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