# --------------------------------------------------------
# Pytorch Meta R-CNN
# Written by Anny Xu, Xiaopeng Yan, based on code from Jianwei Yang
# --------------------------------------------------------
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

from model.utils.config import cfg
from model.faster_rcnn.faster_rcnn import _fasterRCNN

import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.autograd import Variable
from torch.nn import init
import math
import torch.utils.model_zoo as model_zoo
import pdb

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


model_urls = {
  'resnet18': 'https://s3.amazonaws.com/pytorch/models/resnet18-5c106cde.pth',
  'resnet34': 'https://s3.amazonaws.com/pytorch/models/resnet34-333f7ec4.pth',
  'resnet50': 'https://s3.amazonaws.com/pytorch/models/resnet50-19c8e357.pth',
  'resnet101': 'https://s3.amazonaws.com/pytorch/models/resnet101-5d3b4d8f.pth',
  'resnet152': 'https://s3.amazonaws.com/pytorch/models/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)


def init_conv(conv, glu=True):
    init.xavier_uniform(conv.weight)
    if conv.bias is not None:
        conv.bias.data.zero_()


def init_linear(linear):
    init.constant(linear.weight,0)
    init.constant(linear.bias, 1)


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

        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):
        super(Bottleneck, self).__init__()
        self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1, stride=stride, bias=False)
        self.bn1 = nn.BatchNorm2d(planes)
        self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=1, padding=1, bias=False)
        self.bn2 = nn.BatchNorm2d(planes)
        self.conv3 = nn.Conv2d(planes, planes * 4, kernel_size=1, 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

        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, num_classes=1000):
        self.inplanes = 64
        super(ResNet, self).__init__()
        self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3, bias=False)
        self.bn1 = nn.BatchNorm2d(64)
        self.relu = nn.ReLU(inplace=True)
        self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=0, ceil_mode=True)
        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)
        # it is slightly better whereas slower to set stride = 1
        # self.layer4 = self._make_layer(block, 512, layers[3], stride=1)
        self.avgpool = nn.AvgPool2d(7)
        self.fc = nn.Linear(512 * block.expansion, 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, 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(
                nn.Conv2d(self.inplanes, planes * block.expansion, kernel_size=1, stride=stride, bias=False),
                nn.BatchNorm2d(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):
        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)

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

        return x


def resnet18(pretrained=False):
    """Constructs a ResNet-18 model.
      Args:
        pretrained (bool): If True, returns a model pre-trained on ImageNet
      """
    model = ResNet(BasicBlock, [2, 2, 2, 2])
    if pretrained:
        model.load_state_dict(model_zoo.load_url(model_urls['resnet18']))
    return model


def resnet34(pretrained=False):
    """Constructs a ResNet-34 model.
      Args:
        pretrained (bool): If True, returns a model pre-trained on ImageNet
      """
    model = ResNet(BasicBlock, [3, 4, 6, 3])
    if pretrained:
        model.load_state_dict(model_zoo.load_url(model_urls['resnet34']))
    return model


def resnet50(pretrained=False):
    """Constructs a ResNet-50 model.
      Args:
        pretrained (bool): If True, returns a model pre-trained on ImageNet
      """
    model = ResNet(Bottleneck, [3, 4, 6, 3])
    if pretrained:
        model.load_state_dict(model_zoo.load_url(model_urls['resnet50']))
    return model


def resnet101(pretrained=False):
    """Constructs a ResNet-101 model.
      Args:
        pretrained (bool): If True, returns a model pre-trained on ImageNet
      """
    model = ResNet(Bottleneck, [3, 4, 23, 3])
    if pretrained:
        model.load_state_dict(model_zoo.load_url(model_urls['resnet101']))
    return model


def resnet152(pretrained=False):
    """Constructs a ResNet-152 model.
      Args:
        pretrained (bool): If True, returns a model pre-trained on ImageNet
      """
    model = ResNet(Bottleneck, [3, 8, 36, 3])
    if pretrained:
        model.load_state_dict(model_zoo.load_url(model_urls['resnet152']))
    return model


class resnet(_fasterRCNN):
    def __init__(self, n_classes, num_layers=101, pretrained=False, class_agnostic=False,
                 meta_train=True, meta_test=None, meta_loss=None):
        self.model_path = 'data/pretrained_model/resnet101_caffe.pth'
        self.dout_base_model = 1024
        self.pretrained = pretrained
        self.class_agnostic = class_agnostic
        self.meta_train = meta_train
        self.meta_test = meta_test
        self.meta_loss = meta_loss
        self.num_layers = num_layers

