import torch
import torch.nn as nn
import torch.nn.functional as F
import torchvision.models as models
import torch.utils.model_zoo as model_zoo

class ResNetEncoder(models.ResNet):
    """Constructs a resnet model with varying number of input images.
    Adapted from https://github.com/pytorch/vision/blob/master/torchvision/models/resnet.py
    """
    def __init__(self, num_layers=18, num_input_images=1, pretrained=True, out_chs=32, stride=8):
        layers = {18: [2, 2, 2, 2], 50: [3, 4, 6, 3]}[num_layers]
        block = {18: models.resnet.BasicBlock, 50: models.resnet.Bottleneck}[num_layers]    
        self.upsample_mode = "bilinear"
        super(ResNetEncoder, self).__init__(block, layers)
        
        self.inplanes = 64
        self.conv1 = nn.Conv2d(
            num_input_images * 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, 64, layers[0])
        self.layer2 = self._make_layer(block, 128, layers[1], stride=2)
        self.layer3 = self._make_layer(block, 256, layers[2], stride=2)
        
        self.stride = stride
        if stride == 8:
            self.upconv1 = nn.Sequential(nn.Conv2d(256, 128, 3, 1, padding=1), nn.ReLU(inplace=True))
            self.upconv1_fusion = nn.Sequential(nn.Conv2d(256, 128, 3, 1, padding=1), nn.ReLU(inplace=True))
            self.out_conv = nn.Conv2d(128, out_chs, 3, 1, padding=1)
                
        elif stride == 4:
            self.upconv1 = nn.Sequential(nn.Conv2d(256, 128, 3, 1, padding=1), nn.ReLU(inplace=True))
            self.upconv1_fusion = nn.Sequential(nn.Conv2d(256, 128, 3, 1, padding=1), nn.ReLU(inplace=True))
            self.upconv2 = nn.Sequential(nn.Conv2d(128, 64, 3, 1, padding=1), nn.ReLU(inplace=True))
            self.upconv2_fusion = nn.Sequential(nn.Conv2d(128, 64, 3, 1, padding=1), nn.ReLU(inplace=True))
            self.out_conv = nn.Conv2d(64, out_chs, 3, 1, padding=1)
            
        else:
            raise NotImplementedError 

        # self.layer4 = self._make_layer(block, 512, layers[3], stride=2)
        # del self.layer3
        del self.layer4
        del self.fc
        del self.avgpool

        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 pretrained:
            loaded = model_zoo.load_url(models.resnet.model_urls['resnet{}'.format(num_layers)])
            loaded['conv1.weight'] = torch.cat([loaded['conv1.weight']] * num_input_images, 1) / num_input_images
            loaded_flilter = {k:v for k, v in loaded.items() if "layer4" not in k and "fc" not in k}
            try:
                print("load pretrained model from:", models.resnet.model_urls['resnet{}'.format(num_layers)])
                self.load_state_dict(loaded_flilter)
            except Exception as e:
                # print(e)
                self.load_state_dict(loaded_flilter, strict=False)
        
    def forward(self, x):
        feats = {}
        # if input is list, combine batch dimension
        is_list = isinstance(x, tuple) or isinstance(x, list)
        if is_list:
            num = len(x)
            batch_dim = x[0].shape[0]
            x = torch.cat(x, dim=0)

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

        x = self.layer1(x)
        feats["s4"] = x
        x = self.layer2(x)
        feats["s8"] = x
        x = self.layer3(x)

        if self.stride == 8:
            x = F.interpolate(x, scale_factor=2, mode=self.upsample_mode)
            x = self.upconv1(x)
            x = self.upconv1_fusion(torch.cat([x, feats["s8"]], dim=1))
            x = self.out_conv(x)
            
        elif self.stride == 4:
            x = F.interpolate(x, scale_factor=2, mode=self.upsample_mode)
            x = self.upconv1(x)
            x = self.upconv1_fusion(torch.cat([x, feats["s8"]], dim=1)) 
            
