GatherND - 11 vs 12#

Next section compares an older to a newer version of the same operator after both definition are converted into markdown text. Green means an addition to the newer version, red means a deletion. Anything else is unchanged.

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GatherND11 → GatherND12 +13 -44

GatherND11 → GatherND12 RENAMED Viewed

@@ -1 +1 @@
- Given data tensor of rank r >= 1, indices tensor of rank q >= 1, and batch_dims integer b, this operator gathers
+ Given data tensor of rank r >= 1, and indices tensor of rank q >= 1, this operator gathers
- slices of data into an output tensor of rank q + r - indices_shape[-1] - 1 - b.
+ slices of data into an output tensor of rank q + r - indices_shape[-1] - 1.
  indices is an q-dimensional integer tensor, best thought of as a (q-1)-dimensional tensor of index-tuples into data,
  where each element defines a slice of data
- batch_dims (denoted as b) is an integer indicating the number of batch dimensions, i.e the leading b number of dimensions of
- data tensor and indices are representing the batches, and the gather starts from the b+1 dimension.
  Some salient points about the inputs' rank and shape:
  1) r >= 1 and q >= 1 are to be honored. There is no dependency condition to be met between ranks r and q
- 2) The first b dimensions of the shape of indices tensor and data tensor must be equal.
+ 2) The indices_shape[-1] should have a value between 1 (inclusive) and rank r (inclusive)
- 3) b < min(q, r) is to be honored.
- 4) The indices_shape[-1] should have a value between 1 (inclusive) and rank r-b (inclusive)
- 5) All values in indices are expected to be within bounds [-s, s-1] along axis of size s (i.e.) -data_shape[i] <= indices[...,i] <= data_shape[i] - 1.
+ 3) All values in indices are expected to be within bounds [-s, s-1] along axis of size s (i.e.) -data_shape[i] <= indices[...,i] <= data_shape[i] - 1.
     It is an error if any of the index values are out of bounds.
  The output is computed as follows:
  The output tensor is obtained by mapping each index-tuple in the indices tensor to the corresponding slice of the input data.
- 1) If indices_shape[-1] > r-b => error condition
+ 1) If indices_shape[-1] > r => error condition
- 2) If indices_shape[-1] == r-b, since the rank of indices is q, indices can be thought of as N (q-b-1)-dimensional tensors
+ 2) If indices_shape[-1] == r, since the rank of indices is q, indices can be thought of as a (q-1)-dimensional tensor
-    containing 1-D tensors of dimension r-b, where N is an integer equals to the product of 1 and all the elements in the batch dimensions
+    containing 1-D tensors of dimension r. Let us think of each such r ranked tensor as indices_slice.
-    of the indices_shape. Let us think of each such r-b ranked tensor as indices_slice. Each *scalar value* corresponding to data[0:b-1,indices_slice]
+    Each *scalar value* corresponding to data[indices_slice] is filled into the corresponding location of the (q-1)-dimensional tensor
-    is filled into the corresponding location of the (q-b-1)-dimensional tensor to form the output tensor (Example 1 below)
+    to form the output tensor (Example 1 below)
- 3) If indices_shape[-1] < r-b, since the rank of indices is q, indices can be thought of as N (q-b-1)-dimensional tensor
+ 3) If indices_shape[-1] < r, since the rank of indices is q, indices can be thought of as a (q-1)-dimensional tensor
-    containing 1-D tensors of dimension < r-b. Let us think of each such tensors as indices_slice. Each *tensor slice* corresponding
+    containing 1-D tensors of dimension < r. Let us think of each such tensors as indices_slice.
-    to data[0:b-1, indices_slice , :] is filled into the corresponding location of the (q-b-1)-dimensional tensor
+    Each *tensor slice* corresponding to data[indices_slice , :] is filled into the corresponding location of the (q-1)-dimensional tensor
-    to form the output tensor (Examples 2, 3, 4 and 5 below)
+    to form the output tensor (Examples 2, 3, and 4 below)
  This operator is the inverse of ScatterND.
  Example 1
-   batch_dims = 0
    data    = [[0,1],[2,3]]   # data_shape = [2, 2]
    indices = [[0,0],[1,1]]   # indices_shape = [2, 2]
    output  = [0,3]           # output_shape = [2]
  Example 2
-   batch_dims = 0
    data    = [[0,1],[2,3]]  # data_shape = [2, 2]
    indices = [[1],[0]]      # indices_shape = [2, 1]
    output  = [[2,3],[0,1]]  # output_shape = [2, 2]
  Example 3
-   batch_dims = 0
    data    = [[[0,1],[2,3]],[[4,5],[6,7]]] # data_shape = [2, 2, 2]
    indices = [[0,1],[1,0]]                 # indices_shape = [2, 2]
    output  = [[2,3],[4,5]]                 # output_shape = [2, 2]
  Example 4
-   batch_dims = 0
    data    = [[[0,1],[2,3]],[[4,5],[6,7]]] # data_shape = [2, 2, 2]
    indices = [[[0,1]],[[1,0]]]             # indices_shape = [2, 1, 2]
    output  = [[[2,3]],[[4,5]]]             # output_shape = [2, 1, 2]
- Example 5
-   batch_dims = 1
-   data    = [[[0,1],[2,3]],[[4,5],[6,7]]] # data_shape = [2, 2, 2]
-   indices = [[1],[0]]             # indices_shape = [2, 1]
-   output  = [[2,3],[4,5]]             # output_shape = [2, 2]
- **Attributes**
- * **batch_dims**:
-   The number of batch dimensions. The gather of indexing starts from
-   dimension of data[batch_dims:]
  **Inputs**
  * **data** (heterogeneous) - **T**:
    Tensor of rank r >= 1.
  * **indices** (heterogeneous) - **tensor(int64)**:
    Tensor of rank q >= 1. All index values are expected to be within
    bounds [-s, s-1] along axis of size s. It is an error if any of the
    index values are out of bounds.
  **Outputs**
  * **output** (heterogeneous) - **T**:
    Tensor of rank q + r - indices_shape[-1] - 1.
  **Type Constraints**
  * **T** in (
    tensor(bool),
    tensor(complex128),
    tensor(complex64),
    tensor(double),
    tensor(float),
    tensor(float16),
    tensor(int16),
    tensor(int32),
    tensor(int64),
    tensor(int8),
    tensor(string),
    tensor(uint16),
    tensor(uint32),
    tensor(uint64),
    tensor(uint8)
    ):
    Constrain input and output types to any tensor type.