This branch contains codes of the PyTorch version of 3D-R2N2.
If you find this work useful in your research, please consider citing:
@inproceedings{choy20163d,
title={3D-R2N2: A Unified Approach for Single and Multi-view 3D Object Reconstruction},
author={Choy, Christopher B and Xu, Danfei and Gwak, JunYoung and Chen, Kevin and Savarese, Silvio},
booktitle = {Proceedings of the European Conference on Computer Vision ({ECCV})},
year={2016}
}
The project page is available at http://cvgl.stanford.edu/3d-r2n2/.
We used ShapeNet models to generate rendered images and voxelized models which are available below (you can follow the installation instruction below to extract it to the default directory).
- ShapeNet rendered images ftp://cs.stanford.edu/cs/cvgl/ShapeNetRendering.tgz
- ShapeNet voxelized models ftp://cs.stanford.edu/cs/cvgl/ShapeNetVox32.tgz
- Trained ResidualGRUNet Weights ftp://cs.stanford.edu/cs/cvgl/ResidualGRUNet.npy
The package requires python3. You can follow the direction below to install virtual environment within the repository or install anaconda for python 3.
- Download the repository
git clone https://github.com/chrischoy/3D-R2N2.git
- Setup virtual environment and install requirements and copy the theanorc file to the
$HOMEdirectory
cd 3D-R2N2
pip install virtualenv
virtualenv -p python3 --system-site-packages py3
source py3/bin/activate
pip install -r requirements.txt
cp .theanorc ~/.theanorc
学校装好了Nvidia driver和cuda
- 查看gpu状态
nvidia-smi
+-----------------------------------------------------------------------------+
| NVIDIA-SMI 384.90 Driver Version: 384.90 |
|-------------------------------+----------------------+----------------------+
| GPU Name Persistence-M| Bus-Id Disp.A | Volatile Uncorr. ECC |
| Fan Temp Perf Pwr:Usage/Cap| Memory-Usage | GPU-Util Compute M. |
|===============================+======================+======================|
| 0 GeForce GTX 108... Off | 00000000:03:00.0 Off | N/A |
| 50% 83C P2 89W / 250W | 1329MiB / 11172MiB | 1% Default |
+-------------------------------+----------------------+----------------------+
+-----------------------------------------------------------------------------+
| Processes: GPU Memory |
| GPU PID Type Process name Usage |
|=============================================================================|
+-----------------------------------------------------------------------------+
- 查看cuda版本
nvcc --version
nvcc: NVIDIA (R) Cuda compiler driver
Copyright (c) 2005-2016 NVIDIA Corporation
Built on Tue_Jan_10_13:22:03_CST_2017
Cuda compilation tools, release 8.0, V8.0.61
- 安装cudnn(来自installation guide但是cuda和cudnn版本不一样)
cd <cudnnpath>
Install the runtime library
sudo dpkg -i libcudnn7_7.1.1.5-1+cuda8.0_amd64.deb
Install the developer library
sudo dpkg -i libcudnn7-dev_7.1.1.5-1+cuda8.0_amd64.deb
Install the code samples and the cuDNN Library User Guide
sudo dpkg -i libcudnn7-doc_7.1.1.5-1+cuda8.0_amd64.deb
- Verify cudnn(和它网站上的一样)
http://docs.nvidia.com/deeplearning/sdk/cudnn-install/index.html#verify
- 安装anaconda
bash Anaconda3-5.1.0-Linux-x86_64.sh
- 用conda安装libpygpu(theano gpu backend)
这个gpu backend好像是老的，新的是pygpu，在theano的conda-forge里装的有
conda install -c anaconda libgpuarray
- 用conda安装theano
conda install -c conda-forge theano
- 用theano可能有的MKL2018 bug
RuntimeError: To use MKL 2018 with Theano you MUST set "MKL_THREADING_LAYER=GNU" in your environement.
把下面一行加到~/.bashrc中
export MKL_THREADING_LAYER=GNU
- 测试theano能不能用gpu和cudnn(来自testing theano with gpu)
拷贝链接中的代码，并命名为testing_theano_with_gpu.py
THEANO_FLAGS='device=cuda0, floatX=float32, force_device=True, optimizer_including=cudnn' python3 testing_theano_with_gpu.py
结果如下
Using cuDNN version 7101 on context None
Disabling allocation cache on cuda0
Mapped name None to device cuda0: GeForce GTX 1080 Ti (0000:03:00.0)
[GpuElemwise{exp,no_inplace}(<GpuArrayType<None>(float32, vector)>), HostFromGpu(gpuarray)(GpuElemwise{exp,no_inplace}.0)]
Looping 1000 times took 0.445036 seconds
Result is [1.2317803 1.6187935 1.5227807 ... 2.2077181 2.2996776 1.623233 ]
Used the gpu
- 用pip安装easydict
anaconda有requirements.txt里要求的大部分库，除了easydict。但是用conda装easydict会和conda里的其他库冲突，所以用pip安装
pip install easydict
- Thanks my GD
- Install meshlab (skip if you have another mesh viewer). If you skip this step, demo code will not visualize the final prediction.
sudo apt-get install meshlab
- Run the demo code and save the final 3D reconstruction to a mesh file named
prediction.obj
python demo.py prediction.obj
The demo code takes 3 images of the same chair and generates the following reconstruction.
| Image 1 | Image 2 | Image 3 | Reconstruction |
|---|---|---|---|
 |
 |
 |
 |
- Deactivate your environment when you are done
deactivate
- Activate the virtual environment before you run the experiments.
source py3/bin/activate
- Download datasets and place them in a folder named
ShapeNet
mkdir ShapeNet/
wget ftp://cs.stanford.edu/cs/cvgl/ShapeNetRendering.tgz
wget ftp://cs.stanford.edu/cs/cvgl/ShapeNetVox32.tgz
tar -xzf ShapeNetRendering.tgz -C ShapeNet/
tar -xzf ShapeNetVox32.tgz -C ShapeNet/
- Train and test the network using the training shell script
./experiments/scripts/res_gru_net.sh
Note: The initial compilation might take awhile if you run the theano for the first time due to various compilations. The problem will not persist for the subsequent runs.
pygpu.gpuarray.GpuArrayException: Out of memory
解决方法(来自https://groups.google.com/d/msg/theano-users/kpfsem4tabA/bjOuVtCIAwAJ)
1.在~/.theanorc中设置
[gpuarray]
preallocate = -1
2.在所有processes spawn后再标注使用gpu
详见lib/train_net.py第70、71行和lib/test_net.py第45、46行
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