深度学习入门（自用）

环境搭建记录# 已经配置conda24 python3.10 创建新的深度学习环境 conda create -n d2l-zh python3.10 pip -y conda activate d2l-zh # 注意后面都是在这环境执行的 python --version which python gcc --version nvidia-smi free -h df -h 安装jupyter、d2l和pytorch python -m pip install jupyter python -m pip install d2l -i https://pypi.tuna.tsinghua.edu.cn/simple 检查显卡驱动版本并安装torch nvidia-smi python -m pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu118# ubuntu20.04 sudo nvidia-modprobe -u -c0 nvidia-smiJupytershift 回车 运行conda activate d2l-zh jupyter notebook鱼书基础知识训练集 → 被切成很多个 batch训练 1 个 batch → 叫 1 次 iterationiteration 总次数 → 叫 iters_num完整看完一遍训练集 → 叫 1 个 epochAffine层激活函数sigmoidReLU输出层softmax...懒得写了看书吧小土堆教程一、基础教程1. pytorch加载数据from torch.utils.data import Dataset from PIL import Image import os # Python 标准库用于操作系统相关功能 # 自定义数据集类需要重写 getitem、len 和 add 方法 class MyData(Dataset): # 继承Dataset类语法 class 子类名(父类名) # 自定义数据集类示例 # 这里的数据标签格式是文件夹名字为标签文件夹里面放待识别的图图的名字差不多随便命名的 def __init__(self, root_dir, label_dir): 初始化方法 self.root_dir root_dir self.label_dir label_dir # 为了让linux和windows都可以用(linux是/,而windows是//) self.path os.path.join(self.root_dir, self.label_dir) self.img_path os.listdir(self.path) # 列出某个文件夹下面有哪些文件和文件夹并且是列表格式的 def __getitem__(self, idx): # idx 为索引参数 根据索引获取数据项 img_name self.img_path[idx] img_item_path os.path.join(self.root_dir, self.label_dir, img_name) img Image.open(img_item_path) label self.label_dir return img, label def __len__(self): 返回数据集大小 return len(self.img_path) root_dir dataset/train ants_label_dir ants ants_dataset MyData(root_dir, ants_label_dir) img, label ants_dataset[0] # 这个类里的方法比较特殊是双下划线的 len(ants_dataset) # 用同样的方法可以创建bees_dataset还可以合并两个 train_dataset ants_dataset bees_dataset2. TensorBoard使用时要在终端输入tensorboard --logdirlogsfrom torch.utils.tensorboard import SummaryWriter # SummaryWriter是一个类 import numpy as np from PIL import Image # SummaryWriter是直接向log_dir文件夹写事件文件的类这个事件文件可以被tensoroboard解析 writer SummaryWriter(logs) # 需要输入的核心参数就是文件夹名称这里就是“log” # 常用的两个方法writer.add_image()和writer.add_scalar() image_path img_PIL Image.open(image_path) img_array np.array(img_PIL) # 1.writer.add_image() # def add_image( # self, tag, img_tensor, # global_stepNone, walltimeNone, dataformatsCHW # ): # tag (string): Data identifier # img_tensor (torch.Tensor, numpy.array, or string/blobname): Image data # global_step (int): Global step value to record # walltime (float): Optional override default walltime (time.time()) # seconds after epoch of event # dataformats (string): Image data format specification of the form # CHW, HWC, HW, WH, etc. writer.add_image(test, img_array, 1, dataformatsHWC) #高 宽 通道数 # 这里可以通过123滑动那个滑杆看到不同阶段的图片1表示第一阶段 # 2.writer.add_scalar() # def add_scalar( # self, # tag, 就是图标的标题 # scalar_value, 需要保存的数值y # global_stepNone, 训练的次数(x) # walltimeNone, # new_styleFalse, # double_precisionFalse, # ): for i in range(100): writer.add_scalar(yx, i, i) writer.close() writer.close()3. TransformsToTensorfrom torchvision import transforms from PIL import Image import cv2 from torch.utils.tensorboard import SummaryWriter # 最常用的有ToTesor Compose, Normalize, To PILImage # tensor 数据类型 # class ToTensor: # Convert a PIL Image or numpy.ndarray to tensor. This transform does not support torchscript. # Converts a PIL Image or numpy.ndarray (H x W x C) in the range # [0, 255] to a torch.FloatTensor