ResNet bottleneck structure

for this resnet, what does buttleneck class do and why it is named bottleneck

def resnet50nodown(device, filename, num_classes=1):
    """Constructs a ResNet-50 nodown model.
    """
    model = ResNet(Bottleneck, [3, 4, 6, 3], num_classes=num_classes, stride0=1)
    model.load_state_dict(torch.load(filename, map_location=torch.device('cpu'))['model'])
    model = model.to(device).eval()
    return model
class Bottleneck(nn.Module):
    expansion = 4

    def __init__(self, inplanes, planes, stride=1, downsample=None):
        super(Bottleneck, self).__init__()
        self.conv1 = conv1x1(inplanes, planes)
        self.bn1 = nn.BatchNorm2d(planes)
        self.conv2 = conv3x3(planes, planes, stride)
        self.bn2 = nn.BatchNorm2d(planes)
        self.conv3 = conv1x1(planes, planes * self.expansion)
        self.bn3 = nn.BatchNorm2d(planes * self.expansion)
        self.relu = nn.ReLU(inplace=True)
        self.downsample = downsample
        self.stride = stride

    def forward(self, x):
        identity = 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)

        if self.downsample is not None:
            identity = self.downsample(x)

        out += identity
        out = self.relu(out)

        return out

ans by gpt4

In the context of ResNet (Residual Network), the Bottleneck class is a specific type of residual block used to build the network. The name “bottleneck” comes from the fact that it reduces the dimensionality of the data before expanding it again, similar to the shape of a physical bottle’s neck.

In a Bottleneck block, the flow of the data can be seen as follows:

1. A 1x1 convolution is applied first to reduce dimensionality (from inplanes to planes). This is followed by batch normalization and a ReLU activation function. This can be seen as the top of the bottle.

2. Then, a 3x3 convolution is applied without changing the dimensionality (remains as planes). This is followed by batch normalization and a ReLU activation function. This can be seen as the narrow neck of the bottle.

3. Finally, a 1x1 convolution is applied to increase the dimensionality (planes to planes * self.expansion). This is followed by batch normalization. This can be seen as the bottom of the bottle.

The purpose of the bottleneck design is to make the network deeper with fewer parameters. By first reducing and then increasing the dimensionality, the model can learn complex patterns with fewer parameters and less computational cost.

The term “downsample” in the code refers to the optional operation used when the input and output dimensions of the residual block don’t match. In such cases, downsampling (usually done by convolution with stride more than 1 or max pooling) is applied to the input before adding it to the output of the bottleneck, which ensures that their dimensions match.

In the forward method of the Bottleneck class, the output from the 3-layer convolutions is added to the original input, which is a key part of the ResNet architecture known as a shortcut or skip connection. This helps to mitigate the vanishing gradient problem in deep neural networks, enabling the training of very deep models. After adding the input (identity) and the output of the convolutions, a ReLU activation function is applied. This overall design follows the philosophy of ResNet, i.e., learning residual functions with reference to the layer inputs.

how to know this is a ResNet-50 or ResNet-101？

The ResNet model is defined by the block used (either BasicBlock or Bottleneck) and the number of these blocks in each layer. The layers parameter in the ResNet class’ constructor represents the number of blocks in each of the four layers of the network.

For ResNet-50 and ResNet-101, the Bottleneck block is used. The difference between them is the number of these blocks in each layer:

• For ResNet-50, the layers list is [3, 4, 6, 3].
• For ResNet-101, the layers list is [3, 4, 23, 3].

So, you can determine which version of ResNet is used by looking at the layers list provided when the ResNet object is created. In the code you provided, ResNet-50 would be created by calling ResNet(Bottleneck, [3, 4, 6, 3]), while ResNet-101 would be created by calling ResNet(Bottleneck, [3, 4, 23, 3]).

why [3,4,6,3] is resnet 50? there are 16 layers instead 50

It’s a great observation. The numbers [3, 4, 6, 3] indeed refer to the number of Bottleneck blocks in each of the four layers in the ResNet model, but it’s not the total number of layers in the ResNet-50 model.

Each Bottleneck block is composed of three convolutional layers, so in total, these blocks contribute 3*16 = 48 layers.

Now let’s add the first convolutional layer (self.conv1 in the __init__ method of ResNet class) and the final fully-connected layer (self.fc), this gives us 48 + 1 + 1 = 50 layers, hence the name ResNet-50.

The same logic applies to ResNet-101 and other similar models. They’re named based on the total number of layers, including both the convolutional and fully-connected layers.

• pytorch
• python