acc = F.accuracy(logits[test_mask], labels[test_mask])报错,AttributeError: module 'torch.nn.functional' has no attribute 'accuracy'
时间: 2024-01-21 21:04:18 浏览: 84
`torch.nn.functional` 模块确实没有 `accuracy` 方法。如果您想计算模型的准确率,可以使用下面的代码:
```python
# 计算预测值
with torch.no_grad():
logits = model(features)
preds = logits.argmax(dim=1)
# 计算准确率
correct = preds[test_mask].eq(labels[test_mask]).sum().item()
acc = correct / test_mask.sum().item()
```
这里我们首先使用模型生成预测值,然后计算预测值与真实标签相等的数量,最后除以测试集样本总数就可以得到准确率。
相关问题
Traceback (most recent call last): File "E:/403/myworld/GraphNet.py", line 62, in <module> test(model, g, features, labels, test_mask) File "E:/403/myworld/GraphNet.py", line 47, in test acc = F.accuracy(logits[test_mask], labels[test_mask]) AttributeError: module 'torch.nn.functional' has no attribute 'accuracy'
这个错误提示是因为 PyTorch 的 `torch.nn.functional` 模块中没有 `accuracy` 方法。如果你想使用 accuracy 方法,你需要先导入 `torchmetrics` 库:
```python
from torchmetrics import accuracy
acc = accuracy(logits[test_mask], labels[test_mask])
```
这样可以解决你遇到的问题。
def loss_labels(self, outputs, targets, indices, num_boxes, log=True): assert 'pred_logits' in outputs src_logits = outputs['pred_logits'] idx = self._get_src_permutation_idx(indices) target_classes_o = torch.cat([t["labels"][J] for t, (_, J) in zip(targets, indices)]) target_classes = torch.full(src_logits.shape[:2], self.num_classes, dtype=torch.int64, device=src_logits.device) target_classes[idx] = target_classes_o loss_ce = F.cross_entropy(src_logits.transpose(1, 2), target_classes, self.empty_weight) losses = {'loss_ce': loss_ce} if log: # TODO this should probably be a separate loss, not hacked in this one here losses['class_error'] = 100 - accuracy(src_logits[idx], target_classes_o)[0] return losses怎么替换为focal损失函数
要将上述代码替换为Focal Loss,可以修改loss_labels函数的第8行,将F.cross_entropy替换为我们刚刚实现的FocalLoss。修改后的代码如下:
```python
def loss_labels(self, outputs, targets, indices, num_boxes, log=True):
assert 'pred_logits' in outputs
src_logits = outputs['pred_logits']
idx = self._get_src_permutation_idx(indices)
target_classes_o = torch.cat([t["labels"][J] for t, (_, J) in zip(targets, indices)])
target_classes = torch.full(src_logits.shape[:2], self.num_classes,
dtype=torch.int64, device=src_logits.device)
target_classes[idx] = target_classes_o
loss_fn = FocalLoss(gamma=2)
loss_ce = loss_fn(src_logits.transpose(1, 2), target_classes)
losses = {'loss_ce': loss_ce}
if log:
losses['class_error'] = 100 - accuracy(src_logits[idx], target_classes_o)[0]
return losses
```
在代码中,我们首先创建一个Focal Loss函数loss_fn,然后将src_logits和target_classes作为输入,计算Focal Loss。最后返回Focal Loss作为损失值。这样,就将DETR模型中的分类损失替换为Focal Loss,可以更好地处理类别不平衡的情况。
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