def _gcn(self, X):
if not self.specs:
X = global_contrast_normalize(X, scale=True)
return X
pre = 0
for n in feature_ns:
if n <= X.shape[1]:
X[:, pre:n] = global_contrast_normalize(X[:, pre:n], scale=True)
pre = n
return X
def __init__(self,
which_set,
data_path=None,
center=True,
rescale=True,
gcn=True):
self.class_name = ['neg', 'pos']
# load data
path = "${PYLEARN2_DATA_PATH}/cin/"
#datapath = path + 'feature850-2-1.pkl'
if data_path is None:
data_path = path + 'feature850-2-1.pkl'
else:
data_path = path + data_path
data_path = serial.preprocess(data_path)
with open(data_path, 'rb') as f:
#f = open(datapath, 'rb')
train_set, valid_set, test_set = cPickle.load(f)
#f.close()
self.train_set = train_set
self.valid_set = valid_set
self.test_set = test_set
if which_set == 'train':
X, Y = self.train_set
elif which_set == 'valid':
X, Y = self.valid_set
else:
X, Y = self.test_set
X.astype(float)
axis = 0
_max = np.max(X, axis=axis)
_min = np.min(X, axis=axis)
_mean = np.mean(X, axis=axis)
_std = np.std(X, axis=axis)
_scale = _max - _min
# print _max
# print _min
# print _mean
# print _std
if gcn:
X = global_contrast_normalize(X, scale=gcn)
else:
if center:
X[:, ] -= _mean
if rescale:
X[:, ] /= _scale
# topo_view = X.reshape(X.shape[0], X.shape[1], 1, 1)
# y = np.reshape(Y, (Y.shape[0], 1))
# y = np.atleast_2d(Y).T
y = np.zeros((Y.shape[0], 2))
y[:, 0] = Y
y[:, 0] = 1 - Y
print X.shape, y.shape
super(CIN_FEATURE2, self).__init__(X=X, y=y)
def apply(self, dataset, can_fit=False):
if self.skip:
return
if self._batch_size is None:
X = global_contrast_normalize(dataset.get_design_matrix(),
scale=self._scale,
subtract_mean=self._subtract_mean,
use_std=self._use_std,
sqrt_bias=self._sqrt_bias,
min_divisor=self._min_divisor)
dataset.set_design_matrix(X)
else:
X = dataset.get_design_matrix()
data_size = X.shape[0]
last = (np.floor(data_size / float(self._batch_size)) *
self._batch_size)
for i in xrange(0, data_size, self._batch_size):
if i >= last:
stop = i + np.mod(data_size, self._batch_size)
else:
stop = i + self._batch_size
log.info("GCN processing data from %d to %d" % (i, stop))
data = self.transform(X[i:stop])
dataset.set_design_matrix(data, start = i)
def apply(self, dataset, can_fit=False):
#check if we have already flattened patches
if self.normalized_data_key in dataset.keys():
print "skipping normalization, this has already been run"
return
else:
print "normalizing patches"
in_data = dataset[self.data_to_normalize_key]
data_size = in_data.shape[0]
dataset.create_dataset(self.normalized_data_key, in_data.shape, chunks=((self.batch_size,)+in_data.shape[1:]))
out_data = dataset[self.normalized_data_key]
#iterate over patches
for patch_index in range(data_size):
if patch_index % 2000 == 0:
print str(patch_index) + '/' + str(data_size)
#iterate over rgbd so they are all normalized separately at this point
for channel in range(4):
out_data[patch_index, :, :, channel] = global_contrast_normalize(in_data[patch_index, :, :, channel],
scale=self.scale,
subtract_mean=self.subtract_mean,
use_std=self.use_std,
sqrt_bias=self.sqrt_bias,
min_divisor=self.min_divisor)
def test_subtract_mean_false():
rng = numpy.random.RandomState(0)
X = abs(rng.randn(50, 70))
Y = global_contrast_normalize(X, subtract_mean=False, scale=5)
numpy.testing.assert_allclose(numpy.sqrt((Y ** 2).sum(axis=1)), 5)
numpy.testing.assert_raises(AssertionError,
numpy.testing.assert_allclose,
Y.mean(axis=1), 0, atol=1e-10)
def __init__(self,
which_set = 'full',
path = 'train.mat',
one_hot = False,
colorspace = 'none',
step = 1,
start = None,
stop = None,
center = False,
rescale = False,
gcn = None,
toronto_prepro = False,
axes=('b', 0, 1, 'c')):
self.__dict__.update(locals())
del self.self
#
#self.one_hot = one_hot
#self.colorspace = colorspace
#self.step=step
#self.which_set=which_set
self.view_converter = None
self.path = preprocess(self.path)
X, y = self._load_data()
if center:
X -= 127.5
#self.center = center
if rescale:
X /= 127.5
#self.rescale = rescale
if toronto_prepro:
assert not center
assert not gcn
X = X / 255.
if which_set == 'test':
other = MATDATA(which_set='train')
oX = other.X
oX /= 255.
X = X - oX.mean(axis=0)
else:
X = X - X.mean(axis=0)
#self.toronto_prepro = toronto_prepro
#self.gcn = gcn
if gcn is not None:
gcn = float(gcn)
X = global_contrast_normalize(X, scale=gcn, min_divisor=1e-8)
view_converter = DefaultViewConverter((
self.windowSize,self.windowSize,self.channels), axes)
super(MATDATA, self).__init__(X=X, y=y, view_converter=view_converter)
def __init__(self,
which_set='full',
path='train.mat',
one_hot=False,
colorspace='none',
step=1,
start=None,
stop=None,
center=False,
rescale=False,
gcn=None,
toronto_prepro=False,
axes=('b', 0, 1, 'c')):
self.__dict__.update(locals())
del self.self
#
#self.one_hot = one_hot
#self.colorspace = colorspace
#self.step=step
#self.which_set=which_set
self.view_converter = None
self.path = preprocess(self.path)
X, y = self._load_data()
if center:
X -= 127.5
#self.center = center
if rescale:
X /= 127.5
#self.rescale = rescale
if toronto_prepro:
assert not center
assert not gcn
X = X / 255.
if which_set == 'test':
other = MATDATA(which_set='train')
oX = other.X
oX /= 255.
X = X - oX.mean(axis=0)
else:
X = X - X.mean(axis=0)
#self.toronto_prepro = toronto_prepro
#self.gcn = gcn
if gcn is not None:
gcn = float(gcn)
X = global_contrast_normalize(X, scale=gcn, min_divisor=1e-8)
view_converter = DefaultViewConverter(
(self.windowSize, self.windowSize, self.channels), axes)
super(MATDATA, self).__init__(X=X, y=y, view_converter=view_converter)
def normalize(img, prep, img_shape):
# this requires zca from pylearn 2 for all functions prep.
img = prep.inverse(img.reshape(1, -1))[0]
img /= np.abs(img).max()
img = np.clip(img, -1., 1.)
img = (img + 1.) / 2.
img = global_contrast_normalize(img.reshape(1, -1) * 255, scale=55.)
img = prep._gpu_matrix_dot(img - prep.mean_, prep.P_)
return img.reshape(img_shape)
def test_subtract_mean_false():
rng = numpy.random.RandomState(0)
X = abs(rng.randn(50, 70))
Y = global_contrast_normalize(X, subtract_mean=False, scale=5)
numpy.testing.assert_allclose(numpy.sqrt((Y**2).sum(axis=1)), 5)
numpy.testing.assert_raises(AssertionError,
numpy.testing.assert_allclose,
Y.mean(axis=1),
0,
atol=1e-10)
def transform(self, X):
if self.flag_gcn:
X = global_contrast_normalize(X)
if self.flag_lcn:
X = self.lcn_transform(X)
if self.flag_zca:
X, _ = centersphere(X, method='ZCA', A=self.zca_mat)
return X
def real_time_prediction():
### loading new images for classification starts here
fo = open(save_path, 'rb') # batch path
batch1 = pickle.load(fo)
fo.close()
xarr = np.array(batch1['data'], dtype='float32')
xarr = global_contrast_normalize(xarr, scale=55.)
