Vision Longformer

This project provides the source code for the vision longformer paper.

Multi-Scale Vision Longformer: A New Vision Transformer for High-Resolution Image Encoding

Highlights

Fast Pytorch implementation of conv-like sliding-window local attention
Fast random-shifting training strategy of vision longformer
A versatile multi-scale vision transformer class (MsViT) that can support various efficient attention mechanisms
Compare multiple efficient attention mechanisms: vision-longformer ("global + conv_like local") attention, performer attention, global-memory attention, linformer attention and spatial reduction attention.
Provides pre-trained models for different attention mechanisms.

Updates

03/29/2021: First version of vision longformer paper posted on Arxiv.
04/30/2021: Performance improved by adding relative positional bias, inspired by Swin Transformer! Training is accelerated significantly by adding random-shifting training strategy. First version of code released.
05/17/2021: First version of Object Detection code and checkpoints released at VisionLongformerForObjectDetection.
05/27/2021: vision longformer paper is updated on Arxiv to reflect the recent changes. The object detection results are improved significantly due to the relative positional bias and tuning of the drop_path hyper-parameter.
07/23/2021: Vision Longformer is officially accepted by ICCV 2021.

Multi-scale Vision Transformer Architecture

Vision Longformer, and more generally the Multi-scale Vision Transformer (MsViT), follows the multi-stage design of ResNet. Each stage is a (slightly modified) vision transformer with some user-specified attenion mechanism. Currently, five attention mechanisms are supported:

# choices=['full', 'longformerhand', 'linformer', 'srformer', 'performer', 'longformerauto', 'longformer_cuda']
_C.MODEL.VIT.MSVIT.ATTN_TYPE = 'longformerhand'

As an example, a 3-stage multi-scale model architecture is specified by the MODEL.VIT.MSVIT.ARCH:

_C.MODEL.VIT.MSVIT.ARCH = 'l1,h3,d192,n1,s1,g1,p16,f7,a1_l2,h6,d384,n10,s0,g1,p2,f7,a1_l3,h12,d796,n1,s0,g1,p2,f7,a1'

Configs of different stages are separated by _. For each stage, the meaning of the config l*,h*,d*,n*,s*,g*,p*,f*,a* is specified as below.

symbol	l	h	d	n	s	g	p	f	a
Name	stage	num_heads	hidden_dim	num_layers	is_parse_attention	num_global_tokens	patch_size	num_feats	absolute_position_embedding
Range	[1,2,3,4]	N+	N+	N+	[0, 1]	N	N	N	[0,1]

Here, N stands for natural numbers including 0, and N+ stands for positive integers.

The num_feats (number of features) field, i.e., f, is overloaded for different attention mechanisms:

linformer: number of features

performer: number of (random orthogonal) features

srformer: spatial reduction ratio

longformer: one sided window size (not including itself, actual window size is 2 * f + 1 for MSVIT.SW_EXACT = 1 and 3 * f for MSVIT.SW_EXACT = 0/-1).

The following are the main model architectures used in Vision Longformer paper.

Model size	stage_1	stage_2	stage_3	stage_4
Tiny	n1,p4,h1,d48	n1,p2,h3,d96	n9,p2,h3,d192	n1,p2,h6,d384
Small	n1,p4,h3,d96	n2,p2,h3,d192	n8,p2,h6,d384	n1,p2,h12,d768
Medium-Deep	n1,p4,h3,d96	n4,p2,h3,d192	n16,p2,h6,d384	n1,p2,h12,d768
Medium-Wide	n1,p4,h3,d192	n2,p2,h6,d384	n8,p2,h8,d512	n1,p2,h12,d768
Base-Deep	n1,p4,h3,d96	n8,p2,h3,d192	n24,p2,h6,d384	n1,p2,h12,d768
Base-Wide	n1,p4,h3,d192	n2,p2,h6,d384	n8,p2,h12,d768	n1,p2,h16,d1024

