import torch
from torch.nn import functional as F
from models.casper_utils.casper_model import CasperModel
from utils.args import ArgumentParser, add_rehearsal_args
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class DerppCasper(CasperModel):
"""Continual learning via Dark Experience Replay++. Treated with CaSpeR!"""
NAME = 'derpp_casper'
COMPATIBILITY = ['class-il', 'domain-il', 'task-il', 'general-continual']
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@staticmethod
def get_parser(parser) -> ArgumentParser:
add_rehearsal_args(parser)
parser.add_argument('--alpha', type=float, required=True, help='Penalty weight.')
parser.add_argument('--beta', type=float, required=True, help='Penalty weight.')
CasperModel.add_casper_args(parser)
return parser
def __init__(self, backbone, loss, args, transform, dataset=None):
super().__init__(backbone, loss, args, transform, dataset)
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def observe(self, inputs: torch.Tensor, labels: torch.Tensor, not_aug_inputs: torch.Tensor, epoch=None):
self.opt.zero_grad()
outputs = self.net(inputs)
loss = self.loss(outputs, labels)
if not self.buffer.is_empty():
buf_inputs, _, buf_logits = self.buffer.get_data(
self.args.minibatch_size, transform=self.transform)
buf_outputs = self.net(buf_inputs)
derpp_loss = self.args.alpha * F.mse_loss(buf_outputs, buf_logits)
buf_inputs, buf_labels, _ = self.buffer.get_data(
self.args.minibatch_size, transform=self.transform)
buf_outputs = self.net(buf_inputs)
derpp_loss += self.args.beta * self.loss(buf_outputs, buf_labels)
loss += derpp_loss
if self.current_task > 0 and self.args.casper_batch > 0 and self.args.rho > 0:
casper_loss = self.get_casper_loss()
loss += casper_loss * self.args.rho
loss.backward()
self.opt.step()
if self.args.buffer_size > 0:
self.buffer.add_data(examples=not_aug_inputs,
labels=labels,
logits=outputs.data)
return loss.item()