Minimal tutorials for PyTorch

MiniTorch is an educational library that recreates core PyTorch features using only Python and NumPy. For more information, please read the README.md file.

Embeddings Microservice for use in various projects

In this lecture, we are going to build our own Mini GPT Language Model from scratch using PyTorch! This is a beginner-friendly, step-by-step implementation of a tiny language model that learns to predict the next word, just like how GPT and other Large Language Models work internally.

This project is to implement “Attention-Adaptive and Deformable Convolutional Modules for Dynamic Scene Deblurring(with ERCNN)” . To run this project you need to setup the environment, download the dataset, and then you can train and test the network models. ## Prerequiste The project is tested on Ubuntu 16.04, GPU Titan XP. Note that one GPU is required to run the code. Otherwise, you have to modify code a little bit for using CPU. If using CPU for training, it may too slow. So I recommend you using GPU strong enough and about 12G RAM. ## Dependencies Python 3.5 or 3.6 are recommended. ``` tqdm==4.19.9 numpy==1.17.3 torch==1.0.0 Pillow==6.1.0 torchvision==0.2.2 ``` ## Environment I recommend using ```virtualenv``` for making an environment. If you using ```virtualenv```, ## Dataset I use GOPRO dataset for training and testing. __Download links__: [GOPRO_Large](https://drive.google.com/file/d/1H0PIXvJH4c40pk7ou6nAwoxuR4Qh_Sa2/view?usp=sharing) | Statistics | Training | Test | Total | | ----------- | -------- | ---- | ----- | | sequences | 22 | 11 | 33 | | image pairs | 2103 | 1111 | 3214 | After downloading dataset successfully, you need to put images in right folders. By default, you should have images on dataset/train and dataset/valid folders. ## Demo ## Training Run the following command ``` python demo_train.py ('data_dir' is needed before running ) ``` For training other models, you should uncommend lines in scripts/train.sh file. I used ADAM optimizer with a mini-batch size 16 for training. The learning rate is 1e-4. Total training takes 600 epochs to converge. To prevent our network from overfitting, several data augmentation techniques are involved. In terms of geometric transformations, patches are randomly rotated by 90, 180, and 270 degrees. To take image degradations into account, saturation in HSV colorspace is multiplied by a random number within [0.8, 1.2].  ## Testing Run the following command ``` python demo_test.py ('data_dir' is needed before running ) ``` ## pretrained models if you need the pretrained models,please contact us by chenleinj@njust.edu.cn ## Acknowledge Our code is based on Deep Multi-scale Convolutional Neural Network for Dynamic Scene Deblurring [MSCNN](http://openaccess.thecvf.com/content_cvpr_2017/papers/Nah_Deep_Multi-Scale_Convolutional_CVPR_2017_paper.pdf), which is a nice work for dynamic scene deblurring .

Mini-pytorch implemented from scratch using Python

A very basic implementation of Pytorch to learn - automatic differentiation, optimization and stuff

使用pyTorch训练Minist数据集，导出ONNX模型文件，再使用C++ OpenCV DNN进行推理，并用Android做了一个手写板，用于手写数字识别的Demo

A mini, torch-like deep learning library

Cornell CS5781 Machine Learning Engineering

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A small Autograd framework for training

achieve a mini tools like pytorch with cpp and provide cuda compute

Basic neural network made using PyTorch to classify clothes, trained on the Fashion MNIST dataset ✅

Naive torch implementation for unraveling the mystery of Deep Learning.

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Redeveloping Torch basic functions for fun, for EE-559 and for the shire

Solution for Cornell CS5781 Machine Learning Engineering: MiniTorch https://github.com/minitorch/minitorch

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一个简单的深度学习框架. 为"南京大学神经网络课程大作业1"建立

Mini Pytorch

MiniTorch: a DIY teaching library for machine learning engineers. Cornell CS5781 Machine Learning Engineering

A Python framework designed for computer science students building neural networks from scratch

A toy torch-like framework for educational purposes.

extract the core framework of Pytorch

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Pytorch dataloader implemented from scratch, providing introductory learning

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A mini deep learning framework using only the basic functions of Torch and with set_grad_enabled(False)