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Dec 2, 2016
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cifar10/100 only load train/test and not both #12

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46 changes: 23 additions & 23 deletions torchvision/datasets/cifar.py
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Original file line number Diff line number Diff line change
Expand Up @@ -42,35 +42,35 @@ def __init__(self, root, train=True, transform=None, target_transform=None, down
+ ' You can use download=True to download it')

# now load the picked numpy arrays
self.train_data = []
self.train_labels = []
for fentry in self.train_list:
f = fentry[0]
if self.train:
self.train_data = []
self.train_labels = []
for fentry in self.train_list:
f = fentry[0]
file = os.path.join(root, self.base_folder, f)
fo = open(file, 'rb')
entry = pickle.load(fo)
self.train_data.append(entry['data'])
if 'labels' in entry:
self.train_labels += entry['labels']
else:
self.train_labels += entry['fine_labels']
fo.close()

self.train_data = np.concatenate(self.train_data)
self.train_data = self.train_data.reshape((50000, 3, 32, 32))
else:
f = self.test_list[0][0]
file = os.path.join(root, self.base_folder, f)
fo = open(file, 'rb')
entry = pickle.load(fo)
self.train_data.append(entry['data'])
self.test_data = entry['data']
if 'labels' in entry:
self.train_labels += entry['labels']
self.test_labels = entry['labels']
else:
self.train_labels += entry['fine_labels']
self.test_labels = entry['fine_labels']
fo.close()

self.train_data = np.concatenate(self.train_data)

f = self.test_list[0][0]
file = os.path.join(root, self.base_folder, f)
fo = open(file, 'rb')
entry = pickle.load(fo)
self.test_data = entry['data']
if 'labels' in entry:
self.test_labels = entry['labels']
else:
self.test_labels = entry['fine_labels']
fo.close()

self.train_data = self.train_data.reshape((50000, 3, 32, 32))
self.test_data = self.test_data.reshape((10000, 3, 32, 32))
self.test_data = self.test_data.reshape((10000, 3, 32, 32))

def __getitem__(self, index):
if self.train:
Expand Down