def _generate_samples_from_descr(categories, attributes, n_samples_per_class,
augment):
use_cat_id, attributes = attributes
assert use_cat_id and not attributes, \
"usage of attributes isn't supporte in v1."
samples = []
labels = []
for i, cat_concepts in enumerate(categories):
mixture = ComposedConcept(cat_concepts, id=None)
cat_samples = []
cat_labels = []
for s_id, n in enumerate(n_samples_per_class):
split_samples, split_attrs = mixture._get_samples(n,
attributes,
split_id=s_id)
if s_id in augment:
split_samples = augment_samples(split_samples)
split_labels = torch.Tensor().long()
cat_id = torch.tensor([i]).expand(split_samples.shape[0], 1)
split_labels = torch.cat([split_labels, cat_id], dim=1)
cat_samples.append(split_samples)
cat_labels.append(split_labels)
samples.append(cat_samples)
labels.append(cat_labels)
if torch.is_tensor(samples[0][0]):
cat_func = torch.cat
else:
cat_func = np.concatenate
samples = (cat_func(split) for split in zip(*samples))
labels = (torch.cat(split) for split in zip(*labels))
return samples, labels
def _generate_samples_from_descr(categories, attributes, n_samples_per_class,
augment):
use_cat_id, attributes = attributes
assert use_cat_id or attributes, 'Each task should at least use the ' \
'category id or an attribute as labels'
if not use_cat_id:
all_concepts = np.array(
[concept for cat in categories for concept in cat])
all_attrs = np.array([c.attrs for c in all_concepts])
selected_attr = all_attrs[:, attributes[0]]
categories = [tuple(all_concepts[selected_attr == val])
for val in np.unique(selected_attr)]
if use_cat_id or isinstance(all_concepts[0], AtomicConcept):
samples = []
labels = []
for i, cat_concepts in enumerate(categories):
mixture = ComposedConcept(cat_concepts, id=None)
cat_samples = []
cat_labels = []
for s_id, n in enumerate(n_samples_per_class):
split_samples, split_attrs = mixture._get_samples(n, attributes,
split_id=s_id)
if s_id in augment:
split_samples = augment_samples(split_samples)
split_labels = torch.Tensor().long()
if use_cat_id:
cat_id = torch.tensor([i]).expand(split_samples.shape[0], 1)
split_labels = torch.cat([split_labels, cat_id], dim=1)
if attributes:
raise NotImplementedError('Attrs aren\'t supported '
'anymore')
split_labels = torch.cat([split_labels, split_attrs], dim=1)
cat_samples.append(split_samples)
cat_labels.append(split_labels)
samples.append(cat_samples)
labels.append(cat_labels)
if torch.is_tensor(samples[0][0]):
cat_func = torch.cat
else:
cat_func = np.concatenate
samples = (cat_func(split) for split in zip(*samples))
labels = (torch.cat(split) for split in zip(*labels))
else:
# Grouping the concepts by attribute value to create the categories
all_concepts = np.array(
[concept for cat in categories for concept in cat])
samples, labels = get_samples_using_attrs(all_concepts, attributes,
n_samples_per_class)
return samples, labels
def _generate_samples_from_descr(categories, attributes, n_samples_per_class,
augment, rnd):
use_cat_id, attributes = attributes
assert use_cat_id and not attributes, \
"usage of attributes isn't supporte in v1."
samples = []
labels = []
for i, cat_concepts in enumerate(categories):
mixture = ComposedConcept(cat_concepts, id=None)
cat_samples = []
cat_labels = []
for s_id, n in enumerate(n_samples_per_class):
if isinstance(n, list):
n_samples, n_labeled = n[0], n[1]
else:
n_samples, n_labeled = n, n
split_samples, split_attrs = mixture._get_samples(n_samples,
attributes,
split_id=s_id,
rng=rnd)
if s_id in augment:
split_samples = augment_samples(split_samples)
split_labels = torch.Tensor().long()
cat_id = torch.tensor([i]).expand(split_samples.shape[0], 1)
split_labels = torch.cat([split_labels, cat_id], dim=1)
#Create unlabeled samples
n_unlabeled = len(split_samples) - n_labeled
if n_unlabeled > 0:
idx_unlabeled = rnd.choice(list(range(len(split_samples))),
n_unlabeled,
replace=False)
split_labels[
idx_unlabeled] = -1 #mark unlabeled samples with -1
cat_samples.append(split_samples)
cat_labels.append(split_labels)
samples.append(cat_samples)
labels.append(cat_labels)
if torch.is_tensor(samples[0][0]):
cat_func = torch.cat
else:
cat_func = np.concatenate
samples = (cat_func(split) for split in zip(*samples))
labels = (torch.cat(split) for split in zip(*labels))
return samples, labels
