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import numpy as np
from tqdm import tqdm
import torch
from torch import nn
from torch import optim
from torch.nn import functional as F
from torch.utils.data import DataLoader
from models.base import BaseLearner
from models.podnet import pod_spatial_loss
from utils.inc_net import IncrementalNet
from utils.toolkit import target2onehot, tensor2numpy
from torch.utils.tensorboard import SummaryWriter
EPSILON = 1e-8
init_epoch = 200
init_lr = 0.1
init_milestones = [60, 120, 170]
init_lr_decay = 0.1
init_weight_decay = 0.0005
epochs = 200
lrate = 0.1
milestones = [70, 120, 150]
lrate_decay = 0.1
batch_size = 32
weight_decay = 2e-4
num_workers = 2
T = 2
lamda = 1000
fishermax = 0.0001
class EWC(BaseLearner):
def __init__(self, args):
super().__init__(args)
self.fisher = None
self._network = IncrementalNet(args, True)
self.writer = SummaryWriter(log_dir='runs/{}'.format(args['summary_file']))
def after_task(self):
self._known_classes = self._total_classes
def incremental_train(self, data_manager):
self.curr_acc = 0
self._cur_task += 1
self._total_classes = self._known_classes + data_manager.get_task_size(
self._cur_task
)
self._network.update_fc(self._total_classes)
logging.info(
"Learning on {}-{}".format(self._known_classes, self._total_classes)
)
train_dataset = data_manager.get_dataset(
np.arange(self._known_classes, self._total_classes),
source="train",
mode="train",
)
self.train_loader = DataLoader(
train_dataset, batch_size=batch_size, shuffle=True, num_workers=num_workers
)
test_dataset = data_manager.get_dataset(
np.arange(0, self._total_classes), source="test", mode="test"
)
self.test_loader = DataLoader(
test_dataset, batch_size=batch_size, shuffle=False, num_workers=num_workers
)
if len(self._multiple_gpus) > 1:
self._network = nn.DataParallel(self._network, self._multiple_gpus)
self._train(self.train_loader, self.test_loader)
if len(self._multiple_gpus) > 1:
self._network = self._network.module
if self.fisher is None:
self.fisher = self.getFisherDiagonal(self.train_loader)
else:
alpha = self._known_classes / self._total_classes
new_finsher = self.getFisherDiagonal(self.train_loader)
for n, p in new_finsher.items():
new_finsher[n][: len(self.fisher[n])] = (
alpha * self.fisher[n]
+ (1 - alpha) * new_finsher[n][: len(self.fisher[n])]
)
self.fisher = new_finsher
self.mean = {
n: p.clone().detach()
for n, p in self._network.named_parameters()
if p.requires_grad
}
if self._total_classes == data_manager.get_total_classnum():
self.writer.close()
def _train(self, train_loader, test_loader):
self._network.to(self._device)
if self._cur_task == 0:
optimizer = optim.SGD(
self._network.parameters(),
momentum=0.9,
lr=init_lr,
weight_decay=init_weight_decay,
)
scheduler = optim.lr_scheduler.MultiStepLR(
optimizer=optimizer, milestones=init_milestones, gamma=init_lr_decay
)
self._init_train(train_loader, test_loader, optimizer, scheduler)
else:
optimizer = optim.SGD(
self._network.parameters(),
lr=lrate,
momentum=0.9,
weight_decay=weight_decay,
)
scheduler = optim.lr_scheduler.MultiStepLR(
optimizer=optimizer, milestones=milestones, gamma=lrate_decay
)
self._update_representation(train_loader, test_loader, optimizer, scheduler)
self.writer.add_figure("Confusion matrix",
self._createConfusionMatrix(self._network, self.test_loader),
self._cur_task)
self.writer.add_scalar('Accuracies', self.curr_acc, self._cur_task)
def _init_train(self, train_loader, test_loader, optimizer, scheduler):
prog_bar = tqdm(range(init_epoch))
for _, epoch in enumerate(prog_bar):
self._network.train()
losses = 0.0
correct, total = 0, 0
for i, (_, inputs, targets) in enumerate(train_loader):
inputs, targets = inputs.to(self._device), targets.to(self._device)
logits = self._network(inputs)["logits"]
loss = F.cross_entropy(logits, targets)
optimizer.zero_grad()
loss.backward()
optimizer.step()
losses += loss.item()
_, preds = torch.max(logits, dim=1)
correct += preds.eq(targets.expand_as(preds)).cpu().sum()
