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# *****************************************************************************
# Copyright (c) 2018, NVIDIA CORPORATION. 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 NVIDIA CORPORATION 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 NVIDIA CORPORATION 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 logging
import os
import shutil
from typing import Callable, List, Tuple
import torch
from torch import Tensor
def save_checkpoint(state, is_best, filename):
r"""Save the model to a temporary file first, then copy it to filename,
in case signals interrupt the torch.save() process.
"""
torch.save(state, filename)
logging.info(f"Checkpoint saved to {filename}")
if is_best:
path, best_filename = os.path.split(filename)
best_filename = os.path.join(path, "best_" + best_filename)
shutil.copyfile(filename, best_filename)
logging.info(f"Current best checkpoint saved to {best_filename}")
def pad_sequences(batch: List[Tensor]) -> Tuple[Tensor, Tensor]:
r"""Right zero-pad all one-hot text sequences to max input length.
Modified from https://github.com/NVIDIA/DeepLearningExamples.
"""
input_lengths, ids_sorted_decreasing = torch.sort(torch.LongTensor([len(x) for x in batch]), dim=0, descending=True)
max_input_len = input_lengths[0]
text_padded = torch.LongTensor(len(batch), max_input_len)
text_padded.zero_()
for i in range(len(ids_sorted_decreasing)):
text = batch[ids_sorted_decreasing[i]]
text_padded[i, : text.size(0)] = text
return text_padded, input_lengths
def prepare_input_sequence(texts: List[str], text_processor: Callable[[str], List[int]]) -> Tuple[Tensor, Tensor]:
d = []
for text in texts:
d.append(torch.IntTensor(text_processor(text)[:]))
text_padded, input_lengths = pad_sequences(d)
return text_padded, input_lengths
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