Shape-aware Convolutional Layer (ShapeConv)

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

Introduction

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

2. Install dependencies.

pip install -r requirements.txt

Dataset

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

Or download the converted dataset:

• NYU-V2
• SUN-RGBD
• SID

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

   2. 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

   2. Non-distributed training

   python tools/train.py config_path

Result

For more result, please see model zoo.

NYU-V2(40 categories)

SUN-RGBD

SID(Stanford Indoor Dataset)

Acknowledgments

This repo was developed based on vedaseg.