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"""
@inproceedings{DBLP:conf/cvpr/HouPLWL19,
title = {Learning a Unified Classifier Incrementally via Rebalancing},
author = {Saihui Hou and
Xinyu Pan and
Chen Change Loy and
Zilei Wang and
Dahua Lin},
booktitle = {{IEEE} Conference on Computer Vision and Pattern Recognition, {CVPR}
2019, Long Beach, CA, USA, June 16-20, 2019},
pages = {831--839},
publisher = {Computer Vision Foundation / {IEEE}},
year = {2019}
}
https://openaccess.thecvf.com/content_CVPR_2019/html/Hou_Learning_a_Unified_Classifier_Incrementally_via_Rebalancing_CVPR_2019_paper.html
Adapted from https://github.com/hshustc/CVPR19_Incremental_Learning
"""
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.resnet import *
import numpy as np
from torch.utils.data import DataLoader
cur_features = []
ref_features = []
old_scores = []
new_scores = []
def get_ref_features(self, inputs, outputs):
global ref_features
ref_features = inputs[0]
def get_cur_features(self, inputs, outputs):
global cur_features
cur_features = inputs[0]
def get_old_scores_before_scale(self, inputs, outputs):
global old_scores
old_scores = outputs
def get_new_scores_before_scale(self, inputs, outputs):
global new_scores
new_scores = outputs
class Model(nn.Module):
# A model consists with a backbone and a classifier
def __init__(self, backbone, feat_dim, num_class):
super().__init__()
self.backbone = backbone
self.feat_dim = feat_dim
self.num_class = num_class
self.classifier = CosineLinear(feat_dim, num_class)
def forward(self, x):
return self.get_logits(x)
def get_logits(self, x):
logits = self.classifier(self.backbone(x)['features'])
return logits
class LUCIR(Finetune):
def __init__(self, backbone, feat_dim, num_class, **kwargs):
super().__init__(backbone, feat_dim, num_class, **kwargs)
self.kwargs = kwargs
self.network = Model(self.backbone, feat_dim, kwargs['init_cls_num'])
self.K = kwargs['K']
self.lw_mr = kwargs['lw_mr']
self.ref_model = None
self.task_idx = 0
def before_task(self, task_idx, buffer, train_loader, test_loaders):
self.task_idx = task_idx
if task_idx == 1:
self.ref_model = copy.deepcopy(self.network)
in_features = self.network.classifier.in_features
out_features = self.network.classifier.out_features
new_fc = SplitCosineLinear(in_features, out_features, self.kwargs['inc_cls_num'])
new_fc.fc1.weight.data = self.network.classifier.weight.data
new_fc.sigma.data = self.network.classifier.sigma.data
self.network.classifier = new_fc
lamda_mult = out_features*1.0 / self.kwargs['inc_cls_num']
elif task_idx > 1:
self.ref_model = copy.deepcopy(self.network)
in_features = self.network.classifier.in_features
out_features1 = self.network.classifier.fc1.out_features
out_features2 = self.network.classifier.fc2.out_features
new_fc = SplitCosineLinear(in_features, out_features1+out_features2, self.kwargs['inc_cls_num']).to(self.device)
new_fc.fc1.weight.data[:out_features1] = self.network.classifier.fc1.weight.data
new_fc.fc1.weight.data[out_features1:] = self.network.classifier.fc2.weight.data
new_fc.sigma.data = self.network.classifier.sigma.data
self.network.classifier = new_fc
lamda_mult = (out_features1+out_features2)*1.0 / (self.kwargs['inc_cls_num'])
if task_idx > 0:
self.cur_lamda = self.kwargs['lamda'] * math.sqrt(lamda_mult)
else:
self.cur_lamda = self.kwargs['lamda']
self._init_new_fc(task_idx, buffer, train_loader)
if task_idx == 0:
self.loss_fn = nn.CrossEntropyLoss()
else:
self.loss_fn1 = nn.CosineEmbeddingLoss()
self.loss_fn2 = nn.CrossEntropyLoss()
self.loss_fn3 = nn.MarginRankingLoss(margin=self.kwargs['dist'])
self.ref_model.eval()
self.num_old_classes = self.ref_model.classifier.out_features
self.handle_ref_features = self.ref_model.classifier.register_forward_hook(get_ref_features)
self.handle_cur_features = self.network.classifier.register_forward_hook(get_cur_features)
self.handle_old_scores_bs = self.network.classifier.fc1.register_forward_hook(get_old_scores_before_scale)
self.handle_new_scores_bs = self.network.classifier.fc2.register_forward_hook(get_new_scores_before_scale)
self.network = self.network.to(self.device)
if self.ref_model is not None:
self.ref_model = self.ref_model.to(self.device)
