New data files, python requirements, and new noise types

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

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

making_noise.py

@@ -1,16 +1,124 @@
 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([
@@ -19,8 +127,8 @@ 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), batch_size=batch_size,
-        shuffle=True, num_workers=2, drop_last=True
+        datasets.MNIST(root='.data/mnist', train=True, download=True, transform=transform_train),
+        batch_size=batch_size, shuffle=True, num_workers=2, drop_last=True
     )
 
     train_eval_loader = DataLoader(
@@ -36,5 +144,53 @@ 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__':
-    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].permute(1, 2, 0))
+    # plt.imshow(images[0].reshape(28, 28), cmap='gray')
+    plt.show()
+    #print(images[0].shape)

requirements.txt

@@ -0,0 +1,35 @@
+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