Add Powershell scripts for Windows users

scripts/gen_train_eval_nopretrained.ps1

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+# Want to train with wordnet hierarchy? Just set `--hierarchy=wordnet` below.
+# This script is for networks that do NOT come with a pretrained checkpoint provided either by a model zoo or by the NBDT utility itself.
+
+$model="ResNet18"
+$dataset="CIFAR10"
+$weight=1
+
+# 0. train the baseline neural network
+python main.py --dataset=$dataset --arch=$model
+
+# 1. generate hieararchy -- for models without a pretrained checkpoint, use `checkpoint`
+nbdt-hierarchy --dataset=$dataset --checkpoint="./checkpoint/ckpt-$dataset" + "-$model.pth"
+
+# 2. train with soft tree supervision loss -- for models without a pretrained checkpoint, use `path-resume` OR just train from scratch, without `path-resume`
+# python main.py --lr=0.01 --dataset=${dataset} --model=${model} --hierarchy=induced-${model} --path-resume=./checkpoint/ckpt-${dataset}-${model}.pth --loss=SoftTreeSupLoss --tree-supervision-weight=${weight}  # fine-tuning
+python main.py --dataset=$dataset --arch=$model --hierarchy=induced-$model --loss=SoftTreeSupLoss --tree-supervision-weight=$weight  # training from scratch
+
+# 3. evaluate with soft then hard inference
+$analysisRules = @("SoftEmbeddedDecisionRules", "HardEmbeddedDecisionRules")
+
+foreach ($analysis in $analysisRules) {
+  python main.py --dataset=$dataset --model=$model --hierarchy=induced-$model --loss=SoftTreeSupLoss --eval --resume --analysis=$analysis --tree-supervision-weight=$weight
+}

scripts/gen_train_eval_pretrained.ps1

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+# Want to train with wordnet hierarchy? Just set `--hierarchy=wordnet` below.
+# This script is for networks that DO come with a pretrained checkpoint provided either by a model zoo or by the NBDT utility itself.
+
+$model="wrn28_10_cifar10"
+$dataset="CIFAR10"
+$weight=1
+
+# 1. generate hieararchy
+nbdt-hierarchy --dataset=$dataset --arch=$model
+
+# 2. train with soft tree supervision loss
+python main.py --lr=0.01 --dataset=$dataset --model=$model --hierarchy=induced-$model --pretrained --loss=SoftTreeSupLoss --tree-supervision-weight=$weight
+
+# 3. evaluate with soft then hard inference
+$analysisRules = @("SoftEmbeddedDecisionRules", "HardEmbeddedDecisionRules")
+
+foreach ($analysis in $analysisRules) {
+  python main.py --dataset=$dataset --model=$model --hierarchy=induced-$model --loss=SoftTreeSupLoss --eval --resume --analysis=$analysis --tree-supervision-weight=$weight
+} 

scripts/gen_train_eval_resnet.ps1

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+# Want to train with wordnet hierarchy? Just set `--hierarchy=wordnet` below.
+
+$MODELS = @("CIFAR10 1", "CIFAR100 1", "TinyImagenet200 10")
+
+foreach ($model in $MODELS) {
+
+  $params = $model.split(" ")
+
+  $dataset=$params[0]
+  $weight=$params[1]
+
+
+
+  # 1. generate hieararchy
+  nbdt-hierarchy --dataset=$dataset --arch=ResNet18
+
+  # 2. train with soft tree supervision loss
+  python main.py --dataset=$dataset --arch=$model --hierarchy=induced-$model --loss=SoftTreeSupLoss --tree-supervision-weight=$weight
+
+  # 3. evaluate with soft then hard inference
+
+  $analysisRules = @("SoftEmbeddedDecisionRules", "HardEmbeddedDecisionRules")
+
+  foreach ($analysis in $analysisRules) {
+    python main.py --dataset=$dataset --arch=$model --hierarchy=induced-$model --loss=SoftTreeSupLoss --tree-supervision-weight=$weight --eval --resume --analysis=$analysis
+  }
+}

scripts/gen_train_eval_wideresnet.ps1

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+# Want to train with wordnet hierarchy? Just set `--hierarchy=wordnet` below.
+
+$MODEL_NAME="wrn28_10"
+$CIFAR100="CIFAR100" +  " " + $MODEL_NAME + "_cifar100 1"
+$CIFAR10="CIFAR10 $MODEL_NAME" + "_cifar10 1"
+$MODELS=@($CIFAR10, $CIFAR100, "TinyImagenet200 $MODEL_NAME 10")
+
+foreach ($model in $MODELS) {
+
+  $params = $model.split(" ")
+
+  $dataset=$params[0]
+  $model=$params[1]
+  $weight=$params[2]
+
+  # 1. generate hieararchy
+  nbdt-hierarchy  --dataset=$dataset --arch=$model
+
+  # 2. train with soft tree supervision loss
+  python main.py --lr=0.01 --dataset=$dataset --arch=$model --hierarchy=induced-$model --pretrained --loss=SoftTreeSupLoss --tree-supervision-weight=$weight
+
+  # 3. evaluate with soft then hard inference
+  $analysisRules = @("SoftEmbeddedDecisionRules", "HardEmbeddedDecisionRules")
+
+  foreach ($analysis in $analysisRules) {
+    python main.py --dataset=${dataset} --arch=${model} --hierarchy=induced-${model} --loss=SoftTreeSupLoss --eval --resume --analysis=${analysis} --tree-supervision-weight=${weight}
+  }
+}

scripts/generate_hierarchies_wordnet.ps1

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+python -c "import nltk;nltk.download('wordnet')"
+
+# Generate WNIDs
+$DATASETS = @("CIFAR10", "CIFAR100")
+foreach ($dataset in $DATASETS) {
+  nbdt-wnids --dataset=$dataset
+}
+
+# Generate and test hierarchies
+$MORE_DATASETS = @("CIFAR10", "CIFAR100", "TinyImagenet200")
+foreach ($dataset in $MORE_DATASETS) {
+  nbdt-hierarchy --dataset=$dataset --method=wordnet;
+}