            x = F.interpolate(x, scale_factor=2, mode=self.upsample_mode)
            x = self.upconv2(x)
            x = self.upconv2_fusion(torch.cat([x, feats["s4"]], dim=1))
            
            x = self.out_conv(x)
        
        if is_list:
            x = torch.split(x, [batch_dim] * num, dim=0)

        return x
    

########################################################################################################################
class ResNetEncoderV1(models.ResNet):
    """Constructs a resnet model with varying number of input images.
    Adapted from https://github.com/pytorch/vision/blob/master/torchvision/models/resnet.py
    """
    def __init__(self, num_layers=18, num_input_images=1, pretrained=True, out_chs=32, stride=8):
        layers = {18: [2, 2, 2, 2], 50: [3, 4, 6, 3]}[num_layers]
        block = {18: models.resnet.BasicBlock, 50: models.resnet.Bottleneck}[num_layers]    
        self.upsample_mode = "nearest"
        super().__init__(block, layers)
        
        self.inplanes = 64
        self.conv1 = nn.Conv2d(
            num_input_images * 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, 64, layers[0])
        self.layer2 = self._make_layer(block, 128, layers[1], stride=2)
        self.layer3 = self._make_layer(block, 256, layers[2], stride=2)
        
        self.stride = stride
        if stride == 8:
            self.upconv1 = nn.Sequential(nn.Conv2d(256, 128, 3, 1, padding=1), nn.ReLU(inplace=True))
            self.upconv1_fusion = nn.Sequential(nn.Conv2d(256, 128, 3, 1, padding=1), nn.ReLU(inplace=True))
            self.out_conv = nn.Conv2d(128, out_chs, 3, 1, padding=1)
                
        elif stride == 4:
            self.upconv1 = nn.Sequential(nn.Conv2d(256, 128, 3, 1, padding=1), nn.ReLU(inplace=True))
            self.upconv1_fusion = nn.Sequential(nn.Conv2d(256, 128, 3, 1, padding=1), nn.ReLU(inplace=True))
            self.upconv2 = nn.Sequential(nn.Conv2d(128, 64, 3, 1, padding=1), nn.ReLU(inplace=True))
            self.upconv2_fusion = nn.Sequential(nn.Conv2d(128, 64, 3, 1, padding=1), nn.ReLU(inplace=True))
            self.out_conv = nn.Conv2d(64, out_chs, 3, 1, padding=1)
            
        else:
            raise NotImplementedError 

        # self.layer4 = self._make_layer(block, 512, layers[3], stride=2)
        # del self.layer3
        del self.layer4
        del self.fc
        del self.avgpool

        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 pretrained:
            loaded = model_zoo.load_url(models.resnet.model_urls['resnet{}'.format(num_layers)])
            loaded['conv1.weight'] = torch.cat([loaded['conv1.weight']] * num_input_images, 1) / num_input_images
            loaded_flilter = {k:v for k, v in loaded.items() if "layer4" not in k and "fc" not in k}
            try:
                print("load pretrained model from:", models.resnet.model_urls['resnet{}'.format(num_layers)])
                self.load_state_dict(loaded_flilter)
            except Exception as e:
                # print(e)
                self.load_state_dict(loaded_flilter, strict=False)
        
    def forward(self, x):
        feats = {}
        # if input is list, combine batch dimension
        is_list = isinstance(x, tuple) or isinstance(x, list)
        if is_list:
            num = len(x)
            batch_dim = x[0].shape[0]
            x = torch.cat(x, dim=0)

