import os, random, time, copy
from skimage import io, transform
import numpy as np
import os.path as path
import scipy.io as sio
import matplotlib.pyplot as plt

import torch
from torch.utils.data import Dataset, DataLoader
import torch.nn as nn
import torch.optim as optim
from torch.optim import lr_scheduler 
import torch.nn.functional as F
from torch.autograd import Variable

import torchvision
from torchvision import datasets, models, transforms
from torchvision.models import *
    
    
    
    
def conv3x3(in_planes, out_planes, stride=1):
    """3x3 convolution with padding"""
    return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride,
                     padding=1, bias=False)


def conv1x1(in_planes, out_planes, stride=1):
    """1x1 convolution"""
    return nn.Conv2d(in_planes, out_planes, kernel_size=1, stride=stride, bias=False)


class BasicBlock(nn.Module):
    expansion = 1

    def __init__(self, inplanes, planes, stride=1, downsample=None):
        super(BasicBlock, self).__init__()
        self.conv1 = conv3x3(inplanes, planes, stride)
        self.bn1 = nn.BatchNorm2d(planes)
        self.relu = nn.ReLU(inplace=True)
        self.conv2 = conv3x3(planes, planes)
        self.bn2 = nn.BatchNorm2d(planes)
        self.downsample = downsample
        self.stride = stride

    def forward(self, x):
        identity = x

        out = self.conv1(x)
        out = self.bn1(out)
        out = self.relu(out)

        out = self.conv2(out)
        out = self.bn2(out)

        if self.downsample is not None:
            identity = self.downsample(x)

        out += identity
        out = self.relu(out)

        return out


class Bottleneck(nn.Module):
    expansion = 4

    def __init__(self, inplanes, planes, stride=1, downsample=None):
        super(Bottleneck, self).__init__()
        self.conv1 = conv1x1(inplanes, planes)
        self.bn1 = nn.BatchNorm2d(planes)
        self.conv2 = conv3x3(planes, planes, stride)
        self.bn2 = nn.BatchNorm2d(planes)
        self.conv3 = conv1x1(planes, planes * self.expansion)
        self.bn3 = nn.BatchNorm2d(planes * self.expansion)
        self.relu = nn.ReLU(inplace=True)
        self.downsample = downsample
        self.stride = stride

    def forward(self, x):
        identity = x

        out = self.conv1(x)
        out = self.bn1(out)
        out = self.relu(out)

        out = self.conv2(out)
        out = self.bn2(out)
        out = self.relu(out)

        out = self.conv3(out)
        out = self.bn3(out)

        if self.downsample is not None:
            identity = self.downsample(x)

        out += identity
        out = self.relu(out)

        return out

    
    
    
    
class ResNet4MultigridPFF(nn.Module):
    def __init__(self, block, layers, num_classes=10, zero_init_residual=False):
        super(ResNet4MultigridPFF, self).__init__()
        self.inplanes = 64
        self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3,
                               bias=False)
        self.bn1 = nn.BatchNorm2d(64)
        self.relu = nn.ReLU(inplace=True)
        self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
        self.layer1 = self._make_layer(block, 32, layers[0])
        self.layer2 = self._make_layer(block, 64, layers[1], stride=2)
        self.layer3 = self._make_layer(block, 128, layers[2], stride=2)
        self.layer4 = self._make_layer(block, 196, layers[3], stride=2)
        self.avgpool = nn.AdaptiveAvgPool2d((1, 1))
        self.fc = nn.Linear(196 * block.expansion, num_classes)

        for m in self.modules():
            if isinstance(m, nn.Conv2d):
                nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')
            elif isinstance(m, nn.BatchNorm2d):
                nn.init.constant_(m.weight, 1)
                nn.init.constant_(m.bias, 0)

        # Zero-initialize the last BN in each residual branch,
        # so that the residual branch starts with zeros, and each residual block behaves like an identity.
        # This improves the model by 0.2~0.3% according to https://arxiv.org/abs/1706.02677
        if zero_init_residual:
            for m in self.modules():
                if isinstance(m, Bottleneck):
                    nn.init.constant_(m.bn3.weight, 0)
                elif isinstance(m, BasicBlock):
                    nn.init.constant_(m.bn2.weight, 0)

    def _make_layer(self, block, planes, blocks, stride=1):
        downsample = None
        if stride != 1 or self.inplanes != planes * block.expansion:
            downsample = nn.Sequential(
                conv1x1(self.inplanes, planes * block.expansion, stride),
                nn.BatchNorm2d(planes * block.expansion),
            )

        layers = []
        layers.append(block(self.inplanes, planes, stride, downsample))
        self.inplanes = planes * block.expansion
        for _ in range(1, blocks):
            layers.append(block(self.inplanes, planes))

        return nn.Sequential(*layers)

    def forward(self, x):
        x = self.conv1(x)
        x = self.bn1(x)
        x = self.relu(x)
        x = self.maxpool(x)

        x = self.layer1(x)
        x = self.layer2(x)
        x = self.layer3(x)
        x = self.layer4(x)

        x = self.avgpool(x)
        x = x.view(x.size(0), -1)
        x = self.fc(x)

        return x


def compactResNet18forMultigridPFF(**kwargs):
    """Constructs a ResNet-18 model.
    Args:
        pretrained (bool): If True, returns a model pre-trained on ImageNet
    """
    model = ResNet4MultigridPFF(BasicBlock, [2, 2, 2, 2], **kwargs)
    return model




    
    
    
class MultigridPFF_tiny(nn.Module):
    def __init__(self, emb_dimension=128):
        super(MultigridPFF_tiny, self).__init__()
        self.emb_dimension = emb_dimension        
        
        resnet = compactResNet18forMultigridPFF()
        self.layer0 = nn.Sequential(resnet.conv1, resnet.bn1, resnet.relu, resnet.maxpool)
        self.layer1, self.layer2, self.layer3 = resnet.layer1, resnet.layer2, resnet.layer3
        
                
        # tweak resnet backbone to output features 8x smaller than input image
        for n, m in self.layer3.named_modules():
            if 'conv1' in n:
                m.dilation, m.padding, m.stride = (2, 2), (2, 2), (1, 1)
            elif 'conv2' in n:
                m.dilation, m.padding, m.stride = (2, 2), (2, 2), (1, 1)
            elif 'downsample.0' in n:
                m.stride = (1, 1)
        
        self.streamTwo_feats = nn.Sequential(
            nn.Conv2d(3, 16, kernel_size=3, padding=1, bias=False),
            nn.BatchNorm2d(16, momentum=0.001),
            nn.ReLU(True),
            
            nn.Conv2d(16, 16, kernel_size=3, padding=2, dilation=2, bias=False),
            nn.BatchNorm2d(16, momentum=0.001),
            nn.ReLU(True),
            
            nn.Conv2d(16, 16, kernel_size=3, padding=1, bias=False),
            nn.BatchNorm2d(16, momentum=0.001),
            nn.ReLU(True),
            
            nn.Conv2d(16, 16, kernel_size=3, padding=2, dilation=2, bias=False),
            nn.BatchNorm2d(16, momentum=0.001),
            nn.ReLU(True),
            
            nn.Conv2d(16, 16, kernel_size=3, padding=1, bias=False),
            nn.BatchNorm2d(16, momentum=0.001),
            nn.ReLU(True),
        )
        
                
        
        
                
        self.layer1_dimRed = nn.Sequential(
            nn.Conv2d(resnet.layer1[-1].conv2.out_channels, int(emb_dimension/2), 
                      kernel_size=1, stride=1, padding=0, dilation=1, bias=False),
            nn.BatchNorm2d(int(emb_dimension/2), momentum=0.001),
            nn.ReLU(True),
        )
        self.layer1_feats = nn.Sequential(
            nn.Conv2d(int(emb_dimension/2), int(emb_dimension/2), kernel_size=3, padding=1, bias=False),
            nn.BatchNorm2d(int(emb_dimension/2), momentum=0.001),
            nn.ReLU(True),
            
            nn.Conv2d(int(emb_dimension/2), int(emb_dimension/2), kernel_size=3, padding=1, bias=False),
            nn.BatchNorm2d(int(emb_dimension/2), momentum=0.001),
            nn.ReLU(True),
        )
                        
