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import logging |
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import numpy as np |
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from tqdm import tqdm |
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import torch |
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from torch import nn |
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from torch import optim |
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from torch.nn import functional as F |
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from models.base import BaseLearner |
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from utils.inc_net import IncrementalNet |
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from utils.inc_net import CosineIncrementalNet |
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from utils.toolkit import target2onehot, tensor2numpy |
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from torch.utils.data import DataLoader |
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EPSILON = 1e-8 |
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init_epoch = 200 |
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init_lr = 0.1 |
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init_milestones = [60, 120, 170] |
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init_lr_decay = 0.1 |
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init_weight_decay = 0.0005 |
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epochs = 170 |
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lrate = 0.1 |
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milestones = [80, 120] |
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lrate_decay = 0.1 |
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batch_size = 128 |
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weight_decay = 2e-4 |
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num_workers = 0 |
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T = 2 |
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class iCaRL(BaseLearner): |
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def __init__(self, args): |
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super().__init__(args) |
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self.args = args |
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self._network = IncrementalNet(args, False) |
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def after_task(self): |
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self._old_network = self._network.copy().freeze() |
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self._known_classes = self._total_classes |
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logging.info("Exemplar size: {}".format(self.exemplar_size)) |
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def incremental_train(self, data_manager=None): |
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self._cur_task += 1 |
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self._total_classes = self._known_classes + data_manager.get_task_size( |
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self._cur_task |
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) |
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self._network.update_fc(self._total_classes) |
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logging.info( |
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"Learning on {}-{}".format(self._known_classes, self._total_classes) |
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) |
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if not self.args["attack"]: |
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train_dataset = data_manager.get_dataset( |
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np.arange(self._known_classes, self._total_classes), |
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source="train", |
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mode="train", |
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appendent=self._get_memory(), |
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) |
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self.train_loader = DataLoader( |
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train_dataset, batch_size=batch_size, shuffle=True, num_workers=num_workers |
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) |
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test_dataset = data_manager.get_dataset( |
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np.arange(0, self._total_classes), source="test", mode="test" |
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) |
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self.test_loader = DataLoader( |
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test_dataset, batch_size=batch_size, shuffle=False, num_workers=num_workers |
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) |
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if len(self._multiple_gpus) > 1: |
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self._network = nn.DataParallel(self._network, self._multiple_gpus) |
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else: |
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self.train_loader, self.test_loader = None, None |
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test_dataset = data_manager.get_dataset( |
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np.arange(0, self._total_classes), source="test", mode="test" |
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) |
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self.test_loader = DataLoader( |
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test_dataset, batch_size=batch_size, shuffle=False, num_workers=num_workers |
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) |
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self._train(self.train_loader, self.test_loader) |
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if not self.args["attack"]: |
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self.build_rehearsal_memory(data_manager, self.samples_per_class) |
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if len(self._multiple_gpus) > 1: |
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self._network = self._network.module |
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def _train(self, train_loader=None, test_loader=None): |
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self._network.to(self._device) |
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if self.args["attack"]: |
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self._network.eval() |
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if self._old_network is not None: |
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self._old_network.to(self._device) |
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if self.args["attack"]: |
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self._old_network.eval() |
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if self._cur_task == 0: |
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if not self.args["attack"]: |
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optimizer = optim.SGD( |
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self._network.parameters(), |
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momentum=0.9, |
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lr=init_lr, |
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weight_decay=init_weight_decay, |
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) |
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scheduler = optim.lr_scheduler.MultiStepLR( |
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optimizer=optimizer, milestones=init_milestones, gamma=init_lr_decay |
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) |
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self._init_train(train_loader, test_loader, optimizer, scheduler) |
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else: |
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if not self.args["attack"]: |
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optimizer = optim.SGD( |