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

        x = self.layer1(x)
        feats["s4"] = x
        x = self.layer2(x)
        feats["s8"] = x
        x = self.layer3(x)

        if self.stride == 8:
            x = F.interpolate(x, scale_factor=2, mode=self.upsample_mode)
            x = self.upconv1(x)
            x = self.upconv1_fusion(torch.cat([x, feats["s8"]], dim=1))
            x = self.out_conv(x)
            
        elif self.stride == 4:
            x = F.interpolate(x, scale_factor=2, mode=self.upsample_mode)
            x = self.upconv1(x)
            x = self.upconv1_fusion(torch.cat([x, feats["s8"]], dim=1)) 
            
            x = F.interpolate(x, scale_factor=2, mode=self.upsample_mode)
            x = self.upconv2(x)
            x = self.upconv2_fusion(torch.cat([x, feats["s4"]], dim=1))
            
            x = self.out_conv(x)
        
        if is_list:
            x = torch.split(x, [batch_dim] * num, dim=0)

        return x
    


########################################################################################################################
class ResNetEncoderV2(models.ResNet):
    """Constructs a resnet model with varying number of input images.
    Adapted from https://github.com/pytorch/vision/blob/master/torchvision/models/resnet.py
    """
    def __init__(self, num_layers=18, num_input_images=1, pretrained=True, out_chs=32, stride=8):
        layers = {18: [2, 2, 2, 2], 50: [3, 4, 6, 3]}[num_layers]
        block = {18: models.resnet.BasicBlock, 50: models.resnet.Bottleneck}[num_layers]    
        self.upsample_mode = "bilinear"
        super().__init__(block, layers)

        self.inplanes = 64
        self.conv1 = nn.Conv2d(
            num_input_images * 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, 64, layers[0])
        self.layer2 = self._make_layer(block, 128, layers[1], stride=2)
        self.layer3 = self._make_layer(block, 256, layers[2], stride=2)
        self.layer4 = self._make_layer(block, 512, layers[3], stride=2)
        # del self.layer4
        self.upconv0 = nn.Sequential(nn.Conv2d(512, 256, 3, 1, padding=1), nn.ReLU(inplace=True))
        self.upconv0_fusion = nn.Sequential(nn.Conv2d(512, 256, 3, 1, padding=1), nn.ReLU(inplace=True))

         
        self.stride = stride
        if stride == 8:
            self.upconv1 = nn.Sequential(nn.Conv2d(256, 128, 3, 1, padding=1), nn.ReLU(inplace=True))
            self.upconv1_fusion = nn.Sequential(nn.Conv2d(256, 128, 3, 1, padding=1), nn.ReLU(inplace=True))
            self.out_conv = nn.Conv2d(128, out_chs, 3, 1, padding=1)
                
        elif stride == 4:
            self.upconv1 = nn.Sequential(nn.Conv2d(256, 128, 3, 1, padding=1), nn.ReLU(inplace=True))
            self.upconv1_fusion = nn.Sequential(nn.Conv2d(256, 128, 3, 1, padding=1), nn.ReLU(inplace=True))
            self.upconv2 = nn.Sequential(nn.Conv2d(128, 64, 3, 1, padding=1), nn.ReLU(inplace=True))
            self.upconv2_fusion = nn.Sequential(nn.Conv2d(128, 64, 3, 1, padding=1), nn.ReLU(inplace=True))
            self.out_conv = nn.Conv2d(64, out_chs, 3, 1, padding=1)
            
        else:
            raise NotImplementedError 

        del self.fc
        del self.avgpool

        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 pretrained:
            loaded = model_zoo.load_url(models.resnet.model_urls['resnet{}'.format(num_layers)])
            loaded['conv1.weight'] = torch.cat([loaded['conv1.weight']] * num_input_images, 1) / num_input_images
            loaded_flilter = {k:v for k, v in loaded.items() if "layer4" not in k and "fc" not in k}
            try:
                print("load pretrained model from:", models.resnet.model_urls['resnet{}'.format(num_layers)])
                self.load_state_dict(loaded_flilter)
            except Exception as e:
                # print(e)
                self.load_state_dict(loaded_flilter, strict=False)
        
    def forward(self, x):
        feats = {}
        # if input is list, combine batch dimension
        is_list = isinstance(x, tuple) or isinstance(x, list)
        if is_list:
            num = len(x)
            batch_dim = x[0].shape[0]
            x = torch.cat(x, dim=0)

