add a script to run all pytorch examples (#591)

dcgan/main.py

@@ -31,6 +31,7 @@
 parser.add_argument('--netD', default='', help="path to netD (to continue training)")
 parser.add_argument('--outf', default='.', help='folder to output images and model checkpoints')
 parser.add_argument('--manualSeed', type=int, help='manual seed')
+parser.add_argument('--classes', default='bedroom', help='comma separated list of classes for the lsun data set')
 
 opt = parser.parse_args()
 print(opt)
@@ -62,7 +63,8 @@
                                ]))
     nc=3
 elif opt.dataset == 'lsun':
-    dataset = dset.LSUN(root=opt.dataroot, classes=['bedroom_train'],
+    classes = [ c + '_train' for c in opt.classes.split(',')]
+    dataset = dset.LSUN(root=opt.dataroot, classes=classes,
                         transform=transforms.Compose([
                             transforms.Resize(opt.imageSize),
                             transforms.CenterCrop(opt.imageSize),

run_python_examples.sh

@@ -0,0 +1,184 @@
+#!/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"
+else 
+  tput setaf 1
+  echo "Some examples failed:"
+  printf "$ERRORS"
+fi
+
+tput sgr0
+

snli/requirements.txt

@@ -1,2 +1,3 @@
 torch
 torchtext
+spacy

snli/train.py

@@ -14,8 +14,11 @@
 
 
 args = get_args()
-torch.cuda.set_device(args.gpu)
-device = torch.device('cuda:{}'.format(args.gpu))
+if torch.cuda.is_available():
+    torch.cuda.set_device(args.gpu)
+    device = torch.device('cuda:{}'.format(args.gpu))
+else:
+    device = torch.device('cpu')
 
 inputs = data.Field(lower=args.lower, tokenize='spacy')
 answers = data.Field(sequential=False)