def classification_task(self):
X = [[
m['sepal_length'], m['sepal_width'], m['petal_length'],
m['petal_width']
] for m in self.meta]
y = utils.int_labels([m['name'] for m in self.meta])
return np.asarray(X), np.asarray(y)
def raw_classification_task(self, split=None):
"""Return image_paths, labels"""
if split:
inds = self.splits[split]
else:
inds = xrange(len(self.meta))
image_paths = [self.meta[ind]["filename"] for ind in inds]
names = np.asarray([self.meta[ind]["name"] for ind in inds])
labels = int_labels(names)
return image_paths, labels
def raw_classification_task(self, split=None):
"""
:param split: an integer from 0 to 9 inclusive.
:param split_role: either 'train' or 'test'
:returns: either all samples (when split_k=None) or the specific
train/test split
"""
if split is not None:
inds = self.classification_splits[split]
else:
inds = range(len(self.meta))
names = self.names[inds]
paths = [self.meta[ind]['filename'] for ind in inds]
labels = int_labels(names)
return paths, labels, inds
def raw_recognition_task(self):
"""Return image_paths, labels"""
image_paths = [self.image_path(m) for m in self.meta]
names = np.asarray([m["name"] for m in self.meta])
labels = utils.int_labels(names)
return image_paths, labels
def raw_gender_task(self):
genders = [m['gender'] for m in self.meta]
paths = [self.image_path(m) for m in self.meta]
return paths, utils.int_labels(genders)
def raw_recognition_task(self):
names = [m['name'] for m in self.meta]
paths = [self.image_path(m) for m in self.meta]
labels = utils.int_labels(names)
return paths, labels
def raw_gender_task(self):
genders = [m['gender'] for m in self.meta]
paths = [self.image_path(m) for m in self.meta]
return paths, utils.int_labels(genders)
def raw_recognition_task(self):
names = [m['name'] for m in self.meta]
paths = [self.image_path(m) for m in self.meta]
labels = utils.int_labels(names)
return paths, labels
def classification_task(self):
X = [[m['sepal_length'], m['sepal_width'],
m['petal_length'], m['petal_width']]
for m in self.meta]
y = utils.int_labels([m['name'] for m in self.meta])
return np.asarray(X), np.asarray(y)
def raw_classification_task(self):
"""Return image_paths, labels"""
image_paths = [self.image_path(m) for m in self.meta]
names = np.asarray([m['name'] for m in self.meta])
labels = utils.int_labels(names)
return image_paths, labels
def raw_recognition_task(self):
"""Return image_paths, labels"""
image_paths = [self.image_path(m) for m in self.meta]
names = np.asarray([m['name'] for m in self.meta])
labels = utils.int_labels(names)
return image_paths, labels