mirror of
https://github.com/AntoineHX/smart_augmentation.git
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47 lines
2.2 KiB
Python
Executable file
47 lines
2.2 KiB
Python
Executable file
import torch
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from torch.utils.data import SubsetRandomSampler
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import torchvision
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BATCH_SIZE = 300
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TEST_SIZE = 300
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#TEST_SIZE = 10000 #legerement +Rapide / + Consomation memoire !
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download_data=False
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num_workers=2 #4
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pin_memory=False #True :+ GPU memory / + Lent
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#ATTENTION : Dataug (Kornia) Expect image in the range of [0, 1]
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#transform_train = torchvision.transforms.Compose([
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# torchvision.transforms.RandomHorizontalFlip(),
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# torchvision.transforms.ToTensor(),
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# torchvision.transforms.Normalize((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010)), #CIFAR10
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#])
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transform = torchvision.transforms.Compose([
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torchvision.transforms.ToTensor(),
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#torchvision.transforms.Normalize((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010)), #CIFAR10
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])
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#data_train = torchvision.datasets.MNIST(
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# "./data", train=True, download=True,
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# transform=torchvision.transforms.Compose([
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# #torchvision.transforms.RandomAffine(degrees=180, translate=None, scale=None, shear=None, resample=False, fillcolor=0),
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# torchvision.transforms.ToTensor()
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# ])
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#)
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data_test = torchvision.datasets.MNIST(
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"./data", train=False, download=True, transform=torchvision.transforms.ToTensor()
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)
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### Classic Dataset ###
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data_train = torchvision.datasets.CIFAR10("./data", train=True, download=download_data, transform=transform)
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#data_val = torchvision.datasets.CIFAR10("./data", train=True, download=download_data, transform=transform)
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data_test = torchvision.datasets.CIFAR10("./data", train=False, download=download_data, transform=transform)
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train_subset_indices=range(int(len(data_train)/2))
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val_subset_indices=range(int(len(data_train)/2),len(data_train))
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#train_subset_indices=range(BATCH_SIZE*10)
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#val_subset_indices=range(BATCH_SIZE*10, BATCH_SIZE*20)
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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)
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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)
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dl_test = torch.utils.data.DataLoader(data_test, batch_size=TEST_SIZE, shuffle=False, num_workers=num_workers, pin_memory=pin_memory)
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