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97 lines
3.4 KiB
Python
97 lines
3.4 KiB
Python
2 years ago
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# Copyright (c) 2021 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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import csv
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import paddle.nn.functional as F
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from .registry import METRIC
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from .base import BaseMetric
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from paddlevideo.utils import get_logger
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logger = get_logger("paddlevideo")
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@METRIC.register
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class SkeletonMetric(BaseMetric):
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"""
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Test for Skeleton based model.
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note: only support batch size = 1, single card test.
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Args:
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out_file: str, file to save test results.
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"""
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def __init__(self,
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data_size,
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batch_size,
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out_file='submission.csv',
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log_interval=1,
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top_k=5):
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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.top1 = []
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self.top5 = []
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self.values = []
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self.out_file = out_file
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self.k = top_k
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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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if data[0].shape[0] != outputs.shape[0]:
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num_segs = data[0].shape[1]
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batch_size = outputs.shape[0]
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outputs = outputs.reshape(
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[batch_size // num_segs, num_segs, outputs.shape[-1]])
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outputs = outputs.mean(axis=1)
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if len(data) == 2: # data with label
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labels = data[1]
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top1 = paddle.metric.accuracy(input=outputs, label=labels, k=1)
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top5 = paddle.metric.accuracy(input=outputs, label=labels, k=self.k)
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if self.world_size > 1:
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top1 = paddle.distributed.all_reduce(
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top1, op=paddle.distributed.ReduceOp.SUM) / self.world_size
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top5 = paddle.distributed.all_reduce(
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top5, op=paddle.distributed.ReduceOp.SUM) / self.world_size
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self.top1.append(top1.numpy())
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self.top5.append(top5.numpy())
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else: # data without label, only support batch_size=1. Used for fsd-10.
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prob = F.softmax(outputs)
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clas = paddle.argmax(prob, axis=1).numpy()[0]
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self.values.append((batch_id, clas))
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# preds ensemble
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if batch_id % self.log_interval == 0:
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logger.info("[TEST] Processing batch {}/{} ...".format(
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batch_id,
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self.data_size // (self.batch_size * self.world_size)))
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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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if self.top1: # data with label
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logger.info('[TEST] finished, avg_acc1= {}, avg_acc5= {}'.format(
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np.mean(np.array(self.top1)), np.mean(np.array(self.top5))))
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else:
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headers = ['sample_index', 'predict_category']
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with open(
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self.out_file,
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'w',
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) as fp:
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writer = csv.writer(fp)
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writer.writerow(headers)
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writer.writerows(self.values)
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logger.info("Results saved in {} !".format(self.out_file))
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