#!/usr/bin/env python
# -*- coding:utf-8 -*-
# Author: Donny You(youansheng@gmail.com)
import math
import torch.nn as nn
from collections import OrderedDict
from .module_helper import ModuleHelper


model_urls = {
    'resnet18': 'https://download.pytorch.org/backbones/resnet18-5c106cde.pth',
    'resnet34': 'https://download.pytorch.org/backbones/resnet34-333f7ec4.pth',
    'resnet50': 'https://download.pytorch.org/backbones/resnet50-19c8e357.pth',
    'resnet101': 'https://download.pytorch.org/backbones/resnet101-5d3b4d8f.pth',
    'resnet152': 'https://download.pytorch.org/backbones/resnet152-b121ed2d.pth'
}


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


class BasicBlock(nn.Module):
    expansion = 1

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

    def forward(self, x):
        residual = 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:
            residual = self.downsample(x)

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

        return out


class Bottleneck(nn.Module):
    expansion = 4

    def __init__(self, inplanes, planes, stride=1, downsample=None, norm_type=None):
        super(Bottleneck, self).__init__()
        self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1, bias=False)
        self.bn1 = ModuleHelper.BatchNorm2d(norm_type=norm_type)(planes)
        self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=stride,
                               padding=1, bias=False)
        self.bn2 = ModuleHelper.BatchNorm2d(norm_type=norm_type)(planes)
        self.conv3 = nn.Conv2d(planes, planes * 4, kernel_size=1, bias=False)
        self.bn3 = ModuleHelper.BatchNorm2d(norm_type=norm_type)(planes * 4)
        self.relu = nn.ReLU(inplace=True)
        self.downsample = downsample
        self.stride = stride

    def forward(self, x):
        residual = 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:
            residual = self.downsample(x)

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

        return out


class ResNet(nn.Module):
    def __init__(self, block, layers, width_multiplier=1.0, num_classes=1000, deep_base=False, norm_type=None):
        super(ResNet, self).__init__()
        self.inplanes = 128 if deep_base else int(64 * width_multiplier)
        self.width_multiplier = width_multiplier
        if deep_base:
            self.prefix = nn.Sequential(OrderedDict([
                ('conv1', nn.Conv2d(3, 64, kernel_size=3, stride=2, padding=1, bias=False)),
                ('bn1', ModuleHelper.BatchNorm2d(norm_type=norm_type)(64)),
                ('relu1', nn.ReLU(inplace=False)),
                ('conv2', nn.Conv2d(64, 64, kernel_size=3, stride=1, padding=1, bias=False)),
                ('bn2', ModuleHelper.BatchNorm2d(norm_type=norm_type)(64)),
                ('relu2', nn.ReLU(inplace=False)),
                ('conv3', nn.Conv2d(64, 128, kernel_size=3, stride=1, padding=1, bias=False)),
                ('bn3', ModuleHelper.BatchNorm2d(norm_type=norm_type)(self.inplanes)),
                ('relu3', nn.ReLU(inplace=False))]
            ))
        else:
            self.prefix = nn.Sequential(OrderedDict([
                ('conv1', nn.Conv2d(3, self.inplanes, kernel_size=7, stride=2, padding=3, bias=False)),
                ('bn1', ModuleHelper.BatchNorm2d(norm_type=norm_type)(self.inplanes)),
                ('relu', nn.ReLU(inplace=False))]
            ))

        self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1, ceil_mode=False)  # change.

        self.layer1 = self._make_layer(block, int(64 * width_multiplier), layers[0], norm_type=norm_type)
        self.layer2 = self._make_layer(block, int(128 * width_multiplier), layers[1], stride=2, norm_type=norm_type)
        self.layer3 = self._make_layer(block, int(256 * width_multiplier), layers[2], stride=2, norm_type=norm_type)
        self.layer4 = self._make_layer(block, int(512 * width_multiplier), layers[3], stride=2, norm_type=norm_type)
        self.avgpool = nn.AvgPool2d(7, stride=1)
        self.fc = nn.Linear(int(512 * block.expansion * width_multiplier), num_classes)

        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, ModuleHelper.BatchNorm2d(norm_type=norm_type, ret_cls=True)):
                m.weight.data.fill_(1)
                m.bias.data.zero_()

    def _make_layer(self, block, planes, blocks, stride=1, norm_type=None):
        downsample = None
        if stride != 1 or self.inplanes != planes * block.expansion:
            downsample = nn.Sequential(
                nn.Conv2d(self.inplanes, planes * block.expansion,
                          kernel_size=1, stride=stride, bias=False),
                ModuleHelper.BatchNorm2d(norm_type=norm_type)(int(planes * block.expansion * self.width_multiplier)),
            )

        layers = []
        layers.append(block(self.inplanes, planes,
                            stride, downsample, norm_type=norm_type))

