直接搭建网络必须与torchvision自带的网络的权重也就是pth文件的结构、尺寸和变量命名完全一致，否则无法加载权重文件。
此时可比较2个字典逐一加载，详见
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import torchimport torchvisionimport cv2 as cvfrom utils.utils import letter_boxfrom model.backbone import ResNet18model1 = ResNet18(1)model2 = torchvision.models.resnet18(progress=False)fc = model2.fcmodel2.fc = torch.nn.Linear(512, 1)# print(model)model_dict1 = model1.state_dict()model_dict2 = torch.load('resnet18.pth')model_list1 = list(model_dict1.keys())model_list2 = list(model_dict2.keys())len1 = len(model_list1)len2 = len(model_list2)minlen = min(len1, len2)for n in range(minlen): if model_dict1[model_list1[n]].shape != model_dict2[model_list2[n]].shape: continue model_dict1[model_list1[n]] = model_dict2[model_list2[n]]model1.load_state_dict(model_dict1)missing, unspected = model2.load_state_dict(model_dict2)image = cv.imread('zhn1.jpg')image = letter_box(image, 224)image = image[:, :, ::-1].transpose(2, 0, 1)print('Network loading complete.')model1.eval()model2.eval()with torch.no_grad(): image = torch.tensor(image/256, dtype=torch.float32).unsqueeze(0) predict1 = model1(image) predict2 = model2(image)print('finished')# torch.save(model.state_dict(), 'resnet18.pth') |
以上为全部程序，最终可测试原模型与加载了自带权重的自定义模型的输出是否相等。
补充：使用Pytorch搭建ResNet分类网络并基于迁移学习训练
如果stride=1，padding=1
卷积处理是不会改变特征矩阵的高和宽
使用BN层时
卷积中的参数bias置为False(有无偏置BN层的输出都相同)，BN层放在conv层和relu层的中间
复习BN层：
Batch Norm 层是对每层数据归一化后再进行线性变换改善数据分布, 其中的线性变换是可学习的.
Batch Norm优点:减轻过拟合;改善梯度传播（权重不会过高或过低）容许较高的学习率，能够提高训练速度。减轻对初始化权重的强依赖，使得数据分布在激活函数的非饱和区域，一定程度上解决梯度消失问题。作为一种正则化的方式，在某种程度上减少对dropout的使用。
Batch Norm层摆放位置:在激活层（如 ReLU ）之前还是之后，没有一个统一的定论。
BN层与 Dropout 合作：Batch Norm的提出使得dropout的使用减少,但是Batch Norm不能完全取代dropout,保留较小的dropout率,如0.2可能效果更佳。
为什么要先normalize再通过γ,β线性变换恢复接近原来的样子，这不是多此一举吗？
在一定条件下可以纠正原始数据的分布（方差，均值变为新值γ,β），当原始数据分布足够好时就是恒等映射，不改变分布。如果不做BN，方差和均值对前面网络的参数有复杂的关联依赖，具有复杂的非线性。在新参数 γH′ + β 中仅由 γ,β 确定，与前边网络的参数无关，因此新参数很容易通过梯度下降来学习，能够学习到较好的分布。
迁移学习导入权重和下载权重：
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import torchvision.models.resnet#ctrl+鼠标左键点击即可下载权重net = resnet34()#一开始不能设置全连接层的输出种类为自己想要的，必须先将模型参数载入，再修改全连接层# 官方提供载入预训练模型的方法model_weight_path = "./resnet34-pre.pth"#权重路径missing_keys, unexpected_keys = net.load_state_dict(torch.load(model_weight_path), strict=False)#载入模型权重inchannel = net.fc.in_featuresnet.fc = nn.Linear(inchannel, 5)#重新确定全连接层 |
完整代码：
model部分：
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import torch.nn as nnimport torchclass BasicBlock(nn.Module):#对应18层和34层所对应的残差结构（既要有实线残差结构功能，也要有虚线残差结构功能） expansion = 1#残差结构主分支上的三个卷积层是否相同，相同为1，第三层是一二层四倍则为4 def __init__(self, in_channel, out_channel, stride=1, downsample=None):#downsample代表虚线残差结构选项 super(BasicBlock, self).__init__() self.conv1 = nn.Conv2d(in_channels=in_channel, out_channels=out_channel, kernel_size=3, stride=stride, padding=1, bias=False) self.bn1 = nn.BatchNorm2d(out_channel) self.relu = nn.ReLU() self.conv2 = nn.Conv2d(in_channels=out_channel, out_channels=out_channel, kernel_size=3, stride=1, padding=1, bias=False) self.bn2 = nn.BatchNorm2d(out_channel) self.downsample = downsample def forward(self, x): identity = x if self.downsample is not None: identity = self.downsample(x)#得到捷径分支的输出 out = self.conv1(x) out = self.bn1(out) out = self.relu(out) out = self.conv2(out) out = self.bn2(out) out += identity out = self.relu(out) return out#得到残差结构的最终输出class Bottleneck(nn.Module):#对应50层、101层和152层所对应的残差结构 expansion = 4#第三层卷积核个数是第一层和第二层的四倍 def __init__(self, in_channel, out_channel, stride=1, downsample=None): super(Bottleneck, self).