model.py

import torch 
import torch.nn as nn

# config 
architecture_config = [
    #Tuple: (kernel_size, number of filters, strides, padding)
    (7, 64, 2, 3),
    #"M" = Max Pool Layer
    "M",
    (3, 192, 1, 1),
    "M",
    (1, 128, 1, 0),
    (3, 256, 1, 1),
    (1, 256, 1, 0),
    (3, 512, 1, 1),
    "M",
    #List: [(tuple), (tuple), how many times to repeat]
    [(1, 256, 1, 0), (3, 512, 1, 1), 4],
    (1, 512, 1, 0),
    (3, 1024, 1, 1),
    "M",
    [(1, 512, 1, 0), (3, 1024, 1, 1), 2],
    (3, 1024, 1, 1),
    (3, 1024, 2, 1),
    (3, 1024, 1, 1),
    (3, 1024, 1, 1),
    #Doesnt include fc layers
]

class CNNBlock(nn.Module):
    def __init__(self, in_channels, out_channels, **kwargs):
        super(CNNBlock, self).__init__()
        self.conv = nn.Conv2d(in_channels, out_channels, bias=False, **kwargs)
        self.batchnorm = nn.BatchNorm2d(out_channels)
        self.leakyrelu = nn.LeakyReLU(0.1)
        
    def forward(self, x):
        # x = self.conv(x)
        # x = self.batchnorm(x)
        # x = self.leakyrelu(x)
        # return x
        return self.leakyrelu(self.batchnorm(self.conv(x)))

class YoloV1(nn.Module):
    def __init__(self, in_channels=3, **kwargs):
        super(YoloV1, self).__init__()
        self.architecture = architecture_config
        self.in_channels = in_channels
        self.darknet = self._create_conv_layers(self.architecture)
        self.fcs = self._create_fcs(**kwargs)
        
    def forward(self, x):
        x = self.darknet(x)
        return self.fcs(torch.flatten(x, start_dim=1))
    
    def _create_conv_layers(self, architecture):
        layers = []
        in_channels = self.in_channels
        
        for x in architecture:
            if type(x) == tuple:
                layers += [CNNBlock(in_channels, x[1], kernel_size=x[0], stride=x[2], padding=x[3])]
                in_channels = x[1]
            elif type(x) == str:
                layers += [nn.MaxPool2d(kernel_size=2, stride=2)]
            elif type(x) == list:
                conv1 = x[0] #Tuple
                conv2 = x[1] #Tuple
                repeats = x[2] #Int
                
                for _ in range(repeats):
                    layers += [CNNBlock(in_channels, conv1[1], kernel_size=conv1[0], stride=conv1[2], padding=conv1[3])]
                    layers += [CNNBlock(conv1[1], conv2[1], kernel_size=conv2[0], stride=conv2[2], padding=conv2[3])]
                    in_channels = conv2[1]
                    
        return nn.Sequential(*layers)
    
    def _create_fcs(self, split_size, num_boxes, num_classes):
        S, B, C = split_size, num_boxes, num_classes
        return nn.Sequential(nn.Flatten(), 
                             nn.Linear(1024 * S * S, 496),  
                             nn.Dropout(0.0), 
                             nn.LeakyReLU(0.1), 
                             nn.Linear(496, S * S * (C + B * 5))) # (S,S,30) - (C + B*5) = 30
# S - split_size
# B - num_boxes
# C - num_classes
# def test(S=7,B=2,C=20):
#     model = YoloV1(split_size=S, num_boxes=B,num_classes=C)
#     x = torch.randn((2,3,448,448))
#     print(model(x).shape)
# test()