no_of_row = len(batch1['data'])
xdat = np.array(
xarr.reshape((no_of_row, 3, 32, 32)),
dtype='float32') #reshape first parameter = batch matrix no. of row
xdat = np.transpose(xdat[:, :, :, :], (1, 2, 3, 0))
x = dense_design_matrix.DenseDesignMatrix(topo_view=xdat,
axes=['c', 0, 1, 'b'])
x.apply_preprocessor(my_pca_preprocessor, can_fit=False)
tarr = x.get_topological_view()
#print tarr
y = f(tarr)
###########searching max in matrix##################################################
#j = no. of row in prediction_batch
#i = no. of classes (0-9)
#result=('airplane','automobile','bird','cat','deer','dog','frog','horse','ship','truck')
result = ('bottle', 'book', 'toy', 'pen', 'chair', 'coin', 'phone', 'hand',
'note', 'head')
resultString = ''
for j in range(0, no_of_row):
max_index = 0
max_no = y[j][0]
#print max_no
for i in range(0, 10):
if y[j][i] > max_no:
max_no = y[j][i]
max_index = i
# print max_index
print "======================"
print 'Photo', j + 1, ' max=', result[max_index]
if j > 0:
resultString += ','
resultString += result[max_index]
#print 'y =', y
###################################################################################3
return resultString
def __init__(self, which_set=None, file=None, center = False, rescale = False, gcn = None, one_hot = False, start = None, stop = None, axes=('b', 0, 1, 'c'), toronto_prepro = False, preprocessor = None):
# note: there is no such thing as the cifar10 validation set;
# pylearn1 defined one but really it should be user-configurable
# (as it is here)
self.axes = axes
# we define here:
dtype = 'uint8'
ntrain = 0
nvalid = 0 # artefact, we won't use it
ntest = 300000
# we also expose the following details:
self.img_shape = (3,32,32)
self.img_size = N.prod(self.img_shape)
#self.n_classes = 10
#self.label_names = ['airplane', 'automobile', 'bird', 'cat', 'deer',
#'dog', 'frog','horse','ship','truck']
# prepare loading
#fnames = ['data_batch_%i' % i for i in range(1,6)]
#lenx = N.ceil((ntrain + nvalid) / 10000.)*10000
#x = N.zeros((lenx,self.img_size), dtype=dtype)
#y = N.zeros(lenx, dtype=dtype)
X=np.load(file).astype(np.float32)
# load train data
if center:
X -= 127.5
self.center = center
if rescale:
X /= 127.5
self.rescale = rescale
self.toronto_prepro = toronto_prepro
self.gcn = gcn
if gcn is not None:
gcn = float(gcn)
X = global_contrast_normalize(X, scale=gcn)
view_converter = dense_design_matrix.DefaultViewConverter((32,32,3), axes)
super(CIFAR10_TEST, self).__init__(X=X, view_converter=view_converter)
assert not np.any(np.isnan(self.X))
if preprocessor:
preprocessor.apply(self)
def real_time_prediction():
### loading new images for classification starts here
fo = open(save_path,'rb') # batch path
batch1 = pickle.load(fo)
fo.close()
xarr = np.array(batch1['data'],dtype='float32')
xarr = global_contrast_normalize(xarr, scale=55.)
no_of_row=len(batch1['data'])
xdat = np.array(xarr.reshape((no_of_row,3,32,32)),dtype='float32') #reshape first parameter = batch matrix no. of row
xdat = np.transpose(xdat[:,:,:,:],(1,2,3,0))
x = dense_design_matrix.DenseDesignMatrix(topo_view=xdat, axes = ['c', 0, 1, 'b'])
x.apply_preprocessor(my_pca_preprocessor, can_fit = False)
tarr = x.get_topological_view()
#print tarr
y = f(tarr)
###########searching max in matrix##################################################
#j = no. of row in prediction_batch
#i = no. of classes (0-9)
#result=('airplane','automobile','bird','cat','deer','dog','frog','horse','ship','truck')
result=('bottle','book','toy','pen','chair','coin','phone','hand','note','head')
resultString=''
for j in range(0,no_of_row):
max_index=0
max_no=y[j][0]
#print max_no
for i in range(0,10):
if y[j][i]>max_no:
max_no=y[j][i]
max_index=i
# print max_index
print "======================"
print 'Photo',j+1, ' max=', result[max_index]
if j > 0:
resultString += ','
resultString += result[max_index]
#print 'y =', y
###################################################################################3
return resultString
def __init__(self, source_directory, axes=('b', 0, 1, 'c'), remove_misfits = False):
self.axes = axes
# we define here:
dtype = 'uint8'
files = self.file_list_of_source(source_directory)
# we also expose the following details:
self.img_shape = self.determine_shape(files[0]) #this is rather dangerous if the first file is not representative to the remainder
self.img_size = np.prod(self.img_shape)
# prepare loading
x = np.zeros((len(files), self.img_size), dtype=dtype)
# load train data
X = self.flatten_images(x, files)
X = global_contrast_normalize(X)
view_converter = dense_design_matrix.DefaultViewConverter(self.img_shape, self.axes)
super(LocalImages, self).__init__(X = X, view_converter = view_converter)
def apply(self, dataset, can_fit=False):
if self._batch_size is None:
X = global_contrast_normalize(dataset.get_design_matrix(),
scale=self._scale,
subtract_mean=self._subtract_mean,
use_std=self._use_std,
sqrt_bias=self._sqrt_bias,
min_divisor=self._min_divisor)
dataset.set_design_matrix(X)
else:
X = dataset.get_design_matrix()
data_size = X.shape[0]
last = (np.floor(data_size / float(self._batch_size)) *
self._batch_size)
for i in xrange(0, data_size, self._batch_size):
if i >= last:
stop = i + np.mod(data_size, self._batch_size)
else:
stop = i + self._batch_size
log.info("GCN processing data from {} to {}".format(i, stop))
data = self.transform(X[i:stop])
dataset.set_design_matrix(data, start=i)
def __init__(self,
which_set,
data_path=None,
center=True,
rescale=True,
gcn=True,
specs=False):
self.class_name = ['neg', 'pos']
# load data
path = "${PYLEARN2_DATA_PATH}/cin/"
#datapath = path + 'feature850-2-1.pkl'
if data_path is None:
data_path = path + 'feature1406-2-1.pkl'
else:
data_path = path + data_path
data_path = serial.preprocess(data_path)
with open(data_path, 'rb') as f:
#f = open(datapath, 'rb')
train_set, valid_set, test_set = cPickle.load(f)
#f.close()
self.train_set = train_set
self.valid_set = valid_set
self.test_set = test_set
self.specs = specs
if which_set == 'train':
X, Y = self.train_set
elif which_set == 'valid':
X, Y = self.valid_set
else:
X, Y = self.test_set
X.astype(float)
axis = 0
_max = np.max(X, axis=axis)
_min = np.min(X, axis=axis)
_mean = np.mean(X, axis=axis)
_std = np.std(X, axis=axis)
_scale = _max - _min
def features_map_fn(indexes):
rval = []
for sequence_index, example_index in self._fetch_index(indexes):
rval.append(self.samples_sequences[sequence_index][example_index:example_index
+ self.frames_per_example].ravel())
return rval
def targets_map_fn(indexes):
rval = []
for sequence_index, example_index in self._fetch_index(indexes):
rval.append(self.samples_sequences[sequence_index][example_index
+ self.frames_per_example].ravel())
return rval
map_fn_components = [features_map_fn, targets_map_fn]
self.map_functions = tuple(map_fn_components)
self.cumulative_example_indexes = X.shape[0]
# print _max
# print _min
# print _mean
# print _std
if gcn:
X = global_contrast_normalize(X, scale=gcn)
else:
if center:
X[:, ] -= _mean
if rescale:
X[:, ] /= _scale
# topo_view = X.reshape(X.shape[0], X.shape[1], 1, 1)
# y = np.reshape(Y, (Y.shape[0], 1))
# y = np.atleast_2d(Y).T
y = np.zeros((Y.shape[0], 2))
y[:, 0] = Y
y[:, 0] = 1 - Y
print X.shape, y.shape
super(CIN_FEATURE2, self).__init__(X=X, y=y)
# super(CIN_FEATURE2, self).__init__(topo_view=topo_view, y=y, y_labels=2)
if specs:
assert X.shape[1] == (850 + 656)
self.init_data_specs()
self.feature850 = X[:, 0:850]
self.feature656 = X[:, 850:]
self.y = y
def test_scale():
rng = numpy.random.RandomState(0)
X = abs(rng.randn(50, 70))
Y = global_contrast_normalize(X, scale=5)
numpy.testing.assert_allclose(numpy.sqrt((Y**2).sum(axis=1)), 5)
numpy.testing.assert_allclose(Y.mean(axis=1), 0, atol=1e-10)
def __init__(self,
which_set,
center=False,
rescale=False,
gcn=None,
one_hot=False,
start=None,
stop=None,
axes=('b', 0, 1, 'c'),
toronto_prepro=False,
preprocessor=None):
# note: there is no such thing as the cifar10 validation set;
# pylearn1 defined one but really it should be user-configurable
# (as it is here)
self.axes = axes
# we define here:
dtype = 'uint8'
ntrain = 50000
nvalid = 0 # artefact, we won't use it
ntest = 10000
# we also expose the following details:
self.img_shape = (3, 32, 32)
self.img_size = N.prod(self.img_shape)
self.n_classes = 10
self.label_names = [
'airplane', 'automobile', 'bird', 'cat', 'deer', 'dog', 'frog',
'horse', 'ship', 'truck'
]
# prepare loading
fnames = ['data_batch_%i' % i for i in range(1, 6)]
lenx = N.ceil((ntrain + nvalid) / 10000.) * 10000
x = N.zeros((lenx, self.img_size), dtype=dtype)
y = N.zeros(lenx, dtype=dtype)
# load train data
nloaded = 0
for i, fname in enumerate(fnames):
data = CIFAR10._unpickle(fname)
x[i * 10000:(i + 1) * 10000, :] = data['data']
y[i * 10000:(i + 1) * 10000] = data['labels']
nloaded += 10000
if nloaded >= ntrain + nvalid + ntest: break
# load test data
data = CIFAR10._unpickle('test_batch')
# process this data
Xs = {'train': x[0:ntrain], 'test': data['data'][0:ntest]}
Ys = {'train': y[0:ntrain], 'test': data['labels'][0:ntest]}
X = N.cast['float32'](Xs[which_set])
y = Ys[which_set]
if isinstance(y, list):
y = np.asarray(y)
if which_set == 'test':
assert y.shape[0] == 10000
self.one_hot = one_hot
if one_hot:
one_hot = np.zeros((y.shape[0], 10), dtype='float32')
for i in xrange(y.shape[0]):
one_hot[i, y[i]] = 1.
y = one_hot
if center:
X -= 127.5
self.center = center
if rescale:
X /= 127.5
self.rescale = rescale
if toronto_prepro:
assert not center
assert not gcn
X = X / 255.
if which_set == 'test':
other = CIFAR10(which_set='train')
oX = other.X
oX /= 255.
