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Eduardo Henrique Arnold eduardohenriquearnold Coventry, UK https://earnold.me PhD candidate at WMG, University of Warwick. My research interest is within perception methods for autonomous vehicles.

eduardohenriquearnold/tdcfloripa2016 9

Palestra sobre Visão computacional e realidade aumentada no TheDevelopersConference Floripa

eduardohenriquearnold/TFlow 6

Automatic traffic flow and density estimation based on computer vision

eduardohenriquearnold/cagr2gcal 2

Sistema que permite importar o horário de aulas do sistema CAGR da UFSC para o serviço de agenda do Google

eduardohenriquearnold/l3p-ui 1

L3Pilot Project User Interface

eduardohenriquearnold/OCR 1

An OpenCV based OCR system, base to other projects

eduardohenriquearnold/tcc 1

Trabalho de Conclusão de Curso

rjames93/pyCheat 1

Project to explore various strategies for the classic card game (Cheat/Bullshit) and eventually assist a Player

eduardohenriquearnold/basicCC 0

Converts a small set of basic commands into C language script and generate binary

eduardohenriquearnold/Complex-YOLO 0

Complex-YOLO: Real-time 3D Object Detection on Point Clouds pytorch Darknet

eduardohenriquearnold/coopObjectClassification 0

Cooperative Object Classification for Autonomous Driving Applications

created repositoryeduardohenriquearnold/raytracer

Ray Tracer implementation from scratch based on Peter Shirley's 'Ray tracing in one weekend'

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Refactored Query pages, removed unnecessary code

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Merge branch 'refactorQuery' into 'master' Refactored Query pages, removed unnecessary code See merge request l3pilot-sp5/pc_db_ui!58

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Fixed csv format issue regarding headings and extra comma after each page of query

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Merge branch '49-formatting-of-csv-files-for-export' into 'master' Resolve "Formatting of csv files for export" Closes #49 See merge request l3pilot-sp5/pc_db_ui!56

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Improved LoginPage aesthetic

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Merge branch 'newlogin' into 'master' Improved LoginPage aesthetic See merge request l3pilot-sp5/pc_db_ui!57

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Merging github change that wasn't commited to Gitlab

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Download now skip empty measurements,file extentions are now corrected (csv)

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Added Documentation link to nav-bar

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Merge branch 'doc-menu' into 'master' Doc menu See merge request l3pilot-sp5/pc_db_ui!55

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Updated timeseries forecasting blog post and notebook

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Added bayesian linear regression post (from March2019)

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Created a new post on timeseries forecasting

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Added earnold.me domain name to config.toml and the static CNAME required by GH pages

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Updated talks

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issue closedfacebookresearch/pytorch3d

so3_log_map unexpected behaviour

Unexpected output of so3_log_map. so3_log_map and so3_exponential_map are inverse functions. However composing them for some rotation matrices do not result in the original rotation matrix R. Please note the code below.

Instructions To Reproduce the Issue:

import torch
from pytorch3d.transforms import so3_exponential_map, so3_log_map
from pytorch3d.renderer import look_at_view_transform

R,T = look_at_view_transform(1, elev=50, azim=0)
print(R)

log_R = so3_log_map(R, 1e-8)
R_hat = so3_exponential_map(log_R)
print(R_hat)

The output is

tensor([[[-1.0000,  0.0000,  0.0000],
         [ 0.0000,  0.6428, -0.7660],
         [-0.0000, -0.7660, -0.6428]]])
tensor([[[1., 0., 0.],
         [0., 1., 0.],
         [0., 0., 1.]]])

The matrices do not match, as one would expect. I cannot tell why, but I have noticed that this happens for any elevation angle <90 when azim=0.

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eduardohenriquearnold

issue commentfacebookresearch/pytorch3d

so3_log_map unexpected behaviour

Hi. Sorry, I should have realised this earlier. Thanks for the great explanation though.

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Removed old academic theme version

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Updated to new Academic theme

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Added hugo academic theme as a submodule

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Updated about info with Warwick information. Updated portrait pic

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issue openedfacebookresearch/pytorch3d

so3_log_map unexpected behaviour

Unexpected output of so3_log_map. so3_log_map and so3_exponential_map are inverse functions. However composing them for some rotation matrices do not result in the original rotation matrix R. Please note the code below.

Instructions To Reproduce the Issue:

import torch
from pytorch3d.transforms import so3_exponential_map, so3_log_map
from pytorch3d.renderer import look_at_view_transform

R,T = look_at_view_transform(1, elev=50, azim=0)
print(R)

log_R = so3_log_map(R, 1e-8)
R_hat = so3_exponential_map(log_R)
print(R_hat)

The output is

tensor([[[-1.0000,  0.0000,  0.0000],
         [ 0.0000,  0.6428, -0.7660],
         [-0.0000, -0.7660, -0.6428]]])
tensor([[[1., 0., 0.],
         [0., 1., 0.],
         [0., 0., 1.]]])

