（附代码）ResNet网络架构搭建以及基于数据集CIFAR-10对ResNet进行训练

 ​​​​​​为什么ResNet深度残差网络广受好评呢？理论部分参考

一、数据集下载

CIFAR-10 and CIFAR-100 datasets (toronto.edu)

二、预训练权重下载

'resnet18': 'https://download.pytorch.org/models/resnet18-5c106cde.pth','resnet34': 'https://download.pytorch.org/models/resnet34-333f7ec4.pth','resnet50': 'https://download.pytorch.org/models/resnet50-19c8e357.pth','resnet101': 'https://download.pytorch.org/models/resnet101-5d3b4d8f.pth','resnet152': 'https://download.pytorch.org/models/resnet152-b121ed2d.pth',

 三、Demo介绍

构造一个resnet-20，在cifar-10上参考论文中的设置进行训练，达到论文给出的精度。

BASE_DIR = os.path.dirname(os.path.abspath(__file__))device = torch.device("cuda" if torch.cuda.is_available() else "cpu")if __name__ == "__main__":    # config    train_dir = os.path.join("..", "data", "cifar-10",  "cifar10_train")    test_dir = os.path.join("..", "data", "cifar-10", "cifar10_test")    now_time = datetime.now()    time_str = datetime.strftime(now_time, '%m-%d_%H-%M')    log_dir = os.path.join(BASE_DIR, "..", "results", time_str)    if not os.path.exists(log_dir): os.makedirs(log_dir)    class_names = ('plane', 'car', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck')    num_classes = 10    MAX_EPOCH = 182     # 182     # 64000 / (45000 / 128) = 182 epochs    BATCH_SIZE = 128    LR = 0.1    log_interval = 1    val_interval = 1    start_epoch = -1    milestones = [92, 136]  # divide it by 10 at 32k and 48k iterations    # ============================ step 1/5 数据 ============================    norm_mean = [0.485, 0.456, 0.406]    norm_std = [0.229, 0.224, 0.225]    train_transform = transforms.Compose([ transforms.Resize(32), transforms.RandomHorizontalFlip(p=0.5), transforms.RandomCrop(32, padding=4), transforms.ToTensor(), transforms.Normalize(norm_mean, norm_std),    ])    valid_transform = transforms.Compose([ transforms.Resize((32, 32)), transforms.ToTensor(), transforms.Normalize(norm_mean, norm_std),    ])    # 构建MyDataset实例    train_data = CifarDataset(data_dir=train_dir, transform=train_transform)    valid_data = CifarDataset(data_dir=test_dir, transform=valid_transform)    # 构建DataLoder    train_loader = DataLoader(dataset=train_data, batch_size=BATCH_SIZE, shuffle=True, num_workers=2)    valid_loader = DataLoader(dataset=valid_data, batch_size=BATCH_SIZE, num_workers=2)    # ============================ step 2/5 模型 ============================    resnet_model = resnet20()    resnet_model.to(device)    # ============================ step 3/5 损失函数 ============================    criterion = nn.CrossEntropyLoss()    # ============================ step 4/5 优化器 ============================    # 冻结卷积层    optimizer = optim.SGD(resnet_model.parameters(), lr=LR, momentum=0.9, weight_decay=1e-4)  # 选择优化器    scheduler = optim.lr_scheduler.MultiStepLR(optimizer, gamma=0.1, milestones=milestones)# ============================ step 5/5 训练 ============================    loss_rec = {"train": [], "valid": []}    acc_rec = {"train": [], "valid": []}    best_acc, best_epoch = 0, 0    for epoch in range(start_epoch + 1, MAX_EPOCH): # 训练(data_loader, model, loss_f, optimizer, epoch_id, device, max_epoch) loss_train, acc_train, mat_train = ModelTrainer.train(train_loader, resnet_model, criterion, optimizer, epoch, device, MAX_EPOCH) loss_valid, acc_valid, mat_valid = ModelTrainer.valid(valid_loader, resnet_model, criterion, device) print("Epoch[{:0>3}/{:0>3}] Train Acc: {:.2%} Valid Acc:{:.2%} Train loss:{:.4f} Valid loss:{:.4f} LR:{}".format(     epoch + 1, MAX_EPOCH, acc_train, acc_valid, loss_train, loss_valid, optimizer.param_groups[0]["lr"])) scheduler.step()  # 更新学习率 # 绘图 loss_rec["train"].append(loss_train), loss_rec["valid"].append(loss_valid) acc_rec["train"].append(acc_train), acc_rec["valid"].append(acc_valid) show_confMat(mat_train, class_names, "train", log_dir, verbose=epoch == MAX_EPOCH-1) show_confMat(mat_valid, class_names, "valid", log_dir, verbose=epoch == MAX_EPOCH-1) plt_x = np.arange(1, epoch+2) plot_line(plt_x, loss_rec["train"], plt_x, loss_rec["valid"], mode="loss", out_dir=log_dir) plot_line(plt_x, acc_rec["train"], plt_x, acc_rec["valid"], mode="acc", out_dir=log_dir) if epoch > (MAX_EPOCH/2) and best_acc < acc_valid:     best_acc = acc_valid     best_epoch = epoch     checkpoint = {"model_state_dict": resnet_model.state_dict(), "optimizer_state_dict": optimizer.state_dict(), "epoch": epoch, "best_acc": best_acc}     path_checkpoint = os.path.join(log_dir, "checkpoint_best.pkl")     torch.save(checkpoint, path_checkpoint)    print(" done ~~~~ {}, best acc: {} in :{} epochs. ".format(datetime.strftime(datetime.now(), '%m-%d_%H-%M'),     best_acc, best_epoch))    now_time = datetime.now()    time_str = datetime.strftime(now_time, '%m-%d_%H-%M')    print(time_str)

