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Refine docs and license (open-mmlab#11)
* Refine docs and license * Change order of install * Clean unnecessary requirements * Install mmdet manually in readthedocs * delete mmdet install * reformat docstring in code * Refine docs * Reformat * fix format issue
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‎.readthedocs.yml

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version: 3.7
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install:
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- requirements: requirements/docs.txt
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- requirements: requirements/build.txt
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- requirements: requirements/runtime.txt
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- requirements: requirements/readthedocs.txt

‎LICENSE

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‎docs/api.rst

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.. automodule:: mmdet3d.core.evaluation
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:members:
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visualizer
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^^^^^^^^^^^^^^^
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.. automodule:: mmdet3d.core.visualizer
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:members:
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voxel
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^^^^^^^^^^^^^^^
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.. automodule:: mmdet3d.core.voxel
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:members:
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post_processing
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^^^^^^^^^^^^^^^
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.. automodule:: mmdet3d.core.post_processing
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mmdet3d.datasets
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----------------
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datasets
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^^^^^^^^^^
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.. automodule:: mmdet3d.datasets
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:members:
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pipelines
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^^^^^^^^^^
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.. automodule:: mmdet3d.datasets.pipelines
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:members:
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mmdet3d.models
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--------------
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‎docs/config.md

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## Config File Structure
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There are 4 basic component types under `config/_base_`, dataset, model, schedule, default_runtime.
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Many methods could be easily constructed with one of each like SECOND, PointPillars, PartA2 and VoteNet.
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Many methods could be easily constructed with one of each like SECOND, PointPillars, PartA2, and VoteNet.
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The configs that are composed by components from `_base_` are called _primitive_.
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For all configs under the same folder, it is recommended to have only **one** _primitive_ config. All other configs should inherit from the _primitive_ config. In this way, the maximum of inheritance level is 3.

‎docs/getting_started.md

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evaluation = dict(interval=12) # This evaluate the model per 12 epoch.
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```
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**\*Important\***: The default learning rate in config files is for 8 GPUs and the exact batch size is marked by the config's file name, e.g. '2x8' means 2 samples per GPU using 8 GPUs.
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According to the [Linear Scaling Rule](https://arxiv.org/abs/1706.02677), you need to set the learning rate proportional to the batch size if you use different GPUs or images per GPU, e.g., lr=0.01 for 4 GPUs * 2 img/gpu and lr=0.08 for 16 GPUs * 4 img/gpu.
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**Important**: The default learning rate in config files is for 8 GPUs and the exact batch size is marked by the config's file name, e.g. '2x8' means 2 samples per GPU using 8 GPUs.
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According to the [Linear Scaling Rule](https://arxiv.org/abs/1706.02677), you need to set the learning rate proportional to the batch size if you use different GPUs or images per GPU, e.g., lr=0.01 for 4 GPUs * 2 img/gpu and lr=0.08 for 16 GPUs * 4 img/gpu. However, since most of the models in this repo use ADAM rather than SGD for optimization, the rule may not hold and users need to tune the learning rate by themselves.
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### Train with a single GPU
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If you use launch training jobs with Slurm, there are two ways to specify the ports.
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1. Modify the config files (usually the 6th line from the bottom in config files) to set different communication ports.
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1. Set the port through `--options`. This is more recommended since it does not change the original configs.
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```shell
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CUDA_VISIBLE_DEVICES=0,1,2,3 GPUS=4 ./tools/slurm_train.sh ${PARTITION} ${JOB_NAME} config1.py ${WORK_DIR} --options 'dist_params.port=29500'
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CUDA_VISIBLE_DEVICES=4,5,6,7 GPUS=4 ./tools/slurm_train.sh ${PARTITION} ${JOB_NAME} config2.py ${WORK_DIR} --options 'dist_params.port=29501'
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```
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2. Modify the config files (usually the 6th line from the bottom in config files) to set different communication ports.
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```
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2. Set the port through `--options`. This is more recommended since it does not change the original configs.
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```shell
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CUDA_VISIBLE_DEVICES=0,1,2,3 GPUS=4 ./tools/slurm_train.sh ${PARTITION} ${JOB_NAME} config1.py ${WORK_DIR} --options 'dist_params.port=29500'
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CUDA_VISIBLE_DEVICES=4,5,6,7 GPUS=4 ./tools/slurm_train.sh ${PARTITION} ${JOB_NAME} config2.py ${WORK_DIR} --options 'dist_params.port=29501'
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```
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## Useful tools
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We provide lots of useful tools under `tools/` directory.

‎docs/install.md

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If you build PyTorch from source instead of installing the prebuilt pacakge,
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you can use more CUDA versions such as 9.0.
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c. Clone the mmdetection repository.
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c. Install [MMCV](https://mmcv.readthedocs.io/en/latest/).
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*mmcv-full* is necessary since MMDetection3D relies on MMDetection, CUDA ops in *mmcv-full* are required.
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The pre-build *mmcv-full* could be installed by running: (available versions could be found [here](https://mmcv.readthedocs.io/en/latest/#install-with-pip))
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```shell
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git clone https://github.com/open-mmlab/mmdetection.git
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cd mmdetection
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pip install mmcv-full==latest+torch1.5.0+cu101 -f https://openmmlab.oss-accelerate.aliyuncs.com/mmcv/dist/index.html
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```
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Optionally, you could also build the full version from source:
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```shell
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pip install mmcv-full
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```
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d. Install [MMDetection](https://github.com/open-mmlab/mmdetection).
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```shell
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pip install git+https://github.com/open-mmlab/mmdetection.git
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```
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d. Install build requirements and then install mmdetection.
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(We install our forked version of pycocotools via the github repo instead of pypi
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for better compatibility with our repo.)
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Optionally, you could also build MMDetection from source in case you want to modify the code:
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```shell
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git clone https://github.com/open-mmlab/mmdetection.git
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cd mmdetection
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pip install -r requirements/build.txt
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pip install "git+https://github.com/open-mmlab/cocoapi.git#subdirectory=pycocotools"
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CC=clang CXX=clang++ CFLAGS='-stdlib=libc++' pip install -e .
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```
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e. Clone the mmdetection3d repository.
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e. Clone the MMDetection3D repository.
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git clone https://github.com/open-mmlab/mmdetection3d.git
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cd mmdetection3d
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```
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f.Install build requirements and then install mmdetection3d.
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f.Install build requirements and then install MMDetection3D.
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pip install -r requirements/build.txt
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pip install -v -e . # or "python setup.py develop"
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```
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# install latest pytorch prebuilt with the default prebuilt CUDA version (usually the latest)
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conda install -c pytorch pytorch torchvision -y
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# install mmcv
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pip install mmcv-full
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# install mmdetection
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git clone https://github.com/open-mmlab/mmdetection.git
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cd mmdetection
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pip install -r requirements/build.txt
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pip install cython
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pip install "git+https://github.com/open-mmlab/cocoapi.git#subdirectory=pycocotools"
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pip install -v -e .
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cd ..
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# install mmdetection3d
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git clone https://github.com/open-mmlab/mmdetection3d.git
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cd mmdetection3d
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pip install -r requirements/build.txt
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pip install -v -e .
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```
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