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self._network.parameters(), |
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lr=lrate, |
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momentum=0.9, |
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weight_decay=weight_decay, |
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) |
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scheduler = optim.lr_scheduler.MultiStepLR( |
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optimizer=optimizer, milestones=milestones, gamma=lrate_decay |
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) |
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self._update_representation(train_loader, test_loader, optimizer, scheduler) |
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def _init_train(self, train_loader, test_loader, optimizer, scheduler): |
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prog_bar = tqdm(range(init_epoch)) |
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for _, epoch in enumerate(prog_bar): |
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self._network.train() |
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losses = 0.0 |
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correct, total = 0, 0 |
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for i, (_, inputs, targets) in enumerate(train_loader): |
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inputs, targets = inputs.to(self._device), targets.to(self._device).to(torch.int64) |
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logits = self._network(inputs)["logits"] |
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loss = F.cross_entropy(logits, targets) |
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optimizer.zero_grad() |
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loss.backward() |
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optimizer.step() |
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losses += loss.item() |
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_, preds = torch.max(logits, dim=1) |
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correct += preds.eq(targets.expand_as(preds)).cpu().sum() |
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total += len(targets) |
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scheduler.step() |
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train_acc = np.around(tensor2numpy(correct) * 100 / total, decimals=2) |
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if epoch % 5 == 0: |
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test_acc = self._compute_accuracy(self._network, test_loader) |
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info = "Task {}, Epoch {}/{} => Loss {:.3f}, Train_accy {:.2f}, Test_accy {:.2f}".format( |
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self._cur_task, |
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epoch + 1, |
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init_epoch, |
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losses / len(train_loader), |
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train_acc, |
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test_acc, |
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) |
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else: |
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info = "Task {}, Epoch {}/{} => Loss {:.3f}, Train_accy {:.2f}".format( |
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self._cur_task, |
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epoch + 1, |
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init_epoch, |
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losses / len(train_loader), |
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train_acc, |
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) |
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prog_bar.set_description(info) |
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logging.info(info) |
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def _update_representation(self, train_loader, test_loader, optimizer, scheduler): |
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prog_bar = tqdm(range(epochs)) |
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for _, epoch in enumerate(prog_bar): |
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self._network.train() |
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losses = 0.0 |
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correct, total = 0, 0 |
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for i, (_, inputs, targets) in enumerate(train_loader): |
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inputs, targets = inputs.to(self._device), targets.to(self._device).to(torch.int64) |
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logits = self._network(inputs)["logits"] |
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loss_clf = F.cross_entropy(logits, targets) |
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loss_kd = _KD_loss( |
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logits[:, : self._known_classes], |
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self._old_network(inputs)["logits"], |
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T, |
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) |
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loss = loss_clf + loss_kd |
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optimizer.zero_grad() |
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loss.backward() |
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optimizer.step() |
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losses += loss.item() |
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_, preds = torch.max(logits, dim=1) |
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correct += preds.eq(targets.expand_as(preds)).cpu().sum() |
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total += len(targets) |
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scheduler.step() |
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train_acc = np.around(tensor2numpy(correct) * 100 / total, decimals=2) |
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if epoch % 5 == 0: |
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test_acc = self._compute_accuracy(self._network, test_loader) |
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info = "Task {}, Epoch {}/{} => Loss {:.3f}, Train_accy {:.2f}, Test_accy {:.2f}".format( |
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self._cur_task, |
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epoch + 1, |
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epochs, |
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losses / len(train_loader), |
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train_acc, |
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test_acc, |
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) |
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else: |
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info = "Task {}, Epoch {}/{} => Loss {:.3f}, Train_accy {:.2f}".format( |
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self._cur_task, |
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epoch + 1, |
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epochs, |
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losses / len(train_loader), |
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train_acc, |
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) |
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prog_bar.set_description(info) |
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logging.info(info) |
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def _KD_loss(pred, soft, T): |
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pred = torch.log_softmax(pred / T, dim=1) |
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soft = torch.softmax(soft / T, dim=1) |
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return -1 * torch.mul(soft, pred).sum() / pred.shape[0] |
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