of shape (C x H x W) in the range [0.0, 1.0] # if the PIL Image belongs to one of the modes (L, LA, P, I, F, RGB, YCbCr, RGBA, CMYK, 1) # or if the numpy.ndarray has dtype np.uint8 # In the other cases, tensors are returned without scaling. # .. note:: # Because the input image is scaled to [0.0, 1.0], this transformation should not be used when # transforming target image masks. See the references_ for implementing the transforms for image masks. # .. _references: https://github.com/pytorch/vision/tree/main/references/segmentation # # def __init__(self) - None: # _log_api_usage_once(self) # def __call__(self, pic): # # Args: # pic (PIL Image or numpy.ndarray): Image to be converted to tensor. # Returns: # Tensor: Converted image. # # return F.to_tensor(pic) # def __repr__(self) - str: # return f{self.__class__.__name__}() img_path /home/ljy/Projects/DDPM/hymenoptera_data/train/ants/0013035.jpg img Image.open(img_path) # 这就是PIL数据类型 class PIL.JpegImagePlugin.JpegImageFile cv_img cv2.imread(img_path) # class numpy.ndarray writer SummaryWriter(logs) tensor_trans transforms.ToTensor() # 可以实现对Image和Opencv读取的图片进行格式转换 tensor_img tensor_trans(img) # class torch.Tensor writer.add_image(Tensor_img, tensor_img) # torch.tensor本来就是[3, H, W]格式了所以没有写 writer.close()包含totensor在内的常用transfrom功能# 补充一点__call__/class知识 # class Person: # def __call__(self, name): # print(__call__ name) # def call(self, name): # print(call name) # Li Person() # 因为没有__init__所以不能接受参数不可以写Li Person(LiSi) # Li(LiSi) # 用__call__调用可以直接括号内调用而不用.方法 # Li.call(Lisi) # class Person_with_init: # def __init__(self, name): # print(Init name) # def __call__(self, name): # print(__call__ name) # Wa Person_with_init(XiaoWang) # Wa(XiaoWang) # # 输出结果 # # __call__LiSi # # callLisi # # InitXiaoWang # # __call__XiaoWang from torch.utils.tensorboard import SummaryWriter from PIL import Image from torchvision import transforms writer SummaryWriter(logs) img_path /home/ljy/Projects/DDPM/hymenoptera_data/train/ants/0013035.jpg img_PIL Image.open(img_path) # to tensor to_tensor transforms.ToTensor() img_tensor to_tensor(img_PIL) writer.add_image(norm_test, img_tensor, 1) # normalize trans_norm transforms.Normalize([0.5, 0.5, 0.5], [0.5, 0.5, 0.5]) img_norm trans_norm(img_tensor) writer.add_image(norm_test, img_norm, 2) # resize print(img_PIL.size) trans_resize transforms.Resize((512, 512)) img_resize trans_resize(img_PIL) # 也可以用img_tensor但是输出的img_resize还是PIL格式的 print(img_resize.size) img_resize_tensor to_tensor(img_resize) # add_image需要是np.array或者tensor格式的 writer.add_image(norm_test, img_resize_tensor, 3) # compose - resize 即利用compose的resieze的实现方法 trans_resize_2 transforms.Resize(256) # 整体按比例缩放使短边为256 trans_compose transforms.Compose([trans_resize_2, to_tensor]) # 需要给一个列表里面都是transform格式而且要注意前后格式是否匹配 # 常见顺序 # transforms.Compose([ # transforms.Resize(...), # 可以处理 PIL # transforms.ToTensor(), # PIL - Tensor # transforms.Normalize(...) # 只能处理 Tensor # ]) img_compose_resize trans_compose(img_PIL) writer.add_image(norm_test, img_compose_resize, 4) # print(img_PIL.size) (768, 512) # print(img_compose_resize.shape) torch.Size([3, 256, 384]) writer.close()4. DataLoader# 取出64个打乱最后不整的丢掉 test_data DataLoader(datasettest_set, batch_size64, shuffleTrue, num_workers0, drop_lastTrue)二、神经网络搭建1. nn.Modelfrom torch import nn import torch class MYNN(nn.Module): def __init__(self): super().