Python cond_v2.cond_v2函数代码示例

  def testColocateWithBeforeCond(self):
    with ops.Graph().as_default() as g:
      with self.session(graph=g):

        a = constant_op.constant([2.0], name="a")
        b = constant_op.constant([2.0], name="b")

        def fn():
          c = constant_op.constant(3.0)
          self.assertEqual([b"loc:@a"], c.op.colocation_groups())
          return c

        with ops.colocate_with(a.op):
          self.assertEquals(
              cond_v2.cond_v2(constant_op.constant(True), fn, fn).eval(), 3)

        def fn2():
          c = constant_op.constant(3.0)
          self.assertEqual([b"loc:@a", b"loc:@b"], c.op.colocation_groups())
          return c

        with ops.colocate_with(a.op):
          with ops.colocate_with(b.op):
            self.assertEquals(
                cond_v2.cond_v2(constant_op.constant(True), fn2, fn2).eval(), 3)

  def testDeviceBeforeCond(self):
    with ops.Graph().as_default() as g:
      with self.session(graph=g):

        def fn():
          self.assertEqual("", constant_op.constant(3.0).op.device)
          return test_ops.device_placement_op()

        with ops.device("/device:CPU:0"):
          self.assertIn(
              compat.as_bytes("CPU:0"),
              self.evaluate(cond_v2.cond_v2(constant_op.constant(True),
                                            fn, fn)))

        def fn2():
          self.assertEqual("", constant_op.constant(3.0).op.device)
          return test_ops.device_placement_op()

        if test_util.is_gpu_available():
          with ops.device("/device:GPU:0"):
            self.assertIn(
                compat.as_bytes("GPU:0"),
                self.evaluate(cond_v2.cond_v2(constant_op.constant(True),
                                              fn2, fn2)))
        else:
          self.skipTest("Test requires a GPU to check GPU device placement.")

  def testExternalControlDependencies(self):
    with ops.Graph().as_default(), self.test_session():
      v = variables.Variable(1.0)
      v.initializer.run()
      op = v.assign_add(1.0)

      def true_branch():
        with ops.control_dependencies([op]):
          return 1.0

      cond_v2.cond_v2(array_ops.placeholder_with_default(False, None),
                      true_branch,
                      lambda: 2.0).eval()
      self.assertAllEqual(self.evaluate(v), 2.0)

    def func_with_cond():
      pred = constant_op.constant(True, name="pred")
      x = constant_op.constant(1.0, name="x")

      def true_fn():
        intermediate = x + 1
        return intermediate * x

      def false_fn():
        return x + 1

      output = cond_v2.cond_v2(pred, true_fn, false_fn)
      grad = gradients_impl.gradients(output, x)[0]

      forward_if_op = output.op.inputs[0].op
      gradient_if_op = grad.op.inputs[0].op

      def verify_no_optional_ops(op, branch_name):
        branch_function = ops.get_default_graph()._get_function(
            op.get_attr(branch_name).name)
        function_def = branch_function.definition
        for node_def in function_def.node_def:
          self.assertNotIn(node_def.op, _OPTIONAL_OPS)

      verify_no_optional_ops(forward_if_op, "then_branch")
      verify_no_optional_ops(forward_if_op, "else_branch")
      verify_no_optional_ops(gradient_if_op, "then_branch")
      verify_no_optional_ops(gradient_if_op, "else_branch")

      return grad

  def testColocateWithInCondGraphPartitioning(self):
    with ops.Graph().as_default() as g:
      with self.test_session(
          graph=g,
          config=config_pb2.ConfigProto(device_count={"CPU": 2})
      ) as sess:

        with ops.device("/device:CPU:0"):
          a = constant_op.constant([2.0], name="a")
        with ops.device("/device:CPU:1"):
          b = constant_op.constant([2.0], name="b")

        def fn():
          with ops.colocate_with(b.op):
            c = math_ops.add(a, a, name="c")
          return c
        out_cond_2 = cond_v2.cond_v2(True, fn, fn)[0]

        run_options = config_pb2.RunOptions(output_partition_graphs=True)
        run_metadata = config_pb2.RunMetadata()
        sess.run(out_cond_2, options=run_options, run_metadata=run_metadata)

        # We expect there to be two partitions because of the
        # colocate_with. We are only running the cond, which has a data
        # dependency on `a` but not on `b`. So, without the colocate_with
        # we would expect execution on just one device.
        self.assertTrue(len(run_metadata.partition_graphs) >= 2)

  def testSecondDerivative(self):
    with self.test_session() as sess:
      pred = array_ops.placeholder(dtypes.bool, name="pred")
      x = constant_op.constant(3.0, name="x")

      def true_fn():
        return math_ops.pow(x, 3)

      def false_fn():
        return x

      cond = cond_v2.cond_v2(pred, true_fn, false_fn, name="cond")
      cond_grad = gradients_impl.gradients(cond, [x])
      cond_grad_grad = gradients_impl.gradients(cond_grad, [x])

