SoftRas f644279-fosscuda-2019b-Python-3.7.4-PyTorch-1.4.0Soft Rasterizer (SoftRas) is a truly differentiable renderer framework with a novel formulation that views rendering as a differentiable aggregating process that fuses probabilistic contributions of all mesh triangles with respect to the rendered pixels. Thanks to such "soft" formulation, our framework is able to (1) directly render colorized mesh using differentiable functions and (2) back-propagate efficient supervision signals to mesh vertices and their attributes (color, normal, etc.) from various forms of image representations, including silhouette, shading and color images.
Accessing SoftRas f644279-fosscuda-2019b-Python-3.7.4-PyTorch-1.4.0
To load the module for SoftRas f644279-fosscuda-2019b-Python-3.7.4-PyTorch-1.4.0 please use this command on the BEAR systems (BlueBEAR, BEARCloud VMs, and CaStLeS VMs):
module load SoftRas/f644279-fosscuda-2019b-Python-3.7.4-PyTorch-1.4.0
BEAR Apps Version
EL8-haswell (GPUs: NVIDIA P100)
The listed architectures consist of two part: OS-CPU.
- BlueBEAR: The OS used on BlueBEAR is represented by EL and there are several different processor (CPU) types available on BlueBEAR. More information about the processor types on BlueBEAR is available on the BlueBEAR Job Submission page.
- BEAR and CaStLeS Cloud VMs: These VMs can have one of two OSes. Those with access to a BEAR Cloud or CaStLeS VM should check that the listed architectures for an application include the OS of VM being used. The VMs, irrespective of OS, will use the haswell CPU type.
For more information visit the SoftRas website.
This version of SoftRas has a direct dependency on: fosscuda/2019b Python/3.7.4-GCCcore-8.3.0 PyTorch/1.4.0-fosscuda-2019b-Python-3.7.4 scikit-image/0.16.2-fosscuda-2019b-Python-3.7.4 SciPy-bundle/2019.10-fosscuda-2019b-Python-3.7.4 torchvision/0.5.0-fosscuda-2019b-Python-3.7.4-PyTorch-1.4.0 tqdm/4.41.1-GCCcore-8.3.0
This version of SoftRas is a direct dependent of: BEAR-Python-DataScience/2019b-fosscuda-2019b-Python-3.7.4 BEAR-Python-DataScience/2019b-fosscuda-2019b-Python-3.7.4-ppc64le
Last modified on 5th March 2020