import torchvision.transforms as transforms
import torchvision
import torch
import numpy as np
import os
import codecs
from torch.distributions.categorical import Categorical
import torch.utils.data as data
from PIL import Image
import errno
def _reduce_class(set, classes, train, preserve_label_space=True):
if classes is None:
return
new_class_idx = {}
for c in classes:
new_class_idx[c] = new_class_idx.__len__()
new_data = []
new_labels = []
if train:
all_data = set.train_data
labels = set.train_labels
else:
all_data = set.test_data
labels = set.test_labels
for data, label in zip(all_data, labels):
if type(label) == int:
label_val = label
else:
label_val = label.item()
if label_val in classes:
new_data.append(data)
if preserve_label_space:
new_labels += [label_val]
else:
new_labels += [new_class_idx[label_val]]
if type(new_data[0]) == np.ndarray:
new_data = np.array(new_data)
elif type(new_data[0]) == torch.Tensor:
new_data = torch.stack(new_data)
else:
assert False, "Reduce class not supported"
if train:
set.train_data = new_data
set.train_labels = new_labels
else:
set.test_data = new_data
set.test_labels = new_labels
class Permutation(torch.utils.data.Dataset):
"""
A dataset wrapper that permute the position of features
"""
def __init__(self, dataset, permute_idx, target_offset):
super(Permutation,self).__init__()
self.dataset = dataset
self.permute_idx = permute_idx
self.target_offset = target_offset
def __len__(self):
return len(self.dataset)
def __getitem__(self, index):
img, target = self.dataset[index]
target = target + self.target_offset
shape = img.size()
img = img.view(-1)[self.permute_idx].view(shape)
return img, target
class DatasetsLoaders:
def __init__(self, dataset, batch_size=4, num_workers=4, pin_memory=True, **kwargs):
self.dataset_name = dataset
self.valid_loader = None
self.num_workers = num_workers
if self.num_workers is None:
self.num_workers = 4
self.random_erasing = kwargs.get("random_erasing", False)
self.reduce_classes = kwargs.get("reduce_classes", None)
self.permute = kwargs.get("permute", False)
self.target_offset = kwargs.get("target_offset", 0)
pin_memory = pin_memory if torch.cuda.is_available() else False
self.batch_size = batch_size
cifar10_mean = (0.5, 0.5, 0.5)
cifar10_std = (0.5, 0.5, 0.5)
cifar100_mean = (0.5070, 0.4865, 0.4409)
cifar100_std = (0.2673, 0.2564, 0.2761)
mnist_mean = [33.318421449829934]
mnist_std = [78.56749083061408]
fashionmnist_mean = [73.14654541015625]
fashionmnist_std = [89.8732681274414]
if dataset == "CIFAR10":
# CIFAR10:
# type : uint8
# shape : train_set.train_data.shape (50000, 32, 32, 3)
# test data shape : (10000, 32, 32, 3)
# number of channels : 3
# Mean per channel : train_set.train_data[:,:,:,0].mean() 125.306918046875
# train_set.train_data[:,:,:,1].mean() 122.95039414062499
# train_set.train_data[:,:,:,2].mean() 113.86538318359375
# Std per channel : train_set.train_data[:, :, :, 0].std() 62.993219278136884
# train_set.train_data[:, :, :, 1].std() 62.088707640014213
# train_set.train_data[:, :, :, 2].std() 66.704899640630913
self.mean = cifar10_mean
self.std = cifar10_std
transform_train = transforms.Compose([
transforms.RandomCrop(32, padding=4),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
transforms.Normalize((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010)),
])
transform_test = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010)),
])
self.train_set = torchvision.datasets.CIFAR10(root='./data', train=True,
download=True, transform=transform_train)
self.train_loader = torch.utils.data.DataLoader(self.train_set, batch_size=self.batch_size,
shuffle=True, num_workers=self.num_workers,
pin_memory=pin_memory)
self.test_set = torchvision.datasets.CIFAR10(root='./data', train=False,
download=True, transform=transform_test)
self.test_loader = torch.utils.data.DataLoader(self.test_set, batch_size=self.batch_size,
shuffle=False, num_workers=self.num_workers,
pin_memory=pin_memory)
if dataset == "CIFAR100":
