PyTorch自学&遇到的一些错误

为什么是false？

①检查GPU是否支持CUDA？

支持

理解Package结构及法宝函数的作用

pytorch就像一个工具箱

dir():打开操作，能看到里面有什么东西---->dir(torch)

help():说明书---->help(torch.cuda.is_available)

From torch.utils.data import Dataset

pytorch如何读取数据？

①Dataset

提供一种方式去获取数据及其label

如何获取每一个数据及其label

告诉我们总共有多少的数据

②Dataloader

为网络提供不同的数据形式

Tensorboard的使用

Global_step：x轴

Scalar_value：y轴

如何读取logs里面的文件

PS F:\HDU\PyTorchStudy> tensorboard --logdir=logs

指定端口：tensorboard --logdir=logs --port=6007

结果：有图像展示

图像变换，transform的使用

利用numpy.array()，对PIL图片进行转换

torchvision中的transforms:对图像进行一个变换

Transforms.py 工具箱

Totensor/resize/。。。

图片--->工具 --->结果

结果：

tensor( $\[\[0.3137, 0.3137, 0.3137, ..., 0.3176, 0.3098, 0.2980$ ,
$0.3176, 0.3176, 0.3176, ..., 0.3176, 0.3098, 0.2980$ ,
$0.3216, 0.3216, 0.3216, ..., 0.3137, 0.3098, 0.3020$ ,
...,
$0.3412, 0.3412, 0.3373, ..., 0.1725, 0.3725, 0.3529$ ,
$0.3412, 0.3412, 0.3373, ..., 0.3294, 0.3529, 0.3294$ ,
$0.3412, 0.3412, 0.3373, ..., 0.3098, 0.3059, 0.3294$ ],

$\[0.5922, 0.5922, 0.5922, ..., 0.5961, 0.5882, 0.5765$ ,
$0.5961, 0.5961, 0.5961, ..., 0.5961, 0.5882, 0.5765$ ,
$0.6000, 0.6000, 0.6000, ..., 0.5922, 0.5882, 0.5804$ ,
...,
$0.6275, 0.6275, 0.6235, ..., 0.3608, 0.6196, 0.6157$ ,
$0.6275, 0.6275, 0.6235, ..., 0.5765, 0.6275, 0.5961$ ,
$0.6275, 0.6275, 0.6235, ..., 0.6275, 0.6235, 0.6314$ ],

$\[0.9137, 0.9137, 0.9137, ..., 0.9176, 0.9098, 0.8980$ ,
$0.9176, 0.9176, 0.9176, ..., 0.9176, 0.9098, 0.8980$ ,
$0.9216, 0.9216, 0.9216, ..., 0.9137, 0.9098, 0.9020$ ,
...,
$0.9294, 0.9294, 0.9255, ..., 0.5529, 0.9216, 0.8941$ ,
$0.9294, 0.9294, 0.9255, ..., 0.8863, 1.0000, 0.9137$ ,
$0.9294, 0.9294, 0.9255, ..., 0.9490, 0.9804, 0.9137$ ]])

常见的Transforms

|----|---------|--------------|
| 输入 | PIL | Image.open() |
| 输出 | tensor | ToTensor() |
| 作用 | narrays | Cv.imread() |

call的使用

__call__Hello zhangsan
Hellolisi

归一化：Normalize

output $channel$ = (input $channel$ - mean $channel$ ) / std $channel$

<PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=500x333 at 0x232A68BC488>
tensor(0.8863)
tensor(0.7725)

Resize()的使用

(500, 333)
tensor( $\[\[0.8863, 0.8824, 0.8745, ..., 0.8392, 0.8392, 0.8392$ ,
$0.8824, 0.8784, 0.8706, ..., 0.8392, 0.8392, 0.8392$ ,
$0.8784, 0.8745, 0.8667, ..., 0.8353, 0.8353, 0.8353$ ,
...,
$1.0000, 1.0000, 1.0000, ..., 0.8314, 0.8314, 0.8314$ ,
$1.0000, 1.0000, 1.0000, ..., 0.8314, 0.8314, 0.8314$ ,
$1.0000, 1.0000, 1.0000, ..., 0.8314, 0.8314, 0.8314$ ],

$\[0.1451, 0.1412, 0.1333, ..., 0.8471, 0.8471, 0.8471$ ,
$0.1451, 0.1412, 0.1333, ..., 0.8471, 0.8471, 0.8471$ ,
$0.1490, 0.1451, 0.1373, ..., 0.8431, 0.8431, 0.8431$ ,
...,
$0.8275, 0.8275, 0.8275, ..., 0.8549, 0.8549, 0.8549$ ,
$0.8275, 0.8275, 0.8275, ..., 0.8549, 0.8549, 0.8549$ ,
$0.8275, 0.8275, 0.8275, ..., 0.8549, 0.8549, 0.8549$ ],

$\[0.1686, 0.1647, 0.1569, ..., 0.4275, 0.4275, 0.4275$ ,
$0.1686, 0.1647, 0.1569, ..., 0.4275, 0.4275, 0.4275$ ,
$0.1686, 0.1647, 0.1569, ..., 0.4235, 0.4235, 0.4235$ ,
...,
$0.0078, 0.0078, 0.0078, ..., 0.4235, 0.4235, 0.4235$ ,
$0.0078, 0.0078, 0.0078, ..., 0.4235, 0.4235, 0.4235$ ,
$0.0078, 0.0078, 0.0078, ..., 0.4235, 0.4235, 0.4235$ ]])