# Codeblock 5
class DepthwiseSeparableConv(nn.Module):
    def __init__(self, in_channels, out_channels, downsample=False):  #(1)
        super().__init__()
        
        in_channels  = int(in_channels*ALPHA)    #(2)
        out_channels = int(out_channels*ALPHA)   #(3)       
        
        if downsample:    #(4)
            stride = 2
        else:
            stride = 1
        
        self.dwconv = nn.Conv2d(in_channels=in_channels,
                                out_channels=in_channels,     #(5)
                                kernel_size=3,                #(6)
                                stride=stride,                #(7)
                                padding=1,
                                groups=in_channels,           #(8)
                                bias=False)
        self.bn0 = nn.BatchNorm2d(num_features=in_channels)   #(9)
        
        self.pwconv = nn.Conv2d(in_channels=in_channels,   
                                out_channels=out_channels,    #(10)
                                kernel_size=1,                #(11)
                                stride=1,                     #(12)
                                padding=0,                    #(13)
                                groups=1,                     #(14)
                                bias=False)
        self.bn1 = nn.BatchNorm2d(num_features=out_channels)  #(15)
        
        self.relu = nn.ReLU()    #(16)

    def forward(self, x):
        print(f'original\t: {x.size()}')
        
        x = self.relu(self.bn0(self.dwconv(x)))
        print(f'after dw conv\t: {x.size()}')
        
        x = self.relu(self.bn1(self.pwconv(x)))
        print(f'after pw conv\t: {x.size()}')
        
        return x