[onert/test] Remove nnpackage and modelfile options

docs/howto/how-to-build-runtime-tizen-gbs-rpi4.md

@@ -301,7 +301,7 @@ Assume `mobilenet_v2_1.4_224` nnpackage is already copied to
 
 ```
 sh-3.2# BACKENDS="cpu" Product/out/bin/onert_run \
---nnpackage /opt/usr/home/owner/media/models/mobilenet_v2_1.4_224
+/opt/usr/home/owner/media/models/mobilenet_v2_1.4_224
 
 Package Filename /opt/usr/home/owner/media/models/mobilenet_v2_1.4_224
 ===================================

docs/runtime/transfer_learning.md

@@ -98,7 +98,6 @@ The crucial here is proper choosing value of `num_of_trainable_ops` to achieve t
 
 ```bash
 onert_train \
---modelfile customized_mobilenetv2.circle \
 --epoch 5 \
 --loss 1 \                # mean squared error
 --loss_reduction_type 1 \ # sum over batch size
@@ -108,7 +107,8 @@ onert_train \
 --num_of_trainable_ops 5 \
 --load_expected:raw cats_and_dogs.output.bin \
 --load_input:raw cats_and_dogs.input.bin \
---export_path customized_mobilenetv2_trained.circle
+--export_path customized_mobilenetv2_trained.circle \
+customized_mobilenetv2.circle
 ```
 The result of training:
 ```

tests/scripts/benchmark.sh

@@ -92,7 +92,7 @@ $BRIDGE shell tar -zxf $TEST_ROOT/nnpkg.tar.gz -C $TEST_ROOT/nnpkg
 $BRIDGE shell rm $TEST_ROOT/nnpkg.tar.gz
 
 # 1. Run
-$BRIDGE shell LD_LIBRARY_PATH=$TEST_ROOT/Product/out/lib TRACING_MODE=1 WORKSPACE_DIR=$TEST_ROOT BACKENDS=$BACKENDS $TEST_ROOT/Product/out/bin/onert_run --nnpackage $NNPKG_PATH_TARGET -r $NUM_RUNS
+$BRIDGE shell LD_LIBRARY_PATH=$TEST_ROOT/Product/out/lib TRACING_MODE=1 WORKSPACE_DIR=$TEST_ROOT BACKENDS=$BACKENDS $TEST_ROOT/Product/out/bin/onert_run -r $NUM_RUNS $NNPKG_PATH_TARGET
 
 # 2. Pull result file
 echo "Pulling data from target to trace.json"

tests/scripts/command/nnpkg-test

@@ -85,9 +85,9 @@ dumped="$outdir/$tcname".out.h5
 echo -n "[  Run  ] $nnpkg "
 
 if $nnpkg_run \
---nnpackage "$nnpkg" \
 --load "$nnpkg/metadata/tc/input.h5" \
---dump "$dumped" >& /dev/null > "$dumped.log" 2>&1 ; then
+--dump "$dumped" \
+"$nnpkg" >& /dev/null > "$dumped.log" 2>&1 ; then
   echo -e "\tPass"
   rm "$dumped.log"
 else

tests/tools/onert_run/src/args.cc

@@ -153,10 +153,6 @@ void Args::Initialize(void)
     .nargs(0)
     .default_value(false)
     .help("Print version and exit immediately");
-  _arser.add_argument("--nnpackage")
-    .type(arser::DataType::STR)
-    .help("NN Package file(directory) name");
-  _arser.add_argument("--modelfile").type(arser::DataType::STR).help("NN Model filename");
 #if defined(ONERT_HAVE_HDF5) && ONERT_HAVE_HDF5 == 1
   _arser.add_argument("--dump", "-d").type(arser::DataType::STR).help("Output filename");
   _arser.add_argument("--load", "-l").type(arser::DataType::STR).help("Input filename");
@@ -269,32 +265,6 @@ void Args::Parse(const int argc, char **argv)
       return;
     }
 
