"""
Code source: https://github.com/pytorch/vision
"""
from __future__ import division, absolute_import
from torchreid.data.transforms import ColorAugmentation
from typing_extensions import final
from torch.nn.modules.pooling import AvgPool1d
import torch.utils.model_zoo as model_zoo
from torch import nn
import torch
import copy
import torch
import torch.nn.functional as F

__all__ = [
    'resnet18', 'resnet34', 'resnet50', 'resnet101', 'resnet152',
    'resnext50_32x4d', 'resnext101_32x8d', 'resnet50_fc512', 'resnet50_ieee', 'resnet50_ieee_3modal'
]

model_urls = {
    'resnet18':
    'https://download.pytorch.org/models/resnet18-5c106cde.pth',
    'resnet34':
    'https://download.pytorch.org/models/resnet34-333f7ec4.pth',
    'resnet50':
    'https://download.pytorch.org/models/resnet50-19c8e357.pth',
    'resnet101':
    'https://download.pytorch.org/models/resnet101-5d3b4d8f.pth',
    'resnet152':
    'https://download.pytorch.org/models/resnet152-b121ed2d.pth',
    'resnext50_32x4d':
    'https://download.pytorch.org/models/resnext50_32x4d-7cdf4587.pth',
    'resnext101_32x8d':
    'https://download.pytorch.org/models/resnext101_32x8d-8ba56ff5.pth',
}


def conv3x3(in_planes, out_planes, stride=1, groups=1, dilation=1):
    """3x3 convolution with padding"""
    return nn.Conv2d(
        in_planes,
        out_planes,
        kernel_size=3,
        stride=stride,
        padding=dilation,
        groups=groups,
        bias=False,
        dilation=dilation
    )


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 DimReduceLayer(nn.Module):

    def __init__(self, in_channels, out_channels, nonlinear):
        super(DimReduceLayer, self).__init__()
        layers = []
        layers.append(
            nn.Conv2d(
                in_channels, out_channels, 1, stride=1, padding=0, bias=False
            )
        )
        layers.append(nn.BatchNorm2d(out_channels))

        if nonlinear == 'relu':
            layers.append(nn.ReLU(inplace=True))
        elif nonlinear == 'leakyrelu':
            layers.append(nn.LeakyReLU(0.1))

        self.layers = nn.Sequential(*layers)

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



class BasicBlock(nn.Module):
    expansion = 1

    def __init__(
        self,
        inplanes,
        planes,
        stride=1,
        downsample=None,
        groups=1,
        base_width=64,
        dilation=1,
        norm_layer=None
    ):
        super(BasicBlock, self).__init__()
        if norm_layer is None:
            norm_layer = nn.BatchNorm2d
        if groups != 1 or base_width != 64:
            raise ValueError(
                'BasicBlock only supports groups=1 and base_width=64'
            )
        if dilation > 1:
            raise NotImplementedError(
                "Dilation > 1 not supported in BasicBlock"
            )
        # Both self.conv1 and self.downsample layers downsample the input when stride != 1
        self.conv1 = conv3x3(inplanes, planes, stride)
        self.bn1 = norm_layer(planes)
        self.relu = nn.ReLU(inplace=True)
        self.conv2 = conv3x3(planes, planes)
        self.bn2 = norm_layer(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,
        groups=1,
        base_width=64,
        dilation=1,
        norm_layer=None
    ):
        super(Bottleneck, self).__init__()
        if norm_layer is None:
            norm_layer = nn.BatchNorm2d
        width = int(planes * (base_width/64.)) * groups
        # Both self.conv2 and self.downsample layers downsample the input when stride != 1
        self.conv1 = conv1x1(inplanes, width)
        self.bn1 = norm_layer(width)
        self.conv2 = conv3x3(width, width, stride, groups, dilation)
        self.bn2 = norm_layer(width)
        self.conv3 = conv1x1(width, planes * self.expansion)
        self.bn3 = norm_layer(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 ResNet(nn.Module):
    """Residual network.
    
    Reference:
        - He et al. Deep Residual Learning for Image Recognition. CVPR 2016.
        - Xie et al. Aggregated Residual Transformations for Deep Neural Networks. CVPR 2017.
    Public keys:
        - ``resnet18``: ResNet18.
        - ``resnet34``: ResNet34.
        - ``resnet50``: ResNet50.
        - ``resnet101``: ResNet101.
        - ``resnet152``: ResNet152.
        - ``resnext50_32x4d``: ResNeXt50.
        - ``resnext101_32x8d``: ResNeXt101.
        - ``resnet50_fc512``: ResNet50 + FC.
    """

    def __init__(
        self,
        num_classes,
        loss,
        block,
        layers,
        zero_init_residual=False,
        groups=1,
        width_per_group=64,
        replace_stride_with_dilation=None,
        norm_layer=None,
        last_stride=2,
        fc_dims=None,
        dropout_p=None,
        **kwargs
    ):
        super(ResNet, self).__init__()
        if norm_layer is None:
            norm_layer = nn.BatchNorm2d
        self._norm_layer = norm_layer
        self.loss = loss
        self.feature_dim = 512 * block.expansion
        self.inplanes = 64
        self.dilation = 1
        if replace_stride_with_dilation is None:
            # each element in the tuple indicates if we should replace
            # the 2x2 stride with a dilated convolution instead
            replace_stride_with_dilation = [False, False, False]
        if len(replace_stride_with_dilation) != 3:
            raise ValueError(
                "replace_stride_with_dilation should be None "
                "or a 3-element tuple, got {}".
                format(replace_stride_with_dilation)
            )
        self.groups = groups
        self.base_width = width_per_group
        self.conv1 = nn.Conv2d(
            3, self.inplanes, kernel_size=7, stride=2, padding=3, bias=False
        )
        self.bn1 = norm_layer(self.inplanes)
        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,
            dilate=replace_stride_with_dilation[0]
        )
        self.layer3 = self._make_layer(
            block,
            256,
            layers[2],
            stride=2,
            dilate=replace_stride_with_dilation[1]
        )
        self.layer4 = self._make_layer(
            block,
            512,
            layers[3],
            stride=last_stride,
            dilate=replace_stride_with_dilation[2]
        )
        self.global_avgpool = nn.AdaptiveAvgPool2d((1, 1))
        self.fc = self._construct_fc_layer(
            fc_dims, 512 * block.expansion, dropout_p
        )
        self.classifier = nn.Linear(self.feature_dim, num_classes)
        self._init_params()

        # 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, dilate=False):
        norm_layer = self._norm_layer
        downsample = None
        previous_dilation = self.dilation
        if dilate:
            self.dilation *= stride
            stride = 1
        if stride != 1 or self.inplanes != planes * block.expansion:
            downsample = nn.Sequential(
                conv1x1(self.inplanes, planes * block.expansion, stride),
                norm_layer(planes * block.expansion),
            )

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

        return nn.Sequential(*layers)

    def _construct_fc_layer(self, fc_dims, input_dim, dropout_p=None):
        """Constructs fully connected layer
        Args:
            fc_dims (list or tuple): dimensions of fc layers, if None, no fc layers are constructed
            input_dim (int): input dimension
            dropout_p (float): dropout probability, if None, dropout is unused
        """
        if fc_dims is None:
            self.feature_dim = input_dim
            return None

        assert isinstance(
            fc_dims, (list, tuple)
        ), 'fc_dims must be either list or tuple, but got {}'.format(
            type(fc_dims)
        )

        layers = []
        for dim in fc_dims:
            layers.append(nn.Linear(input_dim, dim))
            layers.append(nn.BatchNorm1d(dim))
            layers.append(nn.ReLU(inplace=True))
            if dropout_p is not None:
                layers.append(nn.Dropout(p=dropout_p))
            input_dim = dim

        self.feature_dim = fc_dims[-1]

        return nn.Sequential(*layers)

    def _init_params(self):
        for m in self.modules():
            if isinstance(m, nn.Conv2d):
                nn.init.kaiming_normal_(
                    m.weight, mode='fan_out', nonlinearity='relu'
                )
                if m.bias is not None:
                    nn.init.constant_(m.bias, 0)
            elif isinstance(m, nn.BatchNorm2d):
                nn.init.constant_(m.weight, 1)
                nn.init.constant_(m.bias, 0)
            elif isinstance(m, nn.BatchNorm1d):
                nn.init.constant_(m.weight, 1)
                nn.init.constant_(m.bias, 0)
            elif isinstance(m, nn.Linear):
                nn.init.normal_(m.weight, 0, 0.01)
                if m.bias is not None:
                    nn.init.constant_(m.bias, 0)

    def featuremaps_R(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)
        return x


    def forward(self, x, camid):
        f = self.featuremaps_R(x)
        v = self.global_avgpool(f)
        v = v.view(v.size(0), -1)

        if self.fc is not None:
            v = self.fc(v)

        if not self.training:
            return v

        y = self.classifier(v)

        if self.loss == 'softmax':
            return y
        elif self.loss == 'triplet':
            return y, v
        else:
            raise KeyError("Unsupported loss: {}".format(self.loss))


class DimReduceLayer(nn.Module):

    def __init__(self, in_channels, out_channels, nonlinear):
        super(DimReduceLayer, self).__init__()
        layers = []
        layers.append(
            nn.Conv2d(
                in_channels, out_channels, 1, stride=1, padding=0, bias=False
            )
        )
        layers.append(nn.BatchNorm2d(out_channels))

        if nonlinear == 'relu':
            layers.append(nn.ReLU(inplace=True))
        elif nonlinear == 'leakyrelu':
            layers.append(nn.LeakyReLU(0.1))

        self.layers = nn.Sequential(*layers)

