introduce new opset5, include GRU/RNN/LSTM Sequences to opset5
This commit is contained in:
Ivan Tikhonov
parent
f5a7f7a8f3
commit
73c22a11db
@ -94,6 +94,7 @@
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<!-- IR -->
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<tab 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="opset5 Specification" url="@ref openvino_docs_ops_opset5"/>
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<tab type="user" title="opset4 Specification" url="@ref openvino_docs_ops_opset4"/>
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<tab type="user" title="opset3 Specification" url="@ref openvino_docs_ops_opset3"/>
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<tab type="user" title="opset2 Specification" url="@ref openvino_docs_ops_opset2"/>
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@ -6,6 +6,7 @@ This topic provides a complete list of available sets of operations supported in
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| OpenVINO™ Version | Actual Operations Set |
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| :---------------- | :------------------------------- |
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| 2021.2 | [opset5](opset5.md) |
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| 2021.1 | [opset4](opset4.md) |
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| 2020.4 | [opset3](opset3.md) |
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| 2020.3 | [opset2](opset2.md) |
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152
docs/ops/opset5.md
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152
docs/ops/opset5.md
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# Operation Set `opset5` Specification {#openvino_docs_ops_opset5}
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This specification document describes `opset5` operation set supported in OpenVINO.
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Support for each particular operation from the list below depends on the capabilities available in a inference plugin
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and may vary among different hardware platforms and devices. Examples of operation instances are expressed 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 opset5`.
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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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* [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_1.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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* [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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* [Divide](arithmetic/Divide_1.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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* [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_1.md)
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* [GatherTree](movement/GatherTree_1.md)
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* [Gelu](activation/GELU_2.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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* [HSwish](activation/HSwish_4.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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* [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_1.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_4.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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* [Reverse](movement/Reverse_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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* [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](arithmetic/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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docs/ops/sequence/GRUSequence_5.md
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docs/ops/sequence/GRUSequence_5.md
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## GRUSequence <a name="GRUSequence"></a> {#openvino_docs_ops_sequence_GRUSequence_5}
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**Versioned name**: *GRUSequence-5*
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**Category**: *Sequence processing*
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**Short description**: *GRUSequence* operation represents a series of GRU cells. Each cell is implemented as <a href="#GRUCell">GRUCell</a> operation.
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**Detailed description**
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A single cell in the sequence is implemented in the same way as in <a href="#GRUCell">GRUCell</a> operation. *GRUSequence* represents a sequence of GRU cells. The sequence can be connected differently depending on `direction` attribute that specifies the direction of traversing of input data along sequence dimension or specifies whether it should be a bidirectional sequence. The most of the attributes are in sync with the specification of ONNX GRU operator defined <a href="https://github.com/onnx/onnx/blob/master/docs/Operators.md#gru">GRUCell</a>.
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**Attributes**
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* *hidden_size*
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* **Description**: *hidden_size* specifies hidden state size.
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* **Range of values**: a positive integer
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* **Type**: `int`
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* **Default value**: None
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* **Required**: *yes*
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* *activations*
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* **Description**: *activations* specifies activation functions for gates, there are two gates, so two activation functions should be specified as a value for this attributes
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* **Range of values**: any combination of *relu*, *sigmoid*, *tanh*
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* **Type**: a list of strings
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* **Default value**: *sigmoid,tanh*
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* **Required**: *no*
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* *activations_alpha, activations_beta*
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* **Description**: *activations_alpha, activations_beta* attributes of functions; applicability and meaning of these attributes depends on choosen activation functions
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* **Range of values**: a list of floating-point numbers
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* **Type**: `float[]`
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* **Default value**: None
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* **Required**: *no*
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* *clip*
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* **Description**: *clip* specifies bound values *[-C, C]* for tensor clipping. Clipping is performed before activations.
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* **Range of values**: a positive floating-point number
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* **Type**: `float`
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* **Default value**: *infinity* that means that the clipping is not applied
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* **Required**: *no*
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* *direction*
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* **Description**: Specify if the RNN is forward, reverse, or bidirectional. If it is one of *forward* or *reverse* then `num_directions = 1`, if it is *bidirectional*, then `num_directions = 2`. This `num_directions` value specifies input/output shape requirements.
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* **Range of values**: *forward*, *reverse*, *bidirectional*
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* **Type**: `string`
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* **Default value**: None
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* **Required**: *Yes*
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* *linear_before_reset*
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* **Description**: *linear_before_reset* flag denotes if the layer behaves according to the modification of *GRUCell* described in the formula in the [ONNX documentation](https://github.com/onnx/onnx/blob/master/docs/Operators.md#GRU).
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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**: `X` - 3D tensor of type *T1* `[batch_size, seq_length, input_size]`, input data. It differs from GRUCell 1st input only by additional axis with size `seq_length`. **Required.**
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* **2**: `initial_hidden_state` - 3D tensor of type *T1* `[batch_size, num_directions, hidden_size]`, input hidden state data. **Required.**
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* **3**: `sequence_lengths` - 1D tensor of type *T2* `[batch_size]`, specifies real sequence lengths for each batch element. **Required.**
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* **4**: `W` - 3D tensor of type *T1* `[num_directions, 3 * hidden_size, input_size]`, the weights for matrix multiplication, gate order: zrh. **Required.**
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* **5**: `R` - 3D tensor of type *T1* `[num_directions, 3 * hidden_size, hidden_size]`, the recurrence weights for matrix multiplication, gate order: zrh. **Required.**
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* **6**: `B` - 2D tensor of type *T*. If *linear_before_reset* is set to 1, then the shape is `[num_directions, 4 * hidden_size]` - the sum of biases for z and r gates (weights and recurrence weights), the biases for h gate are placed separately. Otherwise the shape is `[num_directions, 3 * hidden_size]`, the sum of biases (weights and recurrence weights). **Required.**
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**Outputs**
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* **1**: `Y` 3D tensor of type *T1* `[batch_size, num_directions, seq_len, hidden_size]`, concatenation of all the intermediate output values of the hidden.
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* **2**: `Ho` - 3D tensor of type *T1* `[batch_size, num_directions, hidden_size]`, the last output value of hidden state.
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**Types**
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* *T1*: any supported floating point type.
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* *T2*: any supported integer type.
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**Example**
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```xml
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<layer ... type="GRUSequence" ...>
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<data hidden_size="128"/>
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<input>
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<port id="0">
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<dim>1</dim>
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<dim>4</dim>
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<dim>16</dim>
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</port>
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<port id="1">
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<dim>1</dim>
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<dim>1</dim>
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<dim>128</dim>
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</port>
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<port id="2">
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<dim>1</dim>
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</port>
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<port id="3">
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<dim>1</dim>
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<dim>384</dim>
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<dim>16</dim>
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</port>
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<port id="4">
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<dim>1</dim>
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<dim>384</dim>
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<dim>128</dim>
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</port>
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<port id="5">
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<dim>1</dim>
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<dim>384</dim>
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</port>
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</input>
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<output>
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<port id="6">
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<dim>1</dim>
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<dim>1</dim>
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<dim>4</dim>
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<dim>128</dim>
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</port>
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<port id="7">
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<dim>1</dim>
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<dim>1</dim>
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<dim>128</dim>
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</port>
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</output>
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</layer>
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```
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docs/ops/sequence/RNNSequence_5.md
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## RNNSequence <a name="RNNSequence"></a> {#openvino_docs_ops_sequence_RNNSequence_5}
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**Versioned name**: *RNNSequence-5*
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**Category**: *Sequence processing*
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**Short description**: *RNNSequence* operation represents a series of RNN cells. Each cell is implemented as <a href="#RNNCell">RNNCell</a> operation.
