Add metadata to some datasets (#501)

* Add classes metadata to MNIST and FashionMNIST

* Add `targets` property to MNIST and FashionMNIST

* Add class metadata to CIFAR10/CIFAR100

* Add `targets` property to CIFAR10/CIFAR100

* Add targets attribute to DatasetFolder

torchvision/datasets/cifar.py

@@ -45,6 +45,18 @@ class CIFAR10(data.Dataset):
     test_list = [
         ['test_batch', '40351d587109b95175f43aff81a1287e'],
     ]
+    meta = {
+        'filename': 'batches.meta',
+        'key': 'label_names',
+        'md5': '5ff9c542aee3614f3951f8cda6e48888',
+    }
+
+    @property
+    def targets(self):
+        if self.train:
+            return self.train_labels
+        else:
+            return self.test_labels
 
     def __init__(self, root, train=True,
                  transform=None, target_transform=None,
@@ -100,6 +112,21 @@ class CIFAR10(data.Dataset):
             self.test_data = self.test_data.reshape((10000, 3, 32, 32))
             self.test_data = self.test_data.transpose((0, 2, 3, 1))  # convert to HWC
 
+        self._load_meta()
+
+    def _load_meta(self):
+        path = os.path.join(self.root, self.base_folder, self.meta['filename'])
+        if not check_integrity(path, self.meta['md5']):
+            raise RuntimeError('Dataset metadata file not found or corrupted.' +
+                               ' You can use download=True to download it')
+        with open(path, 'rb') as infile:
+            if sys.version_info[0] == 2:
+                data = pickle.load(infile)
+            else:
+                data = pickle.load(infile, encoding='latin1')
+            self.classes = data[self.meta['key']]
+        self.class_to_idx = {_class: i for i, _class in enumerate(self.classes)}
+
     def __getitem__(self, index):
         """
         Args:
@@ -187,3 +214,8 @@ class CIFAR100(CIFAR10):
     test_list = [
         ['test', 'f0ef6b0ae62326f3e7ffdfab6717acfc'],
     ]
+    meta = {
+        'filename': 'meta',
+        'key': 'fine_label_names',
+        'md5': '7973b15100ade9c7d40fb424638fde48',
+    }

torchvision/datasets/folder.py

@@ -69,6 +69,7 @@ class DatasetFolder(data.Dataset):
         classes (list): List of the class names.
         class_to_idx (dict): Dict with items (class_name, class_index).
         samples (list): List of (sample path, class_index) tuples
+        targets (list): The class_index value for each image in the dataset
     """
 
     def __init__(self, root, loader, extensions, transform=None, target_transform=None):
@@ -85,6 +86,7 @@ class DatasetFolder(data.Dataset):
         self.classes = classes
         self.class_to_idx = class_to_idx
         self.samples = samples
+        self.targets = [s[1] for s in samples]
 
         self.transform = transform
         self.target_transform = target_transform

torchvision/datasets/mnist.py

@@ -35,6 +35,16 @@ class MNIST(data.Dataset):
     processed_folder = 'processed'
     training_file = 'training.pt'
     test_file = 'test.pt'
+    classes = ['0 - zero', '1 - one', '2 - two', '3 - three', '4 - four',
+               '5 - five', '6 - six', '7 - seven', '8 - eight', '9 - nine']
+    class_to_idx = {_class: i for i, _class in enumerate(classes)}
+
+    @property
+    def targets(self):
+        if self.train:
+            return self.train_labels
+        else:
+            return self.test_labels
 
     def __init__(self, root, train=True, transform=None, target_transform=None, download=False):
         self.root = os.path.expanduser(root)
@@ -174,6 +184,9 @@ class FashionMNIST(MNIST):
         'http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/t10k-images-idx3-ubyte.gz',
         'http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/t10k-labels-idx1-ubyte.gz',
     ]
+    classes = ['T-shirt/top', 'Trouser', 'Pullover', 'Dress', 'Coat', 'Sandal',
+               'Shirt', 'Sneaker', 'Bag', 'Ankle boot']
+    class_to_idx = {_class: i for i, _class in enumerate(classes)}
 
 
 class EMNIST(MNIST):