Model Performance

Main Results on ImageNet and Pretrained Models

Vision Longformer with absolute positional embedding

name	pretrain	resolution	acc@1	acc@5	#params	FLOPs	22K model	1K model
ViL-Tiny	ImageNet-1K	224x224	76.3	93.3	6.7M	1.43G	-	ckpt, config
ViL-Small	ImageNet-1K	224x224	82.0	95.8	24.6M	5.12G	-	ckpt, config
ViL-Medium-Deep	ImageNet-1K	224x224	83.3	96.3	39.7M	9.1G	-	ckpt, config
ViL-Medium-Wide	ImageNet-1K	224x224	82.9	96.4	39.8M	11.3G	-	ckpt, config
ViL-Medium-Deep	ImageNet-22K	384x384	85.6	97.7	39.7M	29.4G	ckpt, config	ckpt, config
ViL-Medium-Wide	ImageNet-22K	384x384	84.7	97.3	39.8M	35.1G	ckpt, config	ckpt, config
ViL-Base-Deep	ImageNet-22K	384x384	86.0	97.9	55.7M	45.3G	ckpt, config	ckpt, config
ViL-Base-Wide	ImageNet-22K	384x384	86.2	98.0	79.0M	55.8G	ckpt, config	ckpt, config

Vision Longformer with relative positional embedding and comparison with Swin Transformers

name	pretrain	resolution	acc@1	acc@5	#params	FLOPs	22K model	1K model
ViL-Tiny	ImageNet-1K	224x224	76.65	93.55	6.7M	1.43G	-	ckpt config
ViL-Small	ImageNet-1K	224x224	82.39	95.92	24.6M	5.12G	-	ckpt config
ViL-Medium-Deep	ImageNet-1K	224x224	83.52	96.52	39.7M	9.1G	-	ckpt config
ViL-Medium-Deep	ImageNet-22K	384x384	85.73	97.8	39.7M	29.4G	ckpt config	ckpt config
ViL-Base-Deep	ImageNet-22K	384x384	86.11	97.89	55.7M	45.3G	ckpt config	ckpt config
---	---	---	---	---	---	---	---	---
Swin-Tiny (2-2-6-2)	ImageNet-1K	224x224	81.2	95.5	28M	4.5G	-	from swin repo
ViL-Swin-Tiny (2-2-6-2)	ImageNet-1K	224x224	82.71	95.95	28M	5.33G	-	ckpt config
Swin-Small (2-2-18-2)	ImageNet-1K	224x224	83.2	96.2	50M	8.7G	-	from swin repo
ViL-Swin-Small (2-2-18-2)	ImageNet-1K	224x224	83.7	96.43	50M	9.85G	-	ckpt config

Results of other attention mechanims (Small size)

Attention	pretrain	resolution	acc@1	acc@5	#params	FLOPs	22K model	1K model
full	ImageNet-1K	224x224	81.9	95.8	24.6M	6.95G	-	ckpt, config
longformer	ImageNet-1K	224x224	82.0	95.8	24.6M	5.12G	-	ckpt, config
---	---	---	---	---	---	---	---	---
linformer	ImageNet-1K	224x224	81.0	95.4	26.3M	5.62G	-	ckpt, config
srformer/64	ImageNet-1K	224x224	76.4	92.9	52.9M	3.97G	-	ckpt, config
srformer/32	ImageNet-1K	224x224	79.9	94.9	31.1M	4.28G	-	ckpt, config
global	ImageNet-1K	224x224	79.0	94.5	24.9M	6.78G	-	ckpt, config
performer	ImageNet-1K	224x224	78.7	94.3	24.8M	6.26G	-	ckpt, config
---	---	---	---	---	---	---	---	---
partial linformer	ImageNet-1K	224x224	81.8	95.9	25.8M	5.21G	-	ckpt, config
partial srformer/32	ImageNet-1K	224x224	81.6	95.7	26.4M	4.57G	-	ckpt, config
partial global	ImageNet-1K	224x224	81.4	95.7	24.9M	6.3G	-	ckpt, config
partial performer	ImageNet-1K	224x224	81.7	95.7	24.7M	5.52G	-	ckpt, config

See more results on comparing different efficient attention mechanisms in Table 13 and Table 14 in the Vision Longformer paper.

