validating single image predict for fashion_mnist - kamangir/bolt#692

abcli/image_classifier.sh

@@ -8,8 +8,8 @@ function abcli_image_classifier() {
     local task=$(abcli_unpack_keyword "$1" help)
 
     if [ "$task" == "help" ] ; then
-        abcli_help_line "$abcli_cli_name image_classifier describe object_1 [model=object/*saved]" \
-            "describe saved/object model object_1."
+        abcli_help_line "$abcli_cli_name image_classifier list [object_1] [model=object/*saved]" \
+            "list saved/object model object_1."
         abcli_help_line "$abcli_cli_name image_classifier install" \
             "install image_classifier."
         abcli_help_line "$abcli_cli_name image_classifier predict data_1 [name_1] [data=filename/*object/url,model=object/*saved]" \
@@ -30,9 +30,19 @@ function abcli_image_classifier() {
         return
     fi
 
-    if [ "$task" == "describe" ] ; then
+    if [ "$task" == "install" ] ; then
+        conda install -y -c anaconda seaborn
+        return
+    fi
+
+    if [ "$task" == "list" ] ; then
         local model_name=$2
 
+        if [ -z "$model_name" ] ; then
+            ls $abcli_path_git/image-classifier/saved_model
+            return
+        fi
+
         local options=$3
         local model_source=$(abcli_option "$options" "model" saved)
 
@@ -44,18 +54,13 @@ function abcli_image_classifier() {
         fi
 
         python3 -m image_classifier \
-            describe \
+            list \
             --model_path $model_path \
             ${@:4}
 
         return
     fi
 
-    if [ "$task" == "install" ] ; then
-        conda install -y -c anaconda seaborn
-        return
-    fi
-
     if [ "$task" == "save" ] ; then
         abcli_huggingface save \
             image-classifier \

image_classifier/__init__.py

@@ -1,5 +1,5 @@
 name = "image_classifier"
 
-version = "1.1.172"
+version = "1.1.177"
 
 description = "fashion-mnist + hugging-face + awesome-bash-cli"

image_classifier/__main__.py

@@ -15,7 +15,7 @@ parser.add_argument(
     "task",
     type=str,
     default="",
-    help="describe,eval,ingest,predict,predict_image,preprocess,train",
+    help="eval,ingest,list,predict,predict_image,preprocess,train",
 )
 parser.add_argument(
     "--objects",
@@ -91,11 +91,11 @@ parser.add_argument(
 args = parser.parse_args()
 
 success = False
-if args.task == "describe":
+if args.task == "eval":
+    success = eval(args.input_path, args.output_path)
+elif args.task == "list":
     Image_Classifier().load(args.model_path)
     success = True
-elif args.task == "eval":
-    success = eval(args.input_path, args.output_path)
 elif args.task == "predict":
     classifier = Image_Classifier()