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
GNN-based Recommendation Benchma

GRecX A Fair Benchmark for GNN-based Recommendation Preliminary Comparison DiffNet-Yelp dataset (featureless) Algo 73 Oct 17, 2022

A simplified framework and utilities for PyTorch

Here is Poutyne. Poutyne is a simplified framework for PyTorch and handles much of the boilerplating code needed to train neural networks. Use Poutyne

GRAAL/GRAIL 534 Dec 17, 2022
Cave Generation using metaballs in Blender. Originally created by sdfgeoff, Edited by Myself (Archie Jaskowicz).

Blender-Cave-Generation Cave Generation using metaballs in Blender. Originally created by sdfgeoff, Edited by Myself (Archie Jaskowicz). Installation

2 Dec 28, 2022
Explicable Reward Design for Reinforcement Learning Agents [NeurIPS'21]

Explicable Reward Design for Reinforcement Learning Agents [NeurIPS'21]

3 May 12, 2022
Stock-history-display - something like a easy yearly review for your stock performance

Stock History Display Available on Heroku: https://stock-history-display.herokua

LiaoJJ 1 Jan 07, 2022
potpourri3d - An invigorating blend of 3D geometry tools in Python.

A Python library of various algorithms and utilities for 3D triangle meshes and point clouds. Managed by Nicholas Sharp, with new tools added lazily as needed. Currently, mainly bindings to C++ tools

Nicholas Sharp 295 Jan 05, 2023
Reinforcement Learning for Automated Trading

Reinforcement Learning for Automated Trading This thesis has been realized for the obtention of the Master's in Mathematical Engineering at the Polite

Pierpaolo Necchi 80 Jun 19, 2022
A collection of resources and papers on Diffusion Models, a darkhorse in the field of Generative Models

This repository contains a collection of resources and papers on Diffusion Models and Score-based Models. If there are any missing valuable resources

5.1k Jan 08, 2023
An SMPC companion library for Syft

SyMPC A library that extends PySyft with SMPC support SyMPC /ˈsɪmpəθi/ is a library which extends PySyft ≥0.3 with SMPC support. It allows computing o

Arturo Marquez Flores 0 Oct 13, 2021
This repository contains a set of codes to run (i.e., train, perform inference with, evaluate) a diarization method called EEND-vector-clustering.

EEND-vector clustering The EEND-vector clustering (End-to-End-Neural-Diarization-vector clustering) is a speaker diarization framework that integrates

45 Dec 26, 2022
PyTorch implementation for the ICLR 2020 paper "Understanding the Limitations of Variational Mutual Information Estimators"

Smoothed Mutual Information ``Lower Bound'' Estimator PyTorch implementation for the ICLR 2020 paper Understanding the Limitations of Variational Mutu

50 Nov 09, 2022
This repository provides the official code for GeNER (an automated dataset Generation framework for NER).

GeNER This repository provides the official code for GeNER (an automated dataset Generation framework for NER). Overview of GeNER GeNER allows you to

DMIS Laboratory - Korea University 50 Nov 30, 2022
Codebase for the solution that won first place and was awarded the most human-like agent in the 2021 NeurIPS Competition MineRL BASALT Challenge.

KAIROS MineRL BASALT Codebase for the solution that won first place and was awarded the most human-like agent in the 2021 NeurIPS Competition MineRL B

Vinicius G. Goecks 37 Oct 30, 2022
BboxToolkit is a tiny library of special bounding boxes.

BboxToolkit is a light codebase collecting some practical functions for the special-shape detection, such as oriented detection

jbwang1997 73 Jan 01, 2023
[ACM MM 2021] Yes, "Attention is All You Need", for Exemplar based Colorization

Transformer for Image Colorization This is an implemention for Yes, "Attention Is All You Need", for Exemplar based Colorization, and the current soft

Wang Yin 30 Dec 07, 2022
A Python package for faster, safer, and simpler ML processes

Bender 🤖 A Python package for faster, safer, and simpler ML processes. Why use bender? Bender will make your machine learning processes, faster, safe

Otovo 6 Dec 13, 2022
An intelligent, flexible grammar of machine learning.

An english representation of machine learning. Modify what you want, let us handle the rest. Overview Nylon is a python library that lets you customiz

Palash Shah 79 Dec 02, 2022
Unsupervised Image-to-Image Translation

UNIT: UNsupervised Image-to-image Translation Networks Imaginaire Repository We have a reimplementation of the UNIT method that is more performant. It

Ming-Yu Liu 劉洺堉 1.9k Dec 26, 2022
ChainerRL is a deep reinforcement learning library built on top of Chainer.

ChainerRL and PFRL ChainerRL (this repository) is a deep reinforcement learning library that implements various state-of-the-art deep reinforcement al

Chainer 1.1k Jan 01, 2023
PatchMatch-RL: Deep MVS with Pixelwise Depth, Normal, and Visibility

PatchMatch-RL: Deep MVS with Pixelwise Depth, Normal, and Visibility Jae Yong Lee, Joseph DeGol, Chuhang Zou, Derek Hoiem Installation To install nece

31 Apr 19, 2022