A PyTorch implementation of MixNet: Mixed Depthwise Convolutional Kernels.
Now EMA is running on CPU. So It slower than normal runner.
If you running on GPU, then change these lines init, update_ema
python3 main.py -h
usage: main.py [-h] --save_dir SAVE_DIR [--root ROOT] [--gpus GPUS]
[--num_workers NUM_WORKERS] [--model {mixs}] [--epoch EPOCH]
[--batch_size BATCH_SIZE] [--test] [--ema_decay EMA_DECAY]
[--optim {rmsprop,adam}] [--lr LR] [--beta [BETA [BETA ...]]]
[--momentum MOMENTUM] [--eps EPS] [--decay DECAY]
[--scheduler {exp,cosine,none}]
Pytorch Mixnet
optional arguments:
-h, --help show this help message and exit
--save_dir SAVE_DIR Directory name to save the model
--root ROOT The Directory of data path.
--gpus GPUS Select GPU Numbers | 0,1,2,3 |
--num_workers NUM_WORKERS
Select CPU Number workers
--model {mixs} The type of mixnet.
--epoch EPOCH The number of epochs
--batch_size BATCH_SIZE
The size of batch
--test Only Test
--ema_decay EMA_DECAY
Exponential Moving Average Term
--optim {rmsprop,adam}
--lr LR Base learning rate when train batch size is 256.
--beta [BETA [BETA ...]]
--momentum MOMENTUM
--eps EPS
--decay DECAY
--scheduler {exp,cosine,none}
Learning rate scheduler type