Specify AdaptiveAvgPool-8 and AdaptiveMaxPool-8 (#5773)
* Specify AdaptiveAvgPool-8 and AdaptiveMaxPool-8 * Apply review feedback * Describe indices output * Apply review feedback * Apply review feedback * Update AdaptiveMaxPool_8.md * Apply suggestions from code review Co-authored-by: Anastasiya Ageeva <anastasiya.ageeva@intel.com> * Apply review feedback Co-authored-by: Anastasiya Ageeva <anastasiya.ageeva@intel.com>
This commit is contained in:
Maxim Vafin
GitHub
@@ -88,6 +88,7 @@ limitations under the License.
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<!-- Intermediate Representation and Operations Sets -->
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<tab id="intermediate_representaton_and_operations_sets" type="usergroup" title="Intermediate Representation and Operations Sets" url="@ref openvino_docs_MO_DG_IR_and_opsets">
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<tab type="usergroup" title="Available Operations Sets" url="@ref openvino_docs_ops_opset">
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<tab type="user" title="opset8 Specification" url="@ref openvino_docs_ops_opset8"/>
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<tab type="user" title="opset7 Specification" url="@ref openvino_docs_ops_opset7"/>
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<tab type="user" title="opset6 Specification" url="@ref openvino_docs_ops_opset6"/>
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<tab type="user" title="opset5 Specification" url="@ref openvino_docs_ops_opset5"/>
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@@ -100,6 +101,8 @@ limitations under the License.
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<tab type="user" title="Abs-1" url="@ref openvino_docs_ops_arithmetic_Abs_1"/>
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<tab type="user" title="Acos-1" url="@ref openvino_docs_ops_arithmetic_Acos_1"/>
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<tab type="user" title="Acosh-3" url="@ref openvino_docs_ops_arithmetic_Acosh_3"/>
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<tab type="user" title="AdaptiveAvgPool-8" url="@ref openvino_docs_ops_pooling_AdaptiveAvgPool_8"/>
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<tab type="user" title="AdaptiveMaxPool-8" url="@ref openvino_docs_ops_pooling_AdaptiveMaxPool_8"/>
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<tab type="user" title="Add-1" url="@ref openvino_docs_ops_arithmetic_Add_1"/>
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<tab type="user" title="Asin-1" url="@ref openvino_docs_ops_arithmetic_Asin_1"/>
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<tab type="user" title="Asinh-3" url="@ref openvino_docs_ops_arithmetic_Asinh_3"/>
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169
docs/ops/opset8.md
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169
docs/ops/opset8.md
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# Operation Set `opset8` Specification {#openvino_docs_ops_opset8}
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This specification document describes the `opset8` operation set supported in OpenVINO™.
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Support for each particular operation from the list below depends on the capabilities of an inference plugin
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and may vary among different hardware platforms and devices. Examples of operation instances are provided as IR V10 xml
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snippets. Such IR is generated by the Model Optimizer. The semantics match corresponding nGraph operation classes
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declared in `namespace opset8`.
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## Table of Contents <a name="toc"></a>
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* [Abs](arithmetic/Abs_1.md)
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* [Acos](arithmetic/Acos_1.md)
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* [Acosh](arithmetic/Acosh_3.md)
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* [AdaptiveAvgPool](pooling/AdaptiveAvgPool_8.md)
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* [AdaptiveMaxPool](pooling/AdaptiveMaxPool_8.md)
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* [Add](arithmetic/Add_1.md)
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* [Asin](arithmetic/Asin_1.md)
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* [Asinh](arithmetic/Asinh_3.md)
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* [Assign](infrastructure/Assign_3.md)
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* [Atan](arithmetic/Atan_1.md)
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* [Atanh](arithmetic/Atanh_3.md)
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* [AvgPool](pooling/AvgPool_1.md)
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* [BatchNormInference](normalization/BatchNormInference_5.md)
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* [BatchToSpace](movement/BatchToSpace_2.md)
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* [BinaryConvolution](convolution/BinaryConvolution_1.md)
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* [Broadcast](movement/Broadcast_3.md)
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* [Bucketize](condition/Bucketize_3.md)
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* [CTCGreedyDecoder](sequence/CTCGreedyDecoder_1.md)
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* [CTCGreedyDecoderSeqLen](sequence/CTCGreedyDecoderSeqLen_6.md)
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* [CTCLoss](sequence/CTCLoss_4.md)
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* [Ceiling](arithmetic/Ceiling_1.md)
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* [Clamp](activation/Clamp_1.md)
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* [Concat](movement/Concat_1.md)
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* [Constant](infrastructure/Constant_1.md)
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* [Convert](type/Convert_1.md)
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* [ConvertLike](type/ConvertLike_1.md)
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* [Convolution](convolution/Convolution_1.md)
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* [ConvolutionBackpropData](convolution/ConvolutionBackpropData_1.md)
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* [Cos](arithmetic/Cos_1.md)
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* [Cosh](arithmetic/Cosh_1.md)
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* [CumSum](arithmetic/CumSum_3.md)
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* [DeformableConvolution](convolution/DeformableConvolution_1.md)
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* [DeformablePSROIPooling](detection/DeformablePSROIPooling_1.md)
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* [DepthToSpace](movement/DepthToSpace_1.md)
