CIFAR-10 Reproduction

[HieuPhan33]

Hi Shaojie,
I could not reproduce the result for MDEQ on CIFAR-10 image classification.

I only obtained 91.56% using MDEQ_large.
I'm using the same parameters in cls_mdeq_LARGE_reg.yaml, except the batch size and the number of GPUs.
Batch size per gpu are 512 with 2 GPUs.
I'm using 2 RTX 3090 graphic cards.

Hope that you can give me some advice.

Thanks Shaojie.

[jerrybai1995]

Hi @HieuPhan33 ,

Are you using an equivalent batch size of 1024? Could you try a smaller batch size like the default one (I usually use ~100, and found this to be important)?

In addition, when I reproduced the result, I also sometimes (but generally rarely) get <93%, which is part of the fluctuation. If you still encounter the issue, you can also reach me via email and I can send you a sample training log for you to compare... I believe you should expect ~92% after 100 epochs already.

[HieuPhan33]

Hi @jerrybai1995, thanks for quick response. I will reduce the batch size and keep you updated.
Thumbs up.

[HieuPhan33]

Hi, I achieved 92.30% when using a batch size of 128.
Would you have any advice to continue to increase the accuracy to ~93% as expected?

[jerrybai1995]

Hmmm, 92.3% still sounds too low to me for the given default parameters (my logs are usually in the range 92.6% - 93.4%). Could you try increasing f_thres (e.g., 9) and b_thres (e.g., 8 or 9) in the yaml file and using the default batch size? I also think that increasing the momentum (e.g., to 0.99) would improve the performance but I believe you should be able to reproduce the ~93% level performance even without tuning these things.

I'll look into this but in case you might find it useful, feel free to contact me (shaojieb@cs.cmu.edu) and I'll send you some training logs.

[jerrybai1995]

Hi @HieuPhan33 ,

I was able to produce 93.04% and 92.78% on two (slightly different and) independent runs, basically with the modifications/settings mentioned above. E.g., I got 93.04% from the following yaml:

GPUS: (0,)
LOG_DIR: 'log/'
DATA_DIR: ''
OUTPUT_DIR: 'output/'
WORKERS: 2
PRINT_FREQ: 100

MODEL: 
  NAME: mdeq
  NUM_LAYERS: 8
  NUM_CLASSES: 10
  NUM_GROUPS: 8
  DROPOUT: 0.22
  WNORM: true
  DOWNSAMPLE_TIMES: 0
  EXPANSION_FACTOR: 5
  POST_GN_AFFINE: false
  IMAGE_SIZE: 
    - 32
    - 32
  EXTRA:
    FULL_STAGE:
      NUM_MODULES: 1
      NUM_BRANCHES: 4
      BLOCK: BASIC
      BIG_KERNELS:
      - 0
      - 0
      - 0
      - 0
      HEAD_CHANNELS:
      - 14
      - 28
      - 56
      - 112
      FINAL_CHANSIZE: 1680
      NUM_BLOCKS:
      - 1
      - 1
      - 1
      - 1
      NUM_CHANNELS:
      - 32
      - 64
      - 128
      - 256
      FUSE_METHOD: SUM
DEQ:
  F_SOLVER: 'broyden'
  B_SOLVER: 'broyden'
  STOP_MODE: 'rel'
  F_THRES: 8
  B_THRES: 7
  RAND_F_THRES_DELTA: 1
  SPECTRAL_RADIUS_MODE: false
CUDNN:
  BENCHMARK: true
  DETERMINISTIC: false
  ENABLED: true
LOSS:
  JAC_LOSS_FREQ: 0.02
  JAC_LOSS_WEIGHT: 0.4
  PRETRAIN_JAC_LOSS_WEIGHT: 0.0
  JAC_STOP_EPOCH: 90
DATASET:
  DATASET: 'cifar10'
  DATA_FORMAT: 'jpg'
  ROOT: 'data/cifar10/'
  TEST_SET: 'val'
  TRAIN_SET: 'train'
TEST:
  BATCH_SIZE_PER_GPU: 96
  MODEL_FILE: ''
TRAIN:
  BATCH_SIZE_PER_GPU: 96
  BEGIN_EPOCH: 0
  END_EPOCH: 220
  RESUME: false
  LR_SCHEDULER: 'cosine'
  PRETRAIN_STEPS: 12000
  LR_FACTOR: 0.1
  LR_STEP:
  - 30
  - 60
  - 90
  OPTIMIZER: adam
  LR: 0.001
  WD: 0.0
  MOMENTUM: 0.99
  NESTEROV: true
  SHUFFLE: true
DEBUG:
  DEBUG: false

Hope this helps!

[HieuPhan33]

Thanks Shaojie, really appreciate your help!