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2019-03-25 01:22:43 +08:00
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2019-04-18 19:12:17 +08:00
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< h1 > Source code for torchreid.losses.cross_entropy_loss< / h1 > < div class = "highlight" > < pre >
< span > < / span > < span class = "kn" > from< / span > < span class = "nn" > __future__< / span > < span class = "k" > import< / span > < span class = "n" > absolute_import< / span >
< span class = "kn" > from< / span > < span class = "nn" > __future__< / span > < span class = "k" > import< / span > < span class = "n" > division< / span >
< span class = "kn" > import< / span > < span class = "nn" > torch< / span >
< span class = "kn" > import< / span > < span class = "nn" > torch.nn< / span > < span class = "k" > as< / span > < span class = "nn" > nn< / span >
< div class = "viewcode-block" id = "CrossEntropyLoss" > < a class = "viewcode-back" href = "../../../pkg/losses.html#torchreid.losses.cross_entropy_loss.CrossEntropyLoss" > [docs]< / a > < span class = "k" > class< / span > < span class = "nc" > CrossEntropyLoss< / span > < span class = "p" > (< / span > < span class = "n" > nn< / span > < span class = "o" > .< / span > < span class = "n" > Module< / span > < span class = "p" > ):< / span >
< span class = "sa" > r< / span > < span class = "sd" > " " " Cross entropy loss with label smoothing regularizer.< / span >
< span class = "sd" > < / span >
< span class = "sd" > Reference:< / span >
< span class = "sd" > Szegedy et al. Rethinking the Inception Architecture for Computer Vision. CVPR 2016.< / span >
< span class = "sd" > With label smoothing, the label :math:`y` for a class is computed by< / span >
< span class = "sd" > < / span >
< span class = "sd" > .. math::< / span >
< span class = "sd" > \begin{equation}< / span >
< span class = "sd" > (1 - \epsilon) \times y + \frac{\epsilon}{K},< / span >
< span class = "sd" > \end{equation}< / span >
< span class = "sd" > where :math:`K` denotes the number of classes and :math:`\epsilon` is a weight. When< / span >
< span class = "sd" > :math:`\epsilon = 0`, the loss function reduces to the normal cross entropy.< / span >
< span class = "sd" > < / span >
< span class = "sd" > Args:< / span >
< span class = "sd" > num_classes (int): number of classes.< / span >
< span class = "sd" > epsilon (float, optional): weight. Default is 0.1.< / span >
< span class = "sd" > use_gpu (bool, optional): whether to use gpu devices. Default is True.< / span >
< span class = "sd" > label_smooth (bool, optional): whether to apply label smoothing. Default is True.< / span >
< span class = "sd" > " " " < / span >
< span class = "k" > def< / span > < span class = "nf" > __init__< / span > < span class = "p" > (< / span > < span class = "bp" > self< / span > < span class = "p" > ,< / span > < span class = "n" > num_classes< / span > < span class = "p" > ,< / span > < span class = "n" > epsilon< / span > < span class = "o" > =< / span > < span class = "mf" > 0.1< / span > < span class = "p" > ,< / span > < span class = "n" > use_gpu< / span > < span class = "o" > =< / span > < span class = "kc" > True< / span > < span class = "p" > ,< / span > < span class = "n" > label_smooth< / span > < span class = "o" > =< / span > < span class = "kc" > True< / span > < span class = "p" > ):< / span >
< span class = "nb" > super< / span > < span class = "p" > (< / span > < span class = "n" > CrossEntropyLoss< / span > < span class = "p" > ,< / span > < span class = "bp" > self< / span > < span class = "p" > )< / span > < span class = "o" > .< / span > < span class = "fm" > __init__< / span > < span class = "p" > ()< / span >
< span class = "bp" > self< / span > < span class = "o" > .< / span > < span class = "n" > num_classes< / span > < span class = "o" > =< / span > < span class = "n" > num_classes< / span >
< span class = "bp" > self< / span > < span class = "o" > .< / span > < span class = "n" > epsilon< / span > < span class = "o" > =< / span > < span class = "n" > epsilon< / span > < span class = "k" > if< / span > < span class = "n" > label_smooth< / span > < span class = "k" > else< / span > < span class = "mi" > 0< / span >
< span class = "bp" > self< / span > < span class = "o" > .< / span > < span class = "n" > use_gpu< / span > < span class = "o" > =< / span > < span class = "n" > use_gpu< / span >
