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#!/bin/bash
### Utility to test models by using command lines actions to run
### Actions are typically model training / translation
### or options setter.
###
### Actions are executed in the order its provided, therefore setters must
### be first
###
### Example:
### - Run all tests:
### ./test_models.sh all
### or
### ./test_models.sh
###
### - Run all tests using GPU (i.e. -gpuid 0):
### ./test_models.sh set_gpu all
### (note that set_gpu comes first!!!)
### you can set all GPU (i.e. to match CUDA_VISIBLE_DEVICES)!
### ./test_models.sh set_all_gpu all
###
### - Train each models, and run translation (for each!):
### ./test_models.sh translate_each all
### (note that translate_each comes first!!!)
###
### - Train and translate a specific model (e.g. lstm):
### ./test_models.sh lstm translate
### note that translate only consider the last model therefore:
### ./test_models.sh lstm cnn translate
### would actually use CNN model for translation
###
### - Run in debug mode (stops on first error)
### ./test_models set_debug all
###
PYTHON_BIN=python
MODEL_DIR="/tmp"
MODEL_NAME="onmt_tmp_model"
MODEL_PATH="$MODEL_DIR/$MODEL_NAME"
MODEL_FILES_PREFIX="${MODEL_NAME}_acc_"
TEST_DIR="./onmt/tests"
TEST_MODEL_NAME="test_model.pt"
TEST_MODEL_PATH="$TEST_DIR/$TEST_MODEL_NAME"
DATA_DIR="./data"
DATA_PATH="$DATA_DIR/data"
# Do not edit directly, use calls 'set_gpu' and 'translate_each'
GPUID=-1
TRANSLATE_EACH=0
### Some setters
###############################################
set_gpu(){
GPUID=0
}
set_all_gpu(){
GPUID=$(sed 's/,/ /g' <(echo $CUDA_VISIBLE_DEVICES) >&1)
}
print_gpuid(){
echo "$GPUID"
}
set_debug(){
set -e
}
translate_each(){
TRANSLATE_EACH=1
}
### Some utils functions
###############################################
mv_best_checkpoint(){
best_model="$(ls -lsrt $MODEL_DIR | grep "${MODEL_FILES_PREFIX}*" | tail -n 1 | awk '{print $NF}')"
mv "$MODEL_DIR/$best_model" "$TEST_MODEL_PATH"
}
rm_tmp_checkpoints(){
rm -f "$MODEL_DIR/${MODEL_FILES_PREFIX}"*
}
### RNNLM
###############################################
lstm(){
rm -f "$DATA_DIR"/*.pt
$PYTHON_BIN preprocess.py -train_src "$DATA_DIR"/src-train.txt \
-train_tgt "$DATA_DIR"/tgt-train.txt \
-valid_src "$DATA_DIR"/src-val.txt \
-valid_tgt "$DATA_DIR"/tgt-val.txt \
-save_data "$DATA_PATH" \
-src_vocab_size 1000 \
-tgt_vocab_size 1000
$PYTHON_BIN train.py -data "$DATA_PATH" \
-save_model "$MODEL_PATH" \
-gpuid $GPUID \
-rnn_size 512 \
-word_vec_size 512 \
-layers 1 \
-train_steps 10000 \
-optim adam \
-learning_rate 0.001 \
-rnn_type LSTM
mv_best_checkpoint
maybe_translate
rm_tmp_checkpoints
}
### SRU
###############################################
sru(){
rm -f "$DATA_DIR"/*.pt
$PYTHON_BIN preprocess.py -train_src "$DATA_DIR"/src-train.txt \
-train_tgt "$DATA_DIR"/tgt-train.txt \
-valid_src "$DATA_DIR"/src-val.txt \
-valid_tgt "$DATA_DIR"/tgt-val.txt \
-save_data "$DATA_PATH" \
-src_vocab_size 1000 \
-tgt_vocab_size 1000 \
-rnn_type "SRU" \
-input_feed 0
