def fill_buffer(self, mem_buffer: Buffer, dataset, t_idx: int) -> None:
"""
Adds examples from the current task to the memory buffer
by means of the herding strategy.
:param mem_buffer: the memory buffer
:param dataset: the dataset from which take the examples
:param t_idx: the task index
"""
mode = self.net.training
self.net.eval()
samples_per_class = mem_buffer.buffer_size // len(self.classes_so_far)
if t_idx > 0:
# 1) First, subsample prior classes
buf_x, buf_y, buf_l = self.buffer.get_all_data()
mem_buffer.empty()
for _y in buf_y.unique():
idx = (buf_y == _y)
_y_x, _y_y, _y_l = buf_x[idx], buf_y[idx], buf_l[idx]
mem_buffer.add_data(examples=_y_x[:samples_per_class],
labels=_y_y[:samples_per_class],
logits=_y_l[:samples_per_class])
# 2) Then, fill with current tasks
loader = dataset.not_aug_dataloader(self.args, self.args.batch_size)
# 2.1 Extract all features
a_x, a_y, a_f, a_l = [], [], [], []
for x, y, not_norm_x in loader:
x, y, not_norm_x = (a.to(self.device) for a in [x, y, not_norm_x])
a_x.append(not_norm_x.to('cpu'))
a_y.append(y.to('cpu'))
feats = self.net.features(x)
a_f.append(feats.cpu())
a_l.append(torch.sigmoid(self.net.classifier(feats)).cpu())
a_x, a_y, a_f, a_l = torch.cat(a_x), torch.cat(a_y), torch.cat(
a_f), torch.cat(a_l)
# 2.2 Compute class means
for _y in a_y.unique():
idx = (a_y == _y)
_x, _y, _l = a_x[idx], a_y[idx], a_l[idx]
feats = a_f[idx]
mean_feat = feats.mean(0, keepdim=True)
running_sum = torch.zeros_like(mean_feat)
i = 0
while i < samples_per_class and i < feats.shape[0]:
cost = (mean_feat - (feats + running_sum) / (i + 1)).norm(2, 1)
idx_min = cost.argmin().item()
mem_buffer.add_data(
examples=_x[idx_min:idx_min + 1].to(self.device),
labels=_y[idx_min:idx_min + 1].to(self.device),
logits=_l[idx_min:idx_min + 1].to(self.device))
running_sum += feats[idx_min:idx_min + 1]
feats[idx_min] = feats[idx_min] + 1e6
i += 1
assert len(mem_buffer.examples) <= mem_buffer.buffer_size
self.net.train(mode)
class Fdr(ContinualModel):
NAME = 'fdr'
COMPATIBILITY = ['class-il', 'domain-il', 'task-il', 'general-continual']
def __init__(self, backbone, loss, args, transform):
super(Fdr, self).__init__(backbone, loss, args, transform)
self.buffer = Buffer(self.args.buffer_size, self.device)
self.current_task = 0
self.i = 0
self.soft = torch.nn.Softmax(dim=1)
self.logsoft = torch.nn.LogSoftmax(dim=1)
def end_task(self, dataset):
self.current_task += 1
examples_per_task = self.args.buffer_size // self.current_task
if self.current_task > 1:
buf_x, buf_log, buf_tl = self.buffer.get_all_data()
self.buffer.empty()
for ttl in buf_tl.unique():
idx = (buf_tl == ttl)
ex, log, tasklab = buf_x[idx], buf_log[idx], buf_tl[idx]
first = min(ex.shape[0], examples_per_task)
self.buffer.add_data(examples=ex[:first],
logits=log[:first],
task_labels=tasklab[:first])
counter = 0
with torch.no_grad():
for i, data in enumerate(dataset.train_loader):
inputs, labels, not_aug_inputs = data
inputs = inputs.to(self.device)
not_aug_inputs = not_aug_inputs.to(self.device)
outputs = self.net(inputs)
if examples_per_task - counter < 0:
break
self.buffer.add_data(
examples=not_aug_inputs[:(examples_per_task - counter)],
logits=outputs.data[:(examples_per_task - counter)],
task_labels=(torch.ones(self.args.batch_size) *
(self.current_task - 1))[:(examples_per_task -
counter)])
counter += self.args.batch_size
def observe(self, inputs, labels, not_aug_inputs):
self.i += 1
self.opt.zero_grad()
outputs = self.net(inputs)
loss = self.loss(outputs, labels)
loss.backward()
self.opt.step()
if not self.buffer.is_empty():
self.opt.zero_grad()
buf_inputs, buf_logits, _ = self.buffer.get_data(
self.args.minibatch_size, transform=self.transform)
buf_outputs = self.net(buf_inputs)
loss = torch.norm(
self.soft(buf_outputs) - self.soft(buf_logits), 2, 1).mean()
assert not torch.isnan(loss)
loss.backward()
self.opt.step()
return loss.item()