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‎tools/model_converter/README.md‎

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# Model Converter Tool
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A command-line utility to download PyTorch models and convert them to OpenVINO format.
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## Overview
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This tool reads a JSON configuration file containing model specifications, downloads PyTorch weights from URLs, loads the models, and exports them to OpenVINO Intermediate Representation (IR) format.
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## Features
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- **Automatic Download**: Downloads model weights from HTTP/HTTPS URLs with caching support
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- **Dynamic Model Loading**: Dynamically imports and instantiates model classes from Python paths
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- **Metadata Embedding**: Embeds custom metadata into OpenVINO models
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- **Input/Output Naming**: Configurable input and output tensor names
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- **Batch Processing**: Process multiple models from a single configuration file
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- **Selective Conversion**: Convert specific models using the `--model` flag
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## Installation
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### Prerequisites
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```bash
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# Required packages
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uv pip install torch torchvision openvino
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```
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## Usage
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### Basic Usage
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```bash
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python model_converter.py config.json -o ./output_models
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```
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### Command-Line Options
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```
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positional arguments:
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config Path to JSON configuration file
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options:
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-h, --help Show help message and exit
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-o OUTPUT, --output OUTPUT
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Output directory for converted models (default: ./converted_models)
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-c CACHE, --cache CACHE
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Cache directory for downloaded weights (default: ~/.cache/torch/hub/checkpoints)
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--model MODEL Process only the specified model (by model_short_name)
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--list List all models in the configuration file and exit
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-v, --verbose Enable verbose logging
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```
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### Examples
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**List all models in configuration:**
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```bash
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python model_converter.py example_config.json --list
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```
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**Convert all models:**
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```bash
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python model_converter.py example_config.json -o ./converted_models
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```
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**Convert a specific model:**
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```bash
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python model_converter.py example_config.json -o ./converted_models --model resnet50
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```
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**Use custom cache directory:**
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```bash
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python model_converter.py example_config.json -o ./output -c ./my_cache
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```
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**Enable verbose logging:**
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```bash
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python model_converter.py example_config.json -o ./output -v
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```
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## Configuration File Format
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The configuration file is a JSON file with the following structure:
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```json
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{
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"models": [
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{
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"model_short_name": "resnet50",
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"model_class_name": "torchvision.models.resnet.resnet50",
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"model_full_name": "ResNet-50",
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"description": "ResNet-50 image classification model",
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"weights_url": "https://download.pytorch.org/models/resnet50-0676ba61.pth",
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"input_shape": [1, 3, 224, 224],
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"input_names": ["images"],
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"output_names": ["output"],
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"model_params": null,
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"model_type": "Classification"
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}
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]
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}
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```
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**Important**: The `model_type` field enables automatic model detection when using [Intel's model_api](https://github.com/openvinotoolkit/model_api). When specified, this metadata is embedded in the OpenVINO IR, allowing `Model.create_model()` to automatically select the correct model wrapper class.
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Common `model_type` values:
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- `"Classification"` - Image classification models
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- `"DetectionModel"` - Object detection models
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- `"YOLOX"` - YOLOX detection models
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- `"SegmentationModel"` - Segmentation models
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### Configuration Fields
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#### Required Fields
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- **`model_short_name`** (string): Short identifier for the model (used for output filename)
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- **`model_class_name`** (string): Full Python path to the model class (e.g., `torchvision.models.resnet.resnet50`)
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- **`weights_url`** (string): URL to download the PyTorch weights (.pth file)
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#### Optional Fields
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- **`model_full_name`** (string): Full descriptive name of the model
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- **`description`** (string): Description of the model
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- **`input_shape`** (array of integers): Input tensor shape (default: `[1, 3, 224, 224]`)
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- **`input_names`** (array of strings): Names for input tensors (default: `["input"]`)
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- **`output_names`** (array of strings): Names for output tensors (default: auto-generated)
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- **`model_params`** (object): Parameters to pass to model constructor (default: `null`)
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- **`model_type`** (string): Model type for model_api auto-detection (e.g., `"Classification"`, `"DetectionModel"`, `"YOLOX"`, etc.)