# CIFAR100:
# type : uint8
# shape : train_set.train_data.shape (50000, 32, 32, 3)
# test data shape : (10000, 32, 32, 3)
# number of channels : 3
# Mean per channel : train_set.train_data[:,:,:,0].mean() 129.304165605/255=0.5070
# train_set.train_data[:,:,:,1].mean() 124.069962695/255=0.4865
# train_set.train_data[:,:,:,2].mean() 112.434050059/255=0.4409
# Std per channel : train_set.train_data[:, :, :, 0].std() 68.1702428992/255=0.2673
# train_set.train_data[:, :, :, 1].std() 65.3918080439/255=0.2564
# train_set.train_data[:, :, :, 2].std() 70.418370188/255=0.2761
self.mean = cifar100_mean
self.std = cifar100_std
transform = transforms.Compose(
[transforms.ToTensor(),
transforms.Normalize(self.mean, self.std)])
self.train_set = torchvision.datasets.CIFAR100(root='./data', train=True,
download=True, transform=transform)
_reduce_class(self.train_set, self.reduce_classes, train=True,
preserve_label_space=kwargs.get("preserve_label_space"))
self.train_loader = torch.utils.data.DataLoader(self.train_set, batch_size=self.batch_size,
shuffle=True, num_workers=self.num_workers,
pin_memory=pin_memory)
self.test_set = torchvision.datasets.CIFAR100(root='./data', train=False,
download=True, transform=transform)
_reduce_class(self.test_set, self.reduce_classes, train=False,
preserve_label_space=kwargs.get("preserve_label_space"))
self.test_loader = torch.utils.data.DataLoader(self.test_set, batch_size=self.batch_size,
shuffle=False, num_workers=self.num_workers,
pin_memory=pin_memory)
if dataset == "MNIST":
# MNIST:
# type : torch.ByteTensor
# shape : train_set.train_data.shape torch.Size([60000, 28, 28])
# test data shape : [10000, 28, 28]
# number of channels : 1
# Mean per channel : 33.318421449829934
# Std per channel : 78.56749083061408
# Transforms
self.mean = mnist_mean
self.std = mnist_std
if kwargs.get("pad_to_32", False):
transform = transforms.Compose(
[transforms.Pad(2, fill=0, padding_mode='constant'),
transforms.ToTensor(),
transforms.Normalize(mean=(0.1000,), std=(0.2752,))])
else:
transform = transforms.Compose(
[transforms.ToTensor()])
# Create train set
self.train_set = torchvision.datasets.MNIST(root='./data', train=True,
download=True, transform=transform)
if kwargs.get("permutation", False):
# Permute if permutation is provided
self.train_set = Permutation(torchvision.datasets.MNIST(root='./data', train=True,
download=True, transform=transform),
kwargs.get("permutation", False), self.target_offset)
# Reduce classes if necessary
_reduce_class(self.train_set, self.reduce_classes, train=True,
preserve_label_space=kwargs.get("preserve_label_space"))
# Remap labels
if kwargs.get("labels_remapping", False):
labels_remapping = kwargs.get("labels_remapping", False)
for lbl_idx in range(len(self.train_set.train_labels)):
self.train_set.train_labels[lbl_idx] = labels_remapping[self.train_set.train_labels[lbl_idx]]
self.train_loader = torch.utils.data.DataLoader(self.train_set, batch_size=self.batch_size,
shuffle=True, num_workers=self.num_workers,
pin_memory=pin_memory)
# Create test set
self.test_set = torchvision.datasets.MNIST(root='./data', train=False,
download=True, transform=transform)
if kwargs.get("permutation", False):
# Permute if permutation is provided
self.test_set = Permutation(torchvision.datasets.MNIST(root='./data', train=False,
download=True, transform=transform),
kwargs.get("permutation", False), self.target_offset)
# Reduce classes if necessary
_reduce_class(self.test_set, self.reduce_classes, train=False,
preserve_label_space=kwargs.get("preserve_label_space"))
# Remap labels
if kwargs.get("labels_remapping", False):
labels_remapping = kwargs.get("labels_remapping", False)
for lbl_idx in range(len(self.test_set.test_labels)):
self.test_set.test_labels[lbl_idx] = labels_remapping[self.test_set.test_labels[lbl_idx]]
self.test_loader = torch.utils.data.DataLoader(self.test_set, batch_size=self.batch_size,
shuffle=False, num_workers=self.num_workers,
pin_memory=pin_memory)
if dataset == "FashionMNIST":
# MNIST:
# type : torch.ByteTensor
# shape : train_set.train_data.shape torch.Size([60000, 28, 28])
# test data shape : [10000, 28, 28]
# number of channels : 1
# Mean per channel : fm.train_data.type(torch.FloatTensor).mean() is 72.94035223214286
# Std per channel : fm.train_data.type(torch.FloatTensor).std() is 90.0211833054075
self.mean = fashionmnist_mean
self.std = fashionmnist_std
# transform = transforms.Compose(
# [transforms.ToTensor(),
# transforms.Normalize(self.mean, self.std)])
# transform = transforms.Compose(
# [transforms.ToTensor()])
transform = transforms.Compose(