X = X - oX.mean(axis=0)
else:
X = X - X.mean(axis=0)
self.toronto_prepro = toronto_prepro
self.gcn = gcn
if gcn is not None:
gcn = float(gcn)
X = global_contrast_normalize(X, scale=gcn)
if start is not None:
# This needs to come after the prepro so that it doesn't change the pixel
# means computed above for toronto_prepro
assert start >= 0
assert stop > start
assert stop <= X.shape[0]
X = X[start:stop, :]
y = y[start:stop]
assert X.shape[0] == y.shape[0]
if which_set == 'test':
assert X.shape[0] == 10000
view_converter = dense_design_matrix.DefaultViewConverter((32, 32, 3),
axes)
super(CIFAR10, self).__init__(X=X, y=y, view_converter=view_converter)
assert not np.any(np.isnan(self.X))
if preprocessor:
preprocessor.apply(self)
def __init__(self,
which_set,
data_path=None,
center=True,
rescale=True,
gcn=True,
specs=True):
self.class_name = ['neg', 'pos']
# load data
path = "${PYLEARN2_DATA_PATH}/cin/"
#datapath = path + 'feature850-2-1.pkl'
if data_path is None:
data_path = path + 'feature1406-2-1.pkl'
else:
data_path = path + data_path
data_path = serial.preprocess(data_path)
with open(data_path, 'rb') as f:
#f = open(datapath, 'rb')
train_set, valid_set, test_set = cPickle.load(f)
#f.close()
self.train_set = train_set
self.valid_set = valid_set
self.test_set = test_set
self.specs = specs
if which_set == 'train':
X, Y = self.train_set
elif which_set == 'valid':
X, Y = self.valid_set
else:
X, Y = self.test_set
X.astype(float)
axis = 0
_max = np.max(X, axis=axis)
_min = np.min(X, axis=axis)
_mean = np.mean(X, axis=axis)
_std = np.std(X, axis=axis)
_scale = _max - _min
if gcn:
X = global_contrast_normalize(X, scale=gcn)
else:
if center:
X[:, ] -= _mean
if rescale:
X[:, ] /= _scale
# topo_view = X.reshape(X.shape[0], X.shape[1], 1, 1)
# y = np.reshape(Y, (Y.shape[0], 1))
# y = np.atleast_2d(Y).T
self.raw_X = X
self.raw_y = Y
y = np.zeros((Y.shape[0], 2))
y[:, 0] = Y
y[:, 1] = 1 - Y
# print "Load CIN_FEATURE2 data: {}, with size X:{}, y:{}".format(data_path, X.shape, y.shape)
super(CIN_FEATURE2, self).__init__(X=X, y=y)
# super(CIN_FEATURE2, self).__init__(topo_view=topo_view, y=y, y_labels=2)
if specs:
assert X.shape[1] == (850 + 556)
self.init_data_specs()
self.feature850 = X[:, 0:850]
self.feature556 = X[:, 850:]
self.y = y
def test_min_divisor():
rng = numpy.random.RandomState(0)
X = abs(rng.randn(50, 70))
X[0] *= 1e-15
Y = global_contrast_normalize(X, subtract_mean=False, use_std=True)
numpy.testing.assert_array_equal(X[0], Y[0])
def test_basic():
rng = numpy.random.RandomState(0)
X = abs(rng.randn(50, 70))
Y = global_contrast_normalize(X)
numpy.testing.assert_allclose((Y**2).sum(axis=1), 1)
numpy.testing.assert_allclose(Y.mean(axis=1), 0, atol=1e-10)
def __init__(self,
which_set,
center=False,
rescale=False,
gcn=None,
start=None,
stop=None,
axes=('b', 0, 1, 'c'),
toronto_prepro=False,
preprocessor=None):
# note: there is no such thing as the cifar10 validation set;
# pylearn1 defined one but really it should be user-configurable
# (as it is here)
self.axes = axes
# we define here:
dtype = 'uint8'
ntrain = 50000
nvalid = 0 # artefact, we won't use it
ntest = 10000
# we also expose the following details:
self.img_shape = (3, 32, 32)
self.img_size = numpy.prod(self.img_shape)
self.n_classes = 10
self.label_names = [
'airplane', 'automobile', 'bird', 'cat', 'deer', 'dog', 'frog',
'horse', 'ship', 'truck'
]
# prepare loading
fnames = ['data_batch_%i' % i for i in range(1, 6)]
datasets = {}
datapath = os.path.join(
string_utils.preprocess('${PYLEARN2_DATA_PATH}'), 'cifar10',
'cifar-10-batches-py')
for name in fnames + ['test_batch']:
fname = os.path.join(datapath, name)
if not os.path.exists(fname):
raise IOError(fname + " was not found. You probably need to "
"download the CIFAR-10 dataset by using the "
"download script in "
"pylearn2/scripts/datasets/download_cifar10.sh "
"or manually from "
"http://www.cs.utoronto.ca/~kriz/cifar.html")
datasets[name] = cache.datasetCache.cache_file(fname)
lenx = int(numpy.ceil((ntrain + nvalid) / 10000.) * 10000)
x = numpy.zeros((lenx, self.img_size), dtype=dtype)
y = numpy.zeros((lenx, 1), dtype=dtype)
# load train data
nloaded = 0
for i, fname in enumerate(fnames):
_logger.info('loading file %s' % datasets[fname])
data = serial.load(datasets[fname])
x[i * 10000:(i + 1) * 10000, :] = data['data']
y[i * 10000:(i + 1) * 10000, 0] = data['labels']
nloaded += 10000
if nloaded >= ntrain + nvalid + ntest:
break
# load test data
_logger.info('loading file %s' % datasets['test_batch'])
data = serial.load(datasets['test_batch'])
# process this data
Xs = {'train': x[0:ntrain], 'test': data['data'][0:ntest]}
Ys = {'train': y[0:ntrain], 'test': data['labels'][0:ntest]}
X = numpy.cast['float32'](Xs[which_set])
y = Ys[which_set]
if isinstance(y, list):
y = numpy.asarray(y).astype(dtype)
if which_set == 'test':
assert y.shape[0] == 10000
y = y.reshape((y.shape[0], 1))
if center:
X -= 127.5
self.center = center
if rescale:
X /= 127.5
self.rescale = rescale
if toronto_prepro:
assert not center
assert not gcn
X = X / 255.
if which_set == 'test':
other = CIFAR10(which_set='train')
oX = other.X
oX /= 255.
X = X - oX.mean(axis=0)
else:
X = X - X.mean(axis=0)
self.toronto_prepro = toronto_prepro
self.gcn = gcn
if gcn is not None:
gcn = float(gcn)
X = global_contrast_normalize(X, scale=gcn)
if start is not None:
# This needs to come after the prepro so that it doesn't
# change the pixel means computed above for toronto_prepro
assert start >= 0
assert stop > start
assert stop <= X.shape[0]
X = X[start:stop, :]
y = y[start:stop, :]
assert X.shape[0] == y.shape[0]
if which_set == 'test':
assert X.shape[0] == 10000
view_converter = dense_design_matrix.DefaultViewConverter((32, 32, 3),
axes)
super(CIFAR10, self).__init__(X=X,
y=y,
view_converter=view_converter,
y_labels=self.n_classes)
assert not contains_nan(self.X)
if preprocessor:
preprocessor.apply(self)
def __init__(
self,
lfw_path,
filelist_path,
embedding_file=None,
center=False,
scale=False,
start=None,
stop=None,
gcn=None,
shuffle=False,
rng=None,
seed=132987,
axes=("b", 0, 1, "c"),
img_shape=(3, 250, 250),
):
self.axes = axes
self.img_shape = img_shape
C, H, W = img_shape
self.img_size = np.prod(self.img_shape)
files = []
with open(filelist_path, "r") as filelist_f:
files = [line.strip() for line in filelist_f]
# Load raw pixel integer values
dtype = "uint8"
X = np.zeros((len(files), W, H, C), dtype=dtype)
img_ids = []
for i, line in enumerate(files):
if "\t" in line:
# New format: contains image IDs
img_path, img_id = line.strip().split()
img_ids.append(int(img_id))
else:
img_path = line.strip()
full_path = os.path.join(lfw_path, img_path)
im = image.load(full_path, rescale_image=False, dtype=dtype)
# Handle grayscale images which may not have RGB channels
if len(im.shape) == 2:
W, H = im.shape
# Repeat image 3 times across axis 2
im = im.reshape(W, H, 1).repeat(3, 2)
# Swap color channel to front
X[i] = im
# Cast to float32, center / scale if necessary
X = np.cast["float32"](X)
# Create dense design matrix from topological view
X = X.reshape(X.shape[0], -1)
# Prepare img_ids
if embedding_file is not None:
if len(img_ids) != len(files):
raise ValueError("You must provide a filelist with indexes " "into the embedding array for each image.")