 四、Resnet架构介绍

class ResNet(nn.Module):    def __init__(self, block, layers, num_classes=1000, zero_init_residual=False,   groups=1, width_per_group=64, replace_stride_with_dilation=None,   norm_layer=None): super(ResNet, self).__init__()  # 设置标准化层，默认为BN if norm_layer is None:     norm_layer = nn.BatchNorm2d self._norm_layer = norm_layer self.inplanes = 64   # 第一个卷积卷积核数量，后续各个stage，用planes控制通道数，64-128-256-512 self.dilation = 1  if replace_stride_with_dilation is None:     # each element in the tuple indicates if we should replace     # the 2x2 stride with a dilated convolution instead     replace_stride_with_dilation = [False, False, False] if len(replace_stride_with_dilation) != 3:     raise ValueError("replace_stride_with_dilation should be None " "or a 3-element tuple, got {}".format(replace_stride_with_dilation)) self.groups = groups self.base_width = width_per_group # 64  # stage: 1 self.conv1 = nn.Conv2d(3, self.inplanes, kernel_size=7, stride=2, padding=3,   bias=False) self.bn1 = norm_layer(self.inplanes) self.relu = nn.ReLU(inplace=True) self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)  # stage: 2-5 self.layer1 = self._make_layer(block, 64, layers[0])  # basic, 64, 2 self.layer2 = self._make_layer(block, 128, layers[1], stride=2,    dilate=replace_stride_with_dilation[0]) # basic, 128, 2, 2, false self.layer3 = self._make_layer(block, 256, layers[2], stride=2,    dilate=replace_stride_with_dilation[1]) # basic, 256, 2, 2, false self.layer4 = self._make_layer(block, 512, layers[3], stride=2,    dilate=replace_stride_with_dilation[2]) # basic, 512, 2, 2, false  # stage:6 self.avgpool = nn.AdaptiveAvgPool2d((1, 1)) self.fc = nn.Linear(512 * block.expansion, num_classes)  # block.expansion=1   # 初始化 for m in self.modules():     if isinstance(m, nn.Conv2d):  nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')     elif isinstance(m, (nn.BatchNorm2d, nn.GroupNorm)):  nn.init.constant_(m.weight, 1)  nn.init.constant_(m.bias, 0) # Zero-initialize the last BN in each residual branch, # so that the residual branch starts with zeros, and each residual block behaves like an identity. # This improves the model by 0.2~0.3% according to https://arxiv.org/abs/1706.02677 if zero_init_residual:     for m in self.modules():  if isinstance(m, Bottleneck):      nn.init.constant_(m.bn3.weight, 0)  elif isinstance(m, BasicBlock):      nn.init.constant_(m.bn2.weight, 0)    def _make_layer(self, block, planes, blocks, stride=1, dilate=False): norm_layer = self._norm_layer  #  nn.BatchNorm2d downsample = None   previous_dilation = self.dilation if dilate:     self.dilation *= stride     stride = 1  # shortcut connection 的B策略，当分辨率变化时，采用1*1卷积进行变换特征图分辨率 if stride != 1 or self.inplanes != planes * block.expansion:     downsample = nn.Sequential(  conv1x1(self.inplanes, planes * block.expansion, stride),  norm_layer(planes * block.expansion),     )  layers = [] # 添加第一个building block，由于特征图分辨率下降在第一个building block中进行，因此这一个block比较特别，单独拿出来添加 layers.append(block(self.inplanes, planes, stride, downsample, self.groups,self.base_width, previous_dilation, norm_layer))  # 更新 self.inplanes  self.inplanes = planes * block.expansion  # 添加其余building block for _ in range(1, blocks):     layers.append(block(self.inplanes, planes, groups=self.groups,    base_width=self.base_width, dilation=self.dilation,    norm_layer=norm_layer)) return nn.Sequential(*layers)    def _forward_impl(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) x = self.avgpool(x) x = torch.flatten(x, 1) x = self.fc(x) return x    def forward(self, x): return self._forward_impl(x)#%%def _resnet(arch, block, layers, pretrained, progress, kwargs):    model = ResNet(block, layers, kwargs)    if pretrained: state_dict = load_state_dict_from_url(model_urls[arch],    progress=progress) model.load_state_dict(state_dict)    return model