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

        return nn.Sequential(*layers)

    def forward(self, x):
        x = self.prefix(x)
        x = self.maxpool(x)

        x = self.layer1(x)
        x = self.layer2(x)
        x = self.layer3(x)
        x = self.layer4(x)

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

        return x


def resnet18(num_classes=1000, pretrained=None, norm_type='batchnorm', **kwargs):
    """Constructs a ResNet-18 model.
    Args:
        pretrained (bool): If True, returns a model pre-trained on Places
        norm_type (str): choose norm type
    """
    model = ResNet(BasicBlock, [2, 2, 2, 2], num_classes=num_classes, deep_base=False, norm_type=norm_type)
    model = ModuleHelper.load_model(model, pretrained=pretrained)
    return model


def deepbase_resnet18(num_classes=1000, pretrained=None, norm_type='batchnorm', **kwargs):
    """Constructs a ResNet-18 model.
    Args:
        pretrained (bool): If True, returns a model pre-trained on Places
    """
    model = ResNet(BasicBlock, [2, 2, 2, 2], num_classes=num_classes, deep_base=True, norm_type=norm_type)
    model = ModuleHelper.load_model(model, pretrained=pretrained)
    return model


def resnet34(num_classes=1000, pretrained=None, norm_type='batchnorm', **kwargs):
    """Constructs a ResNet-34 model.
    Args:
        pretrained (bool): If True, returns a model pre-trained on Places
    """
    model = ResNet(BasicBlock, [3, 4, 6, 3], num_classes=num_classes, deep_base=False, norm_type=norm_type)
    model = ModuleHelper.load_model(model, pretrained=pretrained)
    return model


def deepbase_resnet34(num_classes=1000, pretrained=None, norm_type='batchnorm', **kwargs):
    """Constructs a ResNet-34 model.
    Args:
        pretrained (bool): If True, returns a model pre-trained on Places
    """
    model = ResNet(BasicBlock, [3, 4, 6, 3], num_classes=num_classes, deep_base=True, norm_type=norm_type)
    model = ModuleHelper.load_model(model, pretrained=pretrained)
    return model


def resnet50(num_classes=1000, pretrained=None, norm_type='batchnorm', **kwargs):
    """Constructs a ResNet-50 model.
    Args:
        pretrained (bool): If True, returns a model pre-trained on Places
    """
    model = ResNet(Bottleneck, [3, 4, 6, 3], num_classes=num_classes, deep_base=False, norm_type=norm_type,
                   width_multiplier=kwargs["width_multiplier"])
    model = ModuleHelper.load_model(model, pretrained=pretrained)
    return model


def deepbase_resnet50(num_classes=1000, pretrained=None, norm_type='batchnorm', **kwargs):
    """Constructs a ResNet-50 model.
    Args:
        pretrained (bool): If True, returns a model pre-trained on Places
    """
    model = ResNet(Bottleneck, [3, 4, 6, 3], num_classes=num_classes, deep_base=True, norm_type=norm_type)
    model = ModuleHelper.load_model(model, pretrained=pretrained)
    return model


def resnet101(num_classes=1000, pretrained=None, norm_type='batchnorm', **kwargs):
    """Constructs a ResNet-101 model.
    Args:
        pretrained (bool): If True, returns a model pre-trained on Places
    """
    model = ResNet(Bottleneck, [3, 4, 23, 3], num_classes=num_classes, deep_base=False, norm_type=norm_type)
    model = ModuleHelper.load_model(model, pretrained=pretrained)
    return model


def deepbase_resnet101(num_classes=1000, pretrained=None, norm_type='batchnorm', **kwargs):
    """Constructs a ResNet-101 model.
    Args:
        pretrained (bool): If True, returns a model pre-trained on Places
    """
    model = ResNet(Bottleneck, [3, 4, 23, 3], num_classes=num_classes, deep_base=True, norm_type=norm_type)
    model = ModuleHelper.load_model(model, pretrained=pretrained)
    return model


def resnet152(num_classes=1000, pretrained=None, norm_type='batchnorm', **kwargs):
    """Constructs a ResNet-152 model.

    Args:
        pretrained (bool): If True, returns a model pre-trained on Places
    """
    model = ResNet(Bottleneck, [3, 8, 36, 3], num_classes=num_classes, deep_base=False, norm_type=norm_type)
    model = ModuleHelper.load_model(model, pretrained=pretrained)
    return model


def deepbase_resnet152(num_classes=1000, pretrained=None, norm_type='batchnorm', **kwargs):
    """Constructs a ResNet-152 model.

    Args:
        pretrained (bool): If True, returns a model pre-trained on Places
    """
    model = ResNet(Bottleneck, [3, 8, 36, 3], num_classes=num_classes, deep_base=True, norm_type=norm_type)
    model = ModuleHelper.load_model(model, pretrained=pretrained)
    return model