根据现有检查点定义 TensorFlow 网络密钥名称

Question

我使用 Nvidia DIGITS 训练了一个 LeNet-gray-28x28 图像检测 Tensorflow 模型，得到了我期望的结果。 现在，我必须对 DIGITS 之外的一些图像进行分类，并且我想使用我训练过的模型。

所以我得到了 DIGITS 使用的 LeNet 模型，并创建了一个类来使用它：

import tensorflow as tf
import tensorflow.contrib.slim as slim
import tflearn
from tflearn.layers.core import input_data


class LeNetModel():

    def gray28(self, nclasses):
        x = input_data(shape=[None, 28, 28, 1])
        # scale (divide by MNIST std)
        # x = x * 0.0125
        with slim.arg_scope([slim.conv2d, slim.fully_connected],
                            weights_initializer=tf.contrib.layers.xavier_initializer(),
                            weights_regularizer=slim.l2_regularizer(0.0005)):
            model = slim.conv2d(x, 20, [5, 5], padding='VALID', scope='conv1')
            model = slim.max_pool2d(model, [2, 2], padding='VALID', scope='pool1')
            model = slim.conv2d(model, 50, [5, 5], padding='VALID', scope='conv2')
            model = slim.max_pool2d(model, [2, 2], padding='VALID', scope='pool2')
            model = slim.flatten(model)
            model = slim.fully_connected(model, 500, scope='fc1')
            model = slim.dropout(model, 0.5, is_training=False, scope='do1')
            model = slim.fully_connected(model, nclasses, activation_fn=None, scope='fc2')

            return tflearn.DNN(model)

我从 DIGITS 下载了我的模型并使用（在另一个文件中）实例化它：

self.ballmodel = LeNetModel().gray28(2)
self.ballmodel.load("src/perftrack/prototype/models/ball/snapshot_5.ckpt")

但是，当我启动我的脚本时，我得到了这些异常：

2017-11-26 14:55:50.330524: W tensorflow/core/framework/op_kernel.cc:1192] Not found: Key conv1/biases not found in checkpoint
2017-11-26 14:55:50.330948: W tensorflow/core/framework/op_kernel.cc:1192] Not found: Key Global_Step not found in checkpoint
2017-11-26 14:55:50.331270: W tensorflow/core/framework/op_kernel.cc:1192] Not found: Key is_training not found in checkpoint
2017-11-26 14:55:50.331564: W tensorflow/core/framework/op_kernel.cc:1192] Not found: Key conv2/weights not found in checkpoint
2017-11-26 14:55:50.332823: W tensorflow/core/framework/op_kernel.cc:1192] Not found: Key conv1/weights not found in checkpoint
2017-11-26 14:55:50.332891: W tensorflow/core/framework/op_kernel.cc:1192] Not found: Key conv2/biases not found in checkpoint
2017-11-26 14:55:50.333620: W tensorflow/core/framework/op_kernel.cc:1192] Not found: Key fc2/weights not found in checkpoint
2017-11-26 14:55:50.334021: W tensorflow/core/framework/op_kernel.cc:1192] Not found: Key fc1/weights not found in checkpoint
2017-11-26 14:55:50.334173: W tensorflow/core/framework/op_kernel.cc:1192] Not found: Key fc1/biases not found in checkpoint
2017-11-26 14:55:50.334431: W tensorflow/core/framework/op_kernel.cc:1192] Not found: Key fc2/biases not found in checkpoint
...
raise type(e)(node_def, op, message)
tensorflow.python.framework.errors_impl.NotFoundError: Key conv1/biases not found in checkpoint
         [[Node: save_1/RestoreV2_1 = RestoreV2[dtypes=[DT_FLOAT], _device="/job:localhost/replica:0/task:0/cpu:0"](_arg_save_1/Const_0_0, save_1/RestoreV2_1/tensor_names, save_1/RestoreV2_1/shape_and_slices)]]
         [[Node: save_1/RestoreV2_1/_19 = _Recv[client_terminated=false, recv_device="/job:localhost/replica:0/task:0/gpu:0", send_device="/job:localhost/replica:0/task:0/cpu:0", send_device_incarnation=1, tensor_name="edge_38_save_1/RestoreV2_1", tensor_type=DT_FLOAT, _device="/job:localhost/replica:0/task:0/gpu:0"]()]]

所以我使用https://github.com/tensorflow/tensorflow/blob/master/tensorflow/python/tools/inspect_checkpoint.py 脚本来检查我的检查点包含的键名，我得到如下信息：

model/conv1/biases
model/conv2/weights
...

