# Loading Image Dataset in Pytorch
###### tags: `Pytorch` `Machine Learning`
[ToC]
## Imports

import torch
from torchvision import datasets, transforms
from torch.utils.data import DataLoader

## 1. Transform
Image transformation with `torchvision.transforms`.
Common transforms include data augmentation, value scaling, value normalization.
### 1.1 Data Augmentatation
* Common: RandomCrop, RandomRotate, RandomHorizontalFlip...
* In general, only for **training dataset**.
* But if there includes resizing the image to specific size, then it must also be done on the testing data.

Example:
If transforms.CenterCrop(224) is done on training, then it must also be done on the testing to make them both at the same size: 224 x 224.

### 1.2 Convert to torch tensor
Convert a PIL image or ndarray to tensor, and scale the values to [0.0, 1.0] accordingly.
* Necessary for both **training & testing dataset**

### 1.3 Normalization
For better and more stable training, normalize each channel from [0.0, 1.0] to [-1.0, 1.0]. This is done by transforms.Normalize(means, stds).

# For rgb images (3 channels)
transforms.Normalize([0.5, 0.5, 0.5],[0.5, 0.5, 0.5])

* Necessary for both **training & testing dataset**

### 1.4 Example:

train_T = transforms.Compose([
    transforms.Resize(255),
    transforms.RandomRotation(30),
    transforms.CenterCrop(224),                     # random crop and resize to 224 x 224
    transforms.ToTensor(),                                 # convert image to tensor and scale values to [0, 1]
    transforms.Normalize([0.5, 0.5, 0.5],
                         [0.5, 0.5, 0.5])                  # normalize each channel value to [-1,1]
])

val_T = transforms.accimageCompose([
    transforms.Resize(255),
    transforms.CenterCrop(224),
    transforms.ToTensor(),                                 # convert image to tensor and scale values to [0, 1]
    transforms.Normalize([0.5, 0.5, 0.5],
                         [0.5, 0.5, 0.5])                  # normalize each channel value to [-1,1]
])

## 2. ImageFolder

ds = datasets.ImageFolder('path/to/data', transform=transform)

Images of each class are in separate folders. The file level should be like

root/dog/dog_0.png
root/dog/dog_1.png
...

root/cat/cat_0.png
root/cat/cat_1.png
...

## 3. DataLoader

dataloader = DataLoader(ds, batch_size=32, shuffle=True)

Every time we loop through the dataloader, it outputs a batch of data with labels

for images, labels in dataloader:
    ...