How downsample work in ResNet in pytorch code?

Question

In this pytorch ResNet code example they define downsample as variable in line 44. and line 58 use it as function. How this downsample work here as CNN point of view and as python Code point of view.

code example : pytorch ResNet

i searched for if downsample is any pytorch inbuilt function. but it is not.

class BasicBlock(nn.Module):
    expansion = 1

    def __init__(self, inplanes, planes, stride=1, downsample=None, groups=1, norm_layer=None):
        super(BasicBlock, self).__init__()
        if norm_layer is None:
            norm_layer = nn.BatchNorm2d
        if groups != 1:
            raise ValueError('BasicBlock only supports groups=1')
        # Both self.conv1 and self.downsample layers downsample the input when stride != 1
        self.conv1 = conv3x3(inplanes, planes, stride)
        self.bn1 = norm_layer(planes)
        self.relu = nn.ReLU(inplace=True)
        self.conv2 = conv3x3(planes, planes)
        self.bn2 = norm_layer(planes)
        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)

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

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

return out

Answer 1

If you look into the original ResNet Paper (http://openaccess.thecvf.com/content_cvpr_2016/papers/He_Deep_Residual_Learning_CVPR_2016_paper.pdf) they use strided convolutions to downsample the image. The main path is downsampled automatically using these strided convolutions as is done in your code. The residual path uses either (a) identity mapping with zero entries added to add no additional parameters or (b) a 1x1 convolution with the same stride parameter.

The second option could look like follows:

if downsample:
    self.downsample = conv1x1(inplanes, planes, strides)