pulp-dronet | deep learning-powered visual navigation engine | Machine Learning library
kandi X-RAY | pulp-dronet Summary
kandi X-RAY | pulp-dronet Summary
PULP-DroNet is a deep learning-powered visual navigation engine that enables autonomous navigation of a pocket-size quadrotor in a previously unseen environment. Thanks to PULP-DroNet the nano-drone can explore the environment, avoiding collisions also with dynamic obstacles, in complete autonomy -- no human operator, no ad-hoc external signals, and no remote laptop! This means that all the complex computations are done directly aboard the vehicle and very fast. The visual navigation engine is composed of both a software and a hardware part. The former is based on the previous DroNet project developed by the RPG from the University of Zürich (UZH). DroNet is a shallow convolutional neural network (CNN) which has been used to control a standard-size quadrotor in a set of environments via remote computation. The hardware soul of PULP-DroNet is embodied by the PULP-Shield an ultra-low power visual navigation module featuring a Parallel Ultra-Low-Power (PULP) GAP8 System-on-Chip (SoC) from GreenWaves Technologies (GWT), an ultra-low power HiMax HBM01 camera, and off-chip Flash/DRAM memory; the shield is designed as a pluggable PCB for the Crazyflie 2.0/2.1 nano-drone. Then, we developed a general methodology for deploying state-of-the-art deep learning algorithms on top of ultra-low power embedded computation nodes, like a miniaturized drone. Our novel methodology allowed us first to deploy DroNet on the PULP-Shield, and then demonstrating how it enables the execution the CNN on board the CrazyFlie 2.0 within only 64-284mW and with a throughput of 6-18 frame-per-second! Finally, we field-prove our methodology presenting a closed-loop fully working demonstration of vision-driven autonomous navigation relying only on onboard resources, and within an ultra-low power budget. See the videos on the PULP Platform Youtube channel (Video). We release here, as open source, all our code, hardware designs, datasets, and trained networks.
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Trending Discussions on Machine Learning
QUESTION
I have trained an RNN model with pytorch. I need to use the model for prediction in an environment where I'm unable to install pytorch because of some strange dependency issue with glibc. However, I can install numpy and scipy and other libraries. So, I want to use the trained model, with the network definition, without pytorch.
I have the weights of the model as I save the model with its state dict and weights in the standard way, but I can also save it using just json/pickle files or similar.
I also have the network definition, which depends on pytorch in a number of ways. This is my RNN network definition.
...ANSWER
Answered 2022-Feb-17 at 10:47You should try to export the model using torch.onnx. The page gives you an example that you can start with.
An alternative is to use TorchScript, but that requires torch libraries.
Both of these can be run without python. You can load torchscript in a C++ application https://pytorch.org/tutorials/advanced/cpp_export.html
ONNX is much more portable and you can use in languages such as C#, Java, or Javascript https://onnxruntime.ai/ (even on the browser)
A running exampleJust modifying a little your example to go over the errors I found
Notice that via tracing any if/elif/else, for, while will be unrolled
QUESTION
I'm trying to implement a gradient-free optimizer function to train convolutional neural networks with Julia using Flux.jl. The reference paper is this: https://arxiv.org/abs/2005.05955. This paper proposes RSO, a gradient-free optimization algorithm updates single weight at a time on a sampling bases. The pseudocode of this algorithm is depicted in the picture below.
I'm using MNIST dataset.
...ANSWER
Answered 2022-Jan-14 at 23:47Based on the paper you shared, it looks like you need to change the weight arrays per each output neuron per each layer. Unfortunately, this means that the implementation of your optimization routine is going to depend on the layer type, since an "output neuron" for a convolution layer is quite different than a fully-connected layer. In other words, just looping over Flux.params(model) is not going to be sufficient, since this is just a set of all the weight arrays in the model and each weight array is treated differently depending on which layer it comes from.
Fortunately, Julia's multiple dispatch does make this easier to write if you use separate functions instead of a giant loop. I'll summarize the algorithm using the pseudo-code below:
QUESTION
This question is the same with How can I check a confusion_matrix after fine-tuning with custom datasets?, on Data Science Stack Exchange.
BackgroundI would like to check a confusion_matrix, including precision, recall, and f1-score like below after fine-tuning with custom datasets.
Fine tuning process and the task are Sequence Classification with IMDb Reviews on the Fine-tuning with custom datasets tutorial on Hugging face.
After finishing the fine-tune with Trainer, how can I check a confusion_matrix in this case?
An image of confusion_matrix, including precision, recall, and f1-score original site: just for example output image
...ANSWER
Answered 2021-Nov-24 at 13:26What you could do in this situation is to iterate on the validation set(or on the test set for that matter) and manually create a list of y_true and y_pred.
