danczs / Swin-Visformer-Object-Detection

This is forked from "Swin Transformer: Hierarchical Vision Transformer using Shifted Windows"

Swin Visformer for Object Detection

This repo contains the code of object detection for Visformer. It is based on Swin Transformer and mmdetection.

Object Detection on COCO

The standard self-attention is not efficient for high-reolution inputs, so we simply replace the standard self-attention with Swin attention for object detection. Therefore, Swin Transformer is our directly baseline.

Mask R-CNN

Backbone	sched	box mAP	mask mAP	params	FLOPs	FPS
Swin-T	1x	42.6	39.3	48	267	14.8
Visformer-S	1x	43.0	39.6	60	275	13.1
VisformerV2-S	1x	44.8	40.7	43	262	15.2
Swin-T	3x + MS	46.0	41.6	48	367	14.8
VisformerV2-S	3x + MS	47.8	42.5	43	262	15.2

Cascade Mask R-CNN

Backbone	sched	box mAP	mask mAP	params	FLOPs	FPS
Swin-T	1x + MS	48.1	41.7	86	745	9.5
VisformerV2-S	1x + MS	49.3	42.3	81	740	9.6
Swin-T	3x + MS	50.5	43.7	86	745	9.5
VisformerV2-S	3x + MS	51.6	44.1	81	740	9.6

Usage

(Inherited from Swin Transformer)

Installation

Please refer to get_started.md for installation and dataset preparation. (mmcv == 1.3.9)

Inference

# single-gpu testing
python tools/test.py <CONFIG_FILE> <DET_CHECKPOINT_FILE> --eval bbox segm

# multi-gpu testing
tools/dist_test.sh <CONFIG_FILE> <DET_CHECKPOINT_FILE> <GPU_NUM> --eval bbox segm

Training

To train a detector with pre-trained models, run:

# single-gpu training
python tools/train.py <CONFIG_FILE> --cfg-options model.pretrained=<PRETRAIN_MODEL> [model.backbone.use_checkpoint=True] [other optional arguments]

# multi-gpu training
tools/dist_train.sh <CONFIG_FILE> <GPU_NUM> --cfg-options model.pretrained=<PRETRAIN_MODEL> [model.backbone.use_checkpoint=True] [other optional arguments] 

For example, to train a Cascade Mask R-CNN model with a VisformerV2_S backbone and 8 gpus, run:

tools/dist_train.sh configs/swin_visformer/cascade_mask_rcnn_swin_visformer_small_v2_mstrain_480-800_adamw_3x_coco.py 8 --cfg-options model.pretrained=<PRETRAIN_MODEL> 

Note: use_checkpoint is used to save GPU memory. Please refer to this page for more details.

Apex (optional):

We use apex for mixed precision training by default. To install apex, run:

git clone https://github.com/NVIDIA/apex
cd apex
pip install -v --disable-pip-version-check --no-cache-dir --global-option="--cpp_ext" --global-option="--cuda_ext" ./

If you would like to disable apex, modify the type of runner as EpochBasedRunner and comment out the following code block in the configuration files:

# do not use mmdet version fp16
fp16 = None
optimizer_config = dict(
    type="DistOptimizerHook",
    update_interval=1,
    grad_clip=None,
    coalesce=True,
    bucket_size_mb=-1,
    use_fp16=True,
)

Other Links

Visformer for Classification: See Visformer for Image Classification.