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add swin transformer moe as backbone #209

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36 changes: 35 additions & 1 deletion README.md
Original file line number Diff line number Diff line change
Expand Up @@ -133,9 +133,43 @@ optimizer_config = dict(

> **Image Classification**: See [Swin Transformer for Image Classification](https://github.com/microsoft/Swin-Transformer).

> **Semantic Segmentation**: See [Swin Transformer for Semantic Segmentation](https://github.com/SwinTransformer/Swin-Transformer-Semantic-Segmentation).
> **Semantic Segmentation**: See [Swin Transformer for Semantic Segmentation](https://github.com/Transformer/Swin-Transformer-Semantic-Segmentation).

> **Self-Supervised Learning**: See [MoBY with Swin Transformer](https://github.com/SwinTransformer/Transformer-SSL).

> **Video Recognition**, See [Video Swin Transformer](https://github.com/SwinTransformer/Video-Swin-Transformer).

## swin-T moe

I added Swin Transformer MoE (referred to as Swin-T MoE hereafter) to the backbone network. MoE is a method that expands the model parameters and improves the model performance. The implementation of Swin Transformer MoE used Microsoft's Tutel framework.

### Install [Tutel](https://github.com/microsoft/tutel)

```
python3 -m pip uninstall tutel -y
python3 -m pip install --user --upgrade git+https://github.com/microsoft/tutel@main
```

You can check out Swin-T MoE at .

```
.\mmdet\models\backbones\swin_transformer_moe.py.
```

I provided the relevant configuration files for reference:

contains the parameters for the Swin-T MoE backbone network:

```
.\configs\swin\cascade_mask_rcnn_swin_moe_tiny_patch4_window7_mstrain_480-800_giou_4conv1f_adamw_3x_coco.py
```

contains the modified configuration for the backbone network:

```
.\configs\swin\cascade_mask_rcnn_swin_moe_tiny_patch4_window7_mstrain_480-800_giou_4conv1f_adamw_3x_coco.py
```

As the output of Swin-T MoE is different from Swin-T, I modified the `extract_feat` function in `.\mmdet\models\detectors\two_stage.py`.

You can change the config according to your needs.
216 changes: 216 additions & 0 deletions configs/_base_/models/cascade_mask_rcnn_swin_moe_fpn.py
Original file line number Diff line number Diff line change
@@ -0,0 +1,216 @@
# model settings
model = dict(
type='CascadeRCNN',
pretrained=None,
backbone=dict(
type='SwinTransformerMoE',
embed_dim=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.,
attn_drop_rate=0.,
drop_path_rate=0.2,
ape=False,
patch_norm=True,
out_indices=(0, 1, 2, 3),
use_checkpoint=False,
moe_blocks=[ [ -1 ], [ -1 ], [ 1, 3, 5 ], [ 1 ] ],
# moe_blocks=[ [ -1 ], [ -1 ], [ -1 ], [ -1 ] ],
num_local_experts=1,
top_value= 1,
capacity_factor= 1.25,
is_gshard_loss= False,
moe_drop= 0.1,
aux_loss_weight= 0.01
),
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],
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=1.0 / 9.0, loss_weight=1.0)),
roi_head=dict(
type='CascadeRoIHead',
num_stages=3,
stage_loss_weights=[1, 0.5, 0.25],
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=80,
bbox_coder=dict(
type='DeltaXYWHBBoxCoder',
target_means=[0., 0., 0., 0.],
target_stds=[0.1, 0.1, 0.2, 0.2]),
reg_class_agnostic=True,
loss_cls=dict(
type='CrossEntropyLoss',
use_sigmoid=False,
loss_weight=1.0),
loss_bbox=dict(type='SmoothL1Loss', beta=1.0,
loss_weight=1.0)),
dict(
type='Shared2FCBBoxHead',
in_channels=256,
fc_out_channels=1024,
roi_feat_size=7,
num_classes=80,
bbox_coder=dict(
type='DeltaXYWHBBoxCoder',
target_means=[0., 0., 0., 0.],
target_stds=[0.05, 0.05, 0.1, 0.1]),
reg_class_agnostic=True,
loss_cls=dict(
type='CrossEntropyLoss',
use_sigmoid=False,
loss_weight=1.0),
loss_bbox=dict(type='SmoothL1Loss', beta=1.0,
loss_weight=1.0)),
dict(
type='Shared2FCBBoxHead',
in_channels=256,
fc_out_channels=1024,
roi_feat_size=7,
num_classes=80,
bbox_coder=dict(
type='DeltaXYWHBBoxCoder',
target_means=[0., 0., 0., 0.],
target_stds=[0.033, 0.033, 0.067, 0.067]),
reg_class_agnostic=True,
loss_cls=dict(
type='CrossEntropyLoss',
use_sigmoid=False,
loss_weight=1.0),
loss_bbox=dict(type='SmoothL1Loss', beta=1.0, loss_weight=1.0))
],
mask_roi_extractor=dict(
type='SingleRoIExtractor',
roi_layer=dict(type='RoIAlign', output_size=14, sampling_ratio=0),
out_channels=256,
featmap_strides=[4, 8, 16, 32]),
mask_head=dict(
type='FCNMaskHead',
num_convs=4,
in_channels=256,
conv_out_channels=256,
num_classes=80,
loss_mask=dict(
type='CrossEntropyLoss', use_mask=True, loss_weight=1.0))),
# model training and testing settings
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=0,
pos_weight=-1,
debug=False),
rpn_proposal=dict(
nms_across_levels=False,
nms_pre=2000,
nms_post=2000,
max_per_img=2000,
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),
mask_size=28,
pos_weight=-1,
debug=False),
dict(
assigner=dict(
type='MaxIoUAssigner',
pos_iou_thr=0.6,
neg_iou_thr=0.6,
min_pos_iou=0.6,
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),
mask_size=28,
pos_weight=-1,
debug=False),
dict(
assigner=dict(
type='MaxIoUAssigner',
pos_iou_thr=0.7,
neg_iou_thr=0.7,
min_pos_iou=0.7,
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),
mask_size=28,
pos_weight=-1,
debug=False)
]),
test_cfg = dict(
rpn=dict(
nms_across_levels=False,
nms_pre=1000,
nms_post=1000,
max_per_img=1000,
nms=dict(type='nms', iou_threshold=0.7),
min_bbox_size=0),
rcnn=dict(
score_thr=0.05,
nms=dict(type='nms', iou_threshold=0.5),
max_per_img=100,
mask_thr_binary=0.5)))
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