total += len(targets)
scheduler.step()
train_acc = np.around(tensor2numpy(correct) * 100 / total, decimals=2)
if epoch % 5 == 0:
test_acc = self._compute_accuracy(self._network, test_loader)
info = "Task {}, Epoch {}/{} => Loss {:.3f}, Train_accy {:.2f}, Test_accy {:.2f}".format(
self._cur_task,
epoch + 1,
init_epoch,
losses / len(train_loader),
train_acc,
test_acc,
)
self.writer.add_scalar('Task: {} on {}-{} -- Train/Loss'.format(self._cur_task, self._known_classes, self._total_classes), losses / len(train_loader), epoch)
self.writer.add_scalar('Task: {} on {}-{} -- Train/Accuracy'.format(self._cur_task, self._known_classes, self._total_classes), train_acc, epoch)
self.writer.add_scalar('Task: {} on {}-{} -- Test/Accuracy'.format(self._cur_task, self._known_classes, self._total_classes), test_acc, epoch)
info = "Task {}, Epoch {}/{} => Loss {:.3f}, Train_accy {:.2f}".format(
self._cur_task,
epoch + 1,
init_epoch,
losses / len(train_loader),
train_acc,
)
prog_bar.set_description(info)
logging.info(info)
def _update_representation(self, train_loader, test_loader, optimizer, scheduler):
prog_bar = tqdm(range(epochs))
for _, epoch in enumerate(prog_bar):
self._network.train()
losses = 0.0
correct, total = 0, 0
for i, (_, inputs, targets) in enumerate(train_loader):
inputs, targets = inputs.to(self._device), targets.to(self._device)
logits = self._network(inputs)["logits"]
loss_clf = F.cross_entropy(
logits[:, self._known_classes :], targets - self._known_classes
)
loss_ewc = self.compute_ewc()
loss = loss_clf + lamda * loss_ewc
optimizer.zero_grad()
loss.backward()
optimizer.step()
losses += loss.item()
_, preds = torch.max(logits, dim=1)
correct += preds.eq(targets.expand_as(preds)).cpu().sum()
total += len(targets)
scheduler.step()
train_acc = np.around(tensor2numpy(correct) * 100 / total, decimals=2)
if epoch % 5 == 0:
test_acc = self._compute_accuracy(self._network, test_loader)
info = "Task {}, Epoch {}/{} => Loss {:.3f}, Train_accy {:.2f}, Test_accy {:.2f}".format(
self._cur_task,
epoch + 1,
epochs,
losses / len(train_loader),
train_acc,
test_acc,
)
self.writer.add_scalar('Task: {} on {}-{} -- Train/Loss'.format(self._cur_task, self._known_classes, self._total_classes), losses / len(train_loader), epoch)
self.writer.add_scalar('Task: {} on {}-{} -- Train/Accuracy'.format(self._cur_task, self._known_classes, self._total_classes), train_acc, epoch)
self.writer.add_scalar('Task: {} on {}-{} -- Test/Accuracy'.format(self._cur_task, self._known_classes, self._total_classes), test_acc, epoch)
else:
info = "Task {}, Epoch {}/{} => Loss {:.3f}, Train_accy {:.2f}".format(
self._cur_task,
epoch + 1,
epochs,
losses / len(train_loader),
train_acc,
)
prog_bar.set_description(info)
logging.info(info)
def compute_ewc(self):
loss = 0
if len(self._multiple_gpus) > 1:
for n, p in self._network.module.named_parameters():
if n in self.fisher.keys():
loss += (
torch.sum(
(self.fisher[n])
* (p[: len(self.mean[n])] - self.mean[n]).pow(2)
)
/ 2
)
else:
for n, p in self._network.named_parameters():
if n in self.fisher.keys():
loss += (
torch.sum(
(self.fisher[n])
* (p[: len(self.mean[n])] - self.mean[n]).pow(2)
)
/ 2
)
return loss
def getFisherDiagonal(self, train_loader):
fisher = {
n: torch.zeros(p.shape).to(self._device)
for n, p in self._network.named_parameters()
if p.requires_grad
}
self._network.train()
optimizer = optim.SGD(self._network.parameters(), lr=lrate)
for i, (_, inputs, targets) in enumerate(train_loader):
inputs, targets = inputs.to(self._device), targets.to(self._device)
logits = self._network(inputs)["logits"]
loss = torch.nn.functional.cross_entropy(logits, targets)
optimizer.zero_grad()
loss.backward()
for n, p in self._network.named_parameters():
if p.grad is not None:
fisher[n] += p.grad.pow(2).clone()
for n, p in fisher.items():
fisher[n] = p / len(train_loader)
fisher[n] = torch.min(fisher[n], torch.tensor(fishermax))
return fisher
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