[arxiv]

This repo is the CMT model which impelement with pytorch, no reference source code so this is a non-official version.

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

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

• Trainig

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

• Testing

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

1. Training

   CMT-Tiny

   #!/bin/bash
   OMP_NUM_THREADS=1
   MKL_NUM_THREADS=1
   export OMP_NUM_THREADS
   export MKL_NUM_THREADS
   cd CMT-pytorch;
   CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 python -W ignore -m torch.distributed.launch --nproc_per_node 8 train.py --batch_size 512 --num_workers 48 --lr 6e-3 --optimizer_name "adamw" --tf_optimizer 1 --cosine 1 --model_name cmtti --max_epochs 300 \
   --warmup_epochs 5 --num-classes 1000 --input_size 184 \ --crop_size 160 --weight_decay 1e-1 --grad_clip 0 --repeated-aug 0 --max_grad_norm 5.0 
   --drop_path_rate 0.1 --FP16 0 --qkv_bias 1 
   --ape 0 --rpe 1 --pe_nd 0 --mode O2 --amp 1 --apex 0 \ 
   --train_file $file_folder$/train.txt \
   --val_file $file_folder$/val.txt \
   --log-dir $save_folder$/log_dir \
   --checkpoints-path $save_folder$/checkpoints
   

   Note: If you use the bs 128 * 8 may be get more accuracy, balance the acc & speed.

2. Inference

   #!/bin/bash
   cd CMT-pytorch;
   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 
   --batch-size 128 --num-workers 48 --num-classes 1000 --input_size 184 --crop_size 160 \
   --ape 0 --rpe 1 --pe_nd 0 --qkv_bias 1 --swin 0 --model_name cmtti --dropout 0.1 --emb_dropout 0.1 \
   --test_file $file_folder$/val.txt \
   --checkpoints-path $save_folder$/checkpoints/xxx.pth.tar \
   --save_folder $save_folder$/acc_logits/

3. calculate acc

   python utils/calculate_acc.py --logits_file $save_folder$/acc_logits/

• Other result may comming sonn if someone need.
• Release the CMT-XS result on the imagenet.
• Check the diff with papers, author give the hyparameters on the issue
• Adjusting the best hyperparameters for CMT or transformers

If you want to know more, I give the CMT explanation, as well as the tuning and training process on here.