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run_python_examples.sh 5.71 KB
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mattip 提交于 2020-07-06 06:44 . remove tput, causes errors in CI
#!/usr/bin/env bash
#
# 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 and --dry-run.
#
# 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, torchvision to be installed.
BASE_DIR=`pwd`"/"`dirname $0`
EXAMPLES=`echo $1 | sed -e 's/ //g'`
USE_CUDA=$(python -c "import torchvision, torch; print(torch.cuda.is_available())")
case $USE_CUDA in
"True")
echo "using cuda"
CUDA=1
CUDA_FLAG="--cuda"
;;
"False")
echo "not using cuda"
CUDA=0
CUDA_FLAG=""
;;
"")
exit 1;
;;
esac
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 --dry-run || 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 --dry-run || error "mnist example failed"
}
function mnist_hogwild() {
start
python main.py --epochs 1 --dry-run $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 --dev_every 1 --no-bidirectional --dry-run || 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_prediction() {
start
python generate_sine_wave.py || { error "generate sine wave failed"; return; }
python train.py --steps 2 || 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 --dry-run $CUDA_FLAG || error "word_language_model failed"
}
function clean() {
cd $BASE_DIR
echo "running clean to remove cruft"
rm -rf dcgan/_cache_lsun_classroom_train_lmdb \
dcgan/fake_samples_epoch_000.png dcgan/lsun/ \
dcgan/_cache_lsunclassroomtrainlmdb \
dcgan/netD_epoch_0.pth dcgan/netG_epoch_0.pth \
dcgan/real_samples.png \
fast_neural_style/saved_models.zip \
fast_neural_style/saved_models/ \
imagenet/checkpoint.pth.tar \
imagenet/lsun/ \
imagenet/model_best.pth.tar \
imagenet/sample/ \
snli/.data/ \
snli/.vector_cache/ \
snli/results/ \
super_resolution/dataset/ \
super_resolution/model_epoch_1.pth \
time_sequence_prediction/predict*.pdf \
time_sequence_prediction/traindata.pt \
word_language_model/model.pt || error "couldn't clean up some files"
git checkout fast_neural_style/images/output-images/amber-candy.jpg || error "couldn't clean up fast neural style image"
}
function run_all() {
# cpp
dcgan
# distributed
fast_neural_style
imagenet
mnist
mnist_hogwild
regression
reinforcement_learning
snli
super_resolution
time_sequence_prediction
vae
word_language_model
}
# by default, run all examples
if [ "" == "$EXAMPLES" ]; then
run_all
else
for i in $(echo $EXAMPLES | sed "s/,/ /g")
do
echo "Starting $i"
$i
echo "Finished $i, status $?"
done
fi
if [ "" == "$ERRORS" ]; then
echo "Completed successfully with status $?"
else
echo "Some examples failed:"
printf "$ERRORS"
fi
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