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0x0. preface The previous articles in this series have a superficial understanding of the components of MLIR. This article will not continue to talk about the architecture of MLIR. But from a practical point of view, let's take readers to see what MLIR has helped me do. Here we still take OneFlow Dialect as an example. stay Interpretation of ...

1. DatasetFolder When learning PyTorch, one of the first things people need to do is to implement some kind of Dataset. This is a low-level mistake. There is no need to waste time writing such things. Typically, a Dataset is either a data list (or a numpy array) or a file on disk. Therefore, organizing data on disk is better than writing a cus ...

In the network of deep learning, I think the most basic is the residual network. What I share today is not the theoretical part of the residual network. Just remember that the idea of the residual network runs through many network structures behind. If you understand the residual network structure, then some advanced network structures behind a ...

Get more training data (data enhancement) Data enhancement using geometric transformations Geometric transformations such as flip, crop, rotation and translation are some commonly used data enhancement techniques. GAN based data enhancement Reduce network capacity The simplest way to prevent overfitting is to reduce the size of the mod ...

1. Introduction of attention mechanism In the seq2seq model in the previous section, in order to enable the decoder and the linear classification layer to directly obtain more information during decoding, we provide the original context vector to each decoder and the original context vector and word embedding to each linear layer, as shown bel ...

import torch import math import numpy as np from visdom import Visdom import time torch.__version__ '1.0.0' 4.2.1 visualization in PyTorch using Visdom Visdom is a visualization tool for PyTorch released by Facebook in 2017. Official website , because of its simple function, Visdom is generally defined as server-side matplot, that is, ...

import torch import numpy as np import torch.nn as nn import torch.nn.functional as F from PIL import Image from torchvision import transforms from torchvision import models,datasets torch.__version__ '1.3.0' 4.2.2 visualization in PyTorch using Tensorboard Introduction to Tensorboard Tensorboard is a built-in visualization tool of te ...

Introduction to torchvision There are four functional modules in torchvision: model, datasets, transforms and utils. Using datasets, you can download some classic datasets. Here we will focus on how to use the ImageFolder of datasets to process custom datasets, and how to use transforms to preprocess and enhance source data. transforms transfo ...

1, maskRcnn environment configuration (win10) Let's first look at the configuration I successfully run: python3.6.13 tensorflow-gpu 1.5.0 scipy1.2.1 Keras2.2.0 cuda9.0 cudnn7.0 I The default Anaconda is installed MaskRCNN——01. Environment configuration (based on Anaconda) II Download the MaskRCNN source code and check the envir ...

preface    convolutional neural network has made a breakthrough, and the effect is also very ideal. However, the learning process of convolutional neural network is difficult to explain theoretically, so it has been criticized by many people. Therefore, it is very important to visualize its learning process, and so is the purpose ...