update README
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# Nueral Architecture Search
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This project contains the following neural architecture search algorithms, implemented in [PyTorch](http://pytorch.org).
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This project contains the following neural architecture search algorithms, implemented in [PyTorch](http://pytorch.org). More NAS resources can be found in [Awesome-NAS](https://github.com/D-X-Y/Awesome-NAS).
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- Network Pruning via Transformable Architecture Search, NeurIPS 2019
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- One-Shot Neural Architecture Search via Self-Evaluated Template Network, ICCV 2019
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@ -20,6 +20,7 @@ In this paper, we proposed a differentiable searching strategy for transformable
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<img src="https://d-x-y.github.com/resources/paper-icon/NIPS-2019-TAS.png" width="700">
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### Usage
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Use `bash ./scripts/prepare.sh` to prepare data splits for `CIFAR-10`, `CIFARR-100`, and `ILSVRC2012`.
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@ -50,6 +51,7 @@ Highlight: we equip one-shot NAS with an architecture sampler and train network
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<img src="https://d-x-y.github.com/resources/paper-icon/ICCV-2019-SETN.png" width="450">
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### Usage
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Please use the following scripts to train the searched SETN-searched CNN on CIFAR-10, CIFAR-100, and ImageNet.
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```
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CUDA_VISIBLE_DEVICES=0 bash ./scripts/nas-infer-train.sh cifar10 SETN 96 -1
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@ -81,6 +83,7 @@ Searching codes come soon!
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# Citation
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If you find that this project helps your research, please consider citing some of the following papers:
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```
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@inproceedings{dong2019tas,
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@ -10,6 +10,7 @@ from copy import deepcopy
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from pathlib import Path
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lib_dir = (Path(__file__).parent / '..' / 'lib').resolve()
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print ('lib_dir : {:}'.format(lib_dir))
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if str(lib_dir) not in sys.path: sys.path.insert(0, str(lib_dir))
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from config_utils import load_config, configure2str, obtain_search_single_args as obtain_args
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from procedures import prepare_seed, prepare_logger, save_checkpoint, copy_checkpoint
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@ -1,50 +0,0 @@
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#!/bin/bash
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# bash ./scripts/KD-train.sh cifar10 ResNet110 ResNet110 0.5 1 -1
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echo script name: $0
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echo $# arguments
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if [ "$#" -ne 6 ] ;then
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echo "Input illegal number of parameters " $#
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echo "Need 6 parameters for the dataset / the-model-name / the-teacher-path / KD-alpha / KD-temperature / the-random-seed"
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exit 1
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fi
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if [ "$TORCH_HOME" = "" ]; then
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echo "Must set TORCH_HOME envoriment variable for data dir saving"
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exit 1
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else
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echo "TORCH_HOME : $TORCH_HOME"
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fi
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dataset=$1
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model=$2
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teacher=$3
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alpha=$4
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temperature=$5
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epoch=E300
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LR=L1
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batch=256
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rseed=$6
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save_dir=./output/KD/${dataset}-${teacher}.2.${model}-${alpha}-${temperature}
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rm -rf ${save_dir}
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PY_C="./env/bin/python"
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if [ ! -f ${PY_C} ]; then
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echo "Local Run with Python: "`which python`
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PY_C="python"
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else
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echo "Cluster Run with Python: "${PY_C}
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fi
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${PY_C} --version
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${PY_C} ./exps/KD-main.py --dataset ${dataset} \
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--data_path $TORCH_HOME/cifar.python \
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--model_config ./configs/archs/CIFAR-${model}.config \
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--optim_config ./configs/opts/CIFAR-${epoch}-W5-${LR}-COS.config \
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--KD_checkpoint $TORCH_HOME/TAS-checkpoints/basemodels/${dataset}/${teacher}.pth \
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--procedure Simple-KD \
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--save_dir ${save_dir} \
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--KD_alpha ${alpha} --KD_temperature ${temperature} \
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--cutout_length -1 \
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--batch_size ${batch} --rand_seed ${rseed} --workers 4 \
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--eval_frequency 1 --print_freq 100 --print_freq_eval 200
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@ -22,21 +22,13 @@ batch=$5
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rseed=$6
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PY_C="./env/bin/python"
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if [ ! -f ${PY_C} ]; then
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echo "Local Run with Python: "`which python`
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PY_C="python"
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SAVE_ROOT="./output"
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else
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echo "Cluster Run with Python: "${PY_C}
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SAVE_ROOT="./hadoop-data/SearchCheckpoints"
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fi
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SAVE_ROOT="./output"
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save_dir=${SAVE_ROOT}/basic/${dataset}/${model}-${epoch}-${LR}-${batch}
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${PY_C} --version
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python --version
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${PY_C} ./exps/basic-main.py --dataset ${dataset} \
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python ./exps/basic-main.py --dataset ${dataset} \
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--data_path $TORCH_HOME/cifar.python \
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--model_config ./configs/archs/CIFAR-${model}.config \
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--optim_config ./configs/opts/CIFAR-${epoch}-W5-${LR}-COS.config \
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