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**Detailed description**
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A single cell in the sequence is implemented in the same way as in <a href="#RNNCell">RNNCell</a> operation. *RNNSequence* represents a sequence of RNN cells. The sequence can be connected differently depending on `direction` attribute that specifies the direction of traversing of input data along sequence dimension or specifies whether it should be a bidirectional sequence. The most of the attributes are in sync with the specification of ONNX RNN operator defined <a href="https://github.com/onnx/onnx/blob/master/docs/Operators.md#rnn">RNNCell</a>.
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**Attributes**
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* *hidden_size*
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* **Description**: *hidden_size* specifies hidden state size.
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* **Range of values**: a positive integer
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* **Type**: `int`
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* **Default value**: None
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* **Required**: *yes*
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* *activations*
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* **Description**: activation functions for gates
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* **Range of values**: any combination of *relu*, *sigmoid*, *tanh*
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* **Type**: a list of strings
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* **Default value**: *tanh*
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* **Required**: *no*
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* *activations_alpha, activations_beta*
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* **Description**: *activations_alpha, activations_beta* attributes of functions; applicability and meaning of these attributes depends on choosen activation functions
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* **Range of values**: a list of floating-point numbers
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* **Type**: `float[]`
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* **Default value**: None
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* **Required**: *no*
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* *clip*
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* **Description**: *clip* specifies bound values *[-C, C]* for tensor clipping. Clipping is performed before activations.
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* **Range of values**: a positive floating-point number
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* **Type**: `float`
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* **Default value**: *infinity* that means that the clipping is not applied
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* **Required**: *no*
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* *direction*
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* **Description**: Specify if the RNN is forward, reverse, or bidirectional. If it is one of *forward* or *reverse* then `num_directions = 1`, if it is *bidirectional*, then `num_directions = 2`. This `num_directions` value specifies input/output shape requirements.
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* **Range of values**: *forward*, *reverse*, *bidirectional*
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* **Type**: `string`
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* **Default value**: None
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* **Required**: *Yes*
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**Inputs**
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|
||||
* **1**: `X` - 3D tensor of type *T1* `[batch_size, seq_length, input_size]`, input data. It differs from RNNCell 1st input only by additional axis with size `seq_length`. **Required.**
|
||||
|
||||
* **2**: `initial_hidden_state` - 3D tensor of type *T1* `[batch_size, num_directions, hidden_size]`, input hidden state data. **Required.**
|
||||
|
||||
* **3**: `sequence_lengths` - 1D tensor of type *T2* `[batch_size]`, specifies real sequence lengths for each batch element. **Required.**
|
||||
|
||||
* **4**: `W` - 3D tensor of type *T1* `[num_directions, hidden_size, input_size]`, the weights for matrix multiplication. **Required.**
|
||||
|
||||
* **5**: `R` - 3D tensor of type *T1* `[num_directions, hidden_size, hidden_size]`, the recurrence weights for matrix multiplication. **Required.**
|
||||
|
||||
* **6**: `B` - 2D tensor of type *T1* `[num_directions, hidden_size]`, the sum of biases (weights and recurrence weights). **Required.**
|
||||
|
||||
**Outputs**
|
||||
|
||||
* **1**: `Y` 3D tensor of type *T1* `[batch_size, num_directions, seq_len, hidden_size]`, concatenation of all the intermediate output values of the hidden.
|
||||
|
||||
* **2**: `Ho` - 3D tensor of type *T1* `[batch_size, num_directions, hidden_size]`, the last output value of hidden state.
|
||||
|
||||
**Types**
|
||||
|
||||
* *T1*: any supported floating point type.
|
||||
* *T2*: any supported integer type.
|
||||
|
||||
**Example**
|
||||
```xml
|
||||
<layer ... type="RNNSequence" ...>
|
||||
<data hidden_size="128"/>
|
||||
<input>
|
||||
<port id="0">
|
||||
<dim>1</dim>
|
||||
<dim>4</dim>
|
||||
<dim>16</dim>
|
||||
</port>
|
||||
<port id="1">
|
||||
<dim>1</dim>
|
||||
<dim>1</dim>
|
||||
<dim>128</dim>
|
||||
</port>
|
||||
<port id="2">
|
||||
<dim>1</dim>
|
||||
</port>
|
||||
<port id="3">
|
||||
<dim>1</dim>
|
||||
<dim>128</dim>
|
||||
<dim>16</dim>
|
||||
</port>
|
||||
<port id="4">
|
||||
<dim>1</dim>
|
||||
<dim>128</dim>
|
||||
<dim>128</dim>
|
||||
</port>
|
||||
<port id="5">
|
||||
<dim>1</dim>
|
||||
<dim>128</dim>
|
||||
</port>
|
||||
</input>
|
||||
<output>
|
||||
<port id="6">
|
||||
<dim>1</dim>
|
||||
<dim>1</dim>
|
||||
<dim>4</dim>
|
||||
<dim>128</dim>
|
||||
</port>
|
||||
<port id="7">
|
||||
<dim>1</dim>
|
||||
<dim>1</dim>
|
||||
<dim>128</dim>
|
||||
</port>
|
||||
</output>
|
||||
</layer>
|
||||
```
|
||||