img_ids = np.array(img_ids, dtype="uint32")
if center and scale:
X[:] -= 127.5
X[:] /= 127.5
elif center:
X[:] -= 127.5
elif scale:
X[:] /= 255.0
self.gcn = gcn
if gcn is not None:
gcn = float(gcn)
X = global_contrast_normalize(X, scale=gcn)
if shuffle:
rng = make_np_rng(rng, seed, which_method="permutation")
rand_idx = rng.permutation(len(X))
X = X[rand_idx]
img_ids = img_ids[rand_idx]
if start is not None:
assert start >= 0
assert stop > start
assert stop <= X.shape[0]
X = X[start:stop]
if len(img_ids) > 0:
img_ids = img_ids[start:stop]
# Load embeddings if provided
Y = None
if embedding_file is not None:
embeddings = np.load(embedding_file)["arr_0"]
assert embeddings.shape[0] >= len(files)
Y = embeddings[img_ids].astype(theano.config.floatX)
# create view converting for retrieving topological view
self.view_converter = dense_design_matrix.DefaultViewConverter((W, H, C), axes)
# init super class
super(LFW, self).__init__(X=X, y=Y)
assert not contains_nan(self.X)
# Another hack: rename 'targets' to match model expectations
if embedding_file is not None:
space, (X_source, y_source) = self.data_specs
self.data_specs = (space, (X_source, "condition"))
def test_basic():
rng = numpy.random.RandomState(0)
X = abs(rng.randn(50, 70))
Y = global_contrast_normalize(X)
numpy.testing.assert_allclose((Y ** 2).sum(axis=1), 1)
numpy.testing.assert_allclose(Y.mean(axis=1), 0, atol=1e-10)
def test_min_divisor():
rng = numpy.random.RandomState(0)
X = abs(rng.randn(50, 70))
X[0] *= 1e-15
Y = global_contrast_normalize(X, subtract_mean=False, use_std=True)
numpy.testing.assert_array_equal(X[0], Y[0])
def test_std_norm():
rng = numpy.random.RandomState(0)
X = abs(rng.randn(50, 70))
Y = global_contrast_normalize(X, use_std=True, scale=5)
numpy.testing.assert_allclose(Y.std(axis=1, ddof=1), 5)
print batch1_data.shape
print batch1_labels.shape
print batch2_data.shape
print batch2_labels.shape
print batch3_data.shape
print batch3_labels.shape
image = paramgraphics.mat_to_img(batch1_data[:100,:].T, dim_input, colorImg=colorImg, scale=True)
image.save(saveDir+'svhn_train.png', 'PNG')
image = paramgraphics.mat_to_img(batch2_data[:100,:].T, dim_input, colorImg=colorImg, scale=True)
image.save(saveDir+'svhn_valid.png', 'PNG')
image = paramgraphics.mat_to_img(batch3_data[:100,:].T, dim_input, colorImg=colorImg, scale=True)
image.save(saveDir+'svhn_test.png', 'PNG')
if preprocessing == 'gcn_var':
batch1_data = pypp.global_contrast_normalize(batch1_data, subtract_mean=True, use_std=True)
batch2_data = pypp.global_contrast_normalize(batch2_data, subtract_mean=True, use_std=True)
batch3_data = pypp.global_contrast_normalize(batch3_data, subtract_mean=True, use_std=True)
elif preprocessing == 'gcn_norm':
batch1_data = pypp.global_contrast_normalize(batch1_data, subtract_mean=True)
batch2_data = pypp.global_contrast_normalize(batch2_data, subtract_mean=True)
batch3_data = pypp.global_contrast_normalize(batch3_data, subtract_mean=True)
print batch1_data.shape
print batch1_data.max()
print batch1_data.min()
print batch2_data.shape
print batch2_data.max()
print batch2_data.min()
print batch3_data.shape
print batch3_data.max()
print batch3_data.min()
def test_scale():
rng = numpy.random.RandomState(0)
X = abs(rng.randn(50, 70))
Y = global_contrast_normalize(X, scale=5)
numpy.testing.assert_allclose(numpy.sqrt((Y ** 2).sum(axis=1)), 5)
numpy.testing.assert_allclose(Y.mean(axis=1), 0, atol=1e-10)
def __init__(self,
which_set,
center=False,
rescale=False,
gcn=None,
one_hot=None,
start=None,
stop=None,
axes=('b', 0, 1, 'c'),
toronto_prepro=False,
preprocessor=None,
two_image=False):
# note: there is no such thing as the cifar10 validation set;
# pylearn1 defined one but really it should be user-configurable
# (as it is here)
self.axes = axes
# we define here:
dtype = 'uint8'
ntrain = 50000
nvalid = 0 # artefact, we won't use it
ntest = 10000
# we also expose the following details:
self.img_shape = (3, 32, 32)
self.img_shape2 = (32, 32, 3)
self.img_size = N.prod(self.img_shape)
self.n_classes = 10
self.label_names = [
'airplane', 'automobile', 'bird', 'cat', 'deer', 'dog', 'frog',
'horse', 'ship', 'truck'
]
# prepare loading
fnames = ['data_batch_%i' % i for i in range(1, 6)]
lenx = N.ceil((ntrain + nvalid) / 10000.) * 10000
x = N.zeros((lenx, self.img_size), dtype=dtype)
y = N.zeros((lenx, 1), dtype=dtype)
# load train data
nloaded = 0
for i, fname in enumerate(fnames):
data = CIFAR10._unpickle(fname)
x[i * 10000:(i + 1) * 10000, :] = data['data']
y[i * 10000:(i + 1) * 10000, 0] = data['labels']
nloaded += 10000
if nloaded >= ntrain + nvalid + ntest:
break
# load test data
data = CIFAR10._unpickle('test_batch')
# 2value image
# can not use other option when you use two_image option
print x.shape
if two_image:
from PIL import Image
two_value_x = []
self.img_shape = (1, 32, 32)
self.img_shape2 = (32, 32, 1)
for i, pixel in enumerate(x.reshape(50000, 3, 32, 32)):
if i % 1000 == 0:
print i
pixel = np.transpose(pixel, (1, 2, 0))
test_img = Image.new("RGB", (32, 32), (255, 0, 0))
test_img.putdata(
[tuple(x.tolist()) for x in pixel.reshape(1024, 3)])
two_value_x.append(
[x for x in test_img.convert("1").getdata()])
x = np.asarray(two_value_x)
# process this data
Xs = {'train': x[0:ntrain], 'test': data['data'][0:ntest]}
Ys = {'train': y[0:ntrain], 'test': data['labels'][0:ntest]}
X = N.cast['float32'](Xs[which_set])
y = Ys[which_set]
if isinstance(y, list):
y = np.asarray(y).astype(dtype)
if which_set == 'test':
assert y.shape[0] == 10000
y = y.reshape((y.shape[0], 1))
max_labels = 10
if one_hot is not None:
warnings.warn(
"the `one_hot` parameter is deprecated. To get "
"one-hot encoded targets, request that they "
"live in `VectorSpace` through the `data_specs` "
"parameter of MNIST's iterator method. "
"`one_hot` will be removed on or after "
"September 20, 2014.",
stacklevel=2)
if center:
X -= 127.5
self.center = center
if rescale:
X /= 127.5
self.rescale = rescale
if toronto_prepro:
assert not center
assert not gcn
X = X / 255.
if which_set == 'test':
other = CIFAR10(which_set='train')
oX = other.X
oX /= 255.