Main Results on COCO object detection and instance segmentation (with absolute positional embedding)

Vision Longformer with relative positional bias

Backbone	Method	pretrain	drop_path	Lr Schd	box mAP	mask mAP	#params	FLOPs	checkpoints	log
ViL-Tiny	Mask R-CNN	ImageNet-1K	0.05	1x	41.4	38.1	26.9M	145.6G	ckpt config	log
ViL-Tiny	Mask R-CNN	ImageNet-1K	0.1	3x	44.2	40.6	26.9M	145.6G	ckpt config	log
ViL-Small	Mask R-CNN	ImageNet-1K	0.2	1x	45.0	41.2	45.0M	218.3G	ckpt config	log
ViL-Small	Mask R-CNN	ImageNet-1K	0.2	3x	47.1	42.7	45.0M	218.3G	ckpt config	log
ViL-Medium (D)	Mask R-CNN	ImageNet-21K	0.2	1x	47.6	43.0	60.1M	293.8G	ckpt config	log
ViL-Medium (D)	Mask R-CNN	ImageNet-21K	0.3	3x	48.9	44.2	60.1M	293.8G	ckpt config	log
ViL-Base (D)	Mask R-CNN	ImageNet-21K	0.3	1x	48.6	43.6	76.1M	384.4G	ckpt config	log
ViL-Base (D)	Mask R-CNN	ImageNet-21K	0.3	3x	49.6	44.5	76.1M	384.4G	ckpt config	log
---	---	---	---	---	---	---	---	---
ViL-Tiny	RetinaNet	ImageNet-1K	0.05	1x	40.8	--	16.64M	182.7G	ckpt config	log
ViL-Tiny	RetinaNet	ImageNet-1K	0.1	3x	43.6	--	16.64M	182.7G	ckpt config	log
ViL-Small	RetinaNet	ImageNet-1K	0.1	1x	44.2	--	35.68M	254.8G	ckpt config	log
ViL-Small	RetinaNet	ImageNet-1K	0.2	3x	45.9	--	35.68M	254.8G	ckpt config	log
ViL-Medium (D)	RetinaNet	ImageNet-21K	0.2	1x	46.8	--	50.77M	330.4G	ckpt config	log
ViL-Medium (D)	RetinaNet	ImageNet-21K	0.3	3x	47.9	--	50.77M	330.4G	ckpt config	log
ViL-Base (D)	RetinaNet	ImageNet-21K	0.3	1x	47.8	--	66.74M	420.9G	ckpt config	log
ViL-Base (D)	RetinaNet	ImageNet-21K	0.3	3x	48.6	--	66.74M	420.9G	ckpt config	log

See more fine-grained results in Table 6 and Table 7 in the Vision Longformer paper. We use weight decay 0.05 for all experiments, but search for best drop path in [0.05, 0.1, 0.2, 0.3].

Comparison of various efficient attention mechanims with absolute positional embedding (Small size)

Backbone	Method	pretrain	drop_path	Lr Schd	box mAP	mask mAP	#params	FLOPs	Memory	checkpoints	log
srformer/64	Mask R-CNN	ImageNet-1K	0.1	1x	36.4	34.6	73.3M	224.1G	7.1G	ckpt config	log
srformer/32	Mask R-CNN	ImageNet-1K	0.1	1x	39.9	37.3	51.5M	268.3G	13.6G	ckpt config	log
Partial srformer/32	Mask R-CNN	ImageNet-1K	0.1	1x	42.4	39.0	46.8M	352.1G	22.6G	ckpt config	log
global	Mask R-CNN	ImageNet-1K	0.1	1x	34.8	33.4	45.2M	226.4G	7.6G	ckpt config	log
Partial global	Mask R-CNN	ImageNet-1K	0.1	1x	42.5	39.2	45.1M	326.5G	20.1G	ckpt config	log
performer	Mask R-CNN	ImageNet-1K	0.1	1x	36.1	34.3	45.0M	251.5G	8.4G	ckpt config	log
Partial performer	Mask R-CNN	ImageNet-1K	0.05	1x	42.3	39.1	45.0M	343.7G	20.0G	ckpt config	log
ViL	Mask R-CNN	ImageNet-1K	0.1	1x	42.9	39.6	45.0M	218.3G	7.4G	ckpt config	log
Partial ViL	Mask R-CNN	ImageNet-1K	0.1	1x	43.3	39.8	45.0M	326.8G	19.5G	ckpt config	log

We use weight decay 0.05 for all experiments, but search for best drop path in [0.05, 0.1, 0.2].

