Last commit not found
| # Copyright (c) Facebook, Inc. and its affiliates. | |
| # -------------------------------------------------------- | |
| # X-Decoder -- Generalized Decoding for Pixel, Image, and Language | |
| # Copyright (c) 2022 Microsoft | |
| # Licensed under The MIT License [see LICENSE for details] | |
| # Modified by Xueyan Zou ([email protected]) | |
| # -------------------------------------------------------- | |
| import torch | |
| import logging | |
| from detectron2.evaluation.evaluator import DatasetEvaluator | |
| from utilities.misc import AverageMeter | |
| from utilities.distributed import get_world_size | |
| def accuracy(output, target, topk=(1,)): | |
| """Computes the precision@k for the specified values of k""" | |
| if isinstance(output, list): | |
| output = output[-1] | |
| n_classes = output.size()[1] | |
| maxk = min(max(topk), n_classes) | |
| batch_size = target.size(0) | |
| _, pred = output.topk(maxk, 1, True, True) | |
| pred = pred.t() | |
| correct = pred.eq(target.reshape(1, -1).expand_as(pred)) | |
| res = [] | |
| for k in topk: | |
| correct_k = correct[:k].reshape(-1).float().sum(0, keepdim=True) | |
| res.append(correct_k.mul_(100.0 / batch_size).item()) | |
| return res | |
| class ClassificationEvaluator(DatasetEvaluator): | |
| def __init__(self, *args): | |
| self.top1 = AverageMeter() | |
| self.top5 = AverageMeter() | |
| self._logger = logging.getLogger(__name__) | |
| def reset(self): | |
| self.top1.reset() | |
| self.top5.reset() | |
| def process(self, inputs, outputs): | |
| logits = torch.stack([o['pred_class'] for o in outputs]) | |
| y = torch.tensor([t['class_id'] for t in inputs], device=logits.device) | |
| prec1, prec5 = accuracy(logits, y, (1, 5)) | |
| self.top1.update(prec1, y.size(0)) | |
| self.top5.update(prec5, y.size(0)) | |
| def evaluate(self): | |
| if get_world_size() > 1: | |
| tmp_tensor = torch.tensor( | |
| [self.top1.sum, self.top5.sum, self.top1.count], | |
| device=torch.cuda.current_device() | |
| ) | |
| torch.distributed.all_reduce( | |
| tmp_tensor, torch.distributed.ReduceOp.SUM | |
| ) | |
| top1_sum, top5_sum, count = tmp_tensor.tolist() | |
| else: | |
| top1_sum = self.top1.sum | |
| top5_sum = self.top5.sum | |
| count = self.top1.count | |
| results = {} | |
| scores = { | |
| 'top1': top1_sum / count, | |
| "top5": top5_sum / count | |
| } | |
| results['class'] = scores | |
| self._logger.info(results) | |
| return results | |