import logging
import numpy as np
import os
from tqdm import tqdm
import torch
from torch import nn
from torch import optim
from torch.nn import functional as F
from torch.utils.data import DataLoader,Dataset
from models.base import BaseLearner
from utils.inc_net import CosineIncrementalNet, FOSTERNet, IncrementalNet
from utils.toolkit import count_parameters, target2onehot, tensor2numpy
from utils.autoaugment import CIFAR10Policy,ImageNetPolicy
from utils.ops import Cutout
from torchvision import datasets, transforms
EPSILON = 1e-8
class SSRE(BaseLearner):
def __init__(self, args):
super().__init__(args)
self.args = args
self._network = IncrementalNet(args, False)
self._protos = []
def after_task(self):
self._known_classes = self._total_classes
self._old_network = self._network.copy().freeze()
if hasattr(self._old_network,"module"):
self.old_network_module_ptr = self._old_network.module
else:
self.old_network_module_ptr = self._old_network
#self.save_checkpoint("{}_{}_{}".format(self.args["model_name"],self.args["init_cls"],self.args["increment"]))
def incremental_train(self, data_manager):
self.data_manager = data_manager
if self._cur_task == 0:
self.data_manager._train_trsf = [
transforms.RandomCrop(32, padding=4),
transforms.RandomHorizontalFlip(),
transforms.ColorJitter(brightness=63/255),
CIFAR10Policy(),
Cutout(n_holes=1, length=16)
]
else:
self.data_manager._train_trsf = [
transforms.RandomCrop(32, padding=4),
transforms.RandomHorizontalFlip(),
transforms.ColorJitter(brightness=63/255),
]
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)
self._network_module_ptr = self._network
logging.info("Model Expansion!")
self._network_expansion()
logging.info(
'Learning on {}-{}'.format(self._known_classes, self._total_classes))
logging.info('All params: {}'.format(count_parameters(self._network)))
logging.info('Trainable params: {}'.format(
count_parameters(self._network, True)))
train_dataset = data_manager.get_dataset(np.arange(self._known_classes, self._total_classes), source='train',mode='train', appendent=self._get_memory())
if self._cur_task == 0:
batch_size = 64
else:
batch_size = self.args["batch_size"]
self.train_loader = DataLoader(
train_dataset, batch_size=batch_size, shuffle=True, num_workers=self.args["num_workers"], pin_memory=True)
test_dataset = data_manager.get_dataset(
np.arange(0, self._total_classes), source='test', mode='test')
self.test_loader = DataLoader(
test_dataset, batch_size=self.args["batch_size"], shuffle=False, num_workers=self.args["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
logging.info("Model Compression!")
self._network_compression()
def _train(self, train_loader, test_loader):
resume = False
if self._cur_task in []:
self._network.load_state_dict(torch.load("{}_{}_{}_{}.pkl".format(self.args["model_name"],self.args["init_cls"],self.args["increment"],self._cur_task))["model_state_dict"])
resume = True
self._network.to(self._device)
if hasattr(self._network, "module"):
self._network_module_ptr = self._network.module
if not resume:
self._epoch_num = self.args["epochs"]
optimizer = torch.optim.Adam(filter(lambda p: p.requires_grad, self._network.parameters(
)), lr=self.args["lr"], weight_decay=self.args["weight_decay"])
scheduler = torch.optim.lr_scheduler.StepLR(optimizer, step_size=self.args["step_size"], gamma=self.args["gamma"])
self._train_function(train_loader, test_loader, optimizer, scheduler)
self._build_protos()
def _build_protos(self):
with torch.no_grad():
for class_idx in range(self._known_classes, self._total_classes):
data, targets, idx_dataset = self.data_manager.get_dataset(np.arange(class_idx, class_idx+1), source='train',
mode='test', ret_data=True)
idx_loader = DataLoader(idx_dataset, batch_size=self.args["batch_size"], shuffle=False, num_workers=4)
vectors, _ = self._extract_vectors(idx_loader)
class_mean = np.mean(vectors, axis=0)
self._protos.append(class_mean)
def train(self):
if self._cur_task > 0:
self._network.eval()
return
self._network.train()
def _train_function(self, train_loader, test_loader, optimizer, scheduler):
prog_bar = tqdm(range(self._epoch_num))
for _, epoch in enumerate(prog_bar):
self.train()
losses = 0.
losses_clf, losses_fkd, losses_proto = 0., 0., 0.
