"import tools" in my codebase imports detectron2's tools rather than my own library.

It's pretty common practice for pip libraries to put all their code inside a folder with the same name in order to namespace all packages to the outermost unique library name. In the detectron2 repo, there are a few folders that violate this, notably `tools` and `projects`. I would like to understand why they are set up this way rather than living inside the `detectron2` folder. Is there a way I can fix this by modifying the setup.py process? Or will I have to move or rename the folders in my fork and risk merge issues down the line?

## Instructions To Reproduce the Issue:
In my codebase

`from tools import foo`

## Expected behavior:

Imports my script named `foo` living in `/tools/foo.py`

## Actual behavior

Python says `"foo" is an unknown symbol`

## Environment:
```
---------------------- ----------------------------------------------------------------
sys.platform linux
Python 3.8.10 (default, Nov 26 2021, 20:14:08) [GCC 9.3.0]
numpy 1.20.2
detectron2 0.6 @/home/daniel/detectron2/detectron2
detectron2._C not built correctly: No module named 'detectron2._C'
Compiler ($CXX) c++ (Ubuntu 9.3.0-17ubuntu1~20.04) 9.3.0
CUDA compiler Build cuda_11.1.TC455_06.29190527_0
DETECTRON2_ENV_MODULE
PyTorch 1.9.0+cu111 @/usr/local/lib/python3.8/dist-packages/torch
PyTorch debug build False
GPU available Yes
GPU 0,1,2,3 NVIDIA GeForce RTX 2080 Ti (arch=7.5)
Driver version 470.86
CUDA_HOME /usr/local/cuda
Pillow 8.2.0
torchvision 0.10.0+cu111 @/usr/local/lib/python3.8/dist-packages/torchvision
torchvision arch flags 3.5, 5.0, 6.0, 7.0, 7.5, 8.0, 8.6
cv2 3.4.13
---------------------- ----------------------------------------------------------------
PyTorch built with:
- GCC 7.3
- C++ Version: 201402
- Intel(R) Math Kernel Library Version 2020.0.0 Product Build 20191122 for Intel(R) 64 architecture applications
- Intel(R) MKL-DNN v2.1.2 (Git Hash 98be7e8afa711dc9b66c8ff3504129cb82013cdb)
- OpenMP 201511 (a.k.a. OpenMP 4.5)
- NNPACK is enabled
- CPU capability usage: AVX2
- CUDA Runtime 11.1
- NVCC architecture flags: -gencode;arch=compute_37,code=sm_37;-gencode;arch=compute_50,code=sm_50;-gencode;arch=compute_60,code=sm_60;-gencode;arch=compute_70,code=sm_70;-gencode;arch=compute_75,code=sm_75;-gencode;arch=compute_80,code=sm_80;-gencode;arch=compute_86,code=sm_86
- CuDNN 8.0.5
- Magma 2.5.2
- Build settings: BLAS_INFO=mkl, BUILD_TYPE=Release, CUDA_VERSION=11.1, CUDNN_VERSION=8.0.5, CXX_COMPILER=/opt/rh/devtoolset-7/root/usr/bin/c++, CXX_FLAGS= -Wno-deprecated -fvisibility-inlines-hidden -DUSE_PTHREADPOOL -fopenmp -DNDEBUG -DUSE_KINETO -DUSE_FBGEMM -DUSE_QNNPACK -DUSE_PYTORCH_QNNPACK -DUSE_XNNPACK -DSYMBOLICATE_MOBILE_DEBUG_HANDLE -O2 -fPIC -Wno-narrowing -Wall -Wextra -Werror=return-type -Wno-missing-field-initializers -Wno-type-limits -Wno-array-bounds -Wno-unknown-pragmas -Wno-sign-compare -Wno-unused-parameter -Wno-unused-variable -Wno-unused-function -Wno-unused-result -Wno-unused-local-typedefs -Wno-strict-overflow -Wno-strict-aliasing -Wno-error=deprecated-declarations -Wno-stringop-overflow -Wno-psabi -Wno-error=pedantic -Wno-error=redundant-decls -Wno-error=old-style-cast -fdiagnostics-color=always -faligned-new -Wno-unused-but-set-variable -Wno-maybe-uninitialized -fno-math-errno -fno-trapping-math -Werror=format -Wno-stringop-overflow, LAPACK_INFO=mkl, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, TORCH_VERSION=1.9.0, USE_CUDA=ON, USE_CUDNN=ON, USE_EXCEPTION_PTR=1, USE_GFLAGS=OFF, USE_GLOG=OFF, USE_MKL=ON, USE_MKLDNN=ON, USE_MPI=OFF, USE_NCCL=ON, USE_NNPACK=ON, USE_OPENMP=ON,

Testing NCCL connectivity ... this should not hang.
NCCL succeeded.
```