[transforms.Pad(2),
transforms.ToTensor(),
transforms.Normalize((72.94035223214286 / 255,), (90.0211833054075 / 255,))])
self.train_set = torchvision.datasets.FashionMNIST(root='./data/fmnist', train=True,
download=True, transform=transform)
self.train_loader = torch.utils.data.DataLoader(self.train_set, batch_size=self.batch_size,
shuffle=True, num_workers=self.num_workers,
pin_memory=pin_memory)
self.test_set = torchvision.datasets.FashionMNIST(root='./data/fmnist', train=False,
download=True, transform=transform)
self.test_loader = torch.utils.data.DataLoader(self.test_set, batch_size=self.batch_size,
shuffle=False, num_workers=self.num_workers,
pin_memory=pin_memory)
if dataset == "SVHN":
# SVHN:
# type : numpy.ndarray
# shape : self.train_set.data.shape is (73257, 3, 32, 32)
# test data shape : self.test_set.data.shape is (26032, 3, 32, 32)
# number of channels : 3
# Mean per channel : sv.data.mean(axis=0).mean(axis=1).mean(axis=1) is array([111.60893668, 113.16127466, 120.56512767])
# Std per channel : np.transpose(sv.data, (1, 0, 2, 3)).reshape(3,-1).std(axis=1) is array([50.49768174, 51.2589843 , 50.24421614])
self.mean = mnist_mean
self.std = mnist_std
# transform = transforms.Compose(
# [transforms.ToTensor(),
# transforms.Normalize(self.mean, self.std)])
transform = transforms.Compose(
[transforms.ToTensor(),
transforms.Normalize((111.60893668/255, 113.16127466/255, 120.56512767/255), (50.49768174/255, 51.2589843/255, 50.24421614/255))])
self.train_set = torchvision.datasets.SVHN(root='./data', split="train",
download=True, transform=transform)
self.train_loader = torch.utils.data.DataLoader(self.train_set, batch_size=self.batch_size,
shuffle=True, num_workers=self.num_workers,
pin_memory=pin_memory)
self.test_set = torchvision.datasets.SVHN(root='./data', split="test",
download=True, transform=transform)
self.test_loader = torch.utils.data.DataLoader(self.test_set, batch_size=self.batch_size,
shuffle=False, num_workers=self.num_workers,
pin_memory=pin_memory)
if dataset == "NOTMNIST":
# MNIST:
# type : torch.ByteTensor
# shape : train_set.train_data.shape torch.Size([60000, 28, 28])
# test data shape : [10000, 28, 28]
# number of channels : 1
# Mean per channel : nm.train_data.type(torch.FloatTensor).mean() is 106.51712372448979
# Std per channel : nm.train_data.type(torch.FloatTensor).std() is 115.76734631096612
self.mean = mnist_mean
self.std = mnist_std
transform = transforms.Compose(
[transforms.Pad(2),
transforms.ToTensor(),
transforms.Normalize((106.51712372448979 / 255,), (115.76734631096612 / 255,))])
self.train_set = NOTMNIST(root='./data/notmnist', train=True, download=True, transform=transform)
self.train_loader = torch.utils.data.DataLoader(self.train_set, batch_size=self.batch_size,
shuffle=True, num_workers=self.num_workers,
pin_memory=pin_memory)
self.test_set = NOTMNIST(root='./data/notmnist', train=False, download=True, transform=transform)
self.test_loader = torch.utils.data.DataLoader(self.test_set, batch_size=self.batch_size,
shuffle=False, num_workers=self.num_workers,
pin_memory=pin_memory)
if dataset == "CONTPERMUTEDPADDEDMNIST":
transform = transforms.Compose(
[transforms.Pad(2, fill=0, padding_mode='constant'),
transforms.ToTensor(),
transforms.Normalize(mean=(0.1000,), std=(0.2752,))])
# Original MNIST
tasks_datasets = [torchvision.datasets.MNIST(root='./data', train=True, download=True, transform=transform)]
tasks_samples_indices = [torch.tensor(range(len(tasks_datasets[0])), dtype=torch.int32)]
total_len = len(tasks_datasets[0])
test_loaders = [torch.utils.data.DataLoader(torchvision.datasets.MNIST(root='./data', train=False,
download=True, transform=transform),
batch_size=self.batch_size, shuffle=False,
num_workers=self.num_workers, pin_memory=pin_memory)]
self.num_of_permutations = len(kwargs.get("all_permutation"))
all_permutation = kwargs.get("all_permutation", None)
for p_idx in range(self.num_of_permutations):
# Create permuation
permutation = all_permutation[p_idx]
# Add train set:
tasks_datasets.append(Permutation(torchvision.datasets.MNIST(root='./data', train=True,
download=True, transform=transform),
permutation, target_offset=0))
tasks_samples_indices.append(torch.tensor(range(total_len,
total_len + len(tasks_datasets[-1])