__init__() def forward(self, input): output input 1 return output jjj MYNN() x torch.tensor(1.0) output jjj(x) print(output)2. conv2d.functionimport torch import torch.nn.functional as F input torch.tensor([ [1, 2, 3, 4, 5], [6, 7, 8, 9, 10], [11, 12, 13, 14, 15], [16, 17, 18, 19, 20], [21, 22, 23, 24, 25]]) kernal torch.tensor([[1, 2, 1], [0, 1, 0], [2, 1, 0]]) print(input.shape) # conv2d 要求格式是 [batch_size, channel, height, width] input torch.reshape(input, (1, 1, 5, 5)) kernal torch.reshape(kernal, (1, 1, 3, 3)) print(input.shape) print(kernal.shape) output F.conv2d(input, kernal, stride2) #padding表示在外围扩充 print(output)# 输出结果 torch.Size([5, 5]) torch.Size([1, 1, 5, 5]) torch.Size([1, 1, 3, 3]) tensor([[[[ 49, 57, 65], [ 89, 97, 105], [129, 137, 145]]]])3. conv2d卷积核数量是与out_channel有关的import torch import torchvision from torch.utils.data import DataLoader from torch import nn from torch.nn import Conv2d from torch.utils.tensorboard import SummaryWriter dataset torchvision.datasets.CIFAR10(./data, trainFalse, transformtorchvision.transforms.ToTensor(), downloadTrue) dataloader DataLoader(dataset, batch_size64) # class Conv2d( # in_channels: int, # out_channels: int, # kernel_size: _size_2_t, # stride: _size_2_t 1, # padding: _size_2_t | str 0, # dilation: _size_2_t 1, # groups: int 1, # bias: bool True, # padding_mode: str zeros, # device: Any | None None, # dtype: Any | None None # ) class MyC2(nn.Module): def __init__(self): super(MyC2, self).__init__() self.conv1 Conv2d(3, 6, 3, stride1, padding0) def forward(self, x): x self.conv1(x) return x c1 MyC2() writer SummaryWriter(logs) step 0 for data in dataloader: imgs, targets data # imgs:[64, 3, 32, 32] 3个channel是rgb output c1(imgs) # output:[64, 6, 30, 30] 没有padding所以像素值变小了 # print(output.shape) # tensor的shape [batch_size, channel, height, width] output torch.reshape(output, (-1, 3, 30, 30)) # 6个channel会不知道怎么显示-1表示适应后面的变化 writer.add_images(inputs, imgs, step) # 注意是imgs [NCHW] writer.add_images(onputs, output, step) step 1 writer.close()4. maxpool卷积是卷积核求和池化是池化核选最大。池化每个 channel 独立使用同一种池化规则卷积每个输出卷积核生成一个输出 channelimport 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(./data, trainFalse, downloadTrue, transformtorchvision.transforms.ToTensor()) dataloader DataLoader(dataset, batch_size64) # 一种方式 # 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]], dtypetorch.float32) # kernal torch.tensor([[1, 2, 1], # [0, 1, 0], # [2, 1, 0]]) # input torch.reshape(input, (-1, 1, 5, 5)) # print(input.shape) class Maxp2(nn.Module): def __init__(self): super().__init__() self.mp MaxPool2d(kernel_size3, ceil_modeTrue) def forward(self, x): return self.mp(x) maxpool Maxp2() writer SummaryWriter(logs) step 0 for data in dataloader: imgs, targets data output maxpool(imgs) writer.add_images(before_mp, imgs, step) # 用了batch_size一定要记得s writer.add_images(after_mp, output, step) step 1 writer.close()5. 激活relu和sigmoidimport torch from torch import nn from torch.nn import ReLU from torch.nn import Sigmoid import torchvision from torch.utils.data import DataLoader from torch.utils.tensorboard import SummaryWriter dataset torchvision.datasets.CIFAR10(./data, False, torchvision.transforms.ToTensor(), downloadTrue) dataloader DataLoader(dataset, batch_size64) writer SummaryWriter(logs) # 矩阵输入 # input torch.tensor([[1, -0.5], # [-1, 3]]) # output torch.reshape(input,(-1, 1, 2, 2)) # print(input.shape) # print(output.shape) # inplace表示是否替代input class sigd(nn.Module): def __init__(self): super().