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Save metric state variables in state_dict. Move tensors in metric state variables to device. Update states with the ground truth labels and predictions. © Copyright 2022, Meta. I am new to PyTorch and want to efficiently evaluate among others F1 during my Training and my Validation Loop. So far, my approach was to calculate the predictions on GPU, then push them to CPU and append them to a vector for both Training and Validation. After Training and Validation, I would evaluate both for each epoch using sklearn. However, profiling my code it showed, that pushing to cpu is quite a bottleneck. I am certain there is a more efficient way to a) store the predictions without having to push them to cpu each batch. b) calculate the metrics on GPU directly? As I am new to PyTorch, I am very grateful for any hints and feedback :) The metric is only proper defined when \(\text + \text \neq 0 \wedge \text + \text \neq 0\) where \(\text\) , \(\text\) and \(\text\) represent the number of true positives, false positives and false negatives respectively. If this case is encountered for any class/label, the metric for that class/label will be set to 0 and the overall metric may therefore be affected in turn. >>> from torch import tensor >>> target = tensor ([ 0 , 1 , 2 , 0 , 1 , 2 ]) >>> preds = tensor ([ 0 , 2 , 1 , 0 , 0 , 1 ]) >>> f_beta = FBetaScore ( task = ”multiclass” , num_classes = 3 , beta = 0.5 ) >>> f_beta ( preds , target ) tensor(0.3333) Initialize task metric. Stake na net.A mobil verzióval nincsenek nyilvánvaló problémák, és szeretjük a használhatóságot. Nyilvánvaló, hogy a Campobet időt és energiát fektetett a mobil kaszinó létrehozásába.
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