), dtype=torch.int32))
total_len += len(tasks_datasets[-1])
# Add test set:
test_set = Permutation(torchvision.datasets.MNIST(root='./data', train=False,
download=True, transform=transform),
permutation, self.target_offset)
test_loaders.append(torch.utils.data.DataLoader(test_set, batch_size=self.batch_size,
shuffle=False, num_workers=self.num_workers,
pin_memory=pin_memory))
self.test_loader = test_loaders
# Concat datasets
total_iters = kwargs.get("total_iters", None)
assert total_iters is not None
beta = kwargs.get("contpermuted_beta", 3)
all_datasets = torch.utils.data.ConcatDataset(tasks_datasets)
# Create probabilities of tasks over iterations
self.tasks_probs_over_iterations = [_create_task_probs(total_iters, self.num_of_permutations+1, task_id,
beta=beta) for task_id in
range(self.num_of_permutations+1)]
normalize_probs = torch.zeros_like(self.tasks_probs_over_iterations[0])
for probs in self.tasks_probs_over_iterations:
normalize_probs.add_(probs)
for probs in self.tasks_probs_over_iterations:
probs.div_(normalize_probs)
self.tasks_probs_over_iterations = torch.cat(self.tasks_probs_over_iterations).view(-1, self.tasks_probs_over_iterations[0].shape[0])
tasks_probs_over_iterations_lst = []
for col in range(self.tasks_probs_over_iterations.shape[1]):
tasks_probs_over_iterations_lst.append(self.tasks_probs_over_iterations[:, col])
self.tasks_probs_over_iterations = tasks_probs_over_iterations_lst
train_sampler = ContinuousMultinomialSampler(data_source=all_datasets, samples_in_batch=self.batch_size,
tasks_samples_indices=tasks_samples_indices,
tasks_probs_over_iterations=
self.tasks_probs_over_iterations,
num_of_batches=kwargs.get("iterations_per_virtual_epc", 1))
self.train_loader = torch.utils.data.DataLoader(all_datasets, batch_size=self.batch_size,
num_workers=self.num_workers, sampler=train_sampler, pin_memory=pin_memory)
class ContinuousMultinomialSampler(torch.utils.data.Sampler):
r"""Samples elements randomly. If without replacement, then sample from a shuffled dataset.
If with replacement, then user can specify ``num_samples`` to draw.
self.tasks_probs_over_iterations is the probabilities of tasks over iterations.
self.samples_distribution_over_time is the actual distribution of samples over iterations
(the result of sampling from self.tasks_probs_over_iterations).
Arguments:
data_source (Dataset): dataset to sample from
num_samples (int): number of samples to draw, default=len(dataset)
replacement (bool): samples are drawn with replacement if ``True``, default=False
"""
def __init__(self, data_source, samples_in_batch=128, num_of_batches=69, tasks_samples_indices=None,
tasks_probs_over_iterations=None):
self.data_source = data_source
assert tasks_samples_indices is not None, "Must provide tasks_samples_indices - a list of tensors," \
"each item in the list corrosponds to a task, each item of the " \
"tensor corrosponds to index of sample of this task"
self.tasks_samples_indices = tasks_samples_indices
self.num_of_tasks = len(self.tasks_samples_indices)
assert tasks_probs_over_iterations is not None, "Must provide tasks_probs_over_iterations - a list of " \
"probs per iteration"
assert all([isinstance(probs, torch.Tensor) and len(probs) == self.num_of_tasks for
probs in tasks_probs_over_iterations]), "All probs must be tensors of len" \
+ str(self.num_of_tasks) + ", first tensor type is " \
+ str(type(tasks_probs_over_iterations[0])) + ", and " \
" len is " + str(len(tasks_probs_over_iterations[0]))
self.tasks_probs_over_iterations = tasks_probs_over_iterations
self.current_iteration = 0
self.samples_in_batch = samples_in_batch
self.num_of_batches = num_of_batches
# Create the samples_distribution_over_time
self.samples_distribution_over_time = [[] for _ in range(self.num_of_tasks)]
self.iter_indices_per_iteration = []
if not isinstance(self.samples_in_batch, int) or self.samples_in_batch <= 0:
raise ValueError("num_samples should be a positive integeral "
"value, but got num_samples={}".format(self.samples_in_batch))
def generate_iters_indices(self, num_of_iters):
from_iter = len(self.iter_indices_per_iteration)
for iter_num in range(from_iter, from_iter+num_of_iters):
