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 OnEntry(MethodExecutionArgs args)
{
// TODO: func_name can be cache in FullName + Args
func_name = $"{args.Method.DeclaringType.FullName}.{args.Method.Name}_{ops.uid_function()}";
if (functions.ContainsKey(func_name))
{
function = functions[func_name];
if (args.Arguments[0] is Tensors tensor_inputs)
{
args.ReturnValue = ConvertReturnValue(function.FilteredCall(tensor_inputs));
}
else
{
args.ReturnValue = ConvertReturnValue(function.FilteredCall(args.Arguments.Select(x => x as Tensor).ToArray()));
}
args.FlowBehavior = FlowBehavior.Return;
return;
}
// make function as an Operation by autograph
// need to restore mode when exits
function = new ConcreteFunction(func_name);
function.Enter();
// convert to Tensors
if (args.Arguments[0] is Tensors inputs)
{
originalInputs = inputs;
var new_inputs = inputs.Select(x => tf.placeholder(x.dtype, shape: x.shape, name: "inputs")).ToArray();
args.Arguments[0] = new Tensors(new_inputs);
}
else
{
originalInputs = new Tensors();
// convert args to placeholder
for (var i = 0; i < args.Arguments.Length; i++)
{
if (args.Arguments[i] is EagerTensor tensor)
{
originalInputs.Add(tensor);
args.Arguments[i] = tf.placeholder(tensor.dtype, shape: tensor.shape, name: "inputs");
}
}
}
}
public MapDataset(IDatasetV2 input_dataset,
Func <Tensor, Tensor> map_func,
bool use_inter_op_parallelism = true,
bool preserve_cardinality = false,
bool use_legacy_function = false) : base(input_dataset)
{
using var func = new ConcreteFunction($"{map_func.Method.Name}_{Guid.NewGuid()}");
func.Enter();
var input = tf.placeholder(input_dataset.element_spec[0].dtype);
var output = map_func(input);
func.ToGraph(input, output);
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);
}
