initial push

utils.py

+import torch
+import torch.nn as nn
+import torch.optim as optim
+import numpy as np
+import os
+import torchvision as tv
+import torchvision.transforms as transforms
+# device
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+
+
+def dataloader(trainset,testset,batch_size=100):
+    trainloader = torch.utils.data.DataLoader(
+        trainset,
+        batch_size=batch_size,
+        shuffle=True,
+        num_workers=2)
+
+    testloader = torch.utils.data.DataLoader(
+        testset,
+        batch_size=batch_size,
+        shuffle=False,
+        num_workers=2)
+
+    return trainloader,testloader
+
+def CIFAR10_dataset():
+    transform_train = transforms.Compose([
+        transforms.RandomCrop(32, padding=4),
+        transforms.RandomHorizontalFlip(),
+        transforms.ToTensor(),
+        transforms.Normalize((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010)),
+    ])
+
+    transform_test = transforms.Compose([
+        transforms.ToTensor(),
+        transforms.Normalize((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010)),
+    ])
+
+    # datasets
+    trainset = tv.datasets.CIFAR10(
+        root='./data/',
+        train=True,
+        download=True,
+        transform=transform_train)
+
+    testset = tv.datasets.CIFAR10(
+        './data/',
+        train=False,
+        download=True,
+        transform=transform_test)
+
+    return trainset, testset, transform_test
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