Pytorch reimplementation of PSM-Net: "Pyramid Stereo Matching Network" paper (CVPR 2018) by Jia-Ren Chang and Yong-Sheng Chen.
Official repository: JiaRenChang/PSMNet
- Python3.5+
- Pytorch0.4
- Opencv-Python
- Matplotlib
- TensorboardX
- Tensorboard
All dependencies are in requirements.txt, you can follow below command to install dependencies.
pip install requirements.txtusage: train.py [-h] [--maxdisp MAXDISP] [--logdir LOGDIR] [--datadir DATADIR]
[--cuda CUDA] [--batch-size BATCH_SIZE]
[--validate-batch-size VALIDATE_BATCH_SIZE]
[--log-per-step LOG_PER_STEP]
[--save-per-epoch SAVE_PER_EPOCH] [--model-dir MODEL_DIR]
[--lr LR] [--num-epochs NUM_EPOCHS]
[--num-workers NUM_WORKERS]
PSMNet
optional arguments:
-h, --help show this help message and exit
--maxdisp MAXDISP max diparity
--logdir LOGDIR log directory
--datadir DATADIR data directory
--cuda CUDA gpu number
--batch-size BATCH_SIZE
batch size
--validate-batch-size VALIDATE_BATCH_SIZE
batch size
--log-per-step LOG_PER_STEP
log per step
--save-per-epoch SAVE_PER_EPOCH
save model per epoch
--model-dir MODEL_DIR
directory where save model checkpoint
--lr LR learning rate
--num-epochs NUM_EPOCHS
number of training epochs
--num-workers NUM_WORKERS
num workers in loading dataFor example:
python train.py --batch-size 16 \
--logdir log/exmaple \
--num-epochs 500This repository uses tensorboardX to virsualize training result. Find your log directory and launch tensorboard to look over the result. The default log directory is /log
tensorboard --logdir <your_log_dir>Here are some of my training results (have been trained for 1000 epochs on KITTI2015):
usage: inference.py [-h] [--maxdisp MAXDISP] [--left LEFT] [--right RIGHT]
[--model-path MODEL_PATH] [--save-path SAVE_PATH]
PSMNet inference
optional arguments:
-h, --help show this help message and exit
--maxdisp MAXDISP max diparity
--left LEFT path to the left image
--right RIGHT path to the right image
--model-path MODEL_PATH
path to the model
--save-path SAVE_PATH
path to save the disp imageFor example:
python inference.py --left test/left.png \
--right test/right.png \
--model-path checkpoint/08/best_model.ckpt \
--save-path test/disp.pngA model trained for 1000 epochs on KITTI2015 dataset can be download here. (I choose the best model among the 1000 epochs)
state {
'epoch': 857,
'3px-error': 3.466
}
- Train
- Inference
- KITTI2015 dataset
- Scene Flow dataset
- Visualize
- Pretained model
Email: [email protected]
Wellcome for any discussions!
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