QUESTION
I am trying to train a model using PyTorch. When beginning model training I get the following error message:
RuntimeError: CUDA out of memory. Tried to allocate 5.37 GiB (GPU 0; 7.79 GiB total capacity; 742.54 MiB already allocated; 5.13 GiB free; 792.00 MiB reserved in total by PyTorch)
I am wondering why this error is occurring. From the way I see it, I have 7.79 GiB total capacity. The numbers it is stating (742 MiB + 5.13 GiB + 792 MiB) do not add up to be greater than 7.79 GiB. When I check nvidia-smi I see these processes running
ANSWER
Answered 2021-Nov-23 at 06:13This is more of a comment, but worth pointing out.
The reason in general is indeed what talonmies commented, but you are summing up the numbers incorrectly. Let's see what happens when tensors are moved to GPU (I tried this on my PC with RTX2060 with 5.8G usable GPU memory in total):
Let's run the following python commands interactively:
QUESTION
I am a bit confusing with comparing best GridSearchCV model and baseline.
For example, we have classification problem.
As a baseline, we'll fit a model with default settings (let it be logistic regression):
ANSWER
Answered 2021-Nov-04 at 21:17No, they aren't comparable.
Your baseline model used X_train to fit the model. Then you're using the fitted model to score the X_train sample. This is like cheating because the model is going to already perform the best since you're evaluating it based on data that it has already seen.
The grid searched model is at a disadvantage because:
- It's working with less data since you have split the
X_trainsample. - Compound that with the fact that it's getting trained with even less data due to the 5 folds (it's training with only 4/5 of
X_valper fold).
So your score for the grid search is going to be worse than your baseline.
Now you might ask, "so what's the point of best_model.best_score_? Well, that score is used to compare all the models used when searching for the optimal hyperparameters in your search space, but in no way should be used to compare against a model that was trained outside of the grid search context.
So how should one go about conducting a fair comparison?
- Split your training data for both models.
QUESTION
I am not able to access jupyter lab created on google cloud
I created one notebook using Google AI platform. I was able to start it and work but suddenly it stopped and I am not able to start it now. I tried building and restarting the jupyterlab, but of no use. I have checked my disk usages as well, which is only 12%.
I tried the diagnostic tool, which gave the following result:
but didn't fix it.
Thanks in advance.
...ANSWER
Answered 2021-Aug-20 at 14:00You should try this Google Notebook trouble shooting section about 524 errors : https://cloud.google.com/notebooks/docs/troubleshooting?hl=ja#opening_a_notebook_results_in_a_524_a_timeout_occurred_error
QUESTION
I am new to Machine Learning.
Having followed the steps in this simple Maching Learning using the Brain.js library, it beats my understanding why I keep getting the error message below:
I have double-checked my code multiple times. This is particularly frustrating as this is the very first exercise!
Kindly point out what I am missing here!
Find below my code:
...ANSWER
Answered 2021-Sep-29 at 22:47Turns out its just documented incorrectly.
In reality the export from brain.js is this:
QUESTION
IF we are not sure about the nature of categorical features like whether they are nominal or ordinal, which encoding should we use? Ordinal-Encoding or One-Hot-Encoding? Is there a clearly defined rule on this topic?
I see a lot of people using Ordinal-Encoding on Categorical Data that doesn't have a Direction. Suppose a frequency table:
...ANSWER
Answered 2021-Sep-04 at 06:43You're right. Just one thing to consider for choosing OrdinalEncoder or OneHotEncoder is that does the order of data matter?
Most ML algorithms will assume that two nearby values are more similar than two distant values. This may be fine in some cases e.g., for ordered categories such as:
quality = ["bad", "average", "good", "excellent"]orshirt_size = ["large", "medium", "small"]
but it is obviously not the case for the:
color = ["white","orange","black","green"]
column (except for the cases you need to consider a spectrum, say from white to black. Note that in this case, white category should be encoded as 0 and black should be encoded as the highest number in your categories), or if you have some cases for example, say, categories 0 and 4 may be more similar than categories 0 and 1. To fix this issue, a common solution is to create one binary attribute per category (One-Hot encoding)
QUESTION
I am using sentence-transformers for semantic search but sometimes it does not understand the contextual meaning and returns wrong result eg. BERT problem with context/semantic search in italian language
by default the vector side of embedding of the sentence is 78 columns, so how do I increase that dimension so that it can understand the contextual meaning in deep.
code:
...ANSWER
Answered 2021-Aug-10 at 07:39Increasing the dimension of a trained model is not possible (without many difficulties and re-training the model). The model you are using was pre-trained with dimension 768, i.e., all weight matrices of the model have a corresponding number of trained parameters. Increasing the dimensionality would mean adding parameters which however need to be learned.
Also, the dimension of the model does not reflect the amount of semantic or context information in the sentence representation. The choice of the model dimension reflects more a trade-off between model capacity, the amount of training data, and reasonable inference speed.
If the model that you are using does not provide representation that is semantically rich enough, you might want to search for better models, such as RoBERTa or T5.
QUESTION
I have a table with features that were used to build some model to predict whether user will buy a new insurance or not. In the same table I have probability of belonging to the class 1 (will buy) and class 0 (will not buy) predicted by this model. I don't know what kind of algorithm was used to build this model. I only have its predicted probabilities.
