ScatterND - 13 vs 16¶
- ScatterND13 → ScatterND16 +21 -7
ScatterND13 → ScatterND16
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ScatterND takes three inputs data tensor of rank r >= 1, indices tensor of rank q >= 1,
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and updates tensor of rank q + r - indices.shape[-1] - 1. The output of the operation
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is produced by creating a copy of the input data, and then updating its value to values
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specified by updates at specific index positions specified by indices. Its output shape
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is the same as the shape of data. Note that indices should not have duplicate entries.
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That is, two or more updates for the same index-location is not supported.
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indices is an integer tensor. Let k denote indices.shape[-1], the last dimension in the shape of indices.
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indices is treated as a (q-1)-dimensional tensor of k-tuples, where each k-tuple is a partial-index into data.
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Hence, k can be a value at most the rank of data. When k equals rank(data), each update entry specifies an
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update to a single element of the tensor. When k is less than rank(data) each update entry specifies an
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update to a slice of the tensor.
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updates is treated as a (q-1)-dimensional tensor of replacement-slice-values. Thus, the
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first (q-1) dimensions of updates.shape must match the first (q-1) dimensions of indices.shape.
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The remaining dimensions of updates correspond to the dimensions of the
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replacement-slice-values. Each replacement-slice-value is a (r-k) dimensional tensor,
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corresponding to the trailing (r-k) dimensions of data. Thus, the shape of updates
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must equal indices.shape[0:q-1] ++ data.shape[k:r-1], where ++ denotes the concatenation
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of shapes.
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The output is calculated via the following equation:
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-
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output = np.copy(data)
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update_indices = indices.shape[:-1]
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for idx in np.ndindex(update_indices):
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output[indices[idx]] = updates[idx]
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The order of iteration in the above loop is not specified.
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In particular, indices should not have duplicate entries: that is, if idx1 != idx2, then indices[idx1] != indices[idx2].
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This ensures that the output value does not depend on the iteration order.
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reduction allows specification of an optional reduction operation, which is applied to all values in updates
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tensor into output at the specified indices.
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In cases where reduction is set to "none", indices should not have duplicate entries: that is, if idx1 != idx2,
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then indices[idx1] != indices[idx2]. This ensures that the output value does not depend on the iteration order.
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When reduction is set to "add", output is calculated as follows:
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output = np.copy(data)
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update_indices = indices.shape[:-1]
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for idx in np.ndindex(update_indices):
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output[indices[idx]] += updates[idx]
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When reduction is set to "mul", output is calculated as follows:
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output = np.copy(data)
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update_indices = indices.shape[:-1]
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for idx in np.ndindex(update_indices):
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output[indices[idx]] *= updates[idx]
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This operator is the inverse of GatherND.
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-
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Example 1:
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::
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data = [1, 2, 3, 4, 5, 6, 7, 8]
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indices = [[4], [3], [1], [7]]
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updates = [9, 10, 11, 12]
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output = [1, 11, 3, 10, 9, 6, 7, 12]
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Example 2:
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::
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data = [[[1, 2, 3, 4], [5, 6, 7, 8], [8, 7, 6, 5], [4, 3, 2, 1]],
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[[1, 2, 3, 4], [5, 6, 7, 8], [8, 7, 6, 5], [4, 3, 2, 1]],
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[[8, 7, 6, 5], [4, 3, 2, 1], [1, 2, 3, 4], [5, 6, 7, 8]],
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[[8, 7, 6, 5], [4, 3, 2, 1], [1, 2, 3, 4], [5, 6, 7, 8]]]
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indices = [[0], [2]]
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updates = [[[5, 5, 5, 5], [6, 6, 6, 6], [7, 7, 7, 7], [8, 8, 8, 8]],
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[[1, 1, 1, 1], [2, 2, 2, 2], [3, 3, 3, 3], [4, 4, 4, 4]]]
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output = [[[5, 5, 5, 5], [6, 6, 6, 6], [7, 7, 7, 7], [8, 8, 8, 8]],
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[[1, 2, 3, 4], [5, 6, 7, 8], [8, 7, 6, 5], [4, 3, 2, 1]],
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[[1, 1, 1, 1], [2, 2, 2, 2], [3, 3, 3, 3], [4, 4, 4, 4]],
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[[8, 7, 6, 5], [4, 3, 2, 1], [1, 2, 3, 4], [5, 6, 7, 8]]]
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**Attributes**
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* **reduction**:
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Type of reduction to apply: none (default), add, mul. 'none': no
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reduction applied. 'add': reduction using the addition operation.
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'mul': reduction using the multiplication operation.
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**Inputs**
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* **data** (heterogeneous) - **T**:
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Tensor of rank r >= 1.
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* **indices** (heterogeneous) - **tensor(int64)**:
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Tensor of rank q >= 1.
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* **updates** (heterogeneous) - **T**:
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Tensor of rank q + r - indices_shape[-1] - 1.
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**Outputs**
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* **output** (heterogeneous) - **T**:
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Tensor of rank r >= 1.
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**Type Constraints**
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* **T** in (
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tensor(bfloat16),
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tensor(bool),
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tensor(complex128),
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tensor(complex64),
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tensor(double),
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tensor(float),
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tensor(float16),
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tensor(int16),
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tensor(int32),
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tensor(int64),
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tensor(int8),
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tensor(string),
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tensor(uint16),
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tensor(uint32),
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tensor(uint64),
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tensor(uint8)
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):
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Constrain input and output types to any tensor type.
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