[MNT] add CURRENT_DIR

examples/hwf/datasets/get_hwf.py

@@ -1,10 +1,10 @@
-import os
 import json
+import os.path as osp
 
 from PIL import Image
 from torchvision.transforms import transforms
 
-CURRENT_DIR = os.path.abspath(os.path.dirname(__file__))
+CURRENT_DIR = osp.abspath(osp.dirname(__file__))
 
 img_transform = transforms.Compose(
     [transforms.ToTensor(), transforms.Normalize((0.5,), (1,))]
@@ -15,7 +15,7 @@ def get_data(file, get_pseudo_label):
     X, Y = [], []
     if get_pseudo_label:
         Z = []
-    img_dir = os.path.join(CURRENT_DIR, "data/Handwritten_Math_Symbols/")
+    img_dir = osp.join(CURRENT_DIR, "data/Handwritten_Math_Symbols/")
     with open(file) as f:
         data = json.load(f)
         for idx in range(len(data)):
@@ -40,8 +40,8 @@ def get_data(file, get_pseudo_label):
 
 def get_hwf(train=True, get_gt_pseudo_label=False):
     if train:
-        file = os.path.join(CURRENT_DIR, "data/expr_train.json")
+        file = osp.join(CURRENT_DIR, "data/expr_train.json")
     else:
-        file = os.path.join(CURRENT_DIR, "data/expr_test.json")
+        file = osp.join(CURRENT_DIR, "data/expr_test.json")
 
     return get_data(file, get_gt_pseudo_label)

examples/mnist_add/datasets/get_mnist_add.py

@@ -1,40 +1,49 @@
+import os.path as osp
+
 import torchvision
 from torchvision.transforms import transforms
 
+CURRENT_DIR = osp.abspath(osp.dirname(__file__))
+
 
 def get_data(file, img_dataset, get_pseudo_label):
-    X = []
+    X, Y = [], []
     if get_pseudo_label:
         Z = []
-    Y = []
     with open(file) as f:
         for line in f:
-            line = line.strip().split(' ')
+            # if len(X) == 1000:
+            #     break
+            line = line.strip().split(" ")
             X.append([img_dataset[int(line[0])][0], img_dataset[int(line[1])][0]])
             if get_pseudo_label:
                 Z.append([img_dataset[int(line[0])][1], img_dataset[int(line[1])][1]])
             Y.append(int(line[2]))
-            
+
     if get_pseudo_label:
         return X, Z, Y
     else:
         return X, None, Y
 
-def get_mnist_add(train = True, get_pseudo_label = False):
-    transform = transforms.Compose([transforms.ToTensor(), transforms.Normalize((0.1307,), (0.3081, ))])
-    img_dataset = torchvision.datasets.MNIST(root='./datasets/', train=train, download=True, transform=transform)
-    
+
+def get_mnist_add(train=True, get_pseudo_label=False):
+    transform = transforms.Compose(
+        [transforms.ToTensor(), transforms.Normalize((0.1307,), (0.3081,))]
+    )
+    img_dataset = torchvision.datasets.MNIST(
+        root=CURRENT_DIR, train=train, download=True, transform=transform
+    )
+
     if train:
-        file = './datasets/train_data.txt'
+        file = osp.join(CURRENT_DIR, "train_data.txt")
     else:
-        file = './datasets/test_data.txt'
-    
+        file = osp.join(CURRENT_DIR, "test_data.txt")
+
     return get_data(file, img_dataset, get_pseudo_label)
-    
+
 
 if __name__ == "__main__":
-    train_X, train_Y = get_mnist_add(train = True)
-    test_X, test_Y = get_mnist_add(train = False)
+    train_X, train_Z, train_Y = get_mnist_add(train=True)
+    test_X, test_Z, test_Y = get_mnist_add(train=False)
     print(len(train_X), len(test_X))
     print(train_X[0][0].shape, train_X[0][1].shape, train_Y[0])
-