The matrices do not match, as one would expect. I cannot tell why, but I have noticed that this happens for any elevation angle <90 when azim=0.

created time in 2 months

issue closedfacebookresearch/pytorch3d

knn_points unexpected error using CUDA

When using knn_points on CUDA with large K (e.g. 4000) the code fails with the error

THCudaCheck FAIL file=/opt/conda/conda-bld/pytorch_1587428111115/work/aten/src/THC/THCReduceAll.cuh line=327 error=6 : the launch timed out and was terminated
Traceback (most recent call last):
  File "testknn.py", line 11, in <module>
    d = knn_points(g,p,None,None,K).dists
  File "/home/eduardo/Documents/Experiments/pytorch3d/pytorch3d/ops/knn.py", line 161, in knn_points
    p1_dists, p1_idx = _knn_points.apply(p1, p2, lengths1, lengths2, K, version)
  File "/home/eduardo/Documents/Experiments/pytorch3d/pytorch3d/ops/knn.py", line 56, in forward
    if lengths2.min() < K:
RuntimeError: cuda runtime error (6) : the launch timed out and was terminated at /opt/conda/conda-bld/pytorch_1587428111115/work/aten/src/THC/THCReduceAll.cuh:327

Tried all different versions of KNN (0 to 3) and all of them result in the same error. Using a smaller K (e.g. 2000) works just fine. If you change the device to cpu it works just fine. It does not seem to be a memory problem as the GPU memory is way below the maximum RAM of 8Gb.

Using pytorch3d compiled from source on commit 7f1e63aed1252ba8145d4a.

To Reproduce the Issue:

import torch
from pytorch3d.ops import knn_points

K = 4000
device = torch.device('cuda')
p = torch.rand(16, 200000, 3).to(device)
g = torch.rand(16, 8, 3).to(device)
d = knn_points(g, p, None, None, K).dists
print(d.shape)

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eduardohenriquearnold

issue commentfacebookresearch/pytorch3d

knn_points unexpected error using CUDA

Thanks, I believe this solves my problem!

eduardohenriquearnold

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issue commentfacebookresearch/pytorch3d

knn_points unexpected error using CUDA

Thanks for the prompt responses.

The PyTorch3D KNN is optimized for K < 32 and D < 4 (where D is the feature size). What is your use case for needing to use K = 4000?

A very large K doesn't seem to make much sense but I will try and explain my case. I'm optimising the sensor pose such that each object that I want to observe has a very large number of points on their surfaces - more accurately, I want to maximise the average number of points over all objects' surfaces. Since the average number of points on objects' surface is not differentiable itself (the counting operation is not differentiable), I am optimising the L2 distance between the centre of the objects and the K nearest points in their neighbourhood. However I need K >> 32, e.g. 1k or 4k. The KNN with large K seems to work fine on the CPU, but if I move the whole pipeline to the CPU my execution time increases drastically. Perhaps there is a simpler loss function to maximise the number of points on the surface of objects? I tried using the Chamfer loss between all points and the centre of objects, but the sensors tend to just collapse into some of the objects, thus I decided to limit the distance to K points only.

Maybe the calculation is just taking a long time in your case. If you are using the GPU for display as well as computation, the GPU can automatically kill long-running functions. See e.g. here. Can you split your calculation along the batch dimension so that each one will be quicker?

Thanks for the suggestion. Unfortunately I cannot disable my X server since I need to generate a visualisation of the optimisation process. Splitting the calculation to the batch dim did help improve to improve the speed when K=1000, but still resulted in the kernel being killed for K=4000.

eduardohenriquearnold

comment created time in 2 months

issue openedfacebookresearch/pytorch3d

knn_points unexpected error using CUDA

When using knn_points on CUDA with large K (e.g. 4000) the code fails with the error

THCudaCheck FAIL file=/opt/conda/conda-bld/pytorch_1587428111115/work/aten/src/THC/THCReduceAll.cuh line=327 error=6 : the launch timed out and was terminated
Traceback (most recent call last):
  File "testknn.py", line 11, in <module>
    d = knn_points(g,p,None,None,K).dists
  File "/home/eduardo/Documents/Experiments/pytorch3d/pytorch3d/ops/knn.py", line 161, in knn_points
    p1_dists, p1_idx = _knn_points.apply(p1, p2, lengths1, lengths2, K, version)
  File "/home/eduardo/Documents/Experiments/pytorch3d/pytorch3d/ops/knn.py", line 56, in forward
    if lengths2.min() < K:
RuntimeError: cuda runtime error (6) : the launch timed out and was terminated at /opt/conda/conda-bld/pytorch_1587428111115/work/aten/src/THC/THCReduceAll.cuh:327

Tried all different versions of KNN (0 to 3) and all of them result in the same error. If you change the device to cpu it works just fine. It does not seem to be a memory problem as the GPU memory is way below the maximum RAM of 8Gb.

Using pytorch3d compiled from source on commit 7f1e63aed1252ba8145d4a.

To Reproduce the Issue:

import torch
from pytorch3d.ops import knn_points

K = 4000
device = torch.device('cuda')
p = torch.rand(16, 200000, 3).to(device)
g = torch.rand(16, 8, 3).to(device)
d = knn_points(g, p, None, None, K).dists
print(d.shape)

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pull request commentfacebookresearch/pytorch3d

Fix look_at_view_transform when object location is not (0,0,0)

Just added a unit test for look_at_view_transform when at != (0,0,0).

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commit sha 3759186a8ea30cf889a4b4a986fe05b68d5c5c27

Add unit test for look_at_view_transform when 'at' is not (0,0,0)

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PR opened facebookresearch/pytorch3d

Fixing look_at_view_transform documentation, azim angle definition was incorrect

…between the projection on the y=0 plane and reference vector (0,0,1).

Fixes https://github.com/facebookresearch/pytorch3d/issues/229

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