import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.nn import init

# ==========================Core Module================================
class conv_block(nn.Module):
    def __init__(self, ch_in, ch_out):
        super(conv_block, self).__init__()
        self.conv = nn.Sequential(
            nn.Conv2d(ch_in, ch_out, kernel_size=3, stride=1, padding=1, bias=True),
            nn.BatchNorm2d(ch_out),
            nn.ReLU(inplace=True),
            nn.Conv2d(ch_out, ch_out, kernel_size=3, stride=1, padding=1, bias=True),
            nn.BatchNorm2d(ch_out),
            nn.ReLU(inplace=True)
        )

    def forward(self, x):
        x = self.conv(x)
        return x


class up_conv(nn.Module):
    def __init__(self, ch_in, ch_out):
        super(up_conv, self).__init__()
        self.up = nn.Sequential(
            nn.Upsample(scale_factor=2),
            nn.Conv2d(ch_in, ch_out, kernel_size=3, stride=1, padding=1, bias=True),
            nn.BatchNorm2d(ch_out),
            nn.ReLU(inplace=True)
        )

    def forward(self, x):
        x = self.up(x)
        return x


class U_Net(nn.Module):
    def __init__(self, img_ch=3, output_ch=1):
        super(U_Net, self).__init__()

        self.Maxpool = nn.MaxPool2d(kernel_size=2, stride=2)

        self.Conv1 = conv_block(ch_in=img_ch, ch_out=32)
        self.Conv2 = conv_block(ch_in=32, ch_out=64)
        self.Conv3 = conv_block(ch_in=64, ch_out=128)
        self.Conv4 = conv_block(ch_in=128, ch_out=256)
        self.Conv5 = conv_block(ch_in=256, ch_out=512)


        self.Up5 = up_conv(ch_in=512, ch_out=256)
        self.Up_conv5 = conv_block(ch_in=512, ch_out=256)

        self.Up4 = up_conv(ch_in=256, ch_out=128)
        self.Up_conv4 = conv_block(ch_in=256, ch_out=128)

        self.Up3 = up_conv(ch_in=128, ch_out=64)
        self.Up_conv3 = conv_block(ch_in=128, ch_out=64)

        self.Up2 = up_conv(ch_in=64, ch_out=32)
        self.Up_conv2 = conv_block(ch_in=64, ch_out=32)

        self.Conv_1x1 = nn.Conv2d(32, output_ch, kernel_size=1, stride=1, padding=0)

    def forward(self, x):
        # encoding path
        x1 = self.Conv1(x)

        x2 = self.Maxpool(x1)
        x2 = self.Conv2(x2)
        # print("x4", x2.shape)

        x3 = self.Maxpool(x2)
        x3 = self.Conv3(x3)
        # print("x4", x3.shape)

        x4 = self.Maxpool(x3)
        x4 = self.Conv4(x4)
        # print("x4", x4.shape)

        x5 = self.Maxpool(x4)
        x5 = self.Conv5(x5)
        # print("x4", x5.shape)

        # decoding + concat path
        d5 = self.Up5(x5)
        # print("x4", d5.shape)

        d5 = torch.cat((x4, d5), dim=1)
        # print("x4", d5.shape)
        d5 = self.Up_conv5(d5)
        # print("x4", d5.shape)
        d4 = self.Up4(d5)
        d4 = torch.cat((x3, d4), dim=1)
        d4 = self.Up_conv4(d4)

        d3 = self.Up3(d4)
        d3 = torch.cat((x2, d3), dim=1)
        d3 = self.Up_conv3(d3)

        d2 = self.Up2(d3)
        d2 = torch.cat((x1, d2), dim=1)
        d2 = self.Up_conv2(d2)

        d1 = self.Conv_1x1(d2)
        # d1 = F.softmax(d1,dim=1)  # mine
        # return d1
        out = nn.Sigmoid()(d1)
        return out
#
# if __name__ == '__main__':
#     net =U_Net(img_ch=3, output_ch=1)
#     print(net)
#     x = torch.rand((2, 3, 224, 224))
#     print(net.forward(x).shape)



# from torchstat import stat
#
# model = U_Net()
# stat(model, (3, 224, 224))