# Get random number of samples per task (according to iteration distribution)
tsks = Categorical(probs=self.tasks_probs_over_iterations[iter_num]).sample(torch.Size([self.samples_in_batch]))
# Generate samples indices for iter_num
iter_indices = torch.zeros(0, dtype=torch.int32)
for task_idx in range(self.num_of_tasks):
if self.tasks_probs_over_iterations[iter_num][task_idx] > 0:
num_samples_from_task = (tsks == task_idx).sum().item()
self.samples_distribution_over_time[task_idx].append(num_samples_from_task)
# Randomize indices for each task (to allow creation of random task batch)
tasks_inner_permute = np.random.permutation(len(self.tasks_samples_indices[task_idx]))
rand_indices_of_task = tasks_inner_permute[:num_samples_from_task]
iter_indices = torch.cat([iter_indices, self.tasks_samples_indices[task_idx][rand_indices_of_task]])
else:
self.samples_distribution_over_time[task_idx].append(0)
self.iter_indices_per_iteration.append(iter_indices.tolist())
def __iter__(self):
self.generate_iters_indices(self.num_of_batches)
self.current_iteration += self.num_of_batches
return iter([item for sublist in self.iter_indices_per_iteration[self.current_iteration - self.num_of_batches:self.current_iteration] for item in sublist])
def __len__(self):
return len(self.samples_in_batch)
def _get_linear_line(start, end, direction="up"):
if direction == "up":
return torch.FloatTensor([(i - start)/(end-start) for i in range(start, end)])
return torch.FloatTensor([1 - ((i - start) / (end - start)) for i in range(start, end)])
def _create_task_probs(iters, tasks, task_id, beta=3):
if beta <= 1:
peak_start = int((task_id/tasks)*iters)
peak_end = int(((task_id + 1) / tasks)*iters)
start = peak_start
end = peak_end
else:
start = max(int(((beta*task_id - 1)*iters)/(beta*tasks)), 0)
peak_start = int(((beta*task_id + 1)*iters)/(beta*tasks))
peak_end = int(((beta * task_id + (beta - 1)) * iters) / (beta * tasks))
end = min(int(((beta * task_id + (beta + 1)) * iters) / (beta * tasks)), iters)
probs = torch.zeros(iters, dtype=torch.float)
if task_id == 0:
probs[start:peak_start].add_(1)
else:
probs[start:peak_start] = _get_linear_line(start, peak_start, direction="up")
probs[peak_start:peak_end].add_(1)
if task_id == tasks - 1:
probs[peak_end:end].add_(1)
else:
probs[peak_end:end] = _get_linear_line(peak_end, end, direction="down")
return probs
###
# NotMNIST
###
class NOTMNIST(data.Dataset):
"""`MNIST <http://yann.lecun.com/exdb/mnist/>`_ Dataset.
Args:
root (string): Root directory of dataset where ``processed/training.pt``
and ``processed/test.pt`` exist.
train (bool, optional): If True, creates dataset from ``training.pt``,
otherwise from ``test.pt``.
download (bool, optional): If true, downloads the dataset from the internet and
puts it in root directory. If dataset is already downloaded, it is not
downloaded again.
transform (callable, optional): A function/transform that takes in an PIL image
and returns a transformed version. E.g, ``transforms.RandomCrop``
target_transform (callable, optional): A function/transform that takes in the
target and transforms it.
"""
urls = [
'https://github.com/davidflanagan/notMNIST-to-MNIST/raw/master/t10k-images-idx3-ubyte.gz',
'https://github.com/davidflanagan/notMNIST-to-MNIST/raw/master/t10k-labels-idx1-ubyte.gz',
'https://github.com/davidflanagan/notMNIST-to-MNIST/raw/master/train-images-idx3-ubyte.gz',
'https://github.com/davidflanagan/notMNIST-to-MNIST/raw/master/train-labels-idx1-ubyte.gz',
]
raw_folder = 'raw'
processed_folder = 'processed'
training_file = 'training.pt'
test_file = 'test.pt'
def __init__(self, root, train=True, transform=None, target_transform=None, download=False):
self.root = os.path.expanduser(root)
self.transform = transform
self.target_transform = target_transform
self.train = train # training set or test set
if download:
self.download()
if not self._check_exists():
raise RuntimeError('Dataset not found.' +
' You can use download=True to download it')
if self.train:
self.train_data, self.train_labels = torch.load(
os.path.join(self.root, self.processed_folder, self.training_file))
else:
self.test_data, self.test_labels = torch.load(
os.path.join(self.root, self.processed_folder, self.test_file))
def __getitem__(self, index):
"""
Args:
index (int): Index
Returns:
tuple: (image, target) where target is index of the target class.
