Tag: resnet

    # self.inplanes = 64
    # self.layer1 = self._make_layer(block=Bottleneck, 64, layers[0]=3)
    def _make_layer(self, block, planes, blocks, stride=1):
        
        downsample = None
        
        # identity 값을 낮춰서 shape을 맞춰주기 위함. channel도 맞춰주기.
        if stride != 1 or self.inplanes != planes * block.expansion: # 64 != 64 * 4
            
            downsample = nn.Sequential(
                conv1x1(self.inplanes, planes * block.expansion, stride), #conv1x1(256, 512, 2) #conv1x1(64, 256, 2)
                nn.BatchNorm2d(planes * block.expansion), #batchnrom2d(512) #batchnrom2d(256)
            )

        layers = []
        layers.append(block(self.inplanes, planes, stride, downsample))
        # layers.append(Bottleneck(64, 64, 1, downsample))
        
        self.inplanes = planes * block.expansion #self.inplanes = 128 * 4
        
        for _ in range(1, blocks): 
            layers.append(block(self.inplanes, planes)) # * 3

        return nn.Sequential(*layers)