@ -9,12 +9,13 @@
|
||||
#include <ngraph/opsets/opset4.hpp>
|
||||
#include <ngraph/rt_info.hpp>
|
||||
#include <ngraph/pattern/op/wrap_type.hpp>
|
||||
#include <ngraph/opsets/opset5.hpp>
|
||||
|
||||
ngraph::pass::BidirectionalLSTMSequenceDecomposition::BidirectionalLSTMSequenceDecomposition() {
|
||||
auto lstm_sequence_ngraph = ngraph::pattern::wrap_type<ngraph::op::v5::LSTMSequence>();
|
||||
auto lstm_sequence_ngraph = ngraph::pattern::wrap_type<ngraph::opset5::LSTMSequence>();
|
||||
|
||||
ngraph::matcher_pass_callback callback = [](pattern::Matcher &m) {
|
||||
auto lstm_sequence = std::dynamic_pointer_cast<ngraph::op::v5::LSTMSequence>(m.get_match_root());
|
||||
auto lstm_sequence = std::dynamic_pointer_cast<ngraph::opset5::LSTMSequence>(m.get_match_root());
|
||||
if (!lstm_sequence) {
|
||||
return false;
|
||||
}
|
||||
@ -22,14 +23,14 @@ ngraph::pass::BidirectionalLSTMSequenceDecomposition::BidirectionalLSTMSequenceD
|
||||
if (lstm_sequence->get_direction() != ngraph::op::RecurrentSequenceDirection::BIDIRECTIONAL)
|
||||
return false;
|
||||
|
||||
auto axis_0 = ngraph::opset4::Constant::create(element::i64, Shape{}, {0});
|
||||
auto axis_1 = ngraph::opset4::Constant::create(element::i64, Shape{}, {1});
|
||||
auto H = std::make_shared<opset4::Split>(lstm_sequence->input_value(1), axis_1, 2);
|
||||
auto C = std::make_shared<opset4::Split>(lstm_sequence->input_value(2), axis_1, 2);
|
||||
auto W = std::make_shared<opset4::Split>(lstm_sequence->input_value(4), axis_0, 2);
|
||||
auto R = std::make_shared<opset4::Split>(lstm_sequence->input_value(5), axis_0, 2);
|
||||
auto B = std::make_shared<opset4::Split>(lstm_sequence->input_value(6), axis_0, 2);
|
||||
auto lstm_sequence_forward = std::make_shared<ngraph::op::v5::LSTMSequence>(
|
||||
auto axis_0 = ngraph::opset5::Constant::create(element::i64, Shape{}, {0});
|
||||
auto axis_1 = ngraph::opset5::Constant::create(element::i64, Shape{}, {1});
|
||||
auto H = std::make_shared<opset5::Split>(lstm_sequence->input_value(1), axis_1, 2);
|
||||
auto C = std::make_shared<opset5::Split>(lstm_sequence->input_value(2), axis_1, 2);
|
||||
auto W = std::make_shared<opset5::Split>(lstm_sequence->input_value(4), axis_0, 2);
|
||||
auto R = std::make_shared<opset5::Split>(lstm_sequence->input_value(5), axis_0, 2);
|
||||
auto B = std::make_shared<opset5::Split>(lstm_sequence->input_value(6), axis_0, 2);
|
||||
auto lstm_sequence_forward = std::make_shared<ngraph::opset5::LSTMSequence>(
|
||||
lstm_sequence->input_value(0),
|
||||
H->output(0),
|
||||
C->output(0),
|
||||
@ -44,7 +45,7 @@ ngraph::pass::BidirectionalLSTMSequenceDecomposition::BidirectionalLSTMSequenceD
|
||||
lstm_sequence->get_activations(),
|
||||
lstm_sequence->get_clip());
|
||||
|
||||
auto lstm_sequence_reverse = std::make_shared<ngraph::op::v5::LSTMSequence>(
|
||||
auto lstm_sequence_reverse = std::make_shared<ngraph::opset5::LSTMSequence>(
|
||||
lstm_sequence->input_value(0),
|
||||
H->output(1),
|
||||
C->output(1),
|
||||
@ -59,11 +60,11 @@ ngraph::pass::BidirectionalLSTMSequenceDecomposition::BidirectionalLSTMSequenceD
|
||||
lstm_sequence->get_activations(),
|
||||
lstm_sequence->get_clip());
|
||||
|
||||
auto concat_0 = std::make_shared<opset4::Concat>(OutputVector{lstm_sequence_forward->output(0),
|
||||
auto concat_0 = std::make_shared<opset5::Concat>(OutputVector{lstm_sequence_forward->output(0),
|
||||
lstm_sequence_reverse->output(0)}, 1);
|
||||
auto concat_1 = std::make_shared<opset4::Concat>(OutputVector{lstm_sequence_forward->output(1),
|
||||
auto concat_1 = std::make_shared<opset5::Concat>(OutputVector{lstm_sequence_forward->output(1),
|
||||
lstm_sequence_reverse->output(1)}, 1);
|
||||
auto concat_2 = std::make_shared<opset4::Concat>(OutputVector{lstm_sequence_forward->output(2),
|
||||
auto concat_2 = std::make_shared<opset5::Concat>(OutputVector{lstm_sequence_forward->output(2),
|
||||
lstm_sequence_reverse->output(2)}, 1);
|
||||
ngraph::copy_runtime_info(lstm_sequence, {H, C, W, R, B, lstm_sequence_forward, lstm_sequence_reverse,
|
||||
concat_0, concat_1, concat_2});
|
||||
@ -79,10 +80,10 @@ ngraph::pass::BidirectionalLSTMSequenceDecomposition::BidirectionalLSTMSequenceD
|
||||
}
|
||||
|
||||
ngraph::pass::BidirectionalGRUSequenceDecomposition::BidirectionalGRUSequenceDecomposition() {
|
||||
auto gru_sequence_ngraph = ngraph::pattern::wrap_type<ngraph::op::v5::GRUSequence>();
|
||||
auto gru_sequence_ngraph = ngraph::pattern::wrap_type<ngraph::opset5::GRUSequence>();
|
||||
|
||||
ngraph::matcher_pass_callback callback = [](pattern::Matcher &m) {
|
||||
auto gru_sequence = std::dynamic_pointer_cast<ngraph::op::v5::GRUSequence>(m.get_match_root());
|
||||
auto gru_sequence = std::dynamic_pointer_cast<ngraph::opset5::GRUSequence>(m.get_match_root());
|
||||
if (!gru_sequence) {
|
||||
return false;
|
||||
}
|
||||
@ -90,13 +91,13 @@ ngraph::pass::BidirectionalGRUSequenceDecomposition::BidirectionalGRUSequenceDec
|
||||
if (gru_sequence->get_direction() != ngraph::op::RecurrentSequenceDirection::BIDIRECTIONAL)
|
||||
return false;
|
||||
|
||||
auto axis_0 = ngraph::opset4::Constant::create(element::i64, Shape{}, {0});
|
||||
auto axis_1 = ngraph::opset4::Constant::create(element::i64, Shape{}, {1});
|
||||
auto H = std::make_shared<opset4::Split>(gru_sequence->input_value(1), axis_1, 2);
|
||||
auto W = std::make_shared<opset4::Split>(gru_sequence->input_value(3), axis_0, 2);
|
||||
auto R = std::make_shared<opset4::Split>(gru_sequence->input_value(4), axis_0, 2);
|
||||
auto B = std::make_shared<opset4::Split>(gru_sequence->input_value(5), axis_0, 2);
|
||||
auto gru_sequence_forward = std::make_shared<ngraph::op::v5::GRUSequence>(
|
||||
auto axis_0 = ngraph::opset5::Constant::create(element::i64, Shape{}, {0});
|
||||
auto axis_1 = ngraph::opset5::Constant::create(element::i64, Shape{}, {1});
|
||||
auto H = std::make_shared<opset5::Split>(gru_sequence->input_value(1), axis_1, 2);
|
||||
auto W = std::make_shared<opset5::Split>(gru_sequence->input_value(3), axis_0, 2);
|
||||
auto R = std::make_shared<opset5::Split>(gru_sequence->input_value(4), axis_0, 2);
|
||||
auto B = std::make_shared<opset5::Split>(gru_sequence->input_value(5), axis_0, 2);
|
||||
auto gru_sequence_forward = std::make_shared<ngraph::opset5::GRUSequence>(
