ef45b5da8d
* Doc Migration from Gitlab (#1289) * doc migration * fix * Update FakeQuantize_1.md * Update performance_benchmarks.md * Updates graphs for FPGA * Update performance_benchmarks.md * Change DL Workbench structure (#1) * Changed DL Workbench structure * Fixed tags * fixes * Update ie_docs.xml * Update performance_benchmarks_faq.md * Fixes in DL Workbench layout * Fixes for CVS-31290 * [DL Workbench] Minor correction * Fix for CVS-30955 * Added nGraph deprecation notice as requested by Zoe * fix broken links in api doxy layouts * CVS-31131 fixes * Additional fixes * Fixed POT TOC * Update PAC_Configure.md PAC DCP 1.2.1 install guide. * Update inference_engine_intro.md * fix broken link * Update opset.md * fix * added opset4 to layout * added new opsets to layout, set labels for them * Update VisionAcceleratorFPGA_Configure.md Updated from 2020.3 to 2020.4 Co-authored-by: domi2000 <domi2000@users.noreply.github.com>
94 lines
3.2 KiB
Markdown
94 lines
3.2 KiB
Markdown
## ScatterUpdate <a name="ScatterUpdate"></a> {#openvino_docs_ops_movement_ScatterUpdate_3}
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**Versioned name**: *ScatterUpdate-3*
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**Category**: Data movement operations
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**Short description**: *ScatterUpdate* creates a copy of the first input tensor with updated elements specified with second and third input tensors.
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**Detailed description**: *ScatterUpdate* creates a copy of the first input tensor with updated elements in positions specified with `indices` input
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and values specified with `updates` tensor starting from the dimension with index `axis`. For the `data` tensor of shape `[d_0, d_1, ..., d_n]`,
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`indices` tensor of shape `[i_0, i_1, ..., i_k]` and `updates` tensor of shape
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`[d_0, d_1, ... d_(axis - 1), i_0, i_1, ..., i_k, d_(axis + 1), ..., d_n]` the operation computes
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for each `m, n, ..., p` of the `indices` tensor indices:
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```
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data[..., indices[m, n, ..., p], ...] = updates[..., m, n, ..., p, ...]
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```
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where first `...` in the `data` corresponds to first `axis` dimensions, last `...` in the `data` corresponds to the
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`rank(data) - (axis + 1)` dimensions.
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Several examples for case when `axis = 0`:
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1. `indices` is a 0D tensor: `data[indices, ...] = updates[...]`
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2. `indices` is a 1D tensor (for each `i`): `data[indices[i], ...] = updates[i, ...]`
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3. `indices` is a ND tensor (for each `i, ..., j`): `data[indices[i, ..., j], ...] = updates[i, ..., j, ...]`
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This operation is similar to TensorFlow* operation [ScatterUpdate](https://www.tensorflow.org/versions/r1.15/api_docs/python/tf/scatter_update)
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but allows scattering for the arbitrary axis.
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**Attributes**: *ScatterUpdate* does not have attributes.
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**Inputs**:
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* **1**: `data` tensor of arbitrary rank `r` and of type *T*. Required.
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* **2**: `indices` tensor with indices of type *T_IND*.
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All index values are expected to be within bounds `[0, s - 1]` along axis of size `s`. If multiple indices point to the
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same output location then the order of updating the values is undefined. If an index points to non-existing output
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tensor element or is negative then an exception is raised. Required.
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* **3**: `updates` tensor of type *T*. Required.
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* **4**: `axis` tensor with scalar or 1D tensor with one element of type *T_AXIS* specifying axis for scatter.
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The value can be in range `[-r, r - 1]` where `r` is the rank of `data`. Required.
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**Outputs**:
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* **1**: tensor with shape equal to `data` tensor of the type *T*.
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**Types**
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* *T*: any numeric type.
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* *T_IND*: any supported integer types.
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* *T_AXIS*: any supported integer types.
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**Example**
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```xml
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<layer ... type="ScatterUpdate">
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<input>
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<port id="0">
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<dim>1000</dim>
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<dim>256</dim>
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<dim>10</dim>
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<dim>15</dim>
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</port>
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<port id="1">
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<dim>125</dim>
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<dim>20</dim>
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</port>
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<port id="2">
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<dim>1000</dim>
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<dim>125</dim>
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<dim>20</dim>
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<dim>10</dim>
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<dim>15</dim>
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</port>
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<port id="3"> <!-- value [1] -->
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<dim>1</dim>
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</port>
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</input>
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<output>
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<port id="4" precision="FP32">
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<dim>1000</dim>
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<dim>256</dim>
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<dim>10</dim>
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<dim>15</dim>
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</port>
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</output>
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</layer>
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```
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