text: import torch
  import torch.nn as nn
  import torch.nn.functional as F
  import numpy as np
  from region_loss_multi import RegionLoss
  from cfg import *
  
  class MaxPoolStride1(nn.Module):
      def __init__(self):
          super(MaxPoolStride1, self).__init__()
  
      def forward(self, x):
          x = F.max_pool2d(F.pad(x, (0,1,0,1), mode='replicate'), 2, stride=1)
          return x
  
  class Reorg(nn.Module):
      def __init__(self, stride=2):
          super(Reorg, self).__init__()
          self.stride = stride
      def forward(self, x):
          stride = self.stride
          assert(x.data.dim() == 4)
          B = x.data.size(0)
          C = x.data.size(1)
          H = x.data.size(2)
          W = x.data.size(3)
          assert(H % stride == 0)
          assert(W % stride == 0)
          ws = stride
          hs = stride
          x = x.view(B, C, H//hs, hs, W//ws, ws).transpose(3,4).contiguous()
          x = x.view(B, C, (H//hs)*(W//ws), hs*ws).transpose(2,3).contiguous()
          x = x.view(B, C, hs*ws, H//hs, W//ws).transpose(1,2).contiguous()
          x = x.view(B, hs*ws*C, H//hs, W//ws)
          return x
  
  class GlobalAvgPool2d(nn.Module):
      def __init__(self):
          super(GlobalAvgPool2d, self).__init__()
  
      def forward(self, x):
          N = x.data.size(0)
          C = x.data.size(1)
          H = x.data.size(2)
          W = x.data.size(3)
          x = F.avg_pool2d(x, (H, W))
          x = x.view(N, C)
          return x
  
  # for route and shortcut
  class EmptyModule(nn.Module):
      def __init__(self):
          super(EmptyModule, self).__init__()
  
      def forward(self, x):
          return x
  
  # support route shortcut and reorg
  class Darknet(nn.Module):
      def __init__(self, cfgfile):
          super(Darknet, self).__init__()
          self.blocks = parse_cfg(cfgfile)
          self.models = self.create_network(self.blocks) # merge conv, bn,leaky
          self.loss = self.models[len(self.models)-1]
  
          self.width = int(self.blocks[0]['width'])
          self.height = int(self.blocks[0]['height'])
  
          if self.blocks[(len(self.blocks)-1)]['type'] == 'region':
              self.anchors = self.loss.anchors
              self.num_anchors = self.loss.num_anchors
              self.anchor_step = self.loss.anchor_step
              self.num_classes = self.loss.num_classes
  
          self.header = torch.IntTensor([0,0,0,0])
          self.seen = 0
          self.iter = 0
  
      def forward(self, x):
          ind = -2
          self.loss = None
          outputs = dict()
          for block in self.blocks:
              ind = ind + 1
              #if ind > 0:
              #    return x
  
              if block['type'] == 'net':
                  continue
              elif block['type'] == 'convolutional' or block['type'] == 'maxpool' or block['type'] == 'reorg' or block['type'] == 'avgpool' or block['type'] == 'softmax' or block['type'] == 'connected':
                  x = self.models[ind](x)
                  outputs[ind] = x
              elif block['type'] == 'route':
                  layers = block['layers'].split(',')
                  layers = [int(i) if int(i) > 0 else int(i)+ind for i in layers]
                  if len(layers) == 1:
                      x = outputs[layers[0]]
                      outputs[ind] = x
                  elif len(layers) == 2:
                      x1 = outputs[layers[0]]
                      x2 = outputs[layers[1]]
                      x = torch.cat((x1,x2),1)
                      outputs[ind] = x
              elif block['type'] == 'shortcut':
                  from_layer = int(block['from'])
                  activation = block['activation']
                  from_layer = from_layer if from_layer > 0 else from_layer + ind
                  x1 = outputs[from_layer]
                  x2 = outputs[ind-1]
                  x  = x1 + x2
                  if activation == 'leaky':
                      x = F.leaky_relu(x, 0.1, inplace=True)
                  elif activation == 'relu':
                      x = F.relu(x, inplace=True)
                  outputs[ind] = x
              elif block['type'] == 'region':
                  continue
                  if self.loss:
                      self.loss = self.loss + self.models[ind](x)
                  else:
                      self.loss = self.models[ind](x)
                  outputs[ind] = None
              elif block['type'] == 'cost':
                  continue
              else:
                  print('unknown type %s' % (block['type']))
          return x
  
      def print_network(self):
          print_cfg(self.blocks)
  
