# Copyright 2016 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
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# See the License for the specific language governing permissions and
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# ==============================================================================
"""Contains various routines and helper functions for training models.

This script contains various functions for training models. These include
manipulating gradients, creating a `train_op` (an operation that computes the
loss and applies the gradients) and a training loop function. The training loop
allows the user to pass in the `train_op` and runs the optimization according
to user-specified arguments.

************************************
* A simple working training script *
************************************

  # Load data and create the model:
  images, labels = LoadData(...)
  predictions = MyModel(images)

  # Define the loss:
  tf.contrib.losses.log_loss(predictions, labels)
  total_loss = tf.contrib.losses.get_total_loss()

  # Define the optimizer:
  optimizer = tf.train.MomentumOptimizer(FLAGS.learning_rate, FLAGS.momentum)

  # Create the train_op
  train_op = tf.contrib.training.create_train_op(total_loss, optimizer)

  # Run training.
  tf.contrib.training.train(train_op, my_log_dir)

*************************
* Creating the train_op *
*************************

In order to use the `train` function, one needs a train_op: an `Operation` that
(a) computes the loss, (b) applies the gradients to update the weights and
(c) returns the value of the loss. tf.contrib.training.create_train_op creates
such an `Operation`. This function also provides the ability to manipulate
the gradients using a few arguments:

  # Create the train_op and clip the gradient norms:
  train_op = tf.contrib.training.create_train_op(
      total_loss,
      optimizer,
      clip_gradient_norm=4)

  # Create the train_op and scale the gradients by providing a map from variable
  # name (or variable) to a scaling coefficient:
  def transform_grads_fn(grads):
    gradient_multipliers = {
      'conv0/weights': 1.2,
      'fc8/weights': 3.4,
    }
    return tf.contrib.training.multiply_gradients(
            grads, gradient_multipliers)

  train_op = tf.contrib.training.create_train_op(
      total_loss,
      optimizer,
      transform_grads_fn=transform_grads_fn)

****************************************************************
* Performing additional (non-gradient) updates during training *
****************************************************************

Many networks utilize modules, like BatchNorm, that require performing a series
of non-gradient updates during training. tf.contrib.training.create_train_op
allows a user to pass in a list of update_ops to call along with the gradient
updates.

  train_op = tf.contrib.training.create_train_op(
      total_loss, optimizer, update_ops)

By default, tf.contrib.training.create_train_op includes all update ops that are
part of the `tf.GraphKeys.UPDATE_OPS` collection. Additionally, the
tf.contrib.layers.batch_norm function adds the moving mean and moving variance
updates to this collection. Consequently, users who want to use
tf.contrib.layers.batch_norm will not need to take any additional steps in order
to have the moving mean and moving variance updates be computed.

However, users with additional, specialized updates can either override the
default update ops or simply add additional update ops to the
`tf.GraphKeys.UPDATE_OPS` collection:

  # Force `create_train_op` to NOT use ANY update_ops:
  train_op = tf.contrib.training.create_train_op(
     total_loss,
     optimizer,
     update_ops=[])

  # Use an alternative set of update ops:
  train_op = tf.contrib.training.create_train_op(
     total_loss,
     optimizer,
     update_ops=my_other_update_ops)

  # Use a set of update ops in addition to the default updates:
  tf.add_to_collection(tf.GraphKeys.UPDATE_OPS, my_update0)
  tf.add_to_collection(tf.GraphKeys.UPDATE_OPS, my_update1)

  train_op = tf.contrib.training.create_train_op(
     total_loss,
     optimizer)

  # Which is the same as:
  train_op = tf.contrib.training.create_train_op(
     total_loss,
     optimizer,
     update_ops=tf.get_collection(tf.GraphKeys.UPDATE_OPS))

******************************************
* Initializing a model from a checkpoint *
******************************************

It is common to want to 'warm-start' a model from a pre-trained checkpoint.
One can use a tf.Scaffold and an initializing function to do so.

  ...

  # Create the train_op
  train_op = tf.contrib.training.create_train_op(total_loss, optimizer)

  # Create the initial assignment op
  checkpoint_path = '/path/to/old_model_checkpoint'
  variables_to_restore = tf.contrib.framework.get_model_variables()
  init_fn = tf.contrib.framework.assign_from_checkpoint_fn(
      checkpoint_path, variables_to_restore)

  # Run training.
  scaffold = tf.Scaffold(init_fn=init_fn)
  tf.contrib.training.train(train_op, my_log_dir, scaffold=scaffold)

***************************************************************************
* Initializing a model from a checkpoint whose variable names don't match *
***************************************************************************

At times, a user may want to initialize a new model with values from a
checkpoint whose variable names do not match those of the current model. In this
case, one needs to create a mapping from the checkpoint variable names to the
current model variables. This requires only a small modification of the code
above:
  ...
  # Creates a model with two variables, var0 and var1
  predictions = MyModel(images)
  ...

