avalanche_extension/avalanche/examples/wandb_logger.py

################################################################################
# Copyright (c) 2021 ContinualAI.                                              #
# Copyrights licensed under the MIT License.                                   #
# See the accompanying LICENSE file for terms.                                 #
#                                                                              #
# Date: 24-05-2020                                                             #
# Author(s): Andrea Cossu                                                      #
# E-mail: contact@continualai.org                                              #
# Website: avalanche.continualai.org                                           #
################################################################################

"""
This is a simple example that shows how to use the
WandB Logger
"""

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

from os.path import expanduser

import argparse
import torch
from torch.nn import CrossEntropyLoss
from torch.optim import SGD
from torchvision import transforms
from torchvision.datasets import MNIST
from torchvision.transforms import ToTensor, RandomCrop

from avalanche.benchmarks import nc_benchmark
from avalanche.logging import InteractiveLogger, WandBLogger
from avalanche.training.plugins import EvaluationPlugin
from avalanche.evaluation.metrics import forgetting_metrics, \
    accuracy_metrics, loss_metrics, cpu_usage_metrics, \
    timing_metrics, gpu_usage_metrics, ram_usage_metrics, disk_usage_metrics, \
    MAC_metrics, confusion_matrix_metrics
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")
    # ---------

    # --- TRANSFORMATIONS
    train_transform = transforms.Compose([
        RandomCrop(28, padding=4),
        ToTensor(),
        transforms.Normalize((0.1307,), (0.3081,))
    ])
    test_transform = transforms.Compose([
        ToTensor(),
        transforms.Normalize((0.1307,), (0.3081,))
    ])
    # ---------

    # --- SCENARIO CREATION
    mnist_train = MNIST(root=expanduser("~") + "/.avalanche/data/mnist/",
                        train=True,
                        download=True, transform=train_transform)
    mnist_test = MNIST(root=expanduser("~") + "/.avalanche/data/mnist/",
                       train=False,
                       download=True, transform=test_transform)
    scenario = nc_benchmark(
        mnist_train, mnist_test, 5, task_labels=False, seed=1234)
    # ---------

    # MODEL CREATION
    model = SimpleMLP(num_classes=scenario.n_classes)

    interactive_logger = InteractiveLogger()
    wandb_logger = WandBLogger(project_name=args.project, run_name=args.run, 
                               config=args)

    eval_plugin = EvaluationPlugin(
        accuracy_metrics(
            minibatch=True, epoch=True, epoch_running=True,
            experience=True, stream=True),
        loss_metrics(
            minibatch=True, epoch=True, epoch_running=True,
            experience=True, stream=True),
        forgetting_metrics(experience=True, stream=True),
        confusion_matrix_metrics(stream=True, wandb=True,
                                 class_names=[str(i) for i in range(10)]),
        cpu_usage_metrics(
            minibatch=True, epoch=True, experience=True, stream=True),
        timing_metrics(
            minibatch=True, epoch=True, experience=True, stream=True),
        ram_usage_metrics(
            every=0.5, minibatch=True, epoch=True, experience=True,
            stream=True),
        gpu_usage_metrics(
            args.cuda, every=0.5, minibatch=True, epoch=True,
            experience=True, stream=True),
        disk_usage_metrics(
            minibatch=True, epoch=True, experience=True, stream=True),
        MAC_metrics(
            minibatch=True, epoch=True, experience=True),
        loggers=[interactive_logger, wandb_logger]
    )

    # CREATE THE STRATEGY INSTANCE (NAIVE)
    cl_strategy = Naive(
        model, SGD(model.parameters(), lr=0.001, momentum=0.9),
        CrossEntropyLoss(), train_mb_size=100, train_epochs=4, eval_mb_size=100,
        device=device, evaluator=eval_plugin)

    # TRAINING LOOP
    print('Starting experiment...')
    results = []
    for experience in scenario.train_stream:
        print("Start of experience: ", experience.current_experience)
        print("Current Classes: ", experience.classes_in_this_experience)

        cl_strategy.train(experience)
        print('Training completed')

        print('Computing accuracy on the whole test set')
        results.append(cl_strategy.eval(scenario.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.')
    parser.add_argument('--run', type=str, help='Provide a run name for WandB')
    parser.add_argument('--project', type=str,
                        help='Define the name of the WandB project')
    args = parser.parse_args()
    main(args)