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incremental_dataloader.py
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incremental_dataloader.py
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'''
TaICML incremental learning
Copyright (c) Jathushan Rajasegaran, 2019
'''
import random
import numpy as np
import torch
from pathlib import Path
from torch.utils.data import Sampler
from torchvision import datasets, transforms
class SubsetRandomSampler(Sampler):
r"""Samples elements randomly from a given list of indices, without replacement.
Arguments:
indices (sequence): a sequence of indices
"""
def __init__(self, indices, shuffle):
self.indices = indices
self.shuffle = shuffle
def __iter__(self):
if(self.shuffle):
return (self.indices[i] for i in torch.randperm(len(self.indices)))
else:
return (self.indices[i] for i in range(len(self.indices)))
def __len__(self):
return len(self.indices)
class IncrementalDataset:
def __init__(
self,
dataset_name,
root,
order,
workers=16,
batch_size=128,
class_per_task=10,
memory_size=0,
mu=1
):
self._current_task = 0
self.memory_size = memory_size
self.mu = mu
self.class_per_task = class_per_task
self.batch_size = batch_size
self.workers = workers
self.sample_per_task_testing = {}
self._setup_data(
dataset_name,
root,
order,
class_per_task=class_per_task,
)
@property
def n_tasks(self):
return len(self.increments)
def get_same_index(self, target, label, memory=None):
# 获取当前任务的训练数据(通过该任务数据的target获得)的indice
# 训练数据 = 当前任务数据 + memory
label_indices = []
label_targets = []
for i in range(len(target)):
if int(target[i]) in label:
label_indices.append(i)
label_targets.append(target[i])
for_memory = (label_indices.copy(),label_targets.copy())
if memory is not None:
memory_indices, memory_targets = memory
memory_indices2 = np.tile(memory_indices, (self.mu,))
all_indices = np.concatenate([memory_indices2,label_indices]).astype(np.int64)
else:
all_indices = label_indices
return all_indices, for_memory
# all_indices 该任务训练数据下标 当前任务数据+memory;
# for_memory (当前任务数据下标, 标签)
def get_same_index_test_chunk(self, target, label):
label_indices = []
label_targets = []
np_target = np.array(target, dtype="int32")
np_indices = np.array(list(range(len(target))), dtype="int32")
for t in range(len(label)//self.class_per_task):
task_idx = []
for class_id in label[t*self.class_per_task: (t+1)*self.class_per_task]:
idx = np.where(np_target==class_id)[0]
task_idx.extend(list(idx.ravel()))
task_idx = np.array(task_idx, dtype="int32")
task_idx.ravel()
random.shuffle(task_idx)
label_indices.extend(list(np_indices[task_idx]))
label_targets.extend(list(np_target[task_idx]))
if(t not in self.sample_per_task_testing.keys()):
self.sample_per_task_testing[t] = len(task_idx)
label_indices = np.array(label_indices, dtype="int32")
label_indices.ravel()
return list(label_indices), label_targets
def new_task(self, memory=None):
print(self._current_task)
print(self.increments)
min_class = sum(self.increments[:self._current_task])
max_class = sum(self.increments[:self._current_task + 1])
train_indices, for_memory = self.get_same_index(self.train_dataset.targets, list(range(min_class, max_class)), memory=memory)
test_indices, _ = self.get_same_index_test_chunk(self.test_dataset.targets, list(range(max_class)))
train_data_loader = torch.utils.data.DataLoader(self.train_dataset, batch_size=self.batch_size,shuffle=False,num_workers=self.workers, sampler=SubsetRandomSampler(train_indices, True))
test_data_loader = torch.utils.data.DataLoader(self.test_dataset, batch_size=self.batch_size,shuffle=False,num_workers=self.workers, sampler=SubsetRandomSampler(test_indices, False))
task_info = {
"min_class": min_class,
"max_class": max_class,
"task": self._current_task,
"max_task": len(self.increments),
