学习笔记|Pytorch使用教程27(模型保存与加载)

2023-09-15 21:38

学习笔记|Pytorch使用教程27 本学习笔记主要摘自“深度之眼”，做一个总结，方便查阅。使用Pytorch版本为1.2 序列化与反序列化模型保存与加载的两种方式模型断点续训练一.序列化与反序列化是为了将数据可以长久的保存。 1.torch. save 主要参数: obj:对象f:输出路径 2.torch.load 主要参数: f:文件路径map_location: 指定存放位置，cpu or gpu 二.模型保存与加载的两种方式测试代码：保存模型 import torch import numpy as np import torch.nn as nn from tools.common_tools import set_seedclass LeNet2(nn.Module):def __init__(self, classes):super(LeNet2, self).__init__()self.features = nn.Sequential(nn.Conv2d(3, 6, 5),nn.ReLU(),nn.MaxPool2d(2, 2),nn.Conv2d(6, 16, 5),nn.ReLU(),nn.MaxPool2d(2, 2))self.classifier = nn.Sequential(nn.Linear(16*5*5, 120),nn.ReLU(),nn.Linear(120, 84),nn.ReLU(),nn.Linear(84, classes))def forward(self, x):x = self.features(x)x = x.view(x.size()[0], -1)x = self.classifier(x)return xdef initialize(self):for p in self.parameters():p.data.fill_(20191104)net = LeNet2(classes=2019) print("训练前: ", net.features[0].weight[0, ...]) net.initialize() print("训练后: ", net.features[0].weight[0, ...])path_model = "./model.pkl" path_state_dict = "./model_state_dict.pkl" www.hardsalon.com(net, path_model) net_state_dict = net.state_dict() www.hardsalon.com(net_state_dict, path_state_dict) 输出：训练前: tensor([[[-0.0688, 0.0352, -0.0851, 0.0967, 0.0009],[ 0.0147, -0.1099, -0.0771, -0.0724, -0.0115],[ 0.0284, -0.0681, 0.0173, 0.0605, -0.0532],[ 0.0633, 0.0034, -0.0758, -0.0935, -0.0514],[ 0.0535, 0.0779, 0.0763, -0.0133, 0.0908]],[[-0.0762, -0.0750, 0.0036, 0.0081, -0.0599],[ 0.0548, -0.0073, 0.0883, -0.1042, -0.0900],[-0.0010, 0.0885, -0.0534, 0.0154, -0.1147],[ 0.0680, -0.0678, 0.0944, 0.0110, 0.0928],[-0.0784, 0.0951, 0.0315, 0.0429, 0.0558]],[[-0.0358, -0.0080, -0.0291, -0.0984, -0.1106],[-0.0667, -0.0410, 0.0611, 0.1053, -0.0444],[-0.1016, -0.0010, -0.0353, 0.0638, 0.0796],[-0.0542, -0.1152, -0.0167, 0.0984, -0.0854],[-0.0337, -0.0077, -0.0425, 0.0431, -0.0985]]],grad_fn=) 训练后: tensor([[[20191104., 20191104., 20191104., 20191104., 20191104.],[20191104., 20191104., 20191104., 20191104., 20191104.],[20191104., 20191104., 20191104., 20191104., 20191104.],[20191104., 20191104., 20191104., 20191104., 20191104.],[20191104., 20191104., 20191104., 20191104., 20191104.]],[[20191104., 20191104., 20191104., 20191104., 20191104.],[20191104., 20191104., 20191104., 20191104., 20191104.],[20191104., 20191104., 20191104., 20191104., 20191104.],[20191104., 20191104., 20191104., 20191104., 20191104.],[20191104., 20191104., 20191104., 20191104., 20191104.]],[[20191104., 20191104., 20191104., 20191104., 20191104.],[20191104., 20191104., 20191104., 20191104., 20191104.],[20191104., 20191104., 20191104., 20191104., 20191104.],[20191104., 20191104., 20191104., 20191104., 20191104.],[20191104., 20191104., 20191104., 20191104., 20191104.]]],grad_fn=) 加载模型 import torch import numpy as np import torch.nn as nn from tools.common_tools import set_seedclass LeNet2(nn.Module):def __init__(self, classes):super(LeNet2, self).