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79 lines
2.9 KiB
Python
79 lines
2.9 KiB
Python
# 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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import os.path as osp
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import copy
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import random
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import numpy as np
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import pickle
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from ..registry import DATASETS
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from .base import BaseDataset
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from ...utils import get_logger
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logger = get_logger("paddlevideo")
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@DATASETS.register()
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class SkeletonDataset(BaseDataset):
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"""
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Skeleton dataset for action recognition.
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The dataset loads skeleton feature, and apply norm operatations.
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Args:
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file_path (str): Path to the index file.
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pipeline(obj): Define the pipeline of data preprocessing.
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data_prefix (str): directory path of the data. Default: None.
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test_mode (bool): Whether to bulid the test dataset. Default: False.
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"""
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def __init__(self, file_path, pipeline, label_path=None, test_mode=False):
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self.label_path = label_path
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super().__init__(file_path, pipeline, test_mode=test_mode)
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def load_file(self):
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"""Load feature file to get skeleton information."""
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logger.info("Loading data, it will take some moment...")
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self.data = np.load(self.file_path)
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if self.label_path:
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if self.label_path.endswith('npy'):
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self.label = np.load(self.label_path)
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elif self.label_path.endswith('pkl'):
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with open(self.label_path, 'rb') as f:
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sample_name, self.label = pickle.load(f)
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else:
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logger.info(
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"Label path not provided when test_mode={}, here just output predictions."
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.format(self.test_mode))
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logger.info("Data Loaded!")
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return self.data # used for __len__
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def prepare_train(self, idx):
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"""Prepare the feature for training/valid given index. """
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results = dict()
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results['data'] = copy.deepcopy(self.data[idx])
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results['label'] = copy.deepcopy(self.label[idx])
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results = self.pipeline(results)
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return results['data'], results['label']
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def prepare_test(self, idx):
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"""Prepare the feature for test given index. """
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results = dict()
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results['data'] = copy.deepcopy(self.data[idx])
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if self.label_path:
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results['label'] = copy.deepcopy(self.label[idx])
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results = self.pipeline(results)
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return results['data'], results['label']
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else:
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results = self.pipeline(results)
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return [results['data']]
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