X = X - oX.mean(axis=0)
else:
X = X - X.mean(axis=0)
self.toronto_prepro = toronto_prepro
self.gcn = gcn
if gcn is not None:
gcn = float(gcn)
X = global_contrast_normalize(X, scale=gcn)
if start is not None:
# This needs to come after the prepro so that it doesn't
# change the pixel means computed above for toronto_prepro
assert start >= 0
assert stop > start
assert stop <= X.shape[0]
X = X[start:stop, :]
y = y[start:stop, :]
assert X.shape[0] == y.shape[0]
if which_set == 'test':
assert X.shape[0] == 10000
# view_converter = dense_design_matrix.DefaultViewConverter((32, 32, 3),
# axes)
view_converter = dense_design_matrix.DefaultViewConverter(
self.img_shape2, axes)
super(CIFAR10, self).__init__(X=X,
y=y,
view_converter=view_converter,
y_labels=self.n_classes)
assert not contains_nan(self.X)
if preprocessor:
preprocessor.apply(self)
def __init__(self,
which_set,
center=False,
rescale=False,
gcn=None,
one_hot=False,
start=None,
stop=None,
axes=('b', 0, 1, 'c'),
toronto_prepro=False,
preprocessor=None):
# note: there is no such thing as the cifar10 validation set;
# pylearn1 defined one but really it should be user-configurable
# (as it is here)
self.axes = axes
# we define here:
dtype = 'uint8'
ntrain = 50000
nvalid = 0 # artefact, we won't use it
ntest = 10000
# we also expose the following details:
self.img_shape = (3, 32, 32)
self.img_size = np.prod(self.img_shape)
self.n_classes = 10
self.label_names = [
'airplane', 'automobile', 'bird', 'cat', 'deer', 'dog', 'frog',
'horse', 'ship', 'truck'
]
# # prepare loading
# fnames = ['data_batch_%i' % i for i in range(1,6)]
# lenx = np.ceil((ntrain + nvalid) / 10000.)*10000
# x = np.zeros((lenx,self.img_size), dtype=dtype)
# y = np.zeros(lenx, dtype=dtype)
#
# # load train data
# nloaded = 0
# for i, fname in enumerate(fnames):
# data = CIFAR10._unpickle(fname)
# x[i*10000:(i+1)*10000, :] = data['data']
# y[i*10000:(i+1)*10000] = data['labels']
# nloaded += 10000
# if nloaded >= ntrain + nvalid + ntest: break;
#
# # load test data
# data = CIFAR10._unpickle('test_batch')
#
# # process this data
# Xs = {
# 'train' : x[0:ntrain],
# 'test' : data['data'][0:ntest]
# }
#
# Ys = {
# 'train' : y[0:ntrain],
# 'test' : data['labels'][0:ntest]
# }
if which_set == 'train':
# pkl = self._unpickle(os.environ['PYLEARN2_DATA_PATH']+
# 'cifar10/pylearn2_gcn_whitened/train.pkl')
#pkl = self._unpickle(os.environ['PYLEARN2_DATA_PATH']+
# 'cifar10/pylearn2_gcn_whitened/test.pkl')
#X = pkl.X
#y = pkl.y
X = np.load(os.environ['PYLEARN2_DATA_PATH'] +
'/cifar10/train_X.npy')
y = np.load(os.environ['PYLEARN2_DATA_PATH'] +
'/cifar10/train_y.npy')
X = np.cast['float32'](X)
y = np.cast['float32'](y)
elif which_set == 'test':
# pkl = self._unpickle(os.environ['PYLEARN2_DATA_PATH']+
# 'cifar10/pylearn2_gcn_whitened/test.pkl')
# X = pkl.X
# y = pkl.y
X = np.load(os.environ['PYLEARN2_DATA_PATH'] +
'/cifar10/test_X.npy')
y = np.load(os.environ['PYLEARN2_DATA_PATH'] +
'/cifar10/test_y.npy')
X = np.cast['float32'](X)
y = np.cast['float32'](y)
# X = np.cast['float32'](Xs[which_set])
# y = Ys[which_set]
if which_set == 'test':
assert X.shape[0] == 10000
if isinstance(y, list):
y = np.asarray(y)
if center:
X -= 127.5
self.center = center
if rescale:
X /= 127.5
self.rescale = rescale
if toronto_prepro:
assert not center
assert not gcn
X = X / 255.
if which_set == 'test':
other = CIFAR10(which_set='train')
oX = other.X
oX /= 255.
X = X - oX.mean(axis=0)
else:
X = X - X.mean(axis=0)
self.toronto_prepro = toronto_prepro
self.gcn = gcn
if gcn is not None:
gcn = float(gcn)
X = global_contrast_normalize(X, scale=gcn)
if start is not None:
# This needs to come after the prepro so that it doesn't change the pixel
# means computed above for toronto_prepro
assert start >= 0
assert stop > start
assert stop <= X.shape[0]
X = X[start:stop, :]
y = y[start:stop]
assert X.shape[0] == y.shape[0]
view_converter = dense_design_matrix.DefaultViewConverter((32, 32, 3),
axes)
if which_set == 'train':
length = X.shape[0]
def search_right_label(desired_label, i):
for idx in xrange(i, length):
if y[idx] == desired_label:
return idx
def swap_ele(index, i):
x_tmp = X[i]
X[i] = X[index]
X[index] = x_tmp
y_tmp = y[i]
y[i] = y[index]
y[index] = y_tmp
desired_label = 0
for i in xrange(length):
desired_label = i % 10
if y[i] != desired_label:
index = search_right_label(desired_label, i)
swap_ele(index, i)
for i in xrange(length - 100, length):
print y[i]
self.one_hot = one_hot
if one_hot:
one_hot = np.zeros((y.shape[0], 10), dtype='float32')
for i in xrange(y.shape[0]):
one_hot[i, y[i]] = 1.
y = one_hot
super(My_CIFAR10, self).__init__(X=X,
y=y,
view_converter=view_converter)
assert not np.any(np.isnan(self.X))
if preprocessor:
preprocessor.apply(self)
def __init__(self, which_set, center=False, rescale=False, gcn=None,
start=None, stop=None, axes=('b', 0, 1, 'c'),
toronto_prepro = False, preprocessor = None):
# note: there is no such thing as the cifar10 validation set;
# pylearn1 defined one but really it should be user-configurable
# (as it is here)
self.axes = axes
# we define here:
dtype = 'uint8'
ntrain = 50000
nvalid = 0 # artefact, we won't use it
ntest = 10000
# we also expose the following details:
self.img_shape = (3, 32, 32)
self.img_size = numpy.prod(self.img_shape)
self.n_classes = 10
self.label_names = ['airplane', 'automobile', 'bird', 'cat', 'deer',
'dog', 'frog', 'horse', 'ship', 'truck']
# prepare loading
fnames = ['data_batch_%i' % i for i in range(1, 6)]
datasets = {}
datapath = os.path.join(
string_utils.preprocess('${PYLEARN2_DATA_PATH}'),
'cifar10', 'cifar-10-batches-py')
for name in fnames + ['test_batch']:
fname = os.path.join(datapath, name)
if not os.path.exists(fname):
raise IOError(fname + " was not found. You probably need to "
"download the CIFAR-10 dataset by using the "
"download script in "
"pylearn2/scripts/datasets/download_cifar10.sh "
"or manually from "
"http://www.cs.utoronto.ca/~kriz/cifar.html")
datasets[name] = cache.datasetCache.cache_file(fname)
lenx = numpy.ceil((ntrain + nvalid) / 10000.) * 10000
x = numpy.zeros((lenx, self.img_size), dtype=dtype)
y = numpy.zeros((lenx, 1), dtype=dtype)
# load train data
nloaded = 0
for i, fname in enumerate(fnames):
_logger.info('loading file %s' % datasets[fname])
data = serial.load(datasets[fname])
x[i * 10000:(i + 1) * 10000, :] = data['data']
y[i * 10000:(i + 1) * 10000, 0] = data['labels']
nloaded += 10000
if nloaded >= ntrain + nvalid + ntest:
break
# load test data
_logger.info('loading file %s' % datasets['test_batch'])
data = serial.load(datasets['test_batch'])
# process this data
Xs = {'train': x[0:ntrain],
'test': data['data'][0:ntest]}
Ys = {'train': y[0:ntrain],
'test': data['labels'][0:ntest]}
X = numpy.cast['float32'](Xs[which_set])
y = Ys[which_set]
if isinstance(y, list):
y = numpy.asarray(y).astype(dtype)
if which_set == 'test':
assert y.shape[0] == 10000
y = y.reshape((y.shape[0], 1))
if center:
X -= 127.5
self.center = center
if rescale:
X /= 127.5
self.rescale = rescale
if toronto_prepro:
assert not center
assert not gcn
X = X / 255.
if which_set == 'test':
other = CIFAR10(which_set='train')
oX = other.X
oX /= 255.
X = X - oX.mean(axis=0)
else:
X = X - X.mean(axis=0)
self.toronto_prepro = toronto_prepro
self.gcn = gcn
if gcn is not None:
gcn = float(gcn)
X = global_contrast_normalize(X, scale=gcn)
if start is not None:
# This needs to come after the prepro so that it doesn't
# change the pixel means computed above for toronto_prepro
assert start >= 0
assert stop > start
assert stop <= X.shape[0]
X = X[start:stop, :]
y = y[start:stop, :]
assert X.shape[0] == y.shape[0]
if which_set == 'test':
assert X.shape[0] == 10000
view_converter = dense_design_matrix.DefaultViewConverter((32, 32, 3),
axes)
super(CIFAR10, self).__init__(X=X, y=y, view_converter=view_converter,
y_labels=self.n_classes)
assert not contains_nan(self.X)
if preprocessor:
preprocessor.apply(self)
def __init__(self, which_set, center=False, rescale=False, gcn=None,
start=None, stop=None, axes=('b', 0, 1, 'c'),
toronto_prepro = False, preprocessor = None):
# note: there is no such thing as the cifar10 validation set;
# pylearn1 defined one but really it should be user-configurable
# (as it is here)
self.axes = axes
# we define here:
dtype = 'uint8'
ntrain = 50000
nvalid = 0 # artefact, we won't use it
ntest = 10000
# we also expose the following details:
self.img_shape = (3, 32, 32)
self.img_size = numpy.prod(self.img_shape)
self.n_classes = 100
# make sure that this is working (we can also copy it from meta file)
self.label_names = range(1900,2000)
import cPickle
fo = open('datasets/data_batch')
dict = cPickle.load(fo)
fo.close()
lenx = numpy.ceil((ntrain + nvalid) / 10000.) * 10000
x = numpy.zeros((lenx, self.img_size), dtype=dtype)
y = numpy.zeros((lenx, 1), dtype=dtype)
# load train data
#data = serial.load(datasets[fname])
x[0:8305,:] = dict['data']
#x[i * 10000:(i + 1) * 10000, :] = dict['data']
#y[i * 10000:(i + 1) * 10000, 0] = dict['labels']
#X = dict['data']
y[0:8305,0] = dict['labels']
# load test data
#_logger.info('loading file %s' % datasets['test_batch'])
#data = serial.load(datasets['test_batch'])
# process this data
#Xs = {'train': x[0:ntrain],
# 'test': data['data'][0:ntest]}
#Ys = {'train': y[0:ntrain],
# 'test': data['labels'][0:ntest]}
X = numpy.cast['float32'](x[0:8305])
y = y[0:8305]
# y = Ys[which_set]
if isinstance(y, list):
y = numpy.asarray(y).astype(dtype)
self.center = center
self.rescale = rescale
self.toronto_prepro = toronto_prepro
self.gcn = gcn
if gcn is not None:
gcn = float(gcn)
X = global_contrast_normalize(X, scale=gcn)
view_converter = dense_design_matrix.DefaultViewConverter((32, 32, 3),
axes)
super(Timeliner, self).__init__(X=X, y=y, view_converter=view_converter,
y_labels=self.n_classes)
assert not contains_nan(self.X)
if preprocessor:
preprocessor.apply(self)
if __name__ == "__main__":
