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evalmodel_bazel.sh
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evalmodel_bazel.sh
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# Copyright 2020 The Magenta Authors.
#
# 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.
#!/bin/bash
set -x
set -e
# Pass path to checkpoint directory as first argument to this script.
# You can also download a model pretrained on the J.S. Bach chorales dataset from here:
# http://download.magenta.tensorflow.org/models/coconet/checkpoint.zip
# and pass the path up to the inner most directory as first argument when running this
# script.
checkpoint=$1
# Change this to where data is loaded from.
data_dir="data"
# Change this to where evaluation results are stored.
eval_logdir="eval_logdir"
# Evaluation settings.
fold=valid
fold_index=1 # Optionally can specify index of specific piece to be evaluated.
unit=frame
chronological=false
ensemble_size=5 # Number of different orderings to average.
# Run command.
python coconet_evaluate.py \
--data_dir=$data_dir \
--eval_logdir=$eval_logdir \
--checkpoint=$checkpoint \
--fold=$fold \
--unit=$unit \
--chronological=$chronological \
--ensemble_size=5 \
#--fold_index=$fold_index