所以我重写了我的网络，手动添加了模型/前缀：

                model = slim.conv2d(x, 20, [5, 5], padding='VALID', scope='model/conv1')
                model = slim.max_pool2d(model, [2, 2], padding='VALID', scope='model/pool1')
                model = slim.conv2d(model, 50, [5, 5], padding='VALID', scope='model/conv2')
                model = slim.max_pool2d(model, [2, 2], padding='VALID', scope='model/pool2')
                model = slim.flatten(model)
                model = slim.fully_connected(model, 500, scope='model/fc1')
                model = slim.dropout(model, 0.5, is_training=False, scope='model/do1')
                model = slim.fully_connected(model, nclasses, 

它修复了一些缺少键的警告，但是：

• 我觉得这不是解决问题的正确方法
• 我无法修复两个键：
  1. Global_Step（我的检查点中有一个 global_step 键）
  2. is_training（不知道是什么）

所以我的问题是：如何在我的网络中重新定义这些键名以匹配我在检查点中找到的那些？

Answer 1

因为我的问题主要是因为我对TensorFlow的理解不好，所以我去官方文档上一趟，找到了一些答案。

首先，我结合了 contrib/slim 和 contrib/tflearn 的使用，即使可能，它也不是真正相关的。所以我只用 slim 重写了网络：

import tensorflow as tf
import tensorflow.contrib.slim as slim


class LeNetModel():

    def gray28(self, nclasses):
        # x = input_data(shape=[None, 28, 28, 1])
        x = tf.placeholder(tf.float32, shape=[1, 28, 28], name="x")
        rs = tf.reshape(x, shape=[-1, 28, 28, 1])
        # scale (divide by MNIST std)
        # x = x * 0.0125
        with slim.arg_scope([slim.conv2d, slim.fully_connected],
                            weights_initializer=tf.contrib.layers.xavier_initializer(),
                            weights_regularizer=slim.l2_regularizer(0.0005)):
            model = slim.conv2d(rs, 20, [5, 5], padding='VALID', scope='conv1')
            model = slim.max_pool2d(model, [2, 2], padding='VALID', scope='pool1')
            model = slim.conv2d(model, 50, [5, 5], padding='VALID', scope='conv2')
            model = slim.max_pool2d(model, [2, 2], padding='VALID', scope='pool2')
            model = slim.flatten(model)
            model = slim.fully_connected(model, 500, scope='fc1')
            model = slim.dropout(model, 0.5, is_training=True, scope='do1')
            model = slim.fully_connected(model, nclasses, activation_fn=None, scope='fc2')

            return x, model

我返回 x 占位符和模型，并使用它来加载 DIGITS 预训练模型（检查点）：

import tensorflow as tf
import tensorflow.contrib.slim as slim
import cv2
from models.lenet import LeNetModel

# Helper function to load/resize images
def image(path):
    img = cv2.imread(path, 0)
    return cv2.resize(img, dsize=(28,28))

# Define a function that adds the model/ prefix to all variables :
def name_in_checkpoint(var):
  return 'model/' + var.op.name

#Instantiate the model
x, model = LeNetModel().gray28(2)

# Define the variables to restore :
# Exclude the "is_training" that I don't care about
variables_to_restore = slim.get_variables_to_restore(exclude=["is_training"])
# Rename the other variables with the function name_in_checkpoint
variables_to_restore = {name_in_checkpoint(var):var for var in variables_to_restore}

# Create a Saver to restore the checkpoint, given the variables
restorer = tf.train.Saver(variables_to_restore)

#Launch a session to restore the checkpoint and try to infer some images :
with tf.Session() as sess:
    # Restore variables from disk.
    restorer.restore(sess, "src/prototype/models/snapshot_5.ckpt")
    print("Model restored.")
    print(sess.run(model, feed_dict={x:[image("/home/damien/Vidéos/1/positives/img/1-img143.jpg")]}))
    print(sess.run(model, feed_dict={x:[image("/home/damien/Vidéos/0/positives/img/1-img1.jpg")]}))

它有效！