__init__()self.features = nn.Sequential(nn.Conv2d(3, 6, 5),nn.ReLU(),nn.MaxPool2d(2, 2),nn.Conv2d(6, 16, 5),nn.ReLU(),nn.MaxPool2d(2, 2))self.classifier = nn.Sequential(nn.Linear(16*5*5, 120),nn.ReLU(),nn.Linear(120, 84),nn.ReLU(),nn.Linear(84, classes))def forward(self, x):x = self.features(x)x = x.view(x.size()[0], -1)x = self.classifier(x)return xdef initialize(self):for p in self.parameters():p.data.fill_(20191104) flag = 1 if flag:path_model = "./model.pkl"net_load = torch.load(path_model)print(net_load) 输出： LeNet2((features): Sequential((0): Conv2d(3, 6, kernel_size=(5, 5), stride=(1, 1))(1): ReLU()(2): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)(3): Conv2d(6, 16, kernel_size=(5, 5), stride=(1, 1))(4): ReLU()(5): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False))(classifier): Sequential((0): Linear(in_features=400, out_features=120, bias=True)(1): ReLU()(2): Linear(in_features=120, out_features=84, bias=True)(3): ReLU()(4): Linear(in_features=84, out_features=2019, bias=True)) ) 加载和更新state_dict flag = 1 if flag:path_state_dict = "./model_state_dict.pkl"state_dict_load = torch.load(path_state_dict)print(state_dict_load.keys()) flag = 1 if flag:net_new = LeNet2(classes=2019)print("加载前: ", net_new.features[0].weight[0, ...])net_new.load_state_dict(state_dict_load)print("加载后: ", net_new.features[0].weight[0, ...]) 输出： odict_keys(['features.0.weight', 'features.0.bias', 'features.3.weight', 'features.3.bias', 'classifier.0.weight', 'classifier.0.bias', 'classifier.2.weight', 'classifier.2.bias', 'classifier.4.weight', 'classifier.4.bias']) 加载前: tensor([[[ 0.0147, 0.0484, 0.0264, 0.0008, -0.0298],[-0.0247, -0.0993, -0.0027, 0.0430, -0.0955],[ 0.0153, 0.0394, -0.0076, -0.0450, -0.1092],[ 0.0114, 0.1027, -0.0189, -0.0330, 0.0977],[ 0.0666, -0.0971, -0.0930, 0.0110, 0.0638]],[[ 0.0006, -0.0326, 0.0263, 0.0948, -0.0631],[-0.1066, -0.0316, 0.0757, -0.0114, 0.1028],[ 0.0704, 0.0368, 0.1142, -0.0035, -0.1099],[ 0.0567, -0.0815, -0.0002, -0.1015, 0.0845],[ 0.0235, 0.0542, 0.0773, 0.0409, 0.0635]],[[-0.0032, -0.0301, 0.0489, 0.0596, -0.0180],[ 0.0137, 0.0171, 0.0843, -0.1067, 0.0658],[-0.0924, -0.0611, 0.0802, -0.0018, 0.0137],[ 0.0364, -0.1142, -0.0575, 0.0875, -0.0954],[-0.0945, 0.0257, 0.0327, 0.0037, -0.0766]]],grad_fn=) 加载后: tensor([[[20191104., 20191104., 20191104., 20191104., 20191104.],[20191104., 20191104., 20191104., 20191104., 20191104.],[20191104., 20191104., 20191104., 20191104., 20191104.],[20191104., 20191104., 20191104., 20191104., 20191104.],[20191104., 20191104., 20191104., 20191104., 20191104.]],[[20191104., 20191104., 20191104., 20191104., 20191104.],[20191104., 20191104., 20191104., 20191104., 20191104.],[20191104., 20191104., 20191104., 20191104., 20191104.],[20191104., 20191104., 20191104., 20191104., 20191104.],[20191104., 20191104., 20191104., 20191104., 20191104.]],[[20191104., 20191104., 20191104., 20191104., 20191104.],[20191104., 20191104., 20191104., 20191104., 20191104.],[20191104., 20191104., 20191104., 20191104., 20191104.],[20191104., 20191104., 20191104., 20191104., 20191104.],[20191104., 20191104., 20191104., 20191104., 20191104.]]],grad_fn=) 三.