utils.py 文件源码

def model(X, Y, k1, k2, paras, flag='single'):
  assert(flag=='single' or flag=='combine')
  X = tf.reshape(X, shape=[-1, boxheight, boxwidth, 1])
  yreshape = tf.reshape(Y, [-1, boxheight, boxwidth, 1])
  yonehot = tf.concat(3, [1-yreshape, yreshape])
  if flag == 'combine':
    hconv4clip = buildcombmodel(X, paras, fusion=False)
    #h1, h2, h3, h4 = tf.split(3, 4, hconv4clip)
    q_train, q_test = crfrnn(hconv4clip, paras['wsmooth'], paras['wcontra'], k1, k2, 
      trainiter=5, testiter=10, wunary=paras['wunary'])
  else: 
    hconv4clip = buildmodel(X, paras)
    q_train, q_test = crfrnn(hconv4clip, paras['wsmooth'], paras['wcontra'], k1, k2, 
      trainiter=5, testiter=10)
  #hconv4log = -tf.log(hconv4clip)
  #q_train = tf.reshape(q_train, [-1, boxheight, boxwidth, 2])
  #q_train = -tf.log(hconv4clip)
  q_trainclip = tf.clip_by_value(q_train, 1e-6, 1.)
  trainenergy = tf.reduce_sum(-tf.log(q_trainclip)*yonehot, reduction_indices=3)
  #trainenergy = tf.reduce_prod(trainenergy, reduction_indices=[1,2])
  trainenergy = tf.reduce_mean(trainenergy, [0,1,2])

  #q_test = hconv4clip
  #q_test = crfrnn(hconv4, paras['wsmooth'], paras['wcontra'], k1, k2, iter=5)
  q_test = tf.reshape(q_test, [-1, boxheight, boxwidth, 2])
  testenergy = tf.reduce_sum(tf.mul(q_test, yonehot), reduction_indices=3)
  #testenergy = tf.reduce_prod(testenergy, reduction_indices=[1,2])
  testenergy = tf.reduce_mean(testenergy, [0,1,2])
  predarg = tf.argmax(q_test, 3)
  yint64 = tf.to_int64(Y)
  acc = tf.equal(yint64, predarg)
  acc = tf.to_float(acc)
  accuracy = tf.reduce_mean(acc, [0,1,2])
  di = dice_tf(tf.reshape(yint64, [-1,]), tf.reshape(predarg, [-1,]))
  return trainenergy, accuracy, di, testenergy, predarg