An implementation of Efficientnet in PyTorch
Pytorch
B0:
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| call plug#begin("~/.vim/plugged") | |
| " Theme | |
| Plug 'arcticicestudio/nord-vim' | |
| " Language Client | |
| Plug 'neoclide/coc.nvim', {'branch': 'release'} | |
| let g:coc_global_extensions = ['coc-emmet', 'coc-css', 'coc-html', 'coc-json', 'coc-prettier', 'coc-tsserver', 'coc-python'] | |
| " TypeScript Highlighting | |
| Plug 'vim-airline/vim-airline' | |
| Plug 'vim-airline/vim-airline-themes' |
SharanSMenon
/ opencl_matrix_multiply.ipynb
Created
April 19, 2022 00:47
Matrix Multiplication with OpenCL. Performance comparison test.
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SharanSMenon
/ MetalAdder.h
Created
April 8, 2022 23:39
Adding in metal. Borrowed from the apple developer documentation.
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| // | |
| // MetalAdder.h | |
| // MetalLearn1 | |
| // | |
| // Created by Sharan Sajiv Menon on 4/3/22. | |
| // | |
| #import <Foundation/Foundation.h> | |
| #import <Metal/Metal.h> | |
| #ifndef MetalAdder_h |
SharanSMenon
/ vulkano_vector_scalar_product.rs
Created
March 23, 2022 01:12
Vulkano 0.29 Vector-Scalar Product program. Multiplies a vector of 65K numbers by 12 using the GPU device. Vulkano and Vulkano shaders required for this.
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| extern crate vulkano; | |
| extern crate vulkano_shaders; | |
| use vulkano::{ | |
| sync, | |
| buffer::{BufferUsage, CpuAccessibleBuffer}, | |
| command_buffer::{AutoCommandBufferBuilder, CommandBufferUsage}, | |
| device::{ | |
| physical::{PhysicalDevice}, | |
| QueueCreateInfo, |
SharanSMenon
/ Accelerate_sgemm.cpp
Created
February 26, 2022 00:01
SGEMM with Accelerate Frame in C++ (macOS)
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| #include<stdio.h> | |
| #include<iostream> | |
| #include<Accelerate/Accelerate.h> | |
| #include <random> | |
| #include <stdlib.h> | |
| #include <vector> | |
| #include <chrono> | |
| void fill(float *a, int n) { | |
| for (int i = 0; i < n; i++) { |
SharanSMenon
/ efficientnet-pytorch.md
Last active
November 10, 2023 04:00
EfficientNet Implementation PyTorch
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SharanSMenon
/ README.MD
Last active
December 5, 2021 22:40
A simple implementation of evolution strategies to train a classification network. Based on https://openai.com/blog/evolution-strategies/
Based on this paper: https://arxiv.org/pdf/1703.03864.pdf
More info can be found in this blog post: https://openai.com/blog/evolution-strategies/
Based on DCGAN from this paper: https://arxiv.org/pdf/1511.06434.pdf
Implementation of SRGAN in PyTorch. Superresolves to 4x
Based on this paper: https://arxiv.org/pdf/1609.04802.pdf
Model is to be trained for 150 epochs.
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