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import logging
import numpy as np
import torch
from torch import nn
from torch.serialization import load
from tqdm import tqdm
from torch import optim
from torch.nn import functional as F
from torch.utils.data import DataLoader
from utils.inc_net import IncrementalNet
from models.base import BaseLearner
from utils.toolkit import target2onehot, tensor2numpy
init_epoch = 200
init_lr = 0.1
init_milestones = [60, 120, 160]
init_lr_decay = 0.1
init_weight_decay = 0.0005
epochs = 250
lrate = 0.1
milestones = [60, 120, 180, 220]
lrate_decay = 0.1
batch_size = 128
weight_decay = 2e-4
num_workers = 8
T = 2
lamda = 3
class LwF(BaseLearner):
def __init__(self, args):
super().__init__(args)
self._network = IncrementalNet(args, False)
def after_task(self):
self._old_network = self._network.copy().freeze()
self._known_classes = self._total_classes
def incremental_train(self, data_manager):
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
def _train(self, train_loader, test_loader):
self._network.to(self._device)
if self._old_network is not None:
self._old_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)
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,
)
else:
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"]
fake_targets = targets - self._known_classes
loss_clf = F.cross_entropy(
logits[:, self._known_classes :], fake_targets
)
loss_kd = _KD_loss(
logits[:, : self._known_classes],
self._old_network(inputs)["logits"],
T,
)
loss = lamda * loss_kd + loss_clf
optimizer.zero_grad()
loss.backward()
optimizer.step()
losses += loss.item()
with torch.no_grad():
_, 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,
)
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 _KD_loss(pred, soft, T):
pred = torch.log_softmax(pred / T, dim=1)
soft = torch.softmax(soft / T, dim=1)
return -1 * torch.mul(soft, pred).sum() / pred.shape[0]
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