BottleNeck1.py

class Bottleneck(nn.Module):
    """
    Parameters:
        in_channels (int): number of channel of the input tensor
        out_channels (int): number of channel of the output tensor
        width_multiple (float): it controls the number of channels (and weights)
                                of all the convolutions beside the
                                first and last one. If closer to 0,
                                the simpler the modelIf closer to 1,
                                the model becomes more complex
    """
    def __init__(self, in_channels, out_channels, width_multiple=1):
        super(Bottleneck, self).__init__()
        c_ = int(width_multiple*in_channels)
        self.c1 = CBL(in_channels, c_, kernel_size=1, stride=1, padding=0)
        self.c2 = CBL(c_, out_channels, kernel_size=3, stride=1, padding=1)

    def forward(self, x):
        return self.c2(self.c1(x)) + x