.data/cifar/cifar-10-batches-py/readme.html

@@ -1 +0,0 @@
-<meta HTTP-EQUIV="REFRESH" content="0; url=http://www.cs.toronto.edu/~kriz/cifar.html">

making_noise.py

@@ -1,124 +1,16 @@
 import torch
-import random
-import numpy as np
-from tqdm import tqdm
 import matplotlib.pyplot as plt
 from torchvision import datasets
 from torch.utils.data import Dataset
 from torch.utils.data import DataLoader
 import torchvision.transforms as transforms
 
-torch.pi = torch.acos(torch.zeros(1)).item() * 2  # which is 3.1415927410125732
-
-
-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)
-
-
-class AddRaleighNoise(object):
-    def __init__(self, a=0.0, b=0.0):
-        self.std = (b * (4 - np.pi)) / 4
-        self.mean = a + np.sqrt((np.pi * b) / 4)
-
-    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)
-
-
-class AddErlangNoise(object):
-    def __init__(self, a=0.0, b=0.0):
-        if a == 0.0:
-            self.std = 0.0
-            self.mean = 0.0
-        else:
-            self.std = b / a
-            self.mean = b / (2*a)
-
-    def __call__(self, tensor):
-        if self.mean == 0.0:
-            return tensor * self.mean
-        else:
-            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)
-
-
-class AddExponentialNoise(object):
-    def __init__(self, a=0.0):
-        if a == 0.0:
-            self.mean = 0.0
-        else:
-            self.std = 1 / (2*a)
-            self.mean = 1 / a
-
-    def __call__(self, tensor):
-        if self.mean == 0.0:
-            return tensor * self.mean
-        else:
-            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)
-
-
-class AddUniformNoise(object):
-    def __init__(self, a=0.0, b=0.0):
-        if a == 0.0:
-            self.std = 0.0
-            self.mean = 0.0
-        else:
-            self.std = (b - a)**2 / 12
-            self.mean = (b + a) / 2
-
-    def __call__(self, tensor):
-        if self.mean == 0.0:
-            return tensor * self.mean
-        else:
-            print('(mean={0}, std={1})'.format(self.mean, self.std))
-            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)
-
-
-class AddInpulseNoise(object):
-    def __init__(self, a=0.0):
-        self.value = a
-
-    def __call__(self, tensor):
-        if random.gauss(0, 1) > 0:
-            return tensor * self.value
-        elif random.gauss(0, 1) < 0:
-            return tensor * (-1 * self.value)
-        else:
-            return tensor * 0.0
-
-    def __repr__(self):
-        return self.__class__.__name__ + '(a={0})'.format(self.value)
-
 
 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., 0.0),
-            # AddRaleighNoise(1, 1),
-            # AddErlangNoise(0.0001, 0.0001),
-            # AddExponentialNoise(2),
-            # AddUniformNoise(2, 1),
-            AddInpulseNoise(0.5),
         ])
 
     transform_test = transforms.Compose([
@@ -127,8 +19,8 @@ def get_mnist_loaders(batch_size=128, test_batch_size=1000, perc=1.0):
     ])
 
     train_loader = DataLoader(
-        datasets.MNIST(root='.data/mnist', train=True, download=True, transform=transform_train),
+        datasets.MNIST(root='.data/mnist', train=True, download=True, transform=transform_train), batch_size=batch_size,
-        batch_size=batch_size, shuffle=True, num_workers=2, drop_last=True
+        shuffle=True, num_workers=2, drop_last=True
     )
 
     train_eval_loader = DataLoader(
@@ -144,53 +36,5 @@ 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.ToTensor(),
-            transforms.RandomCrop(32, padding=0),
-            # transforms.Normalize((0.5,), (0.5,)),
-            # AddGaussianNoise(0., 0.25),
-            # AddRaleighNoise(1, 2),  # Not worinkg for CIFAR
-            # AddErlangNoise(0.0001, 0.0001),
-            # AddExponentialNoise(2),
-            AddUniformNoise(2, 1),  # Not working for CIFAR
-            # AddInpulseNoise(0.5),
-        ])
-
-    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__':
-    train_loader, test_loader, train_eval_loader\
+    test = get_mnist_loaders()
-        = 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].permute(1, 2, 0))
-    # plt.imshow(images[0].reshape(28, 28), cmap='gray')
-    plt.show()
-    #print(images[0].shape)

requirements.txt

@@ -1,35 +0,0 @@
-certifi
-charset-normalizer
-cmake
-contourpy
-cycler
-filelock
-fonttools
-idna
-Jinja2
-kiwisolver
-lit
-MarkupSafe
-matplotlib
-mpmath
-networkx
-numpy
-packaging
-pandas
-Pillow
-psutil
-pyparsing
-python-dateutil
-pytorch-triton-rocm
-pytz
-requests
-seaborn
-six
-sympy
-torch
-torchaudio
-torchvision
-tqdm
-typing_extensions
-tzdata
-urllib3