public override void OnExit(MethodExecutionArgs args)
{
if (args.ReturnValue is Tensors outputs)
{
if (args.Arguments[0] is Tensors inputs)
{
function.ToGraph(inputs, outputs);
}
else
{
function.ToGraph(args.Arguments.Select(x => x as Tensor).ToArray(), outputs);
}
}
else
{
function.ToGraph(args.Arguments.Select(x => x as Tensor).ToArray(), args.ReturnValue as Tensor);
}
// cache function.
function.ReturnType = args.ReturnValue.GetType();
functions[func_name] = function;
// run function
args.ReturnValue = ConvertReturnValue(function.Invoke(originalInputs));
}
public MapDataset(IDatasetV2 input_dataset,
Func <Tensors, Tensors> map_func,
bool use_inter_op_parallelism = true,
bool preserve_cardinality = false,
bool use_legacy_function = false) : base(input_dataset)
{
var func = new ConcreteFunction($"{map_func.Method.Name}_{Tensorflow.ops.uid_function()}");
func.Enter();
var inputs = new Tensors();
foreach (var input in input_dataset.element_spec)
{
inputs.Add(tf.placeholder(input.dtype, shape: input.shape, name: "arg"));
}
var outputs = map_func(inputs);
func.ToGraph(inputs, outputs);
func.Exit();
structure = func.OutputStructure;
variant_tensor = ops.map_dataset(input_dataset.variant_tensor,
func,
output_types,
output_shapes,
use_inter_op_parallelism: use_inter_op_parallelism,
preserve_cardinality: preserve_cardinality);
}
public FilterDataset(IDatasetV2 input_dataset,
Func <Tensor, bool> predicate_func) : base(input_dataset)
{
Func <Tensors, Tensors> predicate_func_update = x =>
{
var result = predicate_func(x);
return(constant_op.constant(result));
};
var func = new ConcreteFunction($"{predicate_func.Method.Name}_{Tensorflow.ops.uid_function()}");
func.Enter();
var inputs = new Tensors();
foreach (var input in input_dataset.element_spec)
{
inputs.Add(tf.placeholder(input.dtype, shape: input.shape, name: "arg"));
}
var outputs = predicate_func_update(inputs);
func.ToGraph(inputs, outputs);
func.Exit();
structure = func.OutputStructure;
variant_tensor = ops.filter_dataset(input_dataset.variant_tensor,
func,
output_types,
output_shapes);
}
public override void OnExit(MethodExecutionArgs args)
{
if (args.ReturnValue is Tensors outputs)
{
Tensors inputs = null;
outputs = mark_as_return(outputs);
if (args.Arguments[0] is Tensors inputs1)
{
inputs = inputs1;
}
else
{
inputs = args.Arguments.Select(x => x as Tensor).ToArray();
}
inputs = inputs.Where(x => x.op.OpType == "Placeholder" &&
x.op.name.StartsWith("inputs")).ToArray();
function.ToGraph(inputs, outputs);
}
else if (args.ReturnValue is Tensor output)
{
var inputs = args.Arguments.Select(x => x as Tensor)
.Where(x => x.op.type == "Placeholder" && x.op.name.StartsWith("inputs"))
.ToArray();
var outputs2 = array_ops.identity(output);
function.ToGraph(inputs, outputs2);
}
function.Exit();
// cache function.
function.ReturnType = args.ReturnValue.GetType();
functions[func_name] = function;
// run function
args.ReturnValue = ConvertReturnValue(function.FilteredCall(originalInputs));
}
public MapDataset(IDatasetV2 input_dataset,
Func <Tensor, Tensor> map_func,
bool use_inter_op_parallelism = true,
bool preserve_cardinality = false,
bool use_legacy_function = false) : base(input_dataset)
{
using var func = new ConcreteFunction($"autograph_{map_func.Method.Name}");
var input = tf.placeholder(input_dataset.element_spec[0].dtype);
var output = map_func(input);
func.ToGraph(input, output);
structure = func.OutputStructure;
variant_tensor = ops.map_dataset(input_dataset.variant_tensor,
func,
output_types,
output_shapes,
use_inter_op_parallelism: use_inter_op_parallelism,
preserve_cardinality: preserve_cardinality);
}
