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): import time 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 = dl.to_one_hot_enc(targets, dimension=len(self._loaded_images)) _dts = [ dl.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 dl.Datasets.from_list(_dts)
def generate_datasets(self, rand=None, num_classes=None, num_examples=None): rand = dl.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 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 = dl.to_one_hot_enc(targets, dimension=num_classes) _dts.append( dl.Dataset( data=np.array(np.stack(data)), target=targets, sample_info=sample_info, info={"all_classes": random_classes}, )) return dl.Datasets.from_list(_dts)