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import logging
import numpy as np
from PIL import Image
from torch.utils.data import Dataset
from torchvision import transforms
from utils.data import iCIFAR10, iCIFAR100, iImageNet100, iImageNet1000, StanfordCar, GeneralDataset
from tqdm import tqdm
class DataManager(object):
def __init__(self, dataset_name, shuffle, seed, init_cls, increment, resume = False, path = None, class_list = [-1]):
self.dataset_name = dataset_name
self.init_class_list = class_list
if not resume:
data = {
"path": path,
"class_list": [-1],
}
self._setup_data(dataset_name, shuffle, seed, data = data)
if len(self._class_order) < init_cls:
self._increments = [len(self._class_order)]
else:
self._increments = [init_cls]
while sum(self._increments) + increment < len(self._class_order):
self._increments.append(increment)
offset = len(self._class_order) - sum(self._increments)
if offset > 0:
self._increments.append(offset)
else:
self._increments = [max(class_list)]
data = {
"path": path,
"class_list": class_list,
}
self._setup_data(dataset_name, shuffle, seed, data = data)
while sum(self._increments) + increment < len(self._class_order):
self._increments.append(increment)
offset = len(self._class_order) - sum(self._increments) - 1
if offset > 0:
self._increments.append(offset)
def get_class_list(self, task):
return self._class_order[: sum(self._increments[: task + 1])]
def get_label_list(self, task):
cls_list = self.get_class_list(task)
start_index = max(self.init_class_list) + 1
result = {i:self.label_list[i] for i in cls_list}
return result
@property
def nb_tasks(self):
return len(self._increments)
def get_task_size(self, task):
return self._increments[task]
def get_accumulate_tasksize(self,task):
return float(sum(self._increments[:task+1]))
def get_total_classnum(self):
return len(self._class_order)
def get_dataset(
self, indices, source, mode, appendent=None, ret_data=False, m_rate=None
):
if source == "train":
x, y = self._train_data, self._train_targets
elif source == "test":
x, y = self._test_data, self._test_targets
else:
raise ValueError("Unknown data source {}.".format(source))
if mode == "train":
trsf = transforms.Compose([*self._train_trsf, *self._common_trsf])
elif mode == "flip":
trsf = transforms.Compose(
[
*self._test_trsf,
transforms.RandomHorizontalFlip(p=1.0),
*self._common_trsf,
]
)
elif mode == "test":
trsf = transforms.Compose([*self._test_trsf, *self._common_trsf])
else:
raise ValueError("Unknown mode {}.".format(mode))
data, targets = [], []
for idx in indices:
if m_rate is None:
class_data, class_targets = self._select(
x, y, low_range=idx, high_range=idx + 1
)
else:
class_data, class_targets = self._select_rmm(
x, y, low_range=idx, high_range=idx + 1, m_rate=m_rate
)
data.append(class_data)
targets.append(class_targets)
if appendent is not None and len(appendent) != 0:
appendent_data, appendent_targets = appendent
data.append(appendent_data)
targets.append(appendent_targets)
data, targets = np.concatenate(data), np.concatenate(targets)
if ret_data:
return data, targets, DummyDataset(data, targets, trsf, self.use_path)
else:
return DummyDataset(data, targets, trsf, self.use_path)
def get_finetune_dataset(self,known_classes,total_classes,source,mode,appendent,type="ratio"):
if source == 'train':
x, y = self._train_data, self._train_targets
elif source == 'test':
x, y = self._test_data, self._test_targets
else:
raise ValueError('Unknown data source {}.'.format(source))
if mode == 'train':
trsf = transforms.Compose([*self._train_trsf, *self._common_trsf])
elif mode == 'test':
trsf = transforms.Compose([*self._test_trsf, *self._common_trsf])
else:
raise ValueError('Unknown mode {}.'.format(mode))
val_data = []
val_targets = []
old_num_tot = 0
appendent_data, appendent_targets = appendent
for idx in range(0, known_classes):
append_data, append_targets = self._select(appendent_data, appendent_targets,
low_range=idx, high_range=idx+1)
num=len(append_data)
if num == 0:
continue
old_num_tot += num
val_data.append(append_data)
val_targets.append(append_targets)
if type == "ratio":
new_num_tot = int(old_num_tot*(total_classes-known_classes)/known_classes)
elif type == "same":
new_num_tot = old_num_tot
else:
assert 0, "not implemented yet"
new_num_average = int(new_num_tot/(total_classes-known_classes))
for idx in range(known_classes,total_classes):
class_data, class_targets = self._select(x, y, low_range=idx, high_range=idx+1)
val_indx = np.random.choice(len(class_data),new_num_average, replace=False)
val_data.append(class_data[val_indx])
val_targets.append(class_targets[val_indx])
val_data=np.concatenate(val_data)
val_targets = np.concatenate(val_targets)
return DummyDataset(val_data, val_targets, trsf, self.use_path)
def get_dataset_with_split(
self, indices, source, mode, appendent=None, val_samples_per_class=0
):
if source == "train":
x, y = self._train_data, self._train_targets
elif source == "test":
x, y = self._test_data, self._test_targets
else:
raise ValueError("Unknown data source {}.".format(source))
