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hal.py
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# Copyright 2022-present, Lorenzo Bonicelli, Pietro Buzzega, Matteo Boschini, Angelo Porrello, 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 sys
import numpy as np
import torch
from datasets import get_dataset
from torch.optim import SGD
from models.utils.continual_model import ContinualModel
from utils.args import add_management_args, add_experiment_args, add_rehearsal_args, ArgumentParser
from utils.buffer import Buffer
def get_parser() -> ArgumentParser:
parser = ArgumentParser(description='Continual learning via'
' Experience Replay.')
add_management_args(parser)
add_experiment_args(parser)
add_rehearsal_args(parser)
parser.add_argument('--hal_lambda', type=float, default=0.1)
parser.add_argument('--beta', type=float, default=0.5)
parser.add_argument('--gamma', type=float, default=0.1)
return parser
class HAL(ContinualModel):
NAME = 'hal'
COMPATIBILITY = ['class-il', 'domain-il', 'task-il']
def __init__(self, backbone, loss, args, transform):
super(HAL, self).__init__(backbone, loss, args, transform)
self.task_number = 0
self.buffer = Buffer(self.args.buffer_size, self.device, get_dataset(args).N_TASKS, mode='ring')
self.hal_lambda = args.hal_lambda
self.beta = args.beta
self.gamma = args.gamma
self.anchor_optimization_steps = 100
self.finetuning_epochs = 1
self.dataset = get_dataset(args)
self.spare_model = self.dataset.get_backbone()
self.spare_model.to(self.device)
self.spare_opt = SGD(self.spare_model.parameters(), lr=self.args.lr)
def end_task(self, dataset):
self.task_number += 1
# ring buffer mgmt (if we are not loading
if self.task_number > self.buffer.task_number:
self.buffer.num_seen_examples = 0
self.buffer.task_number = self.task_number
# get anchors (provided that we are not loading the model
if len(self.anchors) < self.task_number * dataset.N_CLASSES_PER_TASK:
self.get_anchors(dataset)
del self.phi
def get_anchors(self, dataset):
theta_t = self.net.get_params().detach().clone()
self.spare_model.set_params(theta_t)
# fine tune on memory buffer
for _ in range(self.finetuning_epochs):
inputs, labels = self.buffer.get_data(self.args.batch_size, transform=self.transform)
self.spare_opt.zero_grad()
out = self.spare_model(inputs)
loss = self.loss(out, labels)
loss.backward()
self.spare_opt.step()
theta_m = self.spare_model.get_params().detach().clone()
classes_for_this_task = np.unique(dataset.train_loader.dataset.targets)
for a_class in classes_for_this_task:
e_t = torch.rand(self.input_shape, requires_grad=True, device=self.device)
e_t_opt = SGD([e_t], lr=self.args.lr)
print(file=sys.stderr)
for i in range(self.anchor_optimization_steps):
e_t_opt.zero_grad()
cum_loss = 0
self.spare_opt.zero_grad()
self.spare_model.set_params(theta_m.detach().clone())
loss = -torch.sum(self.loss(self.spare_model(e_t.unsqueeze(0)), torch.tensor([a_class]).to(self.device)))
loss.backward()
cum_loss += loss.item()
self.spare_opt.zero_grad()
self.spare_model.set_params(theta_t.detach().clone())
loss = torch.sum(self.loss(self.spare_model(e_t.unsqueeze(0)), torch.tensor([a_class]).to(self.device)))
loss.backward()
cum_loss += loss.item()
self.spare_opt.zero_grad()
loss = torch.sum(self.gamma * (self.spare_model(e_t.unsqueeze(0), returnt='features') - self.phi) ** 2)
assert not self.phi.requires_grad
loss.backward()
cum_loss += loss.item()
e_t_opt.step()
e_t = e_t.detach()
e_t.requires_grad = False
self.anchors = torch.cat((self.anchors, e_t.unsqueeze(0)))
del e_t
print('Total anchors:', len(self.anchors), file=sys.stderr)
self.spare_model.zero_grad()
def observe(self, inputs, labels, not_aug_inputs):
real_batch_size = inputs.shape[0]
if not hasattr(self, 'input_shape'):
self.input_shape = inputs.shape[1:]
if not hasattr(self, 'anchors'):
self.anchors = torch.zeros(tuple([0] + list(self.input_shape))).to(self.device)
if not hasattr(self, 'phi'):
print('Building phi', file=sys.stderr)
with torch.no_grad():
self.phi = torch.zeros_like(self.net(inputs[0].unsqueeze(0), returnt='features'), requires_grad=False)
assert not self.phi.requires_grad
if not self.buffer.is_empty():
buf_inputs, buf_labels = self.buffer.get_data(
self.args.minibatch_size, transform=self.transform)
inputs = torch.cat((inputs, buf_inputs))
labels = torch.cat((labels, buf_labels))
old_weights = self.net.get_params().detach().clone()
self.opt.zero_grad()
outputs = self.net(inputs)
k = self.task_number
loss = self.loss(outputs, labels)
loss.backward()
self.opt.step()
first_loss = 0
assert len(self.anchors) == self.dataset.N_CLASSES_PER_TASK * k
if len(self.anchors) > 0:
first_loss = loss.item()
with torch.no_grad():
pred_anchors = self.net(self.anchors)
self.net.set_params(old_weights)
pred_anchors -= self.net(self.anchors)
loss = self.hal_lambda * (pred_anchors ** 2).mean()
loss.backward()
self.opt.step()
with torch.no_grad():
self.phi = self.beta * self.phi + (1 - self.beta) * self.net(inputs[:real_batch_size], returnt='features').mean(0)
self.buffer.add_data(examples=not_aug_inputs,
labels=labels[:real_batch_size])
return first_loss + loss.item()