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import torch
import pickle
import numpy as np
from PIL import Image
from torchvision import datasets
from torch.utils.data import Dataset, DataLoader
from torch.utils.data.distributed import DistributedSampler
from continuum.datasets import TinyImageNet200
from continuum import ClassIncremental
def _processed_tiny_dir(data_root):
processed_dir = os.path.join(data_root, "processed")
if os.path.isdir(processed_dir):
return processed_dir
if os.path.basename(os.path.normpath(data_root)) == "processed":
return data_root
return processed_dir
class ContinualDatasets:
def __init__(self, dataset, mode, task_num, init_cls_num, inc_cls_num, data_root, cls_map, trfms, batchsize, num_workers, config):
self.mode = mode
self.task_num = task_num
self.init_cls_num = init_cls_num
self.inc_cls_num = inc_cls_num
self.data_root = data_root
self.cls_map = cls_map
self.trfms = trfms
self.batchsize = batchsize
self.num_workers = num_workers
self.config = config
self.dataset = dataset
if self.dataset in ['binary_cifar10', 'binary_cifar100']:
num_classes = 10 if self.dataset == 'binary_cifar10' else 100
if self.cls_map is None:
class_order = self.config.get('class_order', list(range(num_classes)))
self.cls_map = {label: ori_label for label, ori_label in enumerate(class_order)}
if self.dataset == 'binary_cifar10':
datasets.CIFAR10(self.data_root, download=True)
else:
datasets.CIFAR100(self.data_root, download=True)
elif self.dataset == 'processed_tinyimg':
class_order = self.config.get('class_order', list(range(200)))
self.cls_map = {label: ori_label for label, ori_label in enumerate(class_order)}
self.create_loaders()
def create_loaders(self):
self.dataloaders = []
if self.dataset == 'tiny-imagenet':
if 'class_order' in self.config:
class_order = self.config['class_order']
elif self.config.get('shuffle', False):
rng = np.random.RandomState(self.config['seed'])
class_order = rng.permutation(200).tolist()
else:
class_order = list(range(200))
scenario = ClassIncremental(
TinyImageNet200(self.data_root, train=self.mode == 'train', download=True),
initial_increment=self.init_cls_num,
increment=self.inc_cls_num,
class_order=class_order
)
class_ids_per_task = (
[class_order[:self.init_cls_num]] +
[class_order[i:i + self.inc_cls_num] for i in range(self.init_cls_num, len(class_order), self.inc_cls_num)]
)
with open(os.path.join(os.getcwd(), "core", "data", "dataset_reqs", f"tinyimagenet_classes.txt"), "r") as f:
lines = f.read().splitlines()
classes_names = [line.split("\t")[-1] for line in lines]
for t in range(self.task_num):
cur_scenario = scenario[t:t+1]
dataset = SingleDataset(self.dataset, self.data_root, self.mode, self.init_cls_num, self.inc_cls_num, self.cls_map, self.trfms, init=False)
dataset.images = cur_scenario._x
dataset.labels = cur_scenario._y
dataset.labels_name = [classes_names[class_id] for class_id in class_ids_per_task[t]]
self.dataloaders.append(DataLoader(
dataset,
shuffle = self.mode == 'train',
batch_size = self.batchsize,
drop_last = False,
num_workers = self.num_workers,
pin_memory=self.config['pin_memory']
))
else:
for i in range(self.task_num):
start_idx = 0 if i == 0 else (self.init_cls_num + (i-1) * self.inc_cls_num)
end_idx = start_idx + (self.init_cls_num if i ==0 else self.inc_cls_num)
self.dataloaders.append(DataLoader(
SingleDataset(self.dataset, self.data_root, self.mode, self.init_cls_num, self.inc_cls_num, self.cls_map, self.trfms, start_idx, end_idx),
shuffle = self.mode == 'train',
batch_size = self.batchsize,
drop_last = False,
num_workers = self.num_workers,
pin_memory=self.config['pin_memory']
))
def get_loader(self, task_idx):
assert task_idx >= 0 and task_idx < self.task_num
if self.mode == 'train':
return self.dataloaders[task_idx]
else:
return self.dataloaders[:task_idx+1]
class ImbalancedDatasets(ContinualDatasets):
def __init__(self, mode, task_num, init_cls_num, inc_cls_num, data_root, cls_map, trfms, batchsize, num_workers, imb_type='exp', imb_factor=0.002, shuffle=False):
