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Prepartaion dataset benchmark

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Harle, Antoine (Contracteur) 2020-02-03 12:55:36 -05:00
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higher/smart_aug/datasets.py
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@ -4,6 +4,7 @@
""" """
import torch import torch
from torch.utils.data import SubsetRandomSampler from torch.utils.data import SubsetRandomSampler
from torch.utils.data.dataset import ConcatDataset
import torchvision import torchvision
#Train/Validation batch size. #Train/Validation batch size.
@ -40,14 +41,34 @@ transform_train = torchvision.transforms.Compose([
#transform_train.transforms.insert(0, RandAugment(n=2, m=30)) #transform_train.transforms.insert(0, RandAugment(n=2, m=30))
### Classic Dataset ### ### Classic Dataset ###
dataroot="../data"
#MNIST #MNIST
#data_train = torchvision.datasets.MNIST("../data", train=True, download=True, transform=transform_train) #data_train = torchvision.datasets.MNIST(dataroot, train=True, download=True, transform=transform_train)
#data_val = torchvision.datasets.MNIST("../data", train=True, download=True, transform=transform) #data_val = torchvision.datasets.MNIST(dataroot, train=True, download=True, transform=transform)
#data_test = torchvision.datasets.MNIST("../data", train=False, download=True, transform=transform) #data_test = torchvision.datasets.MNIST(dataroot, train=False, download=True, transform=transform)
#CIFAR #CIFAR
data_train = torchvision.datasets.CIFAR10("../data", train=True, download=download_data, transform=transform_train) data_train = torchvision.datasets.CIFAR10(dataroot, train=True, download=download_data, transform=transform_train)
#data_val = torchvision.datasets.CIFAR10("../data", train=True, download=download_data, transform=transform) #data_val = torchvision.datasets.CIFAR10(dataroot, train=True, download=download_data, transform=transform)
data_test = torchvision.datasets.CIFAR10("../data", train=False, download=download_data, transform=transform) data_test = torchvision.datasets.CIFAR10(dataroot, train=False, download=download_data, transform=transform)
#data_train = torchvision.datasets.CIFAR100(dataroot, train=True, download=download_data, transform=transform_train)
#data_val = torchvision.datasets.CIFAR100(dataroot, train=True, download=download_data, transform=transform)
#data_test = torchvision.datasets.CIFAR100(dataroot, train=False, download=download_data, transform=transform)
#SVHN
#trainset = torchvision.datasets.SVHN(root=dataroot, split='train', download=download_data, transform=transform_train)
#extraset = torchvision.datasets.SVHN(root=dataroot, split='extra', download=download_data, transform=transform_train)
#data_train = ConcatDataset([trainset, extraset])
#data_test = torchvision.datasets.SVHN(dataroot, split='test', download=download_data, transform=transform)
#ImageNet
#Necessite SciPy
# Probleme ? : https://github.com/ildoonet/pytorch-randaugment/blob/48b8f509c4bbda93bbe733d98b3fd052b6e4c8ae/RandAugment/imagenet.py#L28
#data_train = torchvision.datasets.ImageNet(root=os.path.join(dataroot, 'imagenet-pytorch'), split='train', transform=transform_train)
#data_test = torchvision.datasets.ImageNet(root=os.path.join(dataroot, 'imagenet-pytorch'), split='val', transform=transform_test)
train_subset_indices=range(int(len(data_train)/2)) train_subset_indices=range(int(len(data_train)/2))
val_subset_indices=range(int(len(data_train)/2),len(data_train)) val_subset_indices=range(int(len(data_train)/2),len(data_train))