"""
if self.train:
img, target = self.train_data[index], self.train_labels[index]
else:
img, target = self.test_data[index], self.test_labels[index]
# doing this so that it is consistent with all other datasets
# to return a PIL Image
img = Image.fromarray(img.numpy(), mode='L')
if self.transform is not None:
img = self.transform(img)
if self.target_transform is not None:
target = self.target_transform(target)
return img, target
def __len__(self):
if self.train:
return len(self.train_data)
else:
return len(self.test_data)
def _check_exists(self):
return os.path.exists(os.path.join(self.root, self.processed_folder, self.training_file)) and \
os.path.exists(os.path.join(self.root, self.processed_folder, self.test_file))
def download(self):
"""Download the MNIST data if it doesn't exist in processed_folder already."""
from six.moves import urllib
import gzip
if self._check_exists():
return
# download files
try:
os.makedirs(os.path.join(self.root, self.raw_folder))
os.makedirs(os.path.join(self.root, self.processed_folder))
except OSError as e:
if e.errno == errno.EEXIST:
pass
else:
raise
for url in self.urls:
print('Downloading ' + url)
data = urllib.request.urlopen(url)
filename = url.rpartition('/')[2]
file_path = os.path.join(self.root, self.raw_folder, filename)
with open(file_path, 'wb') as f:
f.write(data.read())
with open(file_path.replace('.gz', ''), 'wb') as out_f, \
gzip.GzipFile(file_path) as zip_f:
out_f.write(zip_f.read())
os.unlink(file_path)
# process and save as torch files
print('Processing...')
training_set = (
self.read_image_file(os.path.join(self.root, self.raw_folder, 'train-images-idx3-ubyte')),
self.read_label_file(os.path.join(self.root, self.raw_folder, 'train-labels-idx1-ubyte'))
)
test_set = (
self.read_image_file(os.path.join(self.root, self.raw_folder, 't10k-images-idx3-ubyte')),
self.read_label_file(os.path.join(self.root, self.raw_folder, 't10k-labels-idx1-ubyte'))
)
with open(os.path.join(self.root, self.processed_folder, self.training_file), 'wb') as f:
torch.save(training_set, f)
with open(os.path.join(self.root, self.processed_folder, self.test_file), 'wb') as f:
torch.save(test_set, f)
print('Done!')
def __repr__(self):
fmt_str = 'Dataset ' + self.__class__.__name__ + '\n'
fmt_str += ' Number of datapoints: {}\n'.format(self.__len__())
tmp = 'train' if self.train is True else 'test'
fmt_str += ' Split: {}\n'.format(tmp)
fmt_str += ' Root Location: {}\n'.format(self.root)
tmp = ' Transforms (if any): '
fmt_str += '{0}{1}\n'.format(tmp, self.transform.__repr__().replace('\n', '\n' + ' ' * len(tmp)))
tmp = ' Target Transforms (if any): '
fmt_str += '{0}{1}'.format(tmp, self.target_transform.__repr__().replace('\n', '\n' + ' ' * len(tmp)))
return fmt_str
@staticmethod
def get_int(b):
return int(codecs.encode(b, 'hex'), 16)
def read_label_file(self, path):
with open(path, 'rb') as f:
data = f.read()
assert self.get_int(data[:4]) == 2049
length = self.get_int(data[4:8])
parsed = np.frombuffer(data, dtype=np.uint8, offset=8)
return torch.from_numpy(parsed).view(length).long()
def read_image_file(self, path):
with open(path, 'rb') as f:
data = f.read()
assert self.get_int(data[:4]) == 2051
length = self.get_int(data[4:8])
num_rows = self.get_int(data[8:12])
num_cols = self.get_int(data[12:16])
images = []
parsed = np.frombuffer(data, dtype=np.uint8, offset=16)
return torch.from_numpy(parsed).view(length, num_rows, num_cols)
###########################################################################
# Callable datasets
###########################################################################
def ds_mnist(**kwargs):
"""
MNIST dataset.
:param batch_size: batch size
num_workers: num of workers
pad_to_32: If true, will pad digits to size 32x32 and normalize to zero mean and unit variance.
:return: Tuple with two lists.
First list of the tuple is a list of 1 train loaders.
Second list of the tuple is a list of 1 test loaders.
"""
dataset = [DatasetsLoaders("MNIST", batch_size=kwargs.get("batch_size", 128),
num_workers=kwargs.get("num_workers", 1),
pad_to_32=kwargs.get("pad_to_32", False))]
test_loaders = [ds.test_loader for ds in dataset]
train_loaders = [ds.train_loader for ds in dataset]
return train_loaders, test_loaders
def ds_split_mnist(**kwargs):
"""
Split MNIST dataset. Consists of 5 tasks: digits 0 & 1, 2 & 3, 4 & 5, 6 & 7, and 8 & 9.
