__init__.py

# 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, MultiScaleDataSet
from ppocr.data.lmdb_dataset import LMDBDataSet, LMDBDataSetSR, LMDBDataSetTableMaster
from ppocr.data.pgnet_dataset import PGDataSet
from ppocr.data.pubtab_dataset import PubTabDataSet
from ppocr.data.multi_scale_sampler import MultiScaleSampler

# for PaddleX dataset_type
TextDetDataset = SimpleDataSet
TextRecDataset = SimpleDataSet
MSTextRecDataset = MultiScaleDataSet
PubTabTableRecDataset = PubTabDataSet
KieDataset = SimpleDataSet

__all__ = ["build_dataloader", "transform", "create_operators", "set_signal_handlers"]


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 set_signal_handlers():
    pid = os.getpid()
    try:
        pgid = os.getpgid(pid)
    except AttributeError:
        # In case `os.getpgid` is not available, no signal handler will be set,
        # because we cannot do safe cleanup.
        pass
    else:
        # XXX: `term_mp` kills all processes in the process group, which in
        # some cases includes the parent process of current process and may
        # cause unexpected results. To solve this problem, we set signal
        # handlers only when current process is the group leader. In the
        # future, it would be better to consider killing only descendants of
        # the current process.
        if pid == pgid:
            # support exit using ctrl+c
            signal.signal(signal.SIGINT, term_mp)
            signal.signal(signal.SIGTERM, term_mp)


def build_dataloader(config, mode, device, logger, seed=None):
    config = copy.deepcopy(config)

    support_dict = [
        "SimpleDataSet",
        "LMDBDataSet",
        "PGDataSet",
        "PubTabDataSet",
        "LMDBDataSetSR",
        "LMDBDataSetTableMaster",
        "MultiScaleDataSet",
        "TextDetDataset",
        "TextRecDataset",
        "MSTextRecDataset",
        "PubTabTableRecDataset",
        "KieDataset",
    ]
    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
        if "sampler" in config[mode]:
            config_sampler = config[mode]["sampler"]
            sampler_name = config_sampler.pop("name")
            batch_sampler = eval(sampler_name)(dataset, **config_sampler)
        else:
            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,
    )

    return data_loader