# Bayesian ResNet for ImageNet
# ResNet architecture ref:
# https://arxiv.org/abs/1512.03385
# Code adapted from torchvision package to build Bayesian model from deterministic model

import torch.nn as nn
import math
import torch.utils.model_zoo as model_zoo
import torch.nn as nn
import torch.nn.functional as F
import torch.nn.init as init
from bayesian_torch.layers import Conv2dReparameterization
from bayesian_torch.layers import LinearReparameterization
from bayesian_torch.layers import BatchNorm2dLayer

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

prior_mu = 0.0
prior_sigma = 0.1
posterior_mu_init = 0.0
posterior_rho_init = -9.0

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',
}


def conv3x3(in_planes, out_planes, stride=1):
    """3x3 convolution with padding"""
    return Conv2dReparameterization(in_channels=in_planes,
                                    out_channels=out_planes,
                                    kernel_size=3,
                                    stride=stride,
                                    padding=1,
                                    prior_mean=prior_mu,
                                    prior_variance=prior_sigma,
                                    posterior_mu_init=posterior_mu_init,
                                    posterior_rho_init=posterior_rho_init,
                                    bias=False)


class BasicBlock(nn.Module):
    expansion = 1

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

    def forward(self, x):
        residual = x
        kl_sum = 0
        out, kl = self.conv1(x)
        kl_sum += kl
        out = self.bn1(out)
        out = self.relu(out)

        out, kl = self.conv2(out)
        kl_sum += kl
        out = self.bn2(out)

        if self.downsample is not None:
            residual, kl = self.downsample(x)
            kl_sum += kl

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

        return out, kl_sum


class Bottleneck(nn.Module):
    expansion = 4

    def __init__(self, inplanes, planes, stride=1, downsample=None):
        super(Bottleneck, self).__init__()
        self.conv1 = Conv2dReparameterization(
            in_channels=inplanes,
            out_channels=planes,
            kernel_size=1,
            prior_mean=prior_mu,
            prior_variance=prior_sigma,
            posterior_mu_init=posterior_mu_init,
            posterior_rho_init=posterior_rho_init,
            bias=False)
        self.bn1 = nn.BatchNorm2d(planes)
        self.conv2 = Conv2dReparameterization(
            in_channels=planes,
            out_channels=planes,
            kernel_size=3,
            stride=stride,
            padding=1,
            prior_mean=prior_mu,
            prior_variance=prior_sigma,
            posterior_mu_init=posterior_mu_init,
            posterior_rho_init=posterior_rho_init,
            bias=False)
        self.bn2 = nn.BatchNorm2d(planes)
        self.conv3 = Conv2dReparameterization(
            in_channels=planes,
            out_channels=planes * 4,
            kernel_size=1,
            prior_mean=prior_mu,
            prior_variance=prior_sigma,
            posterior_mu_init=posterior_mu_init,
            posterior_rho_init=posterior_rho_init,
            bias=False)
        self.bn3 = nn.BatchNorm2d(planes * 4)
        self.relu = nn.ReLU(inplace=True)
        self.downsample = downsample
        self.stride = stride

    def forward(self, x):
        residual = x
        kl_sum = 0
        out, kl = self.conv1(x)
        kl_sum += kl
        out = self.bn1(out)
        out = self.relu(out)

        out, kl = self.conv2(out)
        kl_sum += kl
        out = self.bn2(out)
        out = self.relu(out)

        out, kl = self.conv3(out)
        kl_sum += kl
        out = self.bn3(out)

        if self.downsample is not None:
            residual, kl = self.downsample(x)
            kl_sum += kl