if mode == "train":
trsf = transforms.Compose([*self._train_trsf, *self._common_trsf])
elif mode == "test":
trsf = transforms.Compose([*self._test_trsf, *self._common_trsf])
else:
raise ValueError("Unknown mode {}.".format(mode))
train_data, train_targets = [], []
val_data, val_targets = [], []
for idx in indices:
class_data, class_targets = self._select(
x, y, low_range=idx, high_range=idx + 1
)
val_indx = np.random.choice(
len(class_data), val_samples_per_class, replace=False
)
train_indx = list(set(np.arange(len(class_data))) - set(val_indx))
val_data.append(class_data[val_indx])
val_targets.append(class_targets[val_indx])
train_data.append(class_data[train_indx])
train_targets.append(class_targets[train_indx])
if appendent is not None:
appendent_data, appendent_targets = appendent
for idx in range(0, int(np.max(appendent_targets)) + 1):
append_data, append_targets = self._select(
appendent_data, appendent_targets, low_range=idx, high_range=idx + 1
)
val_indx = np.random.choice(
len(append_data), val_samples_per_class, replace=False
)
train_indx = list(set(np.arange(len(append_data))) - set(val_indx))
val_data.append(append_data[val_indx])
val_targets.append(append_targets[val_indx])
train_data.append(append_data[train_indx])
train_targets.append(append_targets[train_indx])
train_data, train_targets = np.concatenate(train_data), np.concatenate(
train_targets
)
val_data, val_targets = np.concatenate(val_data), np.concatenate(val_targets)
return DummyDataset(
train_data, train_targets, trsf, self.use_path
), DummyDataset(val_data, val_targets, trsf, self.use_path)
def _setup_data(self, dataset_name, shuffle, seed, data = None):
idata = _get_idata(dataset_name, data = data)
self.label_list = idata.download_data()
# Data
self._train_data, self._train_targets = idata.train_data, idata.train_targets
self._test_data, self._test_targets = idata.test_data, idata.test_targets
self.use_path = idata.use_path
# Transforms
self._train_trsf = idata.train_trsf
self._test_trsf = idata.test_trsf
self._common_trsf = idata.common_trsf
# Order
order = np.unique(self._train_targets)
if shuffle:
np.random.seed(seed)
order = np.random.permutation(order).tolist()
else:
order = idata.class_order.tolist()
if data['class_list'][0] != -1:
self._class_order = np.concatenate((np.array(data['class_list']), order)).tolist()
else:
self._class_order = order
logging.info(self._class_order)
# Map indices
self._train_targets = _map_new_class_index(
self._train_targets, self._class_order,
)
self._test_targets = _map_new_class_index(self._test_targets, self._class_order)
def _select(self, x, y, low_range, high_range):
idxes = np.where(np.logical_and(y >= low_range, y < high_range))[0]
if isinstance(x,np.ndarray):
x_return = x[idxes]
else:
x_return = []
for id in idxes:
x_return.append(x[id])
return x_return, y[idxes]
def _select_rmm(self, x, y, low_range, high_range, m_rate):
assert m_rate is not None
if m_rate != 0:
idxes = np.where(np.logical_and(y >= low_range, y < high_range))[0]
selected_idxes = np.random.randint(
0, len(idxes), size=int((1 - m_rate) * len(idxes))
)
new_idxes = idxes[selected_idxes]
new_idxes = np.sort(new_idxes)
else:
new_idxes = np.where(np.logical_and(y >= low_range, y < high_range))[0]
return x[new_idxes], y[new_idxes]
def getlen(self, index):
y = self._train_targets
return np.sum(np.where(y == index))
class DummyDataset(Dataset):
def __init__(self, images, labels, trsf, use_path=False):
assert len(images) == len(labels), "Data size error!"
self.images = images
self.labels = labels
self.trsf = trsf
self.use_path = use_path
def __len__(self):
return len(self.images)
def __getitem__(self, idx):
if self.use_path:
image = self.trsf(pil_loader(self.images[idx]))
else:
image = self.trsf(Image.fromarray(self.images[idx]))
label = self.labels[idx]
return idx, image, label
def _map_new_class_index(y, order):
return np.array(list(map(lambda x: order.index(x), y)))
def _get_idata(dataset_name, data = None):
name = dataset_name.lower()
if name == "cifar10":
return iCIFAR10()
elif name == "cifar100":
return iCIFAR100()
elif name == "imagenet1000":
return iImageNet1000()
elif name == "imagenet100":
return iImageNet100()
elif name == 'stanfordcar':
return StanfordCar()
elif name == 'general_dataset':
print(data)
return GeneralDataset(data["path"], init_class_list = data["class_list"]);
else:
raise NotImplementedError("Unknown dataset {}.".format(dataset_name))
def pil_loader(path):
# open path as file to avoid ResourceWarning (https://github.com/python-pillow/Pillow/issues/835)
with open(path, "rb") as f:
img = Image.open(f)
return img.convert("RGB")
def accimage_loader(path):
import accimage
try:
return accimage.Image(path)
except IOError:
# Potentially a decoding problem, fall back to PIL.Image
return pil_loader(path)
def default_loader(path):
from torchvision import get_image_backend
if get_image_backend() == "accimage":
return accimage_loader(path)
else:
return pil_loader(path)
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