コード例 #1
0
ファイル: conversion.py プロジェクト: Alex13Wu/tensorflow-1
def node_to_graph(node, namer, namespace, value_hints, nocompile_decorators):
  """Convert Python code to equivalent TF graph mode code.

  Args:
    node: A Python AST node representing the code to convert.
    namer: A naming.Namer object.
    namespace: Dict mapping symbol names to their corresponding live objects.
    value_hints: A dict containing value hints for symbols like function
        parameters.
    nocompile_decorators: A tuple containing decorators to be stripped from
        functions during conversion.

  Returns:
    A tuple (node, deps):
        * node: A Python ast node, representing the converted code.
        * deps: A set of strings, the fully qualified names of object
            dependencies that this node has.
  """
  # TODO(mdan): Verify arguments for correctness.

  # TODO(mdan): Factor out common elements.
  # These include:
  #   * keeping track of symbols that have been created
  #   * marking nodes (e.g. py_func wrappers) to suppress further processing
  #   * code move between blocks
  #   * insertion of new global references
  #   * visiting blocks in transformers

  # Certain steps, especially canonicalization, insert new symbols into the
  # tree, which must be accounted. Although less efficient, it is most robust
  # to re-run the analysis.

  node = _static_analysis_pass(node, namespace, value_hints)
  node = decorators.transform(node, nocompile_decorators)
  node = break_canonicalization.transform(node, namer)

  # Note: sequencing continue canonicalization before for loop one avoids
  # dealing with the extra loop increment operation that the for
  # canonicalization creates.
  node = continue_canonicalization.transform(node, namer)
  namespace['len'] = len

  node = _static_analysis_pass(node, namespace, value_hints)
  node = for_canonicalization.transform(node, namer)
  # for_canonicalization may insert new global references.
  node = builtin_functions.transform(node)
  # builtin_functions may insert new global references.
  namespace['print'] = print

  node = _static_analysis_pass(node, namespace, value_hints)
  node = print_functions.transform(node)
  node = call_trees.transform(node, namer, namespace,
                              config.DEFAULT_UNCOMPILED_MODULES,
                              nocompile_decorators)
  node = control_flow.transform(node, namer)
  node = logical_expressions.transform(node)
  node = side_effect_guards.transform(node, namer)

  return node
コード例 #2
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    def test_basic_for(self):
        def test_fn(l):
            s = 0
            for e in l:
                s += e
            return s

        node = self._parse_and_analyze(test_fn, {})
        node = for_canonicalization.transform(node, TestNamer())
        result = compiler.ast_to_object(node)

        l = [1, 2, 3]
        self.assertEqual(test_fn(l), result.test_fn(l))
        l = []
        self.assertEqual(test_fn(l), result.test_fn(l))
コード例 #3
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  def test_basic_for(self):

    def test_fn(l):
      s = 0
      for e in l:
        s += e
      return s

    node = self._parse_and_analyze(test_fn, {})
    node = for_canonicalization.transform(node, TestNamer())
    result = compiler.ast_to_object(node)

    l = [1, 2, 3]
    self.assertEqual(test_fn(l), result.test_fn(l))
    l = []
    self.assertEqual(test_fn(l), result.test_fn(l))
コード例 #4
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def node_to_graph(node, namer, namespace, value_hints):
    """Convert Python code to equivalent TF graph mode code.

  Args:
    node: A Python AST node representing the code to convert.
    namer: A naming.Namer object.
    namespace: Dict mapping symbol names to their corresponding live objects.
    value_hints: A dict containing value hints for symbols like function
        parameters.

  Returns:
    A tuple (node, deps):
        * node: A Python ast node, representing the converted code.
        * deps: A set of strings, the fully qualified names of object
            dependencies that this node has.
  """
    node = access.resolve(node)
    node = live_values.resolve(node, namespace, config.PYTHON_LITERALS)
    node = type_info.resolve(node, value_hints)

    # TODO(mdan): Factor out common elements.
    # These include:
    #   * keeping track of symbols that have been created
    #   * marking nodes (e.g. py_func wrappers) to suppress further processing

    node = for_canonicalization.transform(node, namer)
    node = builtin_functions.transform(node)

    # The transformation steps above insert new variables. Although less
    # efficient, it is most robust to re-run the analysis.
    # We also need to ensure the namespace contains any new references that may
    # have been created.
    namespace['len'] = len
    namespace['print'] = print

    node = access.resolve(node)
    node = live_values.resolve(node, namespace, config.PYTHON_LITERALS)
    node = type_info.resolve(node, value_hints)

    node = print_functions.transform(node)
    node = call_trees.transform(node, namer, config.DEFAULT_UNCOMPILED_MODULES)
    node = control_flow.transform(node, namer)
    node = logical_expressions.transform(node)
    node = side_effect_guards.transform(node, namer)

    return node