#Load dataset
train_x, test_x, train_y, test_y = unpack_facedataset(
) # 7:3 ratio for train:test
# preprocess images
# GCN and ZCA object!!
# Normalize and then ZCA whitening
# Normalized data only used on inversion, not in training
try:
zca = load("faces/zca.data")
except Exception as e:
print("Failed to load preprocessed data from disk, computing zca")
train_x_normalized = global_contrast_normalize(train_x * 255,
scale=55.)
zca = ZCA()
zca.fit(train_x_normalized)
save("faces/zca.data", zca)
x = tf.compat.v1.placeholder(tf.float32, shape=[None, 112 * 92])
y_ = tf.compat.v1.placeholder(tf.float32, shape=[None, 40])
model = Model(x, y_)
session = tf.compat.v1.InteractiveSession()
session.run(tf.compat.v1.global_variables_initializer())
#print(f"test {test_y.shape} t: {type(test_y)} ; train {train_y.shape} t: {type(train_y)}")
model.train(train_x, train_y, session, test_x, test_y, 250)
perform_inversion(zca, test_x[0::3], model, session)
# perform_inversion(train_x, test_x[0::3], model, session)
def __init__(self,
lfw_path,
filelist_path,
embedding_file=None,
center=False,
scale=False,
start=None,
stop=None,
gcn=None,
shuffle=False,
rng=None,
seed=132987,
axes=('b', 0, 1, 'c'),
img_shape=(3, 250, 250)):
self.axes = axes
self.img_shape = img_shape
C, H, W = img_shape
self.img_size = np.prod(self.img_shape)
files = []
with open(filelist_path, 'r') as filelist_f:
files = [line.strip() for line in filelist_f]
# Load raw pixel integer values
dtype = 'uint8'
X = np.zeros((len(files), W, H, C), dtype=dtype)
img_ids = []
for i, line in enumerate(files):
if '\t' in line:
# New format: contains image IDs
img_path, img_id = line.strip().split()
img_ids.append(int(img_id))
else:
img_path = line.strip()
full_path = os.path.join(lfw_path, img_path)
im = image.load(full_path, rescale_image=False, dtype=dtype)
# Handle grayscale images which may not have RGB channels
if len(im.shape) == 2:
W, H = im.shape
# Repeat image 3 times across axis 2
im = im.reshape(W, H, 1).repeat(3, 2)
# Swap color channel to front
X[i] = im
# Cast to float32, center / scale if necessary
X = np.cast['float32'](X)
# Create dense design matrix from topological view
X = X.reshape(X.shape[0], -1)
# Prepare img_ids
if embedding_file is not None:
if len(img_ids) != len(files):
raise ValueError("You must provide a filelist with indexes "
"into the embedding array for each image.")
img_ids = np.array(img_ids, dtype='uint32')
if center and scale:
X[:] -= 127.5
X[:] /= 127.5
elif center:
X[:] -= 127.5
elif scale:
X[:] /= 255.
self.gcn = gcn
if gcn is not None:
gcn = float(gcn)
X = global_contrast_normalize(X, scale=gcn)
if shuffle:
rng = make_np_rng(rng, seed, which_method='permutation')
rand_idx = rng.permutation(len(X))
X = X[rand_idx]
img_ids = img_ids[rand_idx]
if start is not None:
assert start >= 0
assert stop > start
assert stop <= X.shape[0]
X = X[start:stop]
if len(img_ids) > 0:
img_ids = img_ids[start:stop]
# Load embeddings if provided
Y = None
if embedding_file is not None:
embeddings = np.load(embedding_file)['arr_0']
assert embeddings.shape[0] >= len(files)
Y = embeddings[img_ids].astype(theano.config.floatX)
# create view converting for retrieving topological view
self.view_converter = dense_design_matrix.DefaultViewConverter(
(W, H, C), axes)
# init super class
super(LFW, self).__init__(X=X, y=Y)
assert not contains_nan(self.X)
# Another hack: rename 'targets' to match model expectations
if embedding_file is not None:
space, (X_source, y_source) = self.data_specs
self.data_specs = (space, (X_source, 'condition'))
def test_std_norm():
rng = numpy.random.RandomState(0)
X = abs(rng.randn(50, 70))
Y = global_contrast_normalize(X, use_std=True, scale=5)
numpy.testing.assert_allclose(Y.std(axis=1, ddof=1), 5)
for i in xrange(0, data_size, batch_size):
stop = i + numpy.mod(data_size, batch_size) if i >= last else i + batch_size
data[i:stop, :,:,0] = lecun_lcn(data[i:stop,:,:,0].astype('float32'), img_shape, kernel_size)
return data
preprocess = True
if preprocess:
print "Pre-processing the data"
features = []
labels = []
for item, y in zip(data_x, data_y):
data_shape = item.shape
item = item / 255.
item = global_contrast_normalize(item.reshape((data_shape[0], 48*48))).reshape(data_shape)
item = apply_lcn(item, img_shape = [48,48], kernel_size=5)
features.append(item.astype('float32'))
labels.append(y)
print "Done pre-preprocessing"
data = {'data_x' : features, 'data_y' : labels, 'clip_ids' : clip_ids}
#save_path = "/data/lisa/data/faces/EmotiW/preproc/samira/KGL-AFEW/"
save_path = "/data/lisa/data/faces/EmotiW/preproc/samira/KGLIS-AFEWIS/"
serial.save(save_path + 'afew2_valid_prep.pkl', data)
#serial.save(save_path + 'afew2_train_prep.pkl', data)
scale=True)
image.save(saveDir + 'svhn_train.png', 'PNG')
image = paramgraphics.mat_to_img(batch2_data[:100, :].T,
dim_input,
colorImg=colorImg,
scale=True)
image.save(saveDir + 'svhn_valid.png', 'PNG')
image = paramgraphics.mat_to_img(batch3_data[:100, :].T,
dim_input,
colorImg=colorImg,
scale=True)
image.save(saveDir + 'svhn_test.png', 'PNG')
if preprocessing == 'gcn_var':
batch1_data = pypp.global_contrast_normalize(batch1_data,
subtract_mean=True,
use_std=True)
batch2_data = pypp.global_contrast_normalize(batch2_data,
subtract_mean=True,
use_std=True)
batch3_data = pypp.global_contrast_normalize(batch3_data,
subtract_mean=True,
use_std=True)
elif preprocessing == 'gcn_norm':
batch1_data = pypp.global_contrast_normalize(batch1_data,
subtract_mean=True)
batch2_data = pypp.global_contrast_normalize(batch2_data,
subtract_mean=True)
batch3_data = pypp.global_contrast_normalize(batch3_data,
subtract_mean=True)
print batch1_data.shape
def __init__(self,
which_set,
data_path=None,
center=True,
rescale=True,
gcn=True,
specs=True,
foldi=1,
foldn=10,
filestr="feature2086-5-{}.pkl"):
self.class_name = ['neg', 'cin1','cin2','cin3','cancer']
# load data
self.specs = specs
self.filestr = filestr
if which_set == 'valid':
i = (foldi) % foldn
filepath = self.filestr.format(str(i + 1))
filepath = self.dirpath + filepath
filepath = serial.preprocess(filepath)
X, Y = self.loadi(filepath)
elif which_set == 'test':
i = (foldi - 1) % foldn
filepath = self.filestr.format(str(i + 1))
filepath = self.dirpath + filepath
filepath = serial.preprocess(filepath)
X, Y = self.loadi(filepath)
else:
indexs = range(foldn)
i = foldi % foldn
indexs.pop(i)
if i == 0:
indexs.pop(-1)
else:
i = (foldi - 1) % foldn
indexs.pop(i)
Xs = []
Ys = []
for i in indexs:
filepath = self.filestr.format(str(i + 1))
filepath = self.dirpath + filepath
filepath = serial.preprocess(filepath)
X, Y = self.loadi(filepath)
Xs.append(X)
Ys.append(Y)
X = np.vstack(Xs)
Y = np.hstack(Ys)
print X.shape, Y.shape
# col0s = np.where(Y == 0)[0]
# print len(col0s)
X.astype(float)
axis = 0
_max = np.max(X, axis=axis)
_min = np.min(X, axis=axis)
_mean = np.mean(X, axis=axis)
_std = np.std(X, axis=axis)
_scale = _max - _min
if gcn:
X[:, :850] = global_contrast_normalize(X[:, :850], scale=gcn)
X[:, 850:850+556] = global_contrast_normalize(X[:, 850:850 + 556], scale=gcn)
X[:, 850+556:] = global_contrast_normalize(X[:, 850+556:], scale=gcn)
