def _prepare_cifar10_data():
data_path = '/home/huwenp/Dataset/CIFAR/'
url = 'http://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz'
file_manager.create_dirname_if_not_exist(data_path)
file_name = os.path.basename(url)
full_path = os.path.join(data_path, file_name)
folder = os.path.join(data_path, 'cifar-10-batches-py')
if not os.path.isdir(folder):
file_manager.download(url, data_path)
with tarfile.open(full_path) as f:
f.extractall(path=data_path)
train_x = []
train_y = []
for i in range(1, 6):
file_path = os.path.join(folder, 'data_batch_{0:d}'.format(i))
data_dict = file_manager.unpickle(file_path)
train_x.append(data_dict['data'])
train_y.append(data_dict['labels'])
train_x = np.concatenate(train_x) / 255.0
pos = 0.006
train_y = np.concatenate(train_y)
train_x = train_x / np.linalg.norm(train_x, axis=1, keepdims=True)
train_x = train_x - np.expand_dims(np.mean(train_x, 1), 1) + pos
data_dict = file_manager.unpickle(os.path.join(folder, 'test_batch'))
test_y = np.array(data_dict['labels'])
test_x = data_dict['data'] / 255.0
test_x = test_x / np.linalg.norm(test_x, axis=1, keepdims=True)
test_x = test_x - np.expand_dims(np.mean(test_x, 1), 1) + pos
train_x = train_x.reshape((train_x.shape[0], 3, -1))
test_x = test_x.reshape((test_x.shape[0], 3, -1))
return train_x, train_y, test_x, test_y
def _prepare_imagenet_test_data(self):
data_dict = file_manager.unpickle(
os.path.join(self.data_path, 'val_data'))
test_x = data_dict['data'] / 255.0
test_y = np.array(data_dict['labels'])
# .transpose([0, 2, 3, 1])
test_x = test_x.reshape((test_x.shape[0], 3, 32, 32))
def _make_Negative_small_data():
train_x = []
train_y = []
for i in range(1, 11):
file_path = os.path.join(data_path, 'train_data_batch_{0:d}'.format(i))
data_dict = file_manager.unpickle(file_path)
train_x.append(data_dict['data'])
train_y.append(data_dict['labels'])
train_x = np.concatenate(train_x) / 255.0
train_y = np.concatenate(train_y)
# pdb.set_trace()
label = _select_pOn_data(train_y, negatives_small)
train_x_ = train_x[label,:]
max_num = 20000
for i in range(10):
lengh = train_x_.shape[0]
if lengh > max_num:
torch.save(train_x_[:max_num, :], data_path_s +
'imagenet_batch_' + str(i) + '.pt')
train_x_ = train_x_[max_num:, :]
else:
torch.save(train_x_[:lengh, :], data_path_s +
'imagenet_batch_' + str(i) + '.pt')
train_x_ = None
if train_x_ is None:
break
def _prepare_cifar50_data():
data_path = '/home/huwenp/Dataset/CIFAR/'
url = 'http://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz'
file_manager.create_dirname_if_not_exist(data_path)
file_name = os.path.basename(url)
full_path = os.path.join(data_path, file_name)
folder = os.path.join(data_path, 'cifar-10-batches-py-feature')
if not os.path.isdir(folder):
file_manager.download(url, data_path)
with tarfile.open(full_path) as f:
f.extractall(path=data_path)
train_x = []
train_y = []
for i in range(0, 5):
file_path = os.path.join(folder, 'traindata_batch_{0:d}.pt'.format(i))
data_dict = file_manager.unpickle(file_path)
pdb.set_trace()
train_x.append(data_dict['data'])
train_y.append(data_dict['labels'])
train_x = np.concatenate(train_x) / 255.0
train_y = np.concatenate(train_y)
data_dict = file_manager.unpickle(
os.path.join(folder, 'testdata_batch_0.pt'))
test_x = data_dict['data'] / 255.0
test_y = np.array(data_dict['labels'])
# pdb.set_trace()
# .transpose([0, 2, 3, 1])
train_x = train_x.reshape((train_x.shape[0], 3, 32, 32))
# .transpose([0, 2, 3, 1])
test_x = test_x.reshape((test_x.shape[0], 3, 32, 32))
# for i in range(10):
# # pdb.set_trace()
# misc.imsave('./images/cifar10' + str(train_y[i]) + '-' + str(i) + '.jpg', train_x[i].transpose(1, 2, 0))
# pdb.set_trace()
# train_y = _binarize(train_y)
# test_y = _binarize(test_y)
return train_x, train_y, test_x, test_y
def _prepare_imagenet_data_all(data_path_t):
# pdb.set_trace()
train_x = []
train_y = []
for i in range(1, 11):
file_path = os.path.join(data_path, 'train_data_batch_{0:d}'.format(i))
data_dict = file_manager.unpickle(file_path)
train_x.append(data_dict['data'])
train_y.append(data_dict['labels'])
# pdb.set_trace()
train_x = np.concatenate(train_x) / 255.0
train_y = np.concatenate(train_y)
data_dict = file_manager.unpickle(
os.path.join(data_path, 'val_data'))
test_x = data_dict['data'] / 255.0
test_y = np.array(data_dict['labels'])
train_x = train_x.reshape((train_x.shape[0], 3, 32, 32))
# .transpose([0, 2, 3, 1])
test_x = test_x.reshape((test_x.shape[0], 3, 32, 32))
# pdb.set_trace()
return train_x, train_y, test_x, test_y
def _make_other_data():
# folder = os.path.join(data_path, 'imagenet_train')
# if not os.path.isdir(data_path):
# file_manager.download(url, data_path)
# with tarfile.open(full_path) as f:
# f.extractall(path=data_path)
train_x = []
train_y = []
for i in range(1, 11):
file_path = os.path.join(data_path, 'train_data_batch_{0:d}'.format(i))
data_dict = file_manager.unpickle(file_path)
train_x.append(data_dict['data'])
train_y.append(data_dict['labels'])
train_x = np.concatenate(train_x) / 255.0
train_y = np.concatenate(train_y)
# pdb.set_trace()
label = _select_data(train_y)
train_x = train_x[label,:]
train_y = train_y[label]
max_num = 5000
for i in range(100000000):
lengh = train_x.shape[0]
if lengh > max_num:
data_save = {'data':train_x[:max_num, :], 'labels':train_y[:max_num]}
torch.save(data_save, data_path_ot +
'imagenet_batch_' + str(i) + '.pt')
train_x = train_x[max_num:, :]
train_y = train_y[max_num:]
else:
data_save = {'data':train_x, 'labels':train_y}
torch.save(data_save, data_path_ot +
'imagenet_batch_' + str(i) + '.pt')
train_x = None
if train_x is None:
break