self.imb_type = imb_type
self.imb_factor = imb_factor
self.shuffle = shuffle
super().__init__(mode, task_num, init_cls_num, inc_cls_num, data_root, cls_map, trfms, batchsize, num_workers)
def create_loaders(self):
self.dataloaders = []
cls_num = self.init_cls_num + self.inc_cls_num * (self.task_num - 1)
img_num_list = self._get_img_num_per_cls(cls_num, self.imb_type, self.imb_factor)
if self.shuffle:
grouped_img_nums = [img_num_list[i:i + self.inc_cls_num] for i in range(0, cls_num, self.inc_cls_num)]
np.random.shuffle(grouped_img_nums)
for group in grouped_img_nums:
np.random.shuffle(group)
shuffled_img_num_list = [num for group in grouped_img_nums for num in group]
img_num_list = shuffled_img_num_list
for i in range(self.task_num):
start_idx = 0 if i == 0 else (self.init_cls_num + (i - 1) * self.inc_cls_num)
end_idx = start_idx + (self.init_cls_num if i == 0 else self.inc_cls_num)
dataset = SingleDataset(self.data_root, self.mode, self.cls_map, self.trfms, start_idx, end_idx)
new_imgs, new_labels = [], []
labels_np = np.array(dataset.labels, dtype=np.int64)
classes = np.unique(labels_np)
for the_class, the_img_num in zip(classes, img_num_list[i * self.inc_cls_num:(i + 1) * self.inc_cls_num]):
idx = np.nonzero(labels_np == the_class)[0]
np.random.shuffle(idx)
selec_idx = idx[:the_img_num]
new_imgs.extend([dataset.images[j] for j in selec_idx])
new_labels.extend([the_class, ] * the_img_num)
dataset.images = new_imgs
dataset.labels = new_labels
self.dataloaders.append(DataLoader(
dataset,
batch_size = self.batchsize,
drop_last = False
))
def _get_img_num_per_cls(self, cls_num, imb_type, imb_factor):
img_max = len(os.listdir(os.path.join(self.data_root, self.mode, self.cls_map[0])))
img_num_per_cls = []
if imb_type == 'exp':
for cls_idx in range(cls_num):
num = img_max * (imb_factor**(cls_idx / (cls_num - 1.0)))
img_num_per_cls.append(max(int(num), 1))
elif imb_type == 'exp_re':
for cls_idx in range(cls_num):
num = img_max * (imb_factor**(cls_idx / (cls_num - 1.0)))
img_num_per_cls.append(max(int(num), 1))
img_num_per_cls.reverse()
elif imb_type == 'exp_max':
cls_per_group = cls_num//self.task_num
for cls_idx in range(cls_num):
if (cls_idx+1)%cls_per_group==1:
num = img_max * (imb_factor**(cls_idx / (cls_num - 1.0)))
img_num_per_cls.append(int(num))
elif imb_type == 'exp_max_re':
cls_per_group = cls_num//self.task_num
for cls_idx in range(cls_num):
if (cls_idx+1)%cls_per_group==1:
# print(cls_idx)
num = img_max * (imb_factor**(cls_idx / (cls_num - 1.0)))
img_num_per_cls.append(int(num))
img_num_per_cls.reverse()
elif imb_type == 'exp_min':
cls_per_group = cls_num//self.task_num
for cls_idx in range(cls_num):
if (cls_idx+1)%cls_per_group==1:
# print(cls_idx)
num = img_max * (imb_factor**((cls_idx+cls_per_group-1) / (cls_num - 1.0)))
# print(num)
img_num_per_cls.append(int(num))
elif imb_type == 'half':
cls_per_group = cls_num // self.task_num
ratio = 2
num = 1
for cls_idx in range(cls_num):
if num > img_max:
num = img_max
img_num_per_cls.append(int(num))
if (cls_idx + 1) % cls_per_group == 0:
num *= ratio
img_num_per_cls.reverse()
elif imb_type == 'half_re':
cls_per_group = cls_num // self.task_num
ratio = 2
num = 1
for cls_idx in range(cls_num):
if num > img_max:
num = img_max
img_num_per_cls.append(int(num))
if (cls_idx + 1) % cls_per_group == 0:
num *= ratio
elif imb_type == 'halfbal':
cls_per_group = cls_num // self.task_num
N = img_max * cls_per_group
total = 0
for i in range(self.task_num):
total += N / (2**i)
print(total)
per_class_count = int(total / cls_num)
img_num_per_cls.extend([per_class_count] * cls_num)
elif imb_type == 'oneshot':
img_num_per_cls.extend([1] * cls_num)
elif imb_type == 'step':
for cls_idx in range(cls_num // 2):
img_num_per_cls.append(int(img_max))
for cls_idx in range(cls_num // 2):
img_num_per_cls.append(int(img_max * imb_factor))
elif imb_type == 'fewshot':
for cls_idx in range(cls_num):
if cls_idx<50:
num = img_max
else:
num = img_max*0.01
img_num_per_cls.append(int(num))
else:
