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data_loader.py
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data_loader.py
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"""
CIFAR-10 CIFAR-100, Tiny-ImageNet data loader
"""
import random
import os
import numpy as np
from PIL import Image
import torch
import torchvision
import torchvision.transforms as transforms
from torch.utils.data.sampler import SubsetRandomSampler
def fetch_dataloader(types, params):
"""
Fetch and return train/dev dataloader with hyperparameters (params.subset_percent = 1.)
"""
# using random crops and horizontal flip for train set
if params.augmentation == "yes":
train_transformer = transforms.Compose([
transforms.RandomCrop(32, padding=4),
transforms.RandomHorizontalFlip(), # randomly flip image horizontally
transforms.RandomRotation(15),
transforms.ToTensor(),
transforms.Normalize((0.5070751592371323, 0.48654887331495095, 0.4409178433670343), (0.2673342858792401, 0.2564384629170883, 0.27615047132568404))])
#transforms.Normalize((0.4914, 0.4822, 0.4465), (0.240, 0.243, 0.261))
# data augmentation can be turned off
else:
train_transformer = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.5070751592371323, 0.48654887331495095, 0.4409178433670343), (0.2673342858792401, 0.2564384629170883, 0.27615047132568404))])
# transformer for dev set
dev_transformer = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.5070751592371323, 0.48654887331495095, 0.4409178433670343), (0.2673342858792401, 0.2564384629170883, 0.27615047132568404))])
if params.dataset == 'cifar10':
trainset = torchvision.datasets.CIFAR10(root='./data/data-cifar10', train=True,
download=True, transform=train_transformer)
devset = torchvision.datasets.CIFAR10(root='./data/data-cifar10', train=False,
download=True, transform=dev_transformer)
elif params.dataset == 'cifar100':
trainset = torchvision.datasets.CIFAR100(root='./data/data-cifar100', train=True,
download=True, transform=train_transformer)
devset = torchvision.datasets.CIFAR100(root='./data/data-cifar100', train=False,
download=True, transform=dev_transformer)
elif params.dataset == 'tiny_imagenet':
data_dir = './data/tiny-imagenet-200/'
data_transforms = {
'train': transforms.Compose([
transforms.RandomRotation(20),
transforms.RandomHorizontalFlip(0.5),
transforms.ToTensor(),
transforms.Normalize([0.4802, 0.4481, 0.3975], [0.2302, 0.2265, 0.2262]),
]),
'val': transforms.Compose([
transforms.ToTensor(),
transforms.Normalize([0.4802, 0.4481, 0.3975], [0.2302, 0.2265, 0.2262]),
])
}
train_dir = data_dir + 'train/'
test_dir = data_dir + 'val/images/'
trainset = torchvision.datasets.ImageFolder(train_dir, data_transforms['train'])
devset = torchvision.datasets.ImageFolder(test_dir, data_transforms['val'])
trainloader = torch.utils.data.DataLoader(trainset, batch_size=params.batch_size,
shuffle=True, num_workers=params.num_workers)
devloader = torch.utils.data.DataLoader(devset, batch_size=params.batch_size,
shuffle=False, num_workers=params.num_workers)
if types == 'train':
dl = trainloader
else:
dl = devloader
return dl
def fetch_subset_dataloader(types, params):
"""
Use only a subset of dataset for KD training, depending on params.subset_percent
"""
# using random crops and horizontal flip for train set
if params.augmentation == "yes":
train_transformer = transforms.Compose([
transforms.RandomCrop(32, padding=4),
transforms.RandomHorizontalFlip(), # randomly flip image horizontally
transforms.ToTensor(),
transforms.Normalize((0.4914, 0.4822, 0.4465), (0.247, 0.243, 0.261))])
# data augmentation can be turned off
else:
train_transformer = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.4914, 0.4822, 0.4465), (0.247, 0.243, 0.261))])
# transformer for dev set
dev_transformer = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.4914, 0.4822, 0.4465), (0.247, 0.243, 0.261))])
if params.dataset=='cifar10':
trainset = torchvision.datasets.CIFAR10(root='./data-cifar10', train=True,
download=True, transform=train_transformer)
devset = torchvision.datasets.CIFAR10(root='./data-cifar10', train=False,
download=True, transform=dev_transformer)
elif params.dataset=='cifar100':
trainset = torchvision.datasets.CIFAR10(root='./data-cifar10', train=True,
download=True, transform=train_transformer)
devset = torchvision.datasets.CIFAR10(root='./data-cifar10', train=False,
download=True, transform=dev_transformer)
elif params.dataset == 'tiny_imagenet':
data_dir = './data/tiny-imagenet-200/'
data_transforms = {
'train': transforms.Compose([
transforms.RandomRotation(20),
transforms.RandomHorizontalFlip(0.5),
transforms.ToTensor(),
transforms.Normalize([0.4802, 0.4481, 0.3975], [0.2302, 0.2265, 0.2262]),
]),
'val': transforms.Compose([
transforms.ToTensor(),
transforms.Normalize([0.4802, 0.4481, 0.3975], [0.2302, 0.2265, 0.2262]),
])
}
train_dir = data_dir + 'train/'
test_dir = data_dir + 'val/images/'
trainset = torchvision.datasets.ImageFolder(train_dir, data_transforms['train'])
devset = torchvision.datasets.ImageFolder(test_dir, data_transforms['val'])
trainset_size = len(trainset)
indices = list(range(trainset_size))
split = int(np.floor(params.subset_percent * trainset_size))
np.random.seed(230)
np.random.shuffle(indices)
train_sampler = SubsetRandomSampler(indices[:split])
trainloader = torch.utils.data.DataLoader(trainset, batch_size=params.batch_size,
sampler=train_sampler, num_workers=params.num_workers, pin_memory=params.cuda)
devloader = torch.utils.data.DataLoader(devset, batch_size=params.batch_size,
shuffle=False, num_workers=params.num_workers, pin_memory=params.cuda)
if types == 'train':
dl = trainloader
else:
dl = devloader
return dl