‎tools/model_converter/config.json‎

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{
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"models": [
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{
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"model_short_name": "mobilenet_v3_small",
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"model_class_name": "torchvision.models.mobilenetv3.mobilenet_v3_small",
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"model_full_name": "MobileNetV3-Small",
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"description": "MobileNetV3 Small - Efficient convolutional neural network for mobile and embedded vision applications",
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"docs": "https://docs.pytorch.org/vision/main/models/generated/torchvision.models.mobilenet_v3_small.html#torchvision.models.mobilenet_v3_small",
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"weights_url": "https://download.pytorch.org/models/mobilenet_v3_small-047dcff4.pth",
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"input_shape": [1, 3, 224, 224],
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"input_names": ["image"],
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"output_names": ["output1"],
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"model_params": null,
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"model_type": "Classification",
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"reverse_input_channels": false,
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"mean_values": "123.675 116.28 103.53",
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"scale_values": "58.395 57.12 57.375",
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"labels": "IMAGENET1K_V1"
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},
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{
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"model_short_name": "efficientnet_b0",
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"model_class_name": "torchvision.models.efficientnet.efficientnet_b0",
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"model_full_name": "EfficientNet-B0",
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"description": "EfficientNet-B0 - Efficient convolutional neural network with compound scaling",
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"docs": "https://docs.pytorch.org/vision/main/models/generated/torchvision.models.efficientnet_b0.html#torchvision.models.efficientnet_b0",
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"weights_url": "https://download.pytorch.org/models/efficientnet_b0_rwightman-3dd342df.pth",
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"input_shape": [1, 3, 224, 224],
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"input_names": ["image"],
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"output_names": ["logits"],
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"model_params": null,
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"model_type": "Classification",
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"reverse_input_channels": true,
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"mean_values": "123.675 116.28 103.53",
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"scale_values": "58.395 57.12 57.375",
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"labels": "IMAGENET1K_V1"
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},
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{
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"model_short_name": "resnet18",
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"model_class_name": "torchvision.models.resnet.resnet18",
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"model_full_name": "ResNet-18",
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"description": "ResNet-18 - 18-layer residual learning network for image classification",
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"weights_url": "https://download.pytorch.org/models/resnet18-f37072fd.pth",
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"input_shape": [1, 3, 224, 224],
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"input_names": ["image"],
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"output_names": ["output"],
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"model_params": null,
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"model_type": "Classification",
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"reverse_input_channels": true,
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"mean_values": "123.675 116.28 103.53",
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"scale_values": "58.395 57.12 57.375",
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"labels": "IMAGENET1K_V1"
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},
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{
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"model_short_name": "resnet50",
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"model_class_name": "torchvision.models.resnet.resnet50",
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"model_full_name": "ResNet-50",
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"description": "ResNet-50 - 50-layer residual learning network for image classification",
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"weights_url": "https://download.pytorch.org/models/resnet50-0676ba61.pth",
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"input_shape": [1, 3, 224, 224],
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"input_names": ["image"],
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"output_names": ["output"],
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"model_params": null,
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"model_type": "Classification",
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"reverse_input_channels": true,
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"mean_values": "123.675 116.28 103.53",
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"scale_values": "58.395 57.12 57.375",
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"labels": "IMAGENET1K_V1"
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},
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{
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"model_short_name": "squeezenet1_0",
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"model_class_name": "torchvision.models.squeezenet.squeezenet1_0",
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"model_full_name": "SqueezeNet 1.0",
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"description": "SqueezeNet 1.0 - Small CNN with AlexNet-level accuracy and 50x fewer parameters",
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"weights_url": "https://download.pytorch.org/models/squeezenet1_0-b66bff10.pth",
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"input_shape": [1, 3, 224, 224],
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"input_names": ["image"],
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"output_names": ["output"],
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"model_params": null,
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"model_type": "Classification",
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"reverse_input_channels": true,
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"mean_values": "123.675 116.28 103.53",
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"scale_values": "58.395 57.12 57.375",
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"labels": "IMAGENET1K_V1"
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},
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{
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"model_short_name": "vgg16",
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"model_class_name": "torchvision.models.vgg.vgg16",
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"model_full_name": "VGG-16",
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"description": "VGG-16 - 16-layer deep convolutional network",
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"weights_url": "https://download.pytorch.org/models/vgg16-397923af.pth",
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"input_shape": [1, 3, 224, 224],
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"input_names": ["image"],
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"output_names": ["output"],
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"model_params": null,
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"model_type": "Classification",
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"reverse_input_channels": true,
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"mean_values": "123.675 116.28 103.53",
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"scale_values": "58.395 57.12 57.375",
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"labels": "IMAGENET1K_V1"
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}
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]
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}

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