import torch
import torch.nn as nn
import torchvision
from torch.utils.data import DataLoader, Dataset
import torchvision.transforms as transforms
import os
import numpy as np
import matplotlib.pyplot as plt
import time


class UNET(nn.Module):

    def __init__(self, i_ch, o_ch):
        super().__init__()
        self.conv1 = self.contraction_block(i_ch, 32, 7, 3)
        self.conv2 = self.contraction_block(32, 64, 3, 1)
        self.conv3 = self.contraction_block(64, 128, 3, 1)
        self.upconv3 = self.expansive_block(128, 64, 3, 1)
        self.upconv2 = self.expansive_block(64 * 2, 32, 3, 1)
        self.upconv1 = self.expansive_block(32 * 2, o_ch, 3, 1)

    def __call__(self, x):
        conv1 = self.conv1(x)
        conv2 = self.conv2(conv1)
        conv3 = self.conv3(conv2)
        upconv3 = self.upconv3(conv3)
        upconv2 = self.upconv2(torch.cat([upconv3, conv2], 1))
        upconv1 = self.upconv1(torch.cat([upconv2, conv1], 1))
        return upconv1

    def expansive_block(self, i_ch, o_ch, filter_size, padding):
        block = nn.Sequential(torch.nn.Conv2d(i_ch, o_ch, kernel_size=
            filter_size, stride=1, padding=padding), torch.nn.BatchNorm2d(
            o_ch), torch.nn.ReLU(), torch.nn.Conv2d(o_ch, o_ch, kernel_size
            =filter_size, stride=1, padding=padding), torch.nn.BatchNorm2d(
            o_ch), torch.nn.ReLU(), torch.nn.ConvTranspose2d(o_ch, o_ch,
            kernel_size=3, stride=2, padding=1, output_padding=1))
        return block

    def contraction_block(self, i_ch, o_ch, filter_size, padding):
        block = nn.Sequential(torch.nn.Conv2d(i_ch, o_ch, kernel_size=
            filter_size, stride=1, padding=padding), torch.nn.BatchNorm2d(
            o_ch), torch.nn.ReLU(), torch.nn.Conv2d(o_ch, o_ch, kernel_size
            =filter_size, stride=1, padding=padding), torch.nn.BatchNorm2d(
            o_ch), torch.nn.ReLU(), torch.nn.MaxPool2d(kernel_size=3,
            stride=2, padding=1))
        return block

2b08659efa6334ab680355e89300412a3a32c8f1
<mask token>


class UNET(nn.Module):

    def __init__(self, i_ch, o_ch):
        super().__init__()
        self.conv1 = self.contraction_block(i_ch, 32, 7, 3)
        self.conv2 = self.contraction_block(32, 64, 3, 1)
        self.conv3 = self.contraction_block(64, 128, 3, 1)
        self.upconv3 = self.expansive_block(128, 64, 3, 1)
        self.upconv2 = self.expansive_block(64 * 2, 32, 3, 1)
        self.upconv1 = self.expansive_block(32 * 2, o_ch, 3, 1)

    def __call__(self, x):
        conv1 = self.conv1(x)
        conv2 = self.conv2(conv1)
        conv3 = self.conv3(conv2)
        upconv3 = self.upconv3(conv3)
        upconv2 = self.upconv2(torch.cat([upconv3, conv2], 1))
        upconv1 = self.upconv1(torch.cat([upconv2, conv1], 1))
        return upconv1
    <mask token>
    <mask token>

<mask token>


class UNET(nn.Module):

    def __init__(self, i_ch, o_ch):
        super().__init__()
        self.conv1 = self.contraction_block(i_ch, 32, 7, 3)
        self.conv2 = self.contraction_block(32, 64, 3, 1)
        self.conv3 = self.contraction_block(64, 128, 3, 1)
        self.upconv3 = self.expansive_block(128, 64, 3, 1)
        self.upconv2 = self.expansive_block(64 * 2, 32, 3, 1)
        self.upconv1 = self.expansive_block(32 * 2, o_ch, 3, 1)

    def __call__(self, x):
        conv1 = self.conv1(x)
        conv2 = self.conv2(conv1)
        conv3 = self.conv3(conv2)
        upconv3 = self.upconv3(conv3)
        upconv2 = self.upconv2(torch.cat([upconv3, conv2], 1))
        upconv1 = self.upconv1(torch.cat([upconv2, conv1], 1))
        return upconv1

    def expansive_block(self, i_ch, o_ch, filter_size, padding):
        block = nn.Sequential(torch.nn.Conv2d(i_ch, o_ch, kernel_size=
            filter_size, stride=1, padding=padding), torch.nn.BatchNorm2d(
            o_ch), torch.nn.ReLU(), torch.nn.Conv2d(o_ch, o_ch, kernel_size
            =filter_size, stride=1, padding=padding), torch.nn.BatchNorm2d(
            o_ch), torch.nn.ReLU(), torch.nn.ConvTranspose2d(o_ch, o_ch,
            kernel_size=3, stride=2, padding=1, output_padding=1))
        return block
    <mask token>