  # Create the train_op
  train_op = tf.contrib.training.create_train_op(total_loss, optimizer)

  checkpoint_path = '/path/to/old_model_checkpoint'

  # Create the mapping:
  variables_to_restore = {
      'name_var_0_in_checkpoint':
          tf.contrib.framework.get_unique_variable('var0'),
      'name_var_1_in_checkpoint':
          tf.contrib.framework.get_unique_variable('var1')
  }
  init_fn = tf.contrib.framework.assign_from_checkpoint_fn(
        checkpoint_path, variables_to_restore)
  scaffold = tf.Scaffold(init_fn=init_fn)

  # Run training.
  tf.contrib.training.train(train_op, my_log_dir, scaffold=scaffold)


*************************************************
* Fine-Tuning Part of a model from a checkpoint *
*************************************************

Rather than initializing all of the weights of a given model, we sometimes
only want to restore some of the weights from a checkpoint. To do this, one
need only filter those variables to initialize as follows:

  ...

  # Create the train_op
  train_op = tf.contrib.training.create_train_op(total_loss, optimizer)

  checkpoint_path = '/path/to/old_model_checkpoint'

  # Specify the variables to restore via a list of inclusion or exclusion
  # patterns:
  variables_to_restore = tf.contrib.framework.get_variables_to_restore(
      include=["conv"], exclude=["fc8", "fc9])
  # or
  variables_to_restore = tf.contrib.framework.get_variables_to_restore(
      exclude=["conv"])

  init_fn = tf.contrib.framework.assign_from_checkpoint_fn(
      checkpoint_path, variables_to_restore)
  scaffold = tf.Scaffold(init_fn=init_fn)

  # Run training.
  tf.contrib.training.train(train_op, my_log_dir, scaffold=scaffold)

******************************************************
* Initializing model variables from values in memory *
******************************************************

One may want to initialize the weights of a model from values coming from an
arbitrary source (a text document, matlab file, etc). While this is technically
feasible using assign operations, this strategy results in the values of your
weights being stored in the graph. For large models, this becomes prohibitively
large. However, it's possible to perform this initial assignment without having
to store the values of the initial model in the graph itself by using
placeholders and a feed dictionary:

  ...

  # Create the train_op
  train_op = tf.contrib.training.create_train_op(total_loss, optimizer)

  # Create the mapping from variable names to values:
  var0_initial_value = ReadFromDisk(...)
  var1_initial_value = ReadFromDisk(...)

  var_names_to_values = {
    'var0': var0_initial_value,
    'var1': var1_initial_value,
  }

  init_fn = tf.contrib.framework.assign_from_values_fn(var_names_to_values)
  scaffold = tf.Scaffold(init_fn=init_fn)

  # Run training.
  tf.contrib.training.train(train_op, my_log_dir, scaffold=scaffold)
"""

from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

from tensorflow.contrib.framework.python.ops import variables
from tensorflow.python.framework import constant_op
from tensorflow.python.framework import ops
from tensorflow.python.ops import array_ops
from tensorflow.python.ops import clip_ops
from tensorflow.python.ops import control_flow_ops
from tensorflow.python.ops import variables as tf_variables
from tensorflow.python.platform import tf_logging as logging
from tensorflow.python.summary import summary
from tensorflow.python.training import monitored_session
from tensorflow.python.training import optimizer as tf_optimizer

# TODO(nsilberman): move add_gradients_summaries, clip_gradient_norms and
# multiply_gradients into contrib/summaries and contrib/optimizers.py
__all__ = [
    'add_gradients_summaries',
    'clip_gradient_norms',
    'create_train_op',
    'multiply_gradients',
    'train',
]


def add_gradients_summaries(grads_and_vars):
  """Add summaries to gradients.

  Args:
    grads_and_vars: A list of gradient to variable pairs (tuples).

  Returns:
    The list of created summaries.
  """
  summaries = []
  for grad, var in grads_and_vars:
    if grad is not None:
      if isinstance(grad, ops.IndexedSlices):
        grad_values = grad.values
      else:
        grad_values = grad
      summaries.append(
          summary.histogram(var.op.name + '_gradient', grad_values))
      summaries.append(
          summary.scalar(var.op.name + '_gradient_norm',
                         clip_ops.global_norm([grad_values])))
    else:
      logging.info('Var %s has no gradient', var.op.name)

  return summaries


def clip_gradient_norms(gradients_to_variables, max_norm):
  """Clips the gradients by the given value.