                
        self.layer2_dimRed = nn.Sequential(
            nn.Conv2d(resnet.layer2[-1].conv2.out_channels, int(emb_dimension/2), 
                      kernel_size=1, stride=1, padding=0, dilation=1, bias=False),
            nn.BatchNorm2d(int(emb_dimension/2), momentum=0.001),
            nn.ReLU(True),
        )
        self.layer2_feats = nn.Sequential(
            nn.Conv2d(int(emb_dimension/2), int(emb_dimension/2), kernel_size=3, padding=1, bias=False),
            nn.BatchNorm2d(int(emb_dimension/2), momentum=0.001),
            nn.ReLU(True),
            
            nn.Conv2d(int(emb_dimension/2), int(emb_dimension/2), kernel_size=3, padding=1, bias=False),
            nn.BatchNorm2d(int(emb_dimension/2), momentum=0.001),
            nn.ReLU(True),
        )
                

        self.layer3_dimRed = nn.Sequential(
            nn.Conv2d(resnet.layer3[-1].conv2.out_channels, int(emb_dimension/2), 
                      kernel_size=1, stride=1, padding=0, dilation=1, bias=False),
            nn.BatchNorm2d(int(emb_dimension/2), momentum=0.001),
            nn.ReLU(True),
        )
        self.layer3_feats = nn.Sequential(
            nn.Conv2d(int(emb_dimension/2), int(emb_dimension/2), kernel_size=3, padding=1, bias=False),
            nn.BatchNorm2d(int(emb_dimension/2), momentum=0.001),
            nn.ReLU(True),
            
            nn.Conv2d(int(emb_dimension/2), int(emb_dimension/2), kernel_size=3, padding=1, bias=False),
            nn.BatchNorm2d(int(emb_dimension/2), momentum=0.001),
            nn.ReLU(True),            
        )
                
        self.emb = nn.Sequential(
            nn.Conv2d(int(emb_dimension*1.5+16), emb_dimension, kernel_size=3, padding=1, bias=False),
            nn.BatchNorm2d(emb_dimension, momentum=0.001),
            nn.ReLU(True),
            
            nn.Conv2d(emb_dimension, emb_dimension, kernel_size=3, padding=1, bias=True),
            nn.BatchNorm2d(emb_dimension, momentum=0.001),
            nn.ReLU(True),
        )
        
        
    def forward(self, inputs):
        self.interp_x4 = nn.Upsample(scale_factor=4, mode='bilinear', align_corners=True)  
        self.interp_x8 = nn.Upsample(scale_factor=8, mode='bilinear', align_corners=True)  
        
        input_size = inputs.size()
        
        out = self.layer0(inputs)
        out_stream2 = self.streamTwo_feats(inputs)        
        
        out = self.layer1(out)        
        out_layer1 = self.layer1_dimRed(out)        
        out_layer1 = self.interp_x4(out_layer1)
        out_layer1 = self.layer2_feats(out_layer1)
        
        out = self.layer2(out)        
        out_layer2 = self.layer2_dimRed(out)        
        out_layer2 = self.interp_x8(out_layer2)
        out_layer2 = self.layer2_feats(out_layer2)
        
        
        out = self.layer3(out)        
        out_layer3 = self.layer3_dimRed(out)        
        out_layer3 = self.interp_x8(out_layer3)
        out_layer3 = self.layer3_feats(out_layer3)        

        out = torch.cat([out_stream2, out_layer1, out_layer2, out_layer3], 1)
        out = self.emb(out)
                
        return out
16100321c7374ed67a23e179bc2fdc40495691d7
<mask token>


class Bottleneck(nn.Module):
    <mask token>
    <mask token>
    <mask token>


class ResNet4MultigridPFF(nn.Module):

    def __init__(self, block, layers, num_classes=10, zero_init_residual=False
        ):
        super(ResNet4MultigridPFF, self).__init__()
        self.inplanes = 64
        self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3,
            bias=False)
        self.bn1 = nn.BatchNorm2d(64)
        self.relu = nn.ReLU(inplace=True)
        self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
        self.layer1 = self._make_layer(block, 32, layers[0])
        self.layer2 = self._make_layer(block, 64, layers[1], stride=2)
        self.layer3 = self._make_layer(block, 128, layers[2], stride=2)
        self.layer4 = self._make_layer(block, 196, layers[3], stride=2)
        self.avgpool = nn.AdaptiveAvgPool2d((1, 1))
        self.fc = nn.Linear(196 * block.expansion, num_classes)
        for m in self.modules():
            if isinstance(m, nn.Conv2d):
                nn.init.kaiming_normal_(m.weight, mode='fan_out',
                    nonlinearity='relu')
            elif isinstance(m, nn.BatchNorm2d):
                nn.init.constant_(m.weight, 1)
                nn.init.constant_(m.bias, 0)
        if zero_init_residual:
            for m in self.modules():
                if isinstance(m, Bottleneck):
                    nn.init.constant_(m.bn3.weight, 0)
                elif isinstance(m, BasicBlock):
                    nn.init.constant_(m.bn2.weight, 0)

    def _make_layer(self, block, planes, blocks, stride=1):
        downsample = None
        if stride != 1 or self.inplanes != planes * block.expansion:
            downsample = nn.Sequential(conv1x1(self.inplanes, planes *
                block.expansion, stride), nn.BatchNorm2d(planes * block.
                expansion))
        layers = []
        layers.append(block(self.inplanes, planes, stride, downsample))
        self.inplanes = planes * block.expansion
        for _ in range(1, blocks):
            layers.append(block(self.inplanes, planes))
        return nn.Sequential(*layers)

    def forward(self, x):
        x = self.conv1(x)
        x = self.bn1(x)
        x = self.relu(x)
        x = self.maxpool(x)
        x = self.layer1(x)
        x = self.layer2(x)
        x = self.layer3(x)
        x = self.layer4(x)
        x = self.avgpool(x)
        x = x.view(x.size(0), -1)
        x = self.fc(x)
        return x


<mask token>


class MultigridPFF_tiny(nn.Module):

