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50 lines
1.8 KiB
50 lines
1.8 KiB
#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(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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