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
DeLighT: Very Deep and Light-Weight Transformers

DeLighT: Very Deep and Light-weight Transformers This repository contains the source code of our work on building efficient sequence models: DeFINE (I

Sachin Mehta 440 Dec 18, 2022
BuildingNet: Learning to Label 3D Buildings

BuildingNet This is the implementation of the BuildingNet architecture described in this paper: Paper: BuildingNet: Learning to Label 3D Buildings Arx

16 Nov 07, 2022
(NeurIPS 2021) Pytorch implementation of paper "Re-ranking for image retrieval and transductive few-shot classification"

SSR (NeurIPS 2021) Pytorch implementation of paper "Re-ranking for image retrieval and transductivefew-shot classification" [Paper] [Project webpage]

xshen 29 Dec 06, 2022
UnpNet - Rethinking 3-D LiDAR Point Cloud Segmentation(IEEE TNNLS)

UnpNet Citation Please cite the following paper if you use this repository in your reseach. @article {PMID:34914599, Title = {Rethinking 3-D LiDAR Po

Shijie Li 4 Jul 15, 2022
A project to build an AI voice assistant using Python . The Voice assistant interacts with the humans to perform basic tasks.

AI_Personal_Voice_Assistant_Using_Python A project to build an AI voice assistant using Python . The Voice assistant interacts with the humans to perf

Chumui Tripura 1 Oct 30, 2021
A simple baseline for 3d human pose estimation in PyTorch.

3d_pose_baseline_pytorch A PyTorch implementation of a simple baseline for 3d human pose estimation. You can check the original Tensorflow implementat

weigq 312 Jan 06, 2023
Multi-Object Tracking in Satellite Videos with Graph-Based Multi-Task Modeling

TGraM Multi-Object Tracking in Satellite Videos with Graph-Based Multi-Task Modeling, Qibin He, Xian Sun, Zhiyuan Yan, Beibei Li, Kun Fu Abstract Rece

Qibin He 6 Nov 25, 2022
Public repository containing materials used for Feed Forward (FF) Neural Networks article.

Art041_NN_Feed_Forward Public repository containing materials used for Feed Forward (FF) Neural Networks article. -- Illustration of a very simple Fee

SolClover 2 Dec 29, 2021
Plug and play transformer you can find network structure and official complete code by clicking List

Plug-and-play Module Plug and play transformer you can find network structure and official complete code by clicking List The following is to quickly

8 Mar 27, 2022
Easily pull telemetry data and create beautiful visualizations for analysis.

This repository is a work in progress. Anything and everything is subject to change. Porpo Table of Contents Porpo Table of Contents General Informati

Ryan Dawes 33 Nov 30, 2022
DeepConsensus uses gap-aware sequence transformers to correct errors in Pacific Biosciences (PacBio) Circular Consensus Sequencing (CCS) data.

DeepConsensus DeepConsensus uses gap-aware sequence transformers to correct errors in Pacific Biosciences (PacBio) Circular Consensus Sequencing (CCS)

Google 149 Dec 19, 2022
Collections for the lasted paper about multi-view clustering methods (papers, codes)

Multi-View Clustering Papers Collections for the lasted paper about multi-view clustering methods (papers, codes). There also exists some repositories

Andrew Guan 10 Sep 20, 2022
Official Implementation of "LUNAR: Unifying Local Outlier Detection Methods via Graph Neural Networks"

LUNAR Official Implementation of "LUNAR: Unifying Local Outlier Detection Methods via Graph Neural Networks" Adam Goodge, Bryan Hooi, Ng See Kiong and

Adam Goodge 25 Dec 28, 2022
PolyTrack: Tracking with Bounding Polygons

PolyTrack: Tracking with Bounding Polygons Abstract In this paper, we present a novel method called PolyTrack for fast multi-object tracking and segme

Gaspar Faure 13 Sep 15, 2022
Neural Motion Learner With Python

Neural Motion Learner Introduction This work is to extract skeletal structure from volumetric observations and to learn motion dynamics from the detec

Jinseok Bae 14 Nov 28, 2022
A small library of 3D related utilities used in my research.

utils3D A small library of 3D related utilities used in my research. Installation Install via GitHub pip install git+https://github.com/Steve-Tod/util

Zhenyu Jiang 8 May 20, 2022
DropNAS: Grouped Operation Dropout for Differentiable Architecture Search

DropNAS: Grouped Operation Dropout for Differentiable Architecture Search DropNAS, a grouped operation dropout method for one-level DARTS, with better

weijunhong 4 Aug 15, 2022
MASS (Mueen's Algorithm for Similarity Search) - a python 2 and 3 compatible library used for searching time series sub-sequences under z-normalized Euclidean distance for similarity.

Introduction MASS allows you to search a time series for a subquery resulting in an array of distances. These array of distances enable you to identif

Matrix Profile Foundation 79 Dec 31, 2022
The repository for freeCodeCamp's YouTube course, Algorithmic Trading in Python

Algorithmic Trading in Python This repository Course Outline Section 1: Algorithmic Trading Fundamentals What is Algorithmic Trading? The Differences

Nick McCullum 1.8k Jan 02, 2023
ICCV2021 - A New Journey from SDRTV to HDRTV.

ICCV2021 - A New Journey from SDRTV to HDRTV.

XyChen 82 Dec 27, 2022