# Codeblock 12a
class MobileNetV3(nn.Module):
    def __init__(self):
        super().__init__()
        
        self.first_conv = ConvBlock(in_channels=3,    #(1)
                                    out_channels=int(WIDTH_MULTIPLIER*16),
                                    kernel_size=3,
                                    stride=2,
                                    padding=1, 
                                    activation=nn.Hardswish())
        
        self.blocks = nn.ModuleList([])    #(2)
        for config in BOTTLENECKS:         #(3)
            in_channels, out_channels, kernel_size, exp_size, se, activation, stride, padding = config
            self.blocks.append(Bottleneck(in_channels=int(WIDTH_MULTIPLIER*in_channels), 
                                          out_channels=int(WIDTH_MULTIPLIER*out_channels), 
                                          kernel_size=kernel_size, 
                                          exp_size=int(WIDTH_MULTIPLIER*exp_size), 
                                          stride=stride, 
                                          padding=padding, 
                                          se=se, 
                                          activation=activation))
        
        self.second_conv = ConvBlock(in_channels=int(WIDTH_MULTIPLIER*160), #(4)
                                     out_channels=int(WIDTH_MULTIPLIER*960),
                                     kernel_size=1,
                                     stride=1,
                                     padding=0, 
                                     activation=nn.Hardswish())
        
        self.avgpool = nn.AdaptiveAvgPool2d(output_size=(1,1))              #(5)
        
        self.third_conv = ConvBlock(in_channels=int(WIDTH_MULTIPLIER*960),  #(6)
                                    out_channels=int(WIDTH_MULTIPLIER*1280),
                                    kernel_size=1,
                                    stride=1,
                                    padding=0, 
                                    batchnorm=False,
                                    activation=nn.Hardswish())
        
        self.dropout = nn.Dropout(p=0.8)    #(7)
        
        self.output = ConvBlock(in_channels=int(WIDTH_MULTIPLIER*1280),     #(8)
                                out_channels=int(NUM_CLASSES),              #(9)
                                kernel_size=1,
                                stride=1,
                                padding=0, 
                                batchnorm=False,
                                activation=nn.Identity())