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import torch |
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import logging |
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from detectron2.evaluation.evaluator import DatasetEvaluator |
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from utilities.misc import AverageMeter |
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from utilities.distributed import get_world_size |
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@torch.no_grad() |
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def accuracy(output, target, topk=(1,)): |
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"""Computes the precision@k for the specified values of k""" |
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if isinstance(output, list): |
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output = output[-1] |
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n_classes = output.size()[1] |
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maxk = min(max(topk), n_classes) |
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batch_size = target.size(0) |
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_, pred = output.topk(maxk, 1, True, True) |
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pred = pred.t() |
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correct = pred.eq(target.reshape(1, -1).expand_as(pred)) |
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res = [] |
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for k in topk: |
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correct_k = correct[:k].reshape(-1).float().sum(0, keepdim=True) |
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res.append(correct_k.mul_(100.0 / batch_size).item()) |
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return res |
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class ClassificationEvaluator(DatasetEvaluator): |
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def __init__(self, *args): |
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self.top1 = AverageMeter() |
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self.top5 = AverageMeter() |
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self._logger = logging.getLogger(__name__) |
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def reset(self): |
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self.top1.reset() |
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self.top5.reset() |
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def process(self, inputs, outputs): |
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logits = torch.stack([o['pred_class'] for o in outputs]) |
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y = torch.tensor([t['class_id'] for t in inputs], device=logits.device) |
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prec1, prec5 = accuracy(logits, y, (1, 5)) |
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self.top1.update(prec1, y.size(0)) |
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self.top5.update(prec5, y.size(0)) |
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def evaluate(self): |
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if get_world_size() > 1: |
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tmp_tensor = torch.tensor( |
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[self.top1.sum, self.top5.sum, self.top1.count], |
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device=torch.cuda.current_device() |
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) |
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torch.distributed.all_reduce( |
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tmp_tensor, torch.distributed.ReduceOp.SUM |
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) |
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top1_sum, top5_sum, count = tmp_tensor.tolist() |
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else: |
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top1_sum = self.top1.sum |
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top5_sum = self.top5.sum |
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count = self.top1.count |
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results = {} |
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scores = { |
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'top1': top1_sum / count, |
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"top5": top5_sum / count |
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} |
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results['class'] = scores |
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self._logger.info(results) |
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return results |
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