1666642380
Masked Siamese Networks for Label-Efficient Learning in Python
MSN Masked Siamese Networks
This repo provides a PyTorch implementation of MSN (Masked Siamese Networks), as described in the paper Masked Siamese Networks for Label-Efficient Learning.
MSN is a self-supervised learning framework that leverages the idea of mask-denoising while avoiding pixel and token-level reconstruction. Given two views of an image, MSN randomly masks patches from one view while leaving the other view unchanged. The objective is to train a neural network encoder, parametrized with a vision transformer (ViT), to output similar embeddings for the two views. In this procedure, MSN does not predict the masked patches at the input level, but rather performs the denoising step implicitly at the representation level by ensuring that the representation of the masked input matches the representation of the unmasked one.
Low-shot evaluation on ImageNet-1K
Low-shot Evaluation of self-supervised models, pre-trained on ImageNet-1K. (Left) MSN surpasses the previous 800M parameter state-of-the-art. (Right) MSN achieves good classification performance using less labels than current mask-based auto-encoders.
Visualizations
We can use the RCDM framework of Bordes et al., 2021 to qualitatively demonstrates the effectiveness of the MSN denoising process.
First column: original image. Second column: image with 90% of patches masked used to compute representations of an MSN pre-trained ViT-L/7 encoder. Other columns: RCDM sampling from generative model conditioned on MSN representation of masked image. Qualities that vary across samples represent information that is not contained in the pre-trained representation; e.g., in this case, MSN discards background, pose, and lighting information. Qualities that are common across samples represent information contained in the pre-trained representation. Even with high-masking ratio, MSN retains semantic information about the images.
Pre-trained models
| ViT Small [16x16] | download [800 epochs] |
| ViT Base [16x16] | download [600 epochs] |
| ViT Large [16x16] | download [600 epochs] |
| ViT Base [4x4] | download [300 epochs] |
| ViT Large [7x7] | download [200 epochs] |
Running MSN self-supervised pre-training
Config files
All experiment parameters are specified in config files (as opposed to command-line-arguments). Config files make it easier to keep track of different experiments, as well as launch batches of jobs at a time. See the configs/ directory for example config files.
Requirements
- Python 3.8 (or newer)
- PyTorch install 1.11.0 (older versions may work too)
- torchvision
- Other dependencies: PyYaml, numpy, opencv, submitit, cyanure
Single-GPU training
Our implementation starts from the main.py, which parses the experiment config file and runs the msn pre-training locally on a multi-GPU (or single-GPU) machine. For example, to run on GPUs "0","1", and "2" on a local machine, use the command:
python main.py \
--fname configs/pretrain/msn_vits16.yaml \
--devices cuda:0 cuda:1 cuda:2
Multi-GPU training
In the multi-GPU setting, the implementation starts from main_distributed.py, which, in addition to parsing the config file, also allows for specifying details about distributed training. For distributed training, we use the popular open-source submitit tool and provide examples for a SLURM cluster. Feel free to edit main_distributed.py for your purposes to specify a different procedure for launching a multi-GPU job on a cluster.
For example, to pre-train with MSN on 16 GPUs using the pre-training experiment configs specificed inside configs/pretrain/msn_vits16.yaml, run:
python main_distributed.py \
--fname configs/pretrain/msn_vits16.yaml \
--folder $path_to_save_submitit_logs \
--partition $slurm_partition \
--nodes 2 --tasks-per-node 8 \
--time 1000
ImageNet-1K Logistic Regression Evaluation
Labeled Training Splits
For reproducibilty, we have pre-specified the labeled training images as .txt files in the imagenet_subsets/ directory. Based on your specifications in your experiment's config file, our implementation will automatically use the images specified in one of these .txt files as the set of labeled images.
To run logistic regression on a pre-trained model using some labeled training split you can directly call the script from the command line:
python logistic_eval.py \
--subset-path imagenet_subsets1/5imgs_class.txt \
--root-path /datasets/ --image-folder imagenet_full_size/061417/ \
--device cuda:0 \
--pretrained $directory_containing_your_model \
--fname $model_filename \
--model-name deit_small \
--penalty l2 \
--lambd 0.0025
ImageNet-1K Linear Evaluation
To run linear evaluation on the entire ImageNet-1K dataset, use the main_distributed.py script and specify the --linear-eval flag.
