深度学习快速入门----Pytorch 系列1

注：参考B站‘小土堆’视频教程

视频链接：【PyTorch深度学习快速入门教程（绝对通俗易懂！）【小土堆】

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一、Tensorboard的使用

tensorboard的作用：可视化神经网络的训练过程

'''
1、tensorboard的使用
2、图像变换，transform的使用
'''

from torch.utils.tensorboard import SummaryWriter
import numpy as np
from PIL import Image

writer = SummaryWriter("logs")
# 可以观察训练过程中不同阶段的显示情况
image_path = "dataset/train/bees_image/16838648_415acd9e3f.jpg"
# 图片类型转换
img_PIL = Image.open(image_path)
img_array = np.array(img_PIL)
writer.add_image("test",img_array,2,dataformats='HWC')
# y=x
for i in range(100):
    writer.add_scalar("y=2x",2*i,i)

writer.close()
# tensorboard --logdir=logs --port=6007

在teiminal中输入“tensorboard --logdir=logs --port=6007”（也可以使用默认端口6006）打开tensorboard界面

展示效果：

二、常见的Transforms

1、ToTensor

通过Image.open（）打开的图片是PIL类型的，转换为Tensor类型：
ToTensor()能够把灰度范围从0-255变换到0-1之间

from torchvision import transforms
from PIL import Image
from torch.utils.tensorboard import SummaryWriter

writer = SummaryWriter("logs")
img = Image.open("dataset/train/ants_image/0013035.jpg")

# ToTensor
trans_totensor = transforms.ToTensor()
img_tensor = trans_totensor(img)
writer.add_image("Tensor_img",img_tensor)
writer.close()

2、Normalize

用均值和标准差归一化张量图像
input[channel] = (input[channel]-mean[channel])/std[channel]
即：(input-0.5)/0.5 = 2*input -1
举例：2 x 0.3137 - 1 = -0.3725

# Normalize归一化
# input[channel] = (input[channel]-mean[channel])/std[channel]
# (input-0.5)/0.5 = 2*input -1
# input[0,1]  result[-1,1]
print(img_tensor[0][0][0])  # tensor(0.3137)
# trans_norm = transforms.Normalize([0.5,0.5,0.5],[0.5,0.5,0.5])
# trans_norm = transforms.Normalize([1,3,5],[3,2,1])
trans_norm = transforms.Normalize([6,3,2],[9,3,5])
img_norm = trans_norm(img_tensor)
print(img_norm[0][0][0])  # tensor(-0.3725)
writer.add_image("Normalize",img_norm,2)

3、Resize

更改图片尺寸，把给定的图片resize到given size；输入图像应该是PIL image类型

# Resize
print(img.size)
trans_resize = transforms.Resize((512,512))
# img PIL -> resize -> img_resize PIL
img_resize = trans_resize(img)
# img_resize PIL -> totensor -> img_reszie tensor
img_resize = trans_totensor(img_resize)
writer.add_image("Resize",img_resize,0)
# print(img_resize)

原来图片尺寸为(768, 512)，压缩后变为（512，512）

4、Compose

Compose的主要作用是串联多个图片变换，其中的参数必须是一个列表，数据需要是Transforms类型

# Compose - resize - 2
trans_resize_2 = transforms.Resize(512)
# PIL -> PIL -> tensor
trans_compose = transforms.Compose([trans_resize_2,trans_totensor])
img_resize_2 = trans_compose(img)
writer.add_image("Resize",img_resize_2,1)

5、RandomCrop

对图片进行随机裁剪

# RandomCrop
trans_random = transforms.RandomCrop(512)
trans_compose_2 = transforms.Compose([trans_random,trans_totensor])
for i in range(10):
    img_crop = trans_compose_2(img)
    writer.add_image("RandomCrop",img_crop,i)
writer.close()

三、torchvision中的数据集使用

CIFAR10数据集

import torchvision
from torch.utils.tensorboard import SummaryWriter

dataset_transform = torchvision.transforms.Compose([
    torchvision.transforms.ToTensor()
])

train_set = torchvision.datasets.CIFAR10(root="./dataset", train=True, transform=dataset_transform, download=True)
test_set = torchvision.datasets.CIFAR10(root="./dataset", train=False, transform=dataset_transform, download=True)

# print(test_set[0])
# print(test_set.classes)
#
# img, target = test_set[0]
# print(img)
# print(target)
# print(test_set.classes[target])
# img.show()

print(test_set[0])

writer = SummaryWriter("p10")
for i in range(10):
    img, target = test_set[i]
    writer.add_image("test_set", img, i)

writer.close()

四、dataloader的使用

PyTorch 提供了两个数据集管理相关的类torch.utils.data.DataLoader/Dataset。其中，Dataset用于存储样本及其对应的标签，而DataLoader则提供了一个高效的迭代器，可以实现对Dataset的数据内容进行高效检索。

dataset

• root :是存储训练/测试数据的路径，
• train :指定训练或测试数据集，
• download=True :如果数据不可用，则从 Internet 下载数据root。
• transform和target_transform :指定特征和标签的格式转换方法，以适配网络计算

dataloader

• dataset: Dataset 类，决定数据从哪里读取以及如何读取
• batch_size: 批大小
• shuffle: 每个 epoch 是否乱序
• num_works: 是否多进程读取数据
• drop_last: 当样本数不能被 batchsize 整除时，是否舍弃最后一批数据

import torchvision

# 准备的测试数据集
from torch.utils.data import DataLoader
from torch.utils.tensorboard import SummaryWriter

test_data = torchvision.datasets.CIFAR10("./dataset", train=False, transform=torchvision.transforms.ToTensor())

test_loader = DataLoader(dataset=test_data, batch_size=64, shuffle=True, num_workers=0, drop_last=True)