|
||||
gru_sequence->input_value(0),
|
||||
H->output(0),
|
||||
gru_sequence->input_value(2),
|
||||
@ -111,7 +112,7 @@ ngraph::pass::BidirectionalGRUSequenceDecomposition::BidirectionalGRUSequenceDec
|
||||
gru_sequence->get_clip(),
|
||||
gru_sequence->get_linear_before_reset());
|
||||
|
||||
auto gru_sequence_reverse = std::make_shared<ngraph::op::v5::GRUSequence>(
|
||||
auto gru_sequence_reverse = std::make_shared<ngraph::opset5::GRUSequence>(
|
||||
gru_sequence->input_value(0),
|
||||
H->output(1),
|
||||
gru_sequence->input_value(2),
|
||||
@ -126,9 +127,9 @@ ngraph::pass::BidirectionalGRUSequenceDecomposition::BidirectionalGRUSequenceDec
|
||||
gru_sequence->get_clip(),
|
||||
gru_sequence->get_linear_before_reset());
|
||||
|
||||
auto concat_0 = std::make_shared<opset4::Concat>(OutputVector{gru_sequence_forward->output(0),
|
||||
auto concat_0 = std::make_shared<opset5::Concat>(OutputVector{gru_sequence_forward->output(0),
|
||||
gru_sequence_reverse->output(0)}, 1);
|
||||
auto concat_1 = std::make_shared<opset4::Concat>(OutputVector{gru_sequence_forward->output(1),
|
||||
auto concat_1 = std::make_shared<opset5::Concat>(OutputVector{gru_sequence_forward->output(1),
|
||||
gru_sequence_reverse->output(1)}, 1);
|
||||
ngraph::copy_runtime_info(gru_sequence, {H, W, R, B, gru_sequence_forward, gru_sequence_reverse,
|
||||
concat_0, concat_1});
|
||||
@ -143,10 +144,10 @@ ngraph::pass::BidirectionalGRUSequenceDecomposition::BidirectionalGRUSequenceDec
|
||||
}
|
||||
|
||||
ngraph::pass::BidirectionalRNNSequenceDecomposition::BidirectionalRNNSequenceDecomposition() {
|
||||
auto rnn_sequence_ngraph = ngraph::pattern::wrap_type<ngraph::op::v5::RNNSequence>();
|
||||
auto rnn_sequence_ngraph = ngraph::pattern::wrap_type<ngraph::opset5::RNNSequence>();
|
||||
|
||||
ngraph::matcher_pass_callback callback = [](pattern::Matcher &m) {
|
||||
auto rnn_sequence = std::dynamic_pointer_cast<ngraph::op::v5::RNNSequence>(m.get_match_root());
|
||||
auto rnn_sequence = std::dynamic_pointer_cast<ngraph::opset5::RNNSequence>(m.get_match_root());
|
||||
if (!rnn_sequence) {
|
||||
return false;
|
||||
}
|
||||
@ -154,13 +155,13 @@ ngraph::pass::BidirectionalRNNSequenceDecomposition::BidirectionalRNNSequenceDec
|
||||
if (rnn_sequence->get_direction() != ngraph::op::RecurrentSequenceDirection::BIDIRECTIONAL)
|
||||
return false;
|
||||
|
||||
auto axis_0 = ngraph::opset4::Constant::create(element::i64, Shape{}, {0});
|
||||
auto axis_1 = ngraph::opset4::Constant::create(element::i64, Shape{}, {1});
|
||||
auto H = std::make_shared<opset4::Split>(rnn_sequence->input_value(1), axis_1, 2);
|
||||
auto W = std::make_shared<opset4::Split>(rnn_sequence->input_value(3), axis_0, 2);
|
||||
auto R = std::make_shared<opset4::Split>(rnn_sequence->input_value(4), axis_0, 2);
|
||||
auto B = std::make_shared<opset4::Split>(rnn_sequence->input_value(5), axis_0, 2);
|
||||
auto rnn_sequence_forward = std::make_shared<ngraph::op::v5::RNNSequence>(
|
||||
auto axis_0 = ngraph::opset5::Constant::create(element::i64, Shape{}, {0});
|
||||
auto axis_1 = ngraph::opset5::Constant::create(element::i64, Shape{}, {1});
|
||||
auto H = std::make_shared<opset5::Split>(rnn_sequence->input_value(1), axis_1, 2);
|
||||
auto W = std::make_shared<opset5::Split>(rnn_sequence->input_value(3), axis_0, 2);
|
||||
auto R = std::make_shared<opset5::Split>(rnn_sequence->input_value(4), axis_0, 2);
|
||||
auto B = std::make_shared<opset5::Split>(rnn_sequence->input_value(5), axis_0, 2);
|
||||
auto rnn_sequence_forward = std::make_shared<ngraph::opset5::RNNSequence>(
|
||||
rnn_sequence->input_value(0),
|
||||
H->output(0),
|
||||
rnn_sequence->input_value(2),
|
||||
@ -174,7 +175,7 @@ ngraph::pass::BidirectionalRNNSequenceDecomposition::BidirectionalRNNSequenceDec
|
||||
rnn_sequence->get_activations_beta(),
|
||||
rnn_sequence->get_clip());
|
||||
|
||||
auto rnn_sequence_reverse = std::make_shared<ngraph::op::v5::RNNSequence>(
|
||||
auto rnn_sequence_reverse = std::make_shared<ngraph::opset5::RNNSequence>(
|
||||
rnn_sequence->input_value(0),
|
||||
H->output(1),
|
||||
rnn_sequence->input_value(2),
|
||||
@ -188,9 +189,9 @@ ngraph::pass::BidirectionalRNNSequenceDecomposition::BidirectionalRNNSequenceDec
|
||||
rnn_sequence->get_activations_beta(),
|
||||
rnn_sequence->get_clip());
|
||||
|
||||
auto concat_0 = std::make_shared<opset4::Concat>(OutputVector{rnn_sequence_forward->output(0),
|
||||
auto concat_0 = std::make_shared<opset5::Concat>(OutputVector{rnn_sequence_forward->output(0),
|
||||
rnn_sequence_reverse->output(0)}, 1);
|
||||
auto concat_1 = std::make_shared<opset4::Concat>(OutputVector{rnn_sequence_forward->output(1),
|
||||
auto concat_1 = std::make_shared<opset5::Concat>(OutputVector{rnn_sequence_forward->output(1),
|
||||
rnn_sequence_reverse->output(1)}, 1);
|
||||
ngraph::copy_runtime_info(rnn_sequence, {H, W, R, B, rnn_sequence_forward, rnn_sequence_reverse,
|
||||
concat_0, concat_1});
|
||||
|
||||
@ -6,7 +6,7 @@
|
||||
|
||||
#include <memory>
|
||||
|
||||
#include <ngraph/opsets/opset4.hpp>
|
||||
#include <ngraph/opsets/opset5.hpp>
|
||||
#include <ngraph/rt_info.hpp>
|
||||
#include <ngraph/pattern/op/wrap_type.hpp>
|
||||
|
||||
@ -15,10 +15,10 @@
|
||||
#include <ngraph_ops/rnn_sequence_ie.hpp>
|
||||
|
||||
ngraph::pass::ConvertLSTMSequenceMatcher::ConvertLSTMSequenceMatcher() {
|
||||
auto lstm_sequence_ngraph = ngraph::pattern::wrap_type<ngraph::op::v5::LSTMSequence>();
|
||||
auto lstm_sequence_ngraph = ngraph::pattern::wrap_type<ngraph::opset5::LSTMSequence>();
|
||||
|
||||
ngraph::matcher_pass_callback callback = [](pattern::Matcher &m) {
|
||||
auto lstm_sequence = std::dynamic_pointer_cast<ngraph::op::v5::LSTMSequence>(m.get_match_root());
|
||||
auto lstm_sequence = std::dynamic_pointer_cast<ngraph::opset5::LSTMSequence>(m.get_match_root());
|
||||
if (!lstm_sequence) {
|
||||
return false;
|
||||
}
|
||||
@ -31,13 +31,13 @@ ngraph::pass::ConvertLSTMSequenceMatcher::ConvertLSTMSequenceMatcher() {
|
||||
return false;
|
||||
|
||||
// for forward/reverse cases we can squeeze num_direction dimension
|
||||
auto axis_1 = ngraph::opset4::Constant::create(ngraph::element::i64, ngraph::Shape{1}, {1});
|
||||
auto in_1 = std::make_shared<ngraph::opset4::Squeeze>(lstm_sequence->input_value(1), axis_1);