    def __init__(self, emb_dimension=128):
        super(MultigridPFF_tiny, self).__init__()
        self.emb_dimension = emb_dimension
        resnet = compactResNet18forMultigridPFF()
        self.layer0 = nn.Sequential(resnet.conv1, resnet.bn1, resnet.relu,
            resnet.maxpool)
        self.layer1, self.layer2, self.layer3 = (resnet.layer1, resnet.
            layer2, resnet.layer3)
        for n, m in self.layer3.named_modules():
            if 'conv1' in n:
                m.dilation, m.padding, m.stride = (2, 2), (2, 2), (1, 1)
            elif 'conv2' in n:
                m.dilation, m.padding, m.stride = (2, 2), (2, 2), (1, 1)
            elif 'downsample.0' in n:
                m.stride = 1, 1
        self.streamTwo_feats = nn.Sequential(nn.Conv2d(3, 16, kernel_size=3,
            padding=1, bias=False), nn.BatchNorm2d(16, momentum=0.001), nn.
            ReLU(True), nn.Conv2d(16, 16, kernel_size=3, padding=2,
            dilation=2, bias=False), nn.BatchNorm2d(16, momentum=0.001), nn
            .ReLU(True), nn.Conv2d(16, 16, kernel_size=3, padding=1, bias=
            False), nn.BatchNorm2d(16, momentum=0.001), nn.ReLU(True), nn.
            Conv2d(16, 16, kernel_size=3, padding=2, dilation=2, bias=False
            ), nn.BatchNorm2d(16, momentum=0.001), nn.ReLU(True), nn.Conv2d
            (16, 16, kernel_size=3, padding=1, bias=False), nn.BatchNorm2d(
            16, momentum=0.001), nn.ReLU(True))
        self.layer1_dimRed = nn.Sequential(nn.Conv2d(resnet.layer1[-1].
            conv2.out_channels, int(emb_dimension / 2), kernel_size=1,
            stride=1, padding=0, dilation=1, bias=False), nn.BatchNorm2d(
            int(emb_dimension / 2), momentum=0.001), nn.ReLU(True))
        self.layer1_feats = nn.Sequential(nn.Conv2d(int(emb_dimension / 2),
            int(emb_dimension / 2), kernel_size=3, padding=1, bias=False),
            nn.BatchNorm2d(int(emb_dimension / 2), momentum=0.001), nn.ReLU
            (True), nn.Conv2d(int(emb_dimension / 2), int(emb_dimension / 2
            ), kernel_size=3, padding=1, bias=False), nn.BatchNorm2d(int(
            emb_dimension / 2), momentum=0.001), nn.ReLU(True))
        self.layer2_dimRed = nn.Sequential(nn.Conv2d(resnet.layer2[-1].
            conv2.out_channels, int(emb_dimension / 2), kernel_size=1,
            stride=1, padding=0, dilation=1, bias=False), nn.BatchNorm2d(
            int(emb_dimension / 2), momentum=0.001), nn.ReLU(True))
        self.layer2_feats = nn.Sequential(nn.Conv2d(int(emb_dimension / 2),
            int(emb_dimension / 2), kernel_size=3, padding=1, bias=False),
            nn.BatchNorm2d(int(emb_dimension / 2), momentum=0.001), nn.ReLU
            (True), nn.Conv2d(int(emb_dimension / 2), int(emb_dimension / 2
            ), kernel_size=3, padding=1, bias=False), nn.BatchNorm2d(int(
            emb_dimension / 2), momentum=0.001), nn.ReLU(True))
        self.layer3_dimRed = nn.Sequential(nn.Conv2d(resnet.layer3[-1].
            conv2.out_channels, int(emb_dimension / 2), kernel_size=1,
            stride=1, padding=0, dilation=1, bias=False), nn.BatchNorm2d(
            int(emb_dimension / 2), momentum=0.001), nn.ReLU(True))
        self.layer3_feats = nn.Sequential(nn.Conv2d(int(emb_dimension / 2),
            int(emb_dimension / 2), kernel_size=3, padding=1, bias=False),
            nn.BatchNorm2d(int(emb_dimension / 2), momentum=0.001), nn.ReLU
            (True), nn.Conv2d(int(emb_dimension / 2), int(emb_dimension / 2
            ), kernel_size=3, padding=1, bias=False), nn.BatchNorm2d(int(
            emb_dimension / 2), momentum=0.001), nn.ReLU(True))
        self.emb = nn.Sequential(nn.Conv2d(int(emb_dimension * 1.5 + 16),
            emb_dimension, kernel_size=3, padding=1, bias=False), nn.
            BatchNorm2d(emb_dimension, momentum=0.001), nn.ReLU(True), nn.
            Conv2d(emb_dimension, emb_dimension, kernel_size=3, padding=1,
            bias=True), nn.BatchNorm2d(emb_dimension, momentum=0.001), nn.
            ReLU(True))

    def forward(self, inputs):
        self.interp_x4 = nn.Upsample(scale_factor=4, mode='bilinear',
            align_corners=True)
        self.interp_x8 = nn.Upsample(scale_factor=8, mode='bilinear',
            align_corners=True)
        input_size = inputs.size()
        out = self.layer0(inputs)
        out_stream2 = self.streamTwo_feats(inputs)
        out = self.layer1(out)
        out_layer1 = self.layer1_dimRed(out)
        out_layer1 = self.interp_x4(out_layer1)
        out_layer1 = self.layer2_feats(out_layer1)
        out = self.layer2(out)
        out_layer2 = self.layer2_dimRed(out)
        out_layer2 = self.interp_x8(out_layer2)
        out_layer2 = self.layer2_feats(out_layer2)
        out = self.layer3(out)
        out_layer3 = self.layer3_dimRed(out)
        out_layer3 = self.interp_x8(out_layer3)
        out_layer3 = self.layer3_feats(out_layer3)
        out = torch.cat([out_stream2, out_layer1, out_layer2, out_layer3], 1)
        out = self.emb(out)
        return out

<mask token>


class BasicBlock(nn.Module):
    <mask token>

    def __init__(self, inplanes, planes, stride=1, downsample=None):
        super(BasicBlock, self).__init__()
        self.conv1 = conv3x3(inplanes, planes, stride)
        self.bn1 = nn.BatchNorm2d(planes)
        self.relu = nn.ReLU(inplace=True)
        self.conv2 = conv3x3(planes, planes)
        self.bn2 = nn.BatchNorm2d(planes)
        self.downsample = downsample
        self.stride = stride
    <mask token>


class Bottleneck(nn.Module):
    expansion = 4

    def __init__(self, inplanes, planes, stride=1, downsample=None):
        super(Bottleneck, self).__init__()
        self.conv1 = conv1x1(inplanes, planes)
        self.bn1 = nn.BatchNorm2d(planes)
        self.conv2 = conv3x3(planes, planes, stride)
        self.bn2 = nn.BatchNorm2d(planes)
        self.conv3 = conv1x1(planes, planes * self.expansion)
        self.bn3 = nn.BatchNorm2d(planes * self.expansion)
        self.relu = nn.ReLU(inplace=True)
        self.downsample = downsample
        self.stride = stride

    def forward(self, x):
        identity = x
        out = self.conv1(x)
        out = self.bn1(out)
        out = self.relu(out)
        out = self.conv2(out)
        out = self.bn2(out)
        out = self.relu(out)
        out = self.conv3(out)
        out = self.bn3(out)
        if self.downsample is not None:
            identity = self.downsample(x)
        out += identity
        out = self.relu(out)
        return out


class ResNet4MultigridPFF(nn.Module):

    def __init__(self, block, layers, num_classes=10, zero_init_residual=False
        ):
        super(ResNet4MultigridPFF, self).__init__()
        self.inplanes = 64
        self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3,
            bias=False)
        self.bn1 = nn.BatchNorm2d(64)
        self.relu = nn.ReLU(inplace=True)
        self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
        self.layer1 = self._make_layer(block, 32, layers[0])
        self.layer2 = self._make_layer(block, 64, layers[1], stride=2)
        self.layer3 = self._make_layer(block, 128, layers[2], stride=2)
        self.layer4 = self._make_layer(block, 196, layers[3], stride=2)
        self.avgpool = nn.AdaptiveAvgPool2d((1, 1))
        self.fc = nn.Linear(196 * block.expansion, num_classes)
        for m in self.modules():
            if isinstance(m, nn.Conv2d):
                nn.init.kaiming_normal_(m.weight, mode='fan_out',
                    nonlinearity='relu')
            elif isinstance(m, nn.BatchNorm2d):
                nn.init.constant_(m.weight, 1)
                nn.init.constant_(m.bias, 0)
        if zero_init_residual:
            for m in self.modules():
                if isinstance(m, Bottleneck):
                    nn.init.constant_(m.bn3.weight, 0)
                elif isinstance(m, BasicBlock):
                    nn.init.constant_(m.bn2.weight, 0)

    def _make_layer(self, block, planes, blocks, stride=1):
        downsample = None
        if stride != 1 or self.inplanes != planes * block.expansion:
            downsample = nn.Sequential(conv1x1(self.inplanes, planes *
                block.expansion, stride), nn.BatchNorm2d(planes * block.
                expansion))
        layers = []
        layers.append(block(self.inplanes, planes, stride, downsample))
        self.inplanes = planes * block.expansion
        for _ in range(1, blocks):
            layers.append(block(self.inplanes, planes))
        return nn.Sequential(*layers)

    def forward(self, x):
        x = self.conv1(x)
        x = self.bn1(x)
        x = self.relu(x)
        x = self.maxpool(x)
        x = self.layer1(x)
        x = self.layer2(x)
        x = self.layer3(x)
        x = self.layer4(x)
        x = self.avgpool(x)
        x = x.view(x.size(0), -1)
        x = self.fc(x)
        return x


