"""

UNet

The main UNet model implementation

"""

import torch

import torch.nn as nn

import torch.nn.functional as F

# Utility Functions

''' when filter kernel= 3x3, padding=1 makes in&out matrix same size'''

def conv_bn_leru(in_channels, out_channels, kernel_size=3, stride=1, padding=1):

    return nn.Sequential(

            nn.Conv2d(in_channels, out_channels, kernel_size=kernel_size, stride=stride, padding=padding),

            nn.BatchNorm2d(out_channels),

            nn.ReLU(inplace=True),

            nn.Conv2d(out_channels, out_channels, kernel_size=kernel_size, stride=stride, padding=padding),

            nn.BatchNorm2d(out_channels),

            nn.ReLU(inplace=True),

    )

def down_pooling():

    return nn.MaxPool2d(2)

def up_pooling(in_channels, out_channels, kernel_size=2, stride=2):

    return nn.Sequential(

        nn.ConvTranspose2d(in_channels, out_channels, kernel_size=kernel_size, stride=stride),

        nn.BatchNorm2d(out_channels),

        nn.ReLU(inplace=True)

    )

# UNet class

class UNet(nn.Module):

    def __init__(self, input_channels, nclasses):

        super().__init__()

        # go down

        self.conv1 = conv_bn_leru(input_channels,64)

        self.conv2 = conv_bn_leru(64, 128)

        self.conv3 = conv_bn_leru(128, 256)

        self.conv4 = conv_bn_leru(256, 512)

        self.conv5 = conv_bn_leru(512, 1024)

        self.down_pooling = nn.MaxPool2d(2)

        # go up

        self.up_pool6 = up_pooling(1024, 512)

        self.conv6 = conv_bn_leru(1024, 512)

        self.up_pool7 = up_pooling(512, 256)

        self.conv7 = conv_bn_leru(512, 256)

        self.up_pool8 = up_pooling(256, 128)

        self.conv8 = conv_bn_leru(256, 128)

        self.up_pool9 = up_pooling(128, 64)

        self.conv9 = conv_bn_leru(128, 64)

        self.conv10 = nn.Conv2d(64, nclasses, 1)

        # test weight init

        for m in self.modules():

            if isinstance(m, nn.Conv2d):

                nn.init.kaiming_normal(m.weight.data, a=0, mode='fan_out')

                if m.bias is not None:

                    m.bias.data.zero_()

    def forward(self, x):

        # go down

        x1 = self.conv1(x)

        p1 = self.down_pooling(x1)

        x2 = self.conv2(p1)

        p2 = self.down_pooling(x2)

        x3 = self.conv3(p2)

        p3 = self.down_pooling(x3)

        x4 = self.conv4(p3)

        p4 = self.down_pooling(x4)

        x5 = self.conv5(p4)

        # go up

        p6 = self.up_pool6(x5)

        x6 = torch.cat([p6, x4], dim=1)

        x6 = self.conv6(x6)

        p7 = self.up_pool7(x6)

        x7 = torch.cat([p7, x3], dim=1)

        x7 = self.conv7(x7)

        p8 = self.up_pool8(x7)

        x8 = torch.cat([p8, x2], dim=1)

        x8 = self.conv8(x8)

        p9 = self.up_pool9(x8)

        x9 = torch.cat([p9, x1], dim=1)

        x9 = self.conv9(x9)

        output = self.conv10(x9)

        output = F.sigmoid(output)

        return output