def _dataset(dataname):
normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225])
transformations = transforms.Compose([
transforms.Scale(256),
transforms.CenterCrop(224),
transforms.ToTensor(), normalize
])
rootpath = os.path.join('/data/dacheng/Datasets/', dataname)
if dataname == 'NUSWIDE':
dset_database = dataset.NUSWIDE('train_img.txt', 'train_label.txt',
transformations)
dset_test = dataset.NUSWIDE('test_img.txt', 'test_label.txt',
transformations)
elif dataname == 'MirFlickr':
dset_database = dataset.MirFlickr('train_img.txt', 'train_label.txt',
transformations)
dset_test = dataset.MirFlickr('test_img.txt', 'test_label.txt',
transformations)
elif dataname == 'COCO':
dset_database = dataset.COCO('train_img.txt', 'train_label.txt',
transformations)
dset_test = dataset.COCO('test_img.txt', 'test_label.txt',
transformations)
num_database, num_test = len(dset_database), len(dset_test)
def load_label(filename, DATA_DIR):
path = os.path.join(DATA_DIR, filename)
fp = open(path, 'r')
labels = [x.strip() for x in fp]
fp.close()
return torch.LongTensor(list(map(int, labels)))
def DC_load_label(filename, DATA_DIR):
path = os.path.join(DATA_DIR, filename)
label = np.loadtxt(path, dtype=np.int64)
return torch.LongTensor(label)
def load_label2(root, train=True):
base_folder = 'cifar-10-batches-py'
train_list = [
['data_batch_1', 'c99cafc152244af753f735de768cd75f'],
['data_batch_2', 'd4bba439e000b95fd0a9bffe97cbabec'],
['data_batch_3', '54ebc095f3ab1f0389bbae665268c751'],
['data_batch_4', '634d18415352ddfa80567beed471001a'],
['data_batch_5', '482c414d41f54cd18b22e5b47cb7c3cb'],
]
test_list = [
['test_batch', '40351d587109b95175f43aff81a1287e'],
]
root = os.path.expanduser(root)
train = train # training set or test set
# now load the picked numpy arrays
if train:
train_data = []
train_labels = []
for fentry in train_list:
f = fentry[0]
file = os.path.join(root, base_folder, f)
fo = open(file, 'rb')
if sys.version_info[0] == 2:
entry = pickle.load(fo)
else:
entry = pickle.load(fo, encoding='latin1')
train_data.append(entry['data'])
if 'labels' in entry:
train_labels += entry['labels']
else:
train_labels += entry['fine_labels']
fo.close()
else:
f = test_list[0][0]
file = os.path.join(root, base_folder, f)
fo = open(file, 'rb')
if sys.version_info[0] == 2:
entry = pickle.load(fo)
else:
entry = pickle.load(fo, encoding='latin1')
test_data = entry['data']
if 'labels' in entry:
test_labels = entry['labels']
else:
test_labels = entry['fine_labels']
fo.close()
if train:
target = train_labels
else:
target = test_labels
return torch.LongTensor(list(map(int, target)))
databaselabels = DC_load_label('train_label.txt', rootpath)
testlabels = DC_load_label('test_label.txt', rootpath)
# testlabels2 = load_label2('/home/dacheng/PycharmProjects/ADSH_pytorch/data', train=False)
# databaselabels2 = load_label2('/home/dacheng/PycharmProjects/ADSH_pytorch/data', train=True)
# testlabels = encoding_onehot(testlabels2)
# databaselabels = encoding_onehot(databaselabels2)
dsets = (dset_database, dset_test)
nums = (num_database, num_test)
labels = (databaselabels, testlabels)
return nums, dsets, labels
def _dataset(dataname):
normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225])
transformations = transforms.Compose([
transforms.Scale(256),
transforms.CenterCrop(224),
transforms.ToTensor(), normalize
])
rootpath = os.path.join('/data/dacheng/Datasets/', dataname)
if dataname == 'NUSWIDE':
dset_database = dataset.NUSWIDE('train_img.txt', 'train_label.txt',
transformations)
dset_test = dataset.NUSWIDE('test_img.txt', 'test_label.txt',
transformations)
elif dataname == 'MirFlickr':
dset_database = dataset.MirFlickr('train_img.txt', 'train_label.txt',
transformations)
dset_test = dataset.MirFlickr('test_img.txt', 'test_label.txt',
transformations)
elif dataname == 'COCO':
dset_database = dataset.COCO('train_img.txt', 'train_label.txt',
transformations)
dset_test = dataset.COCO('test_img.txt', 'test_label.txt',
transformations)
elif dataname == 'CIFAR10':
dset_database = dataset.CIFAR10('train_img.txt', 'train_label.txt',
transformations)
dset_test = dataset.CIFAR10('test_img.txt', 'test_label.txt',
transformations)
elif dataname == 'MNIST':
dset_database = dataset.MNIST(True, transformations)
dset_test = dataset.MNIST(False, transformations)
num_database, num_test = len(dset_database), len(dset_test)
def load_label(filename, DATA_DIR):
path = os.path.join(DATA_DIR, filename)
fp = open(path, 'r')
labels = [x.strip() for x in fp]
fp.close()
return torch.LongTensor(list(map(int, labels)))
def DC_load_label(filename, DATA_DIR):
path = os.path.join(DATA_DIR, filename)
label = np.loadtxt(path, dtype=np.int64)
return torch.LongTensor(label)
def DC_load_label_MNIST(filename, root):
_, labels = torch.load(os.path.join(root, filename))
return torch.LongTensor(labels)
if dataname == 'CIFAR10':
testlabels_ = load_label('test_label.txt', rootpath)
databaselabels_ = load_label('train_label.txt', rootpath)
testlabels = encoding_onehot(testlabels_)
databaselabels = encoding_onehot(databaselabels_)
elif dataname == 'MNIST':
databaselabels_ = DC_load_label_MNIST(
'training.pt',
root='/home/dacheng/PycharmProjects/ADSH_pytorch/data/processed/')
testlabels_ = DC_load_label_MNIST(
'test.pt',
root='/home/dacheng/PycharmProjects/ADSH_pytorch/data/processed/')
testlabels = encoding_onehot(testlabels_)
databaselabels = encoding_onehot(databaselabels_)
else:
databaselabels = DC_load_label('train_label.txt', rootpath)
testlabels = DC_load_label('test_label.txt', rootpath)
dsets = (dset_database, dset_test)
nums = (num_database, num_test)
labels = (databaselabels, testlabels)
return nums, dsets, labels