Shape-aware Convolutional Layer (ShapeConv)

PyTorch implementation of ShapeConv: Shape-aware Convolutional Layer for RGB-D Indoor Semantic Segmentation.

We design a Shape-aware Convolutional(ShapeConv) layer to explicitly model the shape information for enhancing the RGB-D semantic segmentation accuracy. Specifically, we decompose the depth feature into a shape-component and a value component, after which two learnable weights are introduced to handle the shape and value with differentiation. Extensive experiments on three challenging indoor RGB-D semantic segmentation benchmarks, i.e., NYU-Dv2(-13,-40), SUN RGB-D, and SID, demonstrate the effectiveness of our ShapeConv when employing it over five popular architectures.

Usage

Installation

  1. Requirements
  • Linux
  • Python 3.6+
  • PyTorch 1.7.0 or higher
  • CUDA 10.0 or higher

We have tested the following versions of OS and softwares:

  • OS: Ubuntu 16.04.6 LTS
  • CUDA: 10.0
  • PyTorch 1.7.0
  • Python 3.6.9
  1. Install dependencies.
pip install -r requirements.txt

Dataset

Download the offical dataset and convert to a format appropriate for this project. See here.

Evaluation

  1. Model

    Download trained model and put it in folder ./model_zoo.
    See all trained models here.

  2. Config

    Edit config file in ./config.
    The config files in ./config correspond to the model files in ./models.

    1. Set inference.gpu_id = CUDA_VISIBLE_DEVICES.
      CUDA_VISIBLE_DEVICES is used to specify which GPUs should be visible to a CUDA application,
      e.g., inference.gpu_id = "0,1,2,3".
    2. Set dataset_root = path_to_dataset.
      path_to_dataset represents the path of dataset.
      e.g.,dataset_root = "/home/shape_conv/nyu_v2".
  3. Run

    1. Ditributed evaluation, please run:
    ./tools/dist_test.sh config_path checkpoint_path gpu_num
    
    • config_path is path of config file;
    • checkpoint_pathis path of model file;
    • gpu_num is the number of GPUs used, note that gpu_num <= len(inference.gpu_id).

    E.g., evaluate shape-conv model on NYU-V2(40 categories), please run:

    ./tools/dist_test.sh configs/nyu/nyu40_deeplabv3plus_resnext101_shape.py model_zoo/nyu40_deeplabv3plus_resnext101_shape.pth 4
    
    1. Non-distributed evaluation
    python tools/test.py config_path checkpoint_path
    

Train

  1. Config

    Edit config file in ./config.

    1. Set inference.gpu_id = CUDA_VISIBLE_DEVICES.

      E.g.,inference.gpu_id = "0,1,2,3".

    2. Set dataset_root = path_to_dataset.

      E.g.,dataset_root = "/home/shape_conv/nyu_v2".

  2. Run

    1. Ditributed training
    ./tools/dist_train.sh config_path gpu_num
    

    E.g., train shape-conv model on NYU-V2(40 categories) with 4 GPUs, please run:

    ./tools/dist_train.sh configs/nyu/nyu40_deeplabv3plus_resnext101_shape.py 4
    
    1. Non-distributed training
    python tools/train.py config_path
    

Result

For more result, please see model zoo.

NYU-V2(40 categories)

Architecture Backbone MS & Flip Shape Conv mIOU
DeepLabv3plus ResNeXt-101 False False 48.9%
DeepLabv3plus ResNeXt-101 False True 50.2%
DeepLabv3plus ResNeXt-101 True False 50.3%
DeepLabv3plus ResNeXt-101 True True 51.3%

SUN-RGBD

Architecture Backbone MS & Flip Shape Conv mIOU
DeepLabv3plus ResNet-101 False False 46.9%
DeepLabv3plus ResNet-101 False True 47.6%
DeepLabv3plus ResNet-101 True False 47.6%
DeepLabv3plus ResNet-101 True True 48.6%

SID(Stanford Indoor Dataset)

Architecture Backbone MS & Flip Shape Conv mIOU
DeepLabv3plus ResNet-101 False False 54.55%
DeepLabv3plus ResNet-101 False True 60.6%

Acknowledgments

This repo was developed based on vedaseg.

GitHub

https://github.com/hanchaoleng/ShapeConv