public static FloatTensor Concatenate(FloatTensorFactory factory, FloatTensor[] tensors_inp, int axis, FloatTensor result = null)
{
FloatTensor first = tensors_inp[0];
FloatTensor[] tensors = new FloatTensor[tensors_inp.Length - 1];
for (int i = 0; i < tensors_inp.Length - 1; i++)
{
tensors[i] = tensors_inp[i + 1];
}
if (first.DataOnGpu)
{
throw new NotImplementedException("Can't concatenate GPU tensors yet");
}
int num_new_rows = 0;
List <IntTensor> int_tensors_for_index_add = new List <IntTensor>();
int[] first_indices = new int[first.Shape[axis]];
for (int i = 0; i < first.Shape[axis]; i++)
{
first_indices[i] = i + num_new_rows;
}
int_tensors_for_index_add.Add(
factory.ctrl.intTensorFactory.Create(_shape: new int[1] {
first.Shape[axis]
}, _data: first_indices));
num_new_rows += first.Shape[axis];
foreach (FloatTensor tensor in tensors)
{
if (tensor.Shape.Length != first.Shape.Length)
{
throw new InvalidOperationException("Tensors do not have the same number of dimensions.");
}
for (int i = 0; i < tensor.Shape.Length; i++)
{
if (i != axis)
{
if (tensor.Shape[i] != first.Shape[i])
{
throw new InvalidOperationException("Tensors do not have the same shape.");
}
}
}
if (tensor.DataOnGpu != first.DataOnGpu)
{
throw new InvalidOperationException("All tensors must be on the same device...");
}
int[] indices = new int[tensor.Shape[axis]];
for (int i = 0; i < tensor.Shape[axis]; i++)
{
indices[i] = i + num_new_rows;
}
int_tensors_for_index_add.Add(
factory.ctrl.intTensorFactory.Create(_shape: new int[1] {
tensor.Shape[axis]
}, _data: indices));
num_new_rows += tensor.Shape[axis];
}
Debug.Log("Num new Rows:" + num_new_rows);
int[] concat_shape = new int[first.Shape.Length];
for (int i = 0; i < first.Shape.Length; i++)
{
if (i == axis)
{
concat_shape[i] = num_new_rows;
}
else
{
concat_shape[i] = first.Shape[i];
}
}
result = first.HookGraph(ref result, tensor_inputs: tensors, creation_op: "concatenate_" + axis, inline: false, resultShape: concat_shape, indices: int_tensors_for_index_add.ToArray());
if (axis != 0)
{
result.IndexAdd(int_tensors_for_index_add[0], axis, first, inline: true);
for (int i = 0; i < tensors.Length; i++)
{
result.IndexAdd(int_tensors_for_index_add[i + 1], axis, tensors[i], inline: true);
}
}
else
{
//Debug.Log("Result Size:" + result.Size);
//Debug.Log("First Size:" + first.Size);
int result_i = 0;
for (int i = 0; i < first.Data.Length; i++)
{
result.Data[result_i] = first.Data[i];
result_i += 1;
}
foreach (FloatTensor tensor in tensors)
{
//Debug.Log("Tensor Size:" + tensor.Size);
for (int i = 0; i < tensor.Data.Length; i++)
{
result.Data[result_i] = tensor.Data[i];
result_i += 1;
}
}
}
return(result);
}
