public static Tensor assign(ResourceVariable @ref, object value,
bool validate_shape = true,
bool use_locking = true,
string name = null)
{
return(gen_state_ops.assign(@ref,
value,
validate_shape: validate_shape,
use_locking: use_locking,
name: name));
}
/// <summary>
/// Converts the given `value` to a `Tensor`.
/// </summary>
/// <param name="value"></param>
/// <param name="dtype"></param>
/// <param name="name"></param>
/// <returns></returns>
public static Tensor convert_to_tensor(object value,
TF_DataType dtype = TF_DataType.DtInvalid,
string name = null,
bool as_ref = false,
TF_DataType preferred_dtype = TF_DataType.DtInvalid,
Context ctx = null)
{
if (dtype == TF_DataType.DtInvalid)
{
dtype = preferred_dtype;
}
if (value is EagerTensor eager_tensor)
{
if (tf.executing_eagerly())
{
return(eager_tensor);
}
else
{
var graph = get_default_graph();
if (!graph.building_function)
{
throw new RuntimeError("Attempting to capture an EagerTensor without building a function.");
}
return((graph as FuncGraph).capture(eager_tensor, name: name));
}
}
Tensor ret = value switch
{
NDArray nd => constant_op.constant(nd, dtype: dtype, name: name),
EagerTensor tensor => tensor.dtype == TF_DataType.TF_RESOURCE
? tensor.AsPlaceholder(name: name)
: tensor.AsConstant(name: name),
Tensor tensor => tensor,
Tensor[] tensors => array_ops._autopacking_helper(tensors, dtype, name == null ? "packed" : name),
RefVariable varVal => varVal._TensorConversionFunction(dtype: dtype, name: name, as_ref: as_ref),
ResourceVariable varVal => varVal._TensorConversionFunction(dtype: dtype, name: name, as_ref: as_ref),
TensorShape ts => constant_op.constant(ts.dims, dtype: dtype, name: name),
int[] dims => constant_op.constant(dims, dtype: dtype, name: name),
string str => constant_op.constant(str, dtype: tf.@string, name: name),
string[] str => constant_op.constant(str, dtype: tf.@string, name: name),
IEnumerable <object> objects => array_ops._autopacking_conversion_function(objects, dtype: dtype, name: name),
_ => constant_op.constant(value, dtype: dtype, name: name)
};
return(ret);
}
public static ResourceVariable cast(ResourceVariable x, TF_DataType dtype = TF_DataType.DtInvalid, string name = null)
{
var base_type = dtype.as_base_dtype();
if (base_type == x.dtype)
return x;
return tf_with(ops.name_scope(name, "Cast", new { x }), scope =>
{
name = scope;
var t_x = ops.convert_to_tensor(x, name: "x");
if (t_x.dtype.as_base_dtype() != base_type)
t_x = gen_math_ops.cast(t_x, base_type, name: name);
return x;
});
}
public static Tensor assign(ResourceVariable @ref, object value,
bool validate_shape = true,
bool use_locking = true,
string name = null)
{
var _op = tf.OpDefLib._apply_op_helper("Assign", name: name, args: new { @ref, value, validate_shape, use_locking });
var _result = _op.outputs;
var _inputs_flat = _op.inputs;
var _attrs = new Dictionary <string, object>();
_attrs["T"] = _op.get_attr("T");
_attrs["validate_shape"] = _op.get_attr("validate_shape");
_attrs["use_locking"] = _op.get_attr("use_locking");
return(_result[0]);
}
private static Tensor op_helper <T>(string default_name, ResourceVariable x, T y)
=> tf_with(ops.name_scope(null, default_name, new { x, y }), scope =>
{
string name = scope;
var xVal = x.value();
var yTensor = ops.convert_to_tensor(y, xVal.dtype.as_base_dtype(), "y");
Tensor result = null;
switch (default_name)
{
case "add":
result = x.dtype == TF_DataType.TF_STRING ?
gen_math_ops.add(xVal, yTensor, name) :
gen_math_ops.add_v2(xVal, yTensor, name);
break;
case "sub":
result = gen_math_ops.sub(xVal, yTensor, name);
break;
case "mul":
result = gen_math_ops.mul(xVal, yTensor, name: name);
break;
case "less":
result = gen_math_ops.less(xVal, yTensor, name);
break;
case "greater":
result = gen_math_ops.greater(xVal, yTensor, name);
break;
default:
throw new NotImplementedException("");
}
// x.assign(result);
// result.ResourceVar = x;
return(result);
});
public static (Dictionary <string, IVariableV1>, ITensorOrOperation[]) import_scoped_meta_graph_with_return_elements(MetaGraphDef meta_graph_or_file,
bool clear_devices = false,
string import_scope = "",
Dictionary <string, Tensor> input_map = null,
string unbound_inputs_col_name = "unbound_inputs",
string[] return_elements = null)
{
var meta_graph_def = meta_graph_or_file;
if (!string.IsNullOrEmpty(unbound_inputs_col_name))
{
foreach (var col in meta_graph_def.CollectionDef)
{
if (col.Key == unbound_inputs_col_name)
{
throw new NotImplementedException("import_scoped_meta_graph_with_return_elements");
}
}
}
// Sets graph to default graph if it's not passed in.
var graph = ops.get_default_graph();
// Gathers the list of nodes we are interested in.
