Gather#
Versioned name: Gather-7
Category: Data movement
Short description: Gather operation takes slices of data of the first input tensor according to the indices specified with the second input tensor and axis from the third input. Semantics of this operation is identical to TensorFlow Gather operation.
Detailed description
output[p_0, p_1, ..., p_{axis-1}, i_b, ..., i_{M-1}, p_{axis+1}, ..., p_{N-1}] =
data[p_0, p_1, ..., p_{axis-1}, indices[p_0, p_1, ..., p_{b-1}, i_b, ..., i_{M-1}], p_{axis+1}, ..., p_{N-1}]
Where data
, indices
and axis
are tensors from first, second and third inputs correspondingly, b
is
the number of batch dimensions. N
and M
are numbers of dimensions of data
and indices
tensors, respectively.
Attributes:
batch_dims
Description: batch_dims (also denoted as
b
) is a leading number of dimensions ofdata
tensor andindices
representing the batches, and Gather starts to gather from theb
dimension. It requires the firstb
dimensions in data and indices tensors to be equal. Ifbatch_dims
is less than zero, the normalized value is usedbatch_dims = indices.rank + batch_dims
.Range of values:
[-min(data.rank, indices.rank); min(data.rank, indices.rank)]
andbatch_dims' <= axis'
. Wherebatch_dims'
andaxis'
stand for normalizedbatch_dims
andaxis
values.Type: T_AXIS
Default value: 0
Required: no
Example 1 with default batch_dims value:
batch_dims = 0
axis = 0
indices = [0, 0, 4]
data = [1, 2, 3, 4, 5]
output = [1, 1, 5]
Example 2 with non-default batch_dims value:
batch_dims = 1
axis = 1
indices = [[0, 0, 4], <-- this is applied to the first batch
[4, 0, 0]] <-- this is applied to the second batch
indices_shape = (2, 3)
data = [[1, 2, 3, 4, 5], <-- the first batch
[6, 7, 8, 9, 10]] <-- the second batch
data_shape = (2, 5)
output = [[ 1, 1, 5],
[10, 6, 6]]
output_shape = (2, 3)
Example 3 with non-default batch_dims value:
batch_dims = 2
axis = 2
indices = [[[0, 0, 4], <-- this is applied to the first batch, index = (0, 0)
[4, 0, 0]], <-- this is applied to the second batch, index = (0, 1)
[[1, 2, 4], <-- this is applied to the third batch, index = (1, 0)
[4, 3, 2]]] <-- this is applied to the fourth batch, index = (1, 1)
indices_shape = (2, 2, 3)
data = [[[1, 2, 3, 4, 5], <-- the first batch, index = (0, 0)
[6, 7, 8, 9, 10]], <-- the second batch, index = (0, 1)
[[11, 12, 13, 14, 15], <-- the third batch, index = (1, 0)
[16, 17, 18, 19, 20]]] <-- the fourth batch, index = (1, 1)
data_shape = (2, 2, 5)
output = [[[ 1, 1, 5],
[10, 6, 6]],
[[12, 13, 15],
[20, 19, 18]]]
output_shape = (2, 2, 3)
Example 4 with axis > batch_dims:
batch_dims = 1
axis = 2
indices = [[1, 2, 4], <-- this is applied to the first batch
[4, 3, 2]] <-- this is applied to the second batch
indices_shape = (2, 3)
data = [[[[ 1, 2, 3, 4], <-- first batch
[ 5, 6, 7, 8],
[ 9, 10, 11, 12],
[13, 14, 15, 16],
[17, 18, 19, 20]]],
[[[21, 22, 23, 24], <-- second batch
[25, 26, 27, 28],
[29, 30, 31, 32],
[33, 34, 35, 36],
[37, 38, 39, 40]]]]
data_shape = (2, 1, 5, 4)
output = [[[[ 5, 6, 7, 8],
[ 9, 10, 11, 12],
[17, 18, 19, 20]]],
[[[37, 38, 39, 40],
[33, 34, 35, 36],
[29, 30, 31, 32]]]]
output_shape = (2, 1, 3, 4)
Example 5 with negative batch_dims value:
batch_dims = -1 <-- normalized value will be indices.rank + batch_dims = 2 - 1 = 1
axis = 1
indices = [[0, 0, 4], <-- this is applied to the first batch
[4, 0, 0]] <-- this is applied to the second batch
indices_shape = (2, 3)
data = [[1, 2, 3, 4, 5], <-- the first batch
[6, 7, 8, 9, 10]] <-- the second batch
data_shape = (2, 5)
output = [[ 1, 1, 5],
[10, 6, 6]]
output_shape = (2, 3)
Inputs
1:
data
tensor of type T with arbitrary data. Required.2:
indices
tensor of type T_IND with indices to gather. 0D tensor (scalar) for indices is also allowed. The values for indices are in the range[0, data[axis] - 1]
. Required.3: Scalar or 1D tensor
axis
of T_AXIS type is a dimension index to gather data from. For example, axis equal to 1 means that gathering is performed over the first dimension. Negativeaxis
means reverse indexing and will be normalized to valueaxis = data.rank + axis
. Allowed values are from[-len(data.shape), len(data.shape) - 1]
andaxis' >= batch_dims'
. Whereaxis'
andbatch_dims'
stand for normalizedbatch_dims
andaxis
values. Required.
Outputs
1: The resulting tensor of type T that consists of elements from
data
tensor gathered byindices
. The shape of the output tensor isdata.shape[:axis] + indices.shape[batch_dims:] + data.shape[axis + 1:]
Types
T: any supported type.
T_IND: any supported integer types.
T_AXIS: any supported integer types.
Example
<layer ... type="Gather" version="opset7">
<data batch_dims="1" />
<input>
<port id="0">
<dim>2</dim>
<dim>64</dim>
<dim>128</dim>
</port>
<port id="1">
<dim>2</dim>
<dim>32</dim>
<dim>21</dim>
</port>
<port id="2"/> <!-- axis = 1 -->
</input>
<output>
<port id="2">
<dim>2</dim>
<dim>32</dim>
<dim>21</dim>
<dim>128</dim>
</port>
</output>
</layer>