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title: textRCNN intensive reading and reproduction date: 2022-03-03 20:16:16 tags: paper intensive readingReappearancepytorch Thesis address: Recurrent Convolutional Neural Networks for Text Classification | Papers With Code Model architecture Bidirectional cyclic neural network and maximum pooling layer. The embedding mentioned in the pa ...

Original address: Explain ModuleList and Sequential in PyTorch in detail torch.nn.Module() import torch.nn as nn import torch.nn.functional as F class Model(nn.Module): def __init__(self): super(Model, self).__init__() self.conv1 = nn.Conv2d(3, 32, 3) self.conv2 = nn.Conv2d(32, 10, 1) def forward(self, x): x = F.relu(self.conv1( ...

Deep learning environment configuration under Ubuntu 20.04 system (subsystem) (pytoch + GPU) 🍀 Previous sequence (pit avoidance) Last time I installed Ubuntu in the virtual machine and installed Nvidia driver , enter the command Ubuntu drivers devices, and you can see that there are no recommended drivers, as shown below: Finally, sudo ap ...

1 tensor data operation 1.1 torch.reshape() implements data dimension change import torch a = torch.tensor([[1,2],[3,4]]) print(torch.reshape(a,(1,-1))) # It is transformed into a tensor with only one row of data, and the parameter - 1 indicates automatic calculation # tensor([[1, 2, 3, 4]]) print(a.reshape((1,-1))) # # It is transformed into ...

1, NN Functional and NN Module Earlier, we introduced the structure operation of Pytorch's tensor and some common API s in mathematical operation. Using these tensor API s, we can construct neural network related components (such as activation function, model layer, loss function). Most of the functional components related to Pytorch and neu ...

explain According to the official website Working with documents , use transformers for fine tuning. I basically follow the documents. Note: the example on the official website basically saves the model or data set into the local cache through a line of code, but it needs to climb the wall. We can also download the model and data set locall ...

Last DETR code learning notes (I) After recording the backbone of DETR and the part before the data enters the encoder, this article begins to introduce the transformer part of DETR, generally the transformer and Attention is All You Need  The proposed framework is basically consistent, but there are some differences in data. The first part is ...

Github: https://github.com/WXinlong/SOLO My operating system is Ubuntu 18 04. This paper will be divided into the following parts: Create datasetModify configmodel trainingVisual Mask resultsModel evaluationInference predictionReasoning optimization First, install some basic libraries. Please refer to the official installation instructions $ ...

GAN animation character avatar generation 1. Introduction A simple DCGAN network is built to generate the avatars of animation characters. The avatar data set of animation characters is taken from kaggle, and the website is as follows link 2. Network structure data setgenerator Discriminator 2.1 data set The data size is 64x64x3, and the ...

The implementation and explanation of PointPillars' paper and training code have been analyzed in detail in the previous article. Please refer to the previous blog: PointPillars paper analysis and OpenPCDet code analysis_ Nnnathan blog - CSDN blog. This blog will analyze the reasoning code of PointPillars model in OpenPCDet in detail and test ...