|
||||
auto in_2 = std::make_shared<ngraph::opset4::Squeeze>(lstm_sequence->input_value(2), axis_1);
|
||||
auto concat = std::make_shared<ngraph::opset4::Concat>(ngraph::OutputVector{W, R}, 2);
|
||||
auto axis_2 = ngraph::opset4::Constant::create(ngraph::element::i64, ngraph::Shape{1}, {0});
|
||||
auto in_3 = std::make_shared<ngraph::opset4::Squeeze>(concat->output(0), axis_2);
|
||||
auto in_4 = std::make_shared<ngraph::opset4::Squeeze>(lstm_sequence->input_value(6), axis_2);
|
||||
auto axis_1 = ngraph::opset5::Constant::create(ngraph::element::i64, ngraph::Shape{1}, {1});
|
||||
auto in_1 = std::make_shared<ngraph::opset5::Squeeze>(lstm_sequence->input_value(1), axis_1);
|
||||
auto in_2 = std::make_shared<ngraph::opset5::Squeeze>(lstm_sequence->input_value(2), axis_1);
|
||||
auto concat = std::make_shared<ngraph::opset5::Concat>(ngraph::OutputVector{W, R}, 2);
|
||||
auto axis_2 = ngraph::opset5::Constant::create(ngraph::element::i64, ngraph::Shape{1}, {0});
|
||||
auto in_3 = std::make_shared<ngraph::opset5::Squeeze>(concat->output(0), axis_2);
|
||||
auto in_4 = std::make_shared<ngraph::opset5::Squeeze>(lstm_sequence->input_value(6), axis_2);
|
||||
auto lstm_sequence_ie = std::make_shared<ngraph::op::LSTMSequenceIE>(
|
||||
lstm_sequence->input(0).get_source_output(), // X
|
||||
in_1, // initial_hidden_state
|
||||
@ -52,10 +52,10 @@ ngraph::pass::ConvertLSTMSequenceMatcher::ConvertLSTMSequenceMatcher() {
|
||||
lstm_sequence->get_activations_beta(),
|
||||
lstm_sequence->get_clip());
|
||||
|
||||
auto unsqueeze_axis = ngraph::opset4::Constant::create(ngraph::element::i64, ngraph::Shape{1}, {1});
|
||||
auto unsqueeze_1 = std::make_shared<ngraph::opset4::Unsqueeze>(lstm_sequence_ie->output(0), unsqueeze_axis);
|
||||
auto unsqueeze_2 = std::make_shared<ngraph::opset4::Unsqueeze>(lstm_sequence_ie->output(1), unsqueeze_axis);
|
||||
auto unsqueeze_3 = std::make_shared<ngraph::opset4::Unsqueeze>(lstm_sequence_ie->output(2), unsqueeze_axis);
|
||||
auto unsqueeze_axis = ngraph::opset5::Constant::create(ngraph::element::i64, ngraph::Shape{1}, {1});
|
||||
auto unsqueeze_1 = std::make_shared<ngraph::opset5::Unsqueeze>(lstm_sequence_ie->output(0), unsqueeze_axis);
|
||||
auto unsqueeze_2 = std::make_shared<ngraph::opset5::Unsqueeze>(lstm_sequence_ie->output(1), unsqueeze_axis);
|
||||
auto unsqueeze_3 = std::make_shared<ngraph::opset5::Unsqueeze>(lstm_sequence_ie->output(2), unsqueeze_axis);
|
||||
|
||||
ngraph::copy_runtime_info(lstm_sequence, {concat, lstm_sequence_ie, in_1, in_2, in_3, in_4, unsqueeze_1,
|
||||
unsqueeze_2, unsqueeze_3});
|
||||
@ -71,10 +71,10 @@ ngraph::pass::ConvertLSTMSequenceMatcher::ConvertLSTMSequenceMatcher() {
|
||||
}
|
||||
|
||||
ngraph::pass::ConvertGRUSequenceMatcher::ConvertGRUSequenceMatcher() {
|
||||
auto gru_sequence_ngraph = ngraph::pattern::wrap_type<ngraph::op::v5::GRUSequence>();
|
||||
auto gru_sequence_ngraph = ngraph::pattern::wrap_type<ngraph::opset5::GRUSequence>();
|
||||
|
||||
ngraph::matcher_pass_callback callback = [](pattern::Matcher &m) {
|
||||
auto gru_sequence = std::dynamic_pointer_cast<ngraph::op::v5::GRUSequence>(m.get_match_root());
|
||||
auto gru_sequence = std::dynamic_pointer_cast<ngraph::opset5::GRUSequence>(m.get_match_root());
|
||||
if (!gru_sequence) {
|
||||
return false;
|
||||
}
|
||||
@ -87,12 +87,12 @@ ngraph::pass::ConvertGRUSequenceMatcher::ConvertGRUSequenceMatcher() {
|
||||
return false;
|
||||
|
||||
// for forward/reverse cases we can squeeze num_direction dimension
|
||||
auto axis_1 = ngraph::opset4::Constant::create(ngraph::element::i64, ngraph::Shape{1}, {1});
|
||||
auto in_1 = std::make_shared<ngraph::opset4::Squeeze>(gru_sequence->input_value(1), axis_1);
|
||||
auto concat = std::make_shared<ngraph::opset4::Concat>(ngraph::OutputVector{W, R}, 2);
|
||||
auto axis_2 = ngraph::opset4::Constant::create(ngraph::element::i64, ngraph::Shape{1}, {0});
|
||||
auto in_3 = std::make_shared<ngraph::opset4::Squeeze>(concat->output(0), axis_2);
|
||||
auto in_4 = std::make_shared<ngraph::opset4::Squeeze>(gru_sequence->input_value(5), axis_2);
|
||||
auto axis_1 = ngraph::opset5::Constant::create(ngraph::element::i64, ngraph::Shape{1}, {1});
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||||
auto in_1 = std::make_shared<ngraph::opset5::Squeeze>(gru_sequence->input_value(1), axis_1);
|
||||
auto concat = std::make_shared<ngraph::opset5::Concat>(ngraph::OutputVector{W, R}, 2);
|
||||
auto axis_2 = ngraph::opset5::Constant::create(ngraph::element::i64, ngraph::Shape{1}, {0});
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||||
auto in_3 = std::make_shared<ngraph::opset5::Squeeze>(concat->output(0), axis_2);
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||||
auto in_4 = std::make_shared<ngraph::opset5::Squeeze>(gru_sequence->input_value(5), axis_2);
|
||||
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||||
auto gru_sequence_ie = std::make_shared<ngraph::op::GRUSequenceIE>(
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||||
gru_sequence->input_value(0), // X
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||||
@ -108,9 +108,9 @@ ngraph::pass::ConvertGRUSequenceMatcher::ConvertGRUSequenceMatcher() {
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||||
gru_sequence->get_clip(),
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||||
gru_sequence->get_linear_before_reset());
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||||
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||||
auto unsqueeze_axis = ngraph::opset4::Constant::create(ngraph::element::i64, ngraph::Shape{1}, {1});
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auto unsqueeze_1 = std::make_shared<ngraph::opset4::Unsqueeze>(gru_sequence_ie->output(0), unsqueeze_axis);
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auto unsqueeze_2 = std::make_shared<ngraph::opset4::Unsqueeze>(gru_sequence_ie->output(1), unsqueeze_axis);
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auto unsqueeze_axis = ngraph::opset5::Constant::create(ngraph::element::i64, ngraph::Shape{1}, {1});
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auto unsqueeze_1 = std::make_shared<ngraph::opset5::Unsqueeze>(gru_sequence_ie->output(0), unsqueeze_axis);
