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93 lines
3.1 KiB
Python
93 lines
3.1 KiB
Python
2 years ago
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# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
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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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import numpy as np
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import paddle
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from collections import OrderedDict
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from paddlevideo.utils import get_logger, load, log_batch, AverageMeter
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from .registry import METRIC
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from .base import BaseMetric
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import time
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from datetime import datetime
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from .ava_utils import ava_evaluate_results
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logger = get_logger("paddlevideo")
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""" An example for metrics class.
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MultiCropMetric for slowfast.
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"""
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@METRIC.register
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class AVAMetric(BaseMetric):
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def __init__(self,
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data_size,
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batch_size,
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file_path,
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exclude_file,
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label_file,
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custom_classes,
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log_interval=1):
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"""prepare for metrics
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"""
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super().__init__(data_size, batch_size, log_interval)
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self.file_path = file_path
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self.exclude_file = exclude_file
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self.label_file = label_file
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self.custom_classes = custom_classes
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self.results = []
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record_list = [
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("loss", AverageMeter('loss', '7.5f')),
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("recall@thr=0.5", AverageMeter("recall@thr=0.5", '.5f')),
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("prec@thr=0.5", AverageMeter("prec@thr=0.5", '.5f')),
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("recall@top3", AverageMeter("recall@top3", '.5f')),
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("prec@top3", AverageMeter("prec@top3", '.5f')),
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("recall@top5", AverageMeter("recall@top5", '.5f')),
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("prec@top5", AverageMeter("prec@top5", '.5f')),
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("mAP@0.5IOU", AverageMeter("mAP@0.5IOU", '.5f')),
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("batch_time", AverageMeter('batch_cost', '.5f')),
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("reader_time", AverageMeter('reader_cost', '.5f')),
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]
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self.record_list = OrderedDict(record_list)
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self.tic = time.time()
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def update(self, batch_id, data, outputs):
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"""update metrics during each iter
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"""
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self.results.extend(outputs)
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self.record_list['batch_time'].update(time.time() - self.tic)
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tic = time.time()
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ips = "ips: {:.5f} instance/sec.".format(
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self.batch_size / self.record_list["batch_time"].val)
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log_batch(self.record_list, batch_id, 0, 0, "test", ips)
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def set_dataset_info(self, info, dataset_len):
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self.info = info
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self.dataset_len = dataset_len
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def accumulate(self):
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"""accumulate metrics when finished all iters.
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"""
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test_res = ava_evaluate_results(self.info, self.dataset_len,
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self.results, None, self.label_file,
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self.file_path, self.exclude_file)
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for name, value in test_res.items():
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self.record_list[name].update(value, self.batch_size)
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return self.record_list
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