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* [DetectionOutput](detection/DetectionOutput_1.md)
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* [DFT](signals/DFT_7.md)
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* [Divide](arithmetic/Divide_1.md)
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* [Einsum](matrix/Einsum_7.md)
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* [Elu](activation/Elu_1.md)
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* [EmbeddingBagOffsetsSum](sparse/EmbeddingBagOffsetsSum_3.md)
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* [EmbeddingBagPackedSum](sparse/EmbeddingBagPackedSum_3.md)
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* [EmbeddingSegmentsSum](sparse/EmbeddingSegmentsSum_3.md)
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* [Equal](comparison/Equal_1.md)
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* [Erf](arithmetic/Erf_1.md)
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* [Exp](activation/Exp_1.md)
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* [ExperimentalDetectronDetectionOutput_6](detection/ExperimentalDetectronDetectionOutput_6.md)
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* [ExperimentalDetectronGenerateProposalsSingleImage_6](detection/ExperimentalDetectronGenerateProposalsSingleImage_6.md)
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* [ExperimentalDetectronPriorGridGenerator_6](detection/ExperimentalDetectronPriorGridGenerator_6.md)
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* [ExperimentalDetectronROIFeatureExtractor_6](detection/ExperimentalDetectronROIFeatureExtractor_6.md)
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* [ExperimentalDetectronTopKROIs_6](sort/ExperimentalDetectronTopKROIs_6.md)
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* [ExtractImagePatches](movement/ExtractImagePatches_3.md)
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* [FakeQuantize](quantization/FakeQuantize_1.md)
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* [Floor](arithmetic/Floor_1.md)
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* [FloorMod](arithmetic/FloorMod_1.md)
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* [Gather](movement/Gather_7.md)
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* [GatherElements](movement/GatherElements_6.md)
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* [GatherND_5](movement/GatherND_5.md)
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* [GatherTree](movement/GatherTree_1.md)
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* [Gelu](activation/GELU_7.md)
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* [Greater](comparison/Greater_1.md)
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* [GreaterEqual](comparison/GreaterEqual_1.md)
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* [GRN](normalization/GRN_1.md)
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* [GroupConvolution](convolution/GroupConvolution_1.md)
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* [GroupConvolutionBackpropData](convolution/GroupConvolutionBackpropData_1.md)
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* [GRUCell](sequence/GRUCell_3.md)
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* [GRUSequence](sequence/GRUSequence_5.md)
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* [HardSigmoid](activation/HardSigmoid_1.md)
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* [HSigmoid](activation/HSigmoid_5.md)
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* [HSwish](activation/HSwish_4.md)
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* [IDFT](signals/IDFT_7.md)
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* [Interpolate](image/Interpolate_4.md)
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* [Less](comparison/Less_1.md)
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* [LessEqual](comparison/LessEqual_1.md)
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* [Log](arithmetic/Log_1.md)
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* [LogicalAnd](logical/LogicalAnd_1.md)
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* [LogicalNot](logical/LogicalNot_1.md)
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* [LogicalOr](logical/LogicalOr_1.md)
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* [LogicalXor](logical/LogicalXor_1.md)
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* [LogSoftmax](activation/LogSoftmax_5.md)
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* [Loop](infrastructure/Loop_5.md)
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* [LRN](normalization/LRN_1.md)
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* [LSTMCell](sequence/LSTMCell_1.md)
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* [LSTMSequence](sequence/LSTMSequence_1.md)
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* [MatMul](matrix/MatMul_1.md)
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* [MaxPool](pooling/MaxPool_1.md)
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* [Maximum](arithmetic/Maximum_1.md)
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* [Minimum](arithmetic/Minimum_1.md)
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* [Mish](activation/Mish_4.md)
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* [Mod](arithmetic/Mod_1.md)
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* [MVN](normalization/MVN_6.md)
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* [Multiply](arithmetic/Multiply_1.md)
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* [Negative](arithmetic/Negative_1.md)
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* [NonMaxSuppression](sort/NonMaxSuppression_5.md)
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* [NonZero](condition/NonZero_3.md)
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* [NormalizeL2](normalization/NormalizeL2_1.md)
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* [NotEqual](comparison/NotEqual_1.md)
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* [OneHot](sequence/OneHot_1.md)
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* [Pad](movement/Pad_1.md)
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* [Parameter](infrastructure/Parameter_1.md)
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* [Power](arithmetic/Power_1.md)
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* [PReLU](activation/PReLU_1.md)
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* [PriorBoxClustered](detection/PriorBoxClustered_1.md)
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* [PriorBox](detection/PriorBox_1.md)
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* [Proposal](detection/Proposal_4.md)
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* [PSROIPooling](detection/PSROIPooling_1.md)
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* [Range](generation/Range_4.md)
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* [ReLU](activation/ReLU_1.md)
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* [ReadValue](infrastructure/ReadValue_3.md)