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

class ChannelAttention(nn.Module):
    def __init__(self, in_planes, ratio=16):
        super(ChannelAttention, self).__init__()
        self.avg_pool = nn.AdaptiveAvgPool2d(1)
        self.max_pool = nn.AdaptiveMaxPool2d(1)
           
        self.fc = nn.Sequential(nn.Conv2d(in_planes, in_planes // 16, 1, bias=False),
                               nn.ReLU(),
                               nn.Conv2d(in_planes // 16, in_planes, 1, bias=False))
        self.sigmoid = nn.Sigmoid()

    def forward(self, x):
        avg_out = self.fc(self.avg_pool(x))
        max_out = self.fc(self.max_pool(x))
        out = avg_out + max_out
        return self.sigmoid(out)



class ResNetIEEE(nn.Module):
    """Residual network.
    
    Reference:
        - He et al. Deep Residual Learning for Image Recognition. CVPR 2016.
        - Xie et al. Aggregated Residual Transformations for Deep Neural Networks. CVPR 2017.
    Public keys:
        - ``resnet18``: ResNet18.
        - ``resnet34``: ResNet34.
        - ``resnet50``: ResNet50.
        - ``resnet101``: ResNet101.
        - ``resnet152``: ResNet152.
        - ``resnext50_32x4d``: ResNeXt50.
        - ``resnext101_32x8d``: ResNeXt101.
        - ``resnet50_fc512``: ResNet50 + FC.
    """

    def __init__(
        self,
        num_classes,
        loss,
        block,
        layers,
        zero_init_residual=False,
        groups=1,
        width_per_group=64,
        replace_stride_with_dilation=None,
        norm_layer=None,
        last_stride=2,
        fc_dims=None,
        dropout_p=None,
        **kwargs
    ):
        super(ResNetIEEE, self).__init__()
        if norm_layer is None:
            norm_layer = nn.BatchNorm2d
        self._norm_layer = norm_layer
        self.loss = loss
        self.feature_dim = 512 * block.expansion
        self.inplanes = 64
        self.dilation = 1
        if replace_stride_with_dilation is None:
            # each element in the tuple indicates if we should replace
            # the 2x2 stride with a dilated convolution instead
            replace_stride_with_dilation = [False, False, False]
        if len(replace_stride_with_dilation) != 3:
            raise ValueError(
                "replace_stride_with_dilation should be None "
                "or a 3-element tuple, got {}".
                format(replace_stride_with_dilation)
            )
        self.groups = groups
        self.base_width = width_per_group
        self.conv1 = nn.Conv2d(
            3, self.inplanes, kernel_size=7, stride=2, padding=3, bias=False
        )
        self.bn1 = norm_layer(self.inplanes)
        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,
            dilate=replace_stride_with_dilation[0]
        )
        self.layer3 = self._make_layer(
            block,
            256,
            layers[2],
            stride=2,
            dilate=replace_stride_with_dilation[1]
        )
        self.layer4 = self._make_layer(
            block,
            512,
            layers[3],
            stride=last_stride,
            dilate=replace_stride_with_dilation[2]
        )


        self._init_params()

        # 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, dilate=False):
        norm_layer = self._norm_layer
        downsample = None
        previous_dilation = self.dilation
        if dilate:
            self.dilation *= stride
            stride = 1
        if stride != 1 or self.inplanes != planes * block.expansion:
            downsample = nn.Sequential(
                conv1x1(self.inplanes, planes * block.expansion, stride),
                norm_layer(planes * block.expansion),
            )

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

        return nn.Sequential(*layers)

    def _construct_fc_layer(self, fc_dims, input_dim, dropout_p=None):
        """Constructs fully connected layer
        Args:
            fc_dims (list or tuple): dimensions of fc layers, if None, no fc layers are constructed
            input_dim (int): input dimension
            dropout_p (float): dropout probability, if None, dropout is unused
        """
        if fc_dims is None:
            self.feature_dim = input_dim
            return None

        assert isinstance(
            fc_dims, (list, tuple)
        ), 'fc_dims must be either list or tuple, but got {}'.format(
            type(fc_dims)
        )

        layers = []
        for dim in fc_dims:
            layers.append(nn.Linear(input_dim, dim))
            layers.append(nn.BatchNorm1d(dim))
            layers.append(nn.ReLU(inplace=True))
            if dropout_p is not None:
                layers.append(nn.Dropout(p=dropout_p))
            input_dim = dim

        self.feature_dim = fc_dims[-1]

        return nn.Sequential(*layers)

    def _init_params(self):
        for m in self.modules():
            if isinstance(m, nn.Conv2d):
                nn.init.kaiming_normal_(
                    m.weight, mode='fan_out', nonlinearity='relu'
                )
                if m.bias is not None:
                    nn.init.constant_(m.bias, 0)
            elif isinstance(m, nn.BatchNorm2d):
                nn.init.constant_(m.weight, 1)
                nn.init.constant_(m.bias, 0)
            elif isinstance(m, nn.BatchNorm1d):
                nn.init.constant_(m.weight, 1)
                nn.init.constant_(m.bias, 0)
            elif isinstance(m, nn.Linear):
                nn.init.normal_(m.weight, 0, 0.01)
                if m.bias is not None:
                    nn.init.constant_(m.bias, 0)

    def featuremaps(self, x):
        x = self.conv1(x)
        x = self.bn1(x)
        x = self.relu(x)
        x = self.maxpool(x)
        x_layer1 = self.layer1(x)
        x_layer2 = self.layer2(x_layer1)
        x_layer3 = self.layer3(x_layer2)
        x_layer4 = self.layer4(x_layer3)
        return x_layer1, x_layer2, x_layer3, x_layer4

    def forward(self, x):
        x_layer1, x_layer2, x_layer3, x_layer4 = self.featuremaps(x)
        return x_layer1, x_layer2, x_layer3, x_layer4



class ResNetIEEE_3modal(nn.Module):
    """Residual network.
    
    Reference:
        - He et al. Deep Residual Learning for Image Recognition. CVPR 2016.
        - Xie et al. Aggregated Residual Transformations for Deep Neural Networks. CVPR 2017.
    Public keys:
        - ``resnet18``: ResNet18.
        - ``resnet34``: ResNet34.
        - ``resnet50``: ResNet50.
        - ``resnet101``: ResNet101.
        - ``resnet152``: ResNet152.
        - ``resnext50_32x4d``: ResNeXt50.
        - ``resnext101_32x8d``: ResNeXt101.
        - ``resnet50_fc512``: ResNet50 + FC.
    """

    def __init__(
        self,
        num_classes,
        loss,
        block,
        layers,
        zero_init_residual=False,
        groups=1,
        width_per_group=64,
        replace_stride_with_dilation=None,
        norm_layer=None,
        last_stride=2,
        fc_dims=None,
        dropout_p=None,
        **kwargs
    ):
        super(ResNetIEEE_3modal, self).__init__()
        if norm_layer is None:
            norm_layer = nn.BatchNorm2d
        self._norm_layer = norm_layer
        self.loss = loss
        self.feature_dim = 512 * block.expansion
        self.inplanes = 64
        self.dilation = 1
        if replace_stride_with_dilation is None:
            # each element in the tuple indicates if we should replace
            # the 2x2 stride with a dilated convolution instead
            replace_stride_with_dilation = [False, False, False]
        if len(replace_stride_with_dilation) != 3:
            raise ValueError(
                "replace_stride_with_dilation should be None "
                "or a 3-element tuple, got {}".
                format(replace_stride_with_dilation)
            )
        self.groups = groups
        self.base_width = width_per_group
        self.conv1 = nn.Conv2d(
            3, self.inplanes, kernel_size=7, stride=2, padding=3, bias=False
        )