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auto unsqueeze_2 = std::make_shared<ngraph::opset5::Unsqueeze>(gru_sequence_ie->output(1), unsqueeze_axis);
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||||
ngraph::copy_runtime_info(gru_sequence, {concat, gru_sequence_ie, unsqueeze_1, unsqueeze_2, in_1, in_3, in_4});
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unsqueeze_1->set_friendly_name(gru_sequence->get_friendly_name()+".0");
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@ -124,10 +124,10 @@ ngraph::pass::ConvertGRUSequenceMatcher::ConvertGRUSequenceMatcher() {
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||||
}
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||||
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||||
ngraph::pass::ConvertRNNSequenceMatcher::ConvertRNNSequenceMatcher() {
|
||||
auto rnn_sequence_ngraph = ngraph::pattern::wrap_type<ngraph::op::v5::RNNSequence>();
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||||
auto rnn_sequence_ngraph = ngraph::pattern::wrap_type<ngraph::opset5::RNNSequence>();
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||||
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||||
ngraph::matcher_pass_callback callback = [](pattern::Matcher &m) {
|
||||
auto rnn_sequence = std::dynamic_pointer_cast<ngraph::op::v5::RNNSequence>(m.get_match_root());
|
||||
auto rnn_sequence = std::dynamic_pointer_cast<ngraph::opset5::RNNSequence>(m.get_match_root());
|
||||
if (!rnn_sequence) {
|
||||
return false;
|
||||
}
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||||
@ -140,12 +140,12 @@ ngraph::pass::ConvertRNNSequenceMatcher::ConvertRNNSequenceMatcher() {
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||||
auto R = rnn_sequence->input_value(4);
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||||
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||||
// for forward/reverse cases we can squeeze num_direction dimension
|
||||
auto axis_1 = ngraph::opset4::Constant::create(ngraph::element::i64, ngraph::Shape{1}, {1});
|
||||
auto in_1 = std::make_shared<ngraph::opset4::Squeeze>(rnn_sequence->input_value(1), axis_1);
|
||||
auto concat = std::make_shared<ngraph::opset4::Concat>(ngraph::OutputVector{W, R}, 2);
|
||||
auto axis_2 = ngraph::opset4::Constant::create(ngraph::element::i64, ngraph::Shape{1}, {0});
|
||||
auto in_3 = std::make_shared<ngraph::opset4::Squeeze>(concat->output(0), axis_2);
|
||||
auto in_4 = std::make_shared<ngraph::opset4::Squeeze>(rnn_sequence->input_value(5), axis_2);
|
||||
auto axis_1 = ngraph::opset5::Constant::create(ngraph::element::i64, ngraph::Shape{1}, {1});
|
||||
auto in_1 = std::make_shared<ngraph::opset5::Squeeze>(rnn_sequence->input_value(1), axis_1);
|
||||
auto concat = std::make_shared<ngraph::opset5::Concat>(ngraph::OutputVector{W, R}, 2);
|
||||
auto axis_2 = ngraph::opset5::Constant::create(ngraph::element::i64, ngraph::Shape{1}, {0});
|
||||
auto in_3 = std::make_shared<ngraph::opset5::Squeeze>(concat->output(0), axis_2);
|
||||
auto in_4 = std::make_shared<ngraph::opset5::Squeeze>(rnn_sequence->input_value(5), axis_2);
|
||||
auto rnn_sequence_ie = std::make_shared<ngraph::op::RNNSequenceIE>(
|
||||
rnn_sequence->input_value(0), // X
|
||||
in_1, // initial_hidden_state
|
||||
@ -159,9 +159,9 @@ ngraph::pass::ConvertRNNSequenceMatcher::ConvertRNNSequenceMatcher() {
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||||
rnn_sequence->get_activations_beta(),
|
||||
rnn_sequence->get_clip());
|
||||
|
||||
auto unsqueeze_axis = ngraph::opset4::Constant::create(ngraph::element::i64, ngraph::Shape{1}, {1});
|
||||
auto unsqueeze_1 = std::make_shared<ngraph::opset4::Unsqueeze>(rnn_sequence_ie->output(0), unsqueeze_axis);
|
||||
auto unsqueeze_2 = std::make_shared<ngraph::opset4::Unsqueeze>(rnn_sequence_ie->output(1), unsqueeze_axis);
|
||||
auto unsqueeze_axis = ngraph::opset5::Constant::create(ngraph::element::i64, ngraph::Shape{1}, {1});
|
||||
auto unsqueeze_1 = std::make_shared<ngraph::opset5::Unsqueeze>(rnn_sequence_ie->output(0), unsqueeze_axis);
|
||||
auto unsqueeze_2 = std::make_shared<ngraph::opset5::Unsqueeze>(rnn_sequence_ie->output(1), unsqueeze_axis);
|
||||
|
||||
ngraph::copy_runtime_info(rnn_sequence, {concat, rnn_sequence_ie, in_1, in_3, in_4, unsqueeze_1,
|
||||
unsqueeze_2});
|
||||
|
||||
@ -10,7 +10,7 @@
|
||||
|
||||
#include <ngraph/node.hpp>
|
||||
#include <ngraph/pass/manager.hpp>
|
||||
#include <ngraph/opsets/opset4.hpp>
|
||||
#include <ngraph/opsets/opset5.hpp>
|
||||
#include <ngraph/rt_info.hpp>
|
||||
#include <ngraph/graph_util.hpp>
|
||||
#include <ngraph/specialize_function.hpp>
|
||||
@ -18,28 +18,28 @@
|
||||
|
||||
ngraph::pass::ConvertTensorIteratorToLSTMSequence::ConvertTensorIteratorToLSTMSequence() {
|
||||
auto tensor_iterator = std::make_shared<ngraph::pattern::op::Label>(ngraph::element::f32,
|
||||
ngraph::Shape{}, ngraph::pattern::has_class<ngraph::opset4::TensorIterator>());
|
||||
ngraph::Shape{}, ngraph::pattern::has_class<ngraph::opset5::TensorIterator>());
|
||||
ngraph::matcher_pass_callback callback = [this](pattern::Matcher &m) {
|
||||
auto ti = std::dynamic_pointer_cast<ngraph::opset4::TensorIterator>(m.get_match_root());
|
||||
auto ti = std::dynamic_pointer_cast<ngraph::opset5::TensorIterator>(m.get_match_root());
|
||||
if (!ti || !m_transformation_callback(ti))
|
||||
return false;
|
||||
|
||||
// create pattern
|
||||
auto data = std::make_shared<ngraph::opset4::Parameter>(ngraph::element::f32, ngraph::Shape{1, 1, 1});
|
||||
auto axis_squeeze = std::make_shared<ngraph::opset4::Constant>(ngraph::element::i64, ngraph::Shape{1}, 1);
|
||||
auto data = std::make_shared<ngraph::opset5::Parameter>(ngraph::element::f32, ngraph::Shape{1, 1, 1});
|
||||
auto axis_squeeze = std::make_shared<ngraph::opset5::Constant>(ngraph::element::i64, ngraph::Shape{1}, 1);
|
||||
|
||||
auto input_data = std::make_shared<ngraph::opset4::Squeeze>(data, axis_squeeze);
|
||||
auto input_H_state = std::make_shared<ngraph::opset4::Parameter>(ngraph::element::f32, ngraph::Shape{1, 1});
|
||||
auto input_C_state = std::make_shared<ngraph::opset4::Parameter>(ngraph::element::f32, ngraph::Shape{1, 1});
|
||||
auto input_W = std::make_shared<ngraph::opset4::Constant>(ngraph::element::f32, ngraph::Shape{4, 1});
