Anon sub eccv26

.gitignore

@@ -0,0 +1,24 @@
+*.pyc
+*.npy
+*.pth
+*.whl
+*.swp
+*.sif
+
+wandb/
+data/
+ckpt/
+work_dirs*/
+dist_test/
+vis/
+val/
+lib/
+logs/
+
+*.egg-info
+build/
+__pycache__/
+*.so
+
+job_scripts/
+temp_ops/

LICENSE

@@ -1,201 +1,21 @@
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+MIT License
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+SOFTWARE.

docker/Dockerfile

@@ -0,0 +1,22 @@
+FROM pytorch/pytorch:1.13.0-cuda11.6-cudnn8-devel
+ENV TORCH_CUDA_ARCH_LIST="6.0 6.1 7.0 7.5 8.0 8.6+PTX" \
+    TORCH_NVCC_FLAGS="-Xfatbin -compress-all" \
+    CMAKE_PREFIX_PATH="$(dirname $(which conda))/../" \
+    FORCE_CUDA="1"
+
+# Install the MMDetection3D required packages
+RUN apt-get update \
+    && apt-get install -y ffmpeg libsm6 libxext6 git ninja-build libglib2.0-0 libsm6 libxrender-dev libxext6 \
+    && apt-get clean \
+    && rm -rf /var/lib/apt/lists/*
+
+# Install StreamPETR required packages
+WORKDIR /workspace/ForeSight/
+COPY requirement.txt .
+COPY projects/ projects/
+RUN pip install mmcv-full==1.7.1 -f https://download.openmmlab.com/mmcv/dist/cu116/torch1.13.0/index.html
+RUN pip install --no-cache-dir -r requirement.txt
+RUN cd projects/mmdet3d_plugin/ops && python setup.py develop
+RUN git config --global --add safe.directory '*'
+
+WORKDIR /workspace/ForeSight/

docs/quick_start.md

@@ -0,0 +1,64 @@
+# Quick Start
+
+### Set up a new virtual environment
+```bash
+conda create -n sparsedrive python=3.8 -y
+conda activate sparsedrive
+```
+
+### Install dependency packpages
+```bash
+sparsedrive_path="path/to/sparsedrive"
+cd ${sparsedrive_path}
+pip3 install --upgrade pip
+pip3 install torch==1.13.0+cu116 torchvision==0.14.0+cu116 torchaudio==0.13.0 --extra-index-url https://download.pytorch.org/whl/cu116
+pip3 install -r requirement.txt
+```
+
+### Compile the deformable_aggregation CUDA op
+```bash
+cd projects/mmdet3d_plugin/ops
+python3 setup.py develop
+cd ../../../
+```
+
+### Prepare the data
+Download the [NuScenes dataset](https://www.nuscenes.org/nuscenes#download) and CAN bus expansion, put CAN bus expansion in /path/to/nuscenes, create symbolic links.
+```bash
+cd ${sparsedrive_path}
+mkdir data
+ln -s path/to/nuscenes ./data/nuscenes
+```
+
+Pack the meta-information and labels of the dataset, and generate the required pkl files to data/infos. Note that we also generate map_annos in data_converter, with a roi_size of (30, 60) as default, if you want a different range, you can modify roi_size in tools/data_converter/nuscenes_converter.py.
+```bash
+sh scripts/create_data.sh
+```
+
+### Generate anchors by K-means
+Gnerated anchors are saved to data/kmeans and can be visualized in vis/kmeans.
+```bash
+sh scripts/kmeans.sh
+```
+
+
+### Download pre-trained weights
+Download the required backbone [pre-trained weights](https://download.pytorch.org/models/resnet50-19c8e357.pth).
+```bash
+mkdir ckpt
+wget https://download.pytorch.org/models/resnet50-19c8e357.pth -O ckpt/resnet50-19c8e357.pth
+```
+
+### Commence training and testing
+```bash
+# train
+sh scripts/train.sh
+
+# test
+sh scripts/test.sh
+```
+
+### Visualization
+```
+sh scripts/visualize.sh
+```