DocumentAIKit/model_t.py

import torch
from torch import nn
import torch.nn.functional as F
from torchinfo import summary


class ResidualBlock(nn.Module):
    def __init__(self, channels):
        super(ResidualBlock, self).__init__()
        self.conv1 = nn.Conv2d(channels, channels, kernel_size=3, padding=1, bias=False)
        self.bn1 = nn.BatchNorm2d(channels)
        self.relu6_1 = nn.ReLU6(inplace=True)
        self.conv2 = nn.Conv2d(channels, channels, kernel_size=3, padding=1, bias=False)
        self.bn2 = nn.BatchNorm2d(channels)
        self.relu6_2 = nn.ReLU6(inplace=True)
        self.relu6_latest = nn.ReLU6(inplace=True)

    def forward(self, x):
        residual = self.conv1(x)
        residual = self.bn1(residual)
        residual = self.relu6_1(residual)

        residual = self.conv2(residual)
        residual = self.bn2(residual)
        residual = self.relu6_2(residual)

        add = x + residual
        return self.relu6_latest(add)

class M64ColorNet(nn.Module):

    def __init__(self):
        super(M64ColorNet, self).__init__()

        self.block1 = nn.Sequential(
            nn.Conv2d(in_channels=3, out_channels=16, kernel_size=3, padding=1, bias=False, stride=1),
            nn.BatchNorm2d(16),
            nn.ReLU6(inplace=True)
        )
        self.block2 = nn.Sequential(
            nn.Conv2d(in_channels=16, out_channels=32, kernel_size=3, padding=1, bias=False, stride=2),
            nn.BatchNorm2d(32),
            nn.ReLU6(inplace=True)
        )
        self.block3 = nn.Sequential(
            nn.Conv2d(in_channels=32, out_channels=32, kernel_size=3, padding=1, bias=False, stride=2),
            nn.BatchNorm2d(32),
            nn.ReLU6(inplace=True)
        )
        # self.block4 = ResidualBlock(32)
        # self.block5 = ResidualBlock(32)
        # self.block6 = nn.Sequential(
        #     nn.Conv2d(in_channels=64, out_channels=128, kernel_size=3, padding=1, bias=False),
        #     nn.BatchNorm2d(128), nn.ReLU6(inplace=True))
        # self.block6 = ResidualBlock(64)
        # self.block6_1 = nn.Sequential(
        #     nn.Conv2d(in_channels=64, out_channels=128, kernel_size=3, padding=1, bias=False, stride=2),
        #     nn.BatchNorm2d(128),
        #     nn.ReLU6(inplace=True)
        # )
        # self.block6_2 = ResidualBlock(128)
        # self.block6_3 = ResidualBlock(128)
        # self.block6_3 = nn.Sequential(
        #     nn.Conv2d(in_channels=128, out_channels=64, kernel_size=3, padding=1, bias=False),
        #     nn.BatchNorm2d(64),
        #     nn.ReLU6(inplace=True))
        # self.block6_4 = nn.Sequential(
        #     # nn.Conv2d(in_channels=64, out_channels=32, kernel_size=3, padding=1, bias=False),
        #     nn.ConvTranspose2d(in_channels=128, out_channels=64, kernel_size=3, padding=1, bias=False, stride=2,output_padding=1),
        #     nn.BatchNorm2d(64),
        #     nn.ReLU6(inplace=True))
        # self.block6_3 = ResidualBlock(64)
        # self.block6_5 = ResidualBlock(64)
        # self.block7 = ResidualBlock(32)
        self.block8 = ResidualBlock(32)
        # self.block9 = nn.Sequential(
        #     nn.Conv2d(in_channels=64, out_channels=64, kernel_size=3, padding=1, bias=False),
        #     nn.BatchNorm2d(64),
        #     nn.ReLU6(inplace=True))
        self.block10 = nn.Sequential(
            # nn.Conv2d(in_channels=64, out_channels=32, kernel_size=3, padding=1, bias=False),
            nn.ConvTranspose2d(in_channels=32, out_channels=32, kernel_size=3, padding=1, bias=False, stride=2, output_padding=1),
            nn.BatchNorm2d(32),
            nn.ReLU6(inplace=True)
        )
        self.block11 = nn.Sequential(
            # nn.Conv2d(in_channels=32, out_channels=16, kernel_size=3, padding=1, bias=False),
            nn.ConvTranspose2d(in_channels=32, out_channels=16, kernel_size=3, padding=1, bias=False, stride=2, output_padding=1),
            nn.BatchNorm2d(16),
            nn.ReLU6(inplace=True)
        )
        self.block12 = nn.Sequential(
            nn.Conv2d(in_channels=16, out_channels=3, kernel_size=3, padding=1, bias=False),
            nn.BatchNorm2d(3),
            nn.ReLU6(inplace=True)
        )
        # self.dropout = nn.Dropout(0.4)

    def forward(self, x):
        input = x

        x = self.block1(x)
        input2 = x

        x = self.block2(x)
        input3 = x

        x = self.block3(x)
        input4 = x

        # x = self.block4(x)
        # x = self.block5(x)
        # x = self.block6(x)
        # x = self.block6_1(x)
        # input4_1 = x
        # x = self.block6_2(x)
        # x = self.block6_3(x)
        # x = input4_1 + x
        # x = self.block6_4(x)
        # x = self.block6_5(x)
        # x = self.block7(x)
        x = self.block8(x)
        x = input4 + x
        
        # x = self.block9(x)

        x = self.block10(x)
        x = input3 + x

        x = self.block11(x)
        x = input2 + x

        x = self.block12(x)
        x = input + x

        x = torch.sigmoid(x)
        return x

    @staticmethod    
    def load_trained_model(ckpt_path):
        ckpt_dict = torch.load(ckpt_path, map_location=torch.device('cpu'))
        model = M64ColorNet()
        model.load_state_dict(ckpt_dict["model_state_dict"])
        model.eval()
        return model, ckpt_dict["mean"], ckpt_dict["std"], ckpt_dict["loss"], ckpt_dict["ssim_score"], ckpt_dict["psnr_score"]



if __name__ == "__main__":
    model = M64ColorNet()
    summary(model, input_size=(16, 3, 256, 256))