        self.bn1 = norm_layer(self.inplanes)
        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,
            dilate=replace_stride_with_dilation[0]
        )
        self.layer3 = self._make_layer(
            block,
            256,
            layers[2],
            stride=2,
            dilate=replace_stride_with_dilation[1]
        )
        self.layer4 = self._make_layer(
            block,
            512,
            layers[3],
            stride=last_stride,
            dilate=replace_stride_with_dilation[2]
        )

        
        self.improveChannel = DimReduceLayer(2048, 1024, nonlinear='relu')
        layer1_channel_dim = 256
        layer2_channel_dim = 512
        layer3_channel_dim = 1024
        self.convOne_layer = nn.ModuleList(
            [
                # DimReduceLayer(layer1_channel_dim, layer1_channel_dim, nonlinear='relu'),
                # DimReduceLayer(layer2_channel_dim, layer2_channel_dim, nonlinear='relu'),
                DimReduceLayer(layer3_channel_dim, layer3_channel_dim, nonlinear='relu')
            ]
        )
        self.convAvgRest_layer = nn.ModuleList(
            [
                # DimReduceLayer(layer1_channel_dim, layer1_channel_dim, nonlinear='relu'),
                # DimReduceLayer(layer2_channel_dim, layer2_channel_dim, nonlinear='relu'),
                DimReduceLayer(layer3_channel_dim, layer3_channel_dim, nonlinear='relu')
            ]
        )
        self.CA_layer = nn.ModuleList(
            [
                # ChannelAttention(layer1_channel_dim),
                # ChannelAttention(layer2_channel_dim),
                ChannelAttention(layer3_channel_dim)
            ]
        )

        self._init_params()

        # 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 crossModalInteractionModule(self, One, Rest1, index):
        conv_feature_one = self.convOne_layer[index](One)
        # conv_feature_avgRest = self.convAvgRest[index](torch.cat([Rest1, Rest2], dim=1))
        conv_feature_avgRest = self.convAvgRest_layer[index](Rest1)
        conv_feature_avgRest1 = self.CA_layer[index](conv_feature_avgRest) * conv_feature_avgRest + conv_feature_avgRest
        interaction_feature = conv_feature_one + conv_feature_avgRest1
        return interaction_feature


    def _make_layer(self, block, planes, blocks, stride=1, dilate=False):
        norm_layer = self._norm_layer
        downsample = None
        previous_dilation = self.dilation
        if dilate:
            self.dilation *= stride
            stride = 1
        if stride != 1 or self.inplanes != planes * block.expansion:
            downsample = nn.Sequential(
                conv1x1(self.inplanes, planes * block.expansion, stride),
                norm_layer(planes * block.expansion),
            )

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

        return nn.Sequential(*layers)

    def _construct_fc_layer(self, fc_dims, input_dim, dropout_p=None):
        """Constructs fully connected layer
        Args:
            fc_dims (list or tuple): dimensions of fc layers, if None, no fc layers are constructed
            input_dim (int): input dimension
            dropout_p (float): dropout probability, if None, dropout is unused
        """
        if fc_dims is None:
            self.feature_dim = input_dim
            return None

        assert isinstance(
            fc_dims, (list, tuple)
        ), 'fc_dims must be either list or tuple, but got {}'.format(
            type(fc_dims)
        )

        layers = []
        for dim in fc_dims:
            layers.append(nn.Linear(input_dim, dim))
            layers.append(nn.BatchNorm1d(dim))
            layers.append(nn.ReLU(inplace=True))
            if dropout_p is not None:
                layers.append(nn.Dropout(p=dropout_p))
            input_dim = dim

        self.feature_dim = fc_dims[-1]

        return nn.Sequential(*layers)

    def _init_params(self):
        for m in self.modules():
            if isinstance(m, nn.Conv2d):
                nn.init.kaiming_normal_(
                    m.weight, mode='fan_out', nonlinearity='relu'
                )
                if m.bias is not None:
                    nn.init.constant_(m.bias, 0)
            elif isinstance(m, nn.BatchNorm2d):
                nn.init.constant_(m.weight, 1)
                nn.init.constant_(m.bias, 0)
            elif isinstance(m, nn.BatchNorm1d):
                nn.init.constant_(m.weight, 1)
                nn.init.constant_(m.bias, 0)
            elif isinstance(m, nn.Linear):
                nn.init.normal_(m.weight, 0, 0.01)
                if m.bias is not None:
                    nn.init.constant_(m.bias, 0)

    def forward(self, rest, one_feat):
        one_feat = F.interpolate(one_feat, size=None, scale_factor=4, mode='bilinear', align_corners=None)
        one_feat = self.improveChannel(one_feat)
        

        rest = self.conv1(rest)
        rest = self.bn1(rest)
        rest = self.relu(rest)
        rest = self.maxpool(rest)
        ## rest after layer1: torch.Size([32, 256, 64, 32])
        rest = self.layer1(rest)


        ## rest after layer2: torch.Size([32, 512, 32, 16])
        rest = self.layer2(rest)
        # rest = self.crossModalInteractionModule(rest, one_feat, index=0)


        ## rest after layer3: torch.Size([32, 1024, 16, 8])
        rest = self.layer3(rest)


        ## rest after layer4: torch.Size([32, 2048, 16, 8])
        rest = self.layer4(rest)
        return rest


class ResNetBackbone(nn.Module):
    """Residual network.
    
    Reference:
        - He et al. Deep Residual Learning for Image Recognition. CVPR 2016.
        - Xie et al. Aggregated Residual Transformations for Deep Neural Networks. CVPR 2017.

    Public keys:
        - ``resnet18``: ResNet18.
        - ``resnet34``: ResNet34.
        - ``resnet50``: ResNet50.
        - ``resnet101``: ResNet101.
        - ``resnet152``: ResNet152.
        - ``resnext50_32x4d``: ResNeXt50.
        - ``resnext101_32x8d``: ResNeXt101.
        - ``resnet50_fc512``: ResNet50 + FC.
    """

    def __init__(
        self,
        block,
        layers,
        zero_init_residual=False,
        groups=1,
        width_per_group=64,
        replace_stride_with_dilation=None,
        norm_layer=None,
        last_stride=2,
        **kwargs
    ):
        super(ResNetBackbone, self).__init__()
        if norm_layer is None:
            norm_layer = nn.BatchNorm2d
        self._norm_layer = norm_layer
        self.feature_dim = 512 * block.expansion
        self.inplanes = 64
        self.dilation = 1
        if replace_stride_with_dilation is None:
            # each element in the tuple indicates if we should replace
            # the 2x2 stride with a dilated convolution instead
            replace_stride_with_dilation = [False, False, False]
        if len(replace_stride_with_dilation) != 3:
            raise ValueError(
                "replace_stride_with_dilation should be None "
                "or a 3-element tuple, got {}".
                format(replace_stride_with_dilation)
            )
        self.groups = groups
        self.base_width = width_per_group
        self.conv1 = nn.Conv2d(
            3, self.inplanes, kernel_size=7, stride=2, padding=3, bias=False
        )
        self.bn1 = norm_layer(self.inplanes)
        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,
            dilate=replace_stride_with_dilation[0]
        )
        self.layer3 = self._make_layer(
            block,
            256,
            layers[2],
            stride=2,
            dilate=replace_stride_with_dilation[1]
        )
        self.layer4 = self._make_layer(
            block,
            512,
            layers[3],
            stride=last_stride,
            dilate=replace_stride_with_dilation[2]
        )

        self._init_params()

        # 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, dilate=False):
        norm_layer = self._norm_layer
        downsample = None
        previous_dilation = self.dilation
        if dilate:
            self.dilation *= stride
            stride = 1
        if stride != 1 or self.inplanes != planes * block.expansion:
            downsample = nn.Sequential(
                conv1x1(self.inplanes, planes * block.expansion, stride),
                norm_layer(planes * block.expansion),
            )

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

        return nn.Sequential(*layers)

    def _construct_fc_layer(self, fc_dims, input_dim, dropout_p=None):
        """Constructs fully connected layer

        Args:
            fc_dims (list or tuple): dimensions of fc layers, if None, no fc layers are constructed
            input_dim (int): input dimension
            dropout_p (float): dropout probability, if None, dropout is unused
        """
        if fc_dims is None:
            self.feature_dim = input_dim
            return None

        assert isinstance(
            fc_dims, (list, tuple)
        ), 'fc_dims must be either list or tuple, but got {}'.format(
            type(fc_dims)
        )

        layers = []
        for dim in fc_dims:
            layers.append(nn.Linear(input_dim, dim))
            layers.append(nn.BatchNorm1d(dim))
            layers.append(nn.ReLU(inplace=True))
            if dropout_p is not None:
                layers.append(nn.Dropout(p=dropout_p))
            input_dim = dim

        self.feature_dim = fc_dims[-1]

        return nn.Sequential(*layers)

    def _init_params(self):
        for m in self.modules():
            if isinstance(m, nn.Conv2d):
                nn.init.kaiming_normal_(
                    m.weight, mode='fan_out', nonlinearity='relu'
                )
                if m.bias is not None:
                    nn.init.constant_(m.bias, 0)
            elif isinstance(m, nn.BatchNorm2d):
                nn.init.constant_(m.weight, 1)
                nn.init.constant_(m.bias, 0)
            elif isinstance(m, nn.BatchNorm1d):
                nn.init.constant_(m.weight, 1)
                nn.init.constant_(m.bias, 0)
            elif isinstance(m, nn.Linear):
                nn.init.normal_(m.weight, 0, 0.01)
                if m.bias is not None:
                    nn.init.constant_(m.bias, 0)

    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)
        return x







def init_pretrained_weights(model, model_url):
    """Initializes model with pretrained weights.
    