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* [ReduceL1](reduction/ReduceL1_4.md)
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* [ReduceL2](reduction/ReduceL2_4.md)
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* [ReduceLogicalAnd](reduction/ReduceLogicalAnd_1.md)
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* [ReduceLogicalOr](reduction/ReduceLogicalOr_1.md)
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* [ReduceMax](reduction/ReduceMax_1.md)
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* [ReduceMean](reduction/ReduceMean_1.md)
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* [ReduceMin](reduction/ReduceMin_1.md)
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* [ReduceProd](reduction/ReduceProd_1.md)
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* [ReduceSum](reduction/ReduceSum_1.md)
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* [RegionYolo](detection/RegionYolo_1.md)
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* [ReorgYolo](detection/ReorgYolo_1.md)
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* [Reshape](shape/Reshape_1.md)
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* [Result](infrastructure/Result_1.md)
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* [ReverseSequence](movement/ReverseSequence_1.md)
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* [RNNCell](sequence/RNNCell_3.md)
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* [RNNSequence](sequence/RNNSequence_5.md)
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* [ROIAlign](detection/ROIAlign_3.md)
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* [ROIPooling](detection/ROIPooling_1.md)
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* [Roll](movement/Roll_7.md)
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* [Round](arithmetic/Round_5.md)
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* [ScatterElementsUpdate](movement/ScatterElementsUpdate_3.md)
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* [ScatterNDUpdate](movement/ScatterNDUpdate_3.md)
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* [ScatterUpdate](movement/ScatterUpdate_3.md)
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* [Select](condition/Select_1.md)
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* [Selu](activation/Selu_1.md)
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* [ShapeOf](shape/ShapeOf_3.md)
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* [ShuffleChannels](movement/ShuffleChannels_1.md)
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* [Sigmoid](activation/Sigmoid_1.md)
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* [Sign](arithmetic/Sign_1.md)
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* [Sin](arithmetic/Sin_1.md)
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* [Sinh](arithmetic/Sinh_1.md)
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* [SoftMax](activation/SoftMax_1.md)
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* [SoftPlus](activation/SoftPlus_4.md)
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* [SpaceToBatch](movement/SpaceToBatch_2.md)
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* [SpaceToDepth](movement/SpaceToDepth_1.md)
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* [Split](movement/Split_1.md)
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* [Sqrt](arithmetic/Sqrt_1.md)
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* [SquaredDifference](arithmetic/SquaredDifference_1.md)
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* [Squeeze](shape/Squeeze_1.md)
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* [StridedSlice](movement/StridedSlice_1.md)
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* [Subtract](arithmetic/Subtract_1.md)
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* [Swish](activation/Swish_4.md)
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* [Tan](arithmetic/Tan_1.md)
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* [Tanh](arithmetic/Tanh_1.md)
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* [TensorIterator](infrastructure/TensorIterator_1.md)
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* [Tile](movement/Tile_1.md)
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* [TopK](sort/TopK_3.md)
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* [Transpose](movement/Transpose_1.md)
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* [Unsqueeze](shape/Unsqueeze_1.md)
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* [VariadicSplit](movement/VariadicSplit_1.md)
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70
docs/ops/pooling/AdaptiveAvgPool_8.md
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70
docs/ops/pooling/AdaptiveAvgPool_8.md
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## AdaptiveAvgPool<a name="AdaptiveAvgPool"></a> {#openvino_docs_ops_pooling_AdaptiveAvgPool_8}
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**Versioned name**: *AdaptiveAvgPool-8*
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**Category**: *Pooling*
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**Short description**: Applies average pooling with adaptive kernel size over the input.
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**Detailed description**: This operation calculates the output based on the first input and `output_size` determined by the second input.
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The kernel dimensions are calculated using the following formulae for the `NCDHW` input case:
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\f[
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\begin{array}{lcl}
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d_{start} &=& floor(i*D_{in}/D_{out})\\
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d_{end} &=& ceil((i+1)*D_{in}/D_{out})\\
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h_{start} &=& floor(j*H_{in}/H_{out})\\
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h_{end} &=& ceil((j+1)*H_{in}/H_{out})\\
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w_{start} &=& floor(k*W_{in}/W_{out})\\
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w_{end} &=& ceil((k+1)*W_{in}/W_{out})
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\end{array}
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\f]
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The output is calculated with the following formula:
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\f[
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Output(i,j,k) = \frac{Input[d_{start}:d_{end}, h_{start}:h_{end}, w_{start}:w_{end}]}{(d_{end}-d_{start})*(h_{end}-h_{start})*(w_{end}-w_{start})}
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\f]
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**Inputs**:
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* **1**: 3D, 4D, or 5D input tensor of shape `[N, C, H]`, `[N, C, H, W]` or `[N, C, D, H, W]` and type *T*. Required.
