GFPGAN source code analysis - Part 6

2021SC@SDUSC Source code: archs\gfpganv1_clean_arch.py This paper mainly analyzes gfpganv1_ clean_ Under arch.py class GFPGANv1Clean(nn.Module) class_ init_ () method catalogue class GFPGANv1Clean(nn.Module) init() (1) Settings for channels (2) Call torch.nn.Conv2d() to create a convolutional neural network (3) Downsample (4) u ...

Posted by jabapyth on Mon, 06 Dec 2021 11:21:47 -0800

pytorch Basics

1.Autograd (automatic gradient algorithm) autograd is the core package of PyTorch to implement the automatic gradient algorithm mentioned earlier. Let's start by introducing the variables. 2.Variable autograd.Variable is the encapsulation of Tensor. Once we have defined the final variable (i.e., calculated loss, etc.), we can call its backwa ...

Posted by csxpcm on Sun, 05 Dec 2021 15:57:54 -0800

Installation tutorial on CUDA+CUDNN in Windows Environment

Anaconda + pychar package is recommended. This article describes how to install the pytorch and tensorflow frameworks First of all, you should know which version of your graphics card driver is (take 1660S as an example) 1. Open the NVIDIA control panel, which can be opened by right clicking on the desktop or hiding the icon bar in the lower ...

Posted by poltort on Sun, 05 Dec 2021 01:46:12 -0800

#Introduction to PyTorch build MLP model to realize classification tasks

  this is the second article on the introduction to PyTorch. It will be continuously updated as a series of PyTorch articles.   this paper will introduce how to use PyTorch to build a simple MLP (Multi-layer Perceptron) model to realize two classification and multi classification tasks. Data set introduction   the second classi ...

Posted by nepeaNMedia on Fri, 03 Dec 2021 14:11:19 -0800

GCANet (gated context aggregation network for image defogging and raining)

Atomization treatment can be represented by the following model: I (x): foggy picture J (x): picture of defogging     A :     Global atmospheric light T (x): intermediate transformation mapping, depending on unknown depth information, medium transmission map       The previous defogging methods used r ...

Posted by carlos1234 on Thu, 02 Dec 2021 20:49:46 -0800

Calculation of cross_entropy loss function of torch (including python code)

1. Call Firstly, the cross entropy loss function of torch is called as follows: torch.nn.functional.cross_entropy(input, target, weight=None, size_average=None, ignore_index=-100, reduce=None, reduction='mean') It is usually written as: import torch.nn.functional as F F.cross_entropy(input, target) 2. Parameter description Input( tensor ...

Posted by wilhud on Wed, 01 Dec 2021 00:39:19 -0800

Conclusion: the overall structure flow chart of t5 transformers

In order to better understand the content of t5 model structure, the overall structure process of t5 model is given here t5 overall structure and process During the operation of t5, the key is changed_ States and values_ Value of States layerselfattention of 6 encoder parts Enter hidden_staes = (1,8,11,64) First call query_states query_ ...

Posted by EPJS on Tue, 30 Nov 2021 23:13:03 -0800

Automatic driving based on Unity

1. Process Software download and installationdata acquisitionCustom datasetModel buildingmodel trainingtestsummaryfollow-up Download address of this project GitHub 1. Software download and installation 1.Download address: https://github.com/udacity/self-driving-car-sim 2. After entering the link, you can choose your own platform to download ...

Posted by Plex on Mon, 29 Nov 2021 07:47:16 -0800

Computer vision - Attention mechanism (with code)

1. Introduction to attention Attention means attention in Chinese. This mechanism is put into computer vision, which is similar to showing us a picture of a beautiful and handsome man. Where is the person we first pay attention to 😏 Where did you first see 😏 The earliest attention mechanism was applied to computer vision. The mechanism ...

Posted by anthonyfellows on Mon, 29 Nov 2021 04:51:03 -0800

Interpretation of PixPro self-monitoring paper

PixPro is the first to use pixel level contrast learning for feature representation learningThe above figure is the flow chart of the whole algorithm, which will be analyzed in detail nextForward propagationInput is the input image, and the dimension size is (b, c, h, w)augmentation: cut the same input in random size and position and reduce it ...

Posted by Darhazer on Mon, 29 Nov 2021 00:44:25 -0800