  Args:
    gradients_to_variables: A list of gradient to variable pairs (tuples).
    max_norm: the maximum norm value.

  Returns:
    A list of clipped gradient to variable pairs.
  """
  clipped_grads_and_vars = []
  for grad, var in gradients_to_variables:
    if grad is not None:
      if isinstance(grad, ops.IndexedSlices):
        tmp = clip_ops.clip_by_norm(grad.values, max_norm)
        grad = ops.IndexedSlices(tmp, grad.indices, grad.dense_shape)
      else:
        grad = clip_ops.clip_by_norm(grad, max_norm)
    clipped_grads_and_vars.append((grad, var))
  return clipped_grads_and_vars


def multiply_gradients(grads_and_vars, gradient_multipliers):
  """Multiply specified gradients.

  Args:
    grads_and_vars: A list of gradient to variable pairs (tuples).
    gradient_multipliers: A map from either `Variables` or `Variable` op names
      to the coefficient by which the associated gradient should be scaled.

  Returns:
    The updated list of gradient to variable pairs.

  Raises:
    ValueError: If `grads_and_vars` is not a list or if `gradient_multipliers`
    is empty or None or if `gradient_multipliers` is not a dictionary.
  """
  if not isinstance(grads_and_vars, list):
    raise ValueError('`grads_and_vars` must be a list.')
  if not gradient_multipliers:
    raise ValueError('`gradient_multipliers` is empty.')
  if not isinstance(gradient_multipliers, dict):
    raise ValueError('`gradient_multipliers` must be a dict.')

  multiplied_grads_and_vars = []
  for grad, var in grads_and_vars:
    if var in gradient_multipliers or var.op.name in gradient_multipliers:
      key = var if var in gradient_multipliers else var.op.name
      if grad is None:
        raise ValueError('Requested multiple of `None` gradient.')

      if isinstance(grad, ops.IndexedSlices):
        tmp = grad.values * constant_op.constant(
            gradient_multipliers[key], dtype=grad.dtype)
        grad = ops.IndexedSlices(tmp, grad.indices, grad.dense_shape)
      else:
        grad *= constant_op.constant(
            gradient_multipliers[key], dtype=grad.dtype)
    multiplied_grads_and_vars.append((grad, var))
  return multiplied_grads_and_vars


_USE_GLOBAL_STEP = 0


def create_train_op(total_loss,
                    optimizer,
                    global_step=_USE_GLOBAL_STEP,
                    update_ops=None,
                    variables_to_train=None,
                    transform_grads_fn=None,
                    summarize_gradients=False,
                    gate_gradients=tf_optimizer.Optimizer.GATE_OP,
                    aggregation_method=None,
                    colocate_gradients_with_ops=False,
                    check_numerics=True):
  """Creates an `Operation` that evaluates the gradients and returns the loss.

  Args:
    total_loss: A `Tensor` representing the total loss.
    optimizer: A tf.Optimizer to use for computing the gradients.
    global_step: A `Tensor` representing the global step variable. If left as
      `_USE_GLOBAL_STEP`, then tf.contrib.framework.global_step() is used.
    update_ops: An optional list of updates to execute. If `update_ops` is
      `None`, then the update ops are set to the contents of the
      `tf.GraphKeys.UPDATE_OPS` collection. If `update_ops` is not `None`, but
      it doesn't contain all of the update ops in `tf.GraphKeys.UPDATE_OPS`,
      a warning will be displayed.
    variables_to_train: an optional list of variables to train. If None, it will
      default to all tf.trainable_variables().
    transform_grads_fn: A function which takes a single argument, a list of
      gradient to variable pairs (tuples), performs any requested gradient
      updates, such as gradient clipping or multipliers, and returns the updated
      list.
    summarize_gradients: Whether or not add summaries for each gradient.
    gate_gradients: How to gate the computation of gradients. See tf.Optimizer.
    aggregation_method: Specifies the method used to combine gradient terms.
      Valid values are defined in the class `AggregationMethod`.
    colocate_gradients_with_ops: Whether or not to try colocating the gradients
      with the ops that generated them.
    check_numerics: Whether or not we apply check_numerics.

