PyTorch 原文：
https://pytorch.org/tutorials/beginner/transfer_learning_tutorial.html
参考文章：
https://www.cnblogs.com/king-lps/p/8665344.html
https://blog.csdn.net/shaopeng568/article/details/95205345
https://blog.csdn.net/yuyangyg/article/details/80018574
# License: BSD # Author: Sasank Chilamkurthy from __future__ import print_function, division import torch import torch.nn as nn import torch.optim as optim from torch.optim import lr_scheduler import numpy as np import torchvision from torchvision import datasets, models, transforms import matplotlib.pyplot as plt import time import os import copy plt.ion() # interactive mod 打开交互模式e # Data augmentation and normalization for training # Just normalization for validation # torchvision.transforms模块提供了一般的图像转换操作类，一般使用Compose把多个步骤整合到一起 data_transforms = { ‘train‘: transforms.Compose([ transforms.RandomResizedCrop(224), # 先将给定的PIL.Image随机切，然后再resize成给定的尺寸大小,裁剪到224*224 transforms.RandomHorizontalFlip(), # 随机水平翻转给定的PIL.Image，概率为0.5，即一半的概率翻转，一半的概率不翻转 transforms.ToTensor(), # 将 PIL Image 或者 ndarray 转换为 tensor，并且归一化至[0-1], shape=chw transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]) # 给定均值(R,G,B)，方差(R,G,B)，将会把tensor正则化，即 Normalized_image = (image-mean)/std ]), ‘val‘: transforms.Compose([ transforms.Resize(256), # 将输入的PIL图像调整为给定的大小 transforms.CenterCrop(224), # 将给定的PIL.Image进行中心切割 transforms.ToTensor(), transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]) # 对数据按通道进行标准化，即先减均值，再除以标准差，注意是 hwc ]), } # Tensor总结 # （1）Tensor 和 Numpy都是矩阵，区别是前者可以在GPU上运行，后者只能在CPU上； # （2）Tensor和Numpy互相转化很方便，类型也比较兼容 # （3）Tensor可以直接通过print显示数据类型，而Numpy不可以 # Visualize a few images # Let’s visualize a few training images so as to understand the data augmentations. def imshow(inp, title=None): """Imshow for Tensor.""" # transpose是求转置矩阵函数 inp = inp.numpy().transpose((1, 2, 0)) mean = np.array([0.485, 0.456, 0.406]) std = np.array([0.229, 0.224, 0.225]) inp = std * inp + mean # clip(a, a_min, a_max, out=None)表示数组a中所有的数限定到范围a_min和a_max中 inp = np.clip(inp, 0, 1) plt.imshow(inp) if title is not None: plt.title(title) plt.pause(1) # pause a bit so that plots are updated data_dir = ‘hymenoptera_data‘ # ImageFolder的第一个参数是在指定的路径下寻找图片，第二个参数transform是指对图像进行转换操作 image_datasets = {x: datasets.ImageFolder(os.path.join(data_dir, x), data_transforms[x]) for x in [‘train‘, ‘val‘]} # dataLoader第一个参数是传入的数据集，第二个参数batch_size表示每个batch有多少个样本，shuffle表示在每个epoch开始的时候，对数据进行重新排序 # num_workers(int, optional)这个参数决定了有几个进程来处理data loading。0意味着所有的数据都会被load进主进程（默认为0）。 # ！！！！！！！！！注意，这里num_workers的参数要改成0，不然在我电脑上就会出错 dataloaders = {x: torch.utils.data.DataLoader(image_datasets[x], batch_size=4, shuffle=True, num_workers=0) for x in [‘train‘, ‘val‘]} # 得到训练集和测试集的长度 dataset_sizes = {x: len(image_datasets[x]) for x in [‘train‘, ‘val‘]} # 得到类别的名称 class_names = image_datasets[‘train‘].classes # 检验是否可用cuda device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") # ------------------- isualize a few images --------------------- # # Get a batch of training data inputs, classes = next(iter(dataloaders[‘train‘])) # Make a grid from batch # mark_grid的作用是将若干幅图像拼成一幅图像。