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[examples] update to use quantization and torchrun

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This commit is contained in:
mlxu995 2021-12-15 17:13:54 +08:00
parent 2041501f17
commit ceaa60c425
1 changed files with 37 additions and 17 deletions
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54
examples/hey_snips/s0/run.sh
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@ -4,8 +4,6 @@
. ./path.sh
export CUDA_VISIBLE_DEVICES="0"
stage=0
stop_stage=4
num_keywords=1
@ -13,7 +11,7 @@ num_keywords=1
config=conf/ds_tcn.yaml
norm_mean=true
norm_var=true
gpu_id=0
gpus="0"
checkpoint=
dir=exp/ds_tcn
@ -68,7 +66,9 @@ if [ ${stage} -le 2 ] && [ ${stop_stage} -ge 2 ]; then
cmvn_opts=
$norm_mean && cmvn_opts="--cmvn_file data/train/global_cmvn"
$norm_var && cmvn_opts="$cmvn_opts --norm_var"
python kws/bin/train.py --gpu $gpu_id \
num_gpus=$(echo $gpus | awk -F ',' '{print NF}')
torchrun --standalone --nnodes=1 --nproc_per_node=$num_gpus \
kws/bin/train.py --gpus $gpus \
--config $config \
--train_data data/train/data.list \
--cv_data data/dev/data.list \
@ -82,28 +82,21 @@ if [ ${stage} -le 2 ] && [ ${stop_stage} -ge 2 ]; then
fi
if [ ${stage} -le 3 ] && [ ${stop_stage} -ge 3 ]; then
# Do model average
echo "Do model average, Compute FRR/FAR ..."
python kws/bin/average_model.py \
--dst_model $score_checkpoint \
--src_path $dir \
--num ${num_average} \
--val_best
# Compute posterior score
result_dir=$dir/test_$(basename $score_checkpoint)
mkdir -p $result_dir
python kws/bin/score.py --gpu $gpu_id \
python kws/bin/score.py \
--config $dir/config.yaml \
--test_data data/test/data.list \
--batch_size 256 \
--checkpoint $score_checkpoint \
--score_file $result_dir/score.txt \
--num_workers 8
fi
if [ ${stage} -le 4 ] && [ ${stop_stage} -ge 4 ]; then
# Compute detection error tradeoff
result_dir=$dir/test_$(basename $score_checkpoint)
first_keyword=0
last_keyword=$(($num_keywords+$first_keyword-1))
for keyword in $(seq $first_keyword $last_keyword); do
@ -116,10 +109,37 @@ if [ ${stage} -le 4 ] && [ ${stop_stage} -ge 4 ]; then
fi
if [ ${stage} -le 5 ] && [ ${stop_stage} -ge 5 ]; then
python kws/bin/export_jit.py --config $dir/config.yaml \
if [ ${stage} -le 4 ] && [ ${stop_stage} -ge 4 ]; then
echo "Static quantization, compute FRR/FAR..."
# Apply static quantization
quantize_score_checkpoint=$(basename $score_checkpoint | sed -e 's:.pt$:.quant.zip:g')
cat data/train/data.list | python tools/shuffle_list.py --seed 777 | \
head -n 10000 > $dir/calibration.list
python kws/bin/static_quantize.py \
--config $dir/config.yaml \
--test_data $dir/calibration.list \
--checkpoint $score_checkpoint \
--output_file $dir/final.zip \
--output_quant_file $dir/final.quant.zip
--num_workers 8 \
--script_model $dir/$quantize_score_checkpoint
result_dir=$dir/test_$(basename $quantize_score_checkpoint)
mkdir -p $result_dir
python kws/bin/score.py \
--config $dir/config.yaml \
--test_data data/test/data.list \
--batch_size 256 \
--jit_model \
--checkpoint $dir/$quantize_score_checkpoint \
--score_file $result_dir/score.txt \
--num_workers 8
first_keyword=0
last_keyword=$(($num_keywords+$first_keyword-1))
for keyword in $(seq $first_keyword $last_keyword); do
python kws/bin/compute_det.py \
--keyword $keyword \
--test_data data/test/data.list \
--score_file $result_dir/score.txt \
--stats_file $result_dir/stats.${keyword}.txt
done
fi