Compare different implementations of vision longformer

Please go to Implementation for implementation details of vision longformer.

Training/Testing Vision Longformer on Local Machine

Prepare datasets

One needs to download zip files of ImageNet (train.zip, train_map.txt, val.zip, val_map.txt) under the specified data folder, e.g., the default src/datasets/imagenet. The CIFAR10, CIFAR100 and MNIST can be automatically downloaded.

With the default setting, we should have the following files in the /root/datasets directory:

root (root folder)
├── datasets (folder with all the datasets and pretrained models)
├──── imagenet/ (imagenet dataset and pretrained models)
├────── 2012/
├───────── train.zip
├───────── val.zip
├───────── train_map.txt
├───────── val_map.txt
├──── CIFAR10/ (CIFAR10 dataset and pretrained models)
├──── CIFAR100/ (CIFAR100 dataset and pretrained models)
├──── MNIST/ (MNIST dataset and pretrained models)

Environment requirements

It is recommended to use any of the following docker images to run the experiments.

pengchuanzhang/maskrcnn:ubuntu18-py3.7-cuda10.1-pytorch1.7 # recommended
pengchuanzhang/maskrcnn:py3.7-cuda10.0-pytorch1.7 # if you want to try the customized cuda kernel of vision longformer.

For virtual environments, the following packages should be the sufficient.

pytorch >= 1.5
tensorboardx, einops, timm, yacs==0.1.8

Evaluation scripts

Navigate to the src folder, run the following commands to evaluate the pre-trained models above.

Pretrained models of Vision Longformer

# tiny
python run_experiment.py --config-file 'config/msvit.yaml' --data ../datasets/imagenet/2012 --output_dir ../run/imagenet/msvittest MODEL.VIT.MSVIT.ARCH 'l1,h1,d48,n1,s1,g1,p4,f7_l2,h3,d96,n1,s1,g1,p2,f7_l3,h3,d192,n9,s0,g1,p2,f7_l4,h6,d384,n1,s0,g0,p2,f7' EVALUATE True MODEL.MODEL_PATH /home/penzhan/penzhanwu2/imagenet/msvit/visionlongformer/msvit_tiny_longformersw_1191_train/model_best.pth 
INFO:root:ACCURACY: 76.29600524902344%
INFO:root:iter: 0  max mem: 2236
    accuracy_metrics - top1: 76.2960 (76.2960)  top5: 93.2720 (93.2720)
    epoch_metrics    - total_cnt: 50000.0000 (50000.0000)  loss: 0.0040 (0.0040)  time: 0.0022 (0.0022)

# small
python run_experiment.py --config-file 'config/msvit.yaml' --data ../datasets/imagenet/2012 --output_dir ../run/imagenet/msvittest MODEL.VIT.MSVIT.ARCH 'l1,h3,d96,n1,s1,g1,p4,f7_l2,h3,d192,n2,s1,g1,p2,f7_l3,h6,d384,n8,s0,g1,p2,f7_l4,h12,d768,n1,s0,g0,p2,f7' EVALUATE True MODEL.MODEL_PATH /home/penzhan/penzhanwu2/imagenet/msvit/visionlongformer/msvit_small_longformersw_1281_train/model_best.pth 
INFO:root:ACCURACY: 81.97799682617188%
INFO:root:iter: 0  max mem: 6060
    accuracy_metrics - top1: 81.9780 (81.9780)  top5: 95.7880 (95.7880)
    epoch_metrics    - total_cnt: 50000.0000 (50000.0000)  loss: 0.0031 (0.0031)  time: 0.0029 (0.0029)

