FlyEgle/imageclassification

General Image Classification Code base

ImageClassification

1. Introduction

This repo is only used for image classification task, such as imagenet. Include ddp training and inference, calculate the real acc and so on.

2. Enveriments

• python 3.7+
• pytorch 1.7.1
• pillow
• apex
• opencv-python

You can see this repo to find how to install the apex

3. Training & Inference

• dataset prepare

  /data/home/imagenet/xxx.jpeg, 0
  /data/home/imagenet/xxx.jpeg, 1
  ...
  /data/home/imagenet/xxx.jpeg, 999
  

• training
  1. Only used FP16 with bn FP32

     #!/bin/bash
     OMP_NUM_THREADS=1
     MKL_NUM_THREADS=1
     export OMP_NUM_THREADS
     export MKL_NUM_THREADS
     cd ImageClassification;
     CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 python -W ignore -m torch.distributed.launch --nproc_per_node 8 train_lanuch.py \
     --batch_size 512 \
     --num_workers 48 \
     --lr 1.6 \
     --max_epochs 90 \
     --warmup_epochs 5 \
     --num-classes 1000 \
     --input_size 256 \
     --crop_size 224 \
     --FP16 1 \
     --mode O2 \
     --apex 0 \
     --amp 0 \
     --train_file $train_file \
     --val_file $val_file \
     --log-dir $log_dir \
     --checkpoints-path $ckpt_dir

  2. Use Apex training

     #!/bin/bash
     OMP_NUM_THREADS=1
     MKL_NUM_THREADS=1
     export OMP_NUM_THREADS
     export MKL_NUM_THREADS
     cd ImageClassification;
     CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 python -W ignore -m torch.distributed.launch --nproc_per_node 8 train_lanuch.py \
     --batch_size 512 \
     --num_workers 48 \
     --lr 1.6 \
     --max_epochs 90 \
     --warmup_epochs 5 \
     --num-classes 1000 \
     --input_size 256 \
     --crop_size 224 \
     --FP16 0 \
     --mode O1 \
     --apex 1 \
     --amp 0 \
     --train_file $train_file \
     --val_file $val_file \
     --log-dir $log_dir \
     --checkpoints-path $ckpt_dir

  3. Use pytorch amp training

     #!/bin/bash
     OMP_NUM_THREADS=1
     MKL_NUM_THREADS=1
     export OMP_NUM_THREADS
     export MKL_NUM_THREADS
     cd ImageClassification;
     CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 python -W ignore -m torch.distributed.launch --nproc_per_node 8 train_lanuch.py \
     --batch_size 512 \
     --num_workers 48 \
     --lr 1.6 \
     --max_epochs 90 \
     --warmup_epochs 5 \
     --num-classes 1000 \
     --input_size 256 \
     --crop_size 224 \
     --FP16 1 \
     --mode O2 \
     --apex 0 \
     --amp 1 \
     --train_file $train_file \
     --val_file $val_file \
     --log-dir $log_dir \
     --checkpoints-path $ckpt_dir

• inference

  #!/bin/bash
  cd ImageClassification;
  CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 python -W ignore test.py \
  --dist-url 'tcp://127.0.0.1:9966' \
  --dist-backend 'nccl' \
  --multiprocessing-distributed=1 \
  --world-size=1 \
  --rank=0 \
  --test_file $test_file \
  --batch-size 128 \
  --num-workers 48 \
  --num-classes 1000 \
  --swin 0 \
  --checkpoints-path $ckpt_path \
  --save_folder $logits_folder

• calculate acc

python utils/calculate_acc.py --logits_file $logits_folder