layers = [] # 每一个convi_x的结构保存在一个layers列表中,i={2,3,4,5} layers.append(block(in_channel=self.in_channel, out_channel=channel, downsample=downsample, stride=stride)) # 定义convi_x中的第一个残差块,只有第一个需要设置downsample和stride self.in_channel = channel * block.expans...
) layers = [] layers.append(block(self.inplanes, planes, stride, downsample)) self.inplanes = planes * block.expansion for i in range(1, blocks): layers.append(block(self.inplanes, planes)) return nn.Sequential(*layers) 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13. 14. ...
nn.BatchNorm2d(channel * block.expansion)) # layers用于顺序储存各连续残差块 # 每个残差结构,第一个残差块均为需要对X下采样的残差块,后面的残差块不需要对X下采样 layers = [] # 添加第一个残差块,第一个残差块均为需要对X下采样的残差块 layers.append(block(self.in_channel, channel, downsample=do...
AI检测代码解析 PS E:\project\python> & D:/ProgramData/Anaconda3/python.exe e:/project/python/PM/ResNet_PM_test.py layers= 50 W0804 20:41:04.044713 18388 gpu_resources.cc:61] Please NOTE: device: 0, GPU Compute Capability: 6.1, Driver API Version: 12.2, Runtime API Version: 10.2 W0...
layers = [] layers.append(ResidualBlock(inchannel, outchannel,stride, shortcut)) for i in range(1,block_num): layers.append(ResidualBlock(outchannel, outchannel,1)) return nn.Sequential(*layers) def forward(self, x): x = self.pre(x) ...
# 第三个输入是每个blocks中包含多少个residual子结构,相当于layers def _make_layer(self, block, planes, blocks, stride=1): # downsample 主要用来处理H(x)=F(x)+x中F(x)和x的channel维度不匹配问题,即对残差结构的输入进行升维,在做残差相加的时候,必须保证残差的纬度与真正的输出维度(宽、高、以及深...
import keras.layers as klsdef bottleneck_block(input_tensor, filters): ''' filters 表示卷积核数量 ''' residual = kls.Conv2D(filters, (1, 1), strides=1)(input_tensor) residual = kls.BatchNormalization(axis=-1)(residual) residual = kls.Activation('relu')(residual) residual = kls.Conv2D...
return nn.Sequential(*layers) def forward(self, x): x = self.conv1(x) x = self.layer1(x) x = self.layer2(x) x = self.layer3(x) x = self.layer4(x) x = self.avgpool(x) x = x.view(x.size(0), -1) x = self.fc(x) ...
layers = [] # 添加第一个残差块,第一个残差块均为需要对X下采样的残差块 layers.append(block(self.in_channel, channel, downsample=downsample, stride=stride, groups=self.groups, width_per_group=self.width_per_group)) self.in_channel = channel * block.expansion ...
self.layer2 = self._make_layer(Basicblock, 128, block_num=layers[1], stride=2) 1. stride=2 需要downsample,此时downsample的主要修改是将通道由上层的64,变成本层的128,所以第一个BasicBlock的结构是: 64—>128,128—<128,第二个结构是,128—>128.128—>128,此阶段后的 self.inplanes=128,往后类似...