# medium-deep
python run_experiment.py --config-file 'config/msvit.yaml' --data ../datasets/imagenet/2012 --output_dir ../run/imagenet/msvittest MODEL.VIT.MSVIT.ARCH 'l1,h3,d96,n1,s1,g1,p4,f7_l2,h3,d192,n4,s1,g1,p2,f7_l3,h6,d384,n16,s0,g1,p2,f7_l4,h12,d768,n1,s0,g0,p2,f7' EVALUATE True MODEL.MODEL_PATH /home/penzhan/penzhanwu2/imagenet/msvit/visionlongformer/deepmedium_14161_lr8e-4/model_best.pth

# medium-wide
python run_experiment.py --config-file 'config/msvit.yaml' --data ../datasets/imagenet/2012 --output_dir ../run/imagenet/msvittest MODEL.VIT.MSVIT.ARCH 'l1,h3,d192,n1,s1,g1,p4,f7_l2,h6,d384,n2,s1,g1,p2,f7_l3,h8,d512,n8,s0,g1,p2,f7_l4,h12,d768,n1,s0,g0,p2,f7' EVALUATE True MODEL.MODEL_PATH /home/penzhan/penzhanwu2/imagenet/msvit/visionlongformer/wide_medium_1281/model_best.pth

# ImageNet22K pretrained and ImageNet1K finetuned medium-deep
python run_experiment.py --config-file 'config/msvit.yaml' --data ../datasets/imagenet/2012 --output_dir ../run/imagenet/msvittest FINETUNE.FINETUNE True INPUT.IMAGE_SIZE 384 INPUT.CROP_PCT 0.922 MODEL.VIT.MSVIT.ARCH 'l1,h3,d96,n1,s1,g1,p4,f7_l2,h3,d192,n4,s1,g1,p2,f7_l3,h6,d384,n16,s0,g1,p2,f7_l4,h12,d768,n1,s0,g0,p2,f7' EVALUATE True MODEL.MODEL_PATH /home/penzhan/penzhanwu2/imagenet/msvit/IN384_IN22kpretrained/msvitdeepmedium_imagenet384_finetune_bsz256_lr001_wd0/model_best.pth

# ImageNet22K pretrained and ImageNet1K finetuned medium-wide
python run_experiment.py --config-file 'config/msvit.yaml' --data ../datasets/imagenet/2012 --output_dir ../run/imagenet/msvittest FINETUNE.FINETUNE True INPUT.IMAGE_SIZE 384 INPUT.CROP_PCT 0.922 MODEL.VIT.MSVIT.ARCH 'l1,h3,d192,n1,s1,g1,p4,f8_l2,h6,d384,n2,s1,g1,p2,f12_l3,h8,d512,n8,s0,g1,p2,f7_l4,h12,d768,n1,s0,g0,p2,f7' EVALUATE True MODEL.MODEL_PATH /home/penzhan/penzhanwu2/imagenet/msvit/IN384_IN22kpretrained/msvitwidemedium_imagenet384_finetune_bsz512_lr004_wd0/model_best.pth

# ImageNet22K pretrained and ImageNet1K finetuned base-deep
python run_experiment.py --config-file 'config/msvit.yaml' --data ../datasets/imagenet/2012 --output_dir ../run/imagenet/msvittest FINETUNE.FINETUNE True INPUT.IMAGE_SIZE 384 INPUT.CROP_PCT 0.922 MODEL.VIT.MSVIT.LN_EPS 1e-5 MODEL.VIT.MSVIT.ARCH 'l1,h3,d96,n1,s1,g1,p4,f6_l2,h3,d192,n8,s1,g1,p2,f8_l3,h6,d384,n24,s0,g1,p2,f7_l4,h12,d768,n1,s0,g0,p2,f7' EVALUATE True MODEL.MODEL_PATH /home/penzhan/penzhanwu2/imagenet/msvit/IN384_IN22kpretrained/msvitdeepbase_imagenet384_finetune_bsz640_lr003_wd0/model_best.pth

