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data_loader.py
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data_loader.py
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# BSD 3-Clause License
#
# Copyright (C) 2021 THL A29 Limited, a Tencent company. All rights reserved.
#
# Redistribution and use in source and binary forms, with or without modification,
# are permitted provided that the following conditions are met:
#
# * Redistributions of source code must retain the above copyright notice, this
# list of conditions and the following disclaimer.
#
# * Redistributions in binary form must reproduce the above copyright notice,
# this list of conditions and the following disclaimer in the documentation
# and/or other materials provided with the distribution.
#
# * Neither the name of the psutil authors nor the names of its contributors
# may be used to endorse or promote products derived from this software without
# specific prior written permission.
#
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
# ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
# WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
# DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR
# ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
# (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
# LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON
# ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
# (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
# SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
import torch
from torch.utils.data import SequentialSampler
def get_bert_data_loader(
batch_size,
total_samples,
sequence_length,
device,
data_type=torch.float,
is_distrbuted=False,
):
train_data = torch.randint(
low=0,
high=1000,
size=(total_samples, sequence_length),
device=device,
dtype=torch.long,
)
train_label = torch.randint(
low=0, high=2, size=(total_samples,), device=device, dtype=torch.long
)
train_dataset = torch.utils.data.TensorDataset(train_data, train_label)
if is_distrbuted:
sampler = torch.utils.data.distributed.DistributedSampler(train_dataset)
else:
sampler = SequentialSampler(train_dataset)
train_loader = torch.utils.data.DataLoader(
train_dataset, batch_size=batch_size, sampler=sampler
)
return train_loader