基于深度学习CNN网络+pytorch框架实现遥感图像滑坡识别源码+数据集+训练好的模型.zip

""" #-*-coding:utf-8-*- # @anthor: wangyu a beginner programmer, striving to be the strongest. # @date: 2021/7/20 14:48 """ import torch.nn as nn import torch class BasicBlock(nn.Module): # layer18/layer34 expansion = 1 # 对应各层的卷积核的个数是否改变，对于18和34层网络，相邻两层卷积核个数均为64 def __init__(self, in_channel, out_channel, stride=1, downsample=None): """ 初始化函数 :param in_channel: 输入特征矩阵的深度 :param out_channel:输出特征矩阵的深度（卷积核的个数） :param stride: 步长 :param downsample: 下采样，对应虚线处的残差结构，1x1的卷积层 """ super(BasicBlock, self).__init__() self.conv1 = nn.Conv2d(in_channels=in_channel, out_channels=out_channel, kernel_size=3, # 卷积核的大小 stride=stride, padding=1, # 填充步长 bias=False,) # 偏置，使用BatchNormalization时不用偏置 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 # 捷径输出值 if self.downsample is not None: identity = self.downsample(x) # 有下采样函数，捷径输出值为下采样输出值 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 class Bottleneck(nn.Module): """ 注意：原论文中，在虚线残差结构的主分支上，第一个1x1卷积层的步距是2，第二个3x3卷积层步距是1。 但在pytorch官方实现过程中是第一个1x1卷积层的步距是1，第二个3x3卷积层步距是2， 这么做的好处是能够在top1上提升大概0.5%的准确率。 可参考Resnet v1.5 https://ngc.nvidia.com/catalog/model-scripts/nvidia:resnet_50_v1_5_for_pytorch """ expansion = 4 def __init__(self, in_channel, out_channel, stride=1, downsample=None, groups=1, width_per_group=64): super(Bottleneck, self).__init__() width = int(out_channel * (width_per_group / 64.)) * groups self.conv1 = nn.Conv2d(in_channels=in_channel, out_channels=width, kernel_size=1, stride=1, bias=False) # squeeze channels self.bn1 = nn.BatchNorm2d(width) # ----------------------------------------- self.conv2 = nn.Conv2d(in_channels=width, out_channels=width, groups=groups, kernel_size=3, stride=stride, bias=False, padding=1) self.bn2 = nn.BatchNorm2d(width) # ----------------------------------------- self.conv3 = nn.Conv2d(in_channels=width, out_channels=out_channel*self.expansion, kernel_size=1, stride=1, bias=False) # unsqueeze channels 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 out class ResNet(nn.Module): def __init__(self, block, blocks_num, num_classes=1000, include_top=True): """ :param block: BasicBlock(18, 34), Bottleneck(50, 101, 152) :param blocks_num: 残差网络的个数 :param num_classes: 分类个数 :param include_top: 便于扩展网络 :param groups: :param width_per_group: """ super(ResNet, self).__init__() self.include_top = include_top self.in_channel = 64 # self.groups = groups # self.width_per_group = width_per_group 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): """ :param block: :param channel: 残差结构中卷积核的个数 :param block_num: 残差结构的个数 :param stride: :return: """ downsample = None if stride != 1 or self.in_channel != channel * block.expansion: 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: x = self.avgpool(x) x = torch.flatten(x, 1) x = self.fc(x) return x def resnet34(num_classes=1000, include_top=True): # https://download.pytorch.org/models/resnet34-333f7ec4.pth return ResNet(BasicBlock, [3, 4, 6, 3], num_classes=num_classes, include_top=include_top) def resnet50(num_classes=1000, include_top=True): # https://download.pytorch.org/models/resnet50-19c8e357.pth return ResNet(Bottleneck, [3, 4, 6, 3], num_classes=num_classes, include_top=include_top) def resnet101(num_classes=1000, include_top=True): # https://download.pytorch.org/models/resnet101-5d3b4d8f.pth return ResNet(Bottleneck, [3, 4, 23, 3], num_classes=num_classes, include_top=include_top) def resnext50_32x4d(num_classes=1000, include_top=True): # https://download.pytorch.org/models/resnext50_32x4d-7cdf4587.pth groups = 32 width_per_group = 4 return ResNet(Bottleneck, [3, 4, 6, 3], num_classes=num_classes, include_top=include_top, groups=groups, width_per_group=width_per_group) def resnext101_32x8d(num_classes=1000, include_top=True