fix boolean conversion and some spelling

Author: Runinho

README.md

@@ -1,6 +1,6 @@
 # Implementation of CutPaste
 
-This is an **unofficial** work in progress PyTorch reimplementation of [CutPaste: Self-Supervised Learning for Anomaly Detection and  Localization](https://arxiv.org/abs/2104.04015) and in no way affiliated with the original authors. Use at own risk. Pull requests and feedback is appreciated.
+This is an **unofficial** work in progress PyTorch reimplementation of [CutPaste: Self-Supervised Learning for Anomaly Detection and Localization](https://arxiv.org/abs/2104.04015) and in no way affiliated with the original authors. Use at own risk. Pull requests and feedback is appreciated.
 
 ## Setup
 Download the MVTec Anomaly detection Dataset from [here](https://www.mvtec.com/company/research/datasets/mvtec-ad) and extract it into a new folder named `Data`.

dataset.py

@@ -24,7 +24,7 @@ def __init__(self, root_dir, defect_name, size, transform=None, mode="train"):
             root_dir (string): Directory with the MVTec AD dataset.
             defect_name (string): defect to load.
             transform: Transform to apply to data
-            mode: "train" loads training sammples "test" test samples default "train"
+            mode: "train" loads training samples "test" test samples default "train"
         """
         self.root_dir = Path(root_dir)
         self.defect_name = defect_name

eval.py

@@ -17,6 +17,7 @@
 from sklearn.covariance import LedoitWolf
 from collections import defaultdict
 import pandas as pd
+from utils import str2bool
 
 test_data_eval = None
 test_transform = None
@@ -95,8 +96,8 @@ def eval_model(modelname, defect_type, device="cpu", save_plots=False, size=256,
         # also show some of the training data
         show_training_data = False
         if show_training_data:
-            #augmentation settig
-            # TODO: do all of this in a seperate function that we can call in training and evaluation.
+            #augmentation setting
+            # TODO: do all of this in a separate function that we can call in training and evaluation.
             #       very ugly to just copy the code lol
             min_scale = 0.5
 
@@ -243,7 +244,7 @@ def plot_tsne(labels, embeds, filename):
     parser.add_argument('--model_dir', default="models",
                     help=' directory contating models to evaluate (default: models)')
 
-    parser.add_argument('--cuda', default=False,
+    parser.add_argument('--cuda', default=False, type=str2bool,
                     help='use cuda for model predictions (default: False)')
 
     parser.add_argument('--head_layer', default=8, type=int,

model.py

@@ -21,9 +21,6 @@ def __init__(self, pretrained=True, head_layers=[512,512,512,512,512,512,512,512
             last_layer = num_neurons
 
         #the last layer without activation
-        #TODO: is this correct? check one classe representation framework paper/code
-        # sequential_layers.append(nn.Linear(last_layer, head_layers[-1]))
-        # last_layer = head_layers[-1]
 
         head = nn.Sequential(
             *sequential_layers

requirements.txt

@@ -0,0 +1,7 @@
+torch
+torchvision
+sklearn
+pandas
+seaborn
+tqdm
+tensorboard

run_training.py

@@ -17,6 +17,7 @@
 from cutpaste import CutPasteNormal,CutPasteScar, CutPaste3Way, CutPasteUnion, cut_paste_collate_fn
 from model import ProjectionNet
 from eval import eval_model
+from util import str2bool
 
 def run_training(data_type="screw",
                  model_dir="models",
@@ -204,7 +205,7 @@ def get_data_inf():
 
     parser.add_argument('--variant', default="3way", choices=['normal', 'scar', '3way', 'union'], help='cutpaste variant to use (dafault: "3way")')
 
-    parser.add_argument('--cuda', default=False,
+    parser.add_argument('--cuda', default=False, type=str2bool,
                     help='use cuda for training (default: False)')
 
     parser.add_argument('--workers', default=8, type=int, help="number of workers to use for data loading (default:8)")

utils.py

@@ -0,0 +1,15 @@
+import distutils
+
+def str2bool(v):
+    """argparse handels type=bool in a weird way.
+    See this stack overflow: https://stackoverflow.com/questions/15008758/parsing-boolean-values-with-argparse
+    we can use this function as type converter for boolean values
+    """
+    if isinstance(v, bool):
+        return v
+    if v.lower() in ('yes', 'true', 't', 'y', '1'):
+        return True
+    elif v.lower() in ('no', 'false', 'f', 'n', '0'):
+        return False
+    else:
+        raise argparse.ArgumentTypeError('Boolean value expected.')