semvec/pypi-packages huggingface dataset:
bag-convert 0.0.1
Ros bag to Apollo record or reverse conversion
Requires Python: >=3.6

# bag_convert
Ros bag to Apollo record or reverse conversion

# Install
You can install `bag_convert` with the following command
```
pip3 install bag_convert
```

# Quick start
```
-m bag conversion mode(b2r, r2b)
-b bag_file 
-r record_file
```
## Ros bag to Cyber record
```shell
bag_convert -m=b2r -b=data/input.bag -r=data/output.record
```

## Cyber record to Ros bag
```shell
bag_convert -m=r2b -r=data/input.record -b=data/output.bag
```
torchmo 1.0.8
A torch observer that wraps around wanted pytorch modules
Requires Python: >=3.9

# Torch-Module-Observer

Torch-Module-Observer is a tool that uses the internal torch hook functionnalities to provide an API to observe on a model's `nn.Module`s.

Here is an example with a ConvObserver, but this tool can be used to **observe any module**, not only Convs.

![image of dog](https://github.com/AlanBlanchet/torch-module-observer/blob/master/images/dog.jpg?raw=true)

Extract features :

<div style="width:100%;height:400px;display:flex;align-items:center">
    <div style="display:flex;flex-direction:column;align-items:center">
        <img src="https://github.com/AlanBlanchet/torch-module-observer/blob/master/images/conv1.jpg?raw=true"/>
        <label>First layer</label>
    </div>
    <div style="margin:10px;white-space:nowrap">--------------></div>
    <div style="display:flex;flex-direction:column;align-items:center">
        <img src="https://github.com/AlanBlanchet/torch-module-observer/blob/master/images/layer4.1.conv2.jpg?raw=true"/>
        <label>Last layer</label>
    </div>
</div>

## Installation

```bash
pip install torchmo
```

This package also works with conda and poetry.

## Use

```python
from torchmo import ConvObserver

model = ... # nn.Module

observer = ConvObserver(model) # Observe model

features = observer(tensor_imgs) # Forward pass through the observer

# After the forward pass, the observer stored the feature info

out_p = ".cache"
out_p.mkdir(exist_ok=True)

# Save figures
observer.save_figs(cache_p)

# Iterate manually through conv features
for name, output in features:
    # output is a batch of
    # output.shape (B,N,H,W)

    print(output.shape)
    break
```

## Create your own

All you need to do is to inherit the `ModuleObserver` class.

See `ConvObserver` to see how to inherit and use.

## Contribute

The project is small and some peope may have other use cases.

Poetry is being used to manage the environment.

Feel free to open an [issue](https://github.com/AlanBlanchet/torch-module-observer/issues) and/or do a PR when you believe it would help the community.
dagrunner 0.0.6
Run tasks as per dags
Keywords: python,dag


# DAG Runner - Run python programs as DAGs
[![Python 3.9](https://img.shields.io/badge/python-3.9-blue.svg)](https://www.python.org/downloads/release/python-390/)
[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)

DagRunner is a lightweight dag runner that allows you to run tasks as desribed in dependancy graph. Each node gets executed after it's dependencies are completed. Furthermore, failure of a node in the graph leads to failure of all further nodes.
DA-test 0.3.0
Automatic analysis of transversal muscle ultrasonography images
Requires Python: >=3.9

# DeepACSA
*Automatic analysis of human lower limb ultrasonography images*

DeepACSA is an open-source tool to evaluate the anatomical cross-sectional area of muscles in ultrasound images using deep learning.
More information about the installtion and usage of DeepACSA can be found in the online [documentation](https://deepacsa.readthedocs.io/en/latest/index.html). You can find information about contributing, issues and bug reports there as well.
Our trained models, training data, an executable as well as example files can be accessed at [![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.8007009.svg)](https://doi.org/10.5281/zenodo.8007009).
If you find this work useful, please remember to cite the corresponding [paper](https://journals.lww.com/acsm-msse/Abstract/9900/DeepACSA__Automatic_Segmentation_of.87.aspx), where more information about the model architecture and performance can be found as well. 

## Quickstart

To quickly start the DeepACSA either open the executable or type 

``python -m Deep_ACSA``

in your prompt once the package was installed and the DeepACSA_2 env activated.