    Layers that don't match with pretrained layers in name or size are kept unchanged.
    """
    pretrain_dict = model_zoo.load_url(model_url)
    model_dict = model.state_dict()
    
    pretrain_dict = {
        k: v
        for k, v in pretrain_dict.items()
        if k in model_dict and model_dict[k].size() == v.size()
    }
    model_dict.update(pretrain_dict)
    model.load_state_dict(model_dict)


"""ResNet"""


def resnet18(num_classes, loss='softmax', pretrained=True, **kwargs):
    model = ResNet(
        num_classes=num_classes,
        loss=loss,
        block=BasicBlock,
        layers=[2, 2, 2, 2],
        last_stride=2,
        fc_dims=None,
        dropout_p=None,
        **kwargs
    )
    if pretrained:
        init_pretrained_weights(model, model_urls['resnet18'])
    return model


def resnet34(num_classes, loss='softmax', pretrained=True, **kwargs):
    model = ResNet(
        num_classes=num_classes,
        loss=loss,
        block=BasicBlock,
        layers=[3, 4, 6, 3],
        last_stride=2,
        fc_dims=None,
        dropout_p=None,
        **kwargs
    )
    if pretrained:
        init_pretrained_weights(model, model_urls['resnet34'])
    return model


def resnet50(num_classes, loss='softmax', pretrained=True, **kwargs):
    model = ResNet(
        num_classes=num_classes,
        loss=loss,
        block=Bottleneck,
        layers=[3, 4, 6, 3],
        last_stride=2,
        fc_dims=None,
        dropout_p=None,
        **kwargs
    )
    if pretrained:
        init_pretrained_weights(model, model_urls['resnet50'])
    return model


def resnet50backbone(num_classes, pretrained=True, **kwargs):
    model = ResNetBackbone(
        num_classes=num_classes,
        block=Bottleneck,
        layers=[3, 4, 6, 3],
        last_stride=1,
        **kwargs
    )
    if pretrained:
        init_pretrained_weights(model, model_urls['resnet50'])
    return model


def resnet101(num_classes, loss='softmax', pretrained=True, **kwargs):
    model = ResNet(
        num_classes=num_classes,
        loss=loss,
        block=Bottleneck,
        layers=[3, 4, 23, 3],
        last_stride=2,
        fc_dims=None,
        dropout_p=None,
        **kwargs
    )
    if pretrained:
        init_pretrained_weights(model, model_urls['resnet101'])
    return model


def resnet152(num_classes, loss='softmax', pretrained=True, **kwargs):
    model = ResNet(
        num_classes=num_classes,
        loss=loss,
        block=Bottleneck,
        layers=[3, 8, 36, 3],
        last_stride=2,
        fc_dims=None,
        dropout_p=None,
        **kwargs
    )
    if pretrained:
        init_pretrained_weights(model, model_urls['resnet152'])
    return model


"""ResNeXt"""


def resnext50_32x4d(num_classes, loss='softmax', pretrained=True, **kwargs):
    model = ResNet(
        num_classes=num_classes,
        loss=loss,
        block=Bottleneck,
        layers=[3, 4, 6, 3],
        last_stride=2,
        fc_dims=None,
        dropout_p=None,
        groups=32,
        width_per_group=4,
        **kwargs
    )
    if pretrained:
        init_pretrained_weights(model, model_urls['resnext50_32x4d'])
    return model


def resnext101_32x8d(num_classes, loss='softmax', pretrained=True, **kwargs):
    model = ResNet(
        num_classes=num_classes,
        loss=loss,
        block=Bottleneck,
        layers=[3, 4, 23, 3],
        last_stride=2,
        fc_dims=None,
        dropout_p=None,
        groups=32,
        width_per_group=8,
        **kwargs
    )
    if pretrained:
        init_pretrained_weights(model, model_urls['resnext101_32x8d'])
    return model


"""
ResNet + FC
"""


def resnet50_fc512(num_classes, loss='softmax', pretrained=True, **kwargs):
    model = ResNet(
        num_classes=num_classes,
        loss=loss,
        block=Bottleneck,
        layers=[3, 4, 6, 3],
        last_stride=1,
        fc_dims=[512],
        dropout_p=None,
        **kwargs
    )
    if pretrained:
        init_pretrained_weights(model, model_urls['resnet50'])
    return model


def resnet50_ieee(num_classes, loss='softmax', pretrained=True, **kwargs):
    model = ResNetIEEE(
        num_classes=num_classes,
        loss=loss,
        block=Bottleneck,
        layers=[3, 4, 6, 3],
        last_stride=1,
        fc_dims=[512],
        dropout_p=None,
        **kwargs
    )
    if pretrained:
        print("load pretrained weights...")
        init_pretrained_weights(model, model_urls['resnet50'])
    return model


def resnet50_ieee_3modal(num_classes, loss='softmax', pretrained=True, **kwargs):
    model = ResNetIEEE_3modal(
        num_classes=num_classes,
        loss=loss,
        block=Bottleneck,
        layers=[3, 4, 6, 3],
        last_stride=1,
        fc_dims=[512],
        dropout_p=None,
        **kwargs
    )
    if pretrained:
        print("load pretrained weights...")
        init_pretrained_weights(model, model_urls['resnet50'])
    return model


"""
Code source: https://github.com/pytorch/vision
"""
from __future__ import division, absolute_import
from torchreid.data.transforms import ColorAugmentation
from typing_extensions import final
from torch.nn.modules.pooling import AvgPool1d
import torch.utils.model_zoo as model_zoo
from torch import nn
import torch
import copy
import torch
import torch.nn.functional as F

__all__ = [
    'resnet18', 'resnet34', 'resnet50', 'resnet101', 'resnet152',
    'resnext50_32x4d', 'resnext101_32x8d', 'resnet50_fc512', 'resnet50_ieee', 'resnet50_ieee_3modal'
]

model_urls = {
    'resnet18':
    'https://download.pytorch.org/models/resnet18-5c106cde.pth',
    'resnet34':
    'https://download.pytorch.org/models/resnet34-333f7ec4.pth',
    'resnet50':
    'https://download.pytorch.org/models/resnet50-19c8e357.pth',
    'resnet101':
    'https://download.pytorch.org/models/resnet101-5d3b4d8f.pth',
    'resnet152':
    'https://download.pytorch.org/models/resnet152-b121ed2d.pth',
    'resnext50_32x4d':
    'https://download.pytorch.org/models/resnext50_32x4d-7cdf4587.pth',
    'resnext101_32x8d':
    'https://download.pytorch.org/models/resnext101_32x8d-8ba56ff5.pth',
}


def conv3x3(in_planes, out_planes, stride=1, groups=1, dilation=1):
    """3x3 convolution with padding"""
    return nn.Conv2d(
        in_planes,
        out_planes,
        kernel_size=3,
        stride=stride,
        padding=dilation,
        groups=groups,
        bias=False,
        dilation=dilation
    )


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 DimReduceLayer(nn.Module):

    def __init__(self, in_channels, out_channels, nonlinear):
        super(DimReduceLayer, self).__init__()
        layers = []
        layers.append(
            nn.Conv2d(
                in_channels, out_channels, 1, stride=1, padding=0, bias=False
            )
        )
        layers.append(nn.BatchNorm2d(out_channels))

        if nonlinear == 'relu':
            layers.append(nn.ReLU(inplace=True))
        elif nonlinear == 'leakyrelu':
            layers.append(nn.LeakyReLU(0.1))

        self.layers = nn.Sequential(*layers)

