from copy import deepcopy
import torch
import torch.nn.functional as F
from models.casper_utils.casper_model import CasperModel
from utils.args import ArgumentParser, add_rehearsal_args
from utils.batch_norm import bn_track_stats
from utils.buffer import Buffer, fill_buffer, icarl_replay
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class ICarlCasper(CasperModel):
"""Continual Learning via iCaRL. Treated with CaSpeR!"""
NAME = 'icarl_casper'
COMPATIBILITY = ['class-il', 'task-il']
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@staticmethod
def get_parser(parser) -> ArgumentParser:
add_rehearsal_args(parser)
CasperModel.add_casper_args(parser)
return parser
def __init__(self, backbone, loss, args, transform, dataset=None):
super().__init__(backbone, loss, args, transform, dataset=dataset)
self.eye = torch.eye(self.num_classes).to(self.device)
self.class_means = None
self.old_net = None
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def forward(self, x):
if self.class_means is None:
with torch.no_grad():
self.compute_class_means()
self.class_means = self.class_means.squeeze()
feats = self.net(x, returnt='features')
feats = feats.view(feats.size(0), -1)
feats = feats.unsqueeze(1)
pred = (self.class_means.unsqueeze(0) - feats).pow(2).sum(2)
return -pred
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def observe(self, inputs, labels, not_aug_inputs, logits=None, epoch=None):
if not hasattr(self, 'classes_so_far'):
self.register_buffer('classes_so_far', labels.unique().to('cpu'))
else:
self.register_buffer('classes_so_far', torch.cat((
self.classes_so_far, labels.to('cpu'))).unique())
self.class_means = None
if self.current_task > 0:
with torch.no_grad():
logits = torch.sigmoid(self.old_net(inputs))
self.opt.zero_grad()
loss = self.get_loss(inputs, labels, self.current_task, logits)
if self.current_task > 0 and self.args.casper_batch > 0 and self.args.rho > 0:
casper_loss = self.get_casper_loss()
loss += casper_loss * self.args.rho
loss.backward()
self.opt.step()
return loss.item()
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@staticmethod
def binary_cross_entropy(pred, y):
return -(pred.log() * y + (1 - y) * (1 - pred).log()).mean()
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def get_loss(self, inputs: torch.Tensor, labels: torch.Tensor,
task_idx: int, logits: torch.Tensor) -> torch.Tensor:
"""
Computes the loss tensor.
Args:
inputs: the images to be fed to the network
labels: the ground-truth labels
task_idx: the task index
logits: the logits of the old network
Returns:
the differentiable loss value
"""
outputs = self.net(inputs)[:, :self.n_seen_classes]
if task_idx == 0:
# Compute loss on the current task
targets = self.eye[labels][:, :self.n_seen_classes]
loss = F.binary_cross_entropy_with_logits(outputs, targets)
assert loss >= 0
else:
targets = self.eye[labels][:, self.n_past_classes:self.n_seen_classes]
comb_targets = torch.cat((logits[:, :self.n_past_classes], targets), dim=1)
loss = F.binary_cross_entropy_with_logits(outputs, comb_targets)
assert loss >= 0
return loss
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def begin_task(self, dataset):
icarl_replay(self, dataset)
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def end_task(self, dataset) -> None:
self.old_net = deepcopy(self.net.eval())
self.net.train()
with torch.no_grad():
fill_buffer(self.buffer, dataset, self.current_task, net=self.net, use_herding=True)
self.class_means = None
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@torch.no_grad()
def compute_class_means(self) -> None:
"""
Computes a vector representing mean features for each class.
"""
# This function caches class means
transform = self.dataset.get_normalization_transform()
class_means = []
buf_data = self.buffer.get_all_data(transform, device=self.device)
examples, labels = buf_data[0], buf_data[1]
for _y in self.classes_so_far:
x_buf = torch.stack(
[examples[i]
for i in range(0, len(examples))
if labels[i].cpu() == _y]
).to(self.device)
with bn_track_stats(self, False):
allt = None
while len(x_buf):
batch = x_buf[:self.args.batch_size]
x_buf = x_buf[self.args.batch_size:]
feats = self.net(batch, returnt='features').mean(0)
if allt is None:
allt = feats
else:
allt += feats
allt /= 2
class_means.append(allt.flatten())
self.class_means = torch.stack(class_means)