def evaluate_train(self, output_dict: dict, targets: torch.Tensor):
assert isinstance(output_dict, dict) and KEY_OUTPUT in output_dict.keys()
probs = output_dict[KEY_OUTPUT]
res = topk_accuracy(probs, targets, topk=self.topk)
acc_dict = dict()
for i in range(len(self.topk)):
acc_dict[f'tok{self.topk[i]}'] = res[i]
return acc_dict
def evaluate_test(self, output_dict: dict, targets: torch.Tensor):
assert isinstance(output_dict,
dict) and KEY_OUTPUT in output_dict.keys()
probs = output_dict[KEY_OUTPUT]
outputs = probs.to(device=self.device)
targets = targets.to(device=self.device)
res = topk_accuracy(outputs, targets, topk=self.topk)
self.topk_list.append(torch.stack(res))
preds = torch.argmax(outputs, dim=1)
for target, pred in zip(targets.numpy(), preds.numpy()):
self.cate_num_dict.update(
{str(target): self.cate_num_dict.get(str(target), 0) + 1})
self.cate_acc_dict.update({
str(target):
self.cate_acc_dict.get(str(target), 0) + int(target == pred)
})