      def create_network(self, blocks):
          models = nn.ModuleList()
      
          prev_filters = 3
          out_filters =[]
          conv_id = 0
          for block in blocks:
              if block['type'] == 'net':
                  prev_filters = int(block['channels'])
                  continue
              elif block['type'] == 'convolutional':
                  conv_id = conv_id + 1
                  batch_normalize = int(block['batch_normalize'])
                  filters = int(block['filters'])
                  kernel_size = int(block['size'])
                  stride = int(block['stride'])
                  is_pad = int(block['pad'])
                  pad = (kernel_size-1)//2 if is_pad else 0
                  activation = block['activation']
                  model = nn.Sequential()
                  if batch_normalize:
                      model.add_module('conv{0}'.format(conv_id), nn.Conv2d(prev_filters, filters, kernel_size, stride, pad, bias=False))
                      model.add_module('bn{0}'.format(conv_id), nn.BatchNorm2d(filters, eps=1e-4))
                      #model.add_module('bn{0}'.format(conv_id), BN2d(filters))
                  else:
                      model.add_module('conv{0}'.format(conv_id), nn.Conv2d(prev_filters, filters, kernel_size, stride, pad))
                  if activation == 'leaky':
                      model.add_module('leaky{0}'.format(conv_id), nn.LeakyReLU(0.1, inplace=True))
                  elif activation == 'relu':
                      model.add_module('relu{0}'.format(conv_id), nn.ReLU(inplace=True))
                  prev_filters = filters
                  out_filters.append(prev_filters)
                  models.append(model)
              elif block['type'] == 'maxpool':
                  pool_size = int(block['size'])
                  stride = int(block['stride'])
                  if stride > 1:
                      model = nn.MaxPool2d(pool_size, stride)
                  else:
                      model = MaxPoolStride1()
                  out_filters.append(prev_filters)
                  models.append(model)
              elif block['type'] == 'avgpool':
                  model = GlobalAvgPool2d()
                  out_filters.append(prev_filters)
                  models.append(model)
              elif block['type'] == 'softmax':
                  model = nn.Softmax()
                  out_filters.append(prev_filters)
                  models.append(model)
              elif block['type'] == 'cost':
                  if block['_type'] == 'sse':
                      model = nn.MSELoss(size_average=True)
                  elif block['_type'] == 'L1':
                      model = nn.L1Loss(size_average=True)
                  elif block['_type'] == 'smooth':
                      model = nn.SmoothL1Loss(size_average=True)
                  out_filters.append(1)
                  models.append(model)
              elif block['type'] == 'reorg':
                  stride = int(block['stride'])
                  prev_filters = stride * stride * prev_filters
                  out_filters.append(prev_filters)
                  models.append(Reorg(stride))
              elif block['type'] == 'route':
                  layers = block['layers'].split(',')
                  ind = len(models)
                  layers = [int(i) if int(i) > 0 else int(i)+ind for i in layers]
                  if len(layers) == 1:
                      prev_filters = out_filters[layers[0]]
                  elif len(layers) == 2:
                      assert(layers[0] == ind - 1)
                      prev_filters = out_filters[layers[0]] + out_filters[layers[1]]
                  out_filters.append(prev_filters)
                  models.append(EmptyModule())
              elif block['type'] == 'shortcut':
                  ind = len(models)
                  prev_filters = out_filters[ind-1]
                  out_filters.append(prev_filters)
                  models.append(EmptyModule())
              elif block['type'] == 'connected':
                  filters = int(block['output'])
                  if block['activation'] == 'linear':
                      model = nn.Linear(prev_filters, filters)
                  elif block['activation'] == 'leaky':
                      model = nn.Sequential(
                                 nn.Linear(prev_filters, filters),
                                 nn.LeakyReLU(0.1, inplace=True))
                  elif block['activation'] == 'relu':
                      model = nn.Sequential(
                                 nn.Linear(prev_filters, filters),
                                 nn.ReLU(inplace=True))
                  prev_filters = filters
                  out_filters.append(prev_filters)
                  models.append(model)
              elif block['type'] == 'region':
                  loss = RegionLoss()
                  anchors = block['anchors'].split(',')
                  loss.anchors = [float(i) for i in anchors]
                  loss.num_classes = int(block['classes'])
                  loss.num_anchors = int(block['num'])
                  loss.anchor_step = len(loss.anchors)//loss.num_anchors
                  loss.object_scale = float(block['object_scale'])
                  loss.noobject_scale = float(block['noobject_scale'])
                  loss.class_scale = float(block['class_scale'])
                  loss.coord_scale = float(block['coord_scale'])
                  out_filters.append(prev_filters)
                  models.append(loss)
              else:
                  print('unknown type %s' % (block['type']))
      
