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- #copyright (c) 2021 PaddlePaddle Authors. All Rights Reserve.
- #
- #Licensed under the Apache License, Version 2.0 (the "License");
- #you may not use this file except in compliance with the License.
- #You may obtain a copy of the License at
- #
- # http://www.apache.org/licenses/LICENSE-2.0
- #
- #Unless required by applicable law or agreed to in writing, software
- #distributed under the License is distributed on an "AS IS" BASIS,
- #WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- #See the License for the specific language governing permissions and
- #limitations under the License.
- from paddle import nn
- import copy
- from collections import OrderedDict
- from .metrics import TopkAcc, mAP, mINP, Recallk, Precisionk
- from .metrics import DistillationTopkAcc
- from .metrics import GoogLeNetTopkAcc
- from .metrics import HammingDistance, AccuracyScore
- class CombinedMetrics(nn.Layer):
- def __init__(self, config_list):
- super().__init__()
- self.metric_func_list = []
- assert isinstance(config_list, list), (
- 'operator config should be a list')
- for config in config_list:
- assert isinstance(config,
- dict) and len(config) == 1, "yaml format error"
- metric_name = list(config)[0]
- metric_params = config[metric_name]
- if metric_params is not None:
- self.metric_func_list.append(
- eval(metric_name)(**metric_params))
- else:
- self.metric_func_list.append(eval(metric_name)())
- def __call__(self, *args, **kwargs):
- metric_dict = OrderedDict()
- for idx, metric_func in enumerate(self.metric_func_list):
- metric_dict.update(metric_func(*args, **kwargs))
- return metric_dict
- def build_metrics(config):
- metrics_list = CombinedMetrics(copy.deepcopy(config))
- return metrics_list
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