其中padding的作用就是子图像与子图像的pad有多宽 # 此时make_grid的输入仍为Tensor(cwh)，而imshow的时候要转回whc，而后要乘以方差并加上均值 out = torchvision.utils.make_grid(inputs) # imshow(out, title=[class_names[x] for x in classes]) # 导入预训练的模型，如果pretrained=False或者为不带参数表示只导入网络结构，不导入参数 # model_ft即为含训练好参数的残差网络 model_ft = models.resnet18(pretrained=True) # 最后一个全连接的输入维度，这里实为512 num_ftrs = model_ft.fc.in_features # Here the size of each output sample is set to 2. # Alternatively, it can be generalized to nn.Linear(num_ftrs, len(class_names)). # 将最后一个全连接由（512， 1000）改成（512， 2） model_ft.fc = nn.Linear(num_ftrs, 2) # 将所有最开始读取数据时的tensor变量copy一份到device所指定的GPU上去，之后的运算都在GPU上进行 model_ft = model_ft.to(device) # 定义计算损失的函数 criterion = nn.CrossEntropyLoss() # Observe that all parameters are being optimized # 神经网络优化器，优化我们的网络，使网络在训练过程中快起来，节省网络训练时间 # 要想优化网络，并且先构造一个优化器 # SGD(stochastic Gradient Descent),SGD会把数据拆分后再分批不断放入NN中计算，每次使用一批数据，虽然不能反映整体数据的情况，不过却很 # 程度上加速了NN的训练过程，而且不会跌势太多准确率 # Momentum 传统的参数 W 的更新是把原始的 W 累加上一个负的学习率(learning rate) 乘以校正值 (dx)。 # 我们把这个人从平地上放到了一个斜坡上, 只要他往下坡的方向走一点点, 由于向下的惯性, 他不自觉地就一直往下走, 走的弯路也变少了. 这就是 Momentum 参数更新 optimizer_ft = optim.SGD(model_ft.parameters(), lr=0.001, momentum=0.9) # Decay LR by a factor of 0.1 every 7 epochs # lr_scheduler是所有学习率改变策略的基类 # lr_scheduler提供了基于多种epoch数目调整学习率的方法 # step_size为int型，表示学习衰减期，指几个epoch衰减一次，gamma为学习衰减的乘积因子 # 每7个epoch衰减0.1倍 exp_lr_scheduler = lr_scheduler.StepLR(optimizer_ft, step_size=7, gamma=0.1) # 定义样本跑的次数 num_epochs = 25 # ----------------------- 训练模型 ---------------------- # def train_model(model, criterion, optimizer, scheduler, num_epochs=25): # 计算模型初始开始训练时间 since = time.time() # 先拷贝一份当前模型的参数，后面迭代过程中若遇到更优模型则替换 best_model_wts = copy.deepcopy(model.state_dict()) # 初始准确率 best_acc = 0.0 for epoch in range(num_epochs): print(‘Epoch {}/{}‘.format(epoch, num_epochs - 1)) print(‘-‘ * 10) # Each epoch has a training and validation phase for phase in [‘train‘, ‘val‘]: if phase == ‘train‘: model.train() # Set model to training mode，设置为训练模式 else: model.eval() # Set model to evaluate mode，设置为测试模式 running_loss = 0.0 running_corrects = 0 # Iterate over data. for inputs, labels in dataloaders[phase]: inputs = inputs.to(device) labels = labels.to(device) # zero the parameter gradients # 把梯度置零，把loss关于weight的倒数变成0 # 为下一次的训练清空上一步的残余更新参数值 optimizer.zero_grad() # forward # track history if only in train # set_grad_enabled用于设置梯度打开或关闭的上下文管理器 # set_grad_enabled将基于它的参数mode使用或禁用梯度。标记是否使能梯度，主要用在有条件的使能梯度 with torch.set_grad_enabled(phase == ‘train‘): # 前向传播求出预测的值 outputs = model(inputs) _, preds = torch.max(outputs, 1) # 求损失 loss = criterion(outputs, labels) # backward + optimize only if in training phase if phase == ‘train‘: # 误差反向传播，计算参数更新值 loss.backward() # 将参数更新值施加到网络的参数上 optimizer.step() # statistics # 计算损失值和精确度 running_loss += loss.item() * inputs.size(0) running_corrects += torch.sum(preds == labels.data) if phase == ‘train‘: # 应用概率学习率的策略，更新optimizer对象每个para_group字典的lr键的值，param_group[‘lr‘]=lr # 在训练的时候进行迭代 scheduler.step() epoch_loss = running_loss / dataset_sizes[phase] epoch_acc = running_corrects.double() / dataset_sizes[phase] print(‘{} Loss: {:.4f} Acc: {:.4f}‘.format(phase, epoch_loss, epoch_acc)) # deep copy the model # 当验证时遇到了更好的模型则予以保留 if phase == ‘val‘ and epoch_acc > best_acc: best_acc = epoch_acc # 拷贝模型参数 best_model_wts = copy.deepcopy(model.state_dict()) print() # 显示计算模型训练时间 time_elapsed = time.time() - since print(‘Training complete in {:.0f}m {:.0f}s‘.format(time_elapsed // 60, time_elapsed % 60)) print(‘Best val Acc: {:4f}‘.format(best_acc)) # load best model weights # 载入最优模型参数 model.load_state_dict(best_model_wts) return model # model_ft = train_model(model_ft, criterion, optimizer_ft, exp_lr_scheduler, num_epochs=25) # ----------------- visualizing the model predictions ----------------- # def visualize_model(model, num_images=6): # 检验是否为训练模型 was_training = model.training # 把模式设置为测试模式 model.eval() images_so_far = 0 fig = plt.figure() # operations inside don‘t track history # 使下面的计算图不占用内存，不保存梯度，减小内存的占用 with torch.no_grad(): for i, (inputs, labels) in enumerate(dataloaders[‘val‘]): inputs = inputs.to(device) labels = labels.to(device) outputs = model(inputs) _, preds = torch.max(outputs, 1) for j in range(inputs.size()[0]): images_so_far += 1 ax = plt.subplot(num_images//2, 2, images_so_far) ax.axis(‘off‘) ax.set_title(‘predicted: {}‘.format(class_names[preds[j]])) imshow(inputs.cpu().data[j]) if images_so_far == num_images: model.train(mode=was_training) return model.train(mode=was_training) visualize_model(model_ft) # ----------------------------------- 局部微调 --------------------------------------- # # ConvNet as fixed feature extractor # 冻结除全连接层以外的最后所有层参数。我们需要设置 requires_grad == False 来冻结参数以便在反向过程中不需要计算梯度 model_conv = torchvision.models.resnet18(pretrained=True) for param in model_conv.parameters(): # 将所有参数求导设为否 param.requires_grad = False # Parameters of newly constructed modules have requires_grad=True by default # 得到最后一个全连接的输入维度 num_ftrs = model_conv.fc.in_features # 将最后一个全连接由（512， 1000）改成（512， 2）,取代最后一个全连接 model_conv.fc = nn.Linear(num_ftrs, 2) model_conv = model_conv.to(device) criterion = nn.CrossEntropyLoss() # Observe that only parameters of final layer are being optimized as opposed to before. # ！！！！！！！注意，这里只有全连接层参数被优化 optimizer_conv = optim.SGD(model_conv.fc.parameters(), lr=0.001, momentum=0.9) # Decay LR by a factor of 0.1 every 7 epochs exp_lr_scheduler = lr_scheduler.StepLR(optimizer_conv, step_size=7, gamma=0.1) # On CPU this will take about half the time compared to previous scenario. # This is expected as gradients don’t need to be computed for most of the network. However, forward does need to be computed. model_conv = train_model(model_conv, criterion, optimizer_conv, exp_lr_scheduler, num_epochs=25) visualize_model(model_conv) # 显示前关掉交叉模式 plt.ioff() plt.show() pass
本人初学水平，主要参考他人代码，有错误欢迎指正
原文地址：https://www.cnblogs.com/ttweixiao-IT-program/p/11972212.html
时间： 2024-10-10 15:38:04