wrn_deploy.py 文件源码

def construc_net(widening_factor, num_block_per_stage):
  net = caffe.NetSpec()

  net.data  = L.Input(input_param = dict(shape = dict(dim = [1,3,32,32])))

  net.conv1 = L.Convolution(net.data, num_output = 16,
                            kernel_size = 3, stride = 1, pad = 1,
                            bias_term = False)

  # stage 1
  num_out = widening_factor * 16
  block_pre = _block('2_1', net, net.conv1, num_out, has_branch1=True, increasing_dims=False)
  for idx in xrange(2,num_block_per_stage+1,1):
    flag = '2_{}'.format(idx)
    block_pre = _block(flag, net, block_pre, num_out)

  # stage 2
  num_out = widening_factor * 32
  block_pre = _block('3_1', net, block_pre, num_out, has_branch1=True)
  for idx in xrange(2,num_block_per_stage+1,1):
    flag = '3_{}'.format(idx)
    block_pre = _block(flag, net, block_pre, num_out)

  # stage 3
  num_out = widening_factor * 64
  block_pre = _block('4_1', net, block_pre, num_out, has_branch1=True)
  for idx in xrange(2,num_block_per_stage+1,1):
    flag = '4_{}'.format(idx)
    block_pre = _block(flag, net, block_pre, num_out)

  net.bn5    = L.BatchNorm(block_pre)
  net.scale5 = L.Scale(net.bn5, bias_term = True, in_place=True)
  net.relu5  = L.ReLU(net.scale5, in_place = True)
  net.pool5  = L.Pooling(net.relu5, pool = P.Pooling.AVE, global_pooling=True)

  net.fc6 = L.InnerProduct(net.pool5, num_output = 10)
  net.prob = L.Softmax(net.fc6)

  return net.to_proto()