public void SetOpAttrs(SafeEagerOpHandle op, params object[] attrs)
{
var status = tf.Status;
var len = attrs.Length;
for (int i = 0; i < len; i += 2)
{
var key = attrs[i].ToString();
var value = attrs[i + 1];
byte is_list = 0;
var type = c_api.TFE_OpGetAttrType(op, key, ref is_list, status.Handle);
if (!status.ok())
{
return;
}
if (is_list != 0)
{
SetOpAttrList(tf.Context, op, key, value as object[], type, null, status);
}
else
{
SetOpAttrScalar(tf.Context, op, key, value, type, null, status);
}
status.Check(true);
}
}
/// <summary>
/// This function will set the op attrs required. If an attr has the value of
/// None, then it will read the AttrDef to get the default value and set that
/// instead. Any failure in this function will simply fall back to the slow
/// path.
/// </summary>
/// <param name="ctx"></param>
/// <param name="op"></param>
/// <param name="attr"></param>
/// <param name="attr_name"></param>
/// <param name="attr_value"></param>
/// <param name="attr_list_sizes"></param>
/// <param name="status"></param>
void SetOpAttrWithDefaults(Context ctx, SafeEagerOpHandle op, AttrDef attr,
string attr_name, object attr_value,
Dictionary <string, long> attr_list_sizes,
Status status)
{
byte is_list = 0;
var type = c_api.TFE_OpGetAttrType(op, attr_name, ref is_list, status.Handle);
if (status.Code != TF_Code.TF_OK)
{
return;
}
if (attr_value == null)
{
}
else
{
if (is_list != 0)
{
SetOpAttrList(ctx, op, attr_name, attr_value, type, attr_list_sizes, status);
}
else
{
SetOpAttrScalar(ctx, op, attr_name, attr_value, type, attr_list_sizes, status);
}
}
}
/// <summary>
/// Adds input and type attr to the op, and to the list of flattened
/// inputs/attrs.
/// </summary>
/// <param name="inputs"></param>
/// <param name="add_type_attr"></param>
/// <param name="input_arg"></param>
/// <param name="op"></param>
/// <param name="status"></param>
/// <returns></returns>
bool AddInputToOp(object inputs,
bool add_type_attr,
ArgDef input_arg,
List <object> flattened_attrs,
List <Tensor> flattened_inputs,
SafeEagerOpHandle op,
Status status)
{
var tensor = tf.convert_to_tensor(inputs);
flattened_inputs.Add(tensor);
if (add_type_attr && !string.IsNullOrEmpty(input_arg.TypeAttr))
{
var dtype = tensor.dtype;
c_api.TFE_OpSetAttrType(op, input_arg.TypeAttr, dtype);
flattened_attrs.Add(input_arg.TypeAttr);
flattened_attrs.Add(dtype);
}
c_api.TFE_OpAddInput(op, tensor.EagerTensorHandle, status.Handle);
status.Check(true);
return(true);
}
bool SetOpAttrScalar(Context ctx, SafeEagerOpHandle op,
string key, object value, TF_AttrType type,
Dictionary <string, long> attr_list_sizes,
Status status)
{
switch (type)
{
case TF_AttrType.TF_ATTR_STRING:
c_api.TFE_OpSetAttrString(op, key, value.ToString(), (ulong)value.ToString().Length);
break;
case TF_AttrType.TF_ATTR_TYPE:
c_api.TFE_OpSetAttrType(op, key, (TF_DataType)value);
break;
case TF_AttrType.TF_ATTR_BOOL:
c_api.TFE_OpSetAttrBool(op, key, Convert.ToBoolean(value));
break;
case TF_AttrType.TF_ATTR_INT:
var size = Convert.ToInt64(value);
c_api.TFE_OpSetAttrInt(op, key, size);
if (attr_list_sizes != null)
{
attr_list_sizes[key] = size;
}
break;
case TF_AttrType.TF_ATTR_FLOAT:
c_api.TFE_OpSetAttrFloat(op, key, Convert.ToSingle(value));
break;
case TF_AttrType.TF_ATTR_SHAPE:
var dims = (value as long[]).ToArray();
c_api.TFE_OpSetAttrShape(op, key, dims, dims.Length, status.Handle);
status.Check(true);
break;
case TF_AttrType.TF_ATTR_FUNC:
if (value is ConcreteFunction func)
{
c_api.TFE_OpSetAttrFunctionName(op, key, func.Name, func.Name.Length);
}
else
{
throw new NotImplementedException("TF_AttrType.TF_ATTR_FUNC");
}
break;
default:
throw new NotImplementedException($"SetOpAttrScalar for {type}");
}
return(true);
}
bool SetOpAttrList(Context ctx, SafeEagerOpHandle op,
string key, object values, TF_AttrType type,
Dictionary <string, long> attr_list_sizes,
Status status)
{
if (type == TF_AttrType.TF_ATTR_STRING && values is string[] values3)
{
c_api.TFE_OpSetAttrStringList(op, key, values3, values3.Select(x => Convert.ToUInt64(x.Length)).ToArray(), values3.Length);
attr_list_sizes[key] = values3.Length;
}
else if (type == TF_AttrType.TF_ATTR_SHAPE && values is Shape[] values1)
{
// Make one pass through the input counting the total number of
// dims across all the input lists.
var num_values = values1.Length;
attr_list_sizes[key] = num_values;
var dims = new IntPtr[num_values];
var num_dims = values1.Select(x => x.ndim).ToArray();
for (int i = 0; i < num_values; ++i)
{
dims[i] = Marshal.AllocHGlobal(sizeof(long) * values1[i].ndim);
tf.memcpy(dims[i], values1[i].dims, values1[i].ndim * sizeof(long));
}
c_api.TFE_OpSetAttrShapeList(op, key, dims, num_dims, num_values, status.Handle);
Array.ForEach(dims, x => Marshal.FreeHGlobal(x));
}
else if (type == TF_AttrType.TF_ATTR_TYPE && values is TF_DataType[] values2)
{
c_api.TFE_OpSetAttrTypeList(op, key, values2, values2.Length);
attr_list_sizes[key] = values2.Length;
}
else if (type == TF_AttrType.TF_ATTR_INT && values is int[] values4)
{
c_api.TFE_OpSetAttrIntList(op, key, values4.Select(x => Convert.ToInt64(x)).ToArray(), values4.Length);
attr_list_sizes[key] = values4.Length;
}
else
{
throw new NotImplementedException("");
}
return(true);
}