Python gen_logging_ops._assert方法代码示例

# 需要导入模块: from tensorflow.python.ops import gen_logging_ops [as 别名]
# 或者: from tensorflow.python.ops.gen_logging_ops import _assert [as 别名]
def Assert(condition, data, summarize=None, name=None):
  """Asserts that the given condition is true.

  If `condition` evaluates to false, print the list of tensors in `data`.
  `summarize` determines how many entries of the tensors to print.

  NOTE: To ensure that Assert executes, one usually attaches a dependency:

  ```python
  # Ensure maximum element of x is smaller or equal to 1
  assert_op = tf.Assert(tf.less_equal(tf.reduce_max(x), 1.), [x])
  with tf.control_dependencies([assert_op]):
    ... code using x ...
  ```

  Args:
    condition: The condition to evaluate.
    data: The tensors to print out when condition is false.
    summarize: Print this many entries of each tensor.
    name: A name for this operation (optional).

  Returns:
    assert_op: An `Operation` that, when executed, raises a
    `tf.errors.InvalidArgumentError` if `condition` is not true.
  """
  with ops.name_scope(name, "Assert", [condition, data]) as name:
    xs = ops.convert_n_to_tensor(data)
    if all([x.dtype in {dtypes.string, dtypes.int32} for x in xs]):
      # As a simple heuristic, we assume that string and int32 are
      # on host to avoid the need to use cond. If it is not case,
      # we will pay the price copying the tensor to host memory.
      return gen_logging_ops._assert(
          condition, data, summarize, name="Assert")
    else:
      condition = ops.convert_to_tensor(condition, name="Condition")
      def true_assert():
        return gen_logging_ops._assert(
            condition, data, summarize, name="Assert")
      guarded_assert = cond(
          condition, no_op, true_assert, name="AssertGuard")
      return guarded_assert.op 

# 需要导入模块: from tensorflow.python.ops import gen_logging_ops [as 别名]
# 或者: from tensorflow.python.ops.gen_logging_ops import _assert [as 别名]
def testGuardedAssertDoesNotCopyWhenTrue(self):
    with self.test_session(use_gpu=True) as sess:
      with tf.device("/gpu:0"):
        value = tf.constant(1.0)
      with tf.device("/cpu:0"):
        true = tf.constant(True)
        guarded_assert = tf.Assert(true, [value], name="guarded")
        unguarded_assert = gen_logging_ops._assert(
            true, [value], name="unguarded")
      opts = tf.RunOptions(trace_level=tf.RunOptions.FULL_TRACE)
      guarded_metadata = tf.RunMetadata()
      sess.run(guarded_assert, options=opts, run_metadata=guarded_metadata)
      unguarded_metadata = tf.RunMetadata()
      sess.run(unguarded_assert, options=opts, run_metadata=unguarded_metadata)
      guarded_nodestat_names = [
          n.node_name for d in guarded_metadata.step_stats.dev_stats
          for n in d.node_stats]
      unguarded_nodestat_names = [
          n.node_name for d in unguarded_metadata.step_stats.dev_stats
          for n in d.node_stats]
      guarded_memcpy_nodestat_names = [
          n for n in guarded_nodestat_names if "MEMCPYDtoH" in n]
      unguarded_memcpy_nodestat_names = [
          n for n in unguarded_nodestat_names if "MEMCPYDtoH" in n]
      if "GPU" in [d.device_type for d in device_lib.list_local_devices()]:
        # A copy was performed for the unguarded assert
        self.assertLess(0, len(unguarded_memcpy_nodestat_names))
      # No copy was performed for the guarded assert
      self.assertEqual([], guarded_memcpy_nodestat_names) 

# 需要导入模块: from tensorflow.python.ops import gen_logging_ops [as 别名]
# 或者: from tensorflow.python.ops.gen_logging_ops import _assert [as 别名]
def Assert(condition, data, summarize=None, name=None):
  """Asserts that the given condition is true.

  If `condition` evaluates to false, print the list of tensors in `data`.
  `summarize` determines how many entries of the tensors to print.

  NOTE: To ensure that Assert executes, one usually attaches a dependency:

  ```python
   # Ensure maximum element of x is smaller or equal to 1
  assert_op = tf.Assert(tf.less_equal(tf.reduce_max(x), 1.), [x])
  x = tf.with_dependencies([assert_op], x)
  ```

  Args:
    condition: The condition to evaluate.
    data: The tensors to print out when condition is false.
    summarize: Print this many entries of each tensor.
    name: A name for this operation (optional).

  Returns:
    assert_op: An `Operation` that, when executed, raises a
    `tf.errors.InvalidArgumentError` if `condition` is not true.
  """
  with ops.name_scope(name, "Assert", [condition, data]) as name:
    xs = ops.convert_n_to_tensor(data)
    if all([x.dtype in {dtypes.string, dtypes.int32} for x in xs]):
      # As a simple heuristic, we assume that string and int32 are
      # on host to avoid the need to use cond. If it is not case,
      # we will pay the price copying the tensor to host memory.
      return gen_logging_ops._assert(
          condition, data, summarize, name="Assert")
    else:
      condition = ops.convert_to_tensor(condition, name="Condition")
      def true_assert():
        return gen_logging_ops._assert(
            condition, data, summarize, name="Assert")
      guarded_assert = cond(
          condition, no_op, true_assert, name="AssertGuard")
      return guarded_assert.op 

# 需要导入模块: from tensorflow.python.ops import gen_logging_ops [as 别名]
# 或者: from tensorflow.python.ops.gen_logging_ops import _assert [as 别名]
def testAssertOp(self):

    @function.Defun(tf.float32)
    def Foo(x):
      check = gen_logging_ops._assert(tf.greater(x, 0), [x])
      with tf.control_dependencies([check]):
        return x * 2

    g = tf.Graph()
    with g.as_default(), self.test_session():
      self.assertAllEqual(Foo(tf.constant(3.0)).eval(), 6.0)
      with self.assertRaisesRegexp(tf.errors.InvalidArgumentError,
                                   "assertion failed.*-3"):
        self.assertAllEqual(Foo(tf.constant(-3.0)).eval(), 6.0)