Learn all the basics you need to get started with this deep learning framework! In this part we learn how we can use dataset transforms together with the built-in Dataset class. Apply built-in transforms to images, arrays, and tensors. Or write your own custom Transform classes.

- Dataset Transforms
- Use built-in Transforms
- Implement custom Transforms

All code from this course can be found on GitHub.

## Dataset Transforms in PyTorch

```
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)
```