mirror of https://github.com/JDAI-CV/fast-reid.git
82 lines
2.2 KiB
Python
82 lines
2.2 KiB
Python
# encoding: utf-8
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"""
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@author: xingyu liao
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@contact: sherlockliao01@gmail.com
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"""
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import copy
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import itertools
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import logging
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from collections import OrderedDict
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import torch
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from fastreid.utils import comm
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from .evaluator import DatasetEvaluator
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logger = logging.getLogger(__name__)
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def accuracy(output, target, topk=(1,)):
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"""Computes the accuracy over the k top predictions for the specified values of k"""
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with torch.no_grad():
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maxk = max(topk)
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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.view(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))
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return res
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class ClasEvaluator(DatasetEvaluator):
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def __init__(self, cfg, output_dir=None):
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self.cfg = cfg
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self._output_dir = output_dir
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self._cpu_device = torch.device('cpu')
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self._predictions = []
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def reset(self):
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self._predictions = []
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def process(self, inputs, outputs):
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pred_logits = outputs.to(self._cpu_device, torch.float32)
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labels = inputs["targets"].to(self._cpu_device)
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# measure accuracy
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acc1, = accuracy(pred_logits, labels, topk=(1,))
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num_correct_acc1 = acc1 * labels.size(0) / 100
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self._predictions.append({"num_correct": num_correct_acc1, "num_samples": labels.size(0)})
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def evaluate(self):
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if comm.get_world_size() > 1:
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comm.synchronize()
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predictions = comm.gather(self._predictions, dst=0)
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predictions = list(itertools.chain(*predictions))
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if not comm.is_main_process(): return {}
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else:
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predictions = self._predictions
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total_correct_num = 0
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total_samples = 0
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for prediction in predictions:
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total_correct_num += prediction["num_correct"]
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total_samples += prediction["num_samples"]
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acc1 = total_correct_num / total_samples * 100
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self._results = OrderedDict()
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self._results["Acc@1"] = acc1
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self._results["metric"] = acc1
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return copy.deepcopy(self._results)
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