模型断点续训练测试代码：模拟意外中断： import os import random import numpy as np import torch import torch.nn as nn from www.hardsalon.com import DataLoader import torchvision.transforms as transforms import torch.optim as optim from PIL import Image from matplotlib import pyplot as plt from model.lenet import LeNet from www.hardsalon.com_dataset import RMBDataset from tools.common_tools import set_seed import torchvisionset_seed(1) rmb_label = {"1": 0, "100": 1} checkpoint_interval = 5 MAX_EPOCH = 10 BATCH_SIZE = 16 LR = 0.01 log_interval = 10 val_interval = 1split_dir = os.path.join("..", "..", "data", "rmb_split") train_dir = os.path.join(split_dir, "train") valid_dir = os.path.join(split_dir, "valid")norm_mean = [0.485, 0.456, 0.406] norm_std = [0.229, 0.224, 0.225]train_transform = transforms.Compose([transforms.Resize((32, 32)),transforms.RandomCrop(32, padding=4),transforms.RandomGrayscale(p=0.8),transforms.ToTensor(),transforms.Normalize(norm_mean, norm_std), ])valid_transform = transforms.Compose([transforms.Resize((32, 32)),transforms.ToTensor(),transforms.Normalize(norm_mean, norm_std), ]) train_data = RMBDataset(data_dir=train_dir, transform=train_transform) valid_data = RMBDataset(data_dir=valid_dir, transform=valid_transform) train_loader = DataLoader(dataset=train_data, batch_size=BATCH_SIZE, shuffle=True) valid_loader = DataLoader(dataset=valid_data, batch_size=BATCH_SIZE)net = LeNet(classes=2) net.initialize_weights() criterion = nn.CrossEntropyLoss() optimizer = optim.SGD(net.parameters(), lr=LR, momentum=0.9) scheduler = www.hardsalon.com_scheduler.StepLR(optimizer, step_size=6, gamma=0.1) train_curve = list() valid_curve = list()start_epoch = -1 for epoch in range(start_epoch+1, MAX_EPOCH):loss_mean = 0.correct = www.hardsalon.com = 0.net.train()for i, data in enumerate(train_loader):inputs, labels = dataoutputs = net(inputs)www.hardsalon.com_grad()loss = criterion(outputs, labels)loss.backward()optimizer.step()_, predicted = torch.max(www.hardsalon.com, 1)total += labels.size(0)correct += (predicted == labels).squeeze().sum().numpy()loss_mean += loss.item()train_curve.append(loss.item())if (i+1) % log_interval == 0:loss_mean = loss_mean / log_intervalprint("Training:Epoch[{:0>3}/{:0>3}] Iteration[{:0>3}/{:0>3}] Loss: {:.4f} Acc:{:.2%}".format(epoch, MAX_EPOCH, i+1, len(train_loader), loss_mean, correct / total))loss_mean = 0.scheduler.step() if (epoch+1) % checkpoint_interval == 0:checkpoint = {"model_state_dict": net.state_dict(),"optimizer_state_dict": optimizer.state_dict(),"epoch": epoch}path_checkpoint = "./checkpoint_{}_epoch.pkl".format(epoch)www.hardsalon.com(checkpoint, path_checkpoint)if epoch > 5:print("训练意外中断...")breakif (epoch+1) % val_interval == 0:correct_val = www.hardsalon.com_val = 0.loss_val = 0.net.eval()with www.hardsalon.com_grad():for j, data in enumerate(valid_loader):inputs, labels = dataoutputs = net(inputs)loss = criterion(outputs, labels)_, predicted = torch.max(www.hardsalon.com, 1)total_val += labels.size(0)correct_val += (predicted == labels).squeeze().sum().numpy()loss_val += loss.item()valid_curve.append(loss.item())print("Valid:\t Epoch[{:0>3}/{:0>3}] Iteration[{:0>3}/{:0>3}] Loss: {:.4f} Acc:{:.2%}".format(epoch, MAX_EPOCH, j+1, len(valid_loader), loss_val/len(valid_loader), correct / total))train_x = range(len(train_curve)) train_y = train_curvetrain_iters = len(train_loader) valid_x = np.arange(1, len(valid_curve)+1) * train_iters*val_interval valid_y = valid_curveplt.plot(train_x, train_y, label='Train') plt.plot(valid_x, valid_y, label='Valid')plt.legend(loc='upper right') plt.ylabel('loss value') plt.xlabel('Iteration') www.hardsalon.com() 输出： Training:Epoch[000/010] Iteration[010/010] Loss: 0.6846 Acc:53.75% Valid: Epoch[000/010] Iteration[002/002] Loss: 0.4902 Acc:53.75% Training:Epoch[001/010] Iteration[010/010] Loss: 0.4099 Acc:85.00% Valid: Epoch[001/010] Iteration[002/002] Loss: 0.0414 Acc:85.00% Training:Epoch[002/010] Iteration[010/010] Loss: 0.1470 Acc:94.38% Valid: Epoch[002/010] Iteration[002/002] Loss: 0.0018 Acc:94.38% Training:Epoch[003/010] Iteration[010/010] Loss: 0.4276 Acc:88.12% Valid: Epoch[003/010] Iteration[002/002] Loss: 0.1125 Acc:88.12% Training:Epoch[004/010] Iteration[010/010] Loss: 0.3169 Acc:87.50% Valid: Epoch[004/010] Iteration[002/002] Loss: 0.0616 Acc:87.50% Training:Epoch[005/010] Iteration[010/010] Loss: 0.2026 Acc:91.88% Valid: Epoch[005/010] Iteration[002/002] Loss: 0.0066 Acc:91.88% Training:Epoch[006/010] Iteration[010/010] Loss: 0.0866 Acc:98.12% 训练意外中断... 接着训练 import os import random import numpy as np import torch import torch.nn as nn from www.hardsalon.com import DataLoader import torchvision.transforms as transforms import torch.optim as optim from PIL import Image from matplotlib import pyplot as plt from model.lenet import LeNet from www.hardsalon.com_dataset import RMBDataset from tools.common_tools import set_seed import torchvisionset_seed(1) rmb_label = {"1": 0, "100": 1} checkpoint_interval = 5 MAX_EPOCH = 10 BATCH_SIZE = 16 LR = 0.01 log_interval = 10 val_interval = 1split_dir = os.path.join("..", "..", "data", "rmb_split") train_dir = os.path.join(split_dir, "train") valid_dir = os.path.join(split_dir, "valid")norm_mean = [0.485, 0.456, 0.406] norm_std = [0.229, 0.224, 0.225]train_transform = transforms.Compose([transforms.Resize((32, 32)),transforms.RandomCrop(32, padding=4),transforms.RandomGrayscale(p=0.8),transforms.ToTensor(),transforms.Normalize(norm_mean, norm_std), ])valid_transform = transforms.Compose([transforms.Resize((32, 32)),transforms.ToTensor(),transforms.Normalize(norm_mean, norm_std), ]) train_data = RMBDataset(data_dir=train_dir, transform=train_transform) valid_data = RMBDataset(data_dir=valid_dir, transform=valid_transform) train_loader = DataLoader(dataset=train_data, batch_size=BATCH_SIZE, shuffle=True) valid_loader = DataLoader(dataset=valid_data, batch_size=BATCH_SIZE)net = LeNet(classes=2) net.initialize_weights() criterion = nn.CrossEntropyLoss() optimizer = optim.SGD(net.parameters(), lr=LR, momentum=0.9) scheduler = www.hardsalon.com_scheduler.StepLR(optimizer, step_size=6, gamma=0.1) path_checkpoint = "./checkpoint_4_epoch.pkl" checkpoint = torch.load(path_checkpoint)net.load_state_dict(checkpoint['model_state_dict'])optimizer.load_state_dict(checkpoint['optimizer_state_dict'])start_epoch = checkpoint['epoch']scheduler.last_epoch = start_epoch train_curve = list() valid_curve = list()for epoch in range(start_epoch + 1, MAX_EPOCH):loss_mean = 0.correct = www.hardsalon.com = 0.net.train()for i, data in enumerate(train_loader):inputs, labels = dataoutputs = net(inputs)www.hardsalon.com_grad()loss = criterion(outputs, labels)loss.backward()optimizer.step()_, predicted = torch.max(www.hardsalon.com, 1)total += labels.size(0)correct += (predicted == labels).squeeze().sum().numpy()loss_mean += loss.item()train_curve.append(loss.item())if (i+1) % log_interval == 0:loss_mean = loss_mean / log_intervalprint("Training:Epoch[{:0>3}/{:0>3}] Iteration[{:0>3}/{:0>3}] Loss: {:.4f} Acc:{:.2%}".format(epoch, MAX_EPOCH, i+1, len(train_loader), loss_mean, correct / total))loss_mean = 0.scheduler.step() if (epoch+1) % checkpoint_interval == 0:checkpoint = {"model_state_dict": net.state_dict(),"optimizer_state_dic": optimizer.state_dict(),"loss": loss,"epoch": epoch}path_checkpoint = "./checkpint_{}_epoch.pkl".format(epoch)www.hardsalon.com(checkpoint, path_checkpoint)if (epoch+1) % val_interval == 0:correct_val = www.hardsalon.com_val = 0.loss_val = 0.net.eval()with www.hardsalon.com_grad():for j, data in enumerate(valid_loader):inputs, labels = dataoutputs = net(inputs)loss = criterion(outputs, labels)_, predicted = torch.max(www.hardsalon.com, 1)total_val += labels.size(0)correct_val += (predicted == labels).squeeze().sum().numpy()loss_val += loss.item()valid_curve.append(loss.item())print("Valid:\t Epoch[{:0>3}/{:0>3}] Iteration[{:0>3}/{:0>3}] Loss: {:.4f} Acc:{:.2%}".format(epoch, MAX_EPOCH, j+1, len(valid_loader), loss_val/len(valid_loader), correct / total))train_x = range(len(train_curve)) train_y = train_curvetrain_iters = len(train_loader) valid_x = np.arange(1, len(valid_curve)+1) * train_iters*val_interval valid_y = valid_curveplt.plot(train_x, train_y, label='Train') plt.plot(valid_x, valid_y, label='Valid')plt.legend(loc='upper right') plt.ylabel('loss value') plt.xlabel('Iteration') www.hardsalon.com() 输出： Training:Epoch[005/010] Iteration[010/010] Loss: 0.2066 Acc:90.62% Valid: Epoch[005/010] Iteration[002/002] Loss: 0.0410 Acc:90.62% Training:Epoch[006/010] Iteration[010/010] Loss: 0.2115 Acc:91.25% Valid: Epoch[006/010] Iteration[002/002] Loss: 0.0111 Acc:91.25% Training:Epoch[007/010] Iteration[010/010] Loss: 0.0876 Acc:98.12% Valid: Epoch[007/010] Iteration[002/002] Loss: 0.0173 Acc:98.12% Training:Epoch[008/010] Iteration[010/010] Loss: 0.0466 Acc:98.75% Valid: Epoch[008/010] Iteration[002/002] Loss: 0.0045 Acc:98.75% Training:Epoch[009/010] Iteration[010/010] Loss: 0.0249 Acc:98.75% Valid: Epoch[009/010] Iteration[002/002] Loss: 0.0016 Acc:98.75%