深度学习一条龙-研0入门_requirements: ultralytics requirement ['gitpython>-优快云博客

import torchvision

torchvision.datasets.xxxx()
例如：
train_set = torchvision.datasets.CIFAR10(root="./dataset", train=True，download=True)
test_set = torchvision.datasets.CIFAR10(root="./dataset", train=False,download=True)

dataloader

对数据集合进行分批

参数：

dataset：数据集对象，

batch_size：每批次的数量，

shuffle：是否随机选取，

num_workers：线程数量，0代表主线程

drop_last：无法满足batch_size 时是否丢弃数据

from torch.utils.data import DataLoader

test_data = torchvision.datasets.CIFAR1e("./dataset", train=False，transform=torchvision.transforms. ToTensor())

test_loader = DataLoader(dataset=test_data， batch_size=4， shuffle=True，num_workers=0，drop_last=False)

for data in test_loader:
    imgs, targets = data
    print( imgs.shape) // [4,32,32,3] 每一批有四个tensor对象
    print(targets)

tensorboard

用于显示图像

安装

pip/conda install tensorboard

查看图像

命令行

tensorboard --logdir="文件地址" --port=端口号

使用

from torch.util.tensorboard import SummaryWriter

writer = SummaryWriter("log地址")

1、writer.add_scaler("标题",y轴,x轴)
2、writer.add_image("标题",y轴,x轴)

writer.close()

transform

导入

from torchvision import transforms

ToTensor

将图片对象转化为Tensor对象

from PIL import Image
from torchvision import transforms
#绝对路径c: \users \Zhiyao\Desktop\learn_torch\dataset\train\ants_image \0013035.jpg
#耜对路径dataset/train/ants_image/0013035.jpg
img_path = "dataset/train/ants_image/0013035.jpg"

#获取图片
img = Image.open( img_path)


tensor_trans = transforms.ToTensor( )
tensor_img = tensor_trans(img)

Normalize

归一化

计算公式：input = ( input - mean ) / std

from torchvision import transforms
# 参数一：均值    参数二：方差
trans_norm = transforms.Normalize([0.5，0.5，0.5]，[0.5，0.5，0.5])
img_norm = trans_norm( img_tensor)

Resize

缩放图片

from torchvision import transforms
trans_resize = transforms.Resize(( 512，512))
img_resize = trans_resize(img)
print( img_resize)

Compose

组合式操作，有先后顺序之分

#获取图片
img = Image.open( img_path)
tensor_trans = transforms.ToTensor( )
trans_resize_2 = transforms.Resize( 512)

#操作组合
trans_compose = transforms.compose( [trans_resize_2，trans_totensor])
img_resize_2 = trans_compose( img )

RandomCrop

随机裁剪，默认正方形

#RandomCrop
trans_random = transforms.Randomcrop( 裁剪大小 512)

tensor

创建tensor对象

torch.tensor([1,2,3])

reshape

改变尺寸

torch.reshape(tensorObj,[w,h])

Conv2d

二维卷积

in_channels:输入通道数，彩色为3

out_channels:卷积核数量,一般为1

kernel_size:卷积核大小，一般为3

stride：步长

padding：边缘填充，默认0，一般为1

group:卷积核组数,int

bias:是否偏置true，boolean

padding_mode: 填充的数字,str

conv2d=torch.nn.functional.Conv2d(in_channels,out_channels,...)
output=conv2d(输入，卷积核，步长)

Convranspose2d

转置卷积：扩大输入input的shape

padding:减小shape

原理：

输入:[1,2] 卷积核：[0,1] 输出:[ 1x0 1x1 2x0 2x1 ]

[3,4] [1,0] [1x1 1x0 ... ]

nn.Convranspose2d(in_channel, out_channel, kernel_size=2, bias=False,padding = 1)

MaxPool2d

最大池化