img_num_per_cls.extend([int(img_max)] * cls_num)
return img_num_per_cls
class SingleDataset(Dataset):
def __init__(self, dataset, data_root, mode, init_cls_num, inc_cls_num, cls_map, trfms, start_idx=-1, end_idx=-1, init=True):
super().__init__()
self.dataset = dataset
self.data_root = data_root
self.mode = mode
self.init_cls_num = init_cls_num
self.inc_cls_num = inc_cls_num
self.cls_map = cls_map
self.start_idx = start_idx
self.end_idx = end_idx
self.trfms = trfms
if init:
self.images, self.labels, self.labels_name = self._init_datalist()
def __getitem__(self, idx):
if self.dataset in ['binary_cifar10', 'binary_cifar100']:
image = self.images[idx]
image = Image.fromarray(np.uint8(image))
elif self.dataset == 'tiny-imagenet':
img_path = self.images[idx]
image = Image.open(img_path).convert("RGB")
elif self.dataset == 'processed_tinyimg':
image = self.images[idx]
if np.max(image) <= 1.0:
image = np.uint8(255 * image)
image = Image.fromarray(np.uint8(image)).convert("RGB")
else:
img_path = self.images[idx]
image = Image.open(os.path.join(self.data_root, self.mode, img_path)).convert("RGB")
label = self.labels[idx]
image = self.trfms(image)
return {"image": image, "label": label, "index": idx}
def __len__(self,):
return len(self.labels)
def _init_datalist(self):
imgs, labels, labels_name = [], [], []
if self.dataset in ['binary_cifar10', 'binary_cifar100']:
if self.dataset == 'binary_cifar10':
data_dir = os.path.join(self.data_root, 'cifar-10-batches-py')
file_names = [f'data_batch_{idx}' for idx in range(1, 6)] if self.mode == 'train' else ['test_batch']
data_chunks, label_chunks = [], []
for file_name in file_names:
with open(os.path.join(data_dir, file_name), 'rb') as f:
load_data = pickle.load(f, encoding='latin1')
data_chunks.append(load_data['data'])
label_chunks.extend(load_data['labels'])
raw_data = np.concatenate(data_chunks, axis=0)
raw_labels = label_chunks
else:
with open(os.path.join(self.data_root, 'cifar-100-python', self.mode), 'rb') as f:
load_data = pickle.load(f, encoding='latin1')
raw_data = load_data['data']
raw_labels = load_data['fine_labels']
num_classes = 10 if self.dataset == 'binary_cifar10' else 100
cls_map = self.cls_map or {label: label for label in range(num_classes)}
new_label_by_old = {old_label: new_label for new_label, old_label in cls_map.items()}
for data, label in zip(raw_data, raw_labels):
if label not in new_label_by_old:
continue
new_label = new_label_by_old[label]
if new_label in range(self.start_idx, self.end_idx):
r = data[:1024].reshape(32, 32)
g = data[1024:2048].reshape(32, 32)
b = data[2048:].reshape(32, 32)
tt_data = np.dstack((r, g, b))
imgs.append(tt_data)
labels.append(new_label)
labels_name.append(new_label)
elif self.dataset == 'processed_tinyimg':
split = 'train' if self.mode == 'train' else 'val'
processed_dir = _processed_tiny_dir(self.data_root)
label_range = range(self.start_idx, self.end_idx)
ori_to_label = {self.cls_map[id]: id for id in label_range}
target_classes = np.array(list(ori_to_label.keys()))
for num in range(20):
data_path = os.path.join(processed_dir, f'x_{split}_{num + 1:02d}.npy')
label_path = os.path.join(processed_dir, f'y_{split}_{num + 1:02d}.npy')
raw_labels = np.load(label_path)
keep_mask = np.isin(raw_labels, target_classes)
if not np.any(keep_mask):
continue
raw_data = np.load(data_path, mmap_mode='r')
kept_data = raw_data[keep_mask]
kept_labels = raw_labels[keep_mask]
for data, label in zip(kept_data, kept_labels):
label = int(label)
imgs.append(np.asarray(data))
labels.append(ori_to_label[label])
labels_name.append(label)
else:
for id in range(self.start_idx, self.end_idx):
img_list = [self.cls_map[id] + '/' + pic_path for pic_path in os.listdir(os.path.join(self.data_root, self.mode, self.cls_map[id]))]
imgs.extend(img_list)
labels.extend([id for _ in range(len(img_list))])
labels_name.append(self.cls_map[id])
return imgs, labels, labels_name
def get_class_names(self):
return self.labels_name
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