125a462400
* Initial version of ReduceL1, ReduceL2 and ReduceLp enabling in the MO * Added operations ReduceL1 and ReduceL2 to nGraph * Removed ReduceLp. Added ReduceL1 and ReduceL2 * Separated specification of ReduceLp into ReduceL1 and ReduceL2 * Updated ReduceL1 and ReduceL2 specification * Fixed ReduceL1 and ReduceL2 type prop tests * Implemented nGraph transformation to decompose ReduceL1 and ReduceL2. Disabled them for CPU and GPU plugins * Updated supported framework layers * Added unit tests for ReduceL1 and ReduceL2 reference implementation * Fixed ReduceXXX operations reference implementation by adding support for a new parameter 'keep_dims' * Fixed constant folding for v0::Any * Added ReduceL1 and ReduceL2 to Python API * Implemented ReduceL1 and ReduceL2 decomposition tests and fixed ReduceL2 decomposition * Added specific creator for ReduceXXX operations instead of NodeBuilders * Fixed conversion ReduceXXX to CNNLayer * Fixed parser for ReduceLogicalXXX operations
143 lines
4.2 KiB
Markdown
143 lines
4.2 KiB
Markdown
## ReduceLp <a name="ReduceL1"></a> {#openvino_docs_ops_reduction_ReduceL1_4}
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**Versioned name**: *ReduceL1-4*
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**Category**: *Reduction*
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**Short description**: *ReduceL1* operation performs reduction with finding the L1 norm (sum of absolute values) of the 1st input tensor in slices specified by the 2nd input.
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**Attributes**
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* *keep_dims*
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* **Description**: If set to `True` it holds axes that are used for reduction. For each such axis, output dimension is equal to 1.
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* **Range of values**: True or False
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* **Type**: `boolean`
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* **Default value**: False
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* **Required**: *no*
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**Inputs**
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* **1**: Input tensor x of type *T1*. **Required.**
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* **2**: Scalar or 1D tensor of type *T_IND* with axis indices for the 1st input along which reduction is performed. Accepted range is `[-r, r - 1]` where where `r` is the rank of input tensor, all values must be unique, repeats are not allowed. **Required.**
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**Outputs**
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* **1**: Tensor of the same type as the 1st input tensor and `shape[i] = shapeOf(input1)[i]` for all `i` that is not in the list of axes from the 2nd input. For dimensions from the 2nd input tensor, `shape[i] == 1` if `keep_dims == True`, or `i`-th dimension is removed from the output otherwise.
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**Types**
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* *T1*: numeric type.
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* *T2*: `int64` or `int32`.
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**Detailed Description**
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Each element in the output is the result of reduction with finding a Lp norm operation along dimensions specified by the 2nd input:
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`output[i0, i1, ..., iN] = L1[j0,..., jN](x[j0, ..., jN]))`
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Where indices i0, ..., iN run through all valid indices for the 1st input and finding the Lp norm `L1[j0, ..., jN]` have `jk = ik` for those dimensions `k` that are not in the set of indices specified by the 2nd input of the operation.
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Corner cases:
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1. When the 2nd input is an empty list, then this operation does nothing, it is an identity.
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2. When the 2nd input contains all dimensions of the 1st input, this means that a single reduction scalar value is calculated for entire input tensor.
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**Example**
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```xml
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<layer id="1" type="ReduceL1" ...>
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<data keep_dims="True" />
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<input>
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<port id="0">
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<dim>6</dim>
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<dim>12</dim>
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<dim>10</dim>
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<dim>24</dim>
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</port>
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<port id="1">
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<dim>2</dim> <!-- value is [2, 3] that means independent reduction in each channel and batch -->
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</port>
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</input>
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<output>
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<port id="2">
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<dim>6</dim>
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<dim>12</dim>
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<dim>1</dim>
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<dim>1</dim>
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</port>
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</output>
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</layer>
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```
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```xml
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<layer id="1" type="ReduceL1" ...>
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<data keep_dims="False" />
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<input>
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<port id="0">
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<dim>6</dim>
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<dim>12</dim>
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<dim>10</dim>
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<dim>24</dim>
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</port>
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<port id="1">
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<dim>2</dim> <!-- value is [2, 3] that means independent reduction in each channel and batch -->
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</port>
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</input>
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<output>
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<port id="2">
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<dim>6</dim>
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<dim>12</dim>
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</port>
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</output>
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</layer>
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```
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```xml
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<layer id="1" type="ReduceL1" ...>
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<data keep_dims="False" />
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<input>
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<port id="0">
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<dim>6</dim>
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<dim>12</dim>
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<dim>10</dim>
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<dim>24</dim>
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</port>
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<port id="1">
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<dim>1</dim> <!-- value is [1] that means independent reduction in each channel and spatial dimensions -->
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</port>
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</input>
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<output>
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<port id="2">
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<dim>6</dim>
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<dim>10</dim>
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<dim>24</dim>
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</port>
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</output>
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</layer>
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```
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```xml
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<layer id="1" type="ReduceL1" ...>
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<data keep_dims="False" />
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<input>
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<port id="0">
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<dim>6</dim>
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<dim>12</dim>
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<dim>10</dim>
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<dim>24</dim>
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</port>
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<port id="1">
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<dim>1</dim> <!-- value is [-2] that means independent reduction in each channel, batch and second spatial dimension -->
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</port>
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</input>
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<output>
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<port id="2">
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<dim>6</dim>
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<dim>12</dim>
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<dim>24</dim>
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</port>
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</output>
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</layer>
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``` |