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dataloaders.py
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import os
import torch
import torchvision
from torchvision import transforms
from torchvision import datasets
from torch.utils.data import Subset, ConcatDataset
import PIL
from utee import selector
import pdb
import numpy as np
from sklearn.model_selection import train_test_split
def get_dataloader(dataset, datadir, bs, augment=False, additional_idxs=None):
if dataset == 'imnet':
return get_imnet_dataloader(datadir, batch_size=bs, resize_dim=256)
elif dataset == 'cifar10':
if 'val' in datadir:
return get_cifar10_dataloader(train=False, batch_size=bs)
return get_cifar10_dataloader(train=True, batch_size=bs)
elif dataset == 'cifar100':
if 'val' in datadir:
return get_cifar100_dataloader(train=False, batch_size=bs)
return get_cifar100_dataloader(train=True, batch_size=bs)
elif dataset == 'stl10':
if 'val' in datadir:
return get_stl10_dataloader(train=False, batch_size=bs)
return get_stl10_dataloader(train=True, batch_size=bs)
elif dataset == 'svhn':
if 'val' in datadir:
return get_svhn_dataloader(train=False, batch_size=bs)
return get_svhn_dataloader(train=True, batch_size=bs)
elif dataset == 'flowers102':
if 'val' in datadir:
return get_flowers_dataloader(train=False, augment=augment,batch_size=bs)
return get_flowers_dataloader(train=True, augment=augment,batch_size=bs,
additional_idxs=additional_idxs)
elif dataset == 'mnist':
if 'val' in datadir:
return get_mnist_dataloader(train=False, augment=augment, batch_size=bs)
return get_mnist_dataloader(train=True, augment=augment, batch_size=bs)
def get_mnist_dataloader(train, augment, batch_size=32, pct=1):
dataset = datasets.MNIST('../data', train=train, transform=transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.1307,), (0.3081,))]))
dataloader = torch.utils.data.DataLoader(dataset,
batch_size=batch_size, shuffle=False)
return dataloader
def get_flowers_dataloader(train, augment, batch_size=32, additional_idxs=None):
image_size = 256
crop_size = 224
normalize = transforms.Normalize(mean=[0.5208, 0.4205, 0.3441],
std=[0.2944, 0.2465, 0.2735])
shuffle=False
if train and augment:
data_path = './datasets/flowers102/train'
d_transforms = transforms.Compose([
transforms.RandomResizedCrop(crop_size),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
normalize,])
shuffle=True
else:
data_path= './datasets/flowers102/test'
d_transforms = transforms.Compose([
transforms.Resize(image_size),
transforms.CenterCrop(image_size),
transforms.ToTensor(),
normalize
])
# subselect for idxs if idxs are provided
# This subselection is only used for the test_idxs
dataset = torchvision.datasets.ImageFolder(
root=data_path,
transform=d_transforms)
if train and len(additional_idxs):
data_path = './datasets/flowers102/test'
add_dataset = torchvision.datasets.ImageFolder(
root=data_path,
transform=d_transforms)
add_dataset = Subset(add_dataset, additional_idxs)
dataset = ConcatDataset([dataset, add_dataset])
dataloader = torch.utils.data.DataLoader(
dataset,
batch_size=batch_size,
num_workers=0,
shuffle=shuffle)
return dataloader
def get_flowers_dataloader_pct(pct, batch_size=32):
image_size = 256
crop_size = 224
shuffle=True
normalize = transforms.Normalize(mean=[0.5208, 0.4205, 0.3441],
std=[0.2944, 0.2465, 0.2735])
data_path = './datasets/flowers102/train'
d_transforms = transforms.Compose([
transforms.RandomResizedCrop(crop_size),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
normalize,])
dataset = torchvision.datasets.ImageFolder(
root=data_path,
transform=d_transforms)
# subselect for idxs if idxs are provided
# This subselection is only used for the test_idxs
dataset = torchvision.datasets.ImageFolder(
root=data_path,
transform=d_transforms)
idxs = list(range(len(dataset)))
train_idxs, _ = train_test_split(idxs, train_size=pct, stratify=dataset.targets)
subset_dataset = Subset(dataset, train_idxs)
dataloader = torch.utils.data.DataLoader(
subset_dataset,
batch_size=batch_size,
num_workers=0,
shuffle=shuffle)
return dataloader
def get_cifar100_dataloader(train=False, batch_size=32):
data_root='/tmp/public_dataset/pytorch'
data_root = os.path.expanduser(os.path.join(data_root, 'cifar100-data'))
dataset = datasets.CIFAR100(
root=data_root, train=train, download=True,
transform=transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5),
transforms.Pad(4)),
]))
dataloader = torch.utils.data.DataLoader(dataset, batch_size=batch_size, shuffle=False, pin_memory=False)
return dataloader
def get_stl10_dataloader(train=False, batch_size=32):
split = 'test'
if train:
split = 'train'
data_root='/tmp/public_dataset/pytorch'
data_root = os.path.expanduser(os.path.join(data_root, 'stl10-data'))
dataset = datasets.STL10(root=data_root, split=split, download=True,
transform=transforms.Compose([transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))]))
dataloader = torch.utils.data.DataLoader(dataset, batch_size=batch_size, shuffle=False, pin_memory=False)
return dataloader
def get_imnet_dataloader(valdir, batch_size=12, resize_dim=256, normalize=True):
n_workers = 1
normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225])
if normalize:
dataset = datasets.ImageFolder(valdir, transforms.Compose([
transforms.Resize(resize_dim, interpolation=PIL.Image.BICUBIC),
transforms.CenterCrop(256),
transforms.ToTensor(),
normalize,
]))
else:
datasets.ImageFolder(valdir, transforms.Compose([
transforms.Resize(resize_dim, interpolation=PIL.Image.BICUBIC),
transforms.CenterCrop(256),
transforms.ToTensor(),
]))
val_loader = torch.utils.data.DataLoader(dataset,
batch_size=batch_size, shuffle=False,
num_workers=n_workers, pin_memory=False)
return val_loader