import torch.nn as nn
from torchinfo import summary
import torchvision.models
import torch


def convrelu(in_channels, out_channels, kernel, padding):
  return nn.Sequential(
    nn.Conv2d(in_channels, out_channels, kernel, padding=padding),
    nn.ReLU(inplace=True),
  )


class ResNet18UNet(nn.Module):
  def __init__(self, max_depth, n_class=1):
    super().__init__()

    self.base_model = torchvision.models.resnet18(pretrained=True)
    self.base_layers = list(self.base_model.children())

    self.layer0 = nn.Sequential(*self.base_layers[:3]) # size=(N, 64, x.H/2, x.W/2)
    self.layer0_1x1 = convrelu(64, 64, 1, 0)
    self.layer1 = nn.Sequential(*self.base_layers[3:5]) # size=(N, 64, x.H/4, x.W/4)
    self.layer1_1x1 = convrelu(64, 64, 1, 0)
    self.layer2 = self.base_layers[5]  # size=(N, 128, x.H/8, x.W/8)
    self.layer2_1x1 = convrelu(128, 128, 1, 0)
    self.layer3 = self.base_layers[6]  # size=(N, 256, x.H/16, x.W/16)
    self.layer3_1x1 = convrelu(256, 256, 1, 0)
    self.layer4 = self.base_layers[7]  # size=(N, 512, x.H/32, x.W/32)
    self.layer4_1x1 = convrelu(512, 512, 1, 0)

    self.upsample = nn.Upsample(scale_factor=2, mode='bilinear', align_corners=True)

    self.conv_up3 = convrelu(256 + 512, 512, 3, 1)
    self.conv_up2 = convrelu(128 + 512, 256, 3, 1)
    self.conv_up1 = convrelu(64 + 256, 256, 3, 1)
    self.conv_up0 = convrelu(64 + 256, 128, 3, 1)

    self.conv_original_size0 = convrelu(3, 64, 3, 1)
    self.conv_original_size1 = convrelu(64, 64, 3, 1)
    self.conv_original_size2 = convrelu(64 + 128, 64, 3, 1)

    self.conv_last = nn.Conv2d(64, n_class, 1)

    self.max_depth = max_depth

  def forward(self, input):
    x_original = self.conv_original_size0(input)
    x_original = self.conv_original_size1(x_original)

    layer0 = self.layer0(input)
    layer1 = self.layer1(layer0)
    layer2 = self.layer2(layer1)
    layer3 = self.layer3(layer2)
    layer4 = self.layer4(layer3)

    layer4 = self.layer4_1x1(layer4)
    x = self.upsample(layer4)
    layer3 = self.layer3_1x1(layer3)
    x = torch.cat([x, layer3], dim=1)
    x = self.conv_up3(x)

    x = self.upsample(x)
    layer2 = self.layer2_1x1(layer2)
    print(x.shape)
    print(layer2.shape)
    x = torch.cat([x, layer2], dim=1)
    x = self.conv_up2(x)

    x = self.upsample(x)
    layer1 = self.layer1_1x1(layer1)
    x = torch.cat([x, layer1], dim=1)
    x = self.conv_up1(x)

    x = self.upsample(x)
    layer0 = self.layer0_1x1(layer0)
    x = torch.cat([x, layer0], dim=1)
    x = self.conv_up0(x)

    x = self.upsample(x)
    x = torch.cat([x, x_original], dim=1)
    x = self.conv_original_size2(x)

    out = self.conv_last(x)

    out_depth = torch.sigmoid(out) * self.max_depth
        
    return {'pred_d': out_depth}

if __name__ == "__main__":
    model = ResNet18UNet(max_depth=10).cuda()
    # print(model)
    summary(model, input_size=(1,3,256,256))