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

call的使用

__call__Hello zhangsan
Hellolisi

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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)

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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]]])

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Compose()用法

数据需要时transforms类型，所以得到Compose([transforms参数1,transforms参数2,…])

RandomCrop

关注输入和输出类型

多看官方文档

关注方法需要什么参数

不知道返回值的时候

*print

*print(type())

*debug

DataLoader的使用

例：

Dataloader(batch_size=4)

Img0,target0 = dataset[0]

Img1,target1 = dataset[1]

Img2,target2 = dataset[2]

Img3,target3 = dataset[3]

Getitem():

Return img,target

神经网络的搭建nn.Module