AntoineH/smart_augmentation
1
0
Fork 0
mirror of https://github.com/AntoineHX/smart_augmentation.git synced 2025-05-04 12:10:45 +02:00
Code Issues Projects Releases Packages Wiki Activity

Test doxygen

Browse source
This commit is contained in:
Harle, Antoine (Contracteur) 2020-01-24 15:10:08 -05:00
parent f507ff4741
commit 99f15b8946
4 changed files with 54 additions and 25 deletions
Download patch file Download diff file Expand all files Collapse all files

9
higher/smart_aug/datasets.py
View file

@ -6,12 +6,17 @@ import torch
from torch.utils.data import SubsetRandomSampler
import torchvision
#Train/Validation batch size.
BATCH_SIZE = 300
TEST_SIZE = 300
#Test batch size.
TEST_SIZE = BATCH_SIZE
#TEST_SIZE = 10000 #legerement +Rapide / + Consomation memoire !
#Wether to download data.
download_data=False
#Number of worker to use.
num_workers=2 #4
#Pin GPU memory
pin_memory=False #True :+ GPU memory / + Lent
#ATTENTION : Dataug (Kornia) Expect image in the range of [0, 1]
@ -37,8 +42,10 @@ transform = torchvision.transforms.Compose([
#)
### Classic Dataset ###
#Training data
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)
#Testing data
data_test = torchvision.datasets.CIFAR10("../data", train=False, download=download_data, transform=transform)
train_subset_indices=range(int(len(data_train)/2))