      # d[x^3]/dx = 3x^2
      true_val = sess.run(cond_grad, {pred: True})
      self.assertEqual(true_val, [27.0])
      # d[x]/dx = 1
      false_val = sess.run(cond_grad, {pred: False})
      self.assertEqual(false_val, [1.0])

      true_val = sess.run(cond_grad_grad, {pred: True})
      # d2[x^3]/dx2 = 6x
      self.assertEqual(true_val, [18.0])
      false_val = sess.run(cond_grad_grad, {pred: False})
      # d2[x]/dx2 = 0
      self.assertEqual(false_val, [0.0])

  def _testCond(self, true_fn, false_fn, train_vals, feed_dict=None):
    if not feed_dict:
      feed_dict = {}
    with self.test_session(graph=ops.get_default_graph()) as sess:
      pred = array_ops.placeholder(dtypes.bool, name="pred")

      expected = control_flow_ops.cond(pred, true_fn, false_fn, name="expected")
      actual = cond_v2.cond_v2(pred, true_fn, false_fn, name="actual")

      expected_grad = gradients_impl.gradients(expected, train_vals)
      actual_grad = gradients_impl.gradients(actual, train_vals)

      sess_run_args = {pred: True}
      sess_run_args.update(feed_dict)
      expected_val, actual_val, expected_grad_val, actual_grad_val = sess.run(
          (expected, actual, expected_grad, actual_grad), sess_run_args)
      self.assertEqual(expected_val, actual_val)
      self.assertEqual(expected_grad_val, actual_grad_val)

      sess_run_args = {pred: False}
      sess_run_args.update(feed_dict)
      expected_val, actual_val, expected_grad_val, actual_grad_val = sess.run(
          (expected, actual, expected_grad, actual_grad), sess_run_args)
      self.assertEqual(expected_val, actual_val)
      self.assertEqual(expected_grad_val, actual_grad_val)

    def build_graph():
      pred_outer = array_ops.placeholder(dtypes.bool, name="pred_outer")
      pred_inner = array_ops.placeholder(dtypes.bool, name="pred_inner")
      x = constant_op.constant(1.0, name="x")
      y = constant_op.constant(2.0, name="y")

      def true_fn():
        return 2.0

      def false_fn():

        def inner_true_fn():
          return x * y * 2.0

        def inner_false_fn():
          return x * 5.0

        return cond_v2.cond_v2(
            pred_inner, inner_true_fn, inner_false_fn, name="inner_cond")

      cond_outer = cond_v2.cond_v2(
          pred_outer, true_fn, false_fn, name="outer_cond")

      # Compute grads inside a Defun.
      @function.defun
      def nesting_fn():
        return gradients_impl.gradients(cond_outer, [x, y])

      grads = nesting_fn()

      return grads, pred_outer, pred_inner

  def testDeviceBeforeCond(self):
    with ops.Graph().as_default() as g:
      with self.test_session(graph=g):
        def fn():
          c = constant_op.constant(3.0)
          self.assertEqual("/device:CPU:0", c.op.device)
          return c

        with ops.device("/device:CPU:0"):
          self.assertEquals(cond_v2.cond_v2(True, fn, fn)[0].eval(), 3)

        def fn2():
          c = constant_op.constant(3.0)
          self.assertEqual("/device:GPU:0", c.op.device)
          return c

        with ops.device("/device:GPU:0"):
          self.assertEquals(cond_v2.cond_v2(True, fn2, fn2)[0].eval(), 3)

        def false_fn():

          def inner_true_fn():
            return x * y * 2.0

          def inner_false_fn():
            return x * 5.0

          return cond_v2.cond_v2(
              pred_inner, inner_true_fn, inner_false_fn, name="inner_cond")

  def _createCond(self, name):
    pred = constant_op.constant(True, name="pred")
    x = constant_op.constant(1.0, name="x")

    def true_fn():
      return x

    def false_fn():
      return x + 1

    return cond_v2.cond_v2(pred, true_fn, false_fn, name=name)[0].op

      def fnWithCond():  # pylint: disable=invalid-name
        with backprop.GradientTape() as tape:
          pred = constant_op.constant(True, dtype=dtypes.bool)

          def true_fn():
            return math_ops.pow(v, 3)

          def false_fn():
            return v

          cond = cond_v2.cond_v2(pred, true_fn, false_fn, name="cond")
        return tape.gradient(cond, v)

  def testNoInputs(self):
    with self.test_session() as sess:
      pred = array_ops.placeholder(dtypes.bool, name="pred")

      def true_fn():
        return constant_op.constant(1.0)

      def false_fn():
        return constant_op.constant(2.0)

      out = cond_v2.cond_v2(pred, true_fn, false_fn)

      self.assertEqual(sess.run(out, {pred: True}), (1.0,))
      self.assertEqual(sess.run(out, {pred: False}), (2.0,))

  def testCollectionIntValueAccessInCond(self):
    """Read values from graph collections inside of cond_v2."""
    with ops.Graph().as_default() as g:
      with self.test_session(graph=g):
        x = 2
        y = 5
        ops.add_to_collection("x", x)
        ops.add_to_collection("y", y)
        def fn():
          x_const = constant_op.constant(ops.get_collection("x")[0])
          y_const = constant_op.constant(ops.get_collection("y")[0])
          return math_ops.add(x_const, y_const)

        cnd = cond_v2.cond_v2(True, fn, fn)
        self.assertEquals(cnd[0].eval(), 7)

  def _createNestedCond(self, name):
    """Like _createCond but creates a nested cond_v2 call as well."""
    pred = constant_op.constant(True, name="pred")
    x = constant_op.constant(1.0, name="x")

    def true_fn():
      return cond_v2.cond_v2(pred, lambda: x, lambda: x + 1)

    def false_fn():
      return x + 2

    output = cond_v2.cond_v2(pred, true_fn, false_fn, name=name)
    cond_op = output.op.inputs[0].op
    self.assertEqual(cond_op.type, "If")
    return output, cond_op