<mask token>


class MultigridPFF_tiny(nn.Module):

    def __init__(self, emb_dimension=128):
        super(MultigridPFF_tiny, self).__init__()
        self.emb_dimension = emb_dimension
        resnet = compactResNet18forMultigridPFF()
        self.layer0 = nn.Sequential(resnet.conv1, resnet.bn1, resnet.relu,
            resnet.maxpool)
        self.layer1, self.layer2, self.layer3 = (resnet.layer1, resnet.
            layer2, resnet.layer3)
        for n, m in self.layer3.named_modules():
            if 'conv1' in n:
                m.dilation, m.padding, m.stride = (2, 2), (2, 2), (1, 1)
            elif 'conv2' in n:
                m.dilation, m.padding, m.stride = (2, 2), (2, 2), (1, 1)
            elif 'downsample.0' in n:
                m.stride = 1, 1
        self.streamTwo_feats = nn.Sequential(nn.Conv2d(3, 16, kernel_size=3,
            padding=1, bias=False), nn.BatchNorm2d(16, momentum=0.001), nn.
            ReLU(True), nn.Conv2d(16, 16, kernel_size=3, padding=2,
            dilation=2, bias=False), nn.BatchNorm2d(16, momentum=0.001), nn
            .ReLU(True), nn.Conv2d(16, 16, kernel_size=3, padding=1, bias=
            False), nn.BatchNorm2d(16, momentum=0.001), nn.ReLU(True), nn.
            Conv2d(16, 16, kernel_size=3, padding=2, dilation=2, bias=False
            ), nn.BatchNorm2d(16, momentum=0.001), nn.ReLU(True), nn.Conv2d
            (16, 16, kernel_size=3, padding=1, bias=False), nn.BatchNorm2d(
            16, momentum=0.001), nn.ReLU(True))
        self.layer1_dimRed = nn.Sequential(nn.Conv2d(resnet.layer1[-1].
            conv2.out_channels, int(emb_dimension / 2), kernel_size=1,
            stride=1, padding=0, dilation=1, bias=False), nn.BatchNorm2d(
            int(emb_dimension / 2), momentum=0.001), nn.ReLU(True))
        self.layer1_feats = nn.Sequential(nn.Conv2d(int(emb_dimension / 2),
            int(emb_dimension / 2), kernel_size=3, padding=1, bias=False),
            nn.BatchNorm2d(int(emb_dimension / 2), momentum=0.001), nn.ReLU
            (True), nn.Conv2d(int(emb_dimension / 2), int(emb_dimension / 2
            ), kernel_size=3, padding=1, bias=False), nn.BatchNorm2d(int(
            emb_dimension / 2), momentum=0.001), nn.ReLU(True))
        self.layer2_dimRed = nn.Sequential(nn.Conv2d(resnet.layer2[-1].
            conv2.out_channels, int(emb_dimension / 2), kernel_size=1,
            stride=1, padding=0, dilation=1, bias=False), nn.BatchNorm2d(
            int(emb_dimension / 2), momentum=0.001), nn.ReLU(True))
        self.layer2_feats = nn.Sequential(nn.Conv2d(int(emb_dimension / 2),
            int(emb_dimension / 2), kernel_size=3, padding=1, bias=False),
            nn.BatchNorm2d(int(emb_dimension / 2), momentum=0.001), nn.ReLU
            (True), nn.Conv2d(int(emb_dimension / 2), int(emb_dimension / 2
            ), kernel_size=3, padding=1, bias=False), nn.BatchNorm2d(int(
            emb_dimension / 2), momentum=0.001), nn.ReLU(True))
        self.layer3_dimRed = nn.Sequential(nn.Conv2d(resnet.layer3[-1].
            conv2.out_channels, int(emb_dimension / 2), kernel_size=1,
            stride=1, padding=0, dilation=1, bias=False), nn.BatchNorm2d(
            int(emb_dimension / 2), momentum=0.001), nn.ReLU(True))
        self.layer3_feats = nn.Sequential(nn.Conv2d(int(emb_dimension / 2),
            int(emb_dimension / 2), kernel_size=3, padding=1, bias=False),
            nn.BatchNorm2d(int(emb_dimension / 2), momentum=0.001), nn.ReLU
            (True), nn.Conv2d(int(emb_dimension / 2), int(emb_dimension / 2
            ), kernel_size=3, padding=1, bias=False), nn.BatchNorm2d(int(
            emb_dimension / 2), momentum=0.001), nn.ReLU(True))
        self.emb = nn.Sequential(nn.Conv2d(int(emb_dimension * 1.5 + 16),
            emb_dimension, kernel_size=3, padding=1, bias=False), nn.
            BatchNorm2d(emb_dimension, momentum=0.001), nn.ReLU(True), nn.
            Conv2d(emb_dimension, emb_dimension, kernel_size=3, padding=1,
            bias=True), nn.BatchNorm2d(emb_dimension, momentum=0.001), nn.
            ReLU(True))

    def forward(self, inputs):
        self.interp_x4 = nn.Upsample(scale_factor=4, mode='bilinear',
            align_corners=True)
        self.interp_x8 = nn.Upsample(scale_factor=8, mode='bilinear',
            align_corners=True)
        input_size = inputs.size()
        out = self.layer0(inputs)
        out_stream2 = self.streamTwo_feats(inputs)
        out = self.layer1(out)
        out_layer1 = self.layer1_dimRed(out)
        out_layer1 = self.interp_x4(out_layer1)
        out_layer1 = self.layer2_feats(out_layer1)
        out = self.layer2(out)
        out_layer2 = self.layer2_dimRed(out)
        out_layer2 = self.interp_x8(out_layer2)
        out_layer2 = self.layer2_feats(out_layer2)
        out = self.layer3(out)
        out_layer3 = self.layer3_dimRed(out)
        out_layer3 = self.interp_x8(out_layer3)
        out_layer3 = self.layer3_feats(out_layer3)
        out = torch.cat([out_stream2, out_layer1, out_layer2, out_layer3], 1)
        out = self.emb(out)
        return out

<mask token>


class BasicBlock(nn.Module):
    <mask token>

    def __init__(self, inplanes, planes, stride=1, downsample=None):
        super(BasicBlock, self).__init__()
        self.conv1 = conv3x3(inplanes, planes, stride)
        self.bn1 = nn.BatchNorm2d(planes)
        self.relu = nn.ReLU(inplace=True)
        self.conv2 = conv3x3(planes, planes)
        self.bn2 = nn.BatchNorm2d(planes)
        self.downsample = downsample
        self.stride = stride

    def forward(self, x):
        identity = x
        out = self.conv1(x)
        out = self.bn1(out)
        out = self.relu(out)
        out = self.conv2(out)
        out = self.bn2(out)
        if self.downsample is not None:
            identity = self.downsample(x)
        out += identity
        out = self.relu(out)
        return out


class Bottleneck(nn.Module):
    expansion = 4

    def __init__(self, inplanes, planes, stride=1, downsample=None):
        super(Bottleneck, self).__init__()
        self.conv1 = conv1x1(inplanes, planes)
        self.bn1 = nn.BatchNorm2d(planes)
        self.conv2 = conv3x3(planes, planes, stride)
        self.bn2 = nn.BatchNorm2d(planes)
        self.conv3 = conv1x1(planes, planes * self.expansion)
        self.bn3 = nn.BatchNorm2d(planes * self.expansion)
        self.relu = nn.ReLU(inplace=True)
        self.downsample = downsample
        self.stride = stride

