text: # --------------------------------------------------------
  # 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
  from collections import OrderedDict
  
  __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) # change
      self.bn1 = nn.BatchNorm2d(planes)
      self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=1, # change
                   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) # change
      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 NodeUpdateNetwork(nn.Module):
      def __init__(self,
                   in_features,
                   num_features,
                   ratio=[2, 1],
                   dropout=0.0):
          super(NodeUpdateNetwork, self).__init__()
          # set size
          self.in_features = in_features
          self.num_features_list = [num_features * r for r in ratio]
          self.dropout = dropout
  
          # layers
          layer_list = OrderedDict()
          for l in range(len(self.num_features_list)):
  
              layer_list['conv{}'.format(l)] = nn.Conv2d(
                  in_channels=self.num_features_list[l - 1] if l > 0 else self.in_features * 3,
                  out_channels=self.num_features_list[l],
                  kernel_size=1,
                  bias=True)
  
              layer_list['relu{}'.format(l)] = nn.LeakyReLU()
  
              if self.dropout > 0 and l == (len(self.num_features_list) - 1):
                  layer_list['drop{}'.format(l)] = nn.Dropout2d(p=self.dropout)
  
          self.network = nn.Sequential(layer_list)
  
      def forward(self, node_feat, edge_feat):
          # get size
          num_data = node_feat.data.size(0)
  
          # get eye matrix (2 x node_size x node_size)
          diag_mask = 1.0 - torch.eye(num_data).unsqueeze(0).repeat(2, 1, 1).cuda()
  
          # set diagonal as zero and normalize
          edge_feat = Variable(F.normalize(edge_feat.data * diag_mask, p=1, dim=-1))
  
  
          # compute attention and aggregate
          # print(node_feat)
          aggr_feat = torch.mm(torch.cat(torch.split(edge_feat, 1, 0), 1).squeeze(0), node_feat)
          # print(aggr_feat)
          node_feat = torch.cat([node_feat, torch.cat(aggr_feat.split(num_data, 0), -1)], -1).transpose(0, 1)
  
          # non-linear transform
          node_feat = self.network(node_feat.unsqueeze(-1).unsqueeze(0)).squeeze(0).transpose(0, 1).squeeze(-1)
          # print(node_feat)
          return node_feat
  
  
  
  class EdgeUpdateNetwork(nn.Module):
      def __init__(self,
                   in_features,
                   num_features,
                   ratio=[2, 2, 1, 1],
                   separate_dissimilarity=False,
                   dropout=0.0):
          super(EdgeUpdateNetwork, self).__init__()
          # set size
          self.in_features = in_features
          self.num_features_list = [num_features * r for r in ratio]
          self.separate_dissimilarity = separate_dissimilarity
          self.dropout = dropout
  
          # layers
          layer_list = OrderedDict()
          for l in range(len(self.num_features_list)):
              # set layer
              layer_list['conv{}'.format(l)] = nn.Conv2d(in_channels=self.num_features_list[l-1] if l > 0 else self.in_features,
                                                         out_channels=self.num_features_list[l],
                                                         kernel_size=1,
                                                         bias=True)
  
              layer_list['relu{}'.format(l)] = nn.LeakyReLU()
  
              if self.dropout > 0:
                  layer_list['drop{}'.format(l)] = nn.Dropout2d(p=self.dropout)
  
          layer_list['conv_out'] = nn.Conv2d(in_channels=self.num_features_list[-1],
                                             out_channels=1,
                                             kernel_size=1)
          self.sim_network = nn.Sequential(layer_list)
  
          if self.separate_dissimilarity:
              # layers
              layer_list = OrderedDict()
              for l in range(len(self.num_features_list)):
                  # set layer
                  layer_list['conv{}'.format(l)] = nn.Conv2d(in_channels=self.num_features_list[l-1] if l > 0 else self.in_features,
                                                             out_channels=self.num_features_list[l],
                                                             kernel_size=1,
                                                             bias=True)
                  layer_list['relu{}'.format(l)] = nn.LeakyReLU()
  
                  if self.dropout > 0:
                      layer_list['drop{}'.format(l)] = nn.Dropout(p=self.dropout)
  
              layer_list['conv_out'] = nn.Conv2d(in_channels=self.num_features_list[-1],
                                                 out_channels=1,
                                                 kernel_size=1)
              self.dsim_network = nn.Sequential(layer_list)
  
      def forward(self, node_feat, edge_feat):
          # compute abs(x_i, x_j)
          x_i = node_feat.unsqueeze(1)
          x_j = torch.transpose(x_i, 0, 1)
          x_ij = torch.abs(x_i - x_j)
          x_ij = torch.transpose(x_ij, 0, 2)
  
