MengzhangLI d21682da79 [Feature] Support ConvNext (#1216)
* upload original backbone and configs

* ConvNext Refactor

* ConvNext Refactor

* convnext customization refactor with mmseg style

* convnext customization refactor with mmseg style

* add ade20k_640x640.py

* upload files for training

* delete dist_optimizer_hook and remove layer_decay_optimizer_constructor

* check max(out_indices) < num_stages

* add unittest

* fix lint error

* use MMClassification backbone

* fix bugs in base_1k

* add mmcls in requirements/mminstall.txt

* add mmcls in requirements/mminstall.txt

* fix drop_path_rate and layer_scale_init_value

* use logger.info instead of print

* add mmcls in runtime.txt

* fix f string && delete

* add doctring in LearningRateDecayOptimizerConstructor and fix mmcls version in requirements

* fix typo in LearningRateDecayOptimizerConstructor

* use ConvNext models in unit test for LearningRateDecayOptimizerConstructor

* add unit test

* fix typo

* fix typo

* add layer_wise and fix redundant backbone.downsample_norm in it

* fix unit test

* give a ground truth lr_scale and weight_decay

* upload models and readme

* delete 'backbone.stem_norm' and 'backbone.downsample_norm' in get_num_layer()

* fix unit test and use mmcls url

* update md2yml.py and metafile

* fix typo
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Kneron AI Training/Deployment Platform (mmsegmentation-based)

Introduction

kneron-mmsegmentation is a platform built upon the well-known mmsegmentation for mmsegmentation. If you are looking for original mmsegmentation document, please visit mmsegmentation docs for detailed mmsegmentation usage.

In this repository, we provide an end-to-end training/deployment flow to realize on Kneron's AI accelerators:

  1. Training/Evalulation:
  2. Converting to ONNX:
    • tools/pytorch2onnx_kneron.py (beta)
    • Export optimized and Kneron-toolchain supported onnx
      • Automatically modify model for arbitrary data normalization preprocess
  3. Evaluation
    • tools/test_kneron.py (beta)
    • Evaluate the model with pytorch checkpoint, onnx, and kneron-nef
  4. Testing
    • inference_kn (beta)
    • Verify the converted NEF model on Kneron USB accelerator with this API
  5. Converting Kneron-NEF: (toolchain feature)
    • Convert the trained pytorch model to Kneron-NEF model, which could be used on Kneron hardware platform.

License

This project is released under the Apache 2.0 license.

Changelog

N/A

Overview of Benchmark and Kneron Model Zoo

Backbone Crop Size Mem (GB) mIoU Config Download
STDC 1 512x1024 7.15 69.29 config model

NOTE: The performance may slightly differ from the original implementation since the input size is smaller.

Installation

Getting Started

Tutorial - Kneron Edition

  • STDC-Seg: Step-By-Step: A tutorial for users to get started easily. To see detailed documents, please see below.

Documents - Kneron Edition

Original mmsegmentation Documents

Contributing

kneron-mmsegmentation a platform built upon OpenMMLab-mmsegmentation

  • For issues regarding to the original mmsegmentation: We appreciate all contributions to improve OpenMMLab-mmsegmentation. Ongoing projects can be found in out GitHub Projects. Welcome community users to participate in these projects. Please refer to CONTRIBUTING.md for the contributing guideline.

  • For issues regarding to this repository kneron-mmsegmentation: Welcome to leave the comment or submit pull requests here to improve kneron-mmsegmentation

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