Tensorflow Slim入门教程（3）

5. Fine Tuning Existing Models

5.1 tf.train.Saver()

# Create some variables.
v1 = tf.Variable(..., name="v1")
v2 = tf.Variable(..., name="v2")
...
# Add ops to restore all the variables.
restorer = tf.train.Saver()

# Add ops to restore some variables.
restorer = tf.train.Saver([v1, v2])

# Later, launch the model, use the saver to restore variables from disk, and
# do some work with the model.
with tf.Session() as sess:
  # Restore variables from disk.
  restorer.restore(sess, "/tmp/model.ckpt")
  print("Model restored.")
  # Do some work with the model
  ... ...

5.2 导入部分参数

有时我们希望在一个全新的数据集上或面对一个信息任务方向去微调预训练模型。在这些情况下，我们可以使用TF-Slim’s的帮助函数去加载模型中变量的一个子集：

# Create some variables.
v1 = slim.variable(name="v1", ...)
v2 = slim.variable(name="nested/v2", ...)
...

# Get list of variables to restore (which contains only 'v2'). These are all
# equivalent methods:
variables_to_restore = slim.get_variables_by_name("v2")
# or
variables_to_restore = slim.get_variables_by_suffix("2")
# or
variables_to_restore = slim.get_variables(scope="nested")
# or
variables_to_restore = slim.get_variables_to_restore(include=["nested"])
# or
variables_to_restore = slim.get_variables_to_restore(exclude=["v1"])

# Create the saver which will be used to restore the variables.
restorer = tf.train.Saver(variables_to_restore)

with tf.Session() as sess:
  # Restore variables from disk.
  restorer.restore(sess, "/tmp/model.ckpt")
  print("Model restored.")
  # Do some work with the model
  ... ...

5.3 导入名字不同的参数

当从checkpoint加载变量时，Saver先在checkpoint中定位变量名，然后映射到当前图的变量中。我们也可以通过向saver传递一个变量列表来创建saver。这时，在checkpoint文件中用于定位的变量名可以隐式地从各自的var.op.name中获得。
当checkpoint文件中的变量名与当前图中的变量名完全匹配时，这会运行得很好。但是，有时我们想从一个变量名与与当前图的变量名不同的checkpoint文件中装载一个模型。这时，我们必须提供一个saver字典，这个字典对checkpoint中的每个变量和每个图变量进行了一一映射。请看下面这个例子，checkpoint的变量是通过一个简单的函数获得的：

# Assuming than 'conv1/weights' should be restored from 'vgg16/conv1/weights'
def name_in_checkpoint(var):
  return 'vgg16/' + var.op.name

# Assuming than 'conv1/weights' and 'conv1/bias' should be restored from 'conv1/params1' and 'conv1/params2'
def name_in_checkpoint(var):
  if "weights" in var.op.name:
    return var.op.name.replace("weights", "params1")
  if "bias" in var.op.name:
    return var.op.name.replace("bias", "params2")

variables_to_restore = slim.get_model_variables()
variables_to_restore = {name_in_checkpoint(var):var for var in variables_to_restore}
restorer = tf.train.Saver(variables_to_restore)

with tf.Session() as sess:
  # Restore variables from disk.
  restorer.restore(sess, "/tmp/model.ckpt")

5.4 在不同任务上微调模型

假设我们有一个已经预训练好的VGG16的模型。这个模型是在拥有1000分类的ImageNet数据集上进行训练的。但是，现在我们想把它应用在只具有20个分类的Pascal VOC数据集上。为了能这样做，我们可以通过利用除最后一些全连接层的其他预训练模型值来初始化新模型的达到目的：

# Load the Pascal VOC data
image, label = MyPascalVocDataLoader(...)
images, labels = tf.train.batch([image, label], batch_size=32)

# Create the model
predictions = vgg.vgg_16(images)

train_op = slim.learning.create_train_op(...)

# Specify where the Model, trained on ImageNet, was saved.
model_path = '/path/to/pre_trained_on_imagenet.checkpoint'

# Specify where the new model will live:
log_dir = '/path/to/my_pascal_model_dir/'

# Restore only the convolutional layers:
variables_to_restore = slim.get_variables_to_restore(exclude=['fc6', 'fc7', 'fc8'])
init_fn = assign_from_checkpoint_fn(model_path, variables_to_restore)

# Start training.
slim.learning.train(train_op, log_dir, init_fn=init_fn)

6. 评估模型

6.1 Metric

TF-Slim提供了一系列指标操作符，它们可以使模型评估更简单。抽象来讲，计算一个指标值可以分为3步：

   1. 初始化：初始化用于计算指标的变量。

   2. 聚合：执行用于计算指标的运算流程（比如sum）。

   3. 收尾：（可选）执行其他用于计算指标值的操作。例如，计算mean、min、max等。

images, labels = LoadTestData(...)
predictions = MyModel(images)

mae_value_op, mae_update_op = slim.metrics.streaming_mean_absolute_error(predictions, labels)
mre_value_op, mre_update_op = slim.metrics.streaming_mean_relative_error(predictions, labels)
pl_value_op, pl_update_op = slim.metrics.percentage_less(mean_relative_errors, 0.3)

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

slim = tf.contrib.slim
vgg = nets.vgg


# Load the data
images, labels = load_data(...)

# Define the network
predictions = vgg.vgg_16(images)

# Choose the metrics to compute:
names_to_values, names_to_updates = slim.metrics.aggregate_metric_map({
    "eval/mean_absolute_error": slim.metrics.streaming_mean_absolute_error(predictions, labels),
    "eval/mean_squared_error": slim.metrics.streaming_mean_squared_error(predictions, labels),
})

# Evaluate the model using 1000 batches of data:
num_batches = 1000

with tf.Session() as sess:
  sess.run(tf.global_variables_initializer())
  sess.run(tf.local_variables_initializer())

  for batch_id in range(num_batches):
    sess.run(names_to_updates.values())

  metric_values = sess.run(names_to_values.values())
  for metric, value in zip(names_to_values.keys(), metric_values):
    print('Metric %s has value: %f' % (metric, value))