"n_train_data": len(train_indices),
"n_test_data": len(test_indices)
}
self._current_task += 1
return task_info, train_data_loader, test_data_loader, for_memory
def _setup_data(self, dataset_name, root, order, class_per_task=10):
self.increments = []
dataset = _get_dataset(dataset_name)
train_path = Path(root) / "train"
test_path = Path(root) / "test"
train_dataset = dataset.base_dataset(root=str(train_path), transform=dataset.train_transforms)
test_dataset = dataset.base_dataset(root=str(test_path), transform=dataset.test_transforms)
for i,t in enumerate(train_dataset.targets):
train_dataset.targets[i] = order[t]
for i,t in enumerate(test_dataset.targets):
test_dataset.targets[i] = order[t]
self.increments = [class_per_task for _ in range(len(order) // class_per_task)]
self.train_dataset = train_dataset
self.test_dataset = test_dataset
def get_memory(self, memory, for_memory, sess, seed=1):
random.seed(seed)
memory_per_task = self.memory_size // ((sess+1)*self.class_per_task)
self._data_memory, self._targets_memory = np.array([]), np.array([])
mu = 1
#update old memory
if memory is not None:
data_memory, targets_memory = memory
data_memory = np.array(data_memory, dtype="int32")
targets_memory = np.array(targets_memory, dtype="int32")
for class_idx in range(self.class_per_task*(sess)):
tmp_index = np.where(targets_memory==class_idx)[0]
idx = np.random.choice(tmp_index, size=min(len(tmp_index), memory_per_task))
self._data_memory = np.concatenate([self._data_memory, np.tile(data_memory[idx], (mu,)) ])
self._targets_memory = np.concatenate([self._targets_memory, np.tile(targets_memory[idx], (mu,)) ])
#add new classes to the memory
new_indices, new_targets = for_memory
new_indices = np.array(new_indices, dtype="int32")
new_targets = np.array(new_targets, dtype="int32")
for class_idx in range(self.class_per_task*(sess),self.class_per_task*(1+sess)):
tmp_index = np.where(new_targets==class_idx)[0]
idx = np.random.choice(tmp_index,size=min(len(tmp_index),memory_per_task))
self._data_memory = np.concatenate([self._data_memory, np.tile(new_indices[idx],(mu,)) ])
self._targets_memory = np.concatenate([self._targets_memory, np.tile(new_targets[idx],(mu,)) ])
print(len(self._data_memory))
return list(self._data_memory.astype("int32")), list(self._targets_memory.astype("int32"))
def _get_dataset(dataset_name):
dataset_name = dataset_name.lower().strip()
if dataset_name in ['mycifar30']:
return MyCifar30
elif dataset_name == 'animal_imagenet':
return AnimalImagenet
elif dataset_name in ['digit5', 'digit4']:
return Digit5
else:
raise NotImplementedError(f"illegal dataset {dataset_name}.")
class DataHandler:
base_dataset = datasets.ImageFolder
class AnimalImagenet(DataHandler):
train_transforms = transforms.Compose([
transforms.RandomCrop(224, padding=28),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
transforms.Normalize(
mean=[0.5071, 0.4867, 0.4408],
std=[0.2675, 0.2565, 0.2761]
)
])
test_transforms = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize(
mean=[0.5071, 0.4867, 0.4408],
std=[0.2675, 0.2565, 0.2761]
)
])
class MyCifar30(DataHandler):
train_transforms = transforms.Compose([
transforms.RandomCrop(32, padding=4),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
# transforms.Normalize(
# mean=[0.5071, 0.4867, 0.4408],
# std=[0.2675, 0.2565, 0.2761]
# )
])
test_transforms = transforms.Compose([
transforms.ToTensor(),
# transforms.Normalize(
# mean=[0.5071, 0.4867, 0.4408],
# std=[0.2675, 0.2565, 0.2761]
# )
])
class Digit5(DataHandler):
train_transforms = transforms.Compose([
transforms.Resize([32,32]),
transforms.RandomRotation(30),
transforms.ToTensor(),
])
test_transforms = transforms.Compose([
transforms.Resize([32,32]),
transforms.ToTensor(),
])