OpList producer_op_list = null;
if (meta_graph_def.MetaInfoDef.StrippedOpList != null)
{
producer_op_list = meta_graph_def.MetaInfoDef.StrippedOpList;
}
var input_graph_def = meta_graph_def.GraphDef;
// Remove all the explicit device specifications for this node. This helps to
// make the graph more portable.
if (clear_devices)
{
foreach (var node in input_graph_def.Node)
{
node.Device = "";
}
}
var scope_to_prepend_to_names = graph.unique_name("", mark_as_used: false);
var imported_return_elements = importer.import_graph_def(input_graph_def,
name: scope_to_prepend_to_names,
input_map: input_map,
producer_op_list: producer_op_list,
return_elements: return_elements);
// Restores all the other collections.
var variable_objects = new Dictionary <ByteString, IVariableV1>();
foreach (var col in meta_graph_def.CollectionDef.OrderBy(x => x.Key))
{
// Don't add unbound_inputs to the new graph.
if (col.Key == unbound_inputs_col_name)
{
continue;
}
switch (col.Value.KindCase)
{
case KindOneofCase.NodeList:
foreach (var value in col.Value.NodeList.Value)
{
var col_op = graph.as_graph_element(ops.prepend_name_scope(value, scope_to_prepend_to_names));
graph.add_to_collection(col.Key, col_op);
}
break;
case KindOneofCase.BytesList:
//var proto_type = ops.get_collection_proto_type(key)
if (tf.GraphKeys._VARIABLE_COLLECTIONS.Contains(col.Key))
{
foreach (var value in col.Value.BytesList.Value)
{
IVariableV1 variable = null;
if (!variable_objects.ContainsKey(value))
{
var proto = VariableDef.Parser.ParseFrom(value);
if (proto.IsResource)
{
variable = new ResourceVariable(variable_def: proto, import_scope: scope_to_prepend_to_names);
}
else
{
variable = new RefVariable(variable_def: proto, import_scope: scope_to_prepend_to_names);
}
variable_objects[value] = variable;
}
variable = variable_objects[value];
graph.add_to_collection(col.Key, variable);
}
}
else
{
foreach (var value in col.Value.BytesList.Value)
{
switch (col.Key)
{
case "cond_context":
{
var proto = CondContextDef.Parser.ParseFrom(value);
var condContext = new CondContext().from_proto(proto, import_scope);
graph.add_to_collection(col.Key, condContext);
}
break;
case "while_context":
{
var proto = WhileContextDef.Parser.ParseFrom(value);
var whileContext = new WhileContext().from_proto(proto, import_scope);
graph.add_to_collection(col.Key, whileContext);
}
break;
default:
Console.WriteLine($"import_scoped_meta_graph_with_return_elements {col.Key}");
continue;
}
}
}
break;
default:
Console.WriteLine($"Cannot identify data type for collection {col.Key}. Skipping.");
break;
}
}
var variables = graph.get_collection <IVariableV1>(tf.GraphKeys.GLOBAL_VARIABLES,
scope: scope_to_prepend_to_names);
var var_list = new Dictionary <string, IVariableV1>();
variables.ForEach(v => var_list[ops.strip_name_scope(v.Name, scope_to_prepend_to_names)] = v);
return(var_list, imported_return_elements);
}
private IVariableV1 _get_single_variable(string name,
TensorShape shape = null,
TF_DataType dtype = TF_DataType.DtInvalid,
IInitializer initializer = null,
Tensor init_value = null,
bool reuse = false,
bool?trainable = null,
List <string> collections = null,
bool validate_shape = false,
bool?use_resource = null,
VariableSynchronization synchronization = VariableSynchronization.Auto,
VariableAggregation aggregation = VariableAggregation.None)
{
bool initializing_from_value = init_value != null;
if (use_resource == null)
{
use_resource = variable_scope._DEFAULT_USE_RESOURCE;
}
if (_vars.ContainsKey(name))
{
if (!reuse)
{
var var = _vars[name];
}
throw new NotImplementedException("_get_single_variable");
}
IVariableV1 v = null;
// Create the tensor to initialize the variable with default value.
if (initializer == null && init_value == null)
{
if (dtype.is_floating())
{
initializer = tf.glorot_uniform_initializer;
initializing_from_value = false;
}
}
// Create the variable.
ops.init_scope();
{
if (initializing_from_value)
{
v = new ResourceVariable(init_value,
name: name,
validate_shape: validate_shape,
trainable: trainable.Value);
}
else
{
Func <Tensor> init_val = () => initializer.Apply(new InitializerArgs(shape, dtype: dtype));
var variable_dtype = dtype.as_base_dtype();
v = variable_scope.default_variable_creator(init_val,
name: name,
trainable: trainable,
collections: collections,
dtype: variable_dtype,
use_resource: use_resource,
validate_shape: validate_shape,
synchronization: synchronization,
aggregation: aggregation);
}
}
_vars[name] = v;
return(v);
}
public _DenseResourceVariableProcessor(ResourceVariable v)
{
_v = v;
}
public static _OptimizableVariable _get_processor(ResourceVariable v)
{
return(new _DenseResourceVariableProcessor(v));
}
public Tensor assign(ResourceVariable @ref, object value, bool validate_shape = true, bool use_locking = true, string name = null) => state_ops.assign(@ref, value, validate_shape, use_locking, name);