<mask token>


class UNET(nn.Module):

    def __init__(self, i_ch, o_ch):
        super().__init__()
        self.conv1 = self.contraction_block(i_ch, 32, 7, 3)
        self.conv2 = self.contraction_block(32, 64, 3, 1)
        self.conv3 = self.contraction_block(64, 128, 3, 1)
        self.upconv3 = self.expansive_block(128, 64, 3, 1)
        self.upconv2 = self.expansive_block(64 * 2, 32, 3, 1)
        self.upconv1 = self.expansive_block(32 * 2, o_ch, 3, 1)

    def __call__(self, x):
        conv1 = self.conv1(x)
        conv2 = self.conv2(conv1)
        conv3 = self.conv3(conv2)
        upconv3 = self.upconv3(conv3)
        upconv2 = self.upconv2(torch.cat([upconv3, conv2], 1))
        upconv1 = self.upconv1(torch.cat([upconv2, conv1], 1))
        return upconv1

    def expansive_block(self, i_ch, o_ch, filter_size, padding):
        block = nn.Sequential(torch.nn.Conv2d(i_ch, o_ch, kernel_size=
            filter_size, stride=1, padding=padding), torch.nn.BatchNorm2d(
            o_ch), torch.nn.ReLU(), torch.nn.Conv2d(o_ch, o_ch, kernel_size
            =filter_size, stride=1, padding=padding), torch.nn.BatchNorm2d(
            o_ch), torch.nn.ReLU(), torch.nn.ConvTranspose2d(o_ch, o_ch,
            kernel_size=3, stride=2, padding=1, output_padding=1))
        return block

    def contraction_block(self, i_ch, o_ch, filter_size, padding):
        block = nn.Sequential(torch.nn.Conv2d(i_ch, o_ch, kernel_size=
            filter_size, stride=1, padding=padding), torch.nn.BatchNorm2d(
            o_ch), torch.nn.ReLU(), torch.nn.Conv2d(o_ch, o_ch, kernel_size
            =filter_size, stride=1, padding=padding), torch.nn.BatchNorm2d(
            o_ch), torch.nn.ReLU(), torch.nn.MaxPool2d(kernel_size=3,
            stride=2, padding=1))
        return block

import torch
import torch.nn as nn
import torchvision
from torch.utils.data import DataLoader, Dataset
import torchvision.transforms as transforms
import os
import numpy as np
import matplotlib.pyplot as plt
import time


class UNET(nn.Module):

    def __init__(self, i_ch, o_ch):
        super().__init__()
        self.conv1 = self.contraction_block(i_ch, 32, 7, 3)
        self.conv2 = self.contraction_block(32, 64, 3, 1)
        self.conv3 = self.contraction_block(64, 128, 3, 1)
        self.upconv3 = self.expansive_block(128, 64, 3, 1)
        self.upconv2 = self.expansive_block(64 * 2, 32, 3, 1)
        self.upconv1 = self.expansive_block(32 * 2, o_ch, 3, 1)

    def __call__(self, x):
        conv1 = self.conv1(x)
        conv2 = self.conv2(conv1)
        conv3 = self.conv3(conv2)
        upconv3 = self.upconv3(conv3)
        upconv2 = self.upconv2(torch.cat([upconv3, conv2], 1))
        upconv1 = self.upconv1(torch.cat([upconv2, conv1], 1))
        return upconv1

    def expansive_block(self, i_ch, o_ch, filter_size, padding):
        block = nn.Sequential(torch.nn.Conv2d(i_ch, o_ch, kernel_size=
            filter_size, stride=1, padding=padding), torch.nn.BatchNorm2d(
            o_ch), torch.nn.ReLU(), torch.nn.Conv2d(o_ch, o_ch, kernel_size
            =filter_size, stride=1, padding=padding), torch.nn.BatchNorm2d(
            o_ch), torch.nn.ReLU(), torch.nn.ConvTranspose2d(o_ch, o_ch,
            kernel_size=3, stride=2, padding=1, output_padding=1))
        return block

    def contraction_block(self, i_ch, o_ch, filter_size, padding):
        block = nn.Sequential(torch.nn.Conv2d(i_ch, o_ch, kernel_size=
            filter_size, stride=1, padding=padding), torch.nn.BatchNorm2d(
            o_ch), torch.nn.ReLU(), torch.nn.Conv2d(o_ch, o_ch, kernel_size
            =filter_size, stride=1, padding=padding), torch.nn.BatchNorm2d(
            o_ch), torch.nn.ReLU(), torch.nn.MaxPool2d(kernel_size=3,
            stride=2, padding=1))
        return block
