"""
Code source: https://github.com/pytorch/vision
"""
from __future__ import division, absolute_import
import torch.utils.model_zoo as model_zoo
from torch import nn

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

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 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(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):
        f = self.featuremaps(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))


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 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