Skip to main content

Edge MDT Custom Layers (EdgeMDT CL)

Edge MDT Custom Layers (EdgeMDT CL) is an open-source project implementing detection post process NN layers not supported by the TensorFlow Keras API or Torch's torch.nn for the easy integration of those layers into pretrained models.

Table of Contents

Getting Started

This section provides an installation and a quick starting guide.

Installation

To install the latest stable release of SCL, run the following command:

pip install edge-mdt-cl

By default, no framework dependencies are installed. To install SCL including the latest tested dependencies (up to patch version) for TensorFlow:

pip install edge-mdt-cl[tf]

To install SCL including the latest tested dependencies (up to patch version) for PyTorch/ONNX/OnnxRuntime:

pip install edge-mdt-cl[torch]

Supported Versions

TensorFlow

Tested FW versions Tested Python version Serialization
2.14 3.10-3.11 .keras
2.15 3.10-3.11 .keras

PyTorch

Tested FW versions Tested Python version Serialization
torch 2.3-2.6
torchvision 0.18-0.21
onnxruntime 1.15-1.21
onnxruntime_extensions 0.8-0.13
onnx 1.14-1.17
3.10-3.12 .onnx (via torch.onnx.export)

API

For edge-mdt-cl API see https://sonysemiconductorsolutions.github.io/aitrios-edge-mdt-cl

TensorFlow API

For TensorFlow layers see KerasAPI

To load a model with custom layers in TensorFlow, see custom_layers_scope

PyTorch API

For PyTorch layers see PyTorchAPI

No special handling is required for torch.onnx.export and onnx.load.

For OnnxRuntime support see load_custom_ops

License

Apache License 2.0.

Metadata

Release files for edge-mdt-cl 1.1.1

For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.

Built distribution (wheel)

Table of built distributions (wheels) for edge-mdt-cl 1.1.1
File Interpreter ABI Platform
edge_mdt_cl-1.1.1-py3-none-any.whl Python 3 none any Details

Release files / edge_mdt_cl-1.1.1-py3-none-any.whl

Download URL edge_mdt_cl-1.1.1-py3-none-any.whl
Size 45.1 kB
Tags Python 3
SHA-256 checksum
How to use checksums
6b60104a22f5be4daceee44f62528a4c42a03a903840b3ab50f0852953892623
BLAKE2b-256 checksum
How to use checksums
16e0e72db17d6a46f1fea786485ff34581c9a9faca78c4b0ceec2aea0b1a4b7c
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
No
Uploaded via twine/6.2.0 CPython/3.11.14

Release history Release notifications | RSS feed

This release

1.1.1 This release

1 release file

1.1.0

1 release file

1.0.0

1 release file

Anthropic, PBC Visionary sponsor Bloomberg Visionary sponsor Hudson River Trading Visionary sponsor Meta Visionary sponsor NVIDIA Visionary sponsor Microsoft Sustainability sponsor Depot Continuous Integration AWS Cloud computing and Security Sponsor Datadog Monitoring Fastly CDN Google Download Analytics Sentry Error logging StatusPage Status page