    def forward(self, x):
        identity = x
        out = self.conv1(x)
        out = self.bn1(out)
        out = self.relu(out)
        out = self.conv2(out)
        out = self.bn2(out)
        out = self.relu(out)
        out = self.conv3(out)
        out = self.bn3(out)
        if self.downsample is not None:
            identity = self.downsample(x)
        out += identity
        out = self.relu(out)
        return out


class ResNet4MultigridPFF(nn.Module):

    def __init__(self, block, layers, num_classes=10, zero_init_residual=False
        ):
        super(ResNet4MultigridPFF, self).__init__()
        self.inplanes = 64
        self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3,
            bias=False)
        self.bn1 = nn.BatchNorm2d(64)
        self.relu = nn.ReLU(inplace=True)
        self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
        self.layer1 = self._make_layer(block, 32, layers[0])
        self.layer2 = self._make_layer(block, 64, layers[1], stride=2)
        self.layer3 = self._make_layer(block, 128, layers[2], stride=2)
        self.layer4 = self._make_layer(block, 196, layers[3], stride=2)
        self.avgpool = nn.AdaptiveAvgPool2d((1, 1))
        self.fc = nn.Linear(196 * block.expansion, num_classes)
        for m in self.modules():
            if isinstance(m, nn.Conv2d):
                nn.init.kaiming_normal_(m.weight, mode='fan_out',
                    nonlinearity='relu')
            elif isinstance(m, nn.BatchNorm2d):
                nn.init.constant_(m.weight, 1)
                nn.init.constant_(m.bias, 0)
        if zero_init_residual:
            for m in self.modules():
                if isinstance(m, Bottleneck):
                    nn.init.constant_(m.bn3.weight, 0)
                elif isinstance(m, BasicBlock):
                    nn.init.constant_(m.bn2.weight, 0)

    def _make_layer(self, block, planes, blocks, stride=1):
        downsample = None
        if stride != 1 or self.inplanes != planes * block.expansion:
            downsample = nn.Sequential(conv1x1(self.inplanes, planes *
                block.expansion, stride), nn.BatchNorm2d(planes * block.
                expansion))
        layers = []
        layers.append(block(self.inplanes, planes, stride, downsample))
        self.inplanes = planes * block.expansion
        for _ in range(1, blocks):
            layers.append(block(self.inplanes, planes))
        return nn.Sequential(*layers)

    def forward(self, x):
        x = self.conv1(x)
        x = self.bn1(x)
        x = self.relu(x)
        x = self.maxpool(x)
        x = self.layer1(x)
        x = self.layer2(x)
        x = self.layer3(x)
        x = self.layer4(x)
        x = self.avgpool(x)
        x = x.view(x.size(0), -1)
        x = self.fc(x)
        return x


<mask token>


class MultigridPFF_tiny(nn.Module):

    def __init__(self, emb_dimension=128):
        super(MultigridPFF_tiny, self).__init__()
        self.emb_dimension = emb_dimension
        resnet = compactResNet18forMultigridPFF()
        self.layer0 = nn.Sequential(resnet.conv1, resnet.bn1, resnet.relu,
            resnet.maxpool)
        self.layer1, self.layer2, self.layer3 = (resnet.layer1, resnet.
            layer2, resnet.layer3)
        for n, m in self.layer3.named_modules():
            if 'conv1' in n:
                m.dilation, m.padding, m.stride = (2, 2), (2, 2), (1, 1)
            elif 'conv2' in n:
                m.dilation, m.padding, m.stride = (2, 2), (2, 2), (1, 1)
            elif 'downsample.0' in n:
                m.stride = 1, 1
        self.streamTwo_feats = nn.Sequential(nn.Conv2d(3, 16, kernel_size=3,
            padding=1, bias=False), nn.BatchNorm2d(16, momentum=0.001), nn.
            ReLU(True), nn.Conv2d(16, 16, kernel_size=3, padding=2,
            dilation=2, bias=False), nn.BatchNorm2d(16, momentum=0.001), nn
            .ReLU(True), nn.Conv2d(16, 16, kernel_size=3, padding=1, bias=
            False), nn.BatchNorm2d(16, momentum=0.001), nn.ReLU(True), nn.
            Conv2d(16, 16, kernel_size=3, padding=2, dilation=2, bias=False
            ), nn.BatchNorm2d(16, momentum=0.001), nn.ReLU(True), nn.Conv2d
            (16, 16, kernel_size=3, padding=1, bias=False), nn.BatchNorm2d(
            16, momentum=0.001), nn.ReLU(True))
        self.layer1_dimRed = nn.Sequential(nn.Conv2d(resnet.layer1[-1].
            conv2.out_channels, int(emb_dimension / 2), kernel_size=1,
            stride=1, padding=0, dilation=1, bias=False), nn.BatchNorm2d(
            int(emb_dimension / 2), momentum=0.001), nn.ReLU(True))
        self.layer1_feats = nn.Sequential(nn.Conv2d(int(emb_dimension / 2),
            int(emb_dimension / 2), kernel_size=3, padding=1, bias=False),
            nn.BatchNorm2d(int(emb_dimension / 2), momentum=0.001), nn.ReLU
            (True), nn.Conv2d(int(emb_dimension / 2), int(emb_dimension / 2
            ), kernel_size=3, padding=1, bias=False), nn.BatchNorm2d(int(
            emb_dimension / 2), momentum=0.001), nn.ReLU(True))
        self.layer2_dimRed = nn.Sequential(nn.Conv2d(resnet.layer2[-1].
            conv2.out_channels, int(emb_dimension / 2), kernel_size=1,
            stride=1, padding=0, dilation=1, bias=False), nn.BatchNorm2d(
            int(emb_dimension / 2), momentum=0.001), nn.ReLU(True))
        self.layer2_feats = nn.Sequential(nn.Conv2d(int(emb_dimension / 2),
            int(emb_dimension / 2), kernel_size=3, padding=1, bias=False),
            nn.BatchNorm2d(int(emb_dimension / 2), momentum=0.001), nn.ReLU
            (True), nn.Conv2d(int(emb_dimension / 2), int(emb_dimension / 2
            ), kernel_size=3, padding=1, bias=False), nn.BatchNorm2d(int(
            emb_dimension / 2), momentum=0.001), nn.ReLU(True))
        self.layer3_dimRed = nn.Sequential(nn.Conv2d(resnet.layer3[-1].
            conv2.out_channels, int(emb_dimension / 2), kernel_size=1,
            stride=1, padding=0, dilation=1, bias=False), nn.BatchNorm2d(
            int(emb_dimension / 2), momentum=0.001), nn.ReLU(True))
        self.layer3_feats = nn.Sequential(nn.Conv2d(int(emb_dimension / 2),
            int(emb_dimension / 2), kernel_size=3, padding=1, bias=False),
            nn.BatchNorm2d(int(emb_dimension / 2), momentum=0.001), nn.ReLU
            (True), nn.Conv2d(int(emb_dimension / 2), int(emb_dimension / 2
            ), kernel_size=3, padding=1, bias=False), nn.BatchNorm2d(int(
            emb_dimension / 2), momentum=0.001), nn.ReLU(True))
        self.emb = nn.Sequential(nn.Conv2d(int(emb_dimension * 1.5 + 16),
            emb_dimension, kernel_size=3, padding=1, bias=False), nn.
            BatchNorm2d(emb_dimension, momentum=0.001), nn.ReLU(True), nn.
            Conv2d(emb_dimension, emb_dimension, kernel_size=3, padding=1,
            bias=True), nn.BatchNorm2d(emb_dimension, momentum=0.001), nn.
            ReLU(True))

    def forward(self, inputs):
        self.interp_x4 = nn.Upsample(scale_factor=4, mode='bilinear',
            align_corners=True)
        self.interp_x8 = nn.Upsample(scale_factor=8, mode='bilinear',
            align_corners=True)
        input_size = inputs.size()
        out = self.layer0(inputs)
        out_stream2 = self.streamTwo_feats(inputs)
        out = self.layer1(out)
        out_layer1 = self.layer1_dimRed(out)
        out_layer1 = self.interp_x4(out_layer1)
        out_layer1 = self.layer2_feats(out_layer1)
        out = self.layer2(out)
        out_layer2 = self.layer2_dimRed(out)
        out_layer2 = self.interp_x8(out_layer2)
        out_layer2 = self.layer2_feats(out_layer2)
        out = self.layer3(out)
        out_layer3 = self.layer3_dimRed(out)
        out_layer3 = self.interp_x8(out_layer3)
        out_layer3 = self.layer3_feats(out_layer3)
        out = torch.cat([out_stream2, out_layer1, out_layer2, out_layer3], 1)
        out = self.emb(out)
        return out

