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96 lines
3.8 KiB
Python
96 lines
3.8 KiB
Python
# copyright (c) 2020 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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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 VideoDataset(BaseDataset):
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"""Video dataset for action recognition
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The dataset loads raw videos and apply specified transforms on them.
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The index file is a file with multiple lines, and each line indicates
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a sample video with the filepath and label, which are split with a whitesapce.
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Example of a inde file:
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.. code-block:: txt
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path/000.mp4 1
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path/001.mp4 1
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path/002.mp4 2
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path/003.mp4 2
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Args:
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file_path(str): Path to the index file.
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pipeline(XXX): A sequence of data transforms.
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**kwargs: Keyword arguments for ```BaseDataset```.
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"""
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def __init__(self, file_path, pipeline, num_retries=5, suffix='', **kwargs):
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self.num_retries = num_retries
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self.suffix = suffix
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super().__init__(file_path, pipeline, **kwargs)
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def load_file(self):
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"""Load index file to get video information."""
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info = []
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with open(self.file_path, 'r') as fin:
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for line in fin:
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line_split = line.strip().split()
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filename, labels = line_split
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#TODO(hj): Required suffix format: may mp4/avi/wmv
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filename = filename + self.suffix
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if self.data_prefix is not None:
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filename = osp.join(self.data_prefix, filename)
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info.append(dict(filename=filename, labels=int(labels)))
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return info
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def prepare_train(self, idx):
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"""TRAIN & VALID. Prepare the data for training/valid given the index."""
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#Try to catch Exception caused by reading corrupted video file
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for ir in range(self.num_retries):
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try:
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results = copy.deepcopy(self.info[idx])
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results = self.pipeline(results)
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except Exception as e:
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#logger.info(e)
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if ir < self.num_retries - 1:
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logger.info(
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"Error when loading {}, have {} trys, will try again".
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format(results['filename'], ir))
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idx = random.randint(0, len(self.info) - 1)
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continue
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return results['imgs'], np.array([results['labels']])
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def prepare_test(self, idx):
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"""TEST. Prepare the data for test given the index."""
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#Try to catch Exception caused by reading corrupted video file
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for ir in range(self.num_retries):
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try:
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results = copy.deepcopy(self.info[idx])
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results = self.pipeline(results)
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except Exception as e:
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#logger.info(e)
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if ir < self.num_retries - 1:
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logger.info(
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"Error when loading {}, have {} trys, will try again".
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format(results['filename'], ir))
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idx = random.randint(0, len(self.info) - 1)
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continue
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return results['imgs'], np.array([results['labels']])
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