i am tried to implement this model https://github.com/rusty1s/pytorch_geometric/blob/master/benchmark/points/point_cnn.py on 3D Mesh Faust dataset. However, this failure occurs and i could not solve by !pip intall "package". can i get help thank you.
Related
I'm using MiniGrid library to work with different 2D navigation problems as experiments for my reinforcement learning problem. I'm also using stable-baselines3 library to train PPO models. but unfortunately, while training PPO using stable-baselines3 with MiniGrid environment, I got the following error.
Error
I imported the environment as follows,
import gymnasium as gym
from minigrid.wrappers import RGBImgObsWrapper
env = gym.make("MiniGrid-SimpleCrossingS9N1-v0", render_mode="human")
env = RGBImgObsWrapper(env)
env = ImgObsWrapper(env)
The training script using stable-baseline3 is as follows,
model = PPO('CnnPolicy', env, verbose=0)
model.learn(args.timesteps, callback)
I have done a quick debug and found a potential lead. I don't know if this is the real cause.
When I tried to load the environment directly from the gym the action space <class 'gym.spaces.discrete.Discrete'>. But when loaded from MiniGrid the action space is <class 'gymnasium.spaces.discrete.Discrete'>. Any help to sort out the problem is highly appreciated. Thanks in advance
That's the correct analysis, Stable Baselines3 doesn't support Gymnasium yet, so checks on gym.spaces.discrete.Discrete fail against gymnasium.
The following post answer explains how to workaround that, based on a currently open PR: OpenAI Gymnasium, are there any libraries with algorithms supporting it?.
I am trying to build a key-points detection model for human, as there are many pretrained networks available to generate key-points, but i want to practice myself to create a keypoint detection model with custom dataset, cant find anything in web if someone have some info's then please share.
I want more points specified to the human body, but to do so i need to create a custom model to generate such kind of key-points in human body, i checked some annotation tools but those annotation tool helps to adjust the points they have already specified when taking dataset like COCO etc, i think we cant add more points to the image. i just want to build a new model with custom key-points.
please share your views about my view on to the problem and please suggest some links if you have any idea about the same
I have created a detailed github repo Custom Keypoint Detection for dataset preparation, model training and inference on Centernet-hourglass104 keypoint detection model based on Tensorflow Object detection API with examples.
This could help you in training your keypoint detection model on custom dataset.
Any issues related to the project can be raised in the github itself and doubts can be cleared here.
I am trying to use the tensorboard callback in keras. When I run the pretrained inceptionv3 model with the tensorboard callback I am getting the following warning:
INFO:tensorflow:Summary name conv2d_95/kernel:0 is illegal; using conv2d_95/kernel_0 instead.
I saw a comment on Github addressing this issue. SeaFX on his comment pointed out that he solved it by replacing variable.name with variable.name.replace(':','_'). I am unsure how to do that. Can anyone please help me. Thanks in advance :)
Not sure on getting name replacement to work however a workaround that may be sufficient for your needs is:
import tensorflow as tf
tf.logging.set_verbosity(tf.logging.WARN)
import keras
This will turn off all INFO level logging but keep warnings, errors etc.
See this question for a discussion on the various log levels and changing them. Personally I found setting the TF_CPP_MIN_LOG_LEVEL environment variable didn't work under Jupyter notebook but I haven't tested on base Python.
I try to use pretrained model (VGG 19) to DIGITS but I got this error.
ERROR: Your deploy network is missing a Softmax layer! Read the
documentation for custom networks and/or look at the standard networks
for examples
I try to test with my dataset which has only two classes.
I read this and this try to modify last layer but also I got error. How can I modify layers based on new dataset?
I try to modify the last layer and I got error
ERROR: Layer 'softmax' references bottom 'fc8' at the TRAIN stage however this blob is not included at that stage. Please consider using an include directive to limit the scope of this layer.
You're having a problem because you're trying to upload a "train/val" network when you really need to be uploading an "all-in-one" network. Unfortunately, we don't document this very well. I've created an RFE to remind us to improve the documentation.
Try to adjust the last layers in your network to look something like this: https://github.com/NVIDIA/DIGITS/blob/v4.0.0/digits/standard-networks/caffe/lenet.prototxt#L162-L184
For more information, here is how I've proposed updating Caffe's example networks to all-in-one nets, and here is how I updated the default DIGITS networks to be all-in-one nets.
I started using CGAL demos to find the intersection of two polyhedra. I met a problem when the two polyhedra share some vertices and edges. The program extis with "Segmentation fault" call. I assume the problem refers to the shared vertices, since for other case I see no problem. My polyhedra are e.g. rhombic triacontahedra or truncated tiracontahedra (the latter are concave). Is it a problem of demo program or the CGAL algorithm itself?
Input OFF files I use:
https://www.dropbox.com/s/nywdv80yzr0wpxp/henley_3D.off
https://www.dropbox.com/s/slxzfwhchpuix2p/henley_shift_3D.off
I'm using CGAL with Ubuntu 10.04 under VirtualMachine with Win7 x64 as host.
I will be grateful for any help from you.
Best,
Radek
First problem, your polyhedra are inconsistently oriented (which explain the checkerboard coloration when you open them in the demo.
Second problem you have non triangular facets which would not
be a problem for Nef if the vertices of the facets were coplanar
(which is not the case).
To fix this you can use the "Orient polygon soup" plugin and "Inside outside" plugin to fix the orientation.
The plugin "Triangulated facets" will help before creating nef polyhedra.