update README

README.md

@@ -1,6 +1,6 @@
 # Nueral Architecture Search
 
-This project contains the following neural architecture search algorithms, implemented in [PyTorch](http://pytorch.org).
+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).
 
 - Network Pruning via Transformable Architecture Search, NeurIPS 2019
 - One-Shot Neural Architecture Search via Self-Evaluated Template Network, ICCV 2019
@@ -20,6 +20,7 @@ In this paper, we proposed a differentiable searching strategy for transformable
 
 <img src="https://d-x-y.github.com/resources/paper-icon/NIPS-2019-TAS.png" width="700">
 
+
 ### Usage
 
 Use `bash ./scripts/prepare.sh` to prepare data splits for `CIFAR-10`, `CIFARR-100`, and `ILSVRC2012`.
@@ -50,6 +51,7 @@ Highlight: we equip one-shot NAS with an architecture sampler and train network
 <img src="https://d-x-y.github.com/resources/paper-icon/ICCV-2019-SETN.png" width="450">
 
 ### Usage
+
 Please use the following scripts to train the searched SETN-searched CNN on CIFAR-10, CIFAR-100, and ImageNet.
 ```
 CUDA_VISIBLE_DEVICES=0 bash ./scripts/nas-infer-train.sh cifar10  SETN 96 -1
@@ -81,6 +83,7 @@ Searching codes come soon!
 
 
 # Citation
+
 If you find that this project helps your research, please consider citing some of the following papers:
 ```
 @inproceedings{dong2019tas,

exps/search-shape.py

@@ -10,6 +10,7 @@ from copy    import deepcopy
 from pathlib import Path
 
 lib_dir = (Path(__file__).parent / '..' / 'lib').resolve()
+print ('lib_dir : {:}'.format(lib_dir))
 if str(lib_dir) not in sys.path: sys.path.insert(0, str(lib_dir))
 from config_utils import load_config, configure2str, obtain_search_single_args as obtain_args
 from procedures   import prepare_seed, prepare_logger, save_checkpoint, copy_checkpoint

scripts/KD-train.sh

@@ -1,50 +0,0 @@
-#!/bin/bash
-# bash ./scripts/KD-train.sh cifar10 ResNet110 ResNet110 0.5 1 -1
-echo script name: $0
-echo $# arguments
-if [ "$#" -ne 6 ] ;then
-  echo "Input illegal number of parameters " $#
-  echo "Need 6 parameters for the dataset / the-model-name / the-teacher-path / KD-alpha / KD-temperature / the-random-seed"
-  exit 1
-fi
-if [ "$TORCH_HOME" = "" ]; then
-  echo "Must set TORCH_HOME envoriment variable for data dir saving"
-  exit 1
-else
-  echo "TORCH_HOME : $TORCH_HOME"
-fi
-
-dataset=$1
-model=$2
-teacher=$3
-alpha=$4
-temperature=$5
-epoch=E300
-LR=L1
-batch=256
-rseed=$6
-
-save_dir=./output/KD/${dataset}-${teacher}.2.${model}-${alpha}-${temperature}
-rm -rf ${save_dir}
-
-PY_C="./env/bin/python"
-if [ ! -f ${PY_C} ]; then
-  echo "Local Run with Python: "`which python`
-  PY_C="python"
-else
-  echo "Cluster Run with Python: "${PY_C}
-fi
-
-${PY_C} --version
-
-${PY_C} ./exps/KD-main.py --dataset ${dataset} \
-	--data_path $TORCH_HOME/cifar.python \
-	--model_config  ./configs/archs/CIFAR-${model}.config \
-	--optim_config  ./configs/opts/CIFAR-${epoch}-W5-${LR}-COS.config \
-	--KD_checkpoint $TORCH_HOME/TAS-checkpoints/basemodels/${dataset}/${teacher}.pth \
-	--procedure    Simple-KD \
-	--save_dir     ${save_dir} \
-	--KD_alpha ${alpha} --KD_temperature ${temperature} \
-	--cutout_length -1 \
-	--batch_size  ${batch} --rand_seed ${rseed} --workers 4 \
-	--eval_frequency 1 --print_freq 100 --print_freq_eval 200

scripts/base-train.sh

@@ -22,21 +22,13 @@ batch=$5
 rseed=$6
 
 
-PY_C="./env/bin/python"
-if [ ! -f ${PY_C} ]; then
-  echo "Local Run with Python: "`which python`
-  PY_C="python"
 SAVE_ROOT="./output"
-else
-  echo "Cluster Run with Python: "${PY_C}
-  SAVE_ROOT="./hadoop-data/SearchCheckpoints"
-fi
 
 save_dir=${SAVE_ROOT}/basic/${dataset}/${model}-${epoch}-${LR}-${batch}
 
-${PY_C} --version
+python --version
 
-${PY_C} ./exps/basic-main.py --dataset ${dataset} \
+python ./exps/basic-main.py --dataset ${dataset} \
 	--data_path $TORCH_HOME/cifar.python \
 	--model_config ./configs/archs/CIFAR-${model}.config \
 	--optim_config ./configs/opts/CIFAR-${epoch}-W5-${LR}-COS.config \