EfficientNetV2 implementation using PyTorch

Last update: Dec 29, 2022

Overview

EfficientNetV2-S implementation using PyTorch

Train Steps

Configure imagenet path by changing data_dir in train.py
python main.py --benchmark for model information
python -m torch.distributed.launch --nproc_per_node=$ main.py --train for training model, $ is number of GPUs
python main.py --test for testing

Note

The model achieved 82.7 top-1 after 150 epochs

Number of parameters: 23941296
Time per operator type:
        778.049 ms.    70.6258%. Conv
        255.227 ms.    23.1677%. Sigmoid
          56.91 ms.    5.16589%. Mul
         6.1573 ms.   0.558916%. Add
        4.69289 ms.   0.425987%. ReduceMean
       0.613303 ms.  0.0556713%. FC
        1101.65 ms in Total
FLOP per operator type:
         17.277 GFLOP.    99.7074%. Conv
      0.0419251 GFLOP.   0.241954%. Mul
     0.00519322 GFLOP.  0.0299706%. Add
       0.003585 GFLOP.  0.0206894%. FC
        17.3277 GFLOP in Total
Feature Memory Read per operator type:
        295.875 MB.    50.5134%. Mul
        241.136 MB.     41.168%. Conv
        41.5457 MB.     7.0929%. Add
        7.17917 MB.    1.22567%. FC
        585.737 MB in Total
Feature Memory Written per operator type:
          167.7 MB.    49.2361%. Mul
        152.127 MB.    44.6639%. Conv
        20.7729 MB.    6.09882%. Add
          0.004 MB. 0.00117438%. FC
        340.605 MB in Total
Parameter Memory per operator type:
        87.8034 MB.    92.4486%. Conv
          7.172 MB.    7.55143%. FC
              0 MB.          0%. Add
              0 MB.          0%. Mul
        94.9754 MB in Total

EfficientNetV2 implementation using PyTorch

Related tags

Overview

Train Steps

Note

Owner

Jahongir Yunusov

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