PyTorch implementation of SplitNet: Learning to Semantically Split Deep Networks for Parameter Reduction and Model Parallelization, ICML 2017
Learned Convolution Filters of g2-b1-w3 (left) and g3-b1-w3 (right).
Learned Group Indicators of g2-b2-p (left) and g3-b2-p (right).
$ git clone https://github.com:kuc2477/dl-papers && cd dl-papers
$ pip install -r requirements.txt
Implementation CLI is provided by main.py.
$ ./main.py --help
$ usage: SplitNet torch implementation [-h] [--dataset {cifar10,mnist,cifar100}]
[--total-block-number TOTAL_BLOCK_NUMBER]
[--widen-factor WIDEN_FACTOR]
[--baseline-strides BASELINE_STRIDES [BASELINE_STRIDES ...]]
[--baseline-channels BASELINE_CHANNELS [BASELINE_CHANNELS ...]]
[--split-sizes SPLIT_SIZES [SPLIT_SIZES ...]]
[--gamma1 GAMMA1] [--gamma2 GAMMA2]
[--gamma3 GAMMA3]
[--weight-decay WEIGHT_DECAY]
[--dropout-prob DROPOUT_PROB] [--lr LR]
[--lr-decay LR_DECAY]
[--lr-decay-epochs LR_DECAY_EPOCHS [LR_DECAY_EPOCHS ...]]
[--epochs EPOCHS] [--batch-size BATCH_SIZE]
[--test-size TEST_SIZE]
[--eval-log-interval EVAL_LOG_INTERVAL]
[--loss-log-interval LOSS_LOG_INTERVAL]
[--weight-log-interval WEIGHT_LOG_INTERVAL]
[--checkpoint-interval CHECKPOINT_INTERVAL]
[--model-dir MODEL_DIR]
[--resume-best | --resume-latest] [--best]
[--no-gpus] (--test | --train)
$ python -m visom.server &
$ ./main.py --train [--resume-latest|--resume-best]
$ ./main.py --test
- Block diagonal weight utilizing test phase is not implemented yet.
- SplitNet: Learning to Semantically Split Deep Networks for Parameter Reduction and Model Parallelization, ICML 2017
- dalgu90/splitnet-wrn
Ha Junsoo / @kuc2477 / MIT License
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