# ImageNet22K pretrained and ImageNet1K finetuned base-wide
python run_experiment.py --config-file 'config/msvit.yaml' --data ../datasets/imagenet/2012 --output_dir ../run/imagenet/msvittest FINETUNE.FINETUNE True INPUT.IMAGE_SIZE 384 INPUT.CROP_PCT 0.922 MODEL.VIT.MSVIT.ARCH 'l1,h3,d192,n1,s1,g1,p4,f8_l2,h6,d384,n2,s1,g1,p2,f8_l3,h12,d768,n8,s0,g1,p2,f7_l4,h16,d1024,n1,s0,g0,p2,f7' EVALUATE True MODEL.MODEL_PATH /home/penzhan/penzhanwu2/imagenet/msvit/IN384_IN22kpretrained/msvitwidebase_imagenet384_finetune_bsz768_lr001_wd1e-7/model_best.pth DATALOADER.BSZ 64

Pretrained models of other attention mechanisms

# Small full attention
python run_experiment.py --config-file 'config/msvit.yaml' --data ../datasets/imagenet/2012 --output_dir ../run/imagenet/msvittest MODEL.VIT.MSVIT.ATTN_TYPE full MODEL.VIT.MSVIT.ARCH 'l1,h3,d96,n1,s1,g1,p4,f7_l2,h3,d192,n2,s1,g1,p2,f7_l3,h6,d384,n8,s0,g1,p2,f7_l4,h12,d768,n1,s0,g0,p2,f7' EVALUATE True MODEL.MODEL_PATH /home/penzhan/penzhanwu2/imagenet/msvit/fullMSA/small1281/model_best.pth

# Small linformer
python run_experiment.py --config-file 'config/msvit.yaml' --data ../datasets/imagenet/2012 --output_dir ../run/imagenet/msvittest MODEL.VIT.MSVIT.ATTN_TYPE linformer MODEL.VIT.MSVIT.ARCH 'l1,h3,d96,n1,s1,g1,p4,f256_l2,h3,d192,n2,s1,g1,p2,f256_l3,h6,d384,n8,s1,g1,p2,f256_l4,h12,d768,n1,s1,g0,p2,f256' EVALUATE True MODEL.MODEL_PATH /home/penzhan/penzhanwu2/imagenet/msvit/linformer/small1281_full/model_best.pth

# Small partial linformer
python run_experiment.py --config-file 'config/msvit.yaml' --data ../datasets/imagenet/2012 --output_dir ../run/imagenet/msvittest MODEL.VIT.MSVIT.ATTN_TYPE linformer MODEL.VIT.MSVIT.ARCH 'l1,h3,d96,n1,s1,g1,p4,f256_l2,h3,d192,n2,s1,g1,p2,f256_l3,h6,d384,n8,s0,g1,p2,f256_l4,h12,d768,n1,s0,g0,p2,f256' EVALUATE True MODEL.MODEL_PATH /home/penzhan/penzhanwu2/imagenet/msvit/linformer/small1281_partial/model_best.pth

# Small global attention
python run_experiment.py --config-file 'config/msvit.yaml' --data ../datasets/imagenet/2012 --output_dir ../run/imagenet/msvittest MODEL.VIT.AVG_POOL True MODEL.VIT.MSVIT.ONLY_GLOBAL True MODEL.VIT.MSVIT.ATTN_TYPE longformerhand MODEL.VIT.MSVIT.ARCH 'l1,h3,d96,n1,s1,g256,p4,f7_l2,h3,d192,n2,s1,g256,p2,f7_l3,h6,d384,n8,s1,g64,p2,f7_l4,h12,d768,n1,s1,g16,p2,f7' EVALUATE True MODEL.MODEL_PATH /home/penzhan/penzhanwu2/imagenet/msvit/globalformer/globalfull1281/model_best.pth

# Small partial global attention
python run_experiment.py --config-file 'config/msvit.yaml' --data ../datasets/imagenet/2012 --output_dir ../run/imagenet/msvittest MODEL.VIT.AVG_POOL True MODEL.VIT.MSVIT.ONLY_GLOBAL True MODEL.VIT.MSVIT.ATTN_TYPE longformerhand MODEL.VIT.MSVIT.ARCH 'l1,h3,d96,n1,s1,g256,p4,f7_l2,h3,d192,n2,s1,g256,p2,f7_l3,h6,d384,n8,s0,g1,p2,f7_l4,h6,d384,n1,s0,g0,p2,f7' EVALUATE True MODEL.MODEL_PATH /home/penzhan/penzhanwu2/imagenet/msvit/globalformer/globalpartial1281/model_best.pth