Question: how to identify what features affect these prediction results? Do I need to build correlation matrix or conduct any tests?
Table example:
...ANSWER
Answered 2021-Aug-11 at 15:55You could build a model like this.
x = features you have. y = true_lable
from that you can extract features importance. also, if you want to go the extra mile,you can do Bootstrapping, so that the features importance would be more stable (statistical).
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Install pulp-dronet
PULP-Shield: it is our pluggable PCB compatible with the Crazyflie 2.0/2.1 nano-drone. The shield is designed to host either a PULP GAP8 SoC from GWT or a PULP Mr.Wolf SoC (academic chip).
PULP Virtual Platform: it is a software simulation environment running on your machine. It simulates the execution of your application on the selected PULP SoC (e.g., GAP8, Mr.Wolf, etc.).
Autotiler: it is a software tool developed in collaboration with GWT and released as lib.a. It produces a set of C files required to compile PULP-DroNet. The default version of the generated files is already included in this release. Thus the tool is not strictly required.
GAPuino board: it is the GWT Arduino-compatible development board that includes a PULP GAP8 SoC (spec).
the bin folder contains the pre-compiled binary files for the users that want only to execute our version of PULP-DroNet directly on the actual hardware (i.e., PULP-Shield or GAPuino);
the dataset folder contains the datasets used to evaluate the inference Accuracy of our PULP-DroNet;
the imgs folder contains all the auxiliary pictures (as .png files) used in this README.md file;
the PULP-Shield folder contains all the schematic and Altium source files needed to reproduce and modify our PULP-Shield (with the GAP8 or the Mr.Wolf SoC) and the PULP JTAG conversion board, as well;
the src folder contains all the source files required to compile the PULP-DroNet, including the Autotiler git submodule;
the weights folder provides the CNN's weights and biases both in the form of binary data (i.e., .hex) and pre-generated .raw image, ready to be flashed into the Hyper Flash memory.
Run PULP-DroNet binary: you want to run the PULP-DroNet binary (available under the bin folder) as it is, either on the PULP-Shield or on the GAPuino board. In this case, you need to: install the PULP-SDK; program the GAP8 via JTAG.
Compile and run PULP-DroNet: you want to compile either the default PULP-DroNet or a modified version of it and run it. In this case, you need to: install the PULP-SDK; download the basic kernels and generators; compile PULP-DroNet; run PULP-DroNet either on the PULP Virtual Platform or the PULP-Shield/GAPuino.
Test PULP-DroNet on the Dataset: you want to test PULP-DroNet on the testing Dataset. In this case, you need to: install the PULP-SDK; download the basic kernels and generators; download the Dataset; compile PULP-DroNet; test PULP-DroNet on the PULP Virtual Platform.
Modify/Produce the PULP-Shield: you want to modify or reproduce our PULP-Shield, we release all the Altium design of it: you can import the PULP-Shield/GAP8 project into your Altium Designer tool and update the schematics or the PCB files; we also release the fabrication files. Thus, you are ready to go with the production of our PULP-Shield.
Run PULP-DroNet on the CarzyFlie 2.0/2.1: you want to run the PULP-DroNet binary as it is, on a PULP-Shield mounted on a CrazyFlie 2.0/2.1 nano-drone. In this case, you need to: install the PULP-SDK; coming soon...
The PULP-SDK is available here. If you are a Linux Ubuntu user, we suggest you of installing the Release version of the PULP-SDK, if not you can build your own PULP-SDK.
pulp-sdk-ubuntu-16 -- tag 2019.04.05
pulp-riscv-gcc-ubuntu-16 -- version 1.0.13
If you want to compile PULP-DroNet, you need first to download the basic kernels and generators included in the GWT open source Autotiler release. Note that, you do not need the Autotiler library itself (i.e., libtile.dronet.a) but only a few auxiliary files. To get these files automatically, you can execute on your terminal the following:.
The Autotiler is a software tool developed in collaboration with GWT. The tool role is to optimize memory utilization on GAP8, relieving the user from manual coding of the tiling loops and the data movement mechanism. This tool produces a set of C files (i.e., src/PULPDronetKernels.c, src/PULPDronetKernels.h, src/PULPDronetKernelsInit.c, and src/PULPDronetKernelsInit.h) that are then compiled together with the others source files in the src folder. It is important to note that the Autotiler is not compulsory to modify and recompile the project in any part because the default version of the files produced by the tool is already included in this release. Although, we recommend the user to install the Autotiler tool (i.e., autotiler/lib/libtile.dronet.a) if he wants to modify the PULP-DroNet profoundly (e.g., new CNN topology). To install the Autotiler, you can type the following commands in your terminal:. Then, you will be asked to put in the terminal your name, surname, company, country, and email. This information will be sent to GWT, and you will automatically receive at the same email address a link to be copy-pasted in your terminal to complete the download of the Autotiler.
The PULP-DroNet dataset is composed by the following three sub-sets, provided as git submodules:.
Himax Dataset;
Udacity Dataset;
Zurich Bicycle Dataset.
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