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



class BasicBlock(nn.Module):
    expansion = 1

    def __init__(
        self,
        inplanes,
        planes,
        stride=1,
        downsample=None,
        groups=1,
        base_width=64,
        dilation=1,
        norm_layer=None
    ):
        super(BasicBlock, self).__init__()
        if norm_layer is None:
            norm_layer = nn.BatchNorm2d
        if groups != 1 or base_width != 64:
            raise ValueError(
                'BasicBlock only supports groups=1 and base_width=64'
            )
        if dilation > 1:
            raise NotImplementedError(
                "Dilation > 1 not supported in BasicBlock"
            )
        # Both self.conv1 and self.downsample layers downsample the input when stride != 1
        self.conv1 = conv3x3(inplanes, planes, stride)
        self.bn1 = norm_layer(planes)
        self.relu = nn.ReLU(inplace=True)
        self.conv2 = conv3x3(planes, planes)
        self.bn2 = norm_layer(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,
        groups=1,
        base_width=64,
        dilation=1,
        norm_layer=None
    ):
        super(Bottleneck, self).__init__()
        if norm_layer is None:
            norm_layer = nn.BatchNorm2d
        width = int(planes * (base_width/64.)) * groups
        # Both self.conv2 and self.downsample layers downsample the input when stride != 1
        self.conv1 = conv1x1(inplanes, width)
        self.bn1 = norm_layer(width)
        self.conv2 = conv3x3(width, width, stride, groups, dilation)
        self.bn2 = norm_layer(width)
        self.conv3 = conv1x1(width, planes * self.expansion)
        self.bn3 = norm_layer(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 ResNet(nn.Module):
    """Residual network.
    
    Reference:
        - He et al. Deep Residual Learning for Image Recognition. CVPR 2016.
        - Xie et al. Aggregated Residual Transformations for Deep Neural Networks. CVPR 2017.
    Public keys:
        - ``resnet18``: ResNet18.
        - ``resnet34``: ResNet34.
        - ``resnet50``: ResNet50.
        - ``resnet101``: ResNet101.
        - ``resnet152``: ResNet152.
        - ``resnext50_32x4d``: ResNeXt50.
        - ``resnext101_32x8d``: ResNeXt101.
        - ``resnet50_fc512``: ResNet50 + FC.
    """

    def __init__(
        self,
        num_classes,
        loss,
        block,
        layers,
        zero_init_residual=False,
        groups=1,
        width_per_group=64,
        replace_stride_with_dilation=None,
        norm_layer=None,
        last_stride=2,
        fc_dims=None,
        dropout_p=None,
        **kwargs
    ):
        super(ResNet, self).__init__()
        if norm_layer is None:
            norm_layer = nn.BatchNorm2d
        self._norm_layer = norm_layer
        self.loss = loss
        self.feature_dim = 512 * block.expansion
        self.inplanes = 64
        self.dilation = 1
        if replace_stride_with_dilation is None:
            # each element in the tuple indicates if we should replace
            # the 2x2 stride with a dilated convolution instead
            replace_stride_with_dilation = [False, False, False]
        if len(replace_stride_with_dilation) != 3:
            raise ValueError(
                "replace_stride_with_dilation should be None "
                "or a 3-element tuple, got {}".
                format(replace_stride_with_dilation)
            )
        self.groups = groups
        self.base_width = width_per_group
        self.conv1 = nn.Conv2d(
            3, self.inplanes, kernel_size=7, stride=2, padding=3, bias=False
        )
        self.bn1 = norm_layer(self.inplanes)
        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,
            dilate=replace_stride_with_dilation[0]
        )
        self.layer3 = self._make_layer(
            block,
            256,
            layers[2],
            stride=2,
            dilate=replace_stride_with_dilation[1]
        )
        self.layer4 = self._make_layer(
            block,
            512,
            layers[3],
            stride=last_stride,
            dilate=replace_stride_with_dilation[2]
        )
        self.global_avgpool = nn.AdaptiveAvgPool2d((1, 1))
        self.fc = self._construct_fc_layer(
            fc_dims, 512 * block.expansion, dropout_p
        )
        self.classifier = nn.Linear(self.feature_dim, num_classes)
        self._init_params()

        # 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, dilate=False):
        norm_layer = self._norm_layer
        downsample = None
        previous_dilation = self.dilation
        if dilate:
            self.dilation *= stride
            stride = 1
        if stride != 1 or self.inplanes != planes * block.expansion:
            downsample = nn.Sequential(
                conv1x1(self.inplanes, planes * block.expansion, stride),
                norm_layer(planes * block.expansion),
            )

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

        return nn.Sequential(*layers)

    def _construct_fc_layer(self, fc_dims, input_dim, dropout_p=None):
        """Constructs fully connected layer
        Args:
            fc_dims (list or tuple): dimensions of fc layers, if None, no fc layers are constructed
            input_dim (int): input dimension
            dropout_p (float): dropout probability, if None, dropout is unused
        """
        if fc_dims is None:
            self.feature_dim = input_dim
            return None

        assert isinstance(
            fc_dims, (list, tuple)
        ), 'fc_dims must be either list or tuple, but got {}'.format(
            type(fc_dims)
        )

        layers = []
        for dim in fc_dims:
            layers.append(nn.Linear(input_dim, dim))
            layers.append(nn.BatchNorm1d(dim))
            layers.append(nn.ReLU(inplace=True))
            if dropout_p is not None:
                layers.append(nn.Dropout(p=dropout_p))
            input_dim = dim

        self.feature_dim = fc_dims[-1]

        return nn.Sequential(*layers)

    def _init_params(self):
        for m in self.modules():
            if isinstance(m, nn.Conv2d):
                nn.init.kaiming_normal_(
                    m.weight, mode='fan_out', nonlinearity='relu'
                )
                if m.bias is not None:
                    nn.init.constant_(m.bias, 0)
            elif isinstance(m, nn.BatchNorm2d):
                nn.init.constant_(m.weight, 1)
                nn.init.constant_(m.bias, 0)
            elif isinstance(m, nn.BatchNorm1d):
                nn.init.constant_(m.weight, 1)
                nn.init.constant_(m.bias, 0)
            elif isinstance(m, nn.Linear):
                nn.init.normal_(m.weight, 0, 0.01)
                if m.bias is not None:
                    nn.init.constant_(m.bias, 0)

    def featuremaps_R(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)
        return x


    def forward(self, x, camid):
        f = self.featuremaps_R(x)
        v = self.global_avgpool(f)
        v = v.view(v.size(0), -1)

        if self.fc is not None:
            v = self.fc(v)

        if not self.training:
            return v

        y = self.classifier(v)

        if self.loss == 'softmax':
            return y
        elif self.loss == 'triplet':
            return y, v
        else:
            raise KeyError("Unsupported loss: {}".format(self.loss))


class DimReduceLayer(nn.Module):

    def __init__(self, in_channels, out_channels, nonlinear):
        super(DimReduceLayer, self).__init__()
        layers = []
        layers.append(
            nn.Conv2d(
                in_channels, out_channels, 1, stride=1, padding=0, bias=False
            )
        )
        layers.append(nn.BatchNorm2d(out_channels))

        if nonlinear == 'relu':
            layers.append(nn.ReLU(inplace=True))
        elif nonlinear == 'leakyrelu':
            layers.append(nn.LeakyReLU(0.1))

        self.layers = nn.Sequential(*layers)

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

class ChannelAttention(nn.Module):
    def __init__(self, in_planes, ratio=16):
        super(ChannelAttention, self).__init__()
        self.avg_pool = nn.AdaptiveAvgPool2d(1)
        self.max_pool = nn.AdaptiveMaxPool2d(1)
           
        self.fc = nn.Sequential(nn.Conv2d(in_planes, in_planes // 16, 1, bias=False),
                               nn.ReLU(),
                               nn.Conv2d(in_planes // 16, in_planes, 1, bias=False))
        self.sigmoid = nn.Sigmoid()

    def forward(self, x):
        avg_out = self.fc(self.avg_pool(x))
        max_out = self.fc(self.max_pool(x))
        out = avg_out + max_out
        return self.sigmoid(out)



class ResNetIEEE(nn.Module):
    """Residual network.
    