For example, to evaluate MSN on 32 GPUs using the linear evaluation config specificed inside configs/eval/lineval_msn_vits16.yaml, run:
python main_distributed.py \
--linear-eval \
--fname configs/eval/lineval_msn_vits16.yaml \
--folder $path_to_save_submitit_logs \
--partition $slurm_partition \
--nodes 4 --tasks-per-node 8 \
--time 1000
ImageNet-1K Fine-Tuning Evaluation
For fine-tuning evaluation, we use the MAE codebase.
License
See the LICENSE file for details about the license under which this code is made available.
Citation
If you find this repository useful in your research, please consider giving a star :star: and a citation
@article{assran2022masked,
title={Masked Siamese Networks for Label-Efficient Learning},
author={Assran, Mahmoud, and Caron, Mathilde, and Misra, Ishan, and Bojanowski, Piotr, and Bordes, Florian and Vincent, Pascal, and Joulin, Armand, and Rabbat, Michael, and Ballas, Nicolas},
journal={arXiv preprint arXiv:2204.07141},
year={2022}
}Download Details:
Author: facebookresearch
Source Code: https://github.com/facebookresearch/msn
License: View license
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1625843760
Python Packages in SQL Server – Get Started with SQL Server Machine Learning Services
Introduction
When installing Machine Learning Services in SQL Server by default few Python Packages are installed. In this article, we will have a look on how to get those installed python package information.
Python Packages
When we choose Python as Machine Learning Service during installation, the following packages are installed in SQL Server,
- revoscalepy – This Microsoft Python package is used for remote compute contexts, streaming, parallel execution of rx functions for data import and transformation, modeling, visualization, and analysis.
- microsoftml – This is another Microsoft Python package which adds machine learning algorithms in Python.
- Anaconda 4.2 – Anaconda is an opensource Python package
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1619510796
Lambda, Map, Filter functions in python
Welcome to my Blog, In this article, we will learn python lambda function, Map function, and filter function.
Lambda function in python: Lambda is a one line anonymous function and lambda takes any number of arguments but can only have one expression and python lambda syntax is
Syntax: x = lambda arguments : expression
Now i will show you some python lambda function examples:
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1624291780
Learn Python - Full Course for Beginners [Tutorial]
This course will give you a full introduction into all of the core concepts in python. Follow along with the videos and you’ll be a python programmer in no time!
⭐️ Contents ⭐
⌨️ (0:00) Introduction
⌨️ (1:45) Installing Python & PyCharm
⌨️ (6:40) Setup & Hello World
⌨️ (10:23) Drawing a Shape
⌨️ (15:06) Variables & Data Types
⌨️ (27:03) Working With Strings
⌨️ (38:18) Working With Numbers
⌨️ (48:26) Getting Input From Users
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⌨️ (58:27) Mad Libs Game
⌨️ (1:03:10) Lists
⌨️ (1:10:44) List Functions
⌨️ (1:18:57) Tuples
⌨️ (1:24:15) Functions
⌨️ (1:34:11) Return Statement
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⌨️ (1:54:07) If Statements & Comparisons
⌨️ (2:00:37) Building a better Calculator
⌨️ (2:07:17) Dictionaries
⌨️ (2:14:13) While Loop
⌨️ (2:20:21) Building a Guessing Game
⌨️ (2:32:44) For Loops
⌨️ (2:41:20) Exponent Function
⌨️ (2:47:13) 2D Lists & Nested Loops
⌨️ (2:52:41) Building a Translator
⌨️ (3:00:18) Comments
⌨️ (3:04:17) Try / Except
⌨️ (3:12:41) Reading Files
⌨️ (3:21:26) Writing to Files
⌨️ (3:28:13) Modules & Pip
⌨️ (3:43:56) Classes & Objects
⌨️ (3:57:37) Building a Multiple Choice Quiz
⌨️ (4:08:28) Object Functions
⌨️ (4:12:37) Inheritance
⌨️ (4:20:43) Python Interpreter
📺 The video in this post was made by freeCodeCamp.org
The origin of the article: https://www.youtube.com/watch?v=rfscVS0vtbw&list=PLWKjhJtqVAblfum5WiQblKPwIbqYXkDoC&index=3
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1626775355
Why use Python for Software Development
No programming language is pretty much as diverse as Python. It enables building cutting edge applications effortlessly. Developers are as yet investigating the full capability of end-to-end Python development services in various areas.
