import logging import numpy as np import torch from torch import nn from torch.serialization import load from tqdm import tqdm from torch import optim from torch.nn import functional as F from torch.utils.data import DataLoader from utils.inc_net import IncrementalNet from models.base import BaseLearner from utils.toolkit import target2onehot, tensor2numpy, count_parameters, count_parameters from torch.utils.tensorboard import SummaryWriter init_epoch = 200 init_lr = 0.1 init_milestones = [60, 120, 170] init_lr_decay = 0.1 init_weight_decay = 0.0005 epochs = 200 lrate = 0.1 milestones = [40, 70] lrate_decay = 0.1 batch_size = 32 weight_decay = 2e-4 num_workers = 2 class Finetune(BaseLearner): def __init__(self, args): super().__init__(args) self._network = IncrementalNet(args, True) self.writer = SummaryWriter(log_dir='runs/{}'.format(args['summary_file'])) #self.train_mode = args['train'] def after_task(self): self._known_classes = self._total_classes def incremental_train(self, data_manager): self.curr_acc = 0 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) 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)) ) logging.debug("****MODEL ARCHITECTURE****") logging.debug(self._network) train_dataset = data_manager.get_dataset( np.arange(self._known_classes, self._total_classes), source="train", mode="train", ) self.train_loader = DataLoader( train_dataset, batch_size=batch_size, shuffle=True, num_workers=num_workers ) test_dataset = data_manager.get_dataset( np.arange(0, self._total_classes), source="test", mode="test" ) self.test_loader = DataLoader( test_dataset, batch_size=batch_size, shuffle=False, num_workers=num_workers ) if len(self._multiple_gpus) > 1: print('***********RUNNING on multiple devices****************') 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 if self._total_classes == data_manager.get_total_classnum(): self.writer.close() def _train(self, train_loader, test_loader): self._network.to(self._device) if self._cur_task == 0: optimizer = optim.SGD( self._network.parameters(), momentum=0.9, lr=init_lr, weight_decay=init_weight_decay, ) scheduler = optim.lr_scheduler.MultiStepLR( optimizer=optimizer, milestones=init_milestones, gamma=init_lr_decay ) self._init_train(train_loader, test_loader, optimizer, scheduler) else: optimizer = optim.SGD( self._network.parameters(), lr=lrate, momentum=0.9, weight_decay=weight_decay, ) # 1e-5 scheduler = optim.lr_scheduler.MultiStepLR( optimizer=optimizer, milestones=milestones, gamma=lrate_decay ) self._update_representation(train_loader, test_loader, optimizer, scheduler) self.writer.add_figure("Confusion matrix", self._createConfusionMatrix(self._network, self.test_loader), self._cur_task) self.writer.add_scalar('Accuracies', self.curr_acc, self._cur_task) def _init_train(self, train_loader, test_loader, optimizer, scheduler): prog_bar = tqdm(range(init_epoch)) for _, epoch in enumerate(prog_bar): self._network.train() losses = 0.0 correct, total = 0, 0 for i, (_, inputs, targets) in enumerate(train_loader): inputs, targets = inputs.to(self._device), targets.to(self._device) logits = self._network(inputs)["logits"] loss = F.cross_entropy(logits, targets) optimizer.zero_grad() loss.backward() optimizer.step() losses += loss.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: test_acc = self._compute_accuracy(self._network, test_loader) info = "Task {}, Epoch {}/{} => Loss {:.3f}, Train_accy {:.2f}, Test_accy {:.2f}".format( self._cur_task, epoch + 1, init_epoch, losses / len(train_loader), train_acc, test_acc, ) # "Learning on {}-{}".format(self._known_classes, self._total_classes) self.writer.add_scalar('Task: {} on {}-{} -- Train/Loss'.format(self._cur_task, self._known_classes, self._total_classes), losses / len(train_loader), epoch) self.writer.add_scalar('Task: {} on {}-{} -- Train/Accuracy'.format(self._cur_task, self._known_classes, self._total_classes), train_acc, epoch) self.writer.add_scalar('Task: {} on {}-{} -- Test/Accuracy'.format(self._cur_task, self._known_classes, self._total_classes), test_acc, epoch) else: info = "Task {}, Epoch {}/{} => Loss {:.3f}, Train_accy {:.2f}".format( self._cur_task, epoch + 1, init_epoch, losses / len(train_loader), train_acc, ) prog_bar.set_description(info) logging.info(info) def _update_representation(self, train_loader, test_loader, optimizer, scheduler): prog_bar = tqdm(range(epochs)) for _, epoch in enumerate(prog_bar): self._network.train() losses = 0.0 correct, total = 0, 0 for i, (_, inputs, targets) in enumerate(train_loader): inputs, targets = inputs.to(self._device), targets.to(self._device) logits = self._network(inputs)["logits"] fake_targets = targets - self._known_classes loss_clf = F.cross_entropy( logits[:, self._known_classes :], fake_targets ) loss = loss_clf optimizer.zero_grad() loss.backward() optimizer.step() losses += loss.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: test_acc = self._compute_accuracy(self._network, test_loader) info = "Task {}, Epoch {}/{} => Loss {:.3f}, Train_accy {:.2f}, Test_accy {:.2f}".format( self._cur_task, epoch + 1, epochs, losses / len(train_loader), train_acc, test_acc, ) self.writer.add_scalar('Task: {} on {}-{} -- Train/Loss'.format(self._cur_task, self._known_classes, self._total_classes), losses / len(train_loader), epoch) self.writer.add_scalar('Task: {} on {}-{} -- Train/Accuracy'.format(self._cur_task, self._known_classes, self._total_classes), train_acc, epoch) self.writer.add_scalar('Task: {} on {}-{} -- Test/Accuracy'.format(self._cur_task, self._known_classes, self._total_classes), test_acc, epoch) else: info = "Task {}, Epoch {}/{} => Loss {:.3f}, Train_accy {:.2f}".format( self._cur_task, epoch + 1, epochs, losses / len(train_loader), train_acc, ) prog_bar.set_description(info) logging.info(info)