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* **2**: 1D tensor describing output shape for spatial dimensions. Can be `[H_out]` for 3D input, `[H_out, W_out]` for 4D input, `[D_out, H_out, W_out]` for 5D input and of type *T_SHAPE*. Required.
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**Outputs**:
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* **1**: Output of type *T* and shape `[N, C, H_out]`, `[N, C, H_out, W_out]` or `[N, C, D_out, H_out, W_out]`.
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**Types**
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* *T*: floating-point type.
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* *T_SHAPE*: `int32` or `int64`.
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**Examples**
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```xml
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<layer ... type="AdaptiveAvgPool" ... >
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<data output_type="i64"/>
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<input>
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<port id="0">
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<dim>1</dim>
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<dim>3</dim>
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<dim>32</dim>
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<dim>32</dim>
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</port>
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</input>
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<input>
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<port id="1">
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<dim>2</dim>
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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>1</dim>
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<dim>3</dim>
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<dim>16</dim>
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<dim>16</dim>
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</port>
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</output>
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</layer>
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```
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87
docs/ops/pooling/AdaptiveMaxPool_8.md
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87
docs/ops/pooling/AdaptiveMaxPool_8.md
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## AdaptiveMaxPool<a name="AdaptiveMaxPool"></a> {#openvino_docs_ops_pooling_AdaptiveMaxPool_8}
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**Versioned name**: *AdaptiveMaxPool-8*
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**Category**: *Pooling*
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**Short description**: Applies max pooling with adaptive kernel size over the input.
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**Detailed description**: This operation calculates the output based on the first input and `output_size` determined by the second input.
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The kernel dimensions are calculated using the following formulae for the `NCDHW` input case:
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\f[
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\begin{array}{lcl}
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d_{start} &=& floor(i*D_{in}/D_{out})\\
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d_{end} &=& ceil((i+1)*D_{in}/D_{out})\\
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h_{start} &=& floor(j*H_{in}/H_{out})\\
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h_{end} &=& ceil((j+1)*H_{in}/H_{out})\\
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w_{start} &=& floor(k*W_{in}/W_{out})\\
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w_{end} &=& ceil((k+1)*W_{in}/W_{out})
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\end{array}
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\f]
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The output is calculated following this formula:
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\f[
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Output(i,j,k) = max(Input[d_{start}:d_{end}, h_{start}:h_{end}, w_{start}:w_{end}])
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\f]
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**Attributes**:
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* *index_element_type*
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* **Description**: the type of the second output containing indices
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* **Range of values**: "i64" or "i32"
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* **Type**: string
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* **Default value**: "i64"
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* **Required**: *No*
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**Inputs**:
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* **1**: 3D, 4D, or 5D input tensor of shape `[N, C, H]`, `[N, C, H, W]` or `[N, C, D, H, W]` and type *T*. Required.
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* **2**: 1D tensor describing output shape for spatial dimensions. Can be `[H_out]` for 3D input, `[H_out, W_out]` for 4D input, `[D_out, H_out, W_out]` for 5D input and of type *T_SHAPE*. Required.
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**Outputs**:
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* **1**: Output of type *T* and shape `[N, C, H_out]`, `[N, C, H_out, W_out]` or `[N, C, D_out, H_out, W_out]`.
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* **2**: Output of type specified by *index_element_type* and same shape as the first output containing indices of elements in the first output. The values of indices are computed as if input was flatten 1-D tensor, so the values are in the range `[0, N * C * H * W * D)`.
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**Types**
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* *T*: floating-point type.
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* *T_SHAPE*: `int32` or `int64`.
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**Examples**
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```xml
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<layer ... type="AdaptiveMaxPool" ... >
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<data output_type="i64"/>
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<input>
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<port id="0">
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<dim>1</dim>
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<dim>3</dim>
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<dim>32</dim>
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<dim>32</dim>
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</port>
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</input>
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<input>
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<port id="1">
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<dim>2</dim>
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</port>
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</input>
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<output>
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<port id="1">
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<dim>1</dim>
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<dim>3</dim>
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<dim>16</dim>
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<dim>16</dim>
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</port>
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<port id="2">
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<dim>1</dim>
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<dim>3</dim>
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<dim>16</dim>
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<dim>16</dim>
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</port>
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</output>
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</layer>
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```
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