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

Related tags

Deep Learningvision-longformer
Overview

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.

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 [email protected] [email protected] #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 [email protected] [email protected] #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 [email protected] [email protected] #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 absolute positional embedding

Backbone Method pretrain Lr Schd box mAP mask mAP #params FLOPs
ViL-Tiny RetinaNet ImageNet-1K 1x 38.8 -- 16.64M 182.7G
ViL-Tiny RetinaNet ImageNet-1K 3x 40.7 -- 16.64M 182.7G
ViL-Small RetinaNet ImageNet-1K 1x 41.6 -- 35.68M 254.8G
ViL-Small RetinaNet ImageNet-1K 3x 42.9 -- 35.68M 254.8G
ViL-Medium (D) RetinaNet ImageNet-1K 1x 42.9 -- 50.77M 330.4G
ViL-Medium (D) RetinaNet ImageNet-1K 3x 43.7 -- 50.77M 330.4G
ViL-Base (D) RetinaNet ImageNet-1K 1x 44.3 -- 66.74M 420.9G
ViL-Base (D) RetinaNet ImageNet-1K 3x 44.7 -- 66.74M 420.9G
--- --- --- --- --- --- --- ---
ViL-Tiny Mask R-CNN ImageNet-1K 1x 38.7 36.2 26.9M 145.6G
ViL-Tiny Mask R-CNN ImageNet-1K 3x 41.2 37.9 26.9M 145.6G
ViL-Small Mask R-CNN ImageNet-1K 1x 41.8 38.5 45.0M 218.3G
ViL-Small Mask R-CNN ImageNet-1K 3x 43.4 39.6 45.0M 218.3G
ViL-Medium (D) Mask R-CNN ImageNet-1K 1x 43.4 39.7 60.1M 293.8G
ViL-Medium (D) Mask R-CNN ImageNet-1K 3x 44.6 40.7 60.1M 293.8G
ViL-Base (D) Mask R-CNN ImageNet-1K 1x 45.1 41.0 76.1M 384.4G
ViL-Base (D) Mask R-CNN ImageNet-1K 3x 45.7 41.3 76.1M 384.4G

See more fine-grained results in Table 6 and Table 7 in the Vision Longformer paper.

Results of other attention mechanims (Small size)

Backbone Method pretrain Lr Schd box mAP mask mAP #params FLOPs Memory
srformer/64 Mask R-CNN ImageNet-1K 1x 35.7 33.6 73.3M 224.1G 7.1G
srformer/32 Mask R-CNN ImageNet-1K 1x 39.8 36.8 51.5M 268.3G 13.6G
Partial srformer/32 Mask R-CNN ImageNet-1K 1x 41.1 38.1 46.8M 352.1G 22.6G
global Mask R-CNN ImageNet-1K 1x 34.1 32.5 45.2M 226.4G 7.6G
Partial global Mask R-CNN ImageNet-1K 1x 41.3 38.2 45.1M 326.5G 20.1G
performer Mask R-CNN ImageNet-1K 1x 35.0 33.1 45.0M 251.5G 8.4G
Partial performer Mask R-CNN ImageNet-1K 1x 41.7 38.4 45.0M 343.7G 20.0G
ViL Mask R-CNN ImageNet-1K 1x 41.3. 38.1 45.0M 218.3G 7.4G
Partial ViL Mask R-CNN ImageNet-1K 1x 42.6 39.3 45.0M 326.8G 19.5G

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={arXiv preprint arXiv:2103.15358},
  year={2021}
}
Owner
Microsoft
Open source projects and samples from Microsoft
Microsoft
GitHub Repository
Official implementation of the paper "Lightweight Deep CNN for Natural Image Matting via Similarity Preserving Knowledge Distillation"

Lightweight-Deep-CNN-for-Natural-Image-Matting-via-Similarity-Preserving-Knowledge-Distillation Introduction Accepted at IEEE Signal Processing Letter

DongGeun-Yoon 19 Jun 07, 2022
MIMO-UNet - Official Pytorch Implementation

MIMO-UNet - Official Pytorch Implementation This repository provides the official PyTorch implementation of the following paper: Rethinking Coarse-to-

Sungjin Cho 248 Jan 02, 2023
CAPRI: Context-Aware Interpretable Point-of-Interest Recommendation Framework

CAPRI: Context-Aware Interpretable Point-of-Interest Recommendation Framework This repository contains a framework for Recommender Systems (RecSys), a

RecSys Lab 8 Jul 03, 2022
A new benchmark for Icon Question Answering (IconQA) and a large-scale icon dataset Icon645.

