clloader 0.0.2
A DataLoader library for Continual Learning in PyTorch.
Requires Python: >=3.6

# Continual Loader (CLLoader)

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## A library for PyTorch's loading of datasets in the field of Continual Learning

Aka Continual Learning, Lifelong-Learning, Incremental Learning, etc.


### Example:

Install from and PyPi:
```bash
pip3 install clloader
```

And run!
```python
from torch.utils.data import DataLoader

from clloader import CLLoader
from clloader.datasets import MNIST

clloader = CLLoader(
    MNIST("my/data/path", download=True),
    increment=1,
    initial_increment=5
)

print(f"Number of classes: {clloader.nb_classes}.")
print(f"Number of tasks: {clloader.nb_tasks}.")

for task_id, (train_dataset, test_dataset) in enumerate(clloader):
    train_loader = DataLoader(train_dataset)
    test_loader = DataLoader(test_dataset)

    # Do your cool stuff here
```

### Supported Scenarios

|Name | Acronym | Supported |
|:----|:---|:---:|
| **New Instances** | NI | :x: |
| **New Classes** | NC | :white_check_mark: |
| **New Instances & Classes** | NIC | :x: |

### Supported Datasets:

Note that the task sizes are fully customizable.

|Name | Nb classes | Image Size | Automatic Download |
|:----|:---:|:----:|:---:|
| **MNIST** | 10 | 28x28x1 | :white_check_mark: |
| **Fashion MNIST** | 10 | 28x28x1 | :white_check_mark: |
| **KMNIST** | 10 | 28x28x1 | :white_check_mark: |
| **EMNIST** | 10 | 28x28x1 | :white_check_mark: |
| **QMNIST** | 10 | 28x28x1 | :white_check_mark: |
| **MNIST Fellowship** | 30 | 28x28x1 | :white_check_mark: |
| **CIFAR10** | 10 | 32x32x3 | :white_check_mark: |
| **CIFAR100** | 100 | 32x32x3 | :white_check_mark: |
| **CIFAR Fellowship** | 110 | 32x32x3 | :white_check_mark: |
| **ImageNet100** | 100 | 224x224x3 | :x: |
| **ImageNet1000** | 1000 | 224x224x3 | :x: |
| **Permuted MNIST** | 10 | 28x28x1 | :white_check_mark: |
| **Rotated MNIST** | 10 | 28x28x1 | :white_check_mark: |

Furthermore some "Meta"-datasets are available:

**InMemoryDataset**, for in-memory numpy array:
```python
x_train, y_train = gen_numpy_array()
x_test, y_test = gen_numpy_array()

clloader = CLLoader(
    InMemoryDataset(x_train, y_train, x_test, y_test),
    increment=10,
)
```

**PyTorchDataset**,for any dataset defined in torchvision:
```python
clloader = CLLoader(
    PyTorchDataset("/my/data/path", dataset_type=torchvision.datasets.CIFAR10),
    increment=10,
)
```

**ImageFolderDataset**, for datasets having a tree-like structure, with one folder per class:
```python
clloader = CLLoader(
    ImageFolderDataset("/my/train/folder", "/my/test/folder"),
    increment=10,
)
```

**Fellowship**, to combine several continual datasets.:
```python
clloader = CLLoader(
    Fellowship("/my/data/path", dataset_list=[CIFAR10, CIFAR100]),
    increment=10,
)
```

Some datasets cannot provide an automatic download of the data for miscealleneous reasons. For example for ImageNet, you'll need to download the data from the [official page](http://www.image-net.org/challenges/LSVRC/2012/downloads). Then load it likewise:
```python
clloader = CLLoader(
    ImageNet1000("/my/train/folder", "/my/test/folder"),
    increment=10,
)
```

Some papers use a subset, called ImageNet100 or ImageNetSubset. You'll need to get the subset ids. It's either a file in the following format:
```
my/path/to/image0.JPEG target0
my/path/to/image1.JPEG target1
```
Or a list of tuple `[("my/path/to/image0.JPEG", target0), ...]`. Then loading the continual loader is very simple:
```python
clloader = CLLoader(
    ImageNet100(
        "/my/train/folder",
        "/my/test/folder",
        train_subset=... # My subset ids
        test_subset=... # My subset ids
    ),
    increment=10,
)
```

### Continual Loader

The Continual Loader `CLLoader` loads the data and batch it in several tasks. See there some example arguments:

```python
clloader = CLLoader(
    my_continual_dataset,
    increment=10,
    initial_increment=2,
    train_transformations=[transforms.RandomHorizontalFlip()],
    common_transformations=[
        transforms.ToTensor(),
        transforms.Normalize((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010))
    ],
    evaluate_on="seen"
)
```

Here the first task is made of 2 classes, then all following tasks of 10 classes. You can have a more finegrained increment by providing a list of ìncrement=[2, 10, 5, 10]`.

The `train_transformations` is applied only on the training data, while the `common_transformations` on both the training and testing data.

By default, we evaluate our model after each task on `seen` classes. But you can evalute only on `current` classes, or even on `all` classes.


### Sample Images

**MNIST**:

|<img src="images/mnist_0.jpg" width="150">|<img src="images/mnist_1.jpg" width="150">|<img src="images/mnist_2.jpg" width="150">|<img src="images/mnist_3.jpg" width="150">|<img src="images/mnist_4.jpg" width="150">|
|:-------------------------:|:-------------------------:|:-------------------------:|:-------------------------:|:-------------------------:|
|Task 0 | Task 1 | Task 2 | Task 3 | Task 4|

**FashionMNIST**:

|<img src="images/fashion_mnist_0.jpg" width="150">|<img src="images/fashion_mnist_1.jpg" width="150">|<img src="images/fashion_mnist_2.jpg" width="150">|<img src="images/fashion_mnist_3.jpg" width="150">|<img src="images/fashion_mnist_4.jpg" width="150">|
|:-------------------------:|:-------------------------:|:-------------------------:|:-------------------------:|:-------------------------:|
|Task 0 | Task 1 | Task 2 | Task 3 | Task 4|

**CIFAR10**:

|<img src="images/cifar10_0.jpg" width="150">|<img src="images/cifar10_1.jpg" width="150">|<img src="images/cifar10_2.jpg" width="150">|<img src="images/cifar10_3.jpg" width="150">|<img src="images/cifar10_4.jpg" width="150">|
|:-------------------------:|:-------------------------:|:-------------------------:|:-------------------------:|:-------------------------:|
|Task 0 | Task 1 | Task 2 | Task 3 | Task 4|

**MNIST Fellowship (MNIST + FashionMNIST + KMNIST)**:

|<img src="images/mnist_fellowship_0.jpg" width="150">|<img src="images/mnist_fellowship_1.jpg" width="150">|<img src="images/mnist_fellowship_2.jpg" width="150">|
|:-------------------------:|:-------------------------:|:-------------------------:|
|Task 0 | Task 1 | Task 2 |


**PermutedMNIST**:

|<img src="images/mnist_permuted_0.jpg" width="150">|<img src="images/mnist_permuted_1.jpg" width="150">|<img src="images/mnist_permuted_2.jpg" width="150">|<img src="images/mnist_permuted_3.jpg" width="150">|<img src="images/mnist_permuted_4.jpg" width="150">|
|:-------------------------:|:-------------------------:|:-------------------------:|:-------------------------:|:-------------------------:|
|Task 0 | Task 1 | Task 2 | Task 3 | Task 4|