__init__() # self.relul1 ReLU() self.sigmoid Sigmoid() def forward(self, x): return self.sigmoid(x) # relu_my relu() s sigd() step 0 for data in dataloader: imgs, targets data writer.add_images(before, imgs, step) out_imgs s(imgs) writer.add_images(after, out_imgs, step) step 1 # output relu_my(imgs) # print(output)6. 线性层from torch.utils.data import DataLoader from torch.utils.tensorboard import SummaryWriter import torch from torch import nn from torch.nn import Linear import torchvision class my_linear(nn.Module): def __init__(self): super().__init__() self.linear Linear(196608, 10) # 转变列数 def forward(self, input): return self.linear(input) dataset torchvision.datasets.CIFAR10(./data, trainFalse, transformtorchvision.transforms.ToTensor(), downloadTrue) dataloader DataLoader(dataset, batch_size64) writer SummaryWriter(logs) step 0 my my_linear() # 要有含义的才可以在add_image里面用 # C1灰度图 # C3RGB 图 # C4RGBA 图 for data in dataloader: imgs, targets data print(imgs.shape) output torch.reshape(imgs, (1, 1, 1, -1)) # output torch.flatten(imgs) # writer.add_images(before, imgs, step) # writer.add_images(after, output, step) # step 1 out_imgs my(output) print(out_imgs.shape)7. 网络搭建实战import torch from torch import nn from torch.utils.tensorboard import SummaryWriter class classify_10(nn.Module): def __init__(self): super().__init__() # self.conv1 nn.Conv2d(3, 32, 5, padding2) # self.maxpool1 nn.MaxPool2d(2) # self.conv2 nn.Conv2d(32, 32, 5, padding2) # self.maxpool2 nn.MaxPool2d(2) # self.conv3 nn.Conv2d(32, 64, 5, padding2) # self.maxpool3 nn.MaxPool2d(2) # self.flattern nn.Flatten() # self.linear1 nn.Linear(1024, 64) # self.linear2 nn.Linear(64, 10) self.model1 nn.Sequential( nn.Conv2d(3, 32, 5, padding2), nn.MaxPool2d(2), nn.Conv2d(32, 32, 5, padding2), nn.MaxPool2d(2), nn.Conv2d(32, 64, 5, padding2), nn.MaxPool2d(2), nn.Flatten(), nn.Linear(1024, 64), nn.Linear(64, 10) ) def forward(self, x): # x self.conv1(x) # x self.maxpool1(x) # x self.conv2(x) # x self.maxpool2(x) # x self.conv3(x) # x self.maxpool3(x) # x self.flattern(x) # x self.linear1(x) # x self.linear2(x) x self.model1(x) return x my_model classify_10() input torch.ones((64, 3, 32, 32)) # print(input.shape) output my_model(input) # print(output.shape) writer SummaryWriter(logs) writer.add_graph(my_model, input) writer.close()8. 损失函数loss求解方法import torch from torch import nn inputs torch.tensor([1, 2, 3], dtype torch.float32) targets torch.tensor([1, 2 ,5], dtype torch.float32) inputs torch.reshape(inputs, (1, 1, 1, 3)) targets torch.reshape(targets, (1, 1, 1, 3)) # class L1Loss( # size_average: Any | None None, # reduce: Any | None None, # reduction: str mean # ) # 可以是平均也可以是求和默认是求和模式 # L1 loss loss nn.L1Loss() # loss nn.L1Loss(reductionsum) result loss(inputs, targets) # MSE 均方误差 loss_mse nn.MSELoss() result_mse loss_mse(inputs, targets) # 交叉熵 分类问题用的比较多这个数据格式要注意 x torch.tensor([0.1, 0.2, 0.3]) # torch.Size([3]) x torch.reshape(x, (1, 3)) # torch.Size([1, 3]) # print(x.shape) y torch.tensor([1]) loss_cross nn.CrossEntropyLoss() result_corss loss_cross(x, y) print(result) print(result_mse) print(result_corss)比较完整的流程import torch import torchvision from torch.utils.data import DataLoader from torch import nn from torch.nn import Conv2d from torch.utils.tensorboard import SummaryWriter dataset torchvision.datasets.CIFAR10(./data, trainFalse, transformtorchvision.transforms.ToTensor(), downloadTrue) dataloader DataLoader(dataset, batch_size64) class classify_10(nn.Module): def __init__(self): super().