UniConvNet for Image Classification

This folder contains the implementation of the UniConvNet for Image Classification.

Usage

Install

• Clone this repo

• Create a conda virtual environment and activate it:

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

• 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 --extra-index-url https://download.pytorch.org/whl/cu113

• Install timm==0.3.2 and mmcv-full==1.5.0:

pip install -U openmim
mim install mmcv-full==1.5.0
pip install timm==0.3.2 mmdet==2.28.1
pip install mmsegmentation==0.30.0

• Install other requirements:

pip install opencv-python termcolor yacs pyyaml scipy tensorboardX wandb yapf==0.40.1

• Compiling CUDA operators (Modified DCNV3)

cd ./ops_dcnv3
sh ./make.sh
# unit test (should see all checking is True)
python test.py

• modify helpers.py in timm

cd ~/anaconda3/envs/UniConvNet/lib/python3.7/site-packages/timm/models/layers/
vim helpers.py
substitute "from torch._six import container_abcs"
with "import collections.abc as container_abcs"

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