-    // Require modelfile, nnpackage, or path
-    if (!_arser["--nnpackage"] && !_arser["--modelfile"] && !_arser["path"])
-    {
-      std::cerr << "Require one of options modelfile, nnpackage, or path." << std::endl;
-      exit(1);
-    }
-
-    // Cannot use both single model file and nnpackage at once
-    if (_arser["--nnpackage"] && _arser["--modelfile"])
-    {
-      std::cerr << "Cannot use both single model file and nnpackage at once." << std::endl;
-      exit(1);
-    }
-
-    if (_arser["--nnpackage"])
-    {
-      std::cout << "Package Filename " << _package_filename << std::endl;
-      _package_filename = _arser.get<std::string>("--nnpackage");
-    }
-
-    if (_arser["--modelfile"])
-    {
-      std::cout << "Model Filename " << _model_filename << std::endl;
-      _model_filename = _arser.get<std::string>("--modelfile");
-    }
-
     if (_arser["path"])
     {
       auto path = _arser.get<std::string>("path");

tests/tools/onert_train/README.md

@@ -18,27 +18,27 @@ sudo apt install -y libhdf5-dev libboost-program-options-dev
 
 ## Usage
 
-You could train your model using the command like below.  
+You could train your model using the command like below.
 
 ```bash
 onert_train \
---path [circle file or nnpackage] \
 --load_input:raw [training input data] \
 --load_expected:raw [training output data] \
---batch_size 32 \ 
+--batch_size 32 \
 --epoch 5 \
 --optimizer 1 \             # sgd
---learning_rate 0.01 \   
+--learning_rate 0.01 \
 --loss 2 \                  # cateogrical crossentropy
---loss_reduction_type 1     # sum over batch size
---num_of_trainable_ops 30    # number of operations to be trained from the back
+--loss_reduction_type 1 \    # sum over batch size
+--num_of_trainable_ops 30 \    # number of operations to be trained from the back
+[circle file or nnpackage]
 ```
 
 `onert_train --help` would help you to set each parameter.
 
 ## Example
 
-To deliver a quick insight to use `onert_train`, let's train a simple mnist model. You could get a mnist tensroflow model code from [here](https://www.kaggle.com/code/amyjang/tensorflow-mnist-cnn-tutorial). 
+To deliver a quick insight to use `onert_train`, let's train a simple mnist model. You could get a mnist tensroflow model code from [here](https://www.kaggle.com/code/amyjang/tensorflow-mnist-cnn-tutorial).
 
 Before using `onert_train`, training data files and a model file have to be ready.
 
@@ -49,17 +49,17 @@ For convenience, we provide a tool([tf dataset convert](https://github.com/Samsu
 