|
||||
auto input_R = std::make_shared<ngraph::opset4::Constant>(ngraph::element::f32, ngraph::Shape{4, 1});
|
||||
auto input_B = std::make_shared<ngraph::opset4::Constant>(ngraph::element::f32, ngraph::Shape{4});
|
||||
auto input_data = std::make_shared<ngraph::opset5::Squeeze>(data, axis_squeeze);
|
||||
auto input_H_state = std::make_shared<ngraph::opset5::Parameter>(ngraph::element::f32, ngraph::Shape{1, 1});
|
||||
auto input_C_state = std::make_shared<ngraph::opset5::Parameter>(ngraph::element::f32, ngraph::Shape{1, 1});
|
||||
auto input_W = std::make_shared<ngraph::opset5::Constant>(ngraph::element::f32, ngraph::Shape{4, 1});
|
||||
auto input_R = std::make_shared<ngraph::opset5::Constant>(ngraph::element::f32, ngraph::Shape{4, 1});
|
||||
auto input_B = std::make_shared<ngraph::opset5::Constant>(ngraph::element::f32, ngraph::Shape{4});
|
||||
|
||||
auto cell = std::make_shared<ngraph::opset4::LSTMCell>(input_data, input_H_state, input_C_state,
|
||||
auto cell = std::make_shared<ngraph::opset5::LSTMCell>(input_data, input_H_state, input_C_state,
|
||||
input_W, input_R, input_B, 1);
|
||||
|
||||
auto axis_unsqueeze = std::make_shared<ngraph::opset4::Constant>(ngraph::element::i64, ngraph::Shape{1}, 1);
|
||||
auto unsqueeze = std::make_shared<ngraph::opset4::Unsqueeze>(cell, axis_unsqueeze);
|
||||
auto axis_unsqueeze = std::make_shared<ngraph::opset5::Constant>(ngraph::element::i64, ngraph::Shape{1}, 1);
|
||||
auto unsqueeze = std::make_shared<ngraph::opset5::Unsqueeze>(cell, axis_unsqueeze);
|
||||
ngraph::pattern::Matcher matcher(unsqueeze);
|
||||
|
||||
bool match = false;
|
||||
@ -57,7 +57,7 @@ ngraph::pass::ConvertTensorIteratorToLSTMSequence::ConvertTensorIteratorToLSTMSe
|
||||
auto pattern_map = matcher.get_pattern_map();
|
||||
|
||||
auto params = func->get_parameters();
|
||||
std::vector<std::shared_ptr<ngraph::opset4::TensorIterator::InputDescription>> ordered_in_descs(3);
|
||||
std::vector<std::shared_ptr<ngraph::opset5::TensorIterator::InputDescription>> ordered_in_descs(3);
|
||||
int64_t stride = 0, slice_axis = 0;
|
||||
size_t batch_size = 0;
|
||||
for (const auto& input_desc : ti->get_input_descriptions()) {
|
||||
@ -68,7 +68,7 @@ ngraph::pass::ConvertTensorIteratorToLSTMSequence::ConvertTensorIteratorToLSTMSe
|
||||
return false;
|
||||
}
|
||||
auto slice_input
|
||||
= std::dynamic_pointer_cast<ngraph::opset4::TensorIterator::SliceInputDescription>(input_desc);
|
||||
= std::dynamic_pointer_cast<ngraph::opset5::TensorIterator::SliceInputDescription>(input_desc);
|
||||
if (!slice_input)
|
||||
return false;
|
||||
|
||||
@ -90,12 +90,12 @@ ngraph::pass::ConvertTensorIteratorToLSTMSequence::ConvertTensorIteratorToLSTMSe
|
||||
}
|
||||
|
||||
auto results = func->get_results();
|
||||
std::vector<std::shared_ptr<ngraph::opset4::TensorIterator::OutputDescription>> ordered_out_descs(3);
|
||||
std::vector<std::shared_ptr<ngraph::opset5::TensorIterator::OutputDescription>> ordered_out_descs(3);
|
||||
for (const auto& output_desc : ti->get_output_descriptions()) {
|
||||
std::shared_ptr<opset4::Result> res = results[output_desc->m_body_value_index];
|
||||
std::shared_ptr<opset5::Result> res = results[output_desc->m_body_value_index];
|
||||
if (res->get_input_source_output(0) == pattern_map[unsqueeze]) {
|
||||
auto concat_output
|
||||
= std::dynamic_pointer_cast<ngraph::opset4::TensorIterator::ConcatOutputDescription>(output_desc);
|
||||
= std::dynamic_pointer_cast<ngraph::opset5::TensorIterator::ConcatOutputDescription>(output_desc);
|
||||
if (!concat_output)
|
||||
return false;
|
||||
|
||||
@ -110,22 +110,22 @@ ngraph::pass::ConvertTensorIteratorToLSTMSequence::ConvertTensorIteratorToLSTMSe
|
||||
}
|
||||
}
|
||||
|
||||
auto seq_lengths = ngraph::opset4::Constant::create(element::i32, Shape{batch_size}, {ti->get_num_iterations()});
|
||||
const auto& lstm_cell = std::dynamic_pointer_cast<ngraph::opset4::LSTMCell>(pattern_map[cell]);
|
||||
auto seq_lengths = ngraph::opset5::Constant::create(element::i32, Shape{batch_size}, {ti->get_num_iterations()});
|
||||
const auto& lstm_cell = std::dynamic_pointer_cast<ngraph::opset5::LSTMCell>(pattern_map[cell]);
|
||||
auto in_0 = ti->input_values()[ordered_in_descs[0]->m_input_index];
|
||||
if (slice_axis == 0) {
|
||||
auto order = ngraph::opset4::Constant::create(ngraph::element::i64, ngraph::Shape{3}, {1, 0, 2});
|
||||
in_0 = std::make_shared<ngraph::opset4::Transpose>(ti->input_values()[ordered_in_descs[0]->m_input_index], order);
|
||||
auto order = ngraph::opset5::Constant::create(ngraph::element::i64, ngraph::Shape{3}, {1, 0, 2});
|
||||
in_0 = std::make_shared<ngraph::opset5::Transpose>(ti->input_values()[ordered_in_descs[0]->m_input_index], order);
|
||||
}
|
||||
auto axis_1 = ngraph::opset4::Constant::create(ngraph::element::i64, ngraph::Shape{1}, {1});
|
||||
auto in_1 = std::make_shared<ngraph::opset4::Unsqueeze>(ti->input_values()[ordered_in_descs[1]->m_input_index], axis_1);
|
||||
auto in_2 = std::make_shared<ngraph::opset4::Unsqueeze>(ti->input_values()[ordered_in_descs[2]->m_input_index], axis_1);
|
||||
auto axis_1 = ngraph::opset5::Constant::create(ngraph::element::i64, ngraph::Shape{1}, {1});
|
||||
auto in_1 = std::make_shared<ngraph::opset5::Unsqueeze>(ti->input_values()[ordered_in_descs[1]->m_input_index], axis_1);
|
||||
auto in_2 = std::make_shared<ngraph::opset5::Unsqueeze>(ti->input_values()[ordered_in_descs[2]->m_input_index], axis_1);
|
||||
|
||||
auto axis_2 = ngraph::opset4::Constant::create(ngraph::element::i64, ngraph::Shape{1}, {0});
|
||||
auto in_4 = std::make_shared<ngraph::opset4::Unsqueeze>(pattern_map[input_W]->output(0).get_node_shared_ptr(), axis_2);
|
||||
auto in_5 = std::make_shared<ngraph::opset4::Unsqueeze>(pattern_map[input_R]->output(0).get_node_shared_ptr(), axis_2);
|
||||
auto in_6 = std::make_shared<ngraph::opset4::Unsqueeze>(pattern_map[input_B]->output(0).get_node_shared_ptr(), axis_2);
|
||||
auto sequence = std::make_shared<op::v5::LSTMSequence>(
|
||||
auto axis_2 = ngraph::opset5::Constant::create(ngraph::element::i64, ngraph::Shape{1}, {0});
|
||||
auto in_4 = std::make_shared<ngraph::opset5::Unsqueeze>(pattern_map[input_W]->output(0).get_node_shared_ptr(), axis_2);
|
||||