__init__() self.conv1 = nn.Conv2d(in_channels=in_channel, out_channels=out_channel, kernel_size=1, stride=1, bias=False) self.bn1 = nn.BatchNorm2d(out_channel) self.conv2 = nn.Conv2d(in_channels=out_channel, out_channels=out_channel, kernel_size=3, stride=stride, bias=False, padding=1) self.bn2 = nn.BatchNorm2d(out_channel) self.conv3 = nn.Conv2d(in_channels=out_channel, out_channels=out_channel*self.expansion, kernel_size=1, stride=1, bias=False) self.bn3 = nn.BatchNorm2d(out_channel*self.expansion) self.relu = nn.ReLU(inplace=True) self.downsample = downsample def forward(self, x): identity = x if self.downsample is not None: identity = self.downsample(x) out = self.conv1(x) out = self.bn1(out) out = self.relu(out) out = self.conv2(out) out = self.bn2(out) out = self.relu(out) out = self.conv3(out) out = self.bn3(out) out += identity out = self.relu(out) return outclass ResNet(nn.Module):#定义整个网络的框架部分#blocks_num是残差结构的数目，是一个列表参数，block对应哪个残差模块 def __init__(self, block, blocks_num, num_classes=1000, include_top=True): super(ResNet, self).__init__() self.include_top = include_top self.in_channel = 64#通过第一个池化层后所得到的特征矩阵的深度 self.conv1 = nn.Conv2d(3, self.in_channel, kernel_size=7, stride=2, padding=3, bias=False) self.bn1 = nn.BatchNorm2d(self.in_channel) self.relu = nn.ReLU(inplace=True) self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1) self.layer1 = self._make_layer(block, 64, blocks_num[0]) self.layer2 = self._make_layer(block, 128, blocks_num[1], stride=2) self.layer3 = self._make_layer(block, 256, blocks_num[2], stride=2) self.layer4 = self._make_layer(block, 512, blocks_num[3], stride=2) if self.include_top: self.avgpool = nn.AdaptiveAvgPool2d((1, 1)) # output size = (1, 1) self.fc = nn.Linear(512 * block.expansion, num_classes) for m in self.modules(): if isinstance(m, nn.Conv2d): nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu') def _make_layer(self, block, channel, block_num, stride=1):#channel：残差结构中，第一个卷积层所使用的卷积核的个数 downsample = None if stride != 1 or self.in_channel != channel * block.expansion:#18层和34层会直接跳过这个if语句 downsample = nn.Sequential( nn.Conv2d(self.in_channel, channel * block.expansion, kernel_size=1, stride=stride, bias=False), nn.BatchNorm2d(channel * block.expansion)) layers = [] layers.append(block(self.in_channel, channel, downsample=downsample, stride=stride)) self.in_channel = channel * block.expansion for _ in range(1, block_num): layers.append(block(self.in_channel, channel)) return nn.Sequential(*layers) def forward(self, x): x = self.conv1(x) x = self.bn1(x) x = self.relu(x) x = self.maxpool(x) x = self.layer1(x) x = self.layer2(x) x = self.layer3(x) x = self.layer4(x) if self.include_top:#默认是true x = self.avgpool(x) x = torch.flatten(x, 1) x = self.fc(x) return xdef resnet34(num_classes=1000, include_top=True): return ResNet(BasicBlock, [3, 4, 6, 3], num_classes=num_classes, include_top=include_top)def resnet101(num_classes=1000, include_top=True): return ResNet(Bottleneck, [3, 4, 23, 3], num_classes=num_classes, include_top=include_top) |
训练部分：
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import torchimport torch.nn as nnfrom torchvision import transforms, datasetsimport jsonimport matplotlib.pyplot as pltimport osimport torch.optim as optimfrom model import resnet34, resnet101import torchvision.models.resnet#ctrl+鼠标左键点击即可下载权重device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")print(device)data_transform = { "train": transforms.Compose([transforms.RandomResizedCrop(224), transforms.RandomHorizontalFlip(), transforms.ToTensor(), transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])]),#和官网初始化方法保持一致 "val": transforms.Compose([transforms.Resize(256), transforms.CenterCrop(224), transforms.ToTensor(), transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])])}data_root = os.path.abspath(os.path.join(os.getcwd(), "../..")) # get data root pathimage_path = data_root + "/data_set/flower_data/" # flower data set pathtrain_dataset = datasets.ImageFolder(root=image_path+"train", transform=data_transform["train"])train_num = len(train_dataset)# {'daisy':0, 'dandelion':1, 'roses':2, 'sunflower':3, 'tulips':4}flower_list = train_dataset.class_to_idxcla_dict = dict((val, key) for key, val in flower_list.items())# write dict into json filejson_str = json.dumps(cla_dict, indent=4)with open('class_indices.json', 'w') as json_file: json_file.write(json_str)batch_size = 16train_loader = torch.utils.data.DataLoader(train_dataset, batch_size=batch_size, shuffle=True, num_workers=0)validate_dataset = datasets.ImageFolder(root=image_path + "val", transform=data_transform["val"])val_num = len(validate_dataset)validate_loader = torch.utils.data.DataLoader(validate_dataset, batch_size=batch_size, shuffle=False, num_workers=0)net = resnet34()#一开始不能设置全连接层的输出种类为自己想要的，必须先将模型参数载入，再修改全连接层# 官方提供载入预训练模型的方法model_weight_path = "./resnet34-pre.pth"#权重路径missing_keys, unexpected_keys = net.load_state_dict(torch.load(model_weight_path), strict=False)#载入模型权重inchannel = net.fc.in_featuresnet.fc = nn.Linear(inchannel, 5)#重新确定全连接层net.to(device)loss_function = nn.CrossEntropyLoss()optimizer = optim.Adam(net.parameters(), lr=0.0001)best_acc = 0.0save_path = './resNet34.pth'for epoch in range(3): # train net.train()#控制BN层状态 running_loss = 0.0 for step, data in enumerate(train_loader, start=0): images, labels = data optimizer.zero_grad() logits = net(images.to(device)) loss = loss_function(logits, labels.to(device)) loss.backward() optimizer.step() # print statistics running_loss += loss.item() # print train process rate = (step+1)/len(train_loader) a = "*" * int(rate * 50) b = "." * int((1 - rate) * 50) print("\rtrain loss: {:^3.0f}%[{}->{}]{:.4f}".format(int(rate*100), a, b, loss), end="") print() # validate net.eval()#控制BN层状态 acc = 0.0 # accumulate accurate number / epoch with torch.no_grad(): for val_data in validate_loader: val_images, val_labels = val_data outputs = net(val_images.to(device)) # eval model only have last output layer # loss = loss_function(outputs, test_labels) predict_y = torch.max(outputs, dim=1)[1] acc += (predict_y == val_labels.to(device)).sum().item() val_accurate = acc / val_num if val_accurate > best_acc: best_acc = val_accurate torch.save(net.state_dict(), save_path) print('[epoch %d] train_loss: %.3f test_accuracy: %.3f' % (epoch + 1, running_loss / step, val_accurate))print('Finished Training') |
预测部分：
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import torchfrom model import resnet34from PIL import Imagefrom torchvision import transformsimport matplotlib.pyplot as pltimport jsondevice = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")data_transform = transforms.Compose( [transforms.Resize(256), transforms.CenterCrop(224), transforms.ToTensor(), transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])])#采用和训练方法一样的标准化处理# load imageimg = Image.open("../aa.jpg")plt.imshow(img)# [N, C, H, W]img = data_transform(img)# expand batch dimensionimg = torch.unsqueeze(img, dim=0)# read class_indicttry: json_file = open('./class_indices.json', 'r') class_indict = json.load(json_file)except Exception as e: print(e) exit(-1)# create modelmodel = resnet34(num_classes=5)# load model weightsmodel_weight_path = "./resNet34.pth"model.load_state_dict(torch.load(model_weight_path, map_location=device))#载入训练好的模型参数model.eval()#使用eval()模式with torch.no_grad():#不跟踪损失梯度 # predict class output = torch.squeeze(model(img))#压缩batch维度 predict = torch.softmax(output, dim=0)#通过softmax得到概率分布 predict_cla = torch.argmax(predict).numpy()#寻找最大值所对应的索引print(class_indict[str(predict_cla)], predict[predict_cla].numpy())#打印类别信息和概率plt.show() |
以上为个人经验，希望能给大家一个参考，也希望大家多多支持服务器之家。
原文链接：https://blog.csdn.net/qq_34288751/article/details/114163057
