deepview-converter 3.0.3

DeepViewRT Converter

DeepView Converter

deepview-converter is the model compiler for the DeepViewRT inference engine. It takes a trained model from a common training framework and produces a .rtm model — the native format DeepViewRT loads to run inference on the edge.

The converter imports the source model, runs a graph optimization pass, and exports an RTM model with an embedded memory map so the runtime can execute it with a fixed, pre-planned allocation.

Supported inputs

Format	Extension / layout	Notes
TFLite	.tflite	Must already be quantized to convert quantized
ONNX	.onnx
TensorFlow	.pb, SavedModel, Keras .h5	Can be quantized at convert time (--quantize)

The output is always a DeepViewRT .rtm model.

Installation

pip install deepview-converter

This pulls in the matching deepview-rt runtime as a dependency.

Usage

The package installs the rtm-converter console script (equivalently python -m deepview_rtm):

# Basic conversion
rtm-converter model.onnx model.rtm

# Quantize a float TensorFlow/Keras model using calibration samples
rtm-converter model.h5 model.rtm --quantize --samples ./calibration_images

# Constrain the converted graph to a subgraph by I/O layer names
rtm-converter model.tflite model.rtm \
    --input-names input_0 --output-names logits

Run rtm-converter --help for the full set of options, including quantization mode (--quant-channel / --quant-tensor), input/output data types, custom user-op handlers, and optimizer controls (--skip-optimizations).

Set DEEPVIEW_CONVERTER_DEBUG=1 to print a full traceback when a conversion fails.

Testing

pip install pytest pytest-html
python -m pytest --html=tests/report.html -s --capture=tee-sys --self-contained-html

The HTML report is written to tests/report.html.

Changelog

See CHANGELOG.md for release notes.