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utilities.py
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#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Tue Nov 3 00:09:35 2020
"""
import torch
import torchvision.transforms as transforms
import torchvision.datasets as dset
from torch.utils.data.sampler import SequentialSampler, SubsetRandomSampler
import numpy as np
def get_data_loaders(dataset,
data_dir,
batch_size,
augment,
random_seed,
valid_size=0.0,
shuffle=True,
num_workers=1,
pin_memory=True):
error_msg = "[!] valid_size should be in the range [0, 1]."
assert ((valid_size >= 0) and (valid_size <= 1)), error_msg
# if dataset == 'CIFAR10' or dataset == 'cifar10':
# normalize = transforms.Normalize((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010))
# elif dataset == 'imagenet2012':
# normalize = transforms.Normalize((0.485, 0.456, 0.406), (0.229, 0.224, 0.225))
if dataset == 'CIFAR10':
test_transform = transforms.Compose([transforms.ToTensor()])
valid_transform = transforms.Compose([transforms.ToTensor()])
if augment:
train_transform = transforms.Compose([transforms.RandomCrop(size=32, padding=4),
transforms.RandomHorizontalFlip(),
transforms.ToTensor()])
else:
train_transform = transforms.Compose([transforms.ToTensor()])
train_set = dset.CIFAR10(root=data_dir, train=True, transform=train_transform, download=True)
valid_set = dset.CIFAR10(root=data_dir, train=True, transform=valid_transform, download=True)
test_set = dset.CIFAR10(root=data_dir, train=False, transform=test_transform, download=True)
elif dataset == 'imagenet2012':
test_transform = transforms.Compose([transforms.Resize(256),
transforms.CenterCrop(224),
transforms.ToTensor()])
valid_transform = transforms.Compose([transforms.Resize(256),
transforms.CenterCrop(224),
transforms.ToTensor()])
if augment:
train_transform = transforms.Compose([transforms.Resize(256),
transforms.RandomResizedCrop(224),
transforms.RandomHorizontalFlip(),
transforms.ToTensor()])
else:
train_transform = transforms.Compose([transforms.Resize(256),
transforms.CenterCrop(224),
transforms.ToTensor()])
train_set = dset.ImageFolder(root=data_dir + '/train', transform=train_transform)
valid_set = dset.ImageFolder(root=data_dir + '/train', transform=valid_transform)
test_set = dset.ImageFolder(root=data_dir + '/val', transform=test_transform)
print('\nForming the samplers for train and validation splits with split fraction={}'.format(valid_size))
num_train = len(train_set)
indices = list(range(num_train))
split = int(np.floor(valid_size * num_train))
if shuffle:
np.random.seed(random_seed)
np.random.shuffle(indices)
train_idx, valid_idx = indices[split:], indices[:split]
train_sampler = SubsetRandomSampler(train_idx)
valid_sampler = SubsetRandomSampler(valid_idx)
print('Preparing dataloaders...\n')
train_loader = torch.utils.data.DataLoader(train_set, batch_size=batch_size, sampler=train_sampler, num_workers=num_workers, pin_memory=pin_memory)
valid_loader = torch.utils.data.DataLoader(valid_set, batch_size=batch_size, sampler=valid_sampler, num_workers=num_workers, pin_memory=pin_memory)
test_loader = torch.utils.data.DataLoader(test_set, batch_size=batch_size, shuffle=False, num_workers=num_workers, pin_memory=pin_memory)
return train_loader, valid_loader, test_loader