def all_data(self, partition_proportions=None, seed=None):
if not self._loaded_images:
self.load_all_images()
while not self.check_loaded_images(600):
time.sleep(5)
data, targets = [], []
for k, c in enumerate(sorted(self._loaded_images)):
data += list(self._loaded_images[c].values())
targets += [k] * 600
if self.info['one_hot_enc']:
targets = em.to_one_hot_enc(targets,
dimension=len(self._loaded_images))
_dts = [
em.Dataset(data=np.stack(data),
target=np.array(targets),
name='MiniImagenet_full')
]
if seed:
np.random.seed(seed)
if partition_proportions:
_dts = redivide_data(
_dts,
partition_proportions=partition_proportions,
shuffle=True)
return em.Datasets.from_list(_dts)
def generate_datasets(self,
rand=None,
num_classes=None,
num_examples=None,
wait_for_n_min=None):
rand = em.get_rand_state(rand)
if wait_for_n_min:
import time
while not self.check_loaded_images(wait_for_n_min):
time.sleep(5)
if not num_examples: num_examples = self.kwargs['num_examples']
if not num_classes: num_classes = self.kwargs['num_classes']
clss = self._loaded_images if self._loaded_images else self.info[
'classes']
random_classes = rand.choice(list(clss.keys()),
size=(num_classes, ),
replace=False)
rand_class_dict = {rnd: k for k, rnd in enumerate(random_classes)}
_dts = []
for ns in em.as_tuple_or_list(num_examples):
classes = balanced_choice_wr(random_classes, ns, rand)
all_images = {cls: list(clss[cls]) for cls in classes}
data, targets, sample_info = [], [], []
for c in classes:
rand.shuffle(all_images[c])
img_name = all_images[c][0]
all_images[c].remove(img_name)
sample_info.append({'name': img_name, 'label': c})
if self._loaded_images:
data.append(clss[c][img_name])
else:
from scipy.misc import imread, imresize
data.append(
imresize(imread(join(self.info['base_folder'],
join(c, img_name)),
mode='RGB'),
size=(self.info['resize'],
self.info['resize'], 3)) / 255.)
targets.append(rand_class_dict[c])
if self.info['one_hot_enc']:
targets = em.to_one_hot_enc(targets, dimension=num_classes)
_dts.append(
em.Dataset(data=np.array(np.stack(data)),
target=targets,
sample_info=sample_info,
info={'all_classes': random_classes}))
return em.Datasets.from_list(_dts)
def generate_datasets(self,
rand=None,
num_classes=None,
num_examples=None):
rand = em.get_rand_state(rand)
if not num_examples: num_examples = self.kwargs['num_examples']
if not num_classes: num_classes = self.kwargs['num_classes']
clss = self._loaded_images if self._loaded_images else self.info[
'classes']
random_classes = rand.choice(list(clss.keys()),
size=(num_classes, ),
replace=False)
rand_class_dict = {rnd: k for k, rnd in enumerate(random_classes)}
_dts = []
for ns in em.as_tuple_or_list(num_examples):
classes = balanced_choice_wr(random_classes, ns, rand)
all_images = {cls: list(clss[cls]) for cls in classes}
data, targets, sample_info = [], [], []
for c in classes:
rand.shuffle(all_images[c])
img_name = all_images[c][0]
all_images[c].remove(img_name)
sample_info.append({'name': img_name, 'label': c})
data.append(clss[c][img_name])
targets.append(rand_class_dict[c])
if self.info['one_hot_enc']:
targets = em.to_one_hot_enc(targets, dimension=num_classes)
_dts.append(
em.Dataset(data=np.array(np.stack(data)),
target=targets,
sample_info=sample_info,
info={'all_classes': random_classes}))
return em.Datasets.from_list(_dts)