        _fasterRCNN.__init__(self, n_classes, class_agnostic, meta_train, meta_test, meta_loss)

    def _init_modules(self):
        if self.num_layers == 101:
            resnet = resnet101()
        else:
            resnet = resnet50()

        if self.pretrained == True:
            print("Loading pretrained weights from %s" % (self.model_path))
            state_dict = torch.load(self.model_path)
            resnet.load_state_dict({k: v for k, v in state_dict.items() if k in resnet.state_dict()})

        # Build resnet.
        self.meta_conv1 = nn.Conv2d(4, 64, kernel_size=7, stride=2, padding=3, bias=False)
        self.rcnn_conv1 = resnet.conv1

        self.RCNN_base = nn.Sequential(resnet.bn1, resnet.relu, resnet.maxpool,
                                       resnet.layer1, resnet.layer2, resnet.layer3)
        self.RCNN_top = nn.Sequential(resnet.layer4)

        self.sigmoid = nn.Sigmoid()
        self.max_pooled = nn.MaxPool2d(2)

        # Feature dimension doubled when concatenate the original RoI feature with newly combined feature
        feat_dim = 2048 * 2

        # Classification
        self.RCNN_cls_score = nn.Linear(feat_dim, self.n_classes)

        # Box regression
        if self.class_agnostic:
            self.RCNN_bbox_pred = nn.Linear(feat_dim, 4)  # x,y,w,h
        else:
            self.RCNN_bbox_pred = nn.Linear(feat_dim, 4 * self.n_classes)

        # Meta feature diversity
        if self.meta_loss:
            self.Meta_cls_score = nn.Linear(2048, self.n_classes)

        # Feature aggregation module
        self.corr_fc = nn.Sequential(nn.Linear(2048, 1024), nn.BatchNorm1d(1024), nn.ReLU(inplace=True))
        self.diff_fc = nn.Sequential(nn.Linear(2048, 1024), nn.BatchNorm1d(1024), nn.ReLU(inplace=True))

        # Fix blocks
        for p in self.rcnn_conv1.parameters(): p.requires_grad = False
        for p in self.RCNN_base[0].parameters(): p.requires_grad = False

        assert (0 <= cfg.RESNET.FIXED_BLOCKS < 5)
        if cfg.RESNET.FIXED_BLOCKS >= 4:
            for p in self.RCNN_top.parameters(): p.requires_grad = False
        if cfg.RESNET.FIXED_BLOCKS >= 3:
            for p in self.RCNN_base[5].parameters(): p.requires_grad = False
        if cfg.RESNET.FIXED_BLOCKS >= 2:
            for p in self.RCNN_base[4].parameters(): p.requires_grad = False
        if cfg.RESNET.FIXED_BLOCKS >= 1:
            for p in self.RCNN_base[3].parameters(): p.requires_grad = False

        def set_bn_fix(m):
            classname = m.__class__.__name__
            if classname.find('BatchNorm') != -1:
                for p in m.parameters(): p.requires_grad = False

        self.RCNN_base.apply(set_bn_fix)
        self.RCNN_top.apply(set_bn_fix)

    def train(self, mode=True):
        # Override train so that the training mode is set as we want
        nn.Module.train(self, mode)
        if mode:
            # Set fixed blocks to be in eval mode
            self.RCNN_base.eval()
            self.RCNN_base[4].train()
            self.RCNN_base[5].train()

            def set_bn_eval(m):
                classname = m.__class__.__name__
                if classname.find('BatchNorm') != -1:
                    m.eval()

            self.RCNN_base.apply(set_bn_eval)
            self.RCNN_top.apply(set_bn_eval)

    def _head_to_tail(self, pool5):
        fc7 = self.RCNN_top(pool5).mean(3).mean(2)
        return fc7

    def prn_network(self, im_data):
        '''
        the Predictor-head Remodeling Network (PRN)
        :param im_data:
        :return attention vectors:
        '''
        base_feat = self.RCNN_base(self.meta_conv1(im_data))
        feature = self._head_to_tail(self.max_pooled(base_feat))
        attentions = self.sigmoid(feature)
        return attentions

