From 941cb7b00da10426c3cbd158fbddca86d62a0945 Mon Sep 17 00:00:00 2001 From: Eduardo Cueto-Mendoza Date: Fri, 20 Sep 2024 09:26:56 +0100 Subject: [PATCH] New class to make Gaussian noise --- making_noise.py | 59 ++++++++++++++++++++++++++++++++++++- t10k-images-idx3-ubyte.gz | Bin 1602073 -> 0 bytes t10k-labels-idx1-ubyte.gz | Bin 4540 -> 0 bytes train-images-idx3-ubyte.gz | Bin 9630634 -> 0 bytes train-labels-idx1-ubyte.gz | Bin 28902 -> 0 bytes 5 files changed, 58 insertions(+), 1 deletion(-) delete mode 100644 t10k-images-idx3-ubyte.gz delete mode 100644 t10k-labels-idx1-ubyte.gz delete mode 100644 train-images-idx3-ubyte.gz delete mode 100644 train-labels-idx1-ubyte.gz diff --git a/making_noise.py b/making_noise.py index f588680..9f7dff6 100644 --- a/making_noise.py +++ b/making_noise.py @@ -1,4 +1,5 @@ import torch +from tqdm import tqdm import matplotlib.pyplot as plt from torchvision import datasets from torch.utils.data import Dataset @@ -6,11 +7,24 @@ from torch.utils.data import DataLoader import torchvision.transforms as transforms +class AddGaussianNoise(object): + def __init__(self, mean=0., std=1.): + self.std = std + self.mean = mean + + def __call__(self, tensor): + return tensor + torch.randn(tensor.size()) * self.std + self.mean + + def __repr__(self): + return self.__class__.__name__ + '(mean={0}, std={1})'.format(self.mean, self.std) + + def get_mnist_loaders(batch_size=128, test_batch_size=1000, perc=1.0): transform_train = transforms.Compose([ transforms.RandomCrop(28, padding=4), transforms.ToTensor(), transforms.Normalize((0.5,), (0.5,)), + AddGaussianNoise(0., 0.99), ]) transform_test = transforms.Compose([ @@ -36,5 +50,48 @@ def get_mnist_loaders(batch_size=128, test_batch_size=1000, perc=1.0): return train_loader, test_loader, train_eval_loader +def get_cifar_loaders(batch_size=128, test_batch_size=1000, perc=1.0): + transform_train = transforms.Compose([ + #transforms.RandomCrop(32, padding=4), + transforms.ToTensor(), + transforms.Normalize((0.5,), (0.5,)), + AddGaussianNoise(0., 0.99), + ]) + + transform_test = transforms.Compose([ + transforms.ToTensor(), + transforms.Normalize((0.5,), (0.5,)), + ]) + + train_loader = DataLoader( + datasets.CIFAR10(root='.data/cifar', train=True, download=True, transform=transform_train), batch_size=batch_size, + shuffle=True, num_workers=2, drop_last=True + ) + + train_eval_loader = DataLoader( + datasets.CIFAR10(root='.data/cifar', train=True, download=True, transform=transform_test), + batch_size=test_batch_size, shuffle=True, num_workers=2, drop_last=True + ) + + test_loader = DataLoader( + datasets.CIFAR10(root='.data/cifar', train=False, download=True, transform=transform_test), + batch_size=test_batch_size, shuffle=False, num_workers=2, drop_last=True + ) + + return train_loader, test_loader, train_eval_loader + + if __name__ == '__main__': - test = get_mnist_loaders() + train_loader, test_loader, train_eval_loader\ + = get_cifar_loaders() + + #for batch_idx, (data, target) in tqdm(enumerate(train_loader), total=len(train_loader)): + # sample_idx = torch.randint(len(data), size=(1,)).item() + # img = data[sample_idx] + # # print(data) + + images, labels = next(iter(train_loader)) + #plt.imshow(images[0].reshape(3,32,32).transpose(0,2,3,1)) + plt.imshow(images[0]) + plt.show() + #print(images[0].shape) diff --git a/t10k-images-idx3-ubyte.gz b/t10k-images-idx3-ubyte.gz deleted file mode 100644 index 84332eb0da40e2176e9ac1fc28f2b272b37c0306..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 1602073 zcmV(%K;pk2iwFoX&0k&u19UMkYb|MQVP|D?Eoo$UGc9#ud30p}?0p4%6j#^xG(wOB zOK?l^KwBgQN`aCVDORjl@d8DPOKFipihC*UuEC`^Bv_CDA%rAY2nlf`ZhOz!Z)SEk z*_pX#-?w~y`aXr+{+cZN&z-q`j~_uOXe|h}iVK2J(fI#6QhJ=NKK2sDlI(3P z9-o?HttPdbwEH!Nihq$4fw)Xlxx`mRTeWJ{tW-SyNWJ@8@GD6`rDOEpQq_LL#E%H5 zSvGz_Rhy3&Z3(E*{jATaYP0YR1$ERVH6W8iCbB{Dy$4yu!&JkX^%1 z38+O1jZH&*UYo>xOa8a4(J8+k((Bg^;6VpLF3&3Sp@0>~0j6u9Pjldrv%OH@0ch9l 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