def _init_new_fc(self, task_idx, buffer, train_loader):
if task_idx == 0:
return
old_embedding_norm = self.network.classifier.fc1.weight.data.norm(dim=1, keepdim=True)
average_old_embedding_norm = torch.mean(old_embedding_norm, dim=0).to('cpu').type(torch.DoubleTensor)
feature_model = self.network.backbone
num_features = self.network.classifier.in_features
novel_embedding = torch.zeros((self.kwargs['inc_cls_num'], num_features))
tmp_datasets = copy.deepcopy(train_loader.dataset)
for cls_idx in range(self.network.classifier.fc1.out_features, self.network.classifier.fc1.out_features + self.network.classifier.fc2.out_features):
cls_dataset = train_loader.dataset
task_data, task_target = cls_dataset.images, cls_dataset.labels
cls_indices = np.where(np.array(task_target) == cls_idx) # tuple
cls_data, cls_target = np.array([task_data[i] for i in cls_indices[0]]), np.array([task_target[i] for i in cls_indices[0]])
tmp_datasets.images = cls_data
tmp_datasets.labels = cls_target
tmp_loader = DataLoader(tmp_datasets, batch_size=128, shuffle=False, num_workers=2)
num_samples = cls_data.shape[0]
cls_features = self._compute_feature(feature_model, tmp_loader, num_samples, num_features)
norm_features = F.normalize(torch.from_numpy(cls_features), p=2, dim=1)
cls_embedding = torch.mean(norm_features, dim=0)
novel_embedding[cls_idx-self.network.classifier.fc1.out_features] = F.normalize(cls_embedding, p=2, dim=0) * average_old_embedding_norm
self.network.to(self.device)
self.network.classifier.fc2.weight.data = novel_embedding.to(self.device)
def _compute_feature(self, feature_model, loader, num_samples, num_features):
feature_model.eval()
features = np.zeros([num_samples, num_features])
start_idx = 0
with torch.no_grad():
for batch_idx, batch in enumerate(loader):
inputs, labels = batch['image'], batch['label']
inputs = inputs.to(self.device)
features[start_idx:start_idx+inputs.shape[0], :] = np.squeeze(feature_model.feature(inputs).cpu())
start_idx = start_idx+inputs.shape[0]
assert(start_idx==num_samples)
return features
def observe(self, data):
x, y = data['image'], data['label']
x = x.to(self.device)
y = y.to(self.device)
logit = self.network(x)
if self.task_idx == 0:
loss = self.loss_fn(logit, y)
else:
ref_outputs = self.ref_model(x)
loss = self.loss_fn1(cur_features, ref_features.detach(), \
torch.ones(x.size(0)).to(self.device)) * self.cur_lamda
loss += self.loss_fn2(logit, y)
outputs_bs = torch.cat((old_scores, new_scores), dim=1)
assert(outputs_bs.size()==logit.size())
gt_index = torch.zeros(outputs_bs.size()).to(self.device)
gt_index = gt_index.scatter(1, y.view(-1,1), 1).ge(0.5)
gt_scores = outputs_bs.masked_select(gt_index)
max_novel_scores = outputs_bs[:, self.num_old_classes:].topk(self.K, dim=1)[0]
hard_index = y.lt(self.num_old_classes)
hard_num = torch.nonzero(hard_index).size(0)
if hard_num > 0:
gt_scores = gt_scores[hard_index].view(-1, 1).repeat(1, self.K)
max_novel_scores = max_novel_scores[hard_index]
assert(gt_scores.size() == max_novel_scores.size())
assert(gt_scores.size(0) == hard_num)
loss += self.loss_fn3(gt_scores.view(-1, 1), \
max_novel_scores.view(-1, 1), torch.ones(hard_num*self.K, 1).to(self.device)) * self.lw_mr
pred = torch.argmax(logit, dim=1)
acc = torch.sum(pred == y).item()
return pred, acc / x.size(0), loss
def after_task(self, task_idx, buffer, train_loader, test_loaders):
if self.task_idx > 0:
self.handle_ref_features.remove()
self.handle_cur_features.remove()
self.handle_old_scores_bs.remove()
self.handle_new_scores_bs.remove()
def inference(self, data):
x, y = data['image'].to(self.device), data['label'].to(self.device)
logit = self.network(x)
pred = torch.argmax(logit, dim=1)
acc = torch.sum(pred == y).item()
return pred, acc / x.size(0)
def get_parameters(self, config):
if self.task_idx > 0:
#fix the embedding of old classes
ignored_params = list(map(id, self.network.classifier.fc1.parameters()))
base_params = filter(lambda p: id(p) not in ignored_params, \
self.network.parameters())
tg_params =[{'params': base_params, 'lr': 0.1, 'weight_decay': 5e-4}, \
{'params': self.network.classifier.fc1.parameters(), 'lr': 0, 'weight_decay': 0}]
else:
tg_params = self.network.parameters()
return tg_params |