__init__() self.model1 nn.Sequential( nn.Conv2d(3, 32, 5, padding2), nn.MaxPool2d(2), nn.Conv2d(32, 32, 5, padding2), nn.MaxPool2d(2), nn.Conv2d(32, 64, 5, padding2), nn.MaxPool2d(2), nn.Flatten(), nn.Linear(1024, 64), nn.Linear(64, 10) ) def forward(self, x): x self.model1(x) return x my_model classify_10() loss_c nn.CrossEntropyLoss() for data in dataloader: imgs, targets data outputs my_model(imgs) result_loss loss_c(outputs, targets) # print(result_loss) result_loss.backward() # 求梯度 # print(outputs) # print(targets) # writer SummaryWriter(logs) # writer.add_graph(my_model, input) # writer.close()9. 优化器主要的步骤在里面用序号标出来了import torch import torchvision from torch.utils.data import DataLoader from torch import nn from torch.nn import Conv2d from torch.utils.tensorboard import SummaryWriter dataset torchvision.datasets.CIFAR10(./data, trainFalse, transformtorchvision.transforms.ToTensor(), downloadTrue) dataloader DataLoader(dataset, batch_size64) class classify_10(nn.Module): def __init__(self): super().__init__() self.model1 nn.Sequential( nn.Conv2d(3, 32, 5, padding2), nn.MaxPool2d(2), nn.Conv2d(32, 32, 5, padding2), nn.MaxPool2d(2), nn.Conv2d(32, 64, 5, padding2), nn.MaxPool2d(2), nn.Flatten(), nn.Linear(1024, 64), nn.Linear(64, 10) ) def forward(self, x): x self.model1(x) return x my_model classify_10() loss_c nn.CrossEntropyLoss() optim torch.optim.SGD(my_model.parameters(), lr0.01) # 1.首先设置优化器 for epoch in range(20): running_loss 0.0 for data in dataloader: imgs, targets data outputs my_model(imgs) result_loss loss_c(outputs, targets) # print(result_loss) optim.zero_grad() # 2.梯度清除 result_loss.backward() # 3.反向传播求梯度 optim.step() # 4.模型参数进行调节 running_loss result_loss print(running_loss) # print(outputs) # print(targets)三、其他补充知识1. 对现成模型进行微调import torchvision from torch import nn from torchvision.models import vgg16, VGG16_Weights # 这个是已经训练好的一个网络后续要调整他应用到cifar10数据集 vgg16_true torchvision.models.vgg16(weightsVGG16_Weights.DEFAULT ) train_data torchvision.datasets.CIFAR10(./data, trainTrue, transformtorchvision.transforms.ToTensor()) # 模型微调 # VGG16后面输出是1000现在加一个线性层调整成10 # vgg16_true.add_module(add_linear, nn.Linear(1000, 10)) # 替换最后一层的写法最后一层是classifier[6] vgg16_true.classifier[6] nn.Linear(4096, 10) print(vgg16_true)2. 模型保存与读取保存import torch import torchvision vgg16 torchvision.models.vgg16(weightsNone) # 不写就是none # 保存方式 1包含模型结构和参数 torch.save(vgg16, ./models/vgg16_method1.pth) # pth是pytorch后缀名 # 保存方式 2仅保存模型参数官方更推荐的占用空间更小 torch.save(vgg16.state_dict(), ./models/vgg16_method2.pth)读取import torch import torchvision # 加载保存的 1 model1 torch.load(/home/ljy/Projects/DDPM/models/vgg16_method1.pth) # print(model1) # 加载保存的 2 # 里面是字典形式的参数 # model2_param_dict torch.load(/home/ljy/Projects/DDPM/models/vgg16_method2.pth) model2 torchvision.models.vgg16() model2.load_state_dict(torch.load(/home/ljy/Projects/DDPM/models/vgg16_method2.pth)) # print(model2)