bn-relu are duplicated in PreAct ResNet.

[rlatjcj]

I'm reproducing this paper and code and I have one question.
At model/resnet.py, I think that bn-relu are duplicated in PreAct ResNet18.

def CIFAR_ResNet18(pretrained=False, **kwargs):
    return CIFAR_ResNet(PreActBlock, [2,2,2,2], **kwargs)

and

class CIFAR_ResNet(nn.Module):
    def __init__(self, block, num_blocks, num_classes=10, bias=True):
        super(CIFAR_ResNet, self).__init__()
        self.in_planes = 64
        self.conv1 = conv3x3(3,64)
        self.bn1 = nn.BatchNorm2d(64)
        self.layer1 = self._make_layer(block, 64, num_blocks[0], stride=1)
        self.layer2 = self._make_layer(block, 128, num_blocks[1], stride=2)
        self.layer3 = self._make_layer(block, 256, num_blocks[2], stride=2)
        self.layer4 = self._make_layer(block, 512, num_blocks[3], stride=2)
        self.linear = nn.Linear(512*block.expansion, num_classes, bias=bias)


    def _make_layer(self, block, planes, num_blocks, stride):
        strides = [stride] + [1]*(num_blocks-1)
        layers = []
        for stride in strides:
            layers.append(block(self.in_planes, planes, stride))
            self.in_planes = planes * block.expansion
        return nn.Sequential(*layers)

    def forward(self, x, lin=0, lout=5):
        out = x
        out = self.conv1(out)
        out = self.bn1(out) # <----------------------------------------
        out = F.relu(out) # <----------------------------------------
        out1 = self.layer1(out)
        out2 = self.layer2(out1)
        out3 = self.layer3(out2)
        out = self.layer4(out3)
        out = F.avg_pool2d(out, 4)
        out4 = out.view(out.size(0), -1)
        out = self.linear(out4)

        return out

self.layer1 in CIFAR_ResNet is PreActBlock shown below

class PreActBlock(nn.Module):
    '''Pre-activation version of the BasicBlock.'''
    expansion = 1

    def __init__(self, in_planes, planes, stride=1):
        super(PreActBlock, self).__init__()
        self.bn1 = nn.BatchNorm2d(in_planes)
        self.conv1 = conv3x3(in_planes, planes, stride)
        self.bn2 = nn.BatchNorm2d(planes)
        self.conv2 = conv3x3(planes, planes)

        self.shortcut = nn.Sequential()
        if stride != 1 or in_planes != self.expansion*planes:
            self.shortcut = nn.Sequential(
                nn.Conv2d(in_planes, self.expansion*planes, kernel_size=1, stride=stride, bias=False)
            )

    def forward(self, x):
        out = F.relu(self.bn1(x)) # <----------------------------------------
        shortcut = self.shortcut(out)
        out = self.conv1(out)
        out = self.conv2(F.relu(self.bn2(out)))
        out += shortcut
        return out

I think the input of PreActBlock has already passed through bn-relu.

When I printed this network,

==> Building model: CIFAR_ResNet18                                                                                                 
CIFAR_ResNet(                                                                                                                        
    (conv1): Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)                                              
    (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) # <----------------------------------------
    (layer1): Sequential(                                                                                                                
        (0): PreActBlock(                                                                                                                    
            (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) # <----------------------------------------
            (conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)                                             
            (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
            (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
            (shortcut): Sequential()
        )