UnorderedMap <string, UnorderedSet <int> > FunctionsAcceptingNoneForIndicesMap()
{
var m = new UnorderedMap <string, UnorderedSet <int> >();
m.Add("SoftmaxCrossEntropyWithLogits", new UnorderedSet <int>(new[] { 1 }));
m.Add("SparseSoftmaxCrossEntropyWithLogits", new UnorderedSet <int>(new[] { 1 }));
m.Add("FusedBatchNorm", new UnorderedSet <int>(new[] { 1, 2, 3, 4 }));
return(m);
}
public Tensor[] TFE_TapeGradient(ITape tape,
Tensor[] target,
Tensor[] sources,
Tensor[] output_gradients)
{
var target_vec = MakeTensorIDList(target);
var sources_vec = MakeTensorIDList(sources);
var sources_set = sources_vec;
var seq_array = target;
var source_tensors_that_are_targets = new UnorderedMap <long, TapeTensor>();
for (int i = 0; i < target.Length; ++i)
{
var target_id = target_vec[i];
var tensor = seq_array[i];
source_tensors_that_are_targets.Add(target_id, TapeTensorFromTensor(tensor));
}
if (output_gradients != null)
{
throw new NotImplementedException("");
}
else
{
output_gradients = new Tensor[0];
}
var outgrad_vec = MakeTensorList(output_gradients);
return(tape.ComputeGradient(target_vec, sources_vec, source_tensors_that_are_targets, outgrad_vec));
}
public static string Get(T value)
{
if (!map.TryGetValue(value, out string str))
{
str = value.ToString();
map.Add(value, str);
}
return(str);
}
public void Add(TKey key, TValue value)
{
if (!keyToValue.ContainsKey(key))
{
keyToValue.Add(key, value);
}
if (!valueToKey.ContainsKey(value))
{
valueToKey.Add(value, key);
}
}
static AttributedTypeCache()
{
foreach (var type in Meta.kumaAsms.AllTypes())
{
foreach (var attr in type.GetCustomAttributes())
{
if (!catalog.TryGetValue(attr.GetType(), out UnorderedSet <TypeKey> types))
{
types = new UnorderedSet <TypeKey>(4);
catalog.Add(attr.GetType(), types);
}
types.AddIfNone(type);
}
}
}