semvec/pypi-packages huggingface dataset:
timm-vis 2.0.0
Visualizer for PyTorch image models

## PyTorch Image Models Visualizer

Implementation of various visualization techniques for pytorch image classifiers. This library can be used to visualize and understand any PyTorch image classifier. This is NOT an official PyTorch library, nor is it affiliated with Ross Wightman's [pytorch-image-models](https://github.com/rwightman/pytorch-image-models) library. [details.ipynb](https://github.com/novice03/timm-vis/blob/main/details.ipynb) has visual examples of all methods implemented.

Currently, the following methods are implemented:

- Filter visualization
- Activations visualization
- Maximally activated patches 
- Saliency maps [1]
- Synthetic image generation [1]
- Adversarial attacks to fool models 
- Feature inversion [2]
- Grad-CAM [3]
- Deep Dream [4]

Specific examples and details about the implementation and parameters of the above methods are described in details.ipynb. All of the above visualization techniques are discussed in [this](https://www.youtube.com/watch?v=6wcs6szJWMY&list=PL3FW7Lu3i5JvHM8ljYj-zLfQRF3EO8sYv&index=14)
lecture.

## Installation

```bash
$ pip install timm-vis
```

## Usage

```python
from timm_vis.methods import *

# available methods - visualize_filters, visualize_activations, 
#   maximally_activated_patches, saliency_map, 
#   generate_image, fool_model, feature_inversion, deep_dream

```

## Paper References

[1] Karen Simonyan, Andrea Vedaldi, Andrew Zisserman. Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps. [https://arxiv.org/abs/1312.6034](https://arxiv.org/abs/1312.6034). 

[2] Aravindh Mahendran, Andrea Vedaldi. Understanding Deep Image Representations by Inverting Them [https://arxiv.org/abs/1412.0035](https://arxiv.org/abs/1412.0035)

[3] Ramprasaath R. Selvaraju, Michael Cogswell, Abhishek Das, Ramakrishna Vedantam, Devi Parikh, Dhruv Batra. Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization [https://arxiv.org/abs/1610.02391 (https://arxiv.org/abs/1610.02391)]

[4] Alexander Mordvintsev, Christopher Olah, Mike Tyka. Inceptionism: Going Deeper into Neural Networks [https://ai.googleblog.com/2015/06/inceptionism-going-deeper-into-neural.html](https://ai.googleblog.com/2015/06/inceptionism-going-deeper-into-neural.html)

## Code References

[5] Ross Wightman [pytorch-image-models](https://github.com/rwightman/pytorch-image-models)

[6] Irfan Alghani Khalid [Saliency Map for Visualizing Deep Learning Model Using PyTorch](https://towardsdatascience.com/saliency-map-using-pytorch-68270fe45e80)

[7] Utku Ozbulak. [pytorch-cnn-adversarial-attacks](https://github.com/utkuozbulak/pytorch-cnn-adversarial-attacks)

[8] Duc Ngo [deep-dream-in-pytorch](https://github.com/duc0/deep-dream-in-pytorch)
glasswall-visual-layer-comparison-tool-cli 1.0.11
CLI Tool that makes requests to the glasswall-visual-layer-comparison API
Requires Python: >=3.6

<div align="center">

# glasswall-visual-layer-comparison-tool-cli
CLI Tool that makes requests to the glasswall-visual-layer-comparison API



[![CI](https://github.com/filetrust/glasswall-visual-layer-comparison-tool-cli/actions/workflows/CI.yml/badge.svg)](https://github.com/filetrust/glasswall-visual-layer-comparison-tool-cli/actions/workflows/CI.yml) [![CD](https://github.com/filetrust/glasswall-visual-layer-comparison-tool-cli/actions/workflows/CD.yml/badge.svg)](https://github.com/filetrust/glasswall-visual-layer-comparison-tool-cli/actions/workflows/CD.yml)

</div>
  
# Installation
First Install:
  
```
pip install glasswall-visual-layer-comparison-tool-cli
```
  
Upgrading
```
pip install glasswall-visual-layer-comparison-tool-cli --upgrade
```
  
# Usage
```
glasswall_visual_comparison_tool_cli [OPTIONS] COMMAND [ARGS]
```
  
## dir-compare
To run the dir-compare command locally, pass in the URL of the API, the left and right directories -and the path to output logs to.
  
```
glasswall_visual_comparison_tool_cli dir-compare -u <url> -l <left/directory> -r <right/directory> --log <log/directory>
```
  
<b>NOTE:</b> The dir-compare command will NOT process subdirectories, it will log a warning when it encounters a folder in the left/right directories.
  
### Options
<table>
	<thead>
		<tr>
			<th>Option</th>
			<th>Required</th>
			<th>Description</th>
		</tr>
	</thead>
	<tbody>
		<tr>
			<td>-u, --url</td>
			<td>Required</td>
			<td>URL for the GW Comparison API</td>
		</tr>
		<tr>
			<td>-l, --left</td>
			<td>Required</td>
			<td>Directory for the original files<br/>(the left side of the comparison)</td>
		</tr>
		<tr>
			<td>-r, --right</td>
			<td>Required</td>
			<td>Directory for the rebuilt files<br/>(the right side of the comparison)</td>
		</tr>
		<tr>
			<td>--log</td>
			<td>Required</td>
			<td>Directory to store log file</td>
		</tr>
		<tr>
			<td>--non_verbose</td>
			<td>Optional</td>
			<td>Non Verbose logging<br/>(hides filenames)</td>
		</tr>
		<tr>
			<td>--help</td>
			<td>Optional</td>
			<td>Show help message and exit</td>
		</tr>
	</tbody>
</table>
simfin-creei 1.0.1
Modèle de microsimulation des finances publiques SimFin
Requires Python: >=3.6

# Modèle de microsimulation SimFin

Ce package contient le modèle de microsimulation des finances publiques SimFin développé par la chaire [CREEi](http://www.creei.ca). La [documentation](https://creei-models.github.io/simfin) du modèle présente les procédures d'installation et d'utilisation du modèle.