两种残差结构定义

def conv1x1(in_planes, out_planes, stride=1):    """1x1 convolution"""    return nn.Conv2d(in_planes, out_planes, kernel_size=1, stride=stride, bias=False)class BasicBlock(nn.Module):    expansion = 1  # 最后FC层使用    __constants__ = ['downsample']    def __init__(self, inplanes, planes, stride=1, downsample=None, groups=1,   base_width=64, dilation=1, norm_layer=None): super(BasicBlock, self).__init__() if norm_layer is None:     norm_layer = nn.BatchNorm2d if groups != 1 or base_width != 64:     raise ValueError('BasicBlock only supports groups=1 and base_width=64') if dilation > 1:     raise NotImplementedError("Dilation > 1 not supported in BasicBlock") # Both self.conv1 and self.downsample layers downsample the input when stride != 1 self.conv1 = conv3x3(inplanes, planes, stride) self.bn1 = norm_layer(planes) self.relu = nn.ReLU(inplace=True) self.conv2 = conv3x3(planes, planes) self.bn2 = norm_layer(planes) self.downsample = downsample self.stride = stride    def forward(self, x): identity = x out = self.conv1(x) out = self.bn1(out) out = self.relu(out) out = self.conv2(out) out = self.bn2(out) if self.downsample is not None:     identity = self.downsample(x) out += identity out = self.relu(out) return outclass Bottleneck(nn.Module):    expansion = 4    __constants__ = ['downsample']    def __init__(self, inplanes, planes, stride=1, downsample=None, groups=1,   base_width=64, dilation=1, norm_layer=None): super(Bottleneck, self).__init__() if norm_layer is None:     norm_layer = nn.BatchNorm2d      width = int(planes * (base_width / 64.)) * groups  # Both self.conv2 and self.downsample layers downsample the input when stride != 1 self.conv1 = conv1x1(inplanes, width) self.bn1 = norm_layer(width) self.conv2 = conv3x3(width, width, stride, groups, dilation) self.bn2 = norm_layer(width) self.conv3 = conv1x1(width, planes * self.expansion) self.bn3 = norm_layer(planes * self.expansion) self.relu = nn.ReLU(inplace=True) self.downsample = downsample self.stride = stride    def forward(self, x): identity = 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) if self.downsample is not None:     identity = self.downsample(x) out += identity out = self.relu(out) return out