FlashInternImage for Image Classification

This folder contains the implementation of the FlashInternImage for image classification.

Usage

Install

Clone this repo:

git clone https://github.com/OpenGVLab/DCNv4.git
cd DCNv4

Create a conda virtual environment and activate it:

conda create -n internimage python=3.7 -y
conda activate internimage

Install CUDA>=10.2 with cudnn>=7 following the official installation instructions
Install PyTorch>=1.10.0 and torchvision>=0.9.0 with CUDA>=10.2:

For examples, to install torch==1.11 with CUDA==11.3:

pip install torch==1.11.0+cu113 torchvision==0.12.0+cu113  -f https://download.pytorch.org/whl/torch_stable.html

Install timm==0.6.11 and mmcv-full==1.5.0:

pip install -U openmim
mim install mmcv-full==1.5.0
pip install timm==0.6.11 mmdet==2.28.1

Install other requirements:

pip install opencv-python termcolor yacs pyyaml scipy

Install DCNv4

pip install DCNv4==latest

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.txt, val.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 meta_data/val.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 meta_data/train.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

For ImageNet-22K dataset, make a folder named fall11_whole and move all images to labeled sub-folders in this folder. Then download the train-val split file (ILSVRC2011fall_whole_map_train.txt & ILSVRC2011fall_whole_map_val.txt) , and put them in the parent directory of fall11_whole. The file structure should look like:
```
  $ tree imagenet22k/
  imagenet22k/
  └── fall11_whole
      ├── n00004475
      ├── n00005787
      ├── n00006024
      ├── n00006484
      └── ...
```

Evaluation

To evaluate a pretrained FlashInternImage on ImageNet val, run:

python -m torch.distributed.launch --nproc_per_node <num-of-gpus-to-use> --master_port 12345 main.py --eval \
--cfg <config-file> --resume <checkpoint> --data-path <imagenet-path>

For example, to evaluate the FlashInternImage-B with a single GPU:

python -m torch.distributed.launch --nproc_per_node 1 --master_port 12345 main.py --eval \
--cfg configs/flash_intern_image_b_1k_224.yaml --resume flash_intern_image_b_1k_224.pth --data-path <imagenet-path>

Training from Scratch on ImageNet-1K

The paper results were obtained from models trained with configs in configs/without_lr_decay.

To train an InternImage on ImageNet from scratch, run:

python -m torch.distributed.launch --nproc_per_node <num-of-gpus-to-use> --master_port 12345  main.py \ 
--cfg <config-file> --data-path <imagenet-path> [--batch-size <batch-size-per-gpu> --output <output-directory> --tag <job-tag>]

Manage Jobs with Slurm.

For example, to train FlashInternImage with 8 GPU on a single node for 300 epochs, run:

FlashInternImage-T:

GPUS=8 sh train_in1k.sh <partition> <job-name> configs/flash_intern_image_t_1k_224.yaml --resume flash_intern_image_t_1k_224.pth --eval

FlashInternImage-S:

GPUS=8 sh train_in1k.sh <partition> <job-name> configs/flash_intern_image_s_1k_224.yaml --resume flash_intern_image_s_1k_224.pth --eval

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