<mask token>


def conv1x1(in_planes, out_planes, stride=1):
    """1x1 convolution"""
    return nn.Conv2d(in_planes, out_planes, kernel_size=1, stride=stride,
        bias=False)


class BasicBlock(nn.Module):
    expansion = 1

    def __init__(self, inplanes, planes, stride=1, downsample=None):
        super(BasicBlock, self).__init__()
        self.conv1 = conv3x3(inplanes, planes, stride)
        self.bn1 = nn.BatchNorm2d(planes)
        self.relu = nn.ReLU(inplace=True)
        self.conv2 = conv3x3(planes, planes)
        self.bn2 = nn.BatchNorm2d(planes)
        self.downsample = downsample
        self.stride = stride

    def forward(self, x):
        identity = x
        out = self.conv1(x)
        out = self.bn1(out)
        out = self.relu(out)
        out = self.conv2(out)
        out = self.bn2(out)
        if self.downsample is not None:
            identity = self.downsample(x)
        out += identity
        out = self.relu(out)
        return out


class Bottleneck(nn.Module):
    expansion = 4

    def __init__(self, inplanes, planes, stride=1, downsample=None):
        super(Bottleneck, self).__init__()
        self.conv1 = conv1x1(inplanes, planes)
        self.bn1 = nn.BatchNorm2d(planes)
        self.conv2 = conv3x3(planes, planes, stride)
        self.bn2 = nn.BatchNorm2d(planes)
        self.conv3 = conv1x1(planes, planes * self.expansion)
        self.bn3 = nn.BatchNorm2d(planes * self.expansion)
        self.relu = nn.ReLU(inplace=True)
        self.downsample = downsample
        self.stride = stride

    def forward(self, x):
        identity = x
        out = self.conv1(x)
        out = self.bn1(out)
        out = self.relu(out)
        out = self.conv2(out)
        out = self.bn2(out)
        out = self.relu(out)
        out = self.conv3(out)
        out = self.bn3(out)
        if self.downsample is not None:
            identity = self.downsample(x)
        out += identity
        out = self.relu(out)
        return out


class ResNet4MultigridPFF(nn.Module):

    def __init__(self, block, layers, num_classes=10, zero_init_residual=False
        ):
        super(ResNet4MultigridPFF, self).__init__()
        self.inplanes = 64
        self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3,
            bias=False)
        self.bn1 = nn.BatchNorm2d(64)
        self.relu = nn.ReLU(inplace=True)
        self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
        self.layer1 = self._make_layer(block, 32, layers[0])
        self.layer2 = self._make_layer(block, 64, layers[1], stride=2)
        self.layer3 = self._make_layer(block, 128, layers[2], stride=2)
        self.layer4 = self._make_layer(block, 196, layers[3], stride=2)
        self.avgpool = nn.AdaptiveAvgPool2d((1, 1))
        self.fc = nn.Linear(196 * block.expansion, num_classes)
        for m in self.modules():
            if isinstance(m, nn.Conv2d):
                nn.init.kaiming_normal_(m.weight, mode='fan_out',
                    nonlinearity='relu')
            elif isinstance(m, nn.BatchNorm2d):
                nn.init.constant_(m.weight, 1)
                nn.init.constant_(m.bias, 0)
        if zero_init_residual:
            for m in self.modules():
                if isinstance(m, Bottleneck):
                    nn.init.constant_(m.bn3.weight, 0)
                elif isinstance(m, BasicBlock):
                    nn.init.constant_(m.bn2.weight, 0)

    def _make_layer(self, block, planes, blocks, stride=1):
        downsample = None
        if stride != 1 or self.inplanes != planes * block.expansion:
            downsample = nn.Sequential(conv1x1(self.inplanes, planes *
                block.expansion, stride), nn.BatchNorm2d(planes * block.
                expansion))
        layers = []
        layers.append(block(self.inplanes, planes, stride, downsample))
        self.inplanes = planes * block.expansion
        for _ in range(1, blocks):
            layers.append(block(self.inplanes, planes))
        return nn.Sequential(*layers)

    def forward(self, x):
        x = self.conv1(x)
        x = self.bn1(x)
        x = self.relu(x)
        x = self.maxpool(x)
        x = self.layer1(x)
        x = self.layer2(x)
        x = self.layer3(x)
        x = self.layer4(x)
        x = self.avgpool(x)
        x = x.view(x.size(0), -1)
        x = self.fc(x)
        return x


def compactResNet18forMultigridPFF(**kwargs):
    """Constructs a ResNet-18 model.
    Args:
        pretrained (bool): If True, returns a model pre-trained on ImageNet
    """
    model = ResNet4MultigridPFF(BasicBlock, [2, 2, 2, 2], **kwargs)
    return model


class MultigridPFF_tiny(nn.Module):

