Merge branch 'main' into feature/layout-support

.github/workflows/modelconverter_test.yaml

@@ -13,17 +13,27 @@ permissions:
   packages: read
 
 env:
-  DOCKERFILE: docker/${{ inputs.package }}/Dockerfile
-  TAG: luxonis/modelconverter-${{ inputs.package }}:latest
+  AWS_ACCESS_KEY_ID: ${{ secrets.AWS_ACCESS_KEY_ID }}
+  AWS_SECRET_ACCESS_KEY: ${{ secrets.AWS_SECRET_ACCESS_KEY }}
+  AWS_S3_ENDPOINT_URL: ${{ secrets.AWS_S3_ENDPOINT_URL }}
+  PACKAGE: ${{ inputs.package }}
 
 jobs:
   tests:
-    runs-on: ubuntu-latest
+    strategy:
+      fail-fast: false
+      matrix:
+        os: [ubuntu-latest, macos-13]
+
+    runs-on: ${{ matrix.os }}
 
     steps:
     - name: Checkout code
       uses: actions/checkout@v4
 
+    - name: Set up Docker
+      uses: crazy-max/ghaction-setup-docker@v3
+
     - name: Login to GitHub Container Registry
       uses: docker/login-action@v3
       with:
@@ -47,12 +57,12 @@ jobs:
         credentials_json: ${{ secrets.GCP_CREDENTIALS }}
         token_format: access_token
 
-    - name: Run Tests
-      env:
-        AWS_ACCESS_KEY_ID: ${{ secrets.AWS_ACCESS_KEY_ID }}
-        AWS_SECRET_ACCESS_KEY: ${{ secrets.AWS_SECRET_ACCESS_KEY }}
-        AWS_S3_ENDPOINT_URL: ${{ secrets.AWS_S3_ENDPOINT_URL }}
-        PACKAGE: ${{ inputs.package }}
+    - name: Run Tests (Ubuntu)
+      if: ${{ matrix.os == 'ubuntu-latest' }}
       run: |
         pytest -s --verbose "tests/test_packages/test_$PACKAGE.py"
 
+    - name: Run Tests (Windows)
+      if: ${{ matrix.os == 'windows-latest' }}
+      run: |
+        pytest -s --verbose "tests/test_packages/test_${{env.PACKAGE}}.py"

README.md

@@ -1,8 +1,8 @@
 # ModelConverter - Compilation Library
 
 [![License](https://img.shields.io/badge/License-Apache_2.0-blue.svg)](https://opensource.org/licenses/Apache-2.0)
-[![PyPI](https://img.shields.io/pypi/v/luxonis-ml?label=pypi%20package)](https://pypi.org/project/modelconv/)
-[![PyPI - Downloads](https://img.shields.io/pypi/dm/luxonis-ml)](https://pypi.org/project/modelconv/)
+[![PyPI](https://img.shields.io/pypi/v/modelconv?label=pypi%20package)](https://pypi.org/project/modelconv/)
+[![PyPI - Downloads](https://img.shields.io/pypi/dm/modelconv)](https://pypi.org/project/modelconv/)
 
 [![Ruff](https://img.shields.io/endpoint?url=https://raw.githubusercontent.com/astral-sh/ruff/main/assets/badge/v2.json)](https://github.com/astral-sh/ruff)
 [![Docformatter](https://img.shields.io/badge/%20formatter-docformatter-fedcba.svg)](https://github.com/PyCQA/docformatter)
@@ -24,6 +24,8 @@ Convert your **ONNX** models to a format compatible with any generation of Luxon
 - [MLOps - Compilation Library](#mlops---compilation-library)
   - [Table of Contents](#table-of-contents)
   - [Installation](#installation)
+    - [Before You Begin](#before-you-begin)
+    - [Instructions](#instructions)
     - [GPU Support](#gpu-support)
   - [Running ModelConverter](#running-modelconverter)
     - [Sharing Files](#sharing-files)
@@ -38,6 +40,13 @@ Convert your **ONNX** models to a format compatible with any generation of Luxon
 
 ## Installation
 
+### System Requirements
+
+`ModelConverter` requires `docker` to be installed on your system.
+It is recommended to use Ubuntu OS for the best compatibility.
+On Windows or MacOS, it is recommended to install `docker` using the [Docker Desktop](https://www.docker.com/products/docker-desktop).
+Otherwise follow the installation instructions for your OS from the [official website](https://docs.docker.com/engine/install/).
+
 ### Before You Begin
 
 `ModelConverter` is in an experimental public beta stage. Some parts might change in the future.
@@ -57,24 +66,19 @@ Requires `snpe.zip` archive to be present in `docker/extra_packages`. You can do
 
 Requires `hailo_ai_sw_suite_2024-04:1` docker image to be present on the system. You can download the image from the [Hailo website](https://developer.hailo.ai/developer-zone/sw-downloads/).
 