# 测试数据集中第一张图片及target
img, target = test_data[0]
print(img.shape)
print(target)

writer = SummaryWriter("dataloader")
for epoch in range(2):
    step = 0
    for data in test_loader:
        imgs, targets = data
        # print(imgs.shape)
        # print(targets)
        writer.add_images("Epoch: {}".format(epoch), imgs, step)
        step = step + 1

writer.close()

drop_last=True

drop_last=False

五、神经网络基本骨架-nn.Module

import torch
from torch import nn


class Tudui(nn.Module):
    def __init__(self):
        super().__init__()

    def forward(self, input):
        output = input + 1
        return output


tudui = Tudui()
x = torch.tensor(1.0)
output = tudui(x)
print(output)

运行结果：

六、神经网络–卷积层

import torch
import torch.nn.functional as F

input = torch.tensor([[1, 2, 0, 3, 1],
                      [0, 1, 2, 3, 1],
                      [1, 2, 1, 0, 0],
                      [5, 2, 3, 1, 1],
                      [2, 1, 0, 1, 1]])

kernel = torch.tensor([[1, 2, 1],
                       [0, 1, 0],
                       [2, 1, 0]])

input = torch.reshape(input, (1, 1, 5, 5))
kernel = torch.reshape(kernel, (1, 1, 3, 3))

print(input.shape)
print(kernel.shape)

output = F.conv2d(input, kernel, stride=1)
print(output)

output2 = F.conv2d(input, kernel, stride=2)
print(output2)

output3 = F.conv2d(input, kernel, stride=1, padding=1)
print(output3)

运行结果：

实例:

import torch
import torchvision
from torch import nn
from torch.nn import Conv2d
from torch.utils.data import DataLoader
from torch.utils.tensorboard import SummaryWriter

dataset = torchvision.datasets.CIFAR10("dataset", train=False, transform=torchvision.transforms.ToTensor(),
                                       download=True)
dataloader = DataLoader(dataset, batch_size=64)

class Tudui(nn.Module):
    def __init__(self):
        super(Tudui, self).__init__()
        self.conv1 = Conv2d(in_channels=3, out_channels=6, kernel_size=3, stride=1, padding=0)

    def forward(self, x):
        x = self.conv1(x)
        return x

tudui = Tudui()

writer = SummaryWriter("logs")

step = 0
for data in dataloader:
    imgs, targets = data
    output = tudui(imgs)
    # print(imgs.shape)
    # print(output.shape)
    # 输入的大小：torch.Size([64, 3, 32, 32])
    writer.add_images("input", imgs, step)
    # 经过卷积后输出的大小：torch.Size([64, 6, 30, 30])  -> [xxx, 3, 30, 30]

    # 刚开始xxx未知时，先用-1
    output = torch.reshape(output, (-1, 3, 30, 30))
    writer.add_images("output", output, step)

    step = step + 1

运行结果：

七、神经网络–最大池化的使用

Pytorch官方文档

最大池化的目的：保留输入的特征，同时把数据量减小

import torch
from torch import nn
from torch.nn import MaxPool2d

input = torch.tensor([[1,2,0,3,1],
                      [0,1,2,3,1],
                      [1,2,1,0,0],
                      [5,2,3,1,1],
                      [2,1,0,1,1]],dtype=torch.float32)
input = torch.reshape(input,(-1,1,5,5))
print(input.shape)

class Tudui(nn.Module):
    def __init__(self):
        super(Tudui, self).__init__()
        self.maxpool1 = MaxPool2d(kernel_size=3, ceil_mode=True)

    def forward(self, input):
        output = self.maxpool1(input)
        return output

tudui = Tudui()
output = tudui(input)
print(output)

运行结果：

实例：

import torch
import torchvision
from torch import nn
from torch.nn import MaxPool2d
from torch.utils.data import DataLoader
from torch.utils.tensorboard import SummaryWriter

dataset = torchvision.datasets.CIFAR10("dataset", train=False, download=True,
                                       transform=torchvision.transforms.ToTensor())

dataloader = DataLoader(dataset, batch_size=64)

class Tudui(nn.Module):
    def __init__(self):
        super(Tudui, self).__init__()
        self.maxpool1 = MaxPool2d(kernel_size=3, ceil_mode=False)

    def forward(self, input):
        output = self.maxpool1(input)
        return output

tudui = Tudui()

writer = SummaryWriter("logs_maxpool")
step = 0

for data in dataloader:
    imgs, targets = data
    writer.add_images("input", imgs, step)
    output = tudui(imgs)
    writer.add_images("output", output, step)
    step = step + 1

writer.close()

运行结果：