correct, total = 0, 0
for i, (_, inputs, targets) in enumerate(train_loader):
inputs, targets = inputs.to(
self._device, non_blocking=True), targets.to(self._device, non_blocking=True)
logits, loss_clf, loss_fkd, loss_proto = self._compute_ssre_loss(inputs,targets)
loss = loss_clf + loss_fkd + loss_proto
optimizer.zero_grad()
loss.backward()
optimizer.step()
losses += loss.item()
losses_clf += loss_clf.item()
losses_fkd += loss_fkd.item()
losses_proto += loss_proto.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:
info = 'Task {}, Epoch {}/{} => Loss {:.3f}, Loss_clf {:.3f}, Loss_fkd {:.3f}, Loss_proto {:.3f}, Train_accy {:.2f}'.format(
self._cur_task, epoch+1, self._epoch_num, losses/len(train_loader), losses_clf/len(train_loader), losses_fkd/len(train_loader), losses_proto/len(train_loader), train_acc)
else:
test_acc = self._compute_accuracy(self._network, test_loader)
info = 'Task {}, Epoch {}/{} => Loss {:.3f}, Loss_clf {:.3f}, Loss_fkd {:.3f}, Loss_proto {:.3f}, Train_accy {:.2f}, Test_accy {:.2f}'.format(
self._cur_task, epoch+1, self._epoch_num, losses/len(train_loader), losses_clf/len(train_loader), losses_fkd/len(train_loader), losses_proto/len(train_loader), train_acc, test_acc)
prog_bar.set_description(info)
logging.info(info)
def _compute_ssre_loss(self,inputs, targets):
if self._cur_task == 0:
logits = self._network(inputs)["logits"]
loss_clf = F.cross_entropy(logits/self.args["temp"], targets)
return logits, loss_clf, torch.tensor(0.), torch.tensor(0.)
features = self._network_module_ptr.extract_vector(inputs) # N D
with torch.no_grad():
features_old = self.old_network_module_ptr.extract_vector(inputs)
protos = torch.from_numpy(np.array(self._protos)).to(self._device) # C D
with torch.no_grad():
weights = F.normalize(features,p=2,dim=1,eps=1e-12) @ F.normalize(protos,p=2,dim=1,eps=1e-12).T
weights = torch.max(weights,dim=1)[0]
# mask = weights > self.args["threshold"]
mask = weights
logits = self._network(inputs)["logits"]
loss_clf = F.cross_entropy(logits/self.args["temp"],targets,reduction="none")
# loss_clf = torch.mean(loss_clf * ~mask)
loss_clf = torch.mean(loss_clf * (1-mask))
loss_fkd = torch.norm(features - features_old, p=2, dim=1)
loss_fkd = self.args["lambda_fkd"] * torch.sum(loss_fkd * mask)
index = np.random.choice(range(self._known_classes),size=self.args["batch_size"],replace=True)
proto_features = np.array(self._protos)[index]
proto_targets = index
proto_features = proto_features
proto_features = torch.from_numpy(proto_features).float().to(self._device,non_blocking=True)
proto_targets = torch.from_numpy(proto_targets).to(self._device,non_blocking=True)
proto_logits = self._network_module_ptr.fc(proto_features)["logits"]
loss_proto = self.args["lambda_proto"] * F.cross_entropy(proto_logits/self.args["temp"], proto_targets)
return logits, loss_clf, loss_fkd, loss_proto
def eval_task(self, save_conf=False):
y_pred, y_true = self._eval_cnn(self.test_loader)
cnn_accy = self._evaluate(y_pred, y_true)
if hasattr(self, '_class_means'):
y_pred, y_true = self._eval_nme(self.test_loader, self._class_means)
nme_accy = self._evaluate(y_pred, y_true)
elif hasattr(self, '_protos'):
y_pred, y_true = self._eval_nme(self.test_loader, self._protos/np.linalg.norm(self._protos,axis=1)[:,None])
nme_accy = self._evaluate(y_pred, y_true)
else:
nme_accy = None
if save_conf:
_pred = y_pred.T[0]
_pred_path = os.path.join(self.args['logfilename'], "pred.npy")
_target_path = os.path.join(self.args['logfilename'], "target.npy")
np.save(_pred_path, _pred)
np.save(_target_path, y_true)
_save_dir = os.path.join(f"./results/{self.args['model_name']}/conf_matrix/{self.args['prefix']}")
os.makedirs(_save_dir, exist_ok=True)
_save_path = os.path.join(_save_dir, f"{self.args['csv_name']}.csv")
with open(_save_path, "a+") as f:
f.write(f"{self.args['model_name']},{_pred_path},{_target_path} \n")
return cnn_accy, nme_accy
def _network_expansion(self):
if self._cur_task > 0:
for p in self._network.convnet.parameters():
p.requires_grad = True
for k, v in self._network.convnet.named_parameters():
if 'adapter' not in k:
v.requires_grad = False
# self._network.convnet.re_init_params() # do not use!
self._network.convnet.switch("parallel_adapters")
def _network_compression(self):
model_dict = self._network.state_dict()
for k, v in model_dict.items():
if 'adapter' in k:
k_conv3 = k.replace('adapter', 'conv')
if 'weight' in k:
model_dict[k_conv3] = model_dict[k_conv3] + F.pad(v, [1, 1, 1, 1], 'constant', 0)
model_dict[k] = torch.zeros_like(v)
elif 'bias' in k:
model_dict[k_conv3] = model_dict[k_conv3] + v
model_dict[k] = torch.zeros_like(v)
else:
assert 0
self._network.load_state_dict(model_dict)
self._network.convnet.switch("normal")