-### Building the Image
+### Instructions
 
-1. Ensure `docker` is installed on your machine. If not, refer to the installation guide [here](https://docs.docker.com/engine/install/).
+1. Build the docker image:
 
-1. Install the modelconverter package. You can install it from PyPI using the following command:
+   ```bash
+   docker build -f docker/<package>/Dockerfile.public -t luxonis/modelconverter-<package>:latest .
+   ```
+1. For easier use, you can install the ModelConverter CLI. You can install it from PyPI using the following command:
 
    ```bash
    pip install modelconv
-
    ```
-
-1. Build the docker image:
-
-```bash
-docker build -f docker/<package>/Dockerfile.public -t luxonis/modelconverter-<package>:latest .
-```
-
-This will also install the `modelconverter` CLI. For usage instructions, see `modelconverter --help`.
+   For usage instructions, see `modelconverter --help`.
 
 ### GPU Support
 
@@ -105,7 +109,7 @@ shared_with_container/
 │ └── <models will be downloaded here>
 │
 └── outputs/
-  └── <output_dir_name>
+  └── <output_dir>
     ├── resnet18.onnx
     ├── resnet18.dlc
     ├── logs.txt
@@ -118,7 +122,7 @@ While adhering to this structure is not mandatory as long as the files are visib
 
 The converter first searches for files exactly at the provided path. If not found, it searches relative to `/app/shared_with_container/`.
 
-The `output_dir_name` can be specified in the config file. If such a directory already exists, the `output_dir_name` will be appended with the current date and time. If not specified, the `output_dir_name` will be autogenerated in the following format: `<model_name>_to_<target>_<date>_<time>`.
+The `output_dir` can be specified using the `--output-dir` CLI argument. If such a directory already exists, the `output_dir_name` will be appended with the current date and time. If not specified, the `output_dir_name` will be autogenerated in the following format: `<model_name>_to_<target>_<date>_<time>`.
 
 ### Usage
 
@@ -276,7 +280,7 @@ To run the inference, use:
 ```bash
 modelconverter infer rvc4 \
   --model_path <path_to_model.dlc> \
-  --dest <dest> \
+  --output-dir <output_dir_name> \
   --input_path <input_path>
   --path <path_to_config.yaml>
 ```

modelconverter/__main__.py

@@ -95,7 +95,7 @@ class Format(str, Enum):
     ),
 ]
 ModelPathOption: TypeAlias = Annotated[
-    str, typer.Option(help="Path to or url of the model file.")
+    str, typer.Option(help="A URL or a path to the model file.")
 ]
 
 DockerOption: TypeAlias = Annotated[
@@ -112,16 +112,24 @@ class Format(str, Enum):
     typer.Option(help="Use GPU for conversion. Only relevant for HAILO."),
 ]
 
+OutputDirOption: TypeAlias = Annotated[
+    Optional[str],
+    typer.Option(
+        ..., "--output-dir", "-o", help="Name of the output directory."
+    ),
+]
+
 
-def get_output_dir_name(target: Target, config: Config) -> Path:
+def get_output_dir_name(
+    target: Target, name: str, output_dir: Optional[str]
+) -> Path:
     date = datetime.now().strftime("%Y_%m_%d_%H_%M_%S")
-    if config.output_dir_name is not None:
-        output_dir_name = config.output_dir_name
-        if (OUTPUTS_DIR / output_dir_name).exists():
-            shutil.rmtree(OUTPUTS_DIR / output_dir_name)
+    if output_dir is not None:
+        if (OUTPUTS_DIR / output_dir).exists():
+            shutil.rmtree(OUTPUTS_DIR / output_dir)
     else:
-        output_dir_name = f"{config.name}_to_{target.name.lower()}_{date}"
-    return OUTPUTS_DIR / output_dir_name
+        output_dir = f"{name}_to_{target.name.lower()}_{date}"
+    return OUTPUTS_DIR / output_dir
 
 
 def get_configs(
@@ -205,19 +213,22 @@ def infer(
         Path,
         typer.Option(
             ...,
+            "--input-path",
+            "-i",
             help="Path to the directory with data for inference."
             "The directory must contain one subdirectory per input, named the same as the input."
             "Inference data must be provided in the NPY format.",
         ),
     ],
     path: PathOption,
-    dest: Annotated[
-        Path, typer.Option(..., help="Path to the output directory.")
-    ],
+    output_dir: OutputDirOption = None,
     stage: Annotated[
         Optional[str],
         typer.Option(
-            help="Name of the stage to run. Only needed for multistage configs."
+            ...,
+            "--stage",
+            "-s",
+            help="Name of the stage to run. Only needed for multistage configs.",
         ),
     ] = None,
     dev: DevOption = False,
@@ -240,8 +251,11 @@ def infer(
         try:
             mult_cfg, _, _ = get_configs(path, opts)
             cfg = mult_cfg.get_stage_config(stage)
+            output_path = get_output_dir_name(
+                target, mult_cfg.name, output_dir
+            )
             Inferer = get_inferer(target)
-            Inferer.from_config(model_path, input_path, dest, cfg).run()
+            Inferer.from_config(model_path, input_path, output_path, cfg).run()
         except Exception:
             logger.exception("Encountered an unexpected error!")
             exit(2)
@@ -253,11 +267,11 @@ def infer(
             str(model_path),
