"""Loss getter.""" return [getattr(self, name) for name in self.loss_names]def call(self, outputs: TensorDict, **kwargs) -> TensorDict: """Get a dictionary of loss values from all the losses.Args: outputs: A dictionary of model output tensors to feed into the losses. ...
[WACV23] A strong baseline for body part-based person re-identification - bpbreid/torchreid/losses/GiLt_loss.py at main · VlSomers/bpbreid
loss_weights: Optional list or dictionary specifying scalar coefficients (Python floats) to weight the loss contributions of different model outputs. The loss value that will be minimized by the model will then be theweighted sumof all individual losses, weighted by theloss_weightscoefficients. If ...
That being said, please specifically try to avoid using l as a variable name, even for iterators. First, because of E741 (see PEP8 "names to avoid"), second because in the context of Torchélie it might mean layer, label, loss, length, line, or other words that are spread among the...
if self.reduction == LossReduction.NONE.value: return ncc.neg() if self.reduction == LossReduction.MEAN.value: return torch.mean(ncc).neg() # average over the batch, channel and spatial ndims raise ValueError(f'Unsupported reduction: {self.reduction}, available options are ["mean", "...
These weights are intended to be used as multipliers in a keypoint loss function. Groundtruth keypoint weights are constructed as follows: - Initialize the weight for each keypoint type based on user-specified weights in the input_reader proto - Mask out (i.e. make zero) all keypoint ...
[CVPR 2023] SFD2: Semantic-guided Feature Detection and Description. Embedding semantics into local features implicitly for long-term visual localization - sfd2/nets/losses.py at dev · feixue94/sfd2
In Defense of the Triplet Loss for Person Re-Identification. arXiv:1703.07737. Imported from https://github.com/Cysu/open-reid/blob/master/reid/loss/triplet.py. Imported from `<https://github.com/Cysu/open-reid/blob/master/reid/loss/triplet.py>`_. Args: margin (float): margin for ...
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for _ in range(10): step_ = self.prepare_new_leaves_values(terminal_regions, leaf_values=leaf_values, y_pred=y_pred + step)[0] step += 0.5 * step_ return step # region Classification losses class AdaLossFunction(HessianLossFunction): """ AdaLossFunction is the same as Exponential ...