          return models
  
      def load_weights(self, weightfile):
          fp = open(weightfile, 'rb')
          header = np.fromfile(fp, count=4, dtype=np.int32)
          self.header = torch.from_numpy(header)
          self.seen = self.header[3]
          buf = np.fromfile(fp, dtype = np.float32)
          fp.close()
  
          start = 0
          ind = -2
          for block in self.blocks:
              if start >= buf.size:
                  break
              ind = ind + 1
              if block['type'] == 'net':
                  continue
              elif block['type'] == 'convolutional':
                  model = self.models[ind]
                  batch_normalize = int(block['batch_normalize'])
                  if batch_normalize:
                      start = load_conv_bn(buf, start, model[0], model[1])
                  else:
                      start = load_conv(buf, start, model[0])
              elif block['type'] == 'connected':
                  model = self.models[ind]
                  if block['activation'] != 'linear':
                      start = load_fc(buf, start, model[0])
                  else:
                      start = load_fc(buf, start, model)
              elif block['type'] == 'maxpool':
                  pass
              elif block['type'] == 'reorg':
                  pass
              elif block['type'] == 'route':
                  pass
              elif block['type'] == 'shortcut':
                  pass
              elif block['type'] == 'region':
                  pass
              elif block['type'] == 'avgpool':
                  pass
              elif block['type'] == 'softmax':
                  pass
              elif block['type'] == 'cost':
                  pass
              else:
                  print('unknown type %s' % (block['type']))
  
      def load_weights_until_last(self, weightfile):
          fp = open(weightfile, 'rb')
          header = np.fromfile(fp, count=4, dtype=np.int32)
          self.header = torch.from_numpy(header)
          self.seen = self.header[3]
          buf = np.fromfile(fp, dtype = np.float32)
          fp.close()
  
          start = 0
          ind = -2
          blocklen = len(self.blocks)
          for i in range(blocklen-2):
              block = self.blocks[i]
              if start >= buf.size:
                  break
              ind = ind + 1
              if block['type'] == 'net':
                  continue
              elif block['type'] == 'convolutional':
                  model = self.models[ind]
                  batch_normalize = int(block['batch_normalize'])
                  if batch_normalize:
                      start = load_conv_bn(buf, start, model[0], model[1])
                  else:
                      start = load_conv(buf, start, model[0])
              elif block['type'] == 'connected':
                  model = self.models[ind]
                  if block['activation'] != 'linear':
                      start = load_fc(buf, start, model[0])
                  else:
                      start = load_fc(buf, start, model)
              elif block['type'] == 'maxpool':
                  pass
              elif block['type'] == 'reorg':
                  pass
              elif block['type'] == 'route':
                  pass
              elif block['type'] == 'shortcut':
                  pass
              elif block['type'] == 'region':
                  pass
              elif block['type'] == 'avgpool':
                  pass
              elif block['type'] == 'softmax':
                  pass
              elif block['type'] == 'cost':
                  pass
              else:
                  print('unknown type %s' % (block['type']))
  
  
      def save_weights(self, outfile, cutoff=0):
          if cutoff <= 0:
              cutoff = len(self.blocks)-1
  
          fp = open(outfile, 'wb')
          self.header[3] = self.seen
          header = self.header
          header.numpy().tofile(fp)
  
          ind = -1
          for blockId in range(1, cutoff+1):
              ind = ind + 1
              block = self.blocks[blockId]
              if block['type'] == 'convolutional':
                  model = self.models[ind]
                  batch_normalize = int(block['batch_normalize'])
                  if batch_normalize:
                      save_conv_bn(fp, model[0], model[1])
                  else:
                      save_conv(fp, model[0])
              elif block['type'] == 'connected':
                  model = self.models[ind]
                  if block['activation'] != 'linear':
                      save_fc(fc, model)
                  else:
                      save_fc(fc, model[0])
              elif block['type'] == 'maxpool':
                  pass
              elif block['type'] == 'reorg':
                  pass
              elif block['type'] == 'route':
                  pass
              elif block['type'] == 'shortcut':
                  pass
              elif block['type'] == 'region':
                  pass
              elif block['type'] == 'avgpool':
                  pass
              elif block['type'] == 'softmax':
                  pass
              elif block['type'] == 'cost':
                  pass
              else:
                  print('unknown type %s' % (block['type']))
          fp.close()
  