"""
This module implements the simplest form of rehearsal training: Experience Replay. It maintains a buffer
of previously seen examples and uses them to augment the current batch during training.
Example usage:
model = Er(backbone, loss, args, transform, dataset)
loss = model.observe(inputs, labels, not_aug_inputs, epoch)
"""
# Copyright 2020-present, Pietro Buzzega, Matteo Boschini, Angelo Porrello, Davide Abati, Simone Calderara.
# All rights reserved.
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
import torch
from models.utils.continual_model import ContinualModel
from utils.args import add_rehearsal_args, ArgumentParser
from utils.buffer import Buffer
[docs]
class Er(ContinualModel):
"""Continual learning via Experience Replay."""
NAME = 'er'
COMPATIBILITY = ['class-il', 'domain-il', 'task-il', 'general-continual']
[docs]
@staticmethod
def get_parser(parser) -> ArgumentParser:
"""
Returns an ArgumentParser object with predefined arguments for the Er model.
This model requires the `add_rehearsal_args` to include the buffer-related arguments.
"""
add_rehearsal_args(parser)
return parser
def __init__(self, backbone, loss, args, transform, dataset=None):
"""
The ER model maintains a buffer of previously seen examples and uses them to augment the current batch during training.
"""
super(Er, self).__init__(backbone, loss, args, transform, dataset=dataset)
self.buffer = Buffer(self.args.buffer_size)
[docs]
def observe(self, inputs, labels, not_aug_inputs, epoch=None):
"""
ER trains on the current task using the data provided, but also augments the batch with data from the buffer.
"""
real_batch_size = inputs.shape[0]
self.opt.zero_grad()
if not self.buffer.is_empty():
buf_inputs, buf_labels = self.buffer.get_data(
self.args.minibatch_size, transform=self.transform, device=self.device)
inputs = torch.cat((inputs, buf_inputs))
labels = torch.cat((labels, buf_labels))
outputs = self.net(inputs)
loss = self.loss(outputs, labels)
loss.backward()
self.opt.step()
self.buffer.add_data(examples=not_aug_inputs,
labels=labels[:real_batch_size])
return loss.item()