    def __init__(self, emb_dimension=128):
        super(MultigridPFF_tiny, self).__init__()
        self.emb_dimension = emb_dimension
        resnet = compactResNet18forMultigridPFF()
        self.layer0 = nn.Sequential(resnet.conv1, resnet.bn1, resnet.relu,
            resnet.maxpool)
        self.layer1, self.layer2, self.layer3 = (resnet.layer1, resnet.
            layer2, resnet.layer3)
        for n, m in self.layer3.named_modules():
            if 'conv1' in n:
                m.dilation, m.padding, m.stride = (2, 2), (2, 2), (1, 1)
            elif 'conv2' in n:
                m.dilation, m.padding, m.stride = (2, 2), (2, 2), (1, 1)
            elif 'downsample.0' in n:
                m.stride = 1, 1
        self.streamTwo_feats = nn.Sequential(nn.Conv2d(3, 16, kernel_size=3,
            padding=1, bias=False), nn.BatchNorm2d(16, momentum=0.001), nn.
            ReLU(True), nn.Conv2d(16, 16, kernel_size=3, padding=2,
            dilation=2, bias=False), nn.BatchNorm2d(16, momentum=0.001), nn
            .ReLU(True), nn.Conv2d(16, 16, kernel_size=3, padding=1, bias=
            False), nn.BatchNorm2d(16, momentum=0.001), nn.ReLU(True), nn.
            Conv2d(16, 16, kernel_size=3, padding=2, dilation=2, bias=False
            ), nn.BatchNorm2d(16, momentum=0.001), nn.ReLU(True), nn.Conv2d
            (16, 16, kernel_size=3, padding=1, bias=False), nn.BatchNorm2d(
            16, momentum=0.001), nn.ReLU(True))
        self.layer1_dimRed = nn.Sequential(nn.Conv2d(resnet.layer1[-1].
            conv2.out_channels, int(emb_dimension / 2), kernel_size=1,
            stride=1, padding=0, dilation=1, bias=False), nn.BatchNorm2d(
            int(emb_dimension / 2), momentum=0.001), nn.ReLU(True))
        self.layer1_feats = nn.Sequential(nn.Conv2d(int(emb_dimension / 2),
            int(emb_dimension / 2), kernel_size=3, padding=1, bias=False),
            nn.BatchNorm2d(int(emb_dimension / 2), momentum=0.001), nn.ReLU
            (True), nn.Conv2d(int(emb_dimension / 2), int(emb_dimension / 2
            ), kernel_size=3, padding=1, bias=False), nn.BatchNorm2d(int(
            emb_dimension / 2), momentum=0.001), nn.ReLU(True))
        self.layer2_dimRed = nn.Sequential(nn.Conv2d(resnet.layer2[-1].
            conv2.out_channels, int(emb_dimension / 2), kernel_size=1,
            stride=1, padding=0, dilation=1, bias=False), nn.BatchNorm2d(
            int(emb_dimension / 2), momentum=0.001), nn.ReLU(True))
        self.layer2_feats = nn.Sequential(nn.Conv2d(int(emb_dimension / 2),
            int(emb_dimension / 2), kernel_size=3, padding=1, bias=False),
            nn.BatchNorm2d(int(emb_dimension / 2), momentum=0.001), nn.ReLU
            (True), nn.Conv2d(int(emb_dimension / 2), int(emb_dimension / 2
            ), kernel_size=3, padding=1, bias=False), nn.BatchNorm2d(int(
            emb_dimension / 2), momentum=0.001), nn.ReLU(True))
        self.layer3_dimRed = nn.Sequential(nn.Conv2d(resnet.layer3[-1].
            conv2.out_channels, int(emb_dimension / 2), kernel_size=1,
            stride=1, padding=0, dilation=1, bias=False), nn.BatchNorm2d(
            int(emb_dimension / 2), momentum=0.001), nn.ReLU(True))
        self.layer3_feats = nn.Sequential(nn.Conv2d(int(emb_dimension / 2),
            int(emb_dimension / 2), kernel_size=3, padding=1, bias=False),
            nn.BatchNorm2d(int(emb_dimension / 2), momentum=0.001), nn.ReLU
            (True), nn.Conv2d(int(emb_dimension / 2), int(emb_dimension / 2
            ), kernel_size=3, padding=1, bias=False), nn.BatchNorm2d(int(
            emb_dimension / 2), momentum=0.001), nn.ReLU(True))
        self.emb = nn.Sequential(nn.Conv2d(int(emb_dimension * 1.5 + 16),
            emb_dimension, kernel_size=3, padding=1, bias=False), nn.
            BatchNorm2d(emb_dimension, momentum=0.001), nn.ReLU(True), nn.
            Conv2d(emb_dimension, emb_dimension, kernel_size=3, padding=1,
            bias=True), nn.BatchNorm2d(emb_dimension, momentum=0.001), nn.
            ReLU(True))

    def forward(self, inputs):
        self.interp_x4 = nn.Upsample(scale_factor=4, mode='bilinear',
            align_corners=True)
        self.interp_x8 = nn.Upsample(scale_factor=8, mode='bilinear',
            align_corners=True)
        input_size = inputs.size()
        out = self.layer0(inputs)
        out_stream2 = self.streamTwo_feats(inputs)
        out = self.layer1(out)
        out_layer1 = self.layer1_dimRed(out)
        out_layer1 = self.interp_x4(out_layer1)
        out_layer1 = self.layer2_feats(out_layer1)
        out = self.layer2(out)
        out_layer2 = self.layer2_dimRed(out)
        out_layer2 = self.interp_x8(out_layer2)
        out_layer2 = self.layer2_feats(out_layer2)
        out = self.layer3(out)
        out_layer3 = self.layer3_dimRed(out)
        out_layer3 = self.interp_x8(out_layer3)
        out_layer3 = self.layer3_feats(out_layer3)
        out = torch.cat([out_stream2, out_layer1, out_layer2, out_layer3], 1)
        out = self.emb(out)
        return out

import os, random, time, copy
from skimage import io, transform
import numpy as np
import os.path as path
import scipy.io as sio
import matplotlib.pyplot as plt

import torch
from torch.utils.data import Dataset, DataLoader
import torch.nn as nn
import torch.optim as optim
from torch.optim import lr_scheduler 
import torch.nn.functional as F
from torch.autograd import Variable

import torchvision
from torchvision import datasets, models, transforms
from torchvision.models import *
    
    
    
    
def conv3x3(in_planes, out_planes, stride=1):
    """3x3 convolution with padding"""
    return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride,
                     padding=1, bias=False)


def conv1x1(in_planes, out_planes, stride=1):
    """1x1 convolution"""
    return nn.Conv2d(in_planes, out_planes, kernel_size=1, stride=stride, bias=False)


class BasicBlock(nn.Module):
    expansion = 1

    def __init__(self, inplanes, planes, stride=1, downsample=None):
        super(BasicBlock, self).__init__()
        self.conv1 = conv3x3(inplanes, planes, stride)
        self.bn1 = nn.BatchNorm2d(planes)
        self.relu = nn.ReLU(inplace=True)
        self.conv2 = conv3x3(planes, planes)
        self.bn2 = nn.BatchNorm2d(planes)
        self.downsample = downsample
        self.stride = stride

    def forward(self, x):
        identity = x

        out = self.conv1(x)
        out = self.bn1(out)
        out = self.relu(out)

        out = self.conv2(out)
        out = self.bn2(out)

        if self.downsample is not None:
            identity = self.downsample(x)

        out += identity
        out = self.relu(out)

        return out


class Bottleneck(nn.Module):
    expansion = 4

    def __init__(self, inplanes, planes, stride=1, downsample=None):
        super(Bottleneck, self).__init__()
        self.conv1 = conv1x1(inplanes, planes)
        self.bn1 = nn.BatchNorm2d(planes)
        self.conv2 = conv3x3(planes, planes, stride)
        self.bn2 = nn.BatchNorm2d(planes)
        self.conv3 = conv1x1(planes, planes * self.expansion)
        self.bn3 = nn.BatchNorm2d(planes * self.expansion)
        self.relu = nn.ReLU(inplace=True)
        self.downsample = downsample
        self.stride = stride

    def forward(self, x):
        identity = x

        out = self.conv1(x)
        out = self.bn1(out)
        out = self.relu(out)

        out = self.conv2(out)
        out = self.bn2(out)
        out = self.relu(out)

        out = self.conv3(out)
        out = self.bn3(out)

        if self.downsample is not None:
            identity = self.downsample(x)

        out += identity
        out = self.relu(out)

        return out

    
    
    
    
class ResNet4MultigridPFF(nn.Module):
    def __init__(self, block, layers, num_classes=10, zero_init_residual=False):
        super(ResNet4MultigridPFF, self).__init__()
        self.inplanes = 64
        self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3,
                               bias=False)
        self.bn1 = nn.BatchNorm2d(64)
        self.relu = nn.ReLU(inplace=True)
        self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
        self.layer1 = self._make_layer(block, 32, layers[0])
        self.layer2 = self._make_layer(block, 64, layers[1], stride=2)
        self.layer3 = self._make_layer(block, 128, layers[2], stride=2)
        self.layer4 = self._make_layer(block, 196, layers[3], stride=2)
        self.avgpool = nn.AdaptiveAvgPool2d((1, 1))
        self.fc = nn.Linear(196 * block.expansion, num_classes)

        for m in self.modules():
            if isinstance(m, nn.Conv2d):
                nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')
            elif isinstance(m, nn.BatchNorm2d):
                nn.init.constant_(m.weight, 1)
                nn.init.constant_(m.bias, 0)

