hearldssh_example

[hearldsshexample]

ßETAExecutable File 184 lines (158 sloc) 5.15 KB

!/bin/sh

#

This script runs through the code in each of the python examples.

The purpose is just as an integrtion test, not to actually train

models in any meaningful way. For that reason, most of these set

epochs = 1.

#

Optionally specify a comma separated list of examples to run.

can be run as:

./run_python_examples.sh "install_deps,run_all,clean"

to pip install dependencies (other than pytorch), run all examples,

and remove temporary/changed data files.

Expects pytorch to be installed.

BASE_DIR=pwd"/"dirname $0 EXAMPLES=echo $1 | sed -e 's/ //g'

if which nvcc ; then echo "using cuda" CUDA=1 CUDA_FLAG="--cuda" else echo "not using cuda" CUDA=0 CUDA_FLAG="" fi

ERRORS=""

function error() { ERR=$1 ERRORS="$ERRORS\n$ERR" echo $ERR }

function install_deps() { echo "installing requirements" cat $BASE_DIR/*/requirements.txt | \ sort -u | \

testing the installed version of torch, so don't pip install it.

grep -vE '^torch$' | \
pip install -r /dev/stdin || \
{ error "failed to install dependencies"; exit 1; }

}

function start() { EXAMPLE=${FUNCNAME[1]} cd $BASE_DIR/$EXAMPLE echo "Running example: $EXAMPLE" }

function dcgan() { start if [ ! -d "lsun" ]; then echo "cloning repo to get lsun dataset" git clone https://github.com/fyu/lsun || { error "couldn't clone lsun repo needed for dcgan"; return; } fi

'classroom' much smaller than the default 'bedroom' dataset.

DATACLASS="classroom" if [ ! -d "lsun/${DATACLASS}_train_lmdb" ]; then pushd lsun python download.py -c $DATACLASS || { error "couldn't download $DATACLASS for dcgan"; return; } unzip ${DATACLASS}_train_lmdb.zip || { error "couldn't unzip $DATACLASS"; return; } popd fi python main.py --dataset lsun --dataroot lsun --classes $DATACLASS --niter 1 $CUDA_FLAG || error "dcgan failed" }

function fast_neural_style() { start if [ ! -d "saved_models" ]; then echo "downloading saved models for fast neural style" python download_saved_models.py fi test -d "saved_models" || { error "saved models not found"; return; }

echo "running fast neural style model" python neural_style/neural_style.py eval --content-image images/content-images/amber.jpg --model saved_models/candy.pth --output-image images/output-images/amber-candy.jpg --cuda $CUDA || error "neural_style.py failed" }

function imagenet() { start if [[ ! -d "sample/val" || ! -d "sample/train" ]]; then mkdir -p sample/val/n mkdir -p sample/train/n wget "https://upload.wikimedia.org/wikipedia/commons/5/5a/Socks-clinton.jpg" || { error "couldn't download sample image for imagenet"; return; } mv Socks-clinton.jpg sample/train/n cp sample/train/n/* sample/val/n/ fi python main.py --epochs 1 sample/ || error "imagenet example failed" }

function mnist() { start python main.py --epochs 1 || error "mnist example failed" }

function mnist_hogwild() { start python main.py --epochs 1 $CUDA_FLAG || error "mnist hogwild failed" }

function regression() { start python main.py --epochs 1 $CUDA_FLAG || error "regression failed" }

function reinforcement_learning() { start python reinforce.py || error "reinforcement learning failed" }

function snli() { start echo "installing 'en' model if not installed" python -m spacy download en || { error "couldn't download 'en' model needed for snli"; return; } echo "training..." python train.py --epochs 1 --no-bidirectional || error "couldn't train snli" }

function super_resolution() { start python main.py --upscale_factor 3 --batchSize 4 --testBatchSize 100 --nEpochs 1 --lr 0.001 || error "super resolution failed" }

function time_sequence_prediciton() { start python generate_sine_wave.py || { error "generate sine wave failed"; return; } python train.py || error "time sequence prediction training failed" }

function vae() { start python main.py --epochs 1 || error "vae failed" }

function word_language_model() { start python main.py --epochs 1 $CUDA_FLAG || error "word_language_model failed" }

function clean() { cd $BASE_DIR rm -rf dcgan/_cache_lsun_classroom_train_lmdb dcgan/fake_samples_epoch_000.png dcgan/lsun/ dcgan/netD_epoch_0.pth dcgan/netG_epoch_0.pth dcgan/real_samples.png fast_neural_style/saved_models.zip fast_neural_style/saved_models/ imagenet/checkpoint.pth.tar imagenet/lsun/ imagenet/model_best.pth.tar imagenet/sample/ snli/.data/ snli/.vector_cache/ snli/results/ super_resolution/dataset/ super_resolution/model_epoch_1.pth word_language_model/model.pt || error "couldn't clean up some files"

git checkout fast_neural_style/images/output-images/amber-candy.jpg || error "couldn't clean up fast neural style image" }

function run_all() { dcgan fast_neural_style imagenet mnist mnist_hogwild regression reinforcement_learning snli super_resolution time_sequence_prediction vae word_language_model }

by default, run all examples

if [ "" == "$EXAMPLES" ]; then run_all else for i in $(echo $EXAMPLES | sed "s/,/ /g") do $i done fi

if [ "" == "$ERRORS" ]; then tput setaf 2 echo "Completed successfully"

[hearldsshexample]

OPEN_SOURCES