DeepLearningExamples/TensorFlow/LanguageModeling/BERT/scripts/run_squad_inference.sh

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#!/usr/bin/env bash
# Copyright (c) 2019 NVIDIA CORPORATION. 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.
echo "Container nvidia build = " $NVIDIA_BUILD_ID
init_checkpoint=${1:-"/results/model.ckpt"}
batch_size=${2:-"8"}
precision=${3:-"fp16"}
use_xla=${4:-"true"}
seq_length=${5:-"384"}
doc_stride=${6:-"128"}
bert_model=${7:-"large"}
squad_version=${8:-"1.1"}
if [ "$bert_model" = "large" ] ; then
export BERT_DIR=data/download/google_pretrained_weights/uncased_L-24_H-1024_A-16
else
export BERT_DIR=data/download/google_pretrained_weights/uncased_L-12_H-768_A-12
fi
export SQUAD_DIR=data/download/squad/v${squad_version}
if [ "$squad_version" = "1.1" ] ; then
version_2_with_negative="False"
else
version_2_with_negative="True"
fi
echo "Squad directory set as " $SQUAD_DIR " BERT directory set as " $BERT_DIR
echo "Results directory set as " $RESULTS_DIR
use_fp16=""
if [ "$precision" = "fp16" ] ; then
echo "fp16 activated!"
use_fp16="--use_fp16"
fi
if [ "$use_xla" = "true" ] ; then
use_xla_tag="--use_xla"
echo "XLA activated"
else
use_xla_tag=""
fi
ckpt_str=${init_checkpoint//\//-}
printf -v TAG "tf_bert_finetuning_squad_%s_inf_%s_gbs%d_ckpt_%s" "$bert_model" "$precision" $batch_size "$ckpt_str"
DATESTAMP=`date +'%y%m%d%H%M%S'`
#Edit to save logs & checkpoints in a different directory
RESULTS_DIR=/results
LOGFILE=$RESULTS_DIR/$TAG.$DATESTAMP.log
printf "Logs written to %s\n" "$LOGFILE"
#Check if all necessary files are available before training
for DIR_or_file in $SQUAD_DIR $RESULTS_DIR $BERT_DIR/vocab.txt $BERT_DIR/bert_config.json; do
if [ ! -d "$DIR_or_file" ] && [ ! -f "$DIR_or_file" ]; then
echo "Error! $DIR_or_file directory missing. Please mount correctly"
exit -1
fi
done
python run_squad.py \
--vocab_file=$BERT_DIR/vocab.txt \
--bert_config_file=$BERT_DIR/bert_config.json \
--init_checkpoint=$init_checkpoint \
--do_predict=True \
--predict_file=$SQUAD_DIR/dev-v${squad_version}.json \
--predict_batch_size=$batch_size \
--max_seq_length=$seq_length \
--doc_stride=$doc_stride \
--predict_batch_size=$batch_size \
--output_dir=$RESULTS_DIR \
"$use_fp16" \
$use_xla_tag --version_2_with_negative=${version_2_with_negative}
python $SQUAD_DIR/evaluate-v${squad_version}.py $SQUAD_DIR/dev-v${squad_version}.json $RESULTS_DIR/predictions.json