    Reference:
        - He et al. Deep Residual Learning for Image Recognition. CVPR 2016.
        - Xie et al. Aggregated Residual Transformations for Deep Neural Networks. CVPR 2017.
    Public keys:
        - ``resnet18``: ResNet18.
        - ``resnet34``: ResNet34.
        - ``resnet50``: ResNet50.
        - ``resnet101``: ResNet101.
        - ``resnet152``: ResNet152.
        - ``resnext50_32x4d``: ResNeXt50.
        - ``resnext101_32x8d``: ResNeXt101.
        - ``resnet50_fc512``: ResNet50 + FC.
    """

    def __init__(
        self,
        num_classes,
        loss,
        block,
        layers,
        zero_init_residual=False,
        groups=1,
        width_per_group=64,
        replace_stride_with_dilation=None,
        norm_layer=None,
        last_stride=2,
        fc_dims=None,
        dropout_p=None,
        **kwargs
    ):
        super(ResNetIEEE, self).__init__()
        if norm_layer is None:
            norm_layer = nn.BatchNorm2d
        self._norm_layer = norm_layer
        self.loss = loss
        self.feature_dim = 512 * block.expansion
        self.inplanes = 64
        self.dilation = 1
        if replace_stride_with_dilation is None:
            # each element in the tuple indicates if we should replace
            # the 2x2 stride with a dilated convolution instead
            replace_stride_with_dilation = [False, False, False]
        if len(replace_stride_with_dilation) != 3:
            raise ValueError(
                "replace_stride_with_dilation should be None "
                "or a 3-element tuple, got {}".
                format(replace_stride_with_dilation)
            )
        self.groups = groups
        self.base_width = width_per_group
        self.conv1 = nn.Conv2d(
            3, self.inplanes, kernel_size=7, stride=2, padding=3, bias=False
        )
        self.bn1 = norm_layer(self.inplanes)
        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,
            dilate=replace_stride_with_dilation[0]
        )
        self.layer3 = self._make_layer(
            block,
            256,
            layers[2],
            stride=2,
            dilate=replace_stride_with_dilation[1]
        )
        self.layer4 = self._make_layer(
            block,
            512,
            layers[3],
            stride=last_stride,
            dilate=replace_stride_with_dilation[2]
        )


        self._init_params()

        # 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, dilate=False):
        norm_layer = self._norm_layer
        downsample = None
        previous_dilation = self.dilation
        if dilate:
            self.dilation *= stride
            stride = 1
        if stride != 1 or self.inplanes != planes * block.expansion:
            downsample = nn.Sequential(
                conv1x1(self.inplanes, planes * block.expansion, stride),
                norm_layer(planes * block.expansion),
            )

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

        return nn.Sequential(*layers)

    def _construct_fc_layer(self, fc_dims, input_dim, dropout_p=None):
        """Constructs fully connected layer
        Args:
            fc_dims (list or tuple): dimensions of fc layers, if None, no fc layers are constructed
            input_dim (int): input dimension
            dropout_p (float): dropout probability, if None, dropout is unused
        """
        if fc_dims is None:
            self.feature_dim = input_dim
            return None

        assert isinstance(
            fc_dims, (list, tuple)
        ), 'fc_dims must be either list or tuple, but got {}'.format(
            type(fc_dims)
        )

        layers = []
        for dim in fc_dims:
            layers.append(nn.Linear(input_dim, dim))
            layers.append(nn.BatchNorm1d(dim))
            layers.append(nn.ReLU(inplace=True))
            if dropout_p is not None:
                layers.append(nn.Dropout(p=dropout_p))
            input_dim = dim

        self.feature_dim = fc_dims[-1]

        return nn.Sequential(*layers)

    def _init_params(self):
        for m in self.modules():
            if isinstance(m, nn.Conv2d):
                nn.init.kaiming_normal_(
                    m.weight, mode='fan_out', nonlinearity='relu'
                )
                if m.bias is not None:
                    nn.init.constant_(m.bias, 0)
            elif isinstance(m, nn.BatchNorm2d):
                nn.init.constant_(m.weight, 1)
                nn.init.constant_(m.bias, 0)
            elif isinstance(m, nn.BatchNorm1d):
                nn.init.constant_(m.weight, 1)
                nn.init.constant_(m.bias, 0)
            elif isinstance(m, nn.Linear):
                nn.init.normal_(m.weight, 0, 0.01)
                if m.bias is not None:
                    nn.init.constant_(m.bias, 0)

    def featuremaps(self, x):
        x = self.conv1(x)
        x = self.bn1(x)
        x = self.relu(x)
        x = self.maxpool(x)
        x_layer1 = self.layer1(x)
        x_layer2 = self.layer2(x_layer1)
        x_layer3 = self.layer3(x_layer2)
        x_layer4 = self.layer4(x_layer3)
        return x_layer1, x_layer2, x_layer3, x_layer4

    def forward(self, x):
        x_layer1, x_layer2, x_layer3, x_layer4 = self.featuremaps(x)
        return x_layer1, x_layer2, x_layer3, x_layer4



class ResNetIEEE_3modal(nn.Module):
    """Residual network.
    
    Reference:
        - He et al. Deep Residual Learning for Image Recognition. CVPR 2016.
        - Xie et al. Aggregated Residual Transformations for Deep Neural Networks. CVPR 2017.
    Public keys:
        - ``resnet18``: ResNet18.
        - ``resnet34``: ResNet34.
        - ``resnet50``: ResNet50.
        - ``resnet101``: ResNet101.
        - ``resnet152``: ResNet152.
        - ``resnext50_32x4d``: ResNeXt50.
        - ``resnext101_32x8d``: ResNeXt101.
        - ``resnet50_fc512``: ResNet50 + FC.
    """

    def __init__(
        self,
        num_classes,
        loss,
        block,
        layers,
        zero_init_residual=False,
        groups=1,
        width_per_group=64,
        replace_stride_with_dilation=None,
        norm_layer=None,
        last_stride=2,
        fc_dims=None,
        dropout_p=None,
        **kwargs
    ):
        super(ResNetIEEE_3modal, self).__init__()
        if norm_layer is None:
            norm_layer = nn.BatchNorm2d
        self._norm_layer = norm_layer
        self.loss = loss
        self.feature_dim = 512 * block.expansion
        self.inplanes = 64
        self.dilation = 1
        if replace_stride_with_dilation is None:
            # each element in the tuple indicates if we should replace
            # the 2x2 stride with a dilated convolution instead
            replace_stride_with_dilation = [False, False, False]
        if len(replace_stride_with_dilation) != 3:
            raise ValueError(
                "replace_stride_with_dilation should be None "
                "or a 3-element tuple, got {}".
                format(replace_stride_with_dilation)
            )
        self.groups = groups
        self.base_width = width_per_group
        self.conv1 = nn.Conv2d(
            3, self.inplanes, kernel_size=7, stride=2, padding=3, bias=False
        )

        self.bn1 = norm_layer(self.inplanes)
        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,
            dilate=replace_stride_with_dilation[0]
        )
        self.layer3 = self._make_layer(
            block,
            256,
            layers[2],
            stride=2,
            dilate=replace_stride_with_dilation[1]
        )
        self.layer4 = self._make_layer(
            block,
            512,
            layers[3],
            stride=last_stride,
            dilate=replace_stride_with_dilation[2]
        )

        
        self.improveChannel = DimReduceLayer(2048, 1024, nonlinear='relu')
        layer1_channel_dim = 256
        layer2_channel_dim = 512
        layer3_channel_dim = 1024
        self.convOne_layer = nn.ModuleList(
            [
                # DimReduceLayer(layer1_channel_dim, layer1_channel_dim, nonlinear='relu'),
                # DimReduceLayer(layer2_channel_dim, layer2_channel_dim, nonlinear='relu'),
                DimReduceLayer(layer3_channel_dim, layer3_channel_dim, nonlinear='relu')
            ]
        )
        self.convAvgRest_layer = nn.ModuleList(
            [
                # DimReduceLayer(layer1_channel_dim, layer1_channel_dim, nonlinear='relu'),
                # DimReduceLayer(layer2_channel_dim, layer2_channel_dim, nonlinear='relu'),
                DimReduceLayer(layer3_channel_dim, layer3_channel_dim, nonlinear='relu')
            ]
        )
        self.CA_layer = nn.ModuleList(
            [
                # ChannelAttention(layer1_channel_dim),
                # ChannelAttention(layer2_channel_dim),
                ChannelAttention(layer3_channel_dim)
            ]
        )

        self._init_params()

        # 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 crossModalInteractionModule(self, One, Rest1, index):
        conv_feature_one = self.convOne_layer[index](One)
        # conv_feature_avgRest = self.convAvgRest[index](torch.cat([Rest1, Rest2], dim=1))
        conv_feature_avgRest = self.convAvgRest_layer[index](Rest1)
        conv_feature_avgRest1 = self.CA_layer[index](conv_feature_avgRest) * conv_feature_avgRest + conv_feature_avgRest
        interaction_feature = conv_feature_one + conv_feature_avgRest1
        return interaction_feature


    def _make_layer(self, block, planes, blocks, stride=1, dilate=False):
        norm_layer = self._norm_layer
        downsample = None
        previous_dilation = self.dilation
        if dilate:
            self.dilation *= stride
            stride = 1
        if stride != 1 or self.inplanes != planes * block.expansion:
            downsample = nn.Sequential(
                conv1x1(self.inplanes, planes * block.expansion, stride),
                norm_layer(planes * block.expansion),
            )