             "--input-path",
             str(input_path),
-            "--dest",
-            str(dest),
             "--path",
             str(path),
         ]
+        if output_dir is not None:
+            args.extend(["--output-dir", output_dir])
         if opts is not None:
             args.extend(opts)
         docker_exec(target.value, *args, tag=tag, use_gpu=gpu)
@@ -340,6 +354,7 @@ def benchmark(
 def convert(
     target: TargetArgument,
     path: PathOption = None,
+    output_dir: OutputDirOption = None,
     dev: DevOption = False,
     to: FormatOption = Format.NATIVE,
     gpu: GPUOption = True,
@@ -391,24 +406,24 @@ def convert(
             if archive_preprocess:
                 cfg, preprocessing = extract_preprocessing(cfg)
 
-            output_dir = get_output_dir_name(target, cfg)
-            output_dir.mkdir(parents=True, exist_ok=True)
+            output_path = get_output_dir_name(target, cfg.name, output_dir)
+            output_path.mkdir(parents=True, exist_ok=True)
             reset_logging()
             setup_logging(
-                file=str(output_dir / "modelconverter.log"), use_rich=True
+                file=str(output_path / "modelconverter.log"), use_rich=True
             )
             if is_multistage:
                 from modelconverter.packages.multistage_exporter import (
                     MultiStageExporter,
                 )
 
                 exporter = MultiStageExporter(
-                    target=target, config=cfg, output_dir=output_dir
+                    target=target, config=cfg, output_dir=output_path
                 )
             else:
                 exporter = get_exporter(target)(
                     config=next(iter(cfg.stages.values())),
-                    output_dir=output_dir,
+                    output_dir=output_path,
                 )
 
             out_models = exporter.run()
@@ -419,9 +434,12 @@ def convert(
 
                 logger.info("Converting to NN archive")
                 assert main_stage is not None
+                if len(out_models) > 1:
+                    model_name = f"{main_stage}{out_models[0].suffix}"
+                else:
+                    model_name = out_models[0].name
                 nn_archive = modelconverter_config_to_nn(
-                    cfg,
-                    target,
+                    Path(model_name),
                     archive_cfg,
                     preprocessing,
                     main_stage,
@@ -430,13 +448,13 @@ def convert(
                     else exporter.exporters[main_stage].inference_model_path,
                 )
                 generator = ArchiveGenerator(
-                    archive_name=cfg.name,
-                    save_path=str(output_dir),
+                    archive_name=f"{cfg.name}.{target.value.lower()}",
+                    save_path=str(output_path),
                     cfg_dict=nn_archive.model_dump(),
                     executables_paths=[
                         str(out_model) for out_model in out_models
                     ]
-                    + [str(output_dir / "buildinfo.json")],
+                    + [str(output_path / "buildinfo.json")],
                 )
                 out_models = [generator.make_archive()]
                 logger.info(f"Model exported to {out_models[0]}")
@@ -479,6 +497,8 @@ def convert(
         ]
         if main_stage is not None:
             args.extend(["--main-stage", main_stage])
+        if output_dir is not None:
+            args.extend(["--output-dir", output_dir])
         if path is not None:
             args.extend(["--path", path])
         if opts is not None:

modelconverter/packages/rvc2/exporter.py

@@ -21,9 +21,9 @@
 
 logger = getLogger(__name__)
 
-COMPILE_TOOL: Final[
-    str
-] = f'{env["INTEL_OPENVINO_DIR"]}/tools/compile_tool/compile_tool'
+COMPILE_TOOL: Final[str] = (
+    f'{env["INTEL_OPENVINO_DIR"]}/tools/compile_tool/compile_tool'
+)
 
 DEFAULT_SUPER_SHAVES: Final[int] = 8
 
@@ -141,9 +141,9 @@ def export(self) -> Path:
         if self.superblob:
             return self.compile_superblob(args)
 
-        return self.compile(args)
+        return self.compile_blob(args)
 
-    def compile(self, args: list) -> Path:
+    def compile_blob(self, args: list) -> Path:
         output_path = (
             self.output_dir / f"{self.model_name}-{self.target.name.lower()}"
         )
@@ -172,7 +172,7 @@ def compile_superblob(self, args: list) -> Path:
 
         orig_args = args.copy()
 
-        default_blob_path = self.compile(
+        default_blob_path = self.compile_blob(
             orig_args
             + [
                 "-o",

modelconverter/packages/rvc4/benchmark.py

@@ -51,8 +51,10 @@ def _adb_run(self, args, **kwargs) -> Tuple[int, str, str]:
                 f"stdout:\n{stdout}\n"
                 f"stderr:\n{stderr}\n"
             )
-        return result.returncode, stdout.decode("utf-8"), stderr.decode(
-            "utf-8"
+        return (
+            result.returncode,
+            stdout.decode(errors="ignore"),
+            stderr.decode(errors="ignore"),
         )
 
     def shell(self, cmd: str) -> Tuple[int, str, str]: