import os 
import sys

sys.path.append(os.path.abspath(os.path.pardir))
from argparse import ArgumentParser

import torch 
import torch.nn as nn 
import torch.nn.functional as F 
import torch.optim as optim
import pytorch_lightning as pl 
from torchmetrics.functional import iou 

class DoubleConv(nn.Module):
    """(convolution => [BN] => ReLU) * 2"""

    def __init__(self, in_channels, out_channels, mid_channels=None):
        super().__init__()
        if not mid_channels:
            mid_channels = out_channels
        self.double_conv = nn.Sequential(
            nn.Conv2d(in_channels, mid_channels, kernel_size=3, padding=1),
            nn.BatchNorm2d(mid_channels),
            nn.ReLU(inplace=True),
            nn.Conv2d(mid_channels, out_channels, kernel_size=3, padding=1),
            nn.BatchNorm2d(out_channels),
            nn.ReLU(inplace=True)
        )

    def forward(self, x):
        return self.double_conv(x)


class Down(nn.Module):
    """Downscaling with maxpool then double conv"""

    def __init__(self, in_channels, out_channels):
        super().__init__()
        self.maxpool_conv = nn.Sequential(
            nn.MaxPool2d(2),
            DoubleConv(in_channels, out_channels)
        )

    def forward(self, x):
        return self.maxpool_conv(x)


class Up(nn.Module):
    """Upscaling then double conv"""

    def __init__(self, in_channels, out_channels, bilinear=True):
        super().__init__()

        # if bilinear, use the normal convolutions to reduce the number of channels
        if bilinear:
            self.up = nn.Upsample(scale_factor=2, mode='bilinear', align_corners=True)
            self.conv = DoubleConv(in_channels, out_channels, in_channels // 2)
        else:
            self.up = nn.ConvTranspose2d(in_channels , in_channels // 2, kernel_size=2, stride=2)
            self.conv = DoubleConv(in_channels, out_channels)


    def forward(self, x1, x2):
        x1 = self.up(x1)
        # input is CHW
        diffY = x2.size()[2] - x1.size()[2]
        diffX = x2.size()[3] - x1.size()[3]

        x1 = F.pad(x1, [diffX // 2, diffX - diffX // 2,
                        diffY // 2, diffY - diffY // 2])
        # if you have padding issues, see
        # https://github.com/HaiyongJiang/U-Net-Pytorch-Unstructured-Buggy/commit/0e854509c2cea854e247a9c615f175f76fbb2e3a
        # https://github.com/xiaopeng-liao/Pytorch-UNet/commit/8ebac70e633bac59fc22bb5195e513d5832fb3bd
        x = torch.cat([x2, x1], dim=1)
        return self.conv(x)


class OutConv(nn.Module):
    def __init__(self, in_channels, out_channels):
        super(OutConv, self).__init__()
        self.conv = nn.Conv2d(in_channels, out_channels, kernel_size=1)

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


class UNet(pl.LightningModule):
    
    def __init__(self, n_channels, n_classes, learning_rate=1e-3, bilinear=True):
        super(UNet, self).__init__()
        self.save_hyperparameters()
        self.n_channels = n_channels
        self.n_classes = n_classes
        self.bilinear = bilinear
        self.inc = DoubleConv(n_channels, 64)
        self.down1 = Down(64, 128)
        self.down2 = Down(128, 256)
        self.down3 = Down(256, 512)
        factor = 2 if bilinear else 1
        self.down4 = Down(512, 1024 // factor)
        self.up1 = Up(1024, 512 // factor, bilinear)
        self.up2 = Up(512, 256 // factor, bilinear)
        self.up3 = Up(256, 128 // factor, bilinear)
        self.up4 = Up(128, 64, bilinear)
        self.outc = OutConv(64, n_classes)

    def score(self, X, y):
        y_pred = self.decoder(self.encoder(X))
        IoU = iou(y_pred, y)
        return IoU

    def encoder(self, x): 
        x1 = self.inc(x)
        x2 = self.down1(x1)
        x3 = self.down2(x2)
        x4 = self.down3(x3)
        x5 = self.down4(x4)
        return x1, x2, x3, x4, x5 

    def decoder(self, x1, x2, x3, x4, x5): 
        x = self.up1(x5, x4)
        x = self.up2(x, x3)
        x = self.up3(x, x2)
        x = self.up4(x, x1)
        return x

    def forward(self, x):
        x1, x2, x3, x4, x5 = self.encoder(x)
        return x5 

    def training_step(self, batch, batch_idx): 
        X, y = batch 
        x1, x2, x3, x4, x5 = self.encoder(X) 
        x = self.decoder(x1, x2, x3, x4, x5) 
        y_hat = self.outc(x) 

        loss = F.cross_entropy(y_hat, y)
        self.log("train_loss", loss, on_step=True, on_epoch=True, prog_bar=True, logger=True)
        return loss 

    def validation_step(self, batch, batch_idx):
        X, y = batch 
        x1, x2, x3, x4, x5 = self.encoder(X) 
        x = self.decoder(x1, x2, x3, x4, x5) 
        y_hat = self.outc(x) 

        loss = F.cross_entropy(y_hat, y)
        self.log("val_loss", loss, on_step=True, on_epoch=True, prog_bar=True, logger=True)
        return loss 

    def test_step(self, batch, batch_idx):
        X, y = batch 
        x1, x2, x3, x4, x5 = self.encoder(X) 
        x = self.decoder(x1, x2, x3, x4, x5) 
        y_hat = self.outc(x) 

        loss = F.cross_entropy(y_hat, y)
        self.log("test_loss", loss, on_step=True, on_epoch=True, prog_bar=True, logger=True)
        return loss

    def configure_optimizers(self):
        optimizer = optim.Adam(self.parameters(), lr=self.hparams.learning_rate)
        scheduler = optim.lr_scheduler.ReduceLROnPlateau(optimizer, factor=0.1, patience=1)
        return {
            'optimizer': optimizer,
            'lr_scheduler': scheduler,
            'monitor': 'val_loss'
        }

    @staticmethod
    def add_model_specific_args(parent_parser: ArgumentParser) -> ArgumentParser:
        parser = ArgumentParser(parents=[parent_parser], add_help=False)
        parser.add_argument('--n_channels', type=int, default=3)
        parser.add_argument('--n_classes', type=int, default=2)
        parser.add_argument('--learning_rate', type=float, default=1e-3)
        return parser
import os 
import sys

sys.path.append(os.path.abspath(os.path.pardir))
from argparse import ArgumentParser

import torch 
import torch.nn as nn 
import torch.nn.functional as F 
import torch.optim as optim
import pytorch_lightning as pl 
from torchmetrics.functional import iou 

class DoubleConv(nn.Module):
    """(convolution => [BN] => ReLU) * 2"""

    def __init__(self, in_channels, out_channels, mid_channels=None):
        super().__init__()
        if not mid_channels:
            mid_channels = out_channels
        self.double_conv = nn.Sequential(
            nn.Conv2d(in_channels, mid_channels, kernel_size=3, padding=1),
            nn.BatchNorm2d(mid_channels),
            nn.ReLU(inplace=True),
            nn.Conv2d(mid_channels, out_channels, kernel_size=3, padding=1),
            nn.BatchNorm2d(out_channels),
            nn.ReLU(inplace=True)
        )

    def forward(self, x):
        return self.double_conv(x)


class Down(nn.Module):
    """Downscaling with maxpool then double conv"""

    def __init__(self, in_channels, out_channels):
        super().__init__()
        self.maxpool_conv = nn.Sequential(
            nn.MaxPool2d(2),
            DoubleConv(in_channels, out_channels)
        )

    def forward(self, x):
        return self.maxpool_conv(x)


class Up(nn.Module):
    """Upscaling then double conv"""

    def __init__(self, in_channels, out_channels, bilinear=True):
        super().__init__()

        # if bilinear, use the normal convolutions to reduce the number of channels
        if bilinear:
            self.up = nn.Upsample(scale_factor=2, mode='bilinear', align_corners=True)
            self.conv = DoubleConv(in_channels, out_channels, in_channels // 2)
        else:
            self.up = nn.ConvTranspose2d(in_channels , in_channels // 2, kernel_size=2, stride=2)
            self.conv = DoubleConv(in_channels, out_channels)


    def forward(self, x1, x2):
        x1 = self.up(x1)
        # input is CHW
        diffY = x2.size()[2] - x1.size()[2]
        diffX = x2.size()[3] - x1.size()[3]

        x1 = F.pad(x1, [diffX // 2, diffX - diffX // 2,
                        diffY // 2, diffY - diffY // 2])
        # if you have padding issues, see
        # https://github.com/HaiyongJiang/U-Net-Pytorch-Unstructured-Buggy/commit/0e854509c2cea854e247a9c615f175f76fbb2e3a
        # https://github.com/xiaopeng-liao/Pytorch-UNet/commit/8ebac70e633bac59fc22bb5195e513d5832fb3bd
        x = torch.cat([x2, x1], dim=1)
        return self.conv(x)


class OutConv(nn.Module):
    def __init__(self, in_channels, out_channels):
        super(OutConv, self).__init__()
        self.conv = nn.Conv2d(in_channels, out_channels, kernel_size=1)

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


class UNet(pl.LightningModule):
    
    def __init__(self, n_channels, n_classes, learning_rate=1e-3, bilinear=True):
        super(UNet, self).__init__()
        self.save_hyperparameters()
        self.n_channels = n_channels
        self.n_classes = n_classes
        self.bilinear = bilinear
        self.inc = DoubleConv(n_channels, 64)
        self.down1 = Down(64, 128)
        self.down2 = Down(128, 256)
        self.down3 = Down(256, 512)
        factor = 2 if bilinear else 1
        self.down4 = Down(512, 1024 // factor)
        self.up1 = Up(1024, 512 // factor, bilinear)
        self.up2 = Up(512, 256 // factor, bilinear)
        self.up3 = Up(256, 128 // factor, bilinear)
        self.up4 = Up(128, 64, bilinear)
        self.outc = OutConv(64, n_classes)

    def score(self, X, y):
        y_pred = self.decoder(self.encoder(X))
        IoU = iou(y_pred, y)
        return IoU

    def encoder(self, x): 
        x1 = self.inc(x)
        x2 = self.down1(x1)
        x3 = self.down2(x2)
        x4 = self.down3(x3)
        x5 = self.down4(x4)
        return x1, x2, x3, x4, x5 

    def decoder(self, x1, x2, x3, x4, x5): 
        x = self.up1(x5, x4)
        x = self.up2(x, x3)
        x = self.up3(x, x2)
        x = self.up4(x, x1)
        return x

    def forward(self, x):
        x1, x2, x3, x4, x5 = self.encoder(x)
        return x5 

    def training_step(self, batch, batch_idx): 
        X, y = batch 
        x1, x2, x3, x4, x5 = self.encoder(X) 
        x = self.decoder(x1, x2, x3, x4, x5) 
        y_hat = self.outc(x) 

        loss = F.cross_entropy(y_hat, y)
        self.log("train_loss", loss, on_step=True, on_epoch=True, prog_bar=True, logger=True)
        return loss 

    def validation_step(self, batch, batch_idx):
        X, y = batch 
        x1, x2, x3, x4, x5 = self.encoder(X) 
        x = self.decoder(x1, x2, x3, x4, x5) 
        y_hat = self.outc(x) 

        loss = F.cross_entropy(y_hat, y)
        self.log("val_loss", loss, on_step=True, on_epoch=True, prog_bar=True, logger=True)
        return loss 

    def test_step(self, batch, batch_idx):
        X, y = batch 
        x1, x2, x3, x4, x5 = self.encoder(X) 
        x = self.decoder(x1, x2, x3, x4, x5) 
        y_hat = self.outc(x) 

        loss = F.cross_entropy(y_hat, y)
        self.log("test_loss", loss, on_step=True, on_epoch=True, prog_bar=True, logger=True)
        return loss

    def configure_optimizers(self):
        optimizer = optim.Adam(self.parameters(), lr=self.hparams.learning_rate)
        scheduler = optim.lr_scheduler.ReduceLROnPlateau(optimizer, factor=0.1, patience=1)
        return {
            'optimizer': optimizer,
            'lr_scheduler': scheduler,
            'monitor': 'val_loss'
        }

    @staticmethod
    def add_model_specific_args(parent_parser: ArgumentParser) -> ArgumentParser:
        parser = ArgumentParser(parents=[parent_parser], add_help=False)
        parser.add_argument('--n_channels', type=int, default=3)
        parser.add_argument('--n_classes', type=int, default=2)
        parser.add_argument('--learning_rate', type=float, default=1e-3)
        return parser
(convolution => [BN] => ReLU) * 2 Downscaling with maxpool then double conv Upscaling then double conv # if bilinear, use the normal convolutions to reduce the number of channels # input is CHW # if you have padding issues, see # https://github.com/HaiyongJiang/U-Net-Pytorch-Unstructured-Buggy/commit/0e854509c2cea854e247a9c615f175f76fbb2e3a # https://github.com/xiaopeng-liao/Pytorch-UNet/commit/8ebac70e633bac59fc22bb5195e513d5832fb3bd