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Get Started

Usage

Install

  • Clone this repo:
git clone https://github.com/kevin-ssy/ViP.git
cd ViP
  • Create a conda virtual environment and activate it:
conda create -n vip python=3.7 -y
conda activate vip
conda install pytorch==1.7.1 torchvision==0.8.2 cudatoolkit=10.1 -c pytorch
  • Install timm==0.3.4, einops, pyyaml:
pip3 install timm=0.3.4, einops, pyyaml
  • Install Apex:
git clone https://github.com/NVIDIA/apex
cd apex
pip install -v --disable-pip-version-check --no-cache-dir --global-option="--cpp_ext" --global-option="--cuda_ext" ./

Data preparation

We use standard ImageNet dataset, you can download it from http://image-net.org/. We provide the following two ways to load data:

  • For standard folder dataset, move validation images to labeled sub-folders. The file structure should look like:

    $ tree data
imagenet
├── train
│   ├── class1
│   │   ├── img1.jpeg
│   │   ├── img2.jpeg
│   │   └── ...
│   ├── class2
│   │   ├── img3.jpeg
│   │   └── ...
│   └── ...
└── val
    ├── class1
    │   ├── img4.jpeg
    │   ├── img5.jpeg
    │   └── ...
    ├── class2
    │   ├── img6.jpeg
    │   └── ...
    └── ...

  • To boost the slow speed when reading images from massive small files, we also support zipped ImageNet, which includes four files:

    • train.zip, val.zip: which store the zipped folder for train and validate splits.
    • train_map.txt, val_map.txt: which store the relative path in the corresponding zip file and ground truth label. Make sure the data folder looks like this:
    $ tree data
data
└── ImageNet-Zip
    ├── train_map.txt
    ├── train.zip
    ├── val_map.txt
    └── val.zip

$ head -n 5 data/ImageNet-Zip/val_map.txt
ILSVRC2012_val_00000001.JPEG	65
ILSVRC2012_val_00000002.JPEG	970
ILSVRC2012_val_00000003.JPEG	230
ILSVRC2012_val_00000004.JPEG	809
ILSVRC2012_val_00000005.JPEG	516

$ head -n 5 data/ImageNet-Zip/train_map.txt
n01440764/n01440764_10026.JPEG	0
n01440764/n01440764_10027.JPEG	0
n01440764/n01440764_10029.JPEG	0
n01440764/n01440764_10040.JPEG	0
n01440764/n01440764_10042.JPEG	0

Evaluation

To evaluate a pre-trained ViP on ImageNet val, run:

python3 main.py <data-root> --model <model-name> -b <batch-size> --eval_checkpoint <path-to-checkpoint>

Training from scratch

To train a ViP on ImageNet from scratch, run:

bash ./distributed_train.sh <job-name> <config-path> <num-gpus>

For example, to train ViP with 8 GPU on a single node, run:

ViP-Tiny:

bash ./distributed_train.sh vip-t-001 configs/vip_t_bs1024.yaml 8

ViP-Small:

bash ./distributed_train.sh vip-s-001 configs/vip_s_bs1024.yaml 8

ViP-Medium:

bash ./distributed_train.sh vip-m-001 configs/vip_m_bs1024.yaml 8

ViP-Base:

bash ./distributed_train.sh vip-b-001 configs/vip_b_bs1024.yaml 8

Profiling the model

To measure the throughput, run:

python3 test_throughput.py <model-name>

For example, if you want to get the test speed of Vip-Tiny on your device, run:

python3 test_throughput.py vip-tiny

To measure the FLOPS and number of parameters, run:

python3 test_flops.py <model-name>