################################################################################
# Copyright (c) 2020 ContinualAI Research                                      #
# Copyrights licensed under the MIT License.                                   #
# See the accompanying LICENSE file for terms.                                 #
#                                                                              #
# Date: 01-12-2020                                                             #
# Author(s): Andrea Cossu                                                      #
# E-mail: contact@continualai.org                                              #
# Website: clair.continualai.org                                               #
################################################################################
"""
This example trains on Split CIFAR10 with Naive strategy.
In this example each step has a different task label.
The task label, although available, is not used at test time.
"""

from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import argparse
import torch
from torch.nn import CrossEntropyLoss
from torch.optim import Adam

from avalanche.benchmarks.classic import SplitCIFAR10
from avalanche.models import SimpleMLP
from avalanche.training.strategies import Naive


def main(args):

    # Config
    device = torch.device(f"cuda:{args.cuda}"
                          if torch.cuda.is_available() and
                          args.cuda >= 0 else "cpu")
    # model
    model = SimpleMLP(input_size=32*32*3, num_classes=10)

    # CL Benchmark Creation
    scenario = SplitCIFAR10(n_steps=5, return_task_id=True)
    train_stream = scenario.train_stream
    test_stream = scenario.test_stream

    # Prepare for training & testing
    optimizer = Adam(model.parameters(), lr=0.01)
    criterion = CrossEntropyLoss()

    # Choose a CL strategy
    strategy = Naive(
        model=model, optimizer=optimizer, criterion=criterion,
        train_mb_size=128, train_epochs=3, eval_mb_size=128, device=device)

    # train and test loop
    for train_task in train_stream:
        strategy.train(train_task, num_workers=4)
        strategy.eval(test_stream)


if __name__ == '__main__':
    parser = argparse.ArgumentParser()
    parser.add_argument('--cuda', type=int, default=0,
                        help='Select zero-indexed cuda device. -1 to use CPU.')
    args = parser.parse_args()
    main(args)
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