10_transformers.py

'''
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
import numpy as np


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]

 # note that we do not convert to tensor here
 self.x_data = xy[:, 1:]
 self.y_data = xy[:, [0]]

 self.transform = transform

 def __getitem__(self, index):
 sample = self.x_data[index], self.y_data[index]

 if self.transform:
 sample = self.transform(sample)

 return sample

 def __len__(self):
 return self.n_samples

# Custom Transforms
# implement __call__(self, sample)
class ToTensor:
 # Convert ndarrays to Tensors
 def __call__(self, sample):
 inputs, targets = sample
 return torch.from_numpy(inputs), torch.from_numpy(targets)

class MulTransform:
 # multiply inputs with a given factor
 def __init__(self, factor):
 self.factor = factor

 def __call__(self, sample):
 inputs, targets = sample
 inputs *= self.factor
 return inputs, targets

print('Without Transform')
dataset = WineDataset()
first_data = dataset[0]
features, labels = first_data
print(type(features), type(labels))
print(features, labels)

print('\nWith Tensor Transform')
dataset = WineDataset(transform=ToTensor())
first_data = dataset[0]
features, labels = first_data
print(type(features), type(labels))
print(features, labels)

print('\nWith Tensor and Multiplication Transform')
composed = torchvision.transforms.Compose([ToTensor(), MulTransform(4)])
dataset = WineDataset(transform=composed)
first_data = dataset[0]
features, labels = first_data
print(type(features), type(labels))
print(features, labels)