Dockerized version of TransLO

Dockerfile

@@ -0,0 +1,38 @@
+# docker build -t translo .
+
+# docker run --rm -it --gpus all -v ~/KITTY_FULL:/DATASET_DIR:ro -v ~/code/TransLO/:/CHECKPOINT_DIR:ro -v "$(pwd)":/OUTPUT_DIR translo
+
+# python3 infer_seq_abs.py --checkpoint /CHECKPOINT_DIR/translo_model_047_-20.478760.pth.tar --sequence /DATASET_DIR/data_odometry_velodyne/dataset/sequences/00/velodyne/ --output /OUTPUT_DIR/my_output.txt
+
+FROM nvidia/cuda:12.8.2-devel-ubuntu24.04
+
+ENV DEBIAN_FRONTEND=noninteractive
+ENV PYTHONUNBUFFERED=1
+
+WORKDIR /translo_workspace
+
+RUN apt-get update
+RUN apt-get -y upgrade
+
+RUN apt-get -y install g++
+RUN apt-get -y install ninja-build
+RUN apt-get -y install build-essential
+RUN apt-get -y install ca-certificates
+RUN apt-get -y install git
+
+RUN apt-get -y install python3
+RUN apt-get -y install python3-dev
+RUN apt-get -y install python3-pip
+RUN apt-get -y install python3-venv
+
+RUN git clone --recursive https://github.com/mwlasiuk/TransLO.git --branch mw/infer
+
+RUN pip3 install --break-system-packages --no-cache-dir torch==2.11.0 torchvision==0.26.0 torchaudio==2.11.0 --index-url https://download.pytorch.org/whl/cu128
+
+COPY entrypoint.sh /entrypoint.sh
+
+RUN chmod +x /entrypoint.sh
+
+ENTRYPOINT ["/entrypoint.sh"]
+
+CMD ["/bin/bash"]

README.md

@@ -1 +1,33 @@
-# benchmark-HDMapping-AILoopClosure-TransLO
+# benchmark-HDMapping-AILoopClosure-TransLO
+
+Reproducible setup for [TransLO: A Window-Based Masked Point Transformer Framework for Large-Scale LiDAR Odometry](https://github.com/IRMVLab/TransLO).
+
+Only inference.
+
+# Requirements
+- Ubuntu 24.04
+- NVIDIA GPU and CUDA Toolkit 12.x
+- Kitti Velodyne dataset ([download](https://www.cvlibs.net/datasets/kitti/))
+- Docker ([follow install instructions](https://docs.docker.com/engine/install/ubuntu/))
+- Docker NVIDIA runtime ([follow install instructions](https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/latest/install-guide.html))
+- Pretrained model
+
+# Running
+
+``` bash
+# clone repository
+git clone https://github.com/MapsHD/benchmark-HDMapping-AILoopClosure-TransLO.git
+
+# enter repository
+cd benchmark-HDMapping-AILoopClosure-TransLO
+
+# build docker image
+docker build -t translo .
+
+# run docker image
+docker run --rm -it --gpus all -v PATH_TO_KITTY_DATASET:/DATASET_DIR:ro -v PATH_TO_MODEL_DIRECTORY:/CHECKPOINT_DIR:ro -v "$(pwd)":/OUTPUT_DIR translo
+
+# inside docker container
+python3 infer_seq_abs.py --checkpoint /CHECKPOINT_DIR/translo_model_047_-20.478760.pth.tar --sequence /DATASET_DIR/data_odometry_velodyne/dataset/sequences/00/velodyne/ --output /OUTPUT_DIR/output.txt --no-relative
+
+```

entrypoint.sh

@@ -0,0 +1,27 @@
+#!/bin/bash
+set -e
+
+MARKER="/translo_workspace/.cuda_extensions_built"
+
+if [ ! -f "$MARKER" ]; then
+    echo "Building TransLO CUDA extensions..."
+
+    export CUDA_HOME=/usr/local/cuda
+
+    cd /translo_workspace/TransLO/pointnet2
+    python3 setup.py install
+
+    cd /translo_workspace/TransLO/ops_pytorch/fused_conv_random_k
+    python3 setup.py install
+
+    cd /translo_workspace/TransLO/ops_pytorch/fused_conv_select_k
+    python3 setup.py install
+
+    cd /translo_workspace/TransLO
+
+    touch "$MARKER"
+
+    echo "Done."
+fi
+
+exec "$@"