 You could use the tool like this. For detailed usage, please refer [here](https://github.com/Samsung/ONE/tree/master/tools/generate_datafile/tf_dataset_converter#readme).
 ```bash
-# Move to tf_dataset_convert directory 
+# Move to tf_dataset_convert directory
 $ cd ONE/tools/generate_datafile/tf_dataset_converter
 
 # install prerequisites
 $ pip3 install -r requirements.txt
 
 # generate binary data files
-$ python3 main.py \ 
---dataset-name mnist \ 
---prefix-name mnist \ 
---model mnist 
+$ python3 main.py \
+--dataset-name mnist \
+--prefix-name mnist \
+--model mnist
 
 # check data files are generated
 # There are 'mnist.train.input.1000.bin' and 'mnist.train.output.1000.bin'
@@ -70,28 +70,28 @@ $ tree out
 
 `onert_train` use a `*.circle` file or a nnpackage as input. <br/>
 
-<!-- This readme is for the ONE developers, so they might know the onecc usage.--> 
-You could convert tf/tflite/onnx model file into circle file using [`onecc`](https://github.com/Samsung/ONE/tree/master/compiler/one-cmds). <br/> 
-If you start with tensorflow code, you could first save it as saved format and then convert it to a circle file by using `onecc`. 
+<!-- This readme is for the ONE developers, so they might know the onecc usage.-->
+You could convert tf/tflite/onnx model file into circle file using [`onecc`](https://github.com/Samsung/ONE/tree/master/compiler/one-cmds). <br/>
+If you start with tensorflow code, you could first save it as saved format and then convert it to a circle file by using `onecc`.
 
 <!--TODO : Add how to inject training parameter in the circle model -->
- 
+
 ### Run onert_train
 Now you're ready to run `onert_train`. <br/>
-Please pass your model file to `--modelfile` and data files to `--load_input:raw` and `--load_expected:raw`. <br/>
+Please pass your model file as argument and data files with `--load_input:raw` and `--load_expected:raw` options. <br/>
 Also, you could set training parameter using options like `--batch_size`, `--epoch`.. etc.
 Please pay special attention for `num_of_trainable_ops` to determine number of operations to be trained from the back..
 
-```bash 
+```bash
 $ onert_train \
---modelfile mnist.circle \
 --load_input:raw mnist.train.input.1000.bin \
 --load_expected:raw mnist.train.output.1000.bin \
---batch_size 32 \ 
+--batch_size 32 \
 --epoch 5 \
---optimizer 2 \          # adam
+--optimizer 2 \           # adam
 --learning_rate 0.001 \
---loss 2 \               # cateogrical crossentropy
---loss_reduction_type 1  # sum over batch size
---num_of_trainable_ops 10
+--loss 2 \                # cateogrical crossentropy
+--loss_reduction_type 1 \ # sum over batch size
+--num_of_trainable_ops 10 \
+mnist.circle
 ```

tests/tools/onert_train/src/args.cc

@@ -146,10 +146,6 @@ void Args::Initialize(void)
     .nargs(0)
     .default_value(false)
     .help("Print version and exit immediately");
-  _arser.add_argument("--nnpackage")
-    .type(arser::DataType::STR)
-    .help("NN Package file(directory) name");
-  _arser.add_argument("--modelfile").type(arser::DataType::STR).help("NN Model filename");
   _arser.add_argument("--export_circle").type(arser::DataType::STR).help("Path to export circle");
   _arser.add_argument("--export_circleplus")
     .type(arser::DataType::STR)
@@ -220,32 +216,6 @@ void Args::Parse(const int argc, char **argv)
       return;
     }
 
-    // Require modelfile, nnpackage, or path
-    if (!_arser["--nnpackage"] && !_arser["--modelfile"] && !_arser["path"])
-    {
-      std::cerr << "Require one of options modelfile, nnpackage, or path." << std::endl;
-      exit(1);
-    }
-
-    // Cannot use both single model file and nnpackage at once
-    if (_arser["--nnpackage"] && _arser["--modelfile"])
-    {
-      std::cerr << "Cannot use both single model file and nnpackage at once." << std::endl;
-      exit(1);
-    }
-
-    if (_arser["--nnpackage"])
-    {
-      std::cout << "Package Filename " << _package_filename << std::endl;
-      _package_filename = _arser.get<std::string>("--nnpackage");
-    }
-
-    if (_arser["--modelfile"])
-    {
-      std::cout << "Model Filename " << _model_filename << std::endl;
-      _model_filename = _arser.get<std::string>("--modelfile");
-    }
-
     if (_arser["path"])
     {
       auto path = _arser.get<std::string>("path");

tools/stab/remote.py

@@ -79,9 +79,8 @@ def profile_backend(self, backend, backend_op_list):
         cmd += [f"RUY_THREADS={self.num_threads}"]
         cmd += [f"BACKENDS=\'{';'.join(['cpu', backend])}\'"]
         cmd += [f"{nnpkg_run_path}"]
-        cmd += [f"--nnpackage"]
-        cmd += [f"{nnpkg_path}"]
         cmd += [f"-w5 -r50"]
+        cmd += [f"{nnpkg_path}"]
         logging.debug(f"SSH command : {' '.join(cmd)}")
         subprocess.call(cmd)