self.inplanes = planes * block.expansion for i in range(1, num_blocks): layers.append(block(self.inplanes, planes)) return nn.Sequential(*layers) def forward(self, x): outs = [] x = self.conv1(x) x = self.bn1(x) x = self.relu(x) x = self.maxpool(x) x = self.layer1(...
ResNet的主体结构和普通的卷积神经网络差不多,这里也没什么需要说的. classResNet(nn.Module):def__init__(self,in_chans,block,num_block,num_classes=1000):super().__init__()#传入的模块resnet 18/34和resnet 50/101/152不一样self.block=blockself.in_channels=64self.conv1=nn.Sequential(nn.Con...
expansion, num_classes) def _make_layers(self, block, out_channels, num_blocks, stride): # 函数名前面带着下划线,是被保护的名字,不会通过【from module import *】导入该函数 strides = [stride] + [1] * (num_blocks - 1) # 第一个降采样 layers = [] for stride in strides: layers....
self.avgpool = nn.AdaptiveAvgPool2d((1, 1)) self.fc = nn.Linear(512,num_classes) def make_layer(self,block,channels,num_blocks,stride): layers = [] for i in range(num_blocks): if i == 0: layers.append(block(self.inchannel,channels,stride)) else: layers.append(block(channels,ch...
num_blocks: how many blocks per layer stride: the stride of the first block of this layer Return: return a resnet layer """# 扩维# we have num_block blocks per layer, the first block# could be 1 or 2, other blocks would always be 1strides=[stride]+[1]*(num_blocks-1)#减少特征...
bias=False)self.bn1=nn.BatchNorm2d(64)self.layer1=self._make_layer(block,64,num_blocks[0],...
# 残差卷积模块defresnet_block(in_channels,out_channels,num_residuals,first_block=False):resnet_blocks=[]foriinrange(num_residuals):# 如果不是第一个残差卷积模块,并且是第一个残差块,那么需要使用1x1卷积使得通道数加倍,并使长宽减半ifi==0andnotfirst_block:resnet_blocks.append(Residual(in_channels,...
self.inplanes= planes *block.expansionforiinrange(1, num_blocks): layers.append(block(self.inplanes, planes))returnnn.Sequential(*layers)defforward(self, x): outs=[] x=self.conv1(x) x=self.bn1(x) x=self.relu(x) x=self.maxpool(x) ...
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(): ...
num_outputs,[ksize,ksize],activation_fn=None) output = part6 + inputs return output def resnet(X_input,ksize,num_outputs,num_classes,num_blocks): layer1 = slim.conv2d(X_input,num_outputs,[ksize,ksize],normalizer_fn=slim.batch_norm,scope='conv_0') for i in range(num_blocks): ...