# GCNetwork

**Repository Path**: han20121231/GCNetwork

## Basic Information

- **Project Name**: GCNetwork
- **Description**: No description available
- **Primary Language**: Unknown
- **License**: Not specified
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2020-06-22
- **Last Updated**: 2020-12-19

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# Geometry and Context Network (On-Going Project)
   A Keras implementation of GC Network by HungShi Lin(hl2997@columbia.edu). The paper can be found [here](https://arxiv.org/abs/1703.04309).
I do some modifications by adding a linear output function and enable training highway block at the second stage.

### Issue
   1. Model performance can't acheive that in the original paper.

### Update (10/06/2017)
   Model can be trained with image with size (256, 512)
   
### Software Requirement
   tensorflow([install from here](https://www.tensorflow.org/install/)), keras([install from here](https://keras.io/#installation))

### Data used for training model  
   I trained my model with [drivingfinalpass dataset](https://lmb.informatik.uni-freiburg.de/resources/datasets/SceneFlowDatasets.en.html), which contains more than 4000 stereo images with 2 epochs.

### Preprocessing
   We crop training patches with size of 256x256 (different from that in the paper) from training images and normalize each channel.

### Download
   Run the following command:
####   
      git clone https://github.com/LinHungShi/GCNetwork.git
   
### Two ways to download driving dataset:  
  1. create subdirectories sceneflow/driving in data, download and tar driving_final pass and driving_disparity from [here](https://lmb.informatik.uni-freiburg.de/resources/datasets/SceneFlowDatasets.en.html). 
  2. You can also issue command 
####
      “sh download.sh” 
   which will create subdirectories and download datasets.
   
### Train the model by running:
   Run the following command: 
####
      python train.py

### Predict the data with test.py
   1. create a directory, which contains two subdirectories -- left and right. 
   2. Run the following command
####
      python test.py -data <path/to/directory> -wpath <path/to/weight> [option]
   3. The default file will be saved as npy file named prediction.npy, you can replace it with -pspath when issuing the above command.
### (Optional) Specify the pretrained weight by
   1. Set it in train_params.py
   2. python train.py -wpath <path to the pretrained weight>

### Something you might want to do
   1. To enable training with Monkaa dataset, 
      a. Download Monkaa dataset from previous link.
      
      b. Create a directory in data, which has the name as monkaa_root in src/environment.json.
      
      c. Create a subdirectory, which has the name as monkaa_train in src/environment.json.
      
      d. Create a subdirectory, which has the name as monkaa_label in src/environment.json.
      
   2. All hyperparameters used for building the model can be found in src/hyperparams.json

### Reference :
   *Kendall, Alex, et al. "End-to-End Learning of Geometry and Context for Deep Stereo Regression." arXiv preprint arXiv:1703.04309 (2017).*