# Small spatial reduction attention with down-sample ratio 64
python run_experiment.py --config-file 'config/msvit.yaml' --data ../datasets/imagenet/2012 --output_dir ../run/imagenet/msvittest MODEL.VIT.MSVIT.ATTN_TYPE srformer MODEL.VIT.MSVIT.ARCH 'l1,h3,d96,n1,s1,g1,p4,f16_l2,h3,d192,n2,s1,g1,p2,f8_l3,h6,d384,n8,s1,g1,p2,f4_l4,h12,d768,n1,s1,g0,p2,f2' EVALUATE True MODEL.MODEL_PATH /home/penzhan/penzhanwu2/imagenet/msvit/srformer/srformerfull1281/model_best.pth

# Small spatial reduction attention with down-sample ratio 32
python run_experiment.py --config-file 'config/msvit.yaml' --data ../datasets/imagenet/2012 --output_dir ../run/imagenet/msvittest MODEL.VIT.MSVIT.ATTN_TYPE srformer MODEL.VIT.MSVIT.ARCH 'l1,h3,d96,n1,s1,g1,p4,f8_l2,h3,d192,n2,s1,g1,p2,f4_l3,h6,d384,n8,s1,g1,p2,f2_l4,h12,d768,n1,s0,g0,p2,f1' EVALUATE True MODEL.MODEL_PATH /home/penzhan/penzhanwu2/imagenet/msvit/srformer/srformerfull8_1281/model_best.pth

# Small partial spatial reduction attention with down-sample ratio 32
python run_experiment.py --config-file 'config/msvit.yaml' --data ../datasets/imagenet/2012 --output_dir ../run/imagenet/msvittest MODEL.VIT.MSVIT.ATTN_TYPE srformer MODEL.VIT.MSVIT.ARCH 'l1,h3,d96,n1,s1,g1,p4,f8_l2,h3,d192,n2,s1,g1,p2,f4_l3,h6,d384,n8,s0,g1,p2,f2_l4,h12,d768,n1,s0,g0,p2,f1' EVALUATE True MODEL.MODEL_PATH /home/penzhan/penzhanwu2/imagenet/msvit/srformer/srformerpartial1281/model_best.pth

# Small performer
python run_experiment.py --config-file 'config/msvit.yaml' --data ../datasets/imagenet/2012 --output_dir ../run/imagenet/msvittest MODEL.VIT.MSVIT.ATTN_TYPE performer MODEL.VIT.MSVIT.ARCH 'l1,h3,d96,n1,s1,g1,p4,f256_l2,h3,d192,n2,s1,g1,p2,f256_l3,h6,d384,n8,s1,g1,p2,f256_l4,h12,d768,n1,s1,g0,p2,f256' EVALUATE True MODEL.MODEL_PATH /home/penzhan/penzhanwu2/imagenet/msvit/performer/fullperformer1281/model_best.pth

# Small partial performer
python run_experiment.py --config-file 'config/msvit.yaml' --data ../datasets/imagenet/2012 --output_dir ../run/imagenet/msvittest MODEL.VIT.MSVIT.ATTN_TYPE performer MODEL.VIT.MSVIT.ARCH 'l1,h3,d96,n1,s1,g1,p4,f256_l2,h3,d192,n2,s1,g1,p2,f256_l3,h6,d384,n8,s0,g1,p2,f256_l4,h12,d768,n1,s0,g0,p2,f256' EVALUATE True MODEL.MODEL_PATH /home/penzhan/penzhanwu2/imagenet/msvit/performer/partialperformer1281/model_best.pth

Training scripts

We provide three example training scripts as below.

