
mean=[123.675, 116.28, 103.53],
std=[58.395, 57.12, 57.375],
bgr_to_rgb=True,
batch_augments=None),
backbone=dict(
_delete_=True,
type='SwinTransformer',
pretrain_img_size=384,
embed_dims=128,
depths=[2, 2, 18, 2],
num_heads=[4, 8, 16, 32],
window_size=12,
mlp_ratio=4,
qkv_bias=True,
qk_scale=None,
drop_rate=0.,
attn_drop_rate=0.,
drop_path_rate=0.3,
patch_norm=True,
out_indices=(1, 2, 3),
with_cp=True,
convert_weights=True,
init_cfg=dict(type='Pretrained', checkpoint=checkpoint)),
neck=dict(in_channels=[256, 512, 1024], norm_cfg=norm_cfg),
bbox_head=dict(norm_cfg=norm_cfg))

max_epochs = 100
stage2_num_epochs = 10
interval = 10
base_lr = 0.001

train_cfg = dict(
max_epochs=max_epochs,
val_interval=interval,
dynamic_intervals=[(max_epochs - stage2_num_epochs, 1)])

optim_wrapper = dict(optimizer=dict(lr=base_lr))

# learning rate
param_scheduler = [
dict(
type='LinearLR',
start_factor=1.0e-5,
by_epoch=False,
begin=0,
end=1000),
dict(
# use cosine lr from 50 to 100 epoch
type='CosineAnnealingLR',
eta_min=base_lr * 0.05,
begin=max_epochs // 2,
end=max_epochs,
T_max=max_epochs // 2,
by_epoch=True,
convert_to_iter_based=True),
]

custom_hooks = [
dict(
type='EMAHook',
ema_type='ExpMomentumEMA',
momentum=0.0002,
update_buffers=True,
priority=49),
dict(
type='PipelineSwitchHook',
switch_epoch=max_epochs - stage2_num_epochs,
switch_pipeline={{_base_.train_pipeline_stage2}})
]
114 changes: 114 additions & 0 deletions configs/rtmdet/rtmdet_l_swin_b_p6_4xb16-100e_coco.py
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_base_ = './rtmdet_l_swin_b_4xb32-100e_coco.py'

model = dict(
backbone=dict(
depths=[2, 2, 18, 2, 1],
num_heads=[4, 8, 16, 32, 64],
strides=(4, 2, 2, 2, 2),
out_indices=(1, 2, 3, 4)),
neck=dict(in_channels=[256, 512, 1024, 2048]),
bbox_head=dict(
anchor_generator=dict(
type='MlvlPointGenerator', offset=0, strides=[8, 16, 32, 64])))

train_pipeline = [
dict(type='LoadImageFromFile', backend_args={{_base_.backend_args}}),
dict(type='LoadAnnotations', with_bbox=True),
dict(type='CachedMosaic', img_scale=(1280, 1280), pad_val=114.0),
dict(
type='RandomResize',
scale=(2560, 2560),
ratio_range=(0.1, 2.0),
keep_ratio=True),
dict(type='RandomCrop', crop_size=(1280, 1280)),
dict(type='YOLOXHSVRandomAug'),
dict(type='RandomFlip', prob=0.5),
dict(type='Pad', size=(1280, 1280), pad_val=dict(img=(114, 114, 114))),
dict(
type='CachedMixUp',
img_scale=(1280, 1280),
ratio_range=(1.0, 1.0),
max_cached_images=20,
pad_val=(114, 114, 114)),
dict(type='PackDetInputs')
]

train_pipeline_stage2 = [
dict(type='LoadImageFromFile', backend_args={{_base_.backend_args}}),
dict(type='LoadAnnotations', with_bbox=True),
dict(
type='RandomResize',
scale=(1280, 1280),
ratio_range=(0.1, 2.0),
keep_ratio=True),
dict(type='RandomCrop', crop_size=(1280, 1280)),
dict(type='YOLOXHSVRandomAug'),
dict(type='RandomFlip', prob=0.5),
dict(type='Pad', size=(1280, 1280), pad_val=dict(img=(114, 114, 114))),
dict(type='PackDetInputs')
]

test_pipeline = [
dict(type='LoadImageFromFile', backend_args={{_base_.backend_args}}),
dict(type='Resize', scale=(1280, 1280), keep_ratio=True),
dict(type='Pad', size=(1280, 1280), pad_val=dict(img=(114, 114, 114))),
dict(type='LoadAnnotations', with_bbox=True),
dict(
type='PackDetInputs',
meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape',
'scale_factor'))
]

train_dataloader = dict(
batch_size=16, num_workers=20, dataset=dict(pipeline=train_pipeline))
val_dataloader = dict(num_workers=20, dataset=dict(pipeline=test_pipeline))
test_dataloader = val_dataloader

max_epochs = 100
stage2_num_epochs = 10

custom_hooks = [
dict(
type='EMAHook',
ema_type='ExpMomentumEMA',
momentum=0.0002,
update_buffers=True,
priority=49),
dict(
type='PipelineSwitchHook',
switch_epoch=max_epochs - stage2_num_epochs,
switch_pipeline=train_pipeline_stage2)
]

img_scales = [(1280, 1280), (640, 640), (1920, 1920)]
tta_pipeline = [
dict(type='LoadImageFromFile', backend_args=None),
dict(
type='TestTimeAug',
transforms=[
[
dict(type='Resize', scale=s, keep_ratio=True)
for s in img_scales
],
[
# ``RandomFlip`` must be placed before ``Pad``, otherwise
# bounding box coordinates after flipping cannot be
# recovered correctly.
dict(type='RandomFlip', prob=1.),
dict(type='RandomFlip', prob=0.)
],
[
dict(
type='Pad',
size=(1920, 1920),
pad_val=dict(img=(114, 114, 114))),
],
[dict(type='LoadAnnotations', with_bbox=True)],
[
dict(
type='PackDetInputs',
meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape',
'scale_factor', 'flip', 'flip_direction'))
]
])
]