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

        x = self.layer1(x)
        feats["s4"] = x
        x = self.layer2(x)
        feats["s8"] = x
        x = self.layer3(x)
        
        feats["s16"] = x
        x = self.layer4(x)
        x = self.upconv0(x)
        x = F.interpolate(x, scale_factor=2, mode=self.upsample_mode)
        x = self.upconv0_fusion(torch.cat([x, feats["s16"]], dim=1)) 

        if self.stride == 8:
            x = self.upconv1(x)
            x = F.interpolate(x, scale_factor=2, mode=self.upsample_mode)
            x = self.upconv1_fusion(torch.cat([x, feats["s8"]], dim=1))
            x = self.out_conv(x)
            
        elif self.stride == 4:
            x = self.upconv1(x)
            x = F.interpolate(x, scale_factor=2, mode=self.upsample_mode)
            x = self.upconv1_fusion(torch.cat([x, feats["s8"]], dim=1)) 
            
            x = self.upconv2(x)
            x = F.interpolate(x, scale_factor=2, mode=self.upsample_mode)
            x = self.upconv2_fusion(torch.cat([x, feats["s4"]], dim=1))
            
            x = self.out_conv(x)
        
        if is_list:
            x = torch.split(x, [batch_dim] * num, dim=0)

        return x


################################################################################
class FeatBlock(nn.Module):
    def __init__(self, planes=128, out_dim=128):
        super().__init__()
        self.conv1 = nn.Conv2d(planes, planes, 3, padding=1)
        self.conv2 = nn.Conv2d(planes, out_dim, 3, padding=1)
        self.relu = nn.ReLU(inplace=True)

    def forward(self, x):
        x = self.relu(self.conv1(self.relu(x)))
        x = self.conv2(x)
        return x
import torch
import torch.nn as nn
import torch.nn.functional as F
import torchvision.models as models
import torch.utils.model_zoo as model_zoo

class ResNetEncoder(models.ResNet):
    """Constructs a resnet model with varying number of input images.
    Adapted from https://github.com/pytorch/vision/blob/master/torchvision/models/resnet.py
    """
    def __init__(self, num_layers=18, num_input_images=1, pretrained=True, out_chs=32, stride=8):
        layers = {18: [2, 2, 2, 2], 50: [3, 4, 6, 3]}[num_layers]
        block = {18: models.resnet.BasicBlock, 50: models.resnet.Bottleneck}[num_layers]    
        self.upsample_mode = "bilinear"
        super(ResNetEncoder, self).__init__(block, layers)
        
        self.inplanes = 64
        self.conv1 = nn.Conv2d(
            num_input_images * 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, 64, layers[0])
        self.layer2 = self._make_layer(block, 128, layers[1], stride=2)
        self.layer3 = self._make_layer(block, 256, layers[2], stride=2)
        
        self.stride = stride
        if stride == 8:
            self.upconv1 = nn.Sequential(nn.Conv2d(256, 128, 3, 1, padding=1), nn.ReLU(inplace=True))
            self.upconv1_fusion = nn.Sequential(nn.Conv2d(256, 128, 3, 1, padding=1), nn.ReLU(inplace=True))
            self.out_conv = nn.Conv2d(128, out_chs, 3, 1, padding=1)
                
        elif stride == 4:
            self.upconv1 = nn.Sequential(nn.Conv2d(256, 128, 3, 1, padding=1), nn.ReLU(inplace=True))
            self.upconv1_fusion = nn.Sequential(nn.Conv2d(256, 128, 3, 1, padding=1), nn.ReLU(inplace=True))
            self.upconv2 = nn.Sequential(nn.Conv2d(128, 64, 3, 1, padding=1), nn.ReLU(inplace=True))
            self.upconv2_fusion = nn.Sequential(nn.Conv2d(128, 64, 3, 1, padding=1), nn.ReLU(inplace=True))
            self.out_conv = nn.Conv2d(64, out_chs, 3, 1, padding=1)
            
        else:
            raise NotImplementedError 

        # self.layer4 = self._make_layer(block, 512, layers[3], stride=2)
        # del self.layer3
        del self.layer4
        del self.fc
        del self.avgpool