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

        return nn.Sequential(*layers)

    def _construct_fc_layer(self, fc_dims, input_dim, dropout_p=None):
        """Constructs fully connected layer
        Args:
            fc_dims (list or tuple): dimensions of fc layers, if None, no fc layers are constructed
            input_dim (int): input dimension
            dropout_p (float): dropout probability, if None, dropout is unused
        """
        if fc_dims is None:
            self.feature_dim = input_dim
            return None

        assert isinstance(
            fc_dims, (list, tuple)
        ), 'fc_dims must be either list or tuple, but got {}'.format(
            type(fc_dims)
        )

        layers = []
        for dim in fc_dims:
            layers.append(nn.Linear(input_dim, dim))
            layers.append(nn.BatchNorm1d(dim))
            layers.append(nn.ReLU(inplace=True))
            if dropout_p is not None:
                layers.append(nn.Dropout(p=dropout_p))
            input_dim = dim

        self.feature_dim = fc_dims[-1]

        return nn.Sequential(*layers)

    def _init_params(self):
        for m in self.modules():
            if isinstance(m, nn.Conv2d):
                nn.init.kaiming_normal_(
                    m.weight, mode='fan_out', nonlinearity='relu'
                )
                if m.bias is not None:
                    nn.init.constant_(m.bias, 0)
            elif isinstance(m, nn.BatchNorm2d):
                nn.init.constant_(m.weight, 1)
                nn.init.constant_(m.bias, 0)
            elif isinstance(m, nn.BatchNorm1d):
                nn.init.constant_(m.weight, 1)
                nn.init.constant_(m.bias, 0)
            elif isinstance(m, nn.Linear):
                nn.init.normal_(m.weight, 0, 0.01)
                if m.bias is not None:
                    nn.init.constant_(m.bias, 0)

    def forward(self, rest, one_feat):
        one_feat = F.interpolate(one_feat, size=None, scale_factor=4, mode='bilinear', align_corners=None)
        one_feat = self.improveChannel(one_feat)
        

        rest = self.conv1(rest)
        rest = self.bn1(rest)
        rest = self.relu(rest)
        rest = self.maxpool(rest)
        ## rest after layer1: torch.Size([32, 256, 64, 32])
        rest = self.layer1(rest)


        ## rest after layer2: torch.Size([32, 512, 32, 16])
        rest = self.layer2(rest)
        # rest = self.crossModalInteractionModule(rest, one_feat, index=0)


        ## rest after layer3: torch.Size([32, 1024, 16, 8])
        rest = self.layer3(rest)


        ## rest after layer4: torch.Size([32, 2048, 16, 8])
        rest = self.layer4(rest)
        return rest


class ResNetBackbone(nn.Module):
    """Residual network.
    
    Reference:
        - He et al. Deep Residual Learning for Image Recognition. CVPR 2016.
        - Xie et al. Aggregated Residual Transformations for Deep Neural Networks. CVPR 2017.

    Public keys:
        - ``resnet18``: ResNet18.
        - ``resnet34``: ResNet34.
        - ``resnet50``: ResNet50.
        - ``resnet101``: ResNet101.
        - ``resnet152``: ResNet152.
        - ``resnext50_32x4d``: ResNeXt50.
        - ``resnext101_32x8d``: ResNeXt101.
        - ``resnet50_fc512``: ResNet50 + FC.
    """

    def __init__(
        self,
        block,
        layers,
        zero_init_residual=False,
        groups=1,
        width_per_group=64,
        replace_stride_with_dilation=None,
        norm_layer=None,
        last_stride=2,
        **kwargs
    ):
        super(ResNetBackbone, self).__init__()
        if norm_layer is None:
            norm_layer = nn.BatchNorm2d
        self._norm_layer = norm_layer
        self.feature_dim = 512 * block.expansion
        self.inplanes = 64
        self.dilation = 1
        if replace_stride_with_dilation is None:
            # each element in the tuple indicates if we should replace
            # the 2x2 stride with a dilated convolution instead
            replace_stride_with_dilation = [False, False, False]
        if len(replace_stride_with_dilation) != 3:
            raise ValueError(
                "replace_stride_with_dilation should be None "
                "or a 3-element tuple, got {}".
                format(replace_stride_with_dilation)
            )
        self.groups = groups
        self.base_width = width_per_group
        self.conv1 = nn.Conv2d(
            3, self.inplanes, kernel_size=7, stride=2, padding=3, bias=False
        )
        self.bn1 = norm_layer(self.inplanes)
        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,
            dilate=replace_stride_with_dilation[0]
        )
        self.layer3 = self._make_layer(
            block,
            256,
            layers[2],
            stride=2,
            dilate=replace_stride_with_dilation[1]
        )
        self.layer4 = self._make_layer(
            block,
            512,
            layers[3],
            stride=last_stride,
            dilate=replace_stride_with_dilation[2]
        )

        self._init_params()

        # 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, dilate=False):
        norm_layer = self._norm_layer
        downsample = None
        previous_dilation = self.dilation
        if dilate:
            self.dilation *= stride
            stride = 1
        if stride != 1 or self.inplanes != planes * block.expansion:
            downsample = nn.Sequential(
                conv1x1(self.inplanes, planes * block.expansion, stride),
                norm_layer(planes * block.expansion),
            )

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

        return nn.Sequential(*layers)

    def _construct_fc_layer(self, fc_dims, input_dim, dropout_p=None):
        """Constructs fully connected layer

        Args:
            fc_dims (list or tuple): dimensions of fc layers, if None, no fc layers are constructed
            input_dim (int): input dimension
            dropout_p (float): dropout probability, if None, dropout is unused
        """
        if fc_dims is None:
            self.feature_dim = input_dim
            return None

        assert isinstance(
            fc_dims, (list, tuple)
        ), 'fc_dims must be either list or tuple, but got {}'.format(
            type(fc_dims)
        )

        layers = []
        for dim in fc_dims:
            layers.append(nn.Linear(input_dim, dim))
            layers.append(nn.BatchNorm1d(dim))
            layers.append(nn.ReLU(inplace=True))
            if dropout_p is not None:
                layers.append(nn.Dropout(p=dropout_p))
            input_dim = dim

        self.feature_dim = fc_dims[-1]

        return nn.Sequential(*layers)

    def _init_params(self):
        for m in self.modules():
            if isinstance(m, nn.Conv2d):
                nn.init.kaiming_normal_(
                    m.weight, mode='fan_out', nonlinearity='relu'
                )
                if m.bias is not None:
                    nn.init.constant_(m.bias, 0)
            elif isinstance(m, nn.BatchNorm2d):
                nn.init.constant_(m.weight, 1)
                nn.init.constant_(m.bias, 0)
            elif isinstance(m, nn.BatchNorm1d):
                nn.init.constant_(m.weight, 1)
                nn.init.constant_(m.bias, 0)
            elif isinstance(m, nn.Linear):
                nn.init.normal_(m.weight, 0, 0.01)
                if m.bias is not None:
                    nn.init.constant_(m.bias, 0)

    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)
        return x







def init_pretrained_weights(model, model_url):
    """Initializes model with pretrained weights.
    
    Layers that don't match with pretrained layers in name or size are kept unchanged.
    """
    pretrain_dict = model_zoo.load_url(model_url)
    model_dict = model.state_dict()
    
    pretrain_dict = {
        k: v
        for k, v in pretrain_dict.items()
        if k in model_dict and model_dict[k].size() == v.size()
    }
    model_dict.update(pretrain_dict)
    model.load_state_dict(model_dict)


