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- # Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
- #
- # Licensed under the Apache License, Version 2.0 (the "License");
- # you may not use this file except in compliance with the License.
- # You may obtain a copy of the License at
- #
- # http://www.apache.org/licenses/LICENSE-2.0
- #
- # Unless required by applicable law or agreed to in writing, software
- # distributed under the License is distributed on an "AS IS" BASIS,
- # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- # See the License for the specific language governing permissions and
- # limitations under the License.
- from __future__ import absolute_import
- from __future__ import division
- from __future__ import print_function
- from __future__ import unicode_literals
- import os
- import sys
- import numpy as np
- import skimage
- import paddle
- import signal
- import random
- __dir__ = os.path.dirname(os.path.abspath(__file__))
- sys.path.append(os.path.abspath(os.path.join(__dir__, '../..')))
- import copy
- from paddle.io import Dataset, DataLoader, BatchSampler, DistributedBatchSampler
- import paddle.distributed as dist
- from ppocr.data.imaug import transform, create_operators
- from ppocr.data.simple_dataset import SimpleDataSet
- from ppocr.data.lmdb_dataset import LMDBDataSet, LMDBDataSetSR
- from ppocr.data.pgnet_dataset import PGDataSet
- from ppocr.data.pubtab_dataset import PubTabDataSet
- __all__ = ['build_dataloader', 'transform', 'create_operators']
- def term_mp(sig_num, frame):
- """ kill all child processes
- """
- pid = os.getpid()
- pgid = os.getpgid(os.getpid())
- print("main proc {} exit, kill process group " "{}".format(pid, pgid))
- os.killpg(pgid, signal.SIGKILL)
- def build_dataloader(config, mode, device, logger, seed=None):
- config = copy.deepcopy(config)
- support_dict = [
- 'SimpleDataSet', 'LMDBDataSet', 'PGDataSet', 'PubTabDataSet',
- 'LMDBDataSetSR'
- ]
- module_name = config[mode]['dataset']['name']
- assert module_name in support_dict, Exception(
- 'DataSet only support {}'.format(support_dict))
- assert mode in ['Train', 'Eval', 'Test'
- ], "Mode should be Train, Eval or Test."
- dataset = eval(module_name)(config, mode, logger, seed)
- loader_config = config[mode]['loader']
- batch_size = loader_config['batch_size_per_card']
- drop_last = loader_config['drop_last']
- shuffle = loader_config['shuffle']
- num_workers = loader_config['num_workers']
- if 'use_shared_memory' in loader_config.keys():
- use_shared_memory = loader_config['use_shared_memory']
- else:
- use_shared_memory = True
- if mode == "Train":
- # Distribute data to multiple cards
- batch_sampler = DistributedBatchSampler(
- dataset=dataset,
- batch_size=batch_size,
- shuffle=shuffle,
- drop_last=drop_last)
- else:
- # Distribute data to single card
- batch_sampler = BatchSampler(
- dataset=dataset,
- batch_size=batch_size,
- shuffle=shuffle,
- drop_last=drop_last)
- if 'collate_fn' in loader_config:
- from . import collate_fn
- collate_fn = getattr(collate_fn, loader_config['collate_fn'])()
- else:
- collate_fn = None
- data_loader = DataLoader(
- dataset=dataset,
- batch_sampler=batch_sampler,
- places=device,
- num_workers=num_workers,
- return_list=True,
- use_shared_memory=use_shared_memory,
- collate_fn=collate_fn)
- # support exit using ctrl+c
- signal.signal(signal.SIGINT, term_mp)
- signal.signal(signal.SIGTERM, term_mp)
- return data_loader
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