"avg_loss": MeanMetric(), "accuracy": Accuracy(task="multiclass", num_classes=1000), "precision": Precision(task="multiclass", num_classes=1000), "recall": Recall(task="multiclass", num_classes=1000), "f1_score": F1Score(task="multiclass", num_classes=1000), } # Move all metric...
"avg_loss": MeanMetric(), "accuracy": Accuracy(task="multiclass", num_classes=1000), "precision": Precision(task="multiclass", num_classes=1000), "recall": Recall(task="multiclass", num_classes=1000), "f1_score": F1Score(task="multiclass", num_classes=1000), } ## Move all metr...
fromtorchmetrics.classificationimportMulticlassAccuracyNUM_CLASS=100seq_len=50batch_size=2accuracy=MulticlassAccuracy(NUM_CLASS,top_k=10,average="micro",multidim_average="global", )importtorchlogits=torch.rand((batch_size,NUM_CLASS,seq_len))targets=torch.randint(0,NUM_CLASS, (batch_size,seq_len)...
"accuracy": Accuracy(task="multiclass", num_classes=1000), "precision": Precision(task="multiclass", num_classes=1000), "recall": Recall(task="multiclass", num_classes=1000), "f1_score": F1Score(task="multiclass", num_classes=1000), } # Move all metrics to the device # 将所有指标...
Accuracy, Precision, Recall, F1Score, ) # toggle to enable/disable metric collection # 切换以启用/禁用指标收集 capture_metrics=False ifcapture_metrics: metrics= { "avg_loss": MeanMetric(), "accuracy": Accuracy(task="multiclass", num_classes=1000), ...
pytorch 语义分割的TorchMetrics MultiClass准确性看起来你遇到了一个定义上的问题。多类分类的准确性，（...
Accuracy, Precision, Recall, F1Score, ) # toggletoenable/disablemetric collection # 切换以启用/禁用指标收集capture_metrics=Falseifcapture_metrics: metrics= {"avg_loss": MeanMetric(),"accuracy": Accuracy(task="multiclass",num_classes=1000),"precision": Precision(task="multiclass",num_classes=10...
计算F1、准确率(Accuracy)、召回率(Recall)、精确率(Precision)、敏感性(Sensitivity)、特异性(Specificity)需要用到的包(PS:还有一些如AUC等后面再加上用法。) from sklearn.metrics import precision_recall_curve, average_precision_score,roc_curve, auc, precision_score, recall_score, f1_score, confusion_ma...
test_acc = MulticlassAccuracy(num_classes=num_classes) def forward(self, x): return self.model(x) def training_step(self, batch, batch_idx): x, y = batch y_hat = self.model(x) loss = torch.nn.functional.cross_entropy(y_hat, y) with torch.no_grad(): self.train_acc.update(y_...
This can be run on CPU, single GPU or multi-GPUs!For the single GPU/CPU case:import torch # import our library import torchmetrics # initialize metric metric = torchmetrics.classification.Accuracy(task="multiclass", num_classes=5) # move the metric to device you want computations to take ...