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#! /usr/bin/env bash |
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function image_classifier() { |
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abcli_image_classifier $@ |
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} |
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function abcli_image_classifier() { |
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local task=$(abcli_unpack_keyword "$1" help) |
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if [ "$task" == "help" ] ; then |
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abcli_help_line "$abcli_cli_name image_classifier describe object_1" \ |
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"describe model object_1." |
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abcli_help_line "$abcli_cli_name image_classifier install" \ |
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"install image_classifier." |
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abcli_help_line "$abcli_cli_name image_classifier predict object_1 object_2" \ |
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"run image_classifier model object_1 predict on data object_2." |
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abcli_help_line "$abcli_cli_name image_classifier save name_1 object_1 [force]" \ |
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"[force] save image_classifier in object_1 as name_1." |
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abcli_help_line "$abcli_cli_name image_classifier train object_1" \ |
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"train image_classifier on data object_1." |
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if [ "$(abcli_keyword_is $2 verbose)" == true ] ; then |
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python3 -m fashion_mnist.image_classifier --help |
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fi |
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return |
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fi |
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if [[ $(type -t abcli_image_classifier_$task) == "function" ]] ; then |
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abcli_image_classifier_$task ${@:2} |
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return |
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fi |
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if [ "$task" == "describe" ] ; then |
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local model_object_name="$2" |
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abcli_download $model_object_name |
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python3 -m image_classifier \ |
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describe \ |
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--model_path $abcli_object_root/$model_object_name \ |
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${@:3} |
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return |
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fi |
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if [ "$task" == "install" ] ; then |
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conda install -y -c anaconda seaborn |
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return |
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fi |
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if [ "$task" == "release" ] ; then |
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local model_name=$(abcli_clarify_arg "$2" image-classifier) |
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abcli_huggingface release image-classifier $model_name ${@:3} |
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return |
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fi |
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abcli_log_error "-fashion_mnist: image-classifier: $task: command not found." |
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} |
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function abcli_image_classifier_predict() { |
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local model_object=$(abcli_clarify_object "$1") |
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local data_object=$(abcli_clarify_object "$2") |
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abcli_download object $model_object |
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abcli_download object $data_object |
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abcli_log "image_classifier($model_object).predict($data_object)" |
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if [ ! -f "$abcli_object_root/$data_object/test_images.pyndarray" ] ; then |
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python3 -m image_classifier \ |
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preprocess \ |
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--infer_annotation 0 \ |
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--model_path $abcli_object_root/$model_object \ |
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--objects $abcli_object_root/$data_object \ |
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--output_path $abcli_object_root/$data_object \ |
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--purpose predict \ |
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${@:3} |
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fi |
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cp -v ../$data_object/*.pyndarray . |
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cp -v ../$model_object/image_classifier/model/class_names.json . |
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python3 -m image_classifier \ |
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predict \ |
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--data_path $abcli_object_root/$data_object \ |
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--model_path $abcli_object_root/$model_object \ |
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--output_path $abcli_object_path \ |
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${@:4} |
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abcli_tag set . image_classifier,predict |
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} |
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function abcli_image_classifier_train() { |
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local data_object=$(abcli_clarify_object "$1" $abcli_object_name) |
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abcli_download object $data_object |
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local options=$2 |
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local do_color=$(abcli_option_int "$options" "color" 0) |
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local do_convnet=$(abcli_option_int "$options" "convnet" 0) |
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local do_validate=$(abcli_option_int "$options" "validate" 0) |
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local extra_args="" |
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if [ "$do_validate" == 1 ] ; then |
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local extra_args="--epochs 2" |
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fi |
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abcli_log "image_classifier.train($data_object): $options" |
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python3 -m image_classifier \ |
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train \ |
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--color $do_color \ |
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--convnet $do_convnet \ |
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--data_path $abcli_object_root/$data_object \ |
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--model_path $abcli_object_path \ |
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$extra_args \ |
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${@:3} |
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abcli_tag set . image_classifier,train |
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} |
