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datasets.py
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import numpy as np
import pickle as pkl
import cPickle as cPkl
import gzip
import tarfile
import fnmatch
import os
import urllib
from scipy.io import loadmat
def _unpickle(f):
import cPickle
fo = open(f, 'rb')
d = cPickle.load(fo)
fo.close()
return d
def _get_datafolder_path():
full_path = os.path.abspath('.')
path = full_path +'/data'
return path
def norb_random(datasets_dir=_get_datafolder_path()+"/norb_random/"):
# random splited norb
fname = 'norb_random.mat'
x_train = loadmat(datasets_dir+fname)['x_train'].astype(np.float32)
x_test = loadmat(datasets_dir+fname)['x_test'].astype(np.float32)
return x_train, x_test
def CalTech101Silhouettes(datasets_dir=_get_datafolder_path()+"/caltech101_silhouettes/"):
# get CalTech101Silhouettes dataset
fname="caltech101_silhouettes_28_split1.mat"
x_train = loadmat(datasets_dir+fname)['train_data'].astype(np.float32)
x_valid = loadmat(datasets_dir+fname)['val_data'].astype(np.float32)
x_test = loadmat(datasets_dir+fname)['test_data'].astype(np.float32)
return x_train, x_valid, x_test
def svhn(datasets_dir=_get_datafolder_path()+"/svhn/", normalized=True, centered=False):
zz = loadmat(datasets_dir+"train_32x32.mat")
x_train = zz['X'].astype(np.float32)
y_train = zz['y']
x_train = x_train.transpose((2, 0, 1, 3)).reshape((3072, -1)).T
zz = loadmat(datasets_dir+"test_32x32.mat")
x_test = zz['X'].astype(np.float32)
y_test = zz['y']
x_test = x_test.transpose((2, 0, 1, 3)).reshape((3072, -1)).T
if normalized:
x_train = x_train / 256.0
x_test = x_test / 256.0
if centered:
ave = x_train.sum(axis=0, keepdims=True)
x_train = x_train - ave
x_test = x_test - ave
return x_train, y_train, x_test, y_test
def lfw(datasets_dir=_get_datafolder_path()+"/lfw/", normalize=True, colorImg=True, size='large'):
fname = 'lfw_'
if size == 'large':
fname += '62x47'
n_f = 62*47
else:
fname += '31x23'
n_f = 31*23
if not colorImg:
fname += '_gray'
fname += '.npy'
if colorImg:
x_train = np.load(datasets_dir+fname)
print '---', x_train.shape
x_train = x_train.swapaxes(1,3).swapaxes(2,3).reshape((-1, n_f*3))
else:
x_train = np.load(datasets_dir+fname).reshape((-1, n_f*1))
if normalize:
x_train = x_train/256.0
return x_train
def ocr_letter(datasets_dir=_get_datafolder_path()+"/ocr_letter/"):
# get ocr_letter dataset
import h5py
fname="ocr_letters.h5"
f = h5py.File(datasets_dir+fname,'r')
x_train = np.asarray(f['train']).astype(np.float32)
x_valid = np.asarray(f['valid']).astype(np.float32)
x_test = np.asarray(f['test']).astype(np.float32)
return x_train, x_valid, x_test
def oivetti(datasets_dir=_get_datafolder_path()+"/oivetti/", normalize=True):
'''
url: http://www.cs.nyu.edu/~roweis/data.html
Olivetti Faces [data/olivettifaces.mat] [picture]
Grayscale faces 8 bit [0-255], a few images of several different people.
400 total images, 64x64 size.
From the Oivetti database at ATT.
'''
fname="olivettifaces.mat"
x_train = loadmat(datasets_dir+fname)['faces'].astype(np.float32)
x_train = x_train.T
x_train = x_train.reshape((400,64,64))
x_train = np.transpose(x_train, (0,2,1)).reshape((400,-1))
if normalize:
x_train = x_train/256.0
return x_train
def cifar10(datasets_dir=_get_datafolder_path()+'/cifar10', num_val=5000, normalized=True, centered=True):
# this code is largely cp from Kyle Kastner:
#
# https://gist.github.com/kastnerkyle/f3f67424adda343fef40
url = 'http://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz'
data_file = os.path.join(datasets_dir, 'cifar-10-python.tar.gz')
data_dir = os.path.join(datasets_dir, 'cifar-10-batches-py')
if not os.path.exists(datasets_dir):
os.makedirs(datasets_dir)
if not os.path.isfile(data_file):
urllib.urlretrieve(url, data_file)
org_dir = os.getcwd()
with tarfile.open(data_file) as tar:
os.chdir(datasets_dir)
tar.extractall()
os.chdir(org_dir)
train_files = []
for filepath in fnmatch.filter(os.listdir(data_dir), 'data*'):
train_files.append(os.path.join(data_dir, filepath))
train_files = sorted(train_files, key=lambda x: x.split("_")[-1])
test_file = os.path.join(data_dir, 'test_batch')
x_train, targets_train = [], []
for f in train_files:
d = _unpickle(f)
x_train.append(d['data'])
targets_train.append(d['labels'])
x_train = np.array(x_train, dtype='uint8')
shp = x_train.shape
x_train = x_train.reshape(shp[0] * shp[1], 3, 32, 32)
targets_train = np.array(targets_train)
targets_train = targets_train.ravel()
d = _unpickle(test_file)
x_test = d['data']
targets_test = d['labels']
x_test = np.array(x_test, dtype='uint8')
x_test = x_test.reshape(-1, 3, 32, 32)
targets_test = np.array(targets_test)
targets_test = targets_test.ravel()
if normalized:
x_train = x_train/256.0
x_test = x_test/256.0
if centered:
avg = x_train.mean(axis=0,keepdims=True)
x_train = x_train - avg
x_test = x_test - avg
if num_val is not None:
perm = np.random.permutation(x_train.shape[0])
x = x_train[perm]
y = targets_train[perm]
x_valid = x[:num_val]
targets_valid = y[:num_val]
x_train = x[num_val:]
targets_train = y[num_val:]
return (x_train, targets_train,
x_valid, targets_valid,
x_test, targets_test)
else:
return x_train, targets_train, x_test, targets_test
def omniglot_original(datasets_dir=_get_datafolder_path()+"/omniglot_original/"):
# get omniglot dataset
def combine(images):
i_re = []
for i in xrange(images.shape[0]):
j_m = images[i][0].shape[0]
for j in xrange(j_m):
k_m = images[i][0][j][0].shape[0]
for k in xrange(k_m):
i_re.append(images[i][0][j][0][k][0])
return np.asarray(i_re)
fname_train="data_background.mat"
x_train = loadmat(datasets_dir+fname_train)['images']
x_train = combine(x_train)
fname_test="data_evaluation.mat"
x_test = loadmat(datasets_dir+fname_test)['images']
x_test = combine(x_test)
return x_train, x_test
def omniglot(datasets_dir=_get_datafolder_path()+"/omniglot/"):
# get omniglot dataset
def reshape_data(data):
return data.reshape((-1, 28, 28)).reshape((-1, 28*28), order='fortran')
omni_raw = loadmat(datasets_dir+'chardata.mat')
train_data = reshape_data(omni_raw['data'].T.astype('float32'))
test_data = reshape_data(omni_raw['testdata'].T.astype('float32'))
return train_data, test_data