<jupyter_start><jupyter_text>Install Dependencies```bash pip install pytorch=1.6.0 torchvision=0.7.0 cudatoolkit=10.2 -c pytorchpip install matplotlib path.py tqdmpip install tensorboard tensorboardXpip install scipy scikit-image opencv-contrib-python```<jupyter_code>!pip install matplotlib path.py tqdm
!pip install tensorboard tensorboardX
!pip install scipy scikit-image ipykernel
# pip install opencv-contrib-python<jupyter_output>Requirement already satisfied: matplotlib in /home/aditya/VSProjects/LEAStereo/LEAStereo/lib/python3.8/site-packages (3.4.2)
Requirement already satisfied: path.py in /home/aditya/VSProjects/LEAStereo/LEAStereo/lib/python3.8/site-packages (12.5.0)
Requirement already satisfied: tqdm in /home/aditya/VSProjects/LEAStereo/LEAStereo/lib/python3.8/site-packages (4.61.1)
Requirement already satisfied: pillow>=6.2.0 in /home/aditya/VSProjects/LEAStereo/LEAStereo/lib/python3.8/site-packages (from matplotlib) (8.2.0)
Requirement already satisfied: cycler>=0.10 in /home/aditya/VSProjects/LEAStereo/LEAStereo/lib/python3.8/site-packages (from matplotlib) (0.10.0)
Requirement already satisfied: pyparsing>=2.2.1 in /home/aditya/VSProjects/LEAStereo/LEAStereo/lib/python3.8/site-packages (from matplotlib) (2.4.7)
Requirement already satisfied: numpy>=1.16 in /home/aditya/VSProjects/LEAStereo/LEAStereo/lib/python3.8/site-packages (from matplotlib) (1.20.3)
Requirement already satisfied: kiwisolver>=1.0[...]<jupyter_text>Download DatasetTo evaluate/train our LEAStereo network, you will need to download the required datasets.* [SceneFlow](https://lmb.informatik.uni-freiburg.de/resources/datasets/SceneFlowDatasets.en.html)* [KITTI2015](http://www.cvlibs.net/datasets/kitti/eval_scene_flow.php?benchmark=stereo)* [KITTI2012](http://www.cvlibs.net/datasets/kitti/eval_stereo_flow.php?benchmark=stereo)* [Middlebury 2014](https://vision.middlebury.edu/stereo/submit3/)Change the first column path in file `create_link.sh` with your actual dataset location. Then run `create_link.sh` that will create symbolic links to wherever the datasets were downloaded in the `datasets` folder. For Middlebury 2014 dataset, we perform our network on half resolution images.<jupyter_code># TODO : download and link dataset dir
!sh download_kitti12.sh
!sh download_kitti15.sh<jupyter_output><empty_output><jupyter_text>Test PredictionsYou can evaluate a trained model using prediction.sh for each dataset, that would help you generate *.png or *.pfm images correspoding to different datasets.<jupyter_code>!sh predict_kitti12.sh<jupyter_output>Namespace(cell_arch_fea='run/sceneflow/best/architecture/feature_genotype.npy', cell_arch_mat='run/sceneflow/best/architecture/matching_genotype.npy', crop_height=384, crop_width=1248, cuda=True, data_path='./dataset/kitti2012/testing/', fea_block_multiplier=4, fea_filter_multiplier=8, fea_num_layers=6, fea_step=3, kitti2012=1, kitti2015=0, mat_block_multiplier=4, mat_filter_multiplier=8, mat_num_layers=12, mat_step=3, maxdisp=192, middlebury=0, net_arch_fea='run/sceneflow/best/architecture/feature_network_path.npy', net_arch_mat='run/sceneflow/best/architecture/matching_network_path.npy', resume='./run/Kitti12/best/best_1.16.pth', save_path='./predict/kitti2012/images/', sceneflow=0, test_list='./dataloaders/lists/kitti2012_test.list')
===> Building LEAStereo model
Feature network path:[1 0 1 0 0 0]
Matching network path:[1 1 2 2 1 2 2 2 1 1 0 1] 

Total Params = 1.81MB
Feature Net Params = 0.10MB
Matching Net Params = 1.71MB
Traceback (most recent call last):
  File "predict.py", lin[...]
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