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

        return out, kl_sum


class ResNet(nn.Module):
    def __init__(self, block, layers, num_classes=1000):
        self.inplanes = 64
        super(ResNet, self).__init__()
        self.conv1 = Conv2dReparameterization(
            in_channels=3,
            out_channels=64,
            kernel_size=7,
            stride=2,
            padding=3,
            prior_mean=prior_mu,
            prior_variance=prior_sigma,
            posterior_mu_init=posterior_mu_init,
            posterior_rho_init=posterior_rho_init,
            bias=False)
        self.bn1 = nn.BatchNorm2d(64)
        self.relu = nn.ReLU(inplace=True)
        self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
        self.layer1 = self._make_layer(block, 64, layers[0])
        self.layer2 = self._make_layer(block, 128, layers[1], stride=2)
        self.layer3 = self._make_layer(block, 256, layers[2], stride=2)
        self.layer4 = self._make_layer(block, 512, layers[3], stride=2)
        self.avgpool = nn.AvgPool2d(7, stride=1)
        self.fc = LinearReparameterization(
            in_features=512 * block.expansion,
            out_features=num_classes,
            prior_mean=prior_mu,
            prior_variance=prior_sigma,
            posterior_mu_init=posterior_mu_init,
            posterior_rho_init=posterior_rho_init)

        for m in self.modules():
            if isinstance(m, nn.Conv2d):
                n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
                m.weight.data.normal_(0, math.sqrt(2. / n))
            elif isinstance(m, nn.BatchNorm2d):
                m.weight.data.fill_(1)
                m.bias.data.zero_()

    def _make_layer(self, block, planes, blocks, stride=1):
        downsample = None
        if stride != 1 or self.inplanes != planes * block.expansion:
            downsample = nn.Sequential(
                Conv2dReparameterization(in_channels=self.inplanes,
                                         out_channels=planes * block.expansion,
                                         kernel_size=1,
                                         stride=stride,
                                         prior_mean=prior_mu,
                                         prior_variance=prior_sigma,
                                         posterior_mu_init=posterior_mu_init,
                                         posterior_rho_init=posterior_rho_init,
                                         bias=False),
                BatchNorm2dLayer(planes * block.expansion),
            )

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

        return nn.Sequential(*layers)

    def forward(self, x):
        kl_sum = 0
        x, kl = self.conv1(x)
        kl_sum += kl
        x = self.bn1(x)
        x = self.relu(x)
        x = self.maxpool(x)

        for layer in self.layer1:
            if 'Reparameterization' in str(layer):
                x, kl = layer(x)
                if kl is None:
                    kl_sum += kl
            else:
                x = layer(x)

        for layer in self.layer2:
            if 'Reparameterization' in str(layer):
                x, kl = layer(x)
                if kl is None:
                    kl_sum += kl
            else:
                x = layer(x)

        for layer in self.layer3:
            if 'Reparameterization' in str(layer):
                x, kl = layer(x)
                if kl is None:
                    kl_sum += kl
            else:
                x = layer(x)

        for layer in self.layer4:
            if 'Reparameterization' in str(layer):
                x, kl = layer(x)
                if kl is None:
                    kl_sum += kl
            else:
                x = layer(x)

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

        return x, kl_sum


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

    Args:
        pretrained (bool): If True, returns a model pre-trained on ImageNet
    """
    model = ResNet(BasicBlock, [2, 2, 2, 2], **kwargs)
    if pretrained:
        model.load_state_dict(model_zoo.load_url(model_urls['resnet18']))
    return model


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

    Args:
        pretrained (bool): If True, returns a model pre-trained on ImageNet
    """
    model = ResNet(BasicBlock, [3, 4, 6, 3], **kwargs)
    if pretrained:
        model.load_state_dict(model_zoo.load_url(model_urls['resnet34']))
    return model


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

    Args:
        pretrained (bool): If True, returns a model pre-trained on ImageNet
    """
    model = ResNet(Bottleneck, [3, 4, 6, 3], **kwargs)
    if pretrained:
        model.load_state_dict(model_zoo.load_url(model_urls['resnet50']))
    return model


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

    Args:
        pretrained (bool): If True, returns a model pre-trained on ImageNet
    """
    model = ResNet(Bottleneck, [3, 4, 23, 3], **kwargs)
    if pretrained:
        model.load_state_dict(model_zoo.load_url(model_urls['resnet101']))
    return model


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

    Args:
        pretrained (bool): If True, returns a model pre-trained on ImageNet
    """
    model = ResNet(Bottleneck, [3, 8, 36, 3], **kwargs)
    if pretrained:
        model.load_state_dict(model_zoo.load_url(model_urls['resnet152']))
    return model