:param batch_size: batch size
num_workers: num of workers
pad_to_32: If true, will pad digits to size 32x32 and normalize to zero mean and unit variance.
separate_labels_space: If true, each task will have its own label space (e.g. 01, 23 etc.).
If false, all tasks will have label space of 0,1 only.
:return: Tuple with two lists.
First list of the tuple is a list of 5 train loaders, each loader is a task.
Second list of the tuple is a list of 5 test loaders, each loader is a task.
"""
classes_lst = [
[0, 1],
[2, 3],
[4, 5],
[6, 7],
[8, 9]
]
dataset = [DatasetsLoaders("MNIST", batch_size=kwargs.get("batch_size", 128),
num_workers=kwargs.get("num_workers", 1),
reduce_classes=cl, pad_to_32=kwargs.get("pad_to_32", False),
preserve_label_space=kwargs.get("separate_labels_space")) for cl in classes_lst]
test_loaders = [ds.test_loader for ds in dataset]
train_loaders = [ds.train_loader for ds in dataset]
return train_loaders, test_loaders
def ds_padded_split_mnist(**kwargs):
"""
Split MNIST dataset, padded to 32x32 pixels.
"""
return ds_split_mnist(pad_to_32=True, **kwargs)
def ds_split_mnist_offline(**kwargs):
"""
Split MNIST dataset. Offline means that all tasks are mixed together.
"""
if kwargs.get("separate_labels_space"):
return ds_mnist(**kwargs)
else:
return ds_mnist(labels_remapping={l: l % 2 for l in range(10)}, **kwargs)
def ds_padded_split_mnist_offline(**kwargs):
"""
Split MNIST dataset. Padded to 32x32. Offline means that all tasks are mixed together.
"""
return ds_split_mnist_offline(pad_to_32=True, **kwargs)
def ds_permuted_mnist(**kwargs):
"""
Permuted MNIST dataset.
First task is the MNIST datasets (with 10 possible labels).
Other tasks are permutations (pixel-wise) of the MNIST datasets (with 10 possible labels).
:param batch_size: batch size
num_workers: num of workers
pad_to_32: If true, will pad digits to size 32x32 and normalize to zero mean and unit variance.
permutations: A list of permutations. Each permutation should be a list containing new pixel position.
separate_labels_space: True for seperated labels space - task i labels will be (10*i) to (10*i + 9).
False for unified labels space - all tasks will have labels of 0 to 9.
:return: Tuple with two lists.
First list of the tuple is a list of train loaders, each loader is a task.
Second list of the tuple is a list of test loaders, each loader is a task.
"""
# First task
dataset = [DatasetsLoaders("MNIST", batch_size=kwargs.get("batch_size", 128),
num_workers=kwargs.get("num_workers", 1), pad_to_32=kwargs.get("pad_to_32", False))]
target_offset = 0
permutations = kwargs.get("permutations", [])
for pidx in range(len(permutations)):
if kwargs.get("separate_labels_space"):
target_offset = (pidx + 1) * 10
dataset.append(DatasetsLoaders("MNIST", batch_size=kwargs.get("batch_size", 128),
num_workers=kwargs.get("num_workers", 1),
permutation=permutations[pidx], target_offset=target_offset,
pad_to_32=kwargs.get("pad_to_32", False)))
# For offline permuted we take the datasets and mix them.
if kwargs.get("offline", False):
train_sets = []
test_sets = []
for ds in dataset:
train_sets.append(ds.train_set)
test_sets.append(ds.test_set)
train_set = torch.utils.data.ConcatDataset(train_sets)
test_set = torch.utils.data.ConcatDataset(test_sets)
train_loader = torch.utils.data.DataLoader(train_set, batch_size=kwargs.get("batch_size", 128), shuffle=True,
num_workers=kwargs.get("num_workers", 1), pin_memory=True)
test_loader = torch.utils.data.DataLoader(test_set, batch_size=kwargs.get("batch_size", 128), shuffle=False,
num_workers=kwargs.get("num_workers", 1), pin_memory=True)
return [train_loader], [test_loader]
test_loaders = [ds.test_loader for ds in dataset]
train_loaders = [ds.train_loader for ds in dataset]
return train_loaders, test_loaders
def ds_padded_permuted_mnist(**kwargs):
"""
Permuted MNIST dataset, padded to 32x32.
"""
return ds_permuted_mnist(pad_to_32=True, **kwargs)
def ds_permuted_mnist_offline(**kwargs):
"""
Permuted MNIST dataset. Offline means that all tasks are mixed together.