< span class = "bp" > self< / span > < span class = "o" > .< / span > < span class = "n" > logsoftmax< / span > < span class = "o" > =< / span > < span class = "n" > nn< / span > < span class = "o" > .< / span > < span class = "n" > LogSoftmax< / span > < span class = "p" > (< / span > < span class = "n" > dim< / span > < span class = "o" > =< / span > < span class = "mi" > 1< / span > < span class = "p" > )< / span >
< div class = "viewcode-block" id = "CrossEntropyLoss.forward" > < a class = "viewcode-back" href = "../../../pkg/losses.html#torchreid.losses.cross_entropy_loss.CrossEntropyLoss.forward" > [docs]< / a > < span class = "k" > def< / span > < span class = "nf" > forward< / span > < span class = "p" > (< / span > < span class = "bp" > self< / span > < span class = "p" > ,< / span > < span class = "n" > inputs< / span > < span class = "p" > ,< / span > < span class = "n" > targets< / span > < span class = "p" > ):< / span >
< span class = "sd" > " " " < / span >
< span class = "sd" > Args:< / span >
< span class = "sd" > inputs (torch.Tensor): prediction matrix (before softmax) with< / span >
< span class = "sd" > shape (batch_size, num_classes).< / span >
2019-04-17 16:14:41 +08:00
< span class = "sd" > targets (torch.LongTensor): ground truth labels with shape (batch_size).< / span >
< span class = "sd" > Each position contains the label index.< / span >
2019-03-25 01:22:43 +08:00
< span class = "sd" > " " " < / span >
< span class = "n" > log_probs< / span > < span class = "o" > =< / span > < span class = "bp" > self< / span > < span class = "o" > .< / span > < span class = "n" > logsoftmax< / span > < span class = "p" > (< / span > < span class = "n" > inputs< / span > < span class = "p" > )< / span >
< span class = "n" > targets< / span > < span class = "o" > =< / span > < span class = "n" > torch< / span > < span class = "o" > .< / span > < span class = "n" > zeros< / span > < span class = "p" > (< / span > < span class = "n" > log_probs< / span > < span class = "o" > .< / span > < span class = "n" > size< / span > < span class = "p" > ())< / span > < span class = "o" > .< / span > < span class = "n" > scatter_< / span > < span class = "p" > (< / span > < span class = "mi" > 1< / span > < span class = "p" > ,< / span > < span class = "n" > targets< / span > < span class = "o" > .< / span > < span class = "n" > unsqueeze< / span > < span class = "p" > (< / span > < span class = "mi" > 1< / span > < span class = "p" > )< / span > < span class = "o" > .< / span > < span class = "n" > data< / span > < span class = "o" > .< / span > < span class = "n" > cpu< / span > < span class = "p" > (),< / span > < span class = "mi" > 1< / span > < span class = "p" > )< / span >
< span class = "k" > if< / span > < span class = "bp" > self< / span > < span class = "o" > .< / span > < span class = "n" > use_gpu< / span > < span class = "p" > :< / span > < span class = "n" > targets< / span > < span class = "o" > =< / span > < span class = "n" > targets< / span > < span class = "o" > .< / span > < span class = "n" > cuda< / span > < span class = "p" > ()< / span >
< span class = "n" > targets< / span > < span class = "o" > =< / span > < span class = "p" > (< / span > < span class = "mi" > 1< / span > < span class = "o" > -< / span > < span class = "bp" > self< / span > < span class = "o" > .< / span > < span class = "n" > epsilon< / span > < span class = "p" > )< / span > < span class = "o" > *< / span > < span class = "n" > targets< / span > < span class = "o" > +< / span > < span class = "bp" > self< / span > < span class = "o" > .< / span > < span class = "n" > epsilon< / span > < span class = "o" > /< / span > < span class = "bp" > self< / span > < span class = "o" > .< / span > < span class = "n" > num_classes< / span >
< span class = "k" > return< / span > < span class = "p" > (< / span > < span class = "o" > -< / span > < span class = "n" > targets< / span > < span class = "o" > *< / span > < span class = "n" > log_probs< / span > < span class = "p" > )< / span > < span class = "o" > .< / span > < span class = "n" > mean< / span > < span class = "p" > (< / span > < span class = "mi" > 0< / span > < span class = "p" > )< / span > < span class = "o" > .< / span > < span class = "n" > sum< / span > < span class = "p" > ()< / span > < / div > < / div >
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