This repo is inspired by an amazing work of Clément Godard, Oisin Mac Aodha and Gabriel J. Brostow for Unsupervised Monocular Depth Estimation. Original code and paper could be found via following links:
This repository contains code and additional parts for the PyTorch port of the MonoDepth Deep Learning algorithm. For more information about original work please visit author's websiet
Purpose of this repository is to make more lightweighted model for depth estimation with better accuracy.
The following results may be obtained using the model pretrained for 150 epochs on the whole dataset with initial lr = 0.01
This algorithm requires stereo-pair images for training and single images for testing. KITTI dataset was used for training. It contains 38237 training samples. Raw dataset (about 175 GB) can be downloaded by running:
wget -i kitti_archives_to_download.txt -P ~/my/output/folder/kitti_archives_to_download.txt may be found in original repo
Dataloader assumes the following structure of the folder with train examples ('data_dir' argument contains path to that folder):
It contains subfolders with folders "image_02/data" for left images and "image_03/data" for right images. Such structure is default for KITTI dataset
Example of training can be find in Monodepth notebook.
Model class from main_monodepth_pytorch.py should be initialized with following params (as easydict) for training:
- 'data_dir': path to the dataset folder
- 'model_path': path to save the trained model
- 'output_directory': where save dispairities for tested images
- 'input_height'
- 'input_width'
- 'model': model for encoder (resnet18 or resnet50)
- 'mode': train or test
- 'epochs': number of epochs,
- 'learning_rate'
- 'batch_size'
- 'adjust_lr': apply learning rate decay or not
- 'tensor_type':'torch.cuda.FloatTensor' or 'torch.FloatTensor'
- 'do_augmentation':do data augmentation or not
- 'augment_parameters':lowest and highest values for gamma, lightness and color respectively'
- 'print_images'
- 'print_weights'
Optionally after initialization we can load pretrained model via load model
After that calling train() on Model class object starts training process.
Also it can be started via calling main_monodepth_pytorch.py through the terminal and feeding parameters as argparse arguments.
One of our pretrained models which showed best results may be downloaded from here.
Example of testing can be find in Monodepth notebook.
Model class from main_monodepth_pytorch.py should be initialized with following params (as easydict) for testing:
- 'data_dir': path to the dataset folder
- 'model_path': path to save the trained model
- 'output_directory': where save dispairities for tested images
- 'input_height'
- 'input_width'
- 'model': model for encoder (resnet18 or resnet50)
- 'mode': train or test
After that calling test() on Model class object starts testing process
Also it can be started via calling main_monodepth_pytorch.py through the terminal and feeding parameters asargparse arguments.
This code was tested with PyTorch 0.4.0, CUDA 9.1 and Ubuntu 16.04.