# modified from torchvision
import torch.nn as nn
from torch.nn import BatchNorm2d
from torchvision.models import utils as vutils
from .domain_specific_module import BatchNormDomain
from . import utils as model_utils


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


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, num_domains=1):
        super(BasicBlock, self).__init__()
        if norm_layer is None:
            norm_layer = 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 = BatchNormDomain(planes, num_domains, norm_layer)
        self.relu = nn.ReLU(inplace=True)
        self.conv2 = conv3x3(planes, planes)
        self.bn2 = BatchNormDomain(planes, num_domains, norm_layer)
        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, num_domains=1):
        super(Bottleneck, self).__init__()
        if norm_layer is None:
            norm_layer = 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 = BatchNormDomain(width, num_domains, norm_layer)
        self.conv2 = conv3x3(width, width, stride, groups, dilation)
        self.bn2 = BatchNormDomain(width, num_domains, norm_layer)
        self.conv3 = conv1x1(width, planes * self.expansion)
        self.bn3 = BatchNormDomain(planes * self.expansion, num_domains,
                         norm_layer)
        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):

    def __init__(self, block, layers, zero_init_residual=False,
                 groups=1, width_per_group=64, 
                 replace_stride_with_dilation=None, #num_classes=1000, 
                 frozen=[], norm_layer=None, num_domains=1):
        super(ResNet, self).__init__()
        if norm_layer is None:
            norm_layer = BatchNorm2d
        self._norm_layer = norm_layer
        self._num_domains = num_domains

        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 = BatchNormDomain(self.inplanes, num_domains, self._norm_layer)
        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=2,
                                       dilate=replace_stride_with_dilation[2])
        self.avgpool = nn.AdaptiveAvgPool2d((1, 1))
        self.out_dim = 512 * block.expansion
        #self.dropout = nn.Dropout(p=dropout_ratio)
        #self.fc = nn.Linear(512 * block.expansion, num_classes)
        self.frozen = frozen
        self.ordered_module_names = ['conv1', 'bn1', 'relu', 'maxpool',
		'layer1', 'layer2', 'layer3', 'layer4', 'avgpool']
        self.domain = None

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

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

    def set_bn_domain(self, domain=0):
        self.domain = domain
        for m in self.modules():
            if isinstance(m, BatchNormDomain):
                m.set_domain(domain)

    def _make_layer(self, block, planes, blocks, stride=1, dilate=False):
        norm_layer = self._norm_layer
        num_domains = self._num_domains
        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),
                BatchNormDomain(planes * block.expansion, num_domains, norm_layer),
            )

        layers = []
        layers.append(block(self.inplanes, planes, stride, downsample, self.groups,
                            self.base_width, previous_dilation, norm_layer, num_domains=num_domains))
        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, num_domains=num_domains))

        return nn.Sequential(*layers)

    def forward(self, x):
        for name in self.ordered_module_names:
            module = self._modules[name]
            x = module(x)
            x = x.detach() if name in self.frozen else x

        return x

def _resnet(arch, block, layers, num_domains, pretrained, progress, **kwargs):
    model = ResNet(block, layers, num_domains=num_domains, **kwargs)
    BN2BNDomain = True
    if pretrained:
        state_dict = vutils.load_state_dict_from_url(model_urls[arch],
                    progress=progress)
        BN2BNDomain = True
    else:
        state_dict = None
        BN2BNDomain = False

    model_utils.init_weights(model, state_dict, num_domains, BN2BNDomain)
    return model


def resnet18(num_domains=1, pretrained=False, progress=True, **kwargs):
    """Constructs a ResNet-18 model.

    Args:
        pretrained (bool): If True, returns a model pre-trained on ImageNet
        progress (bool): If True, displays a progress bar of the download to stderr
    """
    return _resnet('resnet18', BasicBlock, [2, 2, 2, 2], num_domains, 
                   pretrained, progress, **kwargs)


def resnet34(num_domains=1, pretrained=False, progress=True, **kwargs):
    """Constructs a ResNet-34 model.

    Args:
        pretrained (bool): If True, returns a model pre-trained on ImageNet
        progress (bool): If True, displays a progress bar of the download to stderr
    """
    return _resnet('resnet34', BasicBlock, [3, 4, 6, 3], num_domains, 
                   pretrained, progress, **kwargs)


def resnet50(num_domains=1, pretrained=False, progress=True, **kwargs):
    """Constructs a ResNet-50 model.

    Args:
        pretrained (bool): If True, returns a model pre-trained on ImageNet
        progress (bool): If True, displays a progress bar of the download to stderr
    """
    return _resnet('resnet50', Bottleneck, [3, 4, 6, 3], num_domains, 
                   pretrained, progress, **kwargs)


def resnet101(num_domains=1, pretrained=False, progress=True, **kwargs):
    """Constructs a ResNet-101 model.

    Args:
        pretrained (bool): If True, returns a model pre-trained on ImageNet
        progress (bool): If True, displays a progress bar of the download to stderr
    """
    return _resnet('resnet101', Bottleneck, [3, 4, 23, 3], num_domains, 
                   pretrained, progress, **kwargs)


def resnet152(num_domains=1, pretrained=False, progress=True, **kwargs):
    """Constructs a ResNet-152 model.

    Args:
        pretrained (bool): If True, returns a model pre-trained on ImageNet
        progress (bool): If True, displays a progress bar of the download to stderr
    """
    return _resnet('resnet152', Bottleneck, [3, 8, 36, 3], num_domains, 
                   pretrained, progress, **kwargs)


def resnext50_32x4d(num_domains=1, pretrained=False, progress=True, **kwargs):
    """Constructs a ResNeXt-50 32x4d model.

    Args:
        pretrained (bool): If True, returns a model pre-trained on ImageNet
        progress (bool): If True, displays a progress bar of the download to stderr
    """
    kwargs['groups'] = 32
    kwargs['width_per_group'] = 4
    return _resnet('resnext50_32x4d', Bottleneck, [3, 4, 6, 3], num_domains,
                   pretrained, progress, **kwargs)


def resnext101_32x8d(num_domains=1, pretrained=False, progress=True, **kwargs):
    """Constructs a ResNeXt-101 32x8d model.

    Args:
        pretrained (bool): If True, returns a model pre-trained on ImageNet
        progress (bool): If True, displays a progress bar of the download to stderr
    """
    kwargs['groups'] = 32
    kwargs['width_per_group'] = 8
    return _resnet('resnext101_32x8d', Bottleneck, [3, 4, 23, 3], num_domains,
                   pretrained, progress, **kwargs)