        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 pretrained:
            loaded = model_zoo.load_url(models.resnet.model_urls['resnet{}'.format(num_layers)])
            loaded['conv1.weight'] = torch.cat([loaded['conv1.weight']] * num_input_images, 1) / num_input_images
            loaded_flilter = {k:v for k, v in loaded.items() if "layer4" not in k and "fc" not in k}
            try:
                print("load pretrained model from:", models.resnet.model_urls['resnet{}'.format(num_layers)])
                self.load_state_dict(loaded_flilter)
            except Exception as e:
                # print(e)
                self.load_state_dict(loaded_flilter, strict=False)
        
    def forward(self, x):
        feats = {}
        # if input is list, combine batch dimension
        is_list = isinstance(x, tuple) or isinstance(x, list)
        if is_list:
            num = len(x)
            batch_dim = x[0].shape[0]
            x = torch.cat(x, dim=0)

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

        x = self.layer1(x)
        feats["s4"] = x
        x = self.layer2(x)
        feats["s8"] = x
        x = self.layer3(x)

        if self.stride == 8:
            x = F.interpolate(x, scale_factor=2, mode=self.upsample_mode)
            x = self.upconv1(x)
            x = self.upconv1_fusion(torch.cat([x, feats["s8"]], dim=1))
            x = self.out_conv(x)
            
        elif self.stride == 4:
            x = F.interpolate(x, scale_factor=2, mode=self.upsample_mode)
            x = self.upconv1(x)
            x = self.upconv1_fusion(torch.cat([x, feats["s8"]], dim=1)) 
            
            x = F.interpolate(x, scale_factor=2, mode=self.upsample_mode)
            x = self.upconv2(x)
            x = self.upconv2_fusion(torch.cat([x, feats["s4"]], dim=1))
            
            x = self.out_conv(x)
        
        if is_list:
            x = torch.split(x, [batch_dim] * num, dim=0)

        return x
    

########################################################################################################################
class ResNetEncoderV1(models.ResNet):
    """Constructs a resnet model with varying number of input images.
    Adapted from https://github.com/pytorch/vision/blob/master/torchvision/models/resnet.py
    """
    def __init__(self, num_layers=18, num_input_images=1, pretrained=True, out_chs=32, stride=8):
        layers = {18: [2, 2, 2, 2], 50: [3, 4, 6, 3]}[num_layers]
        block = {18: models.resnet.BasicBlock, 50: models.resnet.Bottleneck}[num_layers]    
        self.upsample_mode = "nearest"
        super().__init__(block, layers)
        
        self.inplanes = 64
        self.conv1 = nn.Conv2d(
            num_input_images * 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, 64, layers[0])
        self.layer2 = self._make_layer(block, 128, layers[1], stride=2)
        self.layer3 = self._make_layer(block, 256, layers[2], stride=2)
        
        self.stride = stride
        if stride == 8:
            self.upconv1 = nn.Sequential(nn.Conv2d(256, 128, 3, 1, padding=1), nn.ReLU(inplace=True))
            self.upconv1_fusion = nn.Sequential(nn.Conv2d(256, 128, 3, 1, padding=1), nn.ReLU(inplace=True))
            self.out_conv = nn.Conv2d(128, out_chs, 3, 1, padding=1)
                
        elif stride == 4:
            self.upconv1 = nn.Sequential(nn.Conv2d(256, 128, 3, 1, padding=1), nn.ReLU(inplace=True))
            self.upconv1_fusion = nn.Sequential(nn.Conv2d(256, 128, 3, 1, padding=1), nn.ReLU(inplace=True))
            self.upconv2 = nn.Sequential(nn.Conv2d(128, 64, 3, 1, padding=1), nn.ReLU(inplace=True))
            self.upconv2_fusion = nn.Sequential(nn.Conv2d(128, 64, 3, 1, padding=1), nn.ReLU(inplace=True))
            self.out_conv = nn.Conv2d(64, out_chs, 3, 1, padding=1)
            
        else:
            raise NotImplementedError 

        # self.layer4 = self._make_layer(block, 512, layers[3], stride=2)
        # del self.layer3
        del self.layer4
        del self.fc
        del self.avgpool

