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"""
@inproceedings{DBLP:conf/cvpr/YanX021,
author = {Shipeng Yan and
Jiangwei Xie and
Xuming He},
title = {{DER:} Dynamically Expandable Representation for Class Incremental
Learning},
booktitle = {{IEEE} Conference on Computer Vision and Pattern Recognition, {CVPR}
2021, virtual, June 19-25, 2021},
pages = {3014--3023},
year = {2021},
}
https://openaccess.thecvf.com/content/CVPR2021/papers/Yan_DER_Dynamically_Expandable_Representation_for_Class_Incremental_Learning_CVPR_2021_paper.pdf
Adapted from https://github.com/G-U-N/PyCIL/blob/master/models/der.py
"""
import math
import copy
import torch
import torch.nn as nn
from torch.nn import Parameter
import torch.nn.functional as F
from .finetune import Finetune
from core.model.backbone import resnet18, resnet34, resnet50
from core.utils import get_instance
def get_convnet(convnet_type, pretrained=False):
name = convnet_type.lower()
if name == "resnet18":
dic = {"num_classes": 10, "args":{'dataset':'cifar100'}}
return resnet18(**dic)
# elif name=="resnet32":
# return resnet32()
elif name == "resnet34":
return resnet34()
elif name == "resnet50":
return resnet50()
else:
raise NotImplementedError("Unknown type {}".format(convnet_type))
class SimpleLinear(nn.Module):
'''
Reference:
https://github.com/pytorch/pytorch/blob/master/torch/nn/modules/linear.py
'''
def __init__(self, in_features, out_features, bias=True):
super(SimpleLinear, self).__init__()
self.in_features = in_features
self.out_features = out_features
self.weight = nn.Parameter(torch.Tensor(out_features, in_features))
if bias:
self.bias = nn.Parameter(torch.Tensor(out_features))
else:
self.register_parameter('bias', None)
self.reset_parameters()
def reset_parameters(self):
nn.init.kaiming_uniform_(self.weight, nonlinearity='linear')
nn.init.constant_(self.bias, 0)
def forward(self, input):
return {'logits': F.linear(input, self.weight, self.bias)}
class DER(Finetune):
def __init__(self, backbone, feat_dim, num_class, **kwargs):
super().__init__(backbone, feat_dim, num_class, **kwargs)
self.convnets = nn.ModuleList()
self.pretrained = None
self.out_dim = None
self.fc = None
self.aux_fc = None
self.task_sizes = []
self.kwargs = kwargs
self.init_cls_num = kwargs['init_cls_num']
self.inc_cls_num = kwargs['inc_cls_num']
self.known_cls_num = 0
self.total_cls_num = 0
self.convnet_type = 'resnet18'
@property
def feature_dim(self):
if self.out_dim is None:
return 0
return self.out_dim * len(self.convnets)
def forward(self, x):
features = [convnet(x)["features"] for convnet in self.convnets]
features = torch.cat(features, 1)
out = self.fc(features) # {logics: self.fc(features)}
aux_logits = self.aux_fc(features[:, -self.out_dim :])["logits"]
out.update({"aux_logits": aux_logits, "features": features})
return out
"""
{
'features': features
'logits': logits
'aux_logits':aux_logits
}
"""
def observe(self, data):
x, y = data['image'], data['label']
x = x.to(self.device)
y = y.to(self.device)
features = [convnet(x)["features"] for convnet in self.convnets]
features = torch.cat(features, 1)
logit = self.fc(features)['logits']
if self.task_idx == 0:
loss = self.loss_fn(logit, y)
else:
loss_clf = self.loss_fn(logit, y)
aux_targets = y.clone()
aux_targets = torch.where(
aux_targets - self.known_cls_num + 1 > 0,
aux_targets - self.known_cls_num + 1,
0,
)
aux_logits = self.aux_fc(features[:, -self.out_dim :])["logits"]
loss_aux = F.cross_entropy(aux_logits, aux_targets)
loss = loss_aux + loss_clf
pred = torch.argmax(logit, dim=1)
acc = torch.sum(pred == y).item()
return pred, acc / x.size(0), loss
def inference(self, data):
x, y = data['image'], data['label']
x = x.to(self.device)
y = y.to(self.device)
features = [convnet(x)["features"] for convnet in self.convnets]
features = torch.cat(features, 1)
logit = self.fc(features)['logits']
pred = torch.argmax(logit, dim=1)
acc = torch.sum(pred == y).item()
return pred, acc / x.size(0)
def update_fc(self, nb_classes):
if len(self.convnets) == 0:
self.convnets.append(get_convnet(self.convnet_type))
else:
self.convnets.append(get_convnet(self.convnet_type))
self.convnets[-1].load_state_dict(self.convnets[-2].state_dict())
if self.out_dim is None:
self.out_dim = self.convnets[-1].out_dim
fc = self.generate_fc(self.feature_dim, nb_classes)
if self.fc is not None:
nb_output = self.fc.out_features
weight = copy.deepcopy(self.fc.weight.data)
bias = copy.deepcopy(self.fc.bias.data)
fc.weight.data[:nb_output, : self.feature_dim - self.out_dim] = weight
fc.bias.data[:nb_output] = bias
del self.fc
self.fc = fc
new_task_size = nb_classes - sum(self.task_sizes)
self.task_sizes.append(new_task_size)
self.aux_fc = self.generate_fc(self.out_dim, new_task_size + 1)
def generate_fc(self, in_dim, out_dim):
fc = SimpleLinear(in_dim, out_dim)
return fc
def freeze_convnets(self):
for param in self.convnets.parameters():
param.requires_grad = False
self.convnets.eval()
def weight_align(self, increment):
weights = self.fc.weight.data
newnorm = torch.norm(weights[-increment:, :], p=2, dim=1)
oldnorm = torch.norm(weights[:-increment, :], p=2, dim=1)
meannew = torch.mean(newnorm)
meanold = torch.mean(oldnorm)
gamma = meanold / meannew
print("alignweights,gamma=", gamma)
self.fc.weight.data[-increment:, :] *= gamma
def before_task(self, task_idx, buffer, train_loader, test_loaders):
self.task_idx = task_idx
self.known_cls_num = self.total_cls_num
self.total_cls_num = self.init_cls_num + self.task_idx*self.inc_cls_num
self.freeze_convnets()
self.update_fc(self.total_cls_num)
self.loss_fn = nn.CrossEntropyLoss()
self.convnets = self.convnets.to(self.device)
self.fc = self.fc.to(self.device)
self.aux_fc = self.aux_fc.to(self.device)
def _train(self):
self.fc.train()
# iffreeze('fc',self.fc)
self.aux_fc.train()
# iffreeze('auxfc',self.aux_fc)
for i in range(self.task_idx -1):
self.convnets[i].eval()
self.convnets[-1].train()
# for i,cov in enumerate(self.convnets):
# iffreeze(f'cov{i}',cov)
def get_parameters(self, config):
train_parameters = []
train_parameters.append({"params": self.convnets.parameters()})
if self.fc is not None:
train_parameters.append({"params": self.fc.parameters()})
if self.aux_fc is not None:
train_parameters.append({"params": self.aux_fc.parameters()})
return train_parameters
def iffreeze(name,net):
for k,v in net.named_parameters():
print('{}{}: {}'.format(name,k, v.requires_grad))
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