import torch
from utils.buffer import Buffer
from utils.args import *
from models.utils.lider_model import LiderOptimizer, add_lipschitz_args
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class ErACELider(LiderOptimizer):
"""ER-ACE with future not fixed (as made by authors). Treated with LiDER!"""
NAME = 'er_ace_lider'
COMPATIBILITY = ['class-il', 'task-il']
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@staticmethod
def get_parser(parser) -> ArgumentParser:
add_rehearsal_args(parser)
add_lipschitz_args(parser)
return parser
def __init__(self, backbone, loss, args, transform, dataset=None):
super().__init__(backbone, loss, args, transform, dataset=dataset)
self.buffer = Buffer(self.args.buffer_size)
self.seen_so_far = torch.tensor([]).long().to(self.device)
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def begin_task(self, dataset):
if self.current_task == 0:
self.net.set_return_prerelu(True)
self.init_net(dataset)
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def to(self, device):
super().to(device)
self.seen_so_far = self.seen_so_far.to(device)
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def observe(self, inputs: torch.Tensor, labels: torch.Tensor, not_aug_inputs: torch.Tensor, epoch=None):
present = labels.unique()
self.seen_so_far = torch.cat([self.seen_so_far, present]).unique()
logits = self.net(inputs)
mask = torch.zeros_like(logits)
mask[:, present] = 1
self.opt.zero_grad()
if self.seen_so_far.max() < (self.num_classes - 1):
mask[:, self.seen_so_far.max():] = 1
if self.current_task > 0:
logits = logits.masked_fill(mask == 0, torch.finfo(logits.dtype).min)
loss = self.loss(logits, labels)
tot_loss = loss.item()
loss.backward()
if self.current_task > 0:
# sample from buffer
buf_inputs, buf_labels = self.buffer.get_data(
self.args.minibatch_size, transform=self.transform, device=self.device)
loss_re = self.loss(self.net(buf_inputs), buf_labels)
tot_loss += loss_re.item()
loss_re.backward()
if not self.buffer.is_empty():
if self.args.alpha_lip_lambda > 0:
buf_inputs, _ = self.buffer.get_data(self.args.minibatch_size, transform=self.transform, device=self.device)
_, buf_output_features = self.net(buf_inputs, returnt='full')
lip_inputs = [buf_inputs] + buf_output_features
loss_lip_minimize = self.args.alpha_lip_lambda * self.minimization_lip_loss(lip_inputs)
tot_loss += loss_lip_minimize.item()
loss_lip_minimize.backward()
if self.args.beta_lip_lambda > 0:
buf_inputs, _ = self.buffer.get_data(self.args.minibatch_size, transform=self.transform, device=self.device)
_, buf_output_features = self.net(buf_inputs, returnt='full')
lip_inputs = [buf_inputs] + buf_output_features
loss_lip_dyn_budget = self.args.beta_lip_lambda * self.dynamic_budget_lip_loss(lip_inputs)
tot_loss += loss_lip_dyn_budget.item()
loss_lip_dyn_budget.backward()
self.opt.step()
self.buffer.add_data(examples=not_aug_inputs,
labels=labels)
return tot_loss