# ViL-Tiny with relative positional embedding: Imagenet1K training with 224x224 resolution
python -m torch.distributed.launch --nproc_per_node=4 run_experiment.py --config-file
    'config/msvit.yaml' --data '../datasets/imagenet/2012/' OPTIM.OPT adamw
    OPTIM.LR 1e-3 OPTIM.WD 0.1 DATALOADER.BSZ 1024 MODEL.VIT.MSVIT.ATTN_TYPE
    longformerhand OPTIM.EPOCHS 300 SOLVER.LR_POLICY cosine INPUT.IMAGE_SIZE 224 MODEL.VIT.MSVIT.ARCH
    "l1,h1,d48,n1,s1,g1,p4,f7,a0_l2,h3,d96,n2,s1,g1,p2,f7,a0_l3,h3,d192,n8,s0,g1,p2,f7,a0_l4,h6,d384,n1,s0,g0,p2,f7,a0"
    AUG.REPEATED_AUG False

# Training with random shifting strategy: accelerate the training significantly
python -m torch.distributed.launch --nproc_per_node=4 run_experiment.py --config-file
    'config/msvit.yaml' --data '../datasets/imagenet/2012/' OPTIM.OPT adamw
    OPTIM.LR 1e-3 OPTIM.WD 0.1 DATALOADER.BSZ 1024 MODEL.VIT.MSVIT.ATTN_TYPE
    longformerhand OPTIM.EPOCHS 300 SOLVER.LR_POLICY cosine INPUT.IMAGE_SIZE 224 MODEL.VIT.MSVIT.ARCH
    "l1,h1,d48,n1,s1,g1,p4,f7,a0_l2,h3,d96,n2,s1,g1,p2,f7,a0_l3,h3,d192,n8,s0,g1,p2,f7,a0_l4,h6,d384,n1,s0,g0,p2,f7,a0"
    AUG.REPEATED_AUG False MODEL.VIT.MSVIT.MODE 1 MODEL.VIT.MSVIT.VIL_MODE_SWITCH 0.875

# ViL-Medium-Deep: Imagenet1K finetuning with 384x384 resolution
python -m torch.distributed.launch --nproc_per_node=8 run_experiment.py --config-file
    'config/msvit_384finetune.yaml' --data '/mnt/default/data/sasa/imagenet/2012/'
    OPTIM.OPT qhm OPTIM.LR 0.01 OPTIM.WD 0.0 DATALOADER.BSZ 256 MODEL.VIT.MSVIT.ATTN_TYPE
    longformerhand OPTIM.EPOCHS 10 SOLVER.LR_POLICY cosine INPUT.IMAGE_SIZE 384 MODEL.VIT.MSVIT.ARCH
    "l1,h3,d96,n1,s1,g1,p4,f8_l2,h3,d192,n4,s1,g1,p2,f12_l3,h6,d384,n16,s0,g1,p2,f7_l4,h12,d768,n1,s0,g0,p2,f7"
    MODEL.MODEL_PATH /home/penzhan/penzhanwu2/imagenet/msvit/IN22kpretrained/deepmedium/model_best.pth

Cite Vision Longformer

Please consider citing vision longformer if it helps your work.

@article{zhang2021multi,
  title={Multi-Scale Vision Longformer: A New Vision Transformer for High-Resolution Image Encoding},
  author={Zhang, Pengchuan and Dai, Xiyang and Yang, Jianwei and Xiao, Bin and Yuan, Lu and Zhang, Lei and Gao, Jianfeng},
  journal={ICCV 2021},
  year={2021}
}

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README.md

README.md

Vision Longformer

Highlights

Updates

Multi-scale Vision Transformer Architecture

Model Performance

Main Results on ImageNet and Pretrained Models

Main Results on COCO object detection and instance segmentation (with absolute positional embedding)

Compare different implementations of vision longformer

Training/Testing Vision Longformer on Local Machine

Prepare datasets

Environment requirements

Evaluation scripts

Training scripts

Cite Vision Longformer

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README.md

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README.md

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Vision Longformer

Highlights

Updates

Multi-scale Vision Transformer Architecture

Model Performance

Main Results on ImageNet and Pretrained Models

Main Results on COCO object detection and instance segmentation (with absolute positional embedding)

Compare different implementations of vision longformer

Training/Testing Vision Longformer on Local Machine

Prepare datasets

Environment requirements

Evaluation scripts

Training scripts

Cite Vision Longformer