"""ResNet"""


def resnet18(num_classes, loss='softmax', pretrained=True, **kwargs):
    model = ResNet(
        num_classes=num_classes,
        loss=loss,
        block=BasicBlock,
        layers=[2, 2, 2, 2],
        last_stride=2,
        fc_dims=None,
        dropout_p=None,
        **kwargs
    )
    if pretrained:
        init_pretrained_weights(model, model_urls['resnet18'])
    return model


def resnet34(num_classes, loss='softmax', pretrained=True, **kwargs):
    model = ResNet(
        num_classes=num_classes,
        loss=loss,
        block=BasicBlock,
        layers=[3, 4, 6, 3],
        last_stride=2,
        fc_dims=None,
        dropout_p=None,
        **kwargs
    )
    if pretrained:
        init_pretrained_weights(model, model_urls['resnet34'])
    return model


def resnet50(num_classes, loss='softmax', pretrained=True, **kwargs):
    model = ResNet(
        num_classes=num_classes,
        loss=loss,
        block=Bottleneck,
        layers=[3, 4, 6, 3],
        last_stride=2,
        fc_dims=None,
        dropout_p=None,
        **kwargs
    )
    if pretrained:
        init_pretrained_weights(model, model_urls['resnet50'])
    return model


def resnet50backbone(num_classes, pretrained=True, **kwargs):
    model = ResNetBackbone(
        num_classes=num_classes,
        block=Bottleneck,
        layers=[3, 4, 6, 3],
        last_stride=1,
        **kwargs
    )
    if pretrained:
        init_pretrained_weights(model, model_urls['resnet50'])
    return model


def resnet101(num_classes, loss='softmax', pretrained=True, **kwargs):
    model = ResNet(
        num_classes=num_classes,
        loss=loss,
        block=Bottleneck,
        layers=[3, 4, 23, 3],
        last_stride=2,
        fc_dims=None,
        dropout_p=None,
        **kwargs
    )
    if pretrained:
        init_pretrained_weights(model, model_urls['resnet101'])
    return model


def resnet152(num_classes, loss='softmax', pretrained=True, **kwargs):
    model = ResNet(
        num_classes=num_classes,
        loss=loss,
        block=Bottleneck,
        layers=[3, 8, 36, 3],
        last_stride=2,
        fc_dims=None,
        dropout_p=None,
        **kwargs
    )
    if pretrained:
        init_pretrained_weights(model, model_urls['resnet152'])
    return model


"""ResNeXt"""


def resnext50_32x4d(num_classes, loss='softmax', pretrained=True, **kwargs):
    model = ResNet(
        num_classes=num_classes,
        loss=loss,
        block=Bottleneck,
        layers=[3, 4, 6, 3],
        last_stride=2,
        fc_dims=None,
        dropout_p=None,
        groups=32,
        width_per_group=4,
        **kwargs
    )
    if pretrained:
        init_pretrained_weights(model, model_urls['resnext50_32x4d'])
    return model


def resnext101_32x8d(num_classes, loss='softmax', pretrained=True, **kwargs):
    model = ResNet(
        num_classes=num_classes,
        loss=loss,
        block=Bottleneck,
        layers=[3, 4, 23, 3],
        last_stride=2,
        fc_dims=None,
        dropout_p=None,
        groups=32,
        width_per_group=8,
        **kwargs
    )
    if pretrained:
        init_pretrained_weights(model, model_urls['resnext101_32x8d'])
    return model


"""
ResNet + FC
"""


def resnet50_fc512(num_classes, loss='softmax', pretrained=True, **kwargs):
    model = ResNet(
        num_classes=num_classes,
        loss=loss,
        block=Bottleneck,
        layers=[3, 4, 6, 3],
        last_stride=1,
        fc_dims=[512],
        dropout_p=None,
        **kwargs
    )
    if pretrained:
        init_pretrained_weights(model, model_urls['resnet50'])
    return model


def resnet50_ieee(num_classes, loss='softmax', pretrained=True, **kwargs):
    model = ResNetIEEE(
        num_classes=num_classes,
        loss=loss,
        block=Bottleneck,
        layers=[3, 4, 6, 3],
        last_stride=1,
        fc_dims=[512],
        dropout_p=None,
        **kwargs
    )
    if pretrained:
        print("load pretrained weights...")
        init_pretrained_weights(model, model_urls['resnet50'])
    return model


def resnet50_ieee_3modal(num_classes, loss='softmax', pretrained=True, **kwargs):
    model = ResNetIEEE_3modal(
        num_classes=num_classes,
        loss=loss,
        block=Bottleneck,
        layers=[3, 4, 6, 3],
        last_stride=1,
        fc_dims=[512],
        dropout_p=None,
        **kwargs
    )
    if pretrained:
        print("load pretrained weights...")
        init_pretrained_weights(model, model_urls['resnet50'])
    return model


Code source: https://github.com/pytorch/vision 3x3 convolution with padding 1x1 convolution # Both self.conv1 and self.downsample layers downsample the input when stride != 1 # Both self.conv2 and self.downsample layers downsample the input when stride != 1 Residual network.          Reference:         - He et al. Deep Residual Learning for Image Recognition. CVPR 2016.         - Xie et al. Aggregated Residual Transformations for Deep Neural Networks. CVPR 2017.     Public keys:         - ``resnet18``: ResNet18.         - ``resnet34``: ResNet34.         - ``resnet50``: ResNet50.         - ``resnet101``: ResNet101.         - ``resnet152``: ResNet152.         - ``resnext50_32x4d``: ResNeXt50.         - ``resnext101_32x8d``: ResNeXt101.         - ``resnet50_fc512``: ResNet50 + FC. # each element in the tuple indicates if we should replace # the 2x2 stride with a dilated convolution instead # 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 Constructs fully connected layer         Args:             fc_dims (list or tuple): dimensions of fc layers, if None, no fc layers are constructed             input_dim (int): input dimension             dropout_p (float): dropout probability, if None, dropout is unused Residual network.          Reference:         - He et al. Deep Residual Learning for Image Recognition. CVPR 2016.         - Xie et al. Aggregated Residual Transformations for Deep Neural Networks. CVPR 2017.     Public keys:         - ``resnet18``: ResNet18.         - ``resnet34``: ResNet34.         - ``resnet50``: ResNet50.         - ``resnet101``: ResNet101.         - ``resnet152``: ResNet152.         - ``resnext50_32x4d``: ResNeXt50.         - ``resnext101_32x8d``: ResNeXt101.         - ``resnet50_fc512``: ResNet50 + FC. # each element in the tuple indicates if we should replace # the 2x2 stride with a dilated convolution instead # 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 Constructs fully connected layer         Args:             fc_dims (list or tuple): dimensions of fc layers, if None, no fc layers are constructed             input_dim (int): input dimension             dropout_p (float): dropout probability, if None, dropout is unused Residual network.          Reference:         - He et al. Deep Residual Learning for Image Recognition. CVPR 2016.         - Xie et al. Aggregated Residual Transformations for Deep Neural Networks. CVPR 2017.     Public keys:         - ``resnet18``: ResNet18.         - ``resnet34``: ResNet34.         - ``resnet50``: ResNet50.         - ``resnet101``: ResNet101.         - ``resnet152``: ResNet152.         - ``resnext50_32x4d``: ResNeXt50.         - ``resnext101_32x8d``: ResNeXt101.         - ``resnet50_fc512``: ResNet50 + FC. # each element in the tuple indicates if we should replace # the 2x2 stride with a dilated convolution instead # DimReduceLayer(layer1_channel_dim, layer1_channel_dim, nonlinear='relu'), # DimReduceLayer(layer2_channel_dim, layer2_channel_dim, nonlinear='relu'), # DimReduceLayer(layer1_channel_dim, layer1_channel_dim, nonlinear='relu'), # DimReduceLayer(layer2_channel_dim, layer2_channel_dim, nonlinear='relu'), # ChannelAttention(layer1_channel_dim), # ChannelAttention(layer2_channel_dim), # 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 # conv_feature_avgRest = self.convAvgRest[index](torch.cat([Rest1, Rest2], dim=1)) Constructs fully connected layer         Args:             fc_dims (list or tuple): dimensions of fc layers, if None, no fc layers are constructed             input_dim (int): input dimension             dropout_p (float): dropout probability, if None, dropout is unused ## rest after layer1: torch.Size([32, 256, 64, 32]) ## rest after layer2: torch.Size([32, 512, 32, 16]) # rest = self.crossModalInteractionModule(rest, one_feat, index=0) ## rest after layer3: torch.Size([32, 1024, 16, 8]) ## rest after layer4: torch.Size([32, 2048, 16, 8]) Residual network.          Reference:         - He et al. Deep Residual Learning for Image Recognition. CVPR 2016.         - Xie et al. Aggregated Residual Transformations for Deep Neural Networks. CVPR 2017.      Public keys:         - ``resnet18``: ResNet18.         - ``resnet34``: ResNet34.         - ``resnet50``: ResNet50.         - ``resnet101``: ResNet101.         - ``resnet152``: ResNet152.         - ``resnext50_32x4d``: ResNeXt50.         - ``resnext101_32x8d``: ResNeXt101.         - ``resnet50_fc512``: ResNet50 + FC. # each element in the tuple indicates if we should replace # the 2x2 stride with a dilated convolution instead # 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 Constructs fully connected layer          Args:             fc_dims (list or tuple): dimensions of fc layers, if None, no fc layers are constructed             input_dim (int): input dimension             dropout_p (float): dropout probability, if None, dropout is unused Initializes model with pretrained weights.          Layers that don't match with pretrained layers in name or size are kept unchanged. ResNet ResNeXt ResNet + FC