from argparse import ArgumentParser
import logging
import torch
from backbone import get_backbone
from utils.args import add_rehearsal_args
from torch.optim import SGD, lr_scheduler
from utils.buffer import Buffer
from models.utils.lider_model import LiderOptimizer, add_lipschitz_args
from utils.augmentations import cutmix_data
from utils.status import progress_bar
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def fit_buffer(self: LiderOptimizer, epochs):
optimizer = SGD(self.get_parameters(), lr=self.args.maxlr, momentum=self.args.optim_mom, weight_decay=self.args.optim_wd, nesterov=self.args.optim_nesterov)
scheduler = lr_scheduler.CosineAnnealingWarmRestarts(optimizer, T_0=1, T_mult=2, eta_min=self.args.minlr)
for epoch in range(epochs):
if epoch <= 0: # Warm start of 1 epoch
for param_group in optimizer.param_groups:
param_group['lr'] = self.args.maxlr * 0.1
elif epoch == 1: # Then set to maxlr
for param_group in optimizer.param_groups:
param_group['lr'] = self.args.maxlr
else:
scheduler.step()
all_inputs, all_labels = self.buffer.get_data(
len(self.buffer.examples), transform=self.transform, device=self.device)
it = 0
while len(all_inputs):
if it > self.get_debug_iters() and self.args.debug_mode:
break
it += 1
optimizer.zero_grad()
buf_inputs, buf_labels = all_inputs[:self.args.batch_size], all_labels[:self.args.batch_size]
all_inputs, all_labels = all_inputs[self.args.batch_size:], all_labels[self.args.batch_size:]
if self.args.cutmix_alpha is not None:
inputs, labels_a, labels_b, lam = cutmix_data(x=buf_inputs.cpu(), y=buf_labels.cpu(), alpha=self.args.cutmix_alpha)
buf_inputs = inputs.to(self.device)
buf_labels_a = labels_a.to(self.device)
buf_labels_b = labels_b.to(self.device)
buf_outputs = self.net(buf_inputs)
loss = lam * self.loss(buf_outputs, buf_labels_a) + (1 - lam) * self.loss(buf_outputs, buf_labels_b)
else:
buf_outputs = self.net(buf_inputs)
loss = self.loss(buf_outputs, buf_labels)
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_buffer = self.minimization_lip_loss(lip_inputs)
loss += self.args.alpha_lip_lambda * loss_lip_buffer
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_budget = self.dynamic_budget_lip_loss(lip_inputs)
loss += self.args.beta_lip_lambda * loss_lip_budget
loss.backward()
optimizer.step()
progress_bar(epoch, epochs, 1, 'G', loss.item())
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class GDumbLider(LiderOptimizer):
"""GDumb learns an empty model only on the buffer. Treated with LiDER!"""
NAME = 'gdumb_lider'
COMPATIBILITY = ['class-il', 'task-il']
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@staticmethod
def get_parser(parser) -> ArgumentParser:
parser.set_defaults(lr=0) # lr is managed elsewhere
add_rehearsal_args(parser)
add_lipschitz_args(parser)
parser.add_argument('--maxlr', type=float, default=5e-2,
help='Max learning rate.')
parser.add_argument('--minlr', type=float, default=5e-4,
help='Min learning rate.')
parser.add_argument('--fitting_epochs', type=int, default=256,
help='Number of epochs to fit the buffer.')
parser.add_argument('--cutmix_alpha', type=float, default=1.0,
help='Alpha parameter for cutmix')
return parser
def __init__(self, backbone, loss, args, transform, dataset=None):
if args.n_epochs != 1:
args.n_epochs = 1
logging.info('GDumb needs only 1 epoch to fill the buffer.')
super().__init__(backbone, loss, args, transform, dataset=dataset)
self.buffer = Buffer(self.args.buffer_size)
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def observe(self, inputs: torch.Tensor, labels: torch.Tensor, not_aug_inputs: torch.Tensor, epoch=None):
self.buffer.add_data(examples=not_aug_inputs,
labels=labels)
return 0
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def end_task(self, dataset):
# new model
if not (self.current_task == dataset.N_TASKS - 1):
return
self.net = get_backbone(self.args).to(self.device)
self.net.set_return_prerelu(True)
self.init_net(dataset)
fit_buffer(self, self.args.fitting_epochs)