finish chapter 12

10_dataset_transforms.py

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+'''
+Transforms can be applied to PIL images, tensors, ndarrays, or custom data
+during creation of the DataSet
+
+complete list of built-in transforms: 
+https://pytorch.org/docs/stable/torchvision/transforms.html
+
+On Images
+---------
+CenterCrop, Grayscale, Pad, RandomAffine
+RandomCrop, RandomHorizontalFlip, RandomRotation
+Resize, Scale
+
+On Tensors
+----------
+LinearTransformation, Normalize, RandomErasing
+
+Conversion
+----------
+ToPILImage: from tensor or ndrarray
+ToTensor : from numpy.ndarray or PILImage
+
+Generic
+-------
+Use Lambda 
+
+Custom
+------
+Write own class
+
+Compose multiple Transforms
+---------------------------
+composed = transforms.Compose([Rescale(256),
+                               RandomCrop(224)])
+'''
+
+import torch
+import torchvision
+from torch.utils.data import Dataset, DataLoader
+import numpy as np
+import math
+
+#example
+dataset = torchvision.datasets.MNIST(root='./data', transform=torchvision.transforms.ToTensor(), download=True)
+
+class WineDataset(Dataset):
+    def __init__(self, transform=None):
+        xy = np.loadtxt('./data/wine/wine.csv', delimiter=',', dtype=np.float32, skiprows=1)
+        self.n_samples = xy.shape[0]
+
+        self.x = xy[:,1:]  # n_samples x features
+        self.y = xy[:,[0]] # n_samples x 1
+
+        self.transform = transform
+
+    def __getitem__(self, idx):
+        sample = self.x[idx], self.y[idx]
+        if self.transform:
+            sample = self.transform(sample)
+        return sample
+
+    def __len__(self):
+        return self.n_samples
+
+class ToTensor:
+    def __call__(self, sample):
+        x, y = sample
+        return torch.from_numpy(x), torch.from_numpy(y)
+
+class MulTransform:
+    def __init__(self, factor):
+        self.factor = factor
+
+    def __call__(self, sample):
+        x, y = sample
+        x *= self.factor
+        return x, y
+
+dataset = WineDataset()
+first_data = dataset[0]
+features, labels = first_data
+print(type(features), type(labels))
+
+dataset = WineDataset(transform=ToTensor())
+first_data = dataset[0]
+features, labels = first_data
+print(features)
+print(type(features), type(labels))
+
+composed = torchvision.transforms.Compose([ToTensor(), MulTransform(4.)])
+
+dataset = WineDataset(transform=composed)
+first_data = dataset[0]
+features, labels = first_data
+print(features)
+print(type(features), type(labels))