avalanche_extension/avalanche/examples/icarl.py

from os.path import expanduser

import torch

from avalanche.benchmarks.datasets import CIFAR100
from avalanche.benchmarks.utils import AvalancheDataset
from avalanche.models import IcarlNet, make_icarl_net, initialize_icarl_net
from avalanche.training.plugins.lr_scheduling import LRSchedulerPlugin
from torch.optim import SGD
from torchvision import transforms
from avalanche.benchmarks.generators import nc_benchmark
from avalanche.training.plugins import EvaluationPlugin
from avalanche.evaluation.metrics import ExperienceAccuracy, StreamAccuracy, \
    EpochAccuracy
from avalanche.logging.interactive_logging import InteractiveLogger
import random
import numpy as np
from torch.optim.lr_scheduler import MultiStepLR

from avalanche.training.strategies.icarl import ICaRL


def get_dataset_per_pixel_mean(dataset):
    result = None
    patterns_count = 0

    for img_pattern, _ in dataset:
        if result is None:
            result = torch.zeros_like(img_pattern, dtype=torch.float)

        result += img_pattern
        patterns_count += 1

    if result is None:
        result = torch.empty(0, dtype=torch.float)
    else:
        result = result / patterns_count

    return result


def icarl_cifar100_augment_data(img):
    img = img.numpy()
    padded = np.pad(img, ((0, 0), (4, 4), (4, 4)), mode='constant')
    random_cropped = np.zeros(img.shape, dtype=np.float32)
    crop = np.random.randint(0, high=8 + 1, size=(2,))

    # Cropping and possible flipping
    if np.random.randint(2) > 0:
        random_cropped[:, :, :] = \
            padded[:, crop[0]:(crop[0]+32), crop[1]:(crop[1]+32)]
    else:
        random_cropped[:, :, :] = \
            padded[:, crop[0]:(crop[0]+32), crop[1]:(crop[1]+32)][:, :, ::-1]
    t = torch.tensor(random_cropped)
    return t


def run_experiment(config):
    device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")

    torch.manual_seed(config.seed)
    torch.cuda.manual_seed(config.seed)
    np.random.seed(config.seed)
    random.seed(config.seed)
    torch.backends.cudnn.enabled = False
    torch.backends.cudnn.deterministic = True

    per_pixel_mean = get_dataset_per_pixel_mean(
        CIFAR100(expanduser("~") + "/.avalanche/data/cifar100/",
                 train=True, download=True,
                 transform=transforms.Compose([transforms.ToTensor()])))

    transforms_group = dict(
        eval=(transforms.Compose([
            transforms.ToTensor(),
            lambda img_pattern: img_pattern - per_pixel_mean]), None),
        train=(transforms.Compose([
            transforms.ToTensor(),
            lambda img_pattern: img_pattern - per_pixel_mean,
            icarl_cifar100_augment_data]), None),
    )

    train_set = CIFAR100(expanduser("~") + "/.avalanche/data/cifar100/",
                         train=True, download=True, )
    test_set = CIFAR100(expanduser("~") + "/.avalanche/data/cifar100/",
                        train=False, download=True, )

    train_set = AvalancheDataset(train_set,
                                 transform_groups=transforms_group,
                                 initial_transform_group='train')
    test_set = AvalancheDataset(test_set,
                                transform_groups=transforms_group,
                                initial_transform_group='eval')

    scenario = nc_benchmark(
        train_dataset=train_set,
        test_dataset=test_set,
        n_experiences=config.nb_exp,
        task_labels=False, seed=config.seed,
        shuffle=False,
        fixed_class_order=config.fixed_class_order)

    evaluator = EvaluationPlugin(EpochAccuracy(), ExperienceAccuracy(),
                                 StreamAccuracy(),
                                 loggers=[InteractiveLogger()])

    model: IcarlNet = make_icarl_net(num_classes=100)
    model.apply(initialize_icarl_net)

    optim = SGD(model.parameters(), lr=config.lr_base,
                weight_decay=config.wght_decay, momentum=0.9)
    sched = LRSchedulerPlugin(
        MultiStepLR(optim, config.lr_milestones, gamma=1.0 / config.lr_factor))

    strategy = ICaRL(
        model.feature_extractor, model.classifier, optim,
        config.memory_size,
        buffer_transform=transforms.Compose([icarl_cifar100_augment_data]),
        fixed_memory=True, train_mb_size=config.batch_size,
        train_epochs=config.epochs, eval_mb_size=config.batch_size,
        plugins=[sched], device=device, evaluator=evaluator
    )

    for i, exp in enumerate(scenario.train_stream):
        eval_exps = [e for e in scenario.test_stream][:i + 1]
        strategy.train(exp, num_workers=4)
        strategy.eval(eval_exps, num_workers=4)


class Config(dict):

    def __getattribute__(self, key):
        try:
            return self[key]
        except KeyError:
            raise AttributeError(key)

    def __setattr__(self, key, value):
        self[key] = value


if __name__ == "__main__":
    config = Config()

    config.batch_size = 128
    config.nb_exp = 10
    config.memory_size = 2000
    config.epochs = 70
    config.lr_base = 2.
    config.lr_milestones = [49, 63]
    config.lr_factor = 5.
    config.wght_decay = 0.00001
    config.fixed_class_order = [87, 0, 52, 58, 44, 91, 68, 97, 51, 15,
                                94, 92, 10, 72, 49, 78, 61, 14, 8, 86,
                                84, 96, 18, 24, 32, 45, 88, 11, 4, 67,
                                69, 66, 77, 47, 79, 93, 29, 50, 57, 83,
                                17, 81, 41, 12, 37, 59, 25, 20, 80, 73,
                                1, 28, 6, 46, 62, 82, 53, 9, 31, 75,
                                38, 63, 33, 74, 27, 22, 36, 3, 16, 21,
                                60, 19, 70, 90, 89, 43, 5, 42, 65, 76,
                                40, 30, 23, 85, 2, 95, 56, 48, 71, 64,
                                98, 13, 99, 7, 34, 55, 54, 26, 35, 39]
    config.seed = 2222

    run_experiment(config)