MNIST-360

MNIST-360 is a scenario proposed in “Dark Experience for General Continual Learning: a Strong, Simple Baseline”

It is composed of 27 tasks with two MNIST classes each. The two classes data have different rotation and the rotation change from one task to another.

Continuum Scenarios

We do not propose a click and play scenario for MNIST, however, we show how to implement it with continuum.

import os
import torch
from continuum.scenarios import ClassIncremental
from torchvision import transforms

folder = "tests/samples/mnist360/"
if not os.path.exists(folder):
    os.makedirs(folder)
continual_dataset = MNIST(data_path=DATA_PATH, download=False, train=True)

for i in range(3):
    start_angle = 120 * i
    angle = int(120 / 9)

    list_rotation_first = [[transforms.RandomAffine(degrees=[start_angle + angle * j, start_angle + angle * j + 5])]
                           for j in range(10)]
    list_rotation_second = [
        [transforms.RandomAffine(degrees=[start_angle + 45 + angle * j, start_angle + 45 + angle * j + 5])] for j in
        range(10)]
    first_digit_scenario = ClassIncremental(continual_dataset, increment=1, transformations=list_rotation_first)
    second_digit_scenario = ClassIncremental(continual_dataset, increment=1, transformations=list_rotation_second)

    for task_id in range(9):
        taskset_1 = first_digit_scenario[task_id]
        taskset_2 = second_digit_scenario[1 + task_id % 9]

        # / ! \ we can not concatenate taskset here, since transformations would not work correctly

        loader_1 = DataLoader(taskset_1, batch_size=64)
        loader_2 = DataLoader(taskset_2, batch_size=64)

        nb_minibatches = min(len(loader_1), len(loader_2))
        for minibatch in range(nb_minibatches):
            x_1, y_1, t_1 = next(iter(loader_1))
            x_2, y_2, t_2 = next(iter(loader_2))

            x, y, t = torch.cat([x_1, x_2]), torch.cat([y_1, y_2]), torch.cat([t_1, t_2])

            # train here on x, y, t

            #### to visualize result ####
            # from continuum.viz import visualize_batch
            # visualize_batch(batch=x[:100], number=100, shape=[28, 28, 1], path=folder + f"MNIST360_{task_id + 9 * i}.jpg")