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Inference with pretrained models

We provide testing scripts to evaluate a whole dataset (KITTI, NYU, etc.), and also some high-level apis for easier integration to other projects.

Test a dataset

  • single GPU
  • CPU
  • single node multiple GPU
  • multiple node

You can use the following commands to test a dataset.

# single-gpu testing (you may need to set PYTHONPATH)
python tools/test.py ${CONFIG_FILE} ${CHECKPOINT_FILE} [--out ${RESULT_FILE}] [--eval ${EVAL_METRICS}] [--show]

# CPU: disable GPUs and run single-gpu testing script (you may need to set PYTHONPATH)
export CUDA_VISIBLE_DEVICES=-1
python tools/test.py ${CONFIG_FILE} ${CHECKPOINT_FILE} [--out ${RESULT_FILE}] [--eval ${EVAL_METRICS}] [--show]

# multi-gpu testing
./tools/dist_test.sh ${CONFIG_FILE} ${CHECKPOINT_FILE} ${GPU_NUM} [--out ${RESULT_FILE}] [--eval ${EVAL_METRICS}]

Optional arguments:

  • RESULT_FILE: Filename of the output results in pickle format. If not specified, the results will not be saved to a file.
  • EVAL_METRICS: In this version, we only utilize common metrics in depth estimation, such as Abs Rel, RMSE, etc. In the feature, we will consider more challengeable 3D metrics. Hence, this arg is in redundancy, where you can pass any character.
  • --show: If specified, depth results will be plotted on the images and shown in a new window. It is only applicable to single GPU testing and used for debugging and visualization. Please make sure that GUI is available in your environment, otherwise you may encounter the error like cannot connect to X server.
  • --show-dir: If specified, depth results will be plotted on the images and saved to the specified directory. It is only applicable to single GPU testing and used for debugging and visualization. You do NOT nconfigs/habins/habins_swinleed a GUI available in your environment for using this option.
  • --eval-options: Optional parameters for dataset.format_results and dataset.evaluate during evaluation. Unuseful right now.

Examples:

Assume that you have already downloaded the checkpoints to the directory checkpoints/.

  1. Test habins and visualize the results. Press any key for the next image.

    python tools/test.py configs/habins/habins_swinl_22k_w7_mix5_bs8.py \
    checkpoints/habins_swinl_22k_w7_mix5_bs8.pth \
    --show

  2. Test habins and save the painted images for latter visualization.

    python tools/test.py configs/habins/habins_swinl_22k_w7_mix5_bs8.py \
    checkpoints/habins_swinl_22k_w7_mix5_bs8.pth \
    --show-dir habins_swinl_22k_w7_mix5_bs8_results

  3. Test habins on NYU (without saving the test results) and evaluate the mIoU.

    python tools/test.py configs/habins/habins_swinl_22k_w7_mix5_bs8.py \
    checkpoints/habins_swinl_22k_w7_mix5_bs8.pth \
    --eval x(can be any arg)

  4. Test habins with 4 GPUs, and evaluate the standard 2D metric.

    bash ./tools/dist_test.sh configs/habins/habins_swinl_22k_w7_mix5_bs8.py \
    checkpoints/habins_swinl_22k_w7_mix5_bs8.pth \
    4 --eval x(can be any arg)

  5. Ensemble model results for KITTI online benchmark.

    Run test (only can be done with single GPU) and save formated results in ./habins_swinl_22k_w7_kitti_benchmark

    python configs/habins/habins_swinl_22k_w7_kitti_benchmark.py \
    checkpoints/habins_swinl_22k_w7_kitti_benchmark.pth \
    --format-only --show-dir habins_swinl_22k_w7_kitti_benchmark

    You will get temp files under ./habins_swinl_22k_w7_kitti_benchmark directory to be ensembled. You can re-run this script to get multi-model results, which are stored in folders such as ./habins_swinl_22k_w7_kitti_benchmark_2.

    Then set paths in tools/ensemble.py and ensemble multi-model results. You may run zip -r results.zip your_results_folder/ and submit the zip file to evaluation server.

  6. For NYU dataset, we provide scripts to visualize reconstructed point clouds.

    Run:

    python ./tools/misc/visualize_point-cloud.py \
    configs/habins/habins_swinl_22k_w7_mix5_bs8.py \
    checkpoints/habins_swinl_22k_w7_mix5_bs8.pth \
    --output-dir point-cloud

    You can find .xyz files in ./point-cloud. Then just use visualization tools, such as MeshLab, to see reconstructed point clouds.

Tips about visualization

In most papers, visualized depth maps are rendered with a specific colormap. These kinds of rendering are not invertible, leading to a difficulty to make qualitative comparisons among various methods. While it is hard to align the results of previous work, we implement a unified and also parameterizable rendering interface (I will add args in test.py in the next version).

In this hack version, please refer to file depth/utils/color_depth.py. The parameters indicate several possible rendering manners. Comparisons can be seen below.

python ./tools/test.py configs/habins/habins_swinl_22k_w7_mix5_bs8.py work_dir/saves/habins/habins_swinl_22k_w7_mix5_bs8/latest.pth --show-dir work_dir/saves/visualization