SimCLR | SimCLR pytorch implementation using DistributedDataParallel | GPU library

by jramapuram Python Version: Current License: MIT

X-Ray Key Features Code Snippets Community Discussions(2)Vulnerabilities Install Support

kandi X-RAY | SimCLR Summary

SimCLR is a Python library typically used in Hardware, GPU, Deep Learning, Pytorch, Neural Network applications. SimCLR has no bugs, it has no vulnerabilities, it has build file available, it has a Permissive License and it has low support. You can download it from GitHub.

An implementation of SimCLR with DistributedDataParallel (1GPU : 1Process) in pytorch. This allows scalability to batch size of 4096 (suggested by authors) using 64 gpus, each with batch size of 64 at a resolution of 224x224x3 in FP32 (see below for FP16 support).

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Quality

Security

License

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Support

SimCLR has a low active ecosystem.

It has 19 star(s) with 0 fork(s). There are 2 watchers for this library.

It had no major release in the last 6 months.

There are 1 open issues and 0 have been closed. On average issues are closed in 120 days. There are 7 open pull requests and 0 closed requests.

It has a neutral sentiment in the developer community.

The latest version of SimCLR is current.

Quality

SimCLR has 0 bugs and 0 code smells.

Security

SimCLR has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

SimCLR code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

SimCLR is licensed under the MIT License. This license is Permissive.

Permissive licenses have the least restrictions, and you can use them in most projects.

Reuse

SimCLR releases are not available. You will need to build from source code and install.

Build file is available. You can build the component from source.

Installation instructions, examples and code snippets are available.

It has 587 lines of code, 39 functions and 4 files.

It has high code complexity. Code complexity directly impacts maintainability of the code.

Top functions reviewed by kandi - BETA

kandi has reviewed SimCLR and discovered the below as its top functions. This is intended to give you an instant insight into SimCLR implemented functionality, and help decide if they suit your requirements.

Compute the similarity between two embedding vectors
Gather all the tensors in the given tensor
L2 norm of x
Run the model
Execute the graph
Build the loader for the loader
Build train and test transforms
Build the optimizer
Lazy loading of modules
Build an LR scheduler
Register images to visdom
Add plots to visdom
Execute a test graph
Train a model
Zero gradient

Get all kandi verified functions for this library.

SimCLR Key Features

No Key Features are available at this moment for SimCLR.

SimCLR Examples and Code Snippets

No Code Snippets are available at this moment for SimCLR.

Community Discussions

Trending Discussions on SimCLR

How to use K means clustering to visualise learnt features of a CNN model?

using tfds for using my custom dataset with tensorflow fails

QUESTION

How to use K means clustering to visualise learnt features of a CNN model?

Asked 2021-Oct-19 at 14:42

Recently I was going through the paper : "Intriguing Properties of Contrastive Losses"(https://arxiv.org/abs/2011.02803). In the paper(section 3.2) the authors try to determine how well the SimCLR framework has allowed the ResNet50 Model to learn good quality/generalised features that exhibit hierarchical properties. To achieve this, they make use of K-means on intermediate features of the ResNet50 model (intermediate means o/p of block 2,3,4..) & quote the reason -> "If the model learns good representations then regions of similar objects should be grouped together".

Final Results : KMeans feature visualisation

I am trying to replicate the same procedure but with a different model (like VggNet, Xception), are there any resources explaining how to perform such visualisations ?

...

ANSWER

Answered 2021-Oct-19 at 14:42

The procedure would be as follow:

Let us assume that you want to visualize the 8th layer from VGG. This layer's output might have the shape (64, 64, 256) (I just took some random numbers, this does not correspond to actual VGG). This means that you have 4096 256-dimensional vectors (for one specific image). Now you can apply K-Means on these vectors (for example with 5 clusters) and then color your image corresponding to the clustering result. The coloring is easy, since the 64x64 feature map represents a scaled down version of your image, and thus you just color the corresponding image region for each of these vectors.

I don't know if it might be a good idea to do the K-Means clustering on the combined output of many images, theoretically doing it on many images and one a single one should both give good results (even though for many images you probably would increase the number of clusters to account for the higher variation in your feature vectors).

Source https://stackoverflow.com/questions/69632019

QUESTION

using tfds for using my custom dataset with tensorflow fails

Asked 2021-Apr-01 at 08:30

according to the tutorial at this link I want to create my custom dataset and use it with tensorflow.

I have installed the tfds command and when I entering tfds new my_dataset command, I will encounter to this error :

...

ANSWER

Answered 2021-Apr-01 at 08:30

this problem solved in tensorflow V2. so use tensorflow V2 or higher versions.

Source https://stackoverflow.com/questions/66857077

Community Discussions, Code Snippets contain sources that include Stack Exchange Network

Vulnerabilities

No vulnerabilities reported

Install SimCLR

Grab imagenet, do standard pre-processing and use --data-dir=${DATA_DIR}. Note: This SimCLR implementation expects two pytorch imagefolder locations: train and test as opposed to val in the preprocessor above.

Support

If you have GPUs that works well with FP16, you can try the --half flag. This will allow faster training with larger batch sizes (~95 with a 12Gb GPU memory). If training doesn't work well try chaning the AMP optimization level here.

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