Pytorch lightning metrics: ValueError: preds and target must have same number of dimensions, or one additional dimension for preds
Googling this gets you no where, so I decided to help future me and others by posting this as a searchable question.

def __init__():
    ...
    self.val_acc = pl.metrics.Accuracy()

def validation_step(self, batch, batch_index):
    ...
    self.val_acc.update(log_probs, label_batch)

gives
ValueError: preds and target must have same number of dimensions, or one additional dimension for preds

for log_probs.shape == (16, 4) and for label_batch.shape == (16, 4)
What's the issue?

pl.metrics.Accuracy() expects a batch of dtype=torch.long labels, not one-hot encoded labels.
Thus, it should be fed
self.val_acc.update(log_probs, torch.argmax(label_batch.squeeze(), dim=1))

This is just the same as torch.nn.CrossEntropyLoss
