AntoineH/smart_augmentation
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Harle, Antoine (Contracteur) 2020-01-24 14:32:37 -05:00
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higher/smart_aug/datasets.py Executable file
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""" Dataset definition.
MNIST / CIFAR10
"""
import torch
from torch.utils.data import SubsetRandomSampler
import torchvision
BATCH_SIZE = 300
TEST_SIZE = 300
#TEST_SIZE = 10000 #legerement +Rapide / + Consomation memoire !
download_data=False
num_workers=2 #4
pin_memory=False #True :+ GPU memory / + Lent
#ATTENTION : Dataug (Kornia) Expect image in the range of [0, 1]
#transform_train = torchvision.transforms.Compose([
# torchvision.transforms.RandomHorizontalFlip(),
# torchvision.transforms.ToTensor(),
# torchvision.transforms.Normalize((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010)), #CIFAR10
#])
transform = torchvision.transforms.Compose([
torchvision.transforms.ToTensor(),
#torchvision.transforms.Normalize((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010)), #CIFAR10
])
#data_train = torchvision.datasets.MNIST(
# "./data", train=True, download=True,
# transform=torchvision.transforms.Compose([
# #torchvision.transforms.RandomAffine(degrees=180, translate=None, scale=None, shear=None, resample=False, fillcolor=0),
# torchvision.transforms.ToTensor()
# ])
#)
#data_test = torchvision.datasets.MNIST(
# "./data", train=False, download=True, transform=torchvision.transforms.ToTensor()
#)
### Classic Dataset ###
data_train = torchvision.datasets.CIFAR10("../data", train=True, download=download_data, transform=transform)
#data_val = torchvision.datasets.CIFAR10("../data", train=True, download=download_data, transform=transform)
data_test = torchvision.datasets.CIFAR10("../data", train=False, download=download_data, transform=transform)
train_subset_indices=range(int(len(data_train)/2))
val_subset_indices=range(int(len(data_train)/2),len(data_train))
#train_subset_indices=range(BATCH_SIZE*10)
#val_subset_indices=range(BATCH_SIZE*10, BATCH_SIZE*20)
dl_train = torch.utils.data.DataLoader(data_train, batch_size=BATCH_SIZE, shuffle=False, sampler=SubsetRandomSampler(train_subset_indices), num_workers=num_workers, pin_memory=pin_memory)
dl_val = torch.utils.data.DataLoader(data_train, batch_size=BATCH_SIZE, shuffle=False, sampler=SubsetRandomSampler(val_subset_indices), num_workers=num_workers, pin_memory=pin_memory)
dl_test = torch.utils.data.DataLoader(data_test, batch_size=TEST_SIZE, shuffle=False, num_workers=num_workers, pin_memory=pin_memory)