# else:
# if center:
# X[:, ] -= _mean
# if rescale:
# X[:, ] /= _scale
# topo_view = X.reshape(X.shape[0], X.shape[1], 1, 1)
# y = np.reshape(Y, (Y.shape[0], 1))
# y = np.atleast_2d(Y).T
self.raw_X = X
self.raw_y = Y
y = np.zeros((Y.shape[0], 5))
for i in range(Y.shape[0]):
j = Y[i]
y[i, j] = 1
# print y[:, :]
# y[:, 0] = Y
# y[:, 1] = 1 - Y
print "Load CIN_FEATURE2086_5 data: {}, with size X:{}, y:{}".format(data_path, X.shape, y.shape)
super(CIN_FEATURE2086_5, self).__init__(X=X, y=y)
# super(CIN_FEATURE2, self).__init__(topo_view=topo_view, y=y, y_labels=2)
if specs:
assert X.shape[1] == (850 + 556 + 680)
self.init_data_specs()
self.feature850 = X[:, 0:850]
self.feature556 = X[:, 850:850 + 556]
self.feature680 = X[:, 850 + 556:]
self.y = y
def __init__(
self,
which_set,
center=False,
rescale=False,
gcn=None,
one_hot=False,
start=None,
stop=None,
axes=("b", 0, 1, "c"),
toronto_prepro=False,
preprocessor=None,
):
# note: there is no such thing as the cifar10 validation set;
# pylearn1 defined one but really it should be user-configurable
# (as it is here)
self.axes = axes
# we define here:
dtype = "uint8"
ntrain = 50000
nvalid = 0 # artefact, we won't use it
ntest = 10000
# we also expose the following details:
self.img_shape = (3, 32, 32)
self.img_size = np.prod(self.img_shape)
self.n_classes = 10
self.label_names = ["airplane", "automobile", "bird", "cat", "deer", "dog", "frog", "horse", "ship", "truck"]
# # prepare loading
# fnames = ['data_batch_%i' % i for i in range(1,6)]
# lenx = np.ceil((ntrain + nvalid) / 10000.)*10000
# x = np.zeros((lenx,self.img_size), dtype=dtype)
# y = np.zeros(lenx, dtype=dtype)
#
# # load train data
# nloaded = 0
# for i, fname in enumerate(fnames):
# data = CIFAR10._unpickle(fname)
# x[i*10000:(i+1)*10000, :] = data['data']
# y[i*10000:(i+1)*10000] = data['labels']
# nloaded += 10000
# if nloaded >= ntrain + nvalid + ntest: break;
#
# # load test data
# data = CIFAR10._unpickle('test_batch')
#
# # process this data
# Xs = {
# 'train' : x[0:ntrain],
# 'test' : data['data'][0:ntest]
# }
#
# Ys = {
# 'train' : y[0:ntrain],
# 'test' : data['labels'][0:ntest]
# }
if which_set == "train":
# pkl = self._unpickle(os.environ['PYLEARN2_DATA_PATH']+
# 'cifar10/pylearn2_gcn_whitened/train.pkl')
# pkl = self._unpickle(os.environ['PYLEARN2_DATA_PATH']+
# 'cifar10/pylearn2_gcn_whitened/test.pkl')
# X = pkl.X
# y = pkl.y
X = np.load(os.environ["PYLEARN2_DATA_PATH"] + "/cifar10/train_X.npy")
y = np.load(os.environ["PYLEARN2_DATA_PATH"] + "/cifar10/train_y.npy")
X = np.cast["float32"](X)
y = np.cast["float32"](y)
elif which_set == "test":
# pkl = self._unpickle(os.environ['PYLEARN2_DATA_PATH']+
# 'cifar10/pylearn2_gcn_whitened/test.pkl')
# X = pkl.X
# y = pkl.y
X = np.load(os.environ["PYLEARN2_DATA_PATH"] + "/cifar10/test_X.npy")
y = np.load(os.environ["PYLEARN2_DATA_PATH"] + "/cifar10/test_y.npy")
X = np.cast["float32"](X)
y = np.cast["float32"](y)
# X = np.cast['float32'](Xs[which_set])
# y = Ys[which_set]
if which_set == "test":
assert X.shape[0] == 10000
if isinstance(y, list):
y = np.asarray(y)
if center:
X -= 127.5
self.center = center
if rescale:
X /= 127.5
self.rescale = rescale
if toronto_prepro:
assert not center
assert not gcn
X = X / 255.0
if which_set == "test":
other = CIFAR10(which_set="train")
oX = other.X
oX /= 255.0
X = X - oX.mean(axis=0)
else:
X = X - X.mean(axis=0)
self.toronto_prepro = toronto_prepro
self.gcn = gcn
if gcn is not None:
gcn = float(gcn)
X = global_contrast_normalize(X, scale=gcn)
if start is not None:
# This needs to come after the prepro so that it doesn't change the pixel
# means computed above for toronto_prepro
assert start >= 0
assert stop > start
assert stop <= X.shape[0]
X = X[start:stop, :]
y = y[start:stop]
assert X.shape[0] == y.shape[0]
view_converter = dense_design_matrix.DefaultViewConverter((32, 32, 3), axes)
if which_set == "train":
length = X.shape[0]
def search_right_label(desired_label, i):
for idx in xrange(i, length):
if y[idx] == desired_label:
return idx
def swap_ele(index, i):
x_tmp = X[i]
X[i] = X[index]
X[index] = x_tmp
y_tmp = y[i]
y[i] = y[index]
y[index] = y_tmp
desired_label = 0
for i in xrange(length):
desired_label = i % 10
if y[i] != desired_label:
index = search_right_label(desired_label, i)
swap_ele(index, i)
for i in xrange(length - 100, length):
print y[i]
self.one_hot = one_hot
if one_hot:
one_hot = np.zeros((y.shape[0], 10), dtype="float32")
for i in xrange(y.shape[0]):
one_hot[i, y[i]] = 1.0
y = one_hot
super(My_CIFAR10, self).__init__(X=X, y=y, view_converter=view_converter)
assert not np.any(np.isnan(self.X))
if preprocessor:
preprocessor.apply(self)
def __init__(self, which_set, center=False, rescale=False, gcn=None,
one_hot=None, start=None, stop=None, axes=('b', 0, 1, 'c'),
toronto_prepro = False, preprocessor = None, two_image=False):
# note: there is no such thing as the cifar10 validation set;
# pylearn1 defined one but really it should be user-configurable
# (as it is here)
self.axes = axes
# we define here:
dtype = 'uint8'
ntrain = 50000
nvalid = 0 # artefact, we won't use it
ntest = 10000
# we also expose the following details:
self.img_shape = (3, 32, 32)
self.img_shape2 = (32, 32,3)
self.img_size = N.prod(self.img_shape)
self.n_classes = 10
self.label_names = ['airplane', 'automobile', 'bird', 'cat', 'deer',
'dog', 'frog', 'horse', 'ship', 'truck']
# prepare loading
fnames = ['data_batch_%i' % i for i in range(1, 6)]
lenx = N.ceil((ntrain + nvalid) / 10000.)*10000
x = N.zeros((lenx, self.img_size), dtype=dtype)
y = N.zeros((lenx, 1), dtype=dtype)
# load train data
nloaded = 0
for i, fname in enumerate(fnames):
data = CIFAR10._unpickle(fname)
x[i*10000:(i+1)*10000, :] = data['data']
y[i*10000:(i+1)*10000, 0] = data['labels']
nloaded += 10000
if nloaded >= ntrain + nvalid + ntest:
break
# load test data
data = CIFAR10._unpickle('test_batch')
# 2value image
# can not use other option when you use two_image option
print x.shape
if two_image:
from PIL import Image
two_value_x = []
self.img_shape = (1, 32, 32)
self.img_shape2 = (32, 32,1)
for i,pixel in enumerate(x.reshape(50000, 3, 32, 32)):
if i % 1000 == 0:
print i
pixel = np.transpose(pixel, (1,2,0))
test_img = Image.new("RGB",(32,32),(255,0,0))
test_img.putdata([tuple(x.tolist()) for x in pixel.reshape(1024,3)])
two_value_x.append([x for x in test_img.convert("1").getdata()] )
x = np.asarray(two_value_x)
# process this data
Xs = {'train': x[0:ntrain],
'test': data['data'][0:ntest]}
Ys = {'train': y[0:ntrain],
'test': data['labels'][0:ntest]}
X = N.cast['float32'](Xs[which_set])
y = Ys[which_set]
if isinstance(y, list):
y = np.asarray(y).astype(dtype)
if which_set == 'test':
assert y.shape[0] == 10000
y = y.reshape((y.shape[0], 1))
max_labels = 10
if one_hot is not None:
warnings.warn("the `one_hot` parameter is deprecated. To get "
"one-hot encoded targets, request that they "
"live in `VectorSpace` through the `data_specs` "
"parameter of MNIST's iterator method. "
"`one_hot` will be removed on or after "
"September 20, 2014.", stacklevel=2)
if center:
X -= 127.5
self.center = center
if rescale:
X /= 127.5
self.rescale = rescale
if toronto_prepro:
assert not center
assert not gcn
X = X / 255.
if which_set == 'test':
other = CIFAR10(which_set='train')
oX = other.X
oX /= 255.
