Implemented CNN filter visualization and Hyperparameter tuning (Ray)

labml_nn/cnn/utils/dataloader.py

@@ -0,0 +1,83 @@
+#!/bin/python
+
+import torch
+import torchvision
+import torchvision.transforms as transforms
+from torch.utils.data import Dataset, random_split
+import matplotlib.pyplot as plt
+import numpy as np
+
+def LoadCifar10DatasetTrain(save, transform=None):
+    trainset = torchvision.datasets.CIFAR10(root=save, train=True,
+                                        download=True, transform=transform)
+    return trainset
+
+def LoadCifar10DatasetTest(save, transform):
+    return torchvision.datasets.CIFAR10(root=save, train=False,
+                                       download=False, transform=transform)
+
+def GetCustTransform():
+    transform_train = transforms.Compose([
+        transforms.RandomRotation(20),
+        transforms.RandomCrop(32, (2, 2), pad_if_needed=False, padding_mode='constant'),
+        transforms.ToTensor(),
+        transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])
+    return transform_train
+
+def Dataloader_train_valid(save, batch_size):
+
+    # See utils/dataloader.py for data augmentations
+    transform_train_valid = GetCustTransform()
+
+    # Get Cifar 10 Datasets
+    trainset = LoadCifar10DatasetTrain(save, transform_train_valid)
+    train_val_abs = int(len(trainset) * 0.8)
+    train_subset, val_subset = random_split(trainset, [train_val_abs, len(trainset) - train_val_abs])
+
+    # Get Cifar 10 Dataloaders
+    trainloader = torch.utils.data.DataLoader(train_subset, batch_size=batch_size,
+                                              shuffle=True, num_workers=4)
+
+    valloader = torch.utils.data.DataLoader(val_subset, batch_size=batch_size,
+                                            shuffle=True, num_workers=4)
+    return trainloader, valloader
+
+def Dataloader_train(save, batch_size):
+
+    # See utils/dataloader.py for data augmentations
+    transform_train = GetCustTransform()
+
+    # Get Cifar 10 Datasets
+    trainset = LoadCifar10DatasetTrain(save, transform_train)
+    # Get Cifar 10 Dataloaders
+    trainloader = torch.utils.data.DataLoader(trainset, batch_size=batch_size,
+                                              shuffle=True, num_workers=4)
+
+    return trainloader
+
+def Dataloader_test(save, batch_size):
+
+    # transformation test set
+    transform_test = transforms.Compose(
+        [transforms.ToTensor(),
+         transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])
+
+    # initialize test dataset and dataloader
+    testset = LoadCifar10DatasetTest(save, transform_test)
+    testloader = torch.utils.data.DataLoader(testset, batch_size=64,
+                                             shuffle=False, num_workers=4)
+
+    return testloader
+
+def imshow(im):
+    image = im.cpu().clone().detach().numpy()
+    image = image.transpose(1, 2, 0)
+    image = image * np.array((0.5, 0.5, 0.5)) + np.array((0.5, 0.5, 0.5)) # unnormalize
+    plt.imshow(image)
+    plt.show()
+
+def imretrun(im):
+    image = im.cpu().clone().detach().numpy()
+    image = image.transpose(1, 2, 0)
+    image = image * np.array((0.5, 0.5, 0.5)) + np.array((0.5, 0.5, 0.5)) # unnormalize
+    return image