"""
return ds_permuted_mnist(offline=True, **kwargs)
def ds_padded_permuted_mnist_offline(**kwargs):
"""
Permuted MNIST dataset, padded to 32x32. Offline means that all tasks are mixed together.
"""
return ds_permuted_mnist(pad_to_32=True, offline=True, **kwargs)
def ds_padded_cont_permuted_mnist(**kwargs):
"""
Continuous Permuted MNIST dataset, padded to 32x32.
Notice that this dataloader is aware to the epoch number, therefore if the training is loaded from a checkpoint
adjustments might be needed.
Access dataset.tasks_probs_over_iterations to see the tasks probabilities for each iteration.
:param num_epochs: Number of epochs for the training (since it builds distribution over iterations,
it needs this information in advance)
:param iterations_per_virtual_epc: In continuous task-agnostic learning, the notion of epoch does not exists,
since we cannot define 'passing over the whole dataset'. Therefore,
we define "iterations_per_virtual_epc" -
how many iterations consist a single epoch.
:param contpermuted_beta: The proportion in which the tasks overlap. 4 means that 1/4 of a task duration will
consist of data from previous/next task. Larger values means less overlapping.
:param permutations: The permutations which will be used (first task is always the original MNIST).
:param batch_size: Batch size.
:param num_workers: Num workers.
:return: A tuple of (train_loaders, test_loaders). train_loaders is a list of 1 data loader - it loads the
permuted MNIST dataset continuously as described in the paper. test_loaders is a list of 1+permutations
data loaders, one for each dataset.
"""
dataset = [DatasetsLoaders("CONTPERMUTEDPADDEDMNIST", batch_size=kwargs.get("batch_size", 128),
num_workers=kwargs.get("num_workers", 1),
total_iters=(kwargs.get("num_epochs")*kwargs.get("iterations_per_virtual_epc")),
contpermuted_beta=kwargs.get("contpermuted_beta"),
iterations_per_virtual_epc=kwargs.get("iterations_per_virtual_epc"),
all_permutation=kwargs.get("permutations", []))]
test_loaders = [tloader for ds in dataset for tloader in ds.test_loader]
train_loaders = [ds.train_loader for ds in dataset]
return train_loaders, test_loaders
def ds_visionmix(**kwargs):
"""
Vision mix dataset. Consists of: MNIST, notMNIST, FashionMNIST, SVHN and CIFAR10.
"""
dataset = [DatasetsLoaders("MNIST", batch_size=kwargs.get("batch_size", 128),
num_workers=kwargs.get("num_workers", 1), pad_to_32=True),
DatasetsLoaders("NOTMNIST", batch_size=kwargs.get("batch_size", 128),
num_workers=kwargs.get("num_workers", 1)),
DatasetsLoaders("FashionMNIST", batch_size=kwargs.get("batch_size", 128),
num_workers=kwargs.get("num_workers", 1)),
DatasetsLoaders("SVHN", batch_size=kwargs.get("batch_size", 128),
num_workers=kwargs.get("num_workers", 1)),
DatasetsLoaders("CIFAR10", batch_size=kwargs.get("batch_size", 128),
num_workers=kwargs.get("num_workers", 1))]
test_loaders = [ds.test_loader for ds in dataset]
train_loaders = [ds.train_loader for ds in dataset]
return train_loaders, test_loaders
def ds_cifar10and100(**kwargs):
"""
CIFAR10 and CIFAR100 dataset. Consists of 6 tasks:
1) CIFAR10
2-6) Subsets of 10 classes from CIFAR100.
"""
classes_lst = [[j for j in range(i * 10, (i + 1) * 10)] for i in range(0, 5)]
dataset = [DatasetsLoaders("CIFAR100", batch_size=kwargs.get("batch_size", 128),
num_workers=kwargs.get("num_workers", 1),
reduce_classes=cl, preserve_label_space=False) for cl in classes_lst]
dataset = [DatasetsLoaders("CIFAR10", batch_size=kwargs.get("batch_size", 128),
num_workers=kwargs.get("num_workers", 1), preserve_label_space=False)] + dataset
test_loaders = [ds.test_loader for ds in dataset]
train_loaders = [ds.train_loader for ds in dataset]
return train_loaders, test_loaders
def ds_cifar10(**kwargs):
"""
CIFAR10 dataset. No tasks.
"""
dataset = [DatasetsLoaders("CIFAR10", batch_size=kwargs.get("batch_size", 128),
num_workers=kwargs.get("num_workers", 1))]
test_loaders = [ds.test_loader for ds in dataset]
train_loaders = [ds.train_loader for ds in dataset]
return train_loaders, test_loaders