        # Zero-initialize the last BN in each residual branch,
        # so that the residual branch starts with zeros, and each residual block behaves like an identity.
        # This improves the model by 0.2~0.3% according to https://arxiv.org/abs/1706.02677
        if zero_init_residual:
            for m in self.modules():
                if isinstance(m, Bottleneck):
                    nn.init.constant_(m.bn3.weight, 0)
                elif isinstance(m, BasicBlock):
                    nn.init.constant_(m.bn2.weight, 0)

    def _make_layer(self, block, planes, blocks, stride=1):
        downsample = None
        if stride != 1 or self.inplanes != planes * block.expansion:
            downsample = nn.Sequential(
                conv1x1(self.inplanes, planes * block.expansion, stride),
                nn.BatchNorm2d(planes * block.expansion),
            )

        layers = []
        layers.append(block(self.inplanes, planes, stride, downsample))
        self.inplanes = planes * block.expansion
        for _ in range(1, blocks):
            layers.append(block(self.inplanes, planes))

        return nn.Sequential(*layers)

    def forward(self, x):
        x = self.conv1(x)
        x = self.bn1(x)
        x = self.relu(x)
        x = self.maxpool(x)

        x = self.layer1(x)
        x = self.layer2(x)
        x = self.layer3(x)
        x = self.layer4(x)

        x = self.avgpool(x)
        x = x.view(x.size(0), -1)
        x = self.fc(x)

        return x


def compactResNet18forMultigridPFF(**kwargs):
    """Constructs a ResNet-18 model.
    Args:
        pretrained (bool): If True, returns a model pre-trained on ImageNet
    """
    model = ResNet4MultigridPFF(BasicBlock, [2, 2, 2, 2], **kwargs)
    return model




    
    
    
class MultigridPFF_tiny(nn.Module):
    def __init__(self, emb_dimension=128):
        super(MultigridPFF_tiny, self).__init__()
        self.emb_dimension = emb_dimension        
        
        resnet = compactResNet18forMultigridPFF()
        self.layer0 = nn.Sequential(resnet.conv1, resnet.bn1, resnet.relu, resnet.maxpool)
        self.layer1, self.layer2, self.layer3 = resnet.layer1, resnet.layer2, resnet.layer3
        
                
        # tweak resnet backbone to output features 8x smaller than input image
        for n, m in self.layer3.named_modules():
            if 'conv1' in n:
                m.dilation, m.padding, m.stride = (2, 2), (2, 2), (1, 1)
            elif 'conv2' in n:
                m.dilation, m.padding, m.stride = (2, 2), (2, 2), (1, 1)
            elif 'downsample.0' in n:
                m.stride = (1, 1)
        
        self.streamTwo_feats = nn.Sequential(
            nn.Conv2d(3, 16, kernel_size=3, padding=1, bias=False),
            nn.BatchNorm2d(16, momentum=0.001),
            nn.ReLU(True),
            
            nn.Conv2d(16, 16, kernel_size=3, padding=2, dilation=2, bias=False),
            nn.BatchNorm2d(16, momentum=0.001),
            nn.ReLU(True),
            
            nn.Conv2d(16, 16, kernel_size=3, padding=1, bias=False),
            nn.BatchNorm2d(16, momentum=0.001),
            nn.ReLU(True),
            
            nn.Conv2d(16, 16, kernel_size=3, padding=2, dilation=2, bias=False),
            nn.BatchNorm2d(16, momentum=0.001),
            nn.ReLU(True),
            
            nn.Conv2d(16, 16, kernel_size=3, padding=1, bias=False),
            nn.BatchNorm2d(16, momentum=0.001),
            nn.ReLU(True),
        )
        
                
        
        
                
        self.layer1_dimRed = nn.Sequential(
            nn.Conv2d(resnet.layer1[-1].conv2.out_channels, int(emb_dimension/2), 
                      kernel_size=1, stride=1, padding=0, dilation=1, bias=False),
            nn.BatchNorm2d(int(emb_dimension/2), momentum=0.001),
            nn.ReLU(True),
        )
        self.layer1_feats = nn.Sequential(
            nn.Conv2d(int(emb_dimension/2), int(emb_dimension/2), kernel_size=3, padding=1, bias=False),
            nn.BatchNorm2d(int(emb_dimension/2), momentum=0.001),
            nn.ReLU(True),
            
            nn.Conv2d(int(emb_dimension/2), int(emb_dimension/2), kernel_size=3, padding=1, bias=False),
            nn.BatchNorm2d(int(emb_dimension/2), momentum=0.001),
            nn.ReLU(True),
        )
                        
                
        self.layer2_dimRed = nn.Sequential(
            nn.Conv2d(resnet.layer2[-1].conv2.out_channels, int(emb_dimension/2), 
                      kernel_size=1, stride=1, padding=0, dilation=1, bias=False),
            nn.BatchNorm2d(int(emb_dimension/2), momentum=0.001),
            nn.ReLU(True),
        )
        self.layer2_feats = nn.Sequential(
            nn.Conv2d(int(emb_dimension/2), int(emb_dimension/2), kernel_size=3, padding=1, bias=False),
            nn.BatchNorm2d(int(emb_dimension/2), momentum=0.001),
            nn.ReLU(True),
            
            nn.Conv2d(int(emb_dimension/2), int(emb_dimension/2), kernel_size=3, padding=1, bias=False),
            nn.BatchNorm2d(int(emb_dimension/2), momentum=0.001),
            nn.ReLU(True),
        )
                

        self.layer3_dimRed = nn.Sequential(
            nn.Conv2d(resnet.layer3[-1].conv2.out_channels, int(emb_dimension/2), 
                      kernel_size=1, stride=1, padding=0, dilation=1, bias=False),
            nn.BatchNorm2d(int(emb_dimension/2), momentum=0.001),
            nn.ReLU(True),
        )
        self.layer3_feats = nn.Sequential(
            nn.Conv2d(int(emb_dimension/2), int(emb_dimension/2), kernel_size=3, padding=1, bias=False),
            nn.BatchNorm2d(int(emb_dimension/2), momentum=0.001),
            nn.ReLU(True),
            
            nn.Conv2d(int(emb_dimension/2), int(emb_dimension/2), kernel_size=3, padding=1, bias=False),
            nn.BatchNorm2d(int(emb_dimension/2), momentum=0.001),
            nn.ReLU(True),            
        )
                
        self.emb = nn.Sequential(
            nn.Conv2d(int(emb_dimension*1.5+16), emb_dimension, kernel_size=3, padding=1, bias=False),
            nn.BatchNorm2d(emb_dimension, momentum=0.001),
            nn.ReLU(True),
            
            nn.Conv2d(emb_dimension, emb_dimension, kernel_size=3, padding=1, bias=True),
            nn.BatchNorm2d(emb_dimension, momentum=0.001),
            nn.ReLU(True),
        )
        
        
    def forward(self, inputs):
        self.interp_x4 = nn.Upsample(scale_factor=4, mode='bilinear', align_corners=True)  
        self.interp_x8 = nn.Upsample(scale_factor=8, mode='bilinear', align_corners=True)  
        
        input_size = inputs.size()
        
        out = self.layer0(inputs)
        out_stream2 = self.streamTwo_feats(inputs)        
        
        out = self.layer1(out)        
        out_layer1 = self.layer1_dimRed(out)        
        out_layer1 = self.interp_x4(out_layer1)
        out_layer1 = self.layer2_feats(out_layer1)
        
        out = self.layer2(out)        
        out_layer2 = self.layer2_dimRed(out)        
        out_layer2 = self.interp_x8(out_layer2)
        out_layer2 = self.layer2_feats(out_layer2)
        
        
        out = self.layer3(out)        
        out_layer3 = self.layer3_dimRed(out)        
        out_layer3 = self.interp_x8(out_layer3)
        out_layer3 = self.layer3_feats(out_layer3)        

        out = torch.cat([out_stream2, out_layer1, out_layer2, out_layer3], 1)
        out = self.emb(out)
                
        return out