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Fix etat Train/Eval pour augmentation differee (Retester !)

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Harle, Antoine (Contracteur) 2020-01-20 17:09:31 -05:00
parent 2d6d2f7397
commit d21a6bbf5c
4 changed files with 38 additions and 29 deletions
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22
higher/datasets.py
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@ -7,31 +7,31 @@ TEST_SIZE = 300
#TEST_SIZE = 10000 #legerement +Rapide / + Consomation memoire !
download_data=False
num_workers=4 #4
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
# 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_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()
)
'''
from torchvision.datasets.vision import VisionDataset
from PIL import Image