        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 pretrained:
            loaded = model_zoo.load_url(models.resnet.model_urls['resnet{}'.format(num_layers)])
            loaded['conv1.weight'] = torch.cat([loaded['conv1.weight']] * num_input_images, 1) / num_input_images
            loaded_flilter = {k:v for k, v in loaded.items() if "layer4" not in k and "fc" not in k}
            try:
                print("load pretrained model from:", models.resnet.model_urls['resnet{}'.format(num_layers)])
                self.load_state_dict(loaded_flilter)
            except Exception as e:
                # print(e)
                self.load_state_dict(loaded_flilter, strict=False)
        
    def forward(self, x):
        feats = {}
        # if input is list, combine batch dimension
        is_list = isinstance(x, tuple) or isinstance(x, list)
        if is_list:
            num = len(x)
            batch_dim = x[0].shape[0]
            x = torch.cat(x, dim=0)

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

        x = self.layer1(x)
        feats["s4"] = x
        x = self.layer2(x)
        feats["s8"] = x
        x = self.layer3(x)

        if self.stride == 8:
            x = F.interpolate(x, scale_factor=2, mode=self.upsample_mode)
            x = self.upconv1(x)
            x = self.upconv1_fusion(torch.cat([x, feats["s8"]], dim=1))
            x = self.out_conv(x)
            
        elif self.stride == 4:
            x = F.interpolate(x, scale_factor=2, mode=self.upsample_mode)
            x = self.upconv1(x)
            x = self.upconv1_fusion(torch.cat([x, feats["s8"]], dim=1)) 
            
            x = F.interpolate(x, scale_factor=2, mode=self.upsample_mode)
            x = self.upconv2(x)
            x = self.upconv2_fusion(torch.cat([x, feats["s4"]], dim=1))
            
            x = self.out_conv(x)
        
        if is_list:
            x = torch.split(x, [batch_dim] * num, dim=0)

        return x
    


########################################################################################################################
class ResNetEncoderV2(models.ResNet):
    """Constructs a resnet model with varying number of input images.
    Adapted from https://github.com/pytorch/vision/blob/master/torchvision/models/resnet.py
    """
    def __init__(self, num_layers=18, num_input_images=1, pretrained=True, out_chs=32, stride=8):
        layers = {18: [2, 2, 2, 2], 50: [3, 4, 6, 3]}[num_layers]
        block = {18: models.resnet.BasicBlock, 50: models.resnet.Bottleneck}[num_layers]    
        self.upsample_mode = "bilinear"
        super().__init__(block, layers)

        self.inplanes = 64
        self.conv1 = nn.Conv2d(
            num_input_images * 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, 64, layers[0])
        self.layer2 = self._make_layer(block, 128, layers[1], stride=2)
        self.layer3 = self._make_layer(block, 256, layers[2], stride=2)
        self.layer4 = self._make_layer(block, 512, layers[3], stride=2)
        # del self.layer4
        self.upconv0 = nn.Sequential(nn.Conv2d(512, 256, 3, 1, padding=1), nn.ReLU(inplace=True))
        self.upconv0_fusion = nn.Sequential(nn.Conv2d(512, 256, 3, 1, padding=1), nn.ReLU(inplace=True))

         
        self.stride = stride
        if stride == 8:
            self.upconv1 = nn.Sequential(nn.Conv2d(256, 128, 3, 1, padding=1), nn.ReLU(inplace=True))
            self.upconv1_fusion = nn.Sequential(nn.Conv2d(256, 128, 3, 1, padding=1), nn.ReLU(inplace=True))
            self.out_conv = nn.Conv2d(128, out_chs, 3, 1, padding=1)
                