$PYTHON_BIN train.py -data "$DATA_PATH" \
-save_model "$MODEL_PATH" \
-gpuid $GPUID \
-rnn_size 512 \
-word_vec_size 512 \
-layers 1 \
-train_steps 10000 \
-optim adam \
-learning_rate 0.001 \
-rnn_type LSTM
mv_best_checkpoint
maybe_translate
rm_tmp_checkpoints
}
### CNN
###############################################
cnn(){
rm -f "$DATA_DIR"/*.pt
$PYTHON_BIN preprocess.py -train_src "$DATA_DIR"/src-train.txt\
-train_tgt "$DATA_DIR"/tgt-train.txt \
-valid_src "$DATA_DIR"/src-val.txt \
-valid_tgt "$DATA_DIR"/tgt-val.txt \
-save_data "$DATA_PATH" \
-src_vocab_size 1000 \
-tgt_vocab_size 1000
$PYTHON_BIN train.py -data "$DATA_PATH" \
-save_model "$MODEL_PATH" \
-gpuid $GPUID \
-rnn_size 256 \
-word_vec_size 256 \
-layers 2 \
-train_steps 10000 \
-optim adam \
-learning_rate 0.001 \
-encoder_type cnn \
-decoder_type cnn
mv_best_checkpoint
maybe_translate
rm_tmp_checkpoints
}
### MORPH DATA
###############################################
morph(){
################# MORPH DATA
rm -f "$DATA_DIR"/morph/*.pt
$PYTHON_BIN preprocess.py -train_src "$DATA_DIR"/morph/src.train \
-train_tgt "$DATA_DIR"/morph/tgt.train \
-valid_src "$DATA_DIR"/morph/src.valid \
-valid_tgt "$DATA_DIR"/morph/tgt.valid \
-save_data "$DATA_DIR"/morph/data
$PYTHON_BIN train.py -data "$DATA_DIR"/morph/data \
-save_model "$MODEL_PATH" \
-gpuid $GPUID \
-rnn_size 400 \
-word_vec_size 100 \
-layers 1 \
-train_steps 10000 \
-optim adam \
-learning_rate 0.001
mv_best_checkpoint
maybe_translate
rm_tmp_checkpoints
}
### TRANSFORMER
###############################################
transformer(){
rm -f "$DATA_DIR"/*.pt
$PYTHON_BIN preprocess.py -train_src "$DATA_DIR"/src-train.txt \
-train_tgt "$DATA_DIR"/tgt-train.txt \
-valid_src "$DATA_DIR"/src-val.txt \
-valid_tgt "$DATA_DIR"/tgt-val.txt \
-save_data "$DATA_PATH" \
-src_vocab_size 1000 \
-tgt_vocab_size 1000 \
-share_vocab
$PYTHON_BIN train.py -data "$DATA_PATH" \
-save_model "$MODEL_PATH" \
-share_embedding \
-batch_type tokens \
-batch_size 1024 \
-accum_count 4 \
-layers 1 \
-rnn_size 256 \
-word_vec_size 256 \
-encoder_type transformer \
-decoder_type transformer \
-train_steps 10000 \
-gpuid $GPUID \
-max_generator_batches 4 \
-dropout 0.1 \
-normalization tokens \
-max_grad_norm 0 \
-optim adam \
-decay_method noam \
-learning_rate 2 \
-position_encoding \
-param_init 0 \
-warmup_steps 100 \
-param_init_glorot \
-adam_beta2 0.998
mv_best_checkpoint
maybe_translate
rm_tmp_checkpoints
}
### TRANSLATION
###############################################
translate(){
$PYTHON_BIN translate.py -gpu "$GPUID" \
-model "$TEST_MODEL_PATH" \
-output "$TEST_DIR"/output_hyp.txt \
-beam 5 \
-batch_size 32 \
-src "$DATA_DIR"/src-val.txt
}
maybe_translate(){
if [ $TRANSLATE_EACH -eq 1 ]
then
translate
fi
}
all(){
lstm
sru
cnn
morph
transformer
translate
}
actions="$@"
# set the default action
if [ -z "$1" ]; then
actions="all"
fi
# Process actions (in order)
for action in $actions; do
echo "Running: $action"
eval "$action"
done
echo "Done."
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