update: modify file structure and tidy code

Howeng98 · Nov 30, 2022 · 5ad0edb · 5ad0edb
1 parent e518acd
commit 5ad0edb
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Showing 13 changed files with 15 additions and 19 deletions.
diff --git a/codes/__pycache__/datasets.cpython-38.pyc b/codes/__pycache__/datasets.cpython-38.pyc
diff --git a/codes/__pycache__/inspection.cpython-38.pyc b/codes/__pycache__/inspection.cpython-38.pyc
diff --git a/codes/__pycache__/mvtecad.cpython-38.pyc b/codes/__pycache__/mvtecad.cpython-38.pyc
diff --git a/codes/__pycache__/nearest_neighbor.cpython-38.pyc b/codes/__pycache__/nearest_neighbor.cpython-38.pyc
diff --git a/codes/__pycache__/networks.cpython-38.pyc b/codes/__pycache__/networks.cpython-38.pyc
diff --git a/codes/__pycache__/utils.cpython-38.pyc b/codes/__pycache__/utils.cpython-38.pyc
diff --git a/codes/codes/datasets.py → codes/datasets.py b/codes/codes/datasets.py → codes/datasets.py
diff --git a/codes/codes/inspection.py → codes/inspection.py b/codes/codes/inspection.py → codes/inspection.py
diff --git a/codes/codes/mvtecad.py → codes/mvtecad.py b/codes/codes/mvtecad.py → codes/mvtecad.py
@@ -6,22 +6,20 @@
 from sklearn.metrics import roc_auc_score, precision_recall_curve, roc_curve
 
 
-DATASET_PATH = '/home/ginger/mvtec_anomaly_detection'
+DATASET_PATH = '../MvTecAD/'
 
-
-__all__ = ['objs', 'set_root_path',
+__all__ = ['objs', 
            'get_x', 'get_x_standardized',
            'detection_auroc', 'segmentation_auroc']
 
 objs = ['bottle', 'cable', 'capsule', 'carpet', 'grid', 'hazelnut',
         'leather', 'metal_nut', 'pill', 'screw', 'tile', 'toothbrush',
         'transistor', 'wood', 'zipper']
 
-
 def resize(image, shape=(256, 256)):
     return np.array(Image.fromarray(image).resize(shape[::-1]))
 
-        
+
 def bilinears(images, shape) -> np.ndarray:
     import cv2
     N = images.shape[0]
@@ -39,14 +37,9 @@ def gray2rgb(images):
     return images
 
 
-def set_root_path(new_path):
-    global DATASET_PATH
-    DATASET_PATH = new_path
-
-
 def get_x(obj, mode='train'):
-    fpattern = os.path.join(DATASET_PATH, f'{obj}/{mode}/*/*.png')
-    fpaths = sorted(glob(fpattern))
+    fpattern = os.path.join(DATASET_PATH, f'{obj}/{mode}/*/*.png')    
+    fpaths = sorted(glob(fpattern))    
 
     if mode == 'test':
         fpaths1 = list(filter(lambda fpath: os.path.basename(os.path.dirname(fpath)) != 'good', fpaths))
@@ -62,13 +55,12 @@ def get_x(obj, mode='train'):
     if images.shape[-1] != 3:
         images = gray2rgb(images)
     images = list(map(resize, images))
-    images = np.asarray(images)
-
+    images = np.asarray(images)    
     return images
 
 
 def get_x_standardized(obj, mode='train'):
-    x = get_x(obj, mode=mode)
+    x = get_x(obj, mode=mode)    
     mean = get_mean(obj)
     return (x.astype(np.float32) - mean) / 255
 

diff --git a/codes/codes/nearest_neighbor.py → codes/nearest_neighbor.py b/codes/codes/nearest_neighbor.py → codes/nearest_neighbor.py
diff --git a/codes/codes/networks.py → codes/networks.py b/codes/codes/networks.py → codes/networks.py
diff --git a/codes/codes/utils.py → codes/utils.py b/codes/codes/utils.py → codes/utils.py
diff --git a/main_train.py b/main_train.py
@@ -34,16 +34,20 @@
 device = 'cuda'
 torch.backends.cudnn.benchmark = True
 
-
 # logging AUROC results
 newline = '\n'
+if not os.path.isdir(f'./log_result'):
+    os.mkdir(f'./log_result')
+if not os.path.isdir(f'./ckpts'):
+    os.mkdir(f'./ckpts')
+
 LOG = f'./log_result/AUROC_{args.obj}.log'
 logging.basicConfig(filename=LOG, filemode="w", level=logging.INFO)
 logging.info(f' [class:{args.obj}, lambda:{args.lambda_value}, learning rate:{args.lr}, total training epochs:{args.epochs}, groups_64:{args.groups_64}, groups_32:{args.groups_32}, groups_16:{args.groups_16}]{newline}{newline}')
 
 
-if not os.path.isdir(f'ckpts/{args.obj}'):
-    os.mkdir(f'ckpts/{args.obj}')
+if not os.path.isdir(f'./ckpts/{args.obj}'):
+    os.mkdir(f'./ckpts/{args.obj}')
 
 
 def train():
@@ -73,7 +77,7 @@ def train():
         opt = torch.optim.Adam(params=params, lr=lr)
 
     with task('Datasets'):
-        train_x = mvtecad.get_x_standardized(obj, mode='train')
+        train_x = mvtecad.get_x_standardized(obj, mode='train')        
         train_x = NHWC2NCHW(train_x)
 
         rep = 100