        elif stride == 4:
            self.upconv1 = nn.Sequential(nn.Conv2d(256, 128, 3, 1, padding=1), nn.ReLU(inplace=True))
            self.upconv1_fusion = nn.Sequential(nn.Conv2d(256, 128, 3, 1, padding=1), nn.ReLU(inplace=True))
            self.upconv2 = nn.Sequential(nn.Conv2d(128, 64, 3, 1, padding=1), nn.ReLU(inplace=True))
            self.upconv2_fusion = nn.Sequential(nn.Conv2d(128, 64, 3, 1, padding=1), nn.ReLU(inplace=True))
            self.out_conv = nn.Conv2d(64, out_chs, 3, 1, padding=1)
            
        else:
            raise NotImplementedError 

        del self.fc
        del self.avgpool

        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 pretrained:
            loaded = model_zoo.load_url(models.resnet.model_urls['resnet{}'.format(num_layers)])
            loaded['conv1.weight'] = torch.cat([loaded['conv1.weight']] * num_input_images, 1) / num_input_images
            loaded_flilter = {k:v for k, v in loaded.items() if "layer4" not in k and "fc" not in k}
            try:
                print("load pretrained model from:", models.resnet.model_urls['resnet{}'.format(num_layers)])
                self.load_state_dict(loaded_flilter)
            except Exception as e:
                # print(e)
                self.load_state_dict(loaded_flilter, strict=False)
        
    def forward(self, x):
        feats = {}
        # if input is list, combine batch dimension
        is_list = isinstance(x, tuple) or isinstance(x, list)
        if is_list:
            num = len(x)
            batch_dim = x[0].shape[0]
            x = torch.cat(x, dim=0)

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

        x = self.layer1(x)
        feats["s4"] = x
        x = self.layer2(x)
        feats["s8"] = x
        x = self.layer3(x)
        
        feats["s16"] = x
        x = self.layer4(x)
        x = self.upconv0(x)
        x = F.interpolate(x, scale_factor=2, mode=self.upsample_mode)
        x = self.upconv0_fusion(torch.cat([x, feats["s16"]], dim=1)) 

        if self.stride == 8:
            x = self.upconv1(x)
            x = F.interpolate(x, scale_factor=2, mode=self.upsample_mode)
            x = self.upconv1_fusion(torch.cat([x, feats["s8"]], dim=1))
            x = self.out_conv(x)
            
        elif self.stride == 4:
            x = self.upconv1(x)
            x = F.interpolate(x, scale_factor=2, mode=self.upsample_mode)
            x = self.upconv1_fusion(torch.cat([x, feats["s8"]], dim=1)) 
            
            x = self.upconv2(x)
            x = F.interpolate(x, scale_factor=2, mode=self.upsample_mode)
            x = self.upconv2_fusion(torch.cat([x, feats["s4"]], dim=1))
            
            x = self.out_conv(x)
        
        if is_list:
            x = torch.split(x, [batch_dim] * num, dim=0)

        return x


################################################################################
class FeatBlock(nn.Module):
    def __init__(self, planes=128, out_dim=128):
        super().__init__()
        self.conv1 = nn.Conv2d(planes, planes, 3, padding=1)
        self.conv2 = nn.Conv2d(planes, out_dim, 3, padding=1)
        self.relu = nn.ReLU(inplace=True)

    def forward(self, x):
        x = self.relu(self.conv1(self.relu(x)))
        x = self.conv2(x)
        return x
Constructs a resnet model with varying number of input images.     Adapted from https://github.com/pytorch/vision/blob/master/torchvision/models/resnet.py # self.layer4 = self._make_layer(block, 512, layers[3], stride=2) # del self.layer3 # print(e) # if input is list, combine batch dimension ######################################################################################################################## Constructs a resnet model with varying number of input images.     Adapted from https://github.com/pytorch/vision/blob/master/torchvision/models/resnet.py # self.layer4 = self._make_layer(block, 512, layers[3], stride=2) # del self.layer3 # print(e) # if input is list, combine batch dimension ######################################################################################################################## Constructs a resnet model with varying number of input images.     Adapted from https://github.com/pytorch/vision/blob/master/torchvision/models/resnet.py # del self.layer4 # print(e) # if input is list, combine batch dimension ################################################################################