Source code for avalanche.training.losses

import copy

import numpy as np
import torch
import torch.nn.functional as F
from torch import nn
from torch.nn import BCELoss

from avalanche.training.plugins import SupervisedPlugin
from avalanche.training.regularization import cross_entropy_with_oh_targets


[docs]class ICaRLLossPlugin(SupervisedPlugin): """ ICaRLLossPlugin Similar to the Knowledge Distillation Loss. Works as follows: The target is constructed by taking the one-hot vector target for the current sample and assigning to the position corresponding to the past classes the output of the old model on the current sample. Doesn't work if classes observed in previous experiences might be observed again in future training experiences. """
[docs] def __init__(self): super().__init__() self.criterion = BCELoss() self.old_classes = [] self.old_model = None self.old_logits = None
def before_forward(self, strategy, **kwargs): if self.old_model is not None: with torch.no_grad(): self.old_logits = self.old_model(strategy.mb_x) def __call__(self, logits, targets): predictions = torch.sigmoid(logits) one_hot = torch.zeros( targets.shape[0], logits.shape[1], dtype=torch.float, device=logits.device, ) one_hot[range(len(targets)), targets.long()] = 1 if self.old_logits is not None: old_predictions = torch.sigmoid(self.old_logits) one_hot[:, self.old_classes] = old_predictions[:, self.old_classes] self.old_logits = None return self.criterion(predictions, one_hot) def after_training_exp(self, strategy, **kwargs): if self.old_model is None: old_model = copy.deepcopy(strategy.model) self.old_model = old_model.to(strategy.device) self.old_model.load_state_dict(strategy.model.state_dict()) self.old_classes += np.unique(strategy.experience.dataset.targets).tolist()
[docs]class SCRLoss(torch.nn.Module): """ Supervised Contrastive Replay Loss as defined in Eq. 5 of https://arxiv.org/pdf/2103.13885.pdf. Author: Yonglong Tian (yonglong@mit.edu) Date: May 07, 2020 Original GitHub repository: https://github.com/HobbitLong/SupContrast/ LICENSE: BSD 2-Clause License """
[docs] def __init__(self, temperature=0.07, contrast_mode="all", base_temperature=0.07): super().__init__() self.temperature = temperature self.contrast_mode = contrast_mode self.base_temperature = base_temperature
def forward(self, features, labels=None, mask=None): """Compute loss for model. If both `labels` and `mask` are None, it degenerates to SimCLR unsupervised loss: https://arxiv.org/pdf/2002.05709.pdf features: [bsz, n_views, f_dim] `n_views` is the number of crops from each image, better be L2 normalized in f_dim dimension Args: features: hidden vector of shape [bsz, n_views, ...]. labels: ground truth of shape [bsz]. mask: contrastive mask of shape [bsz, bsz], mask_{i,j}=1 if sample j has the same class as sample i. Can be asymmetric. Returns: A loss scalar. """ device = features.device if len(features.shape) < 3: raise ValueError( "`features` needs to be [bsz, n_views, ...]," "at least 3 dimensions are required" ) if len(features.shape) > 3: features = features.view(features.shape[0], features.shape[1], -1) batch_size = features.shape[0] if labels is not None and mask is not None: raise ValueError("Cannot define both `labels` and `mask`") elif labels is None and mask is None: mask = torch.eye(batch_size, dtype=torch.float32).to(device) elif labels is not None: labels = labels.contiguous().view(-1, 1) if labels.shape[0] != batch_size: raise ValueError("Num of labels does not match num of features") mask = torch.eq(labels, labels.T).float().to(device) else: mask = mask.float().to(device) contrast_count = features.shape[1] contrast_feature = torch.cat(torch.unbind(features, dim=1), dim=0) if self.contrast_mode == "one": anchor_feature = features[:, 0] anchor_count = 1 elif self.contrast_mode == "all": anchor_feature = contrast_feature anchor_count = contrast_count else: raise ValueError("Unknown mode: {}".format(self.contrast_mode)) # compute logits anchor_dot_contrast = torch.div( torch.matmul(anchor_feature, contrast_feature.T), self.temperature ) # for numerical stability logits_max, _ = torch.max(anchor_dot_contrast, dim=1, keepdim=True) logits = anchor_dot_contrast - logits_max.detach() # tile mask mask = mask.repeat(anchor_count, contrast_count) # mask-out self-contrast cases logits_mask = torch.scatter( torch.ones_like(mask), 1, torch.arange(batch_size * anchor_count).view(-1, 1).to(device), 0, ) mask = mask * logits_mask # compute log_prob exp_logits = torch.exp(logits) * logits_mask log_prob = logits - torch.log(exp_logits.sum(1, keepdim=True)) # compute mean of log-likelihood over positive mean_log_prob_pos = (mask * log_prob).sum(1) / mask.sum(1) # loss loss = -(self.temperature / self.base_temperature) * mean_log_prob_pos loss = loss.view(anchor_count, batch_size).mean() return loss
class MaskedCrossEntropy(SupervisedPlugin): """ Masked Cross Entropy This criterion can be used for instance in Class Incremental Learning Problems when no examplars are used (i.e LwF in Class Incremental Learning would need to use mask="new"). """ def __init__(self, classes=None, mask="seen", reduction="mean"): """ param: classes: Initial value for current classes param: mask: "all" normal cross entropy, uses all the classes seen so far "old" cross entropy only on the old classes "new" cross entropy only on the new classes param: reduction: "mean" or "none", average or per-sample loss """ super().__init__() assert mask in ["seen", "new", "old", "all"] if classes is not None: self.current_classes = set(classes) else: self.current_classes = set() self.old_classes = set() self.reduction = reduction self.mask = mask def __call__(self, logits, targets): oh_targets = F.one_hot(targets, num_classes=logits.shape[1]) oh_targets = oh_targets[:, self.current_mask(logits.shape[1])] logits = logits[:, self.current_mask(logits.shape[1])] return cross_entropy_with_oh_targets( logits, oh_targets.float(), reduction=self.reduction, ) def current_mask(self, logit_shape): if self.mask == "seen": return list(self.current_classes.union(self.old_classes)) elif self.mask == "new": return list(self.current_classes) elif self.mask == "old": return list(self.old_classes) elif self.mask == "all": return list(range(int(logit_shape))) def adaptation(self, new_classes): self.old_classes = self.old_classes.union(self.current_classes) self.current_classes = set(new_classes) def before_training_exp(self, strategy, **kwargs): self.adaptation(strategy.experience.classes_in_this_experience) __all__ = ["ICaRLLossPlugin", "SCRLoss", "MaskedCrossEntropy"]