model.py

import torch
import torch.nn as nn

class conv_block(nn.Module):
    def __init__(self, in_c, out_c):
        super().__init__()

        self.conv = nn.Sequential(
            nn.Conv2d(in_c, out_c, kernel_size=3, padding=1),
            nn.BatchNorm2d(out_c),
            nn.ReLU(inplace=True),
            nn.Conv2d(out_c, out_c, kernel_size=3, padding=1),
            nn.BatchNorm2d(out_c),
            nn.ReLU(inplace=True)
        )

    def forward(self, inputs):
        return self.conv(inputs)

class encoder_block(nn.Module):
    def __init__(self, in_c, out_c):
        super().__init__()

        self.conv = conv_block(in_c, out_c)
        self.pool = nn.MaxPool2d((2, 2))

    def forward(self, inputs):
        x = self.conv(inputs)
        p = self.pool(x)

        return x, p

class decoder_block(nn.Module):
    def __init__(self, in_c, out_c):
        super().__init__()

        self.up = nn.ConvTranspose2d(in_c, out_c, kernel_size=2, stride=2, padding=0)
        self.conv = conv_block(out_c+out_c, out_c)

    def forward(self, inputs, skip):
        x = self.up(inputs)
        x = torch.cat([x, skip], axis=1)
        x = self.conv(x)

        return x


class build_unet(nn.Module):
    def __init__(self, num_classes=4):
        super().__init__()

        """ Encoder """
        self.e1 = encoder_block(3, 64)
        self.e2 = encoder_block(64, 128)
        self.e3 = encoder_block(128, 256)
        self.e4 = encoder_block(256, 512)

        """ Bottleneck """
        self.b = conv_block(512, 1024)

        """ Decoder """
        self.d1 = decoder_block(1024, 512)
        self.d2 = decoder_block(512, 256)
        self.d3 = decoder_block(256, 128)
        self.d4 = decoder_block(128, 64)

        """ Classifier """
        self.outputs = nn.Conv2d(64, num_classes, kernel_size=1, padding=0)

    def forward(self, inputs):
        """ Encoder """
        s1, p1 = self.e1(inputs)
        s2, p2 = self.e2(p1)
        s3, p3 = self.e3(p2)
        s4, p4 = self.e4(p3)

        """ Bottleneck """
        b = self.b(p4)

        """ Decoder """
        d1 = self.d1(b, s4)
        d2 = self.d2(d1, s3)
        d3 = self.d3(d2, s2)
        d4 = self.d4(d3, s1)

        """ Classifier """
        outputs = self.outputs(d4)

        return outputs

if __name__ == "__main__":
    model = build_unet()

    from ptflops import get_model_complexity_info
    flops, params = get_model_complexity_info(model, input_res=(3, 256, 256), as_strings=True, print_per_layer_stat=False)
    print('      - Flops:  ' + flops)
    print('      - Params: ' + params)