IconQA About IconQA is a new diverse abstract visual question answering dataset that highlights the importance of abstract diagram understanding and c

Pan Lu 24 Dec 30, 2022
Tool cek opsi checkpoint facebook!

tool apa ini? cek_opsi_facebook adalah sebuah tool yang mengecek opsi checkpoint akun facebook yang terkena checkpoint! tujuan dibuatnya tool ini? too

Muhammad Latif Harkat 2 Jul 17, 2022
Scripts and a shader to get you started on setting up an exported Koikatsu character in Blender.

KK Blender Shader Pack A plugin and a shader to get you started with setting up an exported Koikatsu character in Blender. The plugin is a Blender add

166 Jan 01, 2023
A new version of the CIDACS-RL linkage tool suitable to a cluster computing environment.

Fully Distributed CIDACS-RL The CIDACS-RL is a brazillian record linkage tool suitable to integrate large amount of data with high accuracy. However,

Robespierre Pita 5 Nov 04, 2022
Research Artifact of USENIX Security 2022 Paper: Automated Side Channel Analysis of Media Software with Manifold Learning

Automated Side Channel Analysis of Media Software with Manifold Learning Official implementation of USENIX Security 2022 paper: Automated Side Channel

Yuanyuan Yuan 175 Jan 07, 2023
Synthetic structured data generators

Join us on What is Synthetic Data? Synthetic data is artificially generated data that is not collected from real world events. It replicates the stati

YData 850 Jan 07, 2023
To model the probability of a soccer coach leave his/her team during Campeonato Brasileiro for 10 chosen teams and considering years 2018, 2019 and 2020.

To model the probability of a soccer coach leave his/her team during Campeonato Brasileiro for 10 chosen teams and considering years 2018, 2019 and 2020.

Larissa Sayuri Futino Castro dos Santos 1 Jan 20, 2022
An self sufficient AI that crawls the web to learn how to generate art from keywords

Roxx-IO - The Smart Artist AI! TO DO / IDEAS Implement Web-Scraping Functionality Figure out a less annoying (and an off button for it) text to speech

Tatz 5 Mar 21, 2022
Unofficial implementation of PatchCore anomaly detection

PatchCore anomaly detection Unofficial implementation of PatchCore(new SOTA) anomaly detection model Original Paper : Towards Total Recall in Industri

Changwoo Ha 268 Dec 22, 2022
Elastic weight consolidation technique for incremental learning.

Overcoming-Catastrophic-forgetting-in-Neural-Networks Elastic weight consolidation technique for incremental learning. About Use this API if you dont

Shivam Saboo 89 Dec 22, 2022
Heart Arrhythmia Classification

This program takes and input of an ECG in European Data Format (EDF) and outputs the classification for heartbeats into normal vs different types of arrhythmia . It uses a deep learning model for cla

4 Nov 02, 2022
[ICCV '21] In this repository you find the code to our paper Keypoint Communities

Keypoint Communities In this repository you will find the code to our ICCV '21 paper: Keypoint Communities Duncan Zauss, Sven Kreiss, Alexandre Alahi,

Duncan Zauss 262 Dec 13, 2022
PyTorch implementation of U-TAE and PaPs for satellite image time series panoptic segmentation.

Panoptic Segmentation of Satellite Image Time Series with Convolutional Temporal Attention Networks (ICCV 2021) This repository is the official implem

71 Jan 04, 2023
Causal-BALD: Deep Bayesian Active Learning of Outcomes to Infer Treatment-Effects from Observational Data.

causal-bald | Abstract | Installation | Example | Citation | Reproducing Results DUE An implementation of the methods presented in Causal-BALD: Deep B

OATML 13 Oct 07, 2022
TensorFlow Implementation of "Show, Attend and Tell"

Show, Attend and Tell Update (December 2, 2016) TensorFlow implementation of Show, Attend and Tell: Neural Image Caption Generation with Visual Attent

Yunjey Choi 902 Nov 29, 2022
Allows including an action inside another action (by preprocessing the Yaml file). This is how composite actions should have worked.

actions-includes Allows including an action inside another action (by preprocessing the Yaml file). Instead of using uses or run in your action step,

Tim Ansell 70 Nov 04, 2022
Emotion classification of online comments based on RNN

emotion_classification Emotion classification of online comments based on RNN, the accuracy of the model in the test set reaches 99% data: Large Movie

1 Nov 23, 2021