X = X - oX.mean(axis=0)
else:
X = X - X.mean(axis=0)
self.toronto_prepro = toronto_prepro
self.gcn = gcn
if gcn is not None:
gcn = float(gcn)
X = global_contrast_normalize(X, scale=gcn)
if start is not None:
# This needs to come after the prepro so that it doesn't
# change the pixel means computed above for toronto_prepro
assert start >= 0
assert stop > start
assert stop <= X.shape[0]
X = X[start:stop, :]
y = y[start:stop, :]
assert X.shape[0] == y.shape[0]
if which_set == 'test':
assert X.shape[0] == 10000
# view_converter = dense_design_matrix.DefaultViewConverter((32, 32, 3),
# axes)
view_converter = dense_design_matrix.DefaultViewConverter(self.img_shape2,
axes)
super(CIFAR10, self).__init__(X=X, y=y, view_converter=view_converter,
y_labels=self.n_classes)
assert not contains_nan(self.X)
if preprocessor:
preprocessor.apply(self)
def __init__(
self,
which_set,
center=False,
rescale=False,
gcn=None,
one_hot=False,
start=None,
stop=None,
axes=("b", 0, 1, "c"),
toronto_prepro=False,
preprocessor=None,
):
"""
Parameters
----------
which_set : str
One of 'train', 'test'
gcn : float, optional
Multiplicative constant to use for global contrast normalization.
No global contrast normalization is applied, if None
.. todo::
WRITEME
"""
# note: there is no such thing as the cifar10 validation set;
# pylearn1 defined one but really it should be user-configurable
# (as it is here)
self.axes = axes
# we define here:
dtype = "uint8"
ntrain = 50000
nvalid = 0 # artefact, we won't use it
ntest = 10000
# we also expose the following details:
self.img_shape = (3, 32, 32)
self.img_size = N.prod(self.img_shape)
self.n_classes = 10
self.label_names = ["airplane", "automobile", "bird", "cat", "deer", "dog", "frog", "horse", "ship", "truck"]
# prepare loading
fnames = ["data_batch_%i" % i for i in range(1, 6)]
lenx = N.ceil((ntrain + nvalid) / 10000.0) * 10000
x = N.zeros((lenx, self.img_size), dtype=dtype)
y = N.zeros(lenx, dtype=dtype)
# load train data
nloaded = 0
for i, fname in enumerate(fnames):
data = CIFAR10._unpickle(fname)
x[i * 10000 : (i + 1) * 10000, :] = data["data"]
y[i * 10000 : (i + 1) * 10000] = data["labels"]
nloaded += 10000
if nloaded >= ntrain + nvalid + ntest:
break
# load test data
data = CIFAR10._unpickle("test_batch")
# process this data
Xs = {"train": x[0:ntrain], "test": data["data"][0:ntest]}
Ys = {"train": y[0:ntrain], "test": data["labels"][0:ntest]}
X = N.cast["float32"](Xs[which_set])
y = Ys[which_set]
if isinstance(y, list):
y = np.asarray(y)
if which_set == "test":
assert y.shape[0] == 10000
self.one_hot = one_hot
if one_hot:
one_hot = np.zeros((y.shape[0], 10), dtype="float32")
for i in xrange(y.shape[0]):
one_hot[i, y[i]] = 1.0
y = one_hot
if center:
X -= 127.5
self.center = center
if rescale:
X /= 127.5
self.rescale = rescale
if toronto_prepro:
assert not center
assert not gcn
X = X / 255.0
if which_set == "test":
other = CIFAR10(which_set="train")
oX = other.X
oX /= 255.0
X = X - oX.mean(axis=0)
else:
X = X - X.mean(axis=0)
self.toronto_prepro = toronto_prepro
self.gcn = gcn
if gcn is not None:
gcn = float(gcn)
X = global_contrast_normalize(X, scale=gcn)
if start is not None:
# This needs to come after the prepro so that it doesn't change the pixel
# means computed above for toronto_prepro
assert start >= 0
assert stop > start
assert stop <= X.shape[0]
X = X[start:stop, :]
y = y[start:stop]
assert X.shape[0] == y.shape[0]
if which_set == "test":
assert X.shape[0] == 10000
view_converter = dense_design_matrix.DefaultViewConverter((32, 32, 3), axes)
super(CIFAR10, self).__init__(X=X, y=y, view_converter=view_converter)
assert not np.any(np.isnan(self.X))
if preprocessor:
preprocessor.apply(self)
def __init__(self, which_set, center = False, rescale = False, gcn = None,
one_hot = False, start = None, stop = None, axes=('b', 0, 1, 'c'),
toronto_prepro = False, preprocessor = None):
# note: there is no such thing as the cifar10 validation set;
# pylearn1 defined one but really it should be user-configurable
# (as it is here)
self.axes = axes
# we define here:
dtype = 'uint8'
ntrain = 50000
nvalid = 0 # artefact, we won't use it
ntest = 10000
# we also expose the following details:
self.img_shape = (3,32,32)
self.img_size = N.prod(self.img_shape)
self.n_classes = 10
self.label_names = ['airplane', 'automobile', 'bird', 'cat', 'deer',
'dog', 'frog','horse','ship','truck']
# prepare loading
fnames = ['data_batch_%i' % i for i in range(1,6)]
lenx = N.ceil((ntrain + nvalid) / 10000.)*10000
x = N.zeros((lenx,self.img_size), dtype=dtype)
y = N.zeros(lenx, dtype=dtype)
# load train data
nloaded = 0
for i, fname in enumerate(fnames):
data = CIFAR10._unpickle(fname)
x[i*10000:(i+1)*10000, :] = data['data']
y[i*10000:(i+1)*10000] = data['labels']
nloaded += 10000
if nloaded >= ntrain + nvalid + ntest: break;
# load test data
data = CIFAR10._unpickle('test_batch')
# process this data
Xs = {
'train' : x[0:ntrain],
'test' : data['data'][0:ntest]
}
Ys = {
'train' : y[0:ntrain],
'test' : data['labels'][0:ntest]
}
X = N.cast['float32'](Xs[which_set])
y = Ys[which_set]
if isinstance(y,list):
y = np.asarray(y)
if which_set == 'test':
assert y.shape[0] == 10000
self.one_hot = one_hot
if one_hot:
one_hot = np.zeros((y.shape[0],10),dtype='float32')
for i in xrange(y.shape[0]):
one_hot[i,y[i]] = 1.
y = one_hot
if center:
X -= 127.5
self.center = center
if rescale:
X /= 127.5
self.rescale = rescale
if toronto_prepro:
assert not center
assert not gcn
X = X / 255.
if which_set == 'test':
other = CIFAR10(which_set='train')
oX = other.X
oX /= 255.
X = X - oX.mean(axis=0)
else:
X = X - X.mean(axis=0)
self.toronto_prepro = toronto_prepro
self.gcn = gcn
if gcn is not None:
gcn = float(gcn)
X = global_contrast_normalize(X, scale=gcn)
if start is not None:
# This needs to come after the prepro so that it doesn't change the pixel
# means computed above for toronto_prepro
assert start >= 0
assert stop > start
assert stop <= X.shape[0]
X = X[start:stop, :]
y = y[start:stop]
assert X.shape[0] == y.shape[0]
if which_set == 'test':
assert X.shape[0] == 10000
view_converter = dense_design_matrix.DefaultViewConverter((32,32,3), axes)
super(CIFAR10, self).__init__(X=X, y=y, view_converter=view_converter)
assert not np.any(np.isnan(self.X))
if preprocessor:
preprocessor.apply(self)
batch_size) if i >= last else i + batch_size
data[i:stop, :, :,
0] = lecun_lcn(data[i:stop, :, :, 0].astype('float32'), img_shape,
kernel_size)
return data
preprocess = True
if preprocess:
print "Pre-processing the data"
features = []
labels = []
for item, y in zip(data_x, data_y):
data_shape = item.shape
item = item / 255.
item = global_contrast_normalize(item.reshape(
(data_shape[0], 48 * 48))).reshape(data_shape)
item = apply_lcn(item, img_shape=[48, 48], kernel_size=5)
features.append(item.astype('float32'))
labels.append(y)
print "Done pre-preprocessing"
data = {'data_x': features, 'data_y': labels, 'clip_ids': clip_ids}
#save_path = "/data/lisa/data/faces/EmotiW/preproc/samira/KGL-AFEW/"
save_path = "/data/lisa/data/faces/EmotiW/preproc/samira/KGLIS-AFEWIS/"
serial.save(save_path + 'afew2_valid_prep.pkl', data)
#serial.save(save_path + 'afew2_train_prep.pkl', data)