By areas, we mean FinTech, HealthTech, InsureTech, Cybersecurity, and that's just the beginning. These are New Economy areas, and Python has the ability to serve every one of them. The vast majority of them require massive computational abilities. Python's code is dynamic and powerful - equipped for taking care of the heavy traffic and substantial algorithmic capacities.
Programming advancement is multidimensional today. Endeavor programming requires an intelligent application with AI and ML capacities. Shopper based applications require information examination to convey a superior client experience. Netflix, Trello, and Amazon are genuine instances of such applications. Python assists with building them effortlessly.
5 Reasons to Utilize Python for Programming Web Apps
Python can do such numerous things that developers can't discover enough reasons to admire it. Python application development isn't restricted to web and enterprise applications. It is exceptionally adaptable and superb for a wide range of uses.
Robust frameworks
Python is known for its tools and frameworks. There's a structure for everything. Django is helpful for building web applications, venture applications, logical applications, and mathematical processing. Flask is another web improvement framework with no conditions.
Web2Py, CherryPy, and Falcon offer incredible capabilities to customize Python development services. A large portion of them are open-source frameworks that allow quick turn of events.
Simple to read and compose
Python has an improved sentence structure - one that is like the English language. New engineers for Python can undoubtedly understand where they stand in the development process. The simplicity of composing allows quick application building.
The motivation behind building Python, as said by its maker Guido Van Rossum, was to empower even beginner engineers to comprehend the programming language. The simple coding likewise permits developers to roll out speedy improvements without getting confused by pointless subtleties.
Utilized by the best
Alright - Python isn't simply one more programming language. It should have something, which is the reason the business giants use it. Furthermore, that too for different purposes. Developers at Google use Python to assemble framework organization systems, parallel information pusher, code audit, testing and QA, and substantially more. Netflix utilizes Python web development services for its recommendation algorithm and media player.
Massive community support
Python has a steadily developing community that offers enormous help. From amateurs to specialists, there's everybody. There are a lot of instructional exercises, documentation, and guides accessible for Python web development solutions.
Today, numerous universities start with Python, adding to the quantity of individuals in the community. Frequently, Python designers team up on various tasks and help each other with algorithmic, utilitarian, and application critical thinking.
Progressive applications
Python is the greatest supporter of data science, Machine Learning, and Artificial Intelligence at any enterprise software development company. Its utilization cases in cutting edge applications are the most compelling motivation for its prosperity. Python is the second most well known tool after R for data analytics.
The simplicity of getting sorted out, overseeing, and visualizing information through unique libraries makes it ideal for data based applications. TensorFlow for neural networks and OpenCV for computer vision are two of Python's most well known use cases for Machine learning applications.
Summary
Thinking about the advances in programming and innovation, Python is a YES for an assorted scope of utilizations. Game development, web application development services, GUI advancement, ML and AI improvement, Enterprise and customer applications - every one of them uses Python to its full potential.
The disadvantages of Python web improvement arrangements are regularly disregarded by developers and organizations because of the advantages it gives. They focus on quality over speed and performance over blunders. That is the reason it's a good idea to utilize Python for building the applications of the future.
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1602968400
Python Tricks Every Developer Should Know
Python is awesome, it’s one of the easiest languages with simple and intuitive syntax but wait, have you ever thought that there might ways to write your python code simpler?
In this tutorial, you’re going to learn a variety of Python tricks that you can use to write your Python code in a more readable and efficient way like a pro.
Let’s get started
Swapping value in Python
Instead of creating a temporary variable to hold the value of the one while swapping, you can do this instead
>>> FirstName = "kalebu"
>>> LastName = "Jordan"
>>> FirstName, LastName = LastName, FirstName
>>> print(FirstName, LastName)
('Jordan', 'kalebu')
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