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dataloaders.py
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'''
A single dataloader is the typical one input - one label mapping. This is the classical supervised learning.
A double dataloader creates 2 data points for the same label and used in training with AgMax.
Copyright 2021 Rowel Atienza
'''
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import torch
import torchvision.transforms as transforms
import torchvision.datasets as datasets
from torch.utils.data import ConcatDataset
import os
class SingleLoader:
def __init__(self,
root='./data',
batch_size=128,
dataset=datasets.CIFAR10,
transform={'train':transforms.ToTensor(), 'test':transforms.ToTensor()},
device=None,
dataset_name="cifar10",
shuffle_test=False,
corruption=None,
num_workers=16):
super(SingleLoader, self).__init__()
self.test = None
self.train = None
self._build(root,
batch_size,
dataset,
transform,
device,
dataset_name,
shuffle_test,
corruption,
num_workers)
def _build(self,
root,
batch_size,
dataset,
transform,
device,
dataset_name,
shuffle_test,
corruption,
num_workers):
DataLoader = torch.utils.data.DataLoader
#workers = torch.cuda.device_count() * 4
if "cuda" in str(device):
print("num_workers: ", num_workers)
kwargs = {'num_workers': num_workers, 'pin_memory': True}
else:
kwargs = {}
if dataset_name == "svhn" or dataset_name == "svhn-core":
x_train = dataset(root=root,
split='train',
download=True,
transform=transform['train'])
if dataset_name == "svhn":
x_extra = dataset(root=root,
split='extra',
download=True,
transform=transform['train'])
x_train = ConcatDataset([x_train, x_extra])
x_test = dataset(root=root,
split='test',
download=True,
transform=transform['test'])
elif dataset_name == "imagenet":
x_train = dataset(root=root,
split='train',
transform=transform['train'])
if corruption is None:
x_test = dataset(root=root,
split='val',
transform=transform['test'])
else:
root = os.path.join(root, corruption)
corrupt_test = []
for i in range(1, 6):
folder = os.path.join(root, str(i))
x_test = datasets.ImageFolder(root=folder,
transform=transform['test'])
corrupt_test.append(x_test)
x_test = ConcatDataset(corrupt_test)
elif dataset_name == "speech_commands":
x_train = dataset(root=root,
split='train',
transform=transform['train'])
x_val = dataset(root=root,
split='valid',
transform=transform['test'])
x_test = dataset(root=root,
split='test',
transform=transform['test'])
self.val = DataLoader(x_val,
shuffle=False,
batch_size=batch_size,
**kwargs)
#self.train = DataLoader(x_train,
# shuffle=True,
# batch_size=batch_size,
# **kwargs)
#self.test = DataLoader(x_test,
# shuffle=False,
# batch_size=batch_size,
# **kwargs)
#return
else:
x_train = dataset(root=root,
train=True,
download=True,
transform=transform['train'])
x_test = dataset(root=root,
train=False,
download=True,
transform=transform['test'])
self.train = DataLoader(x_train,
shuffle=True,
batch_size=batch_size,
**kwargs)
self.test = DataLoader(x_test,
shuffle=shuffle_test,
batch_size=batch_size,
**kwargs)
class DoubleLoader(SingleLoader):
def __init__(self,
root='./data',
batch_size=128,
dataset=[None, None],
transform={'train':transforms.ToTensor(), 'test':transforms.ToTensor()},
device=None,
dataset_name="cifar10",
shuffle_test=False,
corruption=None,
num_workers=16):
super(DoubleLoader, self).__init__(root=root,
batch_size=batch_size,
dataset=dataset,
transform=transform,
device=device,
dataset_name=dataset_name,
shuffle_test=shuffle_test,
corruption=corruption,
num_workers=num_workers)
def _build(self,
root,
batch_size,
dataset,
transform,
device,
dataset_name,
shuffle_test,
corruption,
num_workers):
print(self.__class__.__name__)
DataLoader = torch.utils.data.DataLoader
#workers = torch.cuda.device_count() * 4
if "cuda" in str(device):
print("num_workers: ", num_workers)
kwargs = {'num_workers': num_workers, 'pin_memory': True}
else:
kwargs = {}
if dataset_name == "svhn" or dataset_name == "svhn-core":
x_train = dataset[0](root=root,
split='train',
download=True,
transform=transform['train'],
siamese_transform=transform['train'])
if dataset_name == "svhn":
x_extra = dataset[0](root=root,
split='extra',
download=True,
transform=transform['train'],
siamese_transform=transform['train'])
x_train = ConcatDataset([x_train, x_extra])
x_test = dataset[1](root=root,
split='test',
download=True,
transform=transform['test'])
elif dataset_name == "imagenet":
x_train = dataset[0](root=root,
split='train',
transform=transform['train'],
siamese_transform=transform['train'])
if corruption is None:
x_test = dataset[1](root=root,
split='val',
transform=transform['test'])
else:
root = os.path.join(root, corruption)
corrupt_test = []
for i in range(1, 6):
folder = os.path.join(root, str(i))
x_test = datasets.ImageFolder(root=folder,
transform=transform['test'])
corrupt_test.append(x_test)
x_test = ConcatDataset(corrupt_test)
elif dataset_name == "speech_commands":
x_train = dataset[0](root=root,
split='train',
transform=transform['train'],
siamese_transform=transform['train'])
x_val = dataset[1](root=root,
split='valid',
transform=transform['test'])
x_test = dataset[1](root=root,
split='test',
transform=transform['test'])
self.val = DataLoader(x_val,
shuffle=False,
batch_size=batch_size,
**kwargs)
#from torch.utils.data.sampler import WeightedRandomSampler
#weights = x_train.make_weights_for_balanced_classes()
#sampler = WeightedRandomSampler(weights, len(weights))
# sampler=sampler,
#self.train = DataLoader(x_train,
# shuffle=True,
# batch_size=batch_size,
# **kwargs)
#self.test = DataLoader(x_test,
# shuffle=False,
# batch_size=batch_size,
# **kwargs)
#return
else:
x_train = dataset[0](root=root,
train=True,
download=True,
transform=transform['train'],
siamese_transform=transform['train'])
x_test = dataset[1](root=root,
train=False,
download=True,
transform=transform['test'])
self.train = DataLoader(x_train,
shuffle=True,
batch_size=batch_size,
**kwargs)
self.test = DataLoader(x_test,
shuffle=shuffle_test,
batch_size=batch_size,
**kwargs)