  Returns:
    A `Tensor` that when evaluated, computes the gradients and returns the total
      loss value.
  """
  if global_step is _USE_GLOBAL_STEP:
    global_step = variables.get_or_create_global_step()

  # Update ops use GraphKeys.UPDATE_OPS collection if update_ops is None.
  global_update_ops = set(ops.get_collection(ops.GraphKeys.UPDATE_OPS))
  if update_ops is None:
    update_ops = global_update_ops
  else:
    update_ops = set(update_ops)
  if not global_update_ops.issubset(update_ops):
    logging.warning('update_ops in create_train_op does not contain all the '
                    ' update_ops in GraphKeys.UPDATE_OPS')

  # Make sure update_ops are computed before total_loss.
  if update_ops:
    with ops.control_dependencies(update_ops):
      barrier = control_flow_ops.no_op(name='update_barrier')
    total_loss = control_flow_ops.with_dependencies([barrier], total_loss)

  if variables_to_train is None:
    # Default to tf.trainable_variables()
    variables_to_train = tf_variables.trainable_variables()
  else:
    # Make sure that variables_to_train are in tf.trainable_variables()
    for v in variables_to_train:
      assert v in tf_variables.trainable_variables()

  assert variables_to_train

  # Create the gradients. Note that apply_gradients adds the gradient
  # computation to the current graph.
  grads = optimizer.compute_gradients(
      total_loss,
      variables_to_train,
      gate_gradients=gate_gradients,
      aggregation_method=aggregation_method,
      colocate_gradients_with_ops=colocate_gradients_with_ops)

  if transform_grads_fn:
    grads = transform_grads_fn(grads)

  # Summarize gradients.
  if summarize_gradients:
    with ops.name_scope('summarize_grads'):
      add_gradients_summaries(grads)

  # Create gradient updates.
  grad_updates = optimizer.apply_gradients(grads, global_step=global_step)

  with ops.name_scope('train_op'):
    # Make sure total_loss is valid.
    if check_numerics:
      total_loss = array_ops.check_numerics(total_loss,
                                            'LossTensor is inf or nan')

    # Ensure the train_tensor computes grad_updates.
    train_op = control_flow_ops.with_dependencies([grad_updates], total_loss)

  # Add the operation used for training to the 'train_op' collection
  train_ops = ops.get_collection_ref(ops.GraphKeys.TRAIN_OP)
  if train_op not in train_ops:
    train_ops.append(train_op)

  return train_op


def train(train_op,
          logdir,
          master='',
          is_chief=True,
          scaffold=None,
          hooks=None,
          chief_only_hooks=None,
          save_checkpoint_secs=600,
          save_summaries_steps=100,
          config=None):
  """Runs the training loop.

  Args:
    train_op: A `Tensor` that, when executed, will apply the gradients and
      return the loss value.
    logdir: The directory where the graph and checkpoints are saved.
    master: The URL of the master.
    is_chief: Specifies whether or not the training is being run by the primary
      replica during replica training.
    scaffold: An tf.train.Scaffold instance.
    hooks: List of `tf.train.SessionRunHook` callbacks which are run inside the
      training loop.
    chief_only_hooks: List of `tf.train.SessionRunHook` instances which are run
      inside the training loop for the chief trainer only.
    save_checkpoint_secs: The frequency, in seconds, that a checkpoint is saved
      using a default checkpoint saver. If `save_checkpoint_secs` is set to
      `None`, then the default checkpoint saver isn't used.
    save_summaries_steps: The frequency, in number of global steps, that the
      summaries are written to disk using a default summary saver. If
      `save_summaries_steps` is set to `None`, then the default summary saver
      isn't used.
    config: An instance of `tf.ConfigProto`.

  Returns:
    the value of the loss function after training.

  Raises:
    ValueError: if `logdir` is `None` and either `save_checkpoint_secs` or
    `save_summaries_steps` are `None.
  """
  if logdir is None and is_chief:
    if save_summaries_steps:
      raise ValueError(
          'logdir cannot be None when save_summaries_steps is not None')

    if save_checkpoint_secs:
      raise ValueError(
          'logdir cannot be None when save_checkpoint_secs is not None')

  with monitored_session.MonitoredTrainingSession(
      master=master,
      is_chief=is_chief,
      checkpoint_dir=logdir,
      scaffold=scaffold,
      hooks=hooks,
      chief_only_hooks=chief_only_hooks,
      save_checkpoint_secs=save_checkpoint_secs,
      save_summaries_steps=save_summaries_steps,
      config=config) as session:
    loss = None
    while not session.should_stop():
      loss = session.run(train_op)
  return loss