          # compute similarity/dissimilarity ( feat_size x num_samples x num_samples)
          sim_val = F.sigmoid(self.sim_network(x_ij.unsqueeze(0)).squeeze(0))
  
          if self.separate_dissimilarity:
              dsim_val = F.sigmoid(self.dsim_network(x_ij.unsqueeze(0)).squeeze(0))
          else:
              dsim_val = 1.0 - sim_val
  
          diag_mask = 1.0 - torch.eye(node_feat.size(0)).unsqueeze(0).repeat(2, 1, 1).cuda()
          edge_feat = Variable(edge_feat.data * diag_mask)
          merge_sum = torch.sum(edge_feat, -1, True)
          # set diagonal as zero and normalize
          edge_feat = F.normalize(torch.cat((sim_val, dsim_val), 0) * edge_feat, p=1, dim=-1) * merge_sum
          force_edge_feat = torch.cat((torch.eye(node_feat.size(0)).unsqueeze(0), torch.zeros(node_feat.size(0), node_feat.size(0)).unsqueeze(0)), 0).repeat(1, 1, 1).cuda()
          edge_feat = edge_feat.data + force_edge_feat
          edge_feat = edge_feat + 1e-6
          edge_feat = Variable(edge_feat / torch.sum(edge_feat, dim=0).unsqueeze(0).repeat(2, 1, 1))
  
          return edge_feat
  
  
  class GraphNetwork(nn.Module):
      def __init__(self,
                   in_features,
                   node_features,
                   edge_features,
                   num_layers,
                   dropout=0.0):
          super(GraphNetwork, self).__init__()
          # set size
          self.in_features = in_features
          self.node_features = node_features
          self.edge_features = edge_features
          self.num_layers = num_layers
          self.dropout = dropout
  
          # for each layer
          for l in range(self.num_layers):
              # set edge to node
              edge2node_net = NodeUpdateNetwork(in_features=self.in_features if l == 0 else self.node_features,
                                                num_features=self.node_features,
                                                dropout=self.dropout if l < self.num_layers-1 else 0.0)
  
              # set node to edge
              node2edge_net = EdgeUpdateNetwork(in_features=self.node_features,
                                                num_features=self.edge_features,
                                                separate_dissimilarity=False,
                                                dropout=self.dropout if l < self.num_layers-1 else 0.0)
  
              self.add_module('edge2node_net{}'.format(l), edge2node_net)
              self.add_module('node2edge_net{}'.format(l), node2edge_net)
              
      # forward
      def forward(self, node_feat, edge_feat):
          # for each layer
          edge_feat_list = []
          for l in range(self.num_layers):
              # (1) edge to node
              node_feat = self._modules['edge2node_net{}'.format(l)](node_feat, edge_feat)
  
              # (2) node to edge
              edge_feat = self._modules['node2edge_net{}'.format(l)](node_feat, edge_feat)
  
              # save edge feature
              edge_feat_list.append(edge_feat)
  
          # if tt.arg.visualization:
          #     for l in range(self.num_layers):
          #         ax = sns.heatmap(tt.nvar(edge_feat_list[l][0, 0, :, :]), xticklabels=False, yticklabels=False, linewidth=0.1,  cmap="coolwarm",  cbar=False, square=True)
          #         ax.get_figure().savefig('./visualization/edge_feat_layer{}.png'.format(l))
  
  
          return edge_feat_list
  
  class resnet(_fasterRCNN):
    def __init__(self, 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
  
      _fasterRCNN.__init__(self, classes, class_agnostic, meta_train, meta_test, meta_loss)
  
    def _init_modules(self):
      resnet = resnet101()
  
      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)
  
      # self.RCNN_cls_score = self.get_cls_score(full_data, init_edge, self.n_classes)
      self.gnn_module = GraphNetwork(in_features=2048,
                                 node_features=1536,
                                 edge_features=1536,
                                 num_layers=self.num_layers_g)
      if self.meta_loss:
        self.Meta_cls_score = nn.Linear(2048, self.n_classes)
  
      if self.class_agnostic:
        self.RCNN_bbox_pred = nn.Linear(2048, 4) # x,y,w,h
      else:
        self.RCNN_bbox_pred = nn.Linear(2048, 4 * self.n_classes)
  
  
      # 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()
  
        self.RCNN_base.eval()
  
        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
      
    def RCNN_cls_score(self, full_data, init_edge, n_classes, support_label, num_supports):
      full_logit_layers = self.gnn_module(node_feat=full_data, edge_feat=Variable(init_edge))
      query_node_pred_layers = [torch.mm(full_logit_layer[0, num_supports:, :num_supports],
                                          self.one_hot_encode(n_classes, support_label)) for full_logit_layer in
                                full_logit_layers]
      cls_score = query_node_pred_layers[-1]
  
      return cls_score, full_logit_layers
  
  
    def one_hot_encode(self, num_classes, class_idx):
      return Variable(torch.eye(num_classes)[class_idx]).cuda()
  
  