optional arguments:
  -h, --help            show this help message and exit
  --gpus                Select gpu_id to use. default:[0]
  --data_path           The dictionary where the data is stored.
  --job_dir             The directory where the summaries will be stored.
  --resume              Load the model from the specified checkpoint.
  --pretrain_model      Path of the pre-trained model.
  --pruned_model        Path of the pruned model to evaluate.
  --arch                Architecture of model. For ImageNet :mobilenet_v1, mobilenet_v2, mobilenet_v3_small, mobilenet_v3_large
  --num_epochs          The num of epochs to train. default:180
  --train_batch_size    Batch size for training. default:256
  --eval_batch_size     Batch size for validation. default:100
  --momentum            Momentum for Momentum Optimizer. default:0.9
  --lr LR               Learning rate. default:1e-2
  --lr_decay_step       The iterval of learn rate decay for cifar. default:100 150
  --lr_decay_freq       The frequecy of learn rate decay for Imagenet. default:30
  --weight_decay        The weight decay of loss. default:4e-5
  --lr_type             lr scheduler. default: cos. optional:exp/cos/step/fixed
  --use_dali            If this parameter exists, use dali module to load ImageNet data (benefit in training acceleration).
  --conv_type           Importance criterion of filters. Default: BlockL1Conv. optional: BlockRandomConv, DenseConv
  --pr_target           Pruning rate. default:0.5
  --full                If this parameter exists, prune fully-connected layer.
  --N                   Consecutive N kernels for removal (see paper for details).
  --rearrange           If this parameter exists, filters will be rearranged (see paper for details).
  --export_onnx         If this parameter exists, export onnx model.