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youngLBW / HRN

[CVPR2023] A Hierarchical Representation Network for Accurate and Detailed Face Reconstruction from In-The-Wild Images.
https://younglbw.github.io/HRN-homepage/
Apache License 2.0
407 stars 37 forks source link

Is there any conflict in the provided environment? #45

Closed 675492062 closed 8 months ago

675492062 commented 9 months ago

Requirements This implementation is only tested under Ubuntu/CentOS environment with Nvidia GPUs and CUDA installed.

Python >= 3.8

PyTorch >= 1.6

requirements.txt: numpy==1.18.1

torch==1.6.0

torchvision==0.7.0

tensorflow-gpu==2.3.0

opencv-python==4.5.5.64 opencv-python-headless==4.5.5.64 protobuf==3.20.1 tqdm kornia pillow scipy tensorboard scikit-image albumentations torchsummary numba einops trimesh face-alignment ninja imageio

Is there any confict?

675492062 commented 9 months ago

nvdiffrast Installation Minimum requirements:

Linux or Windows operating system. 64-bit Python 3.6.

PyTorch (recommended) 1.6 or TensorFlow 1.14. TensorFlow 2.x is currently not supported.

A high-end NVIDIA GPU, NVIDIA drivers, CUDA 10.2 toolkit.

675492062 commented 9 months ago

pytorch3d Installation Requirements Core library The core library is written in PyTorch. Several components have underlying implementation in CUDA for improved performance. A subset of these components have CPU implementations in C++/PyTorch. It is advised to use PyTorch3D with GPU support in order to use all the features.

Linux or macOS or Windows

Python 3.8, 3.9 or 3.10

PyTorch 1.10.0, 1.10.1, 1.10.2, 1.11.0, 1.12.0, 1.12.1, 1.13.0, 2.0.0 or 2.0.1.

torchvision that matches the PyTorch installation. You can install them together as explained at pytorch.org to make sure of this. gcc & g++ ≥ 4.9 fvcore ioPath If CUDA is to be used, use a version which is supported by the corresponding pytorch version and at least version 9.2. If CUDA older than 11.7 is to be used and you are building from source, the CUB library must be available. We recommend version 1.10.0.