auto in_5 = std::make_shared<ngraph::opset5::Unsqueeze>(pattern_map[input_R]->output(0).get_node_shared_ptr(), axis_2);
|
||||
auto in_6 = std::make_shared<ngraph::opset5::Unsqueeze>(pattern_map[input_B]->output(0).get_node_shared_ptr(), axis_2);
|
||||
auto sequence = std::make_shared<opset5::LSTMSequence>(
|
||||
in_0,
|
||||
in_1,
|
||||
in_2,
|
||||
@ -140,15 +140,15 @@ ngraph::pass::ConvertTensorIteratorToLSTMSequence::ConvertTensorIteratorToLSTMSe
|
||||
lstm_cell->get_activations(),
|
||||
lstm_cell->get_clip());
|
||||
|
||||
auto axis_out = ngraph::opset4::Constant::create(ngraph::element::i64, ngraph::Shape{1}, {1});
|
||||
auto out_0 = std::make_shared<ngraph::opset4::Squeeze>(sequence->output(0), axis_out);
|
||||
auto out_1 = std::make_shared<ngraph::opset4::Squeeze>(sequence->output(1), axis_out);
|
||||
auto out_2 = std::make_shared<ngraph::opset4::Squeeze>(sequence->output(2), axis_out);
|
||||
auto axis_out = ngraph::opset5::Constant::create(ngraph::element::i64, ngraph::Shape{1}, {1});
|
||||
auto out_0 = std::make_shared<ngraph::opset5::Squeeze>(sequence->output(0), axis_out);
|
||||
auto out_1 = std::make_shared<ngraph::opset5::Squeeze>(sequence->output(1), axis_out);
|
||||
auto out_2 = std::make_shared<ngraph::opset5::Squeeze>(sequence->output(2), axis_out);
|
||||
|
||||
std::shared_ptr<Node> out = out_0;
|
||||
if (slice_axis == 0) {
|
||||
auto order = ngraph::opset4::Constant::create(ngraph::element::i64, ngraph::Shape{3}, {1, 0, 2});
|
||||
out = std::make_shared<ngraph::opset4::Transpose>(out_0, order);
|
||||
auto order = ngraph::opset5::Constant::create(ngraph::element::i64, ngraph::Shape{3}, {1, 0, 2});
|
||||
out = std::make_shared<ngraph::opset5::Transpose>(out_0, order);
|
||||
}
|
||||
|
||||
ngraph::NodeVector outputs = {out, out_1, out_2};
|
||||
@ -176,26 +176,26 @@ ngraph::pass::ConvertTensorIteratorToLSTMSequence::ConvertTensorIteratorToLSTMSe
|
||||
|
||||
ngraph::pass::ConvertTensorIteratorToRNNSequence::ConvertTensorIteratorToRNNSequence() {
|
||||
auto tensor_iterator = std::make_shared<ngraph::pattern::op::Label>(ngraph::element::f32,
|
||||
ngraph::Shape{}, ngraph::pattern::has_class<ngraph::opset4::TensorIterator>());
|
||||
ngraph::Shape{}, ngraph::pattern::has_class<ngraph::opset5::TensorIterator>());
|
||||
ngraph::matcher_pass_callback callback = [this](pattern::Matcher &m) {
|
||||
auto ti = std::dynamic_pointer_cast<ngraph::opset4::TensorIterator>(m.get_match_root());
|
||||
auto ti = std::dynamic_pointer_cast<ngraph::opset5::TensorIterator>(m.get_match_root());
|
||||
if (!ti || !m_transformation_callback(ti))
|
||||
return false;
|
||||
|
||||
// create pattern
|
||||
auto data = std::make_shared<ngraph::opset4::Parameter>(ngraph::element::f32, ngraph::Shape{1, 1, 1});
|
||||
auto axis_squeeze = std::make_shared<ngraph::opset4::Constant>(ngraph::element::i64, ngraph::Shape{1}, 0);
|
||||
auto input_data = std::make_shared<ngraph::opset4::Squeeze>(data, axis_squeeze);
|
||||
auto data = std::make_shared<ngraph::opset5::Parameter>(ngraph::element::f32, ngraph::Shape{1, 1, 1});
|
||||
auto axis_squeeze = std::make_shared<ngraph::opset5::Constant>(ngraph::element::i64, ngraph::Shape{1}, 0);
|
||||
auto input_data = std::make_shared<ngraph::opset5::Squeeze>(data, axis_squeeze);
|
||||
|
||||
auto input_H_state = std::make_shared<ngraph::opset4::Parameter>(ngraph::element::f32, ngraph::Shape{1, 1});
|
||||
auto input_W = std::make_shared<ngraph::opset4::Constant>(ngraph::element::f32, ngraph::Shape{1, 1});
|
||||
auto input_R = std::make_shared<ngraph::opset4::Constant>(ngraph::element::f32, ngraph::Shape{1, 1});
|
||||
auto input_B = std::make_shared<ngraph::opset4::Constant>(ngraph::element::f32, ngraph::Shape{1});
|
||||
auto input_H_state = std::make_shared<ngraph::opset5::Parameter>(ngraph::element::f32, ngraph::Shape{1, 1});
|
||||
auto input_W = std::make_shared<ngraph::opset5::Constant>(ngraph::element::f32, ngraph::Shape{1, 1});
|
||||
auto input_R = std::make_shared<ngraph::opset5::Constant>(ngraph::element::f32, ngraph::Shape{1, 1});
|
||||
auto input_B = std::make_shared<ngraph::opset5::Constant>(ngraph::element::f32, ngraph::Shape{1});
|
||||
|
||||
auto cell = std::make_shared<ngraph::opset4::RNNCell>(input_data, input_H_state, input_W, input_R, input_B, 1);
|
||||
auto cell = std::make_shared<ngraph::opset5::RNNCell>(input_data, input_H_state, input_W, input_R, input_B, 1);
|
||||
|
||||
auto axis_unsqueeze = std::make_shared<ngraph::opset4::Constant>(ngraph::element::i64, ngraph::Shape{1}, 0);
|
||||
auto unsqueeze = std::make_shared<ngraph::opset4::Unsqueeze>(cell, axis_unsqueeze);
|
||||
auto axis_unsqueeze = std::make_shared<ngraph::opset5::Constant>(ngraph::element::i64, ngraph::Shape{1}, 0);
|
||||
auto unsqueeze = std::make_shared<ngraph::opset5::Unsqueeze>(cell, axis_unsqueeze);
|
||||
ngraph::pattern::Matcher matcher(unsqueeze);
|
||||
|
||||
bool match = false;
|
||||
@ -213,7 +213,7 @@ ngraph::pass::ConvertTensorIteratorToRNNSequence::ConvertTensorIteratorToRNNSequ
|
||||
auto pattern_map = matcher.get_pattern_map();
|
||||
|
||||
auto params = func->get_parameters();
|
||||
std::vector<std::shared_ptr<ngraph::opset4::TensorIterator::InputDescription>> ordered_in_descs(3);
|
||||
std::vector<std::shared_ptr<ngraph::opset5::TensorIterator::InputDescription>> ordered_in_descs(3);
|
||||
int64_t stride = 0, slice_axis = 0;
|
||||
size_t batch_size = 0;
|
||||
for (const auto& input_desc : ti->get_input_descriptions()) {
|
||||
@ -224,7 +224,7 @@ ngraph::pass::ConvertTensorIteratorToRNNSequence::ConvertTensorIteratorToRNNSequ
|
||||
return false;
|
||||
}
|
||||
auto slice_input
|
||||
= std::dynamic_pointer_cast<ngraph::opset4::TensorIterator::SliceInputDescription>(input_desc);
|
||||
= std::dynamic_pointer_cast<ngraph::opset5::TensorIterator::SliceInputDescription>(input_desc);
|
||||
if (!slice_input)
|
||||
return false;
|
||||
|
||||
@ -242,15 +242,15 @@ ngraph::pass::ConvertTensorIteratorToRNNSequence::ConvertTensorIteratorToRNNSequ
|
||||
}
|
||||
}
|
||||
|
||||
auto seq_lengths = ngraph::opset4::Constant::create(element::i32, Shape{batch_size}, {ti->get_num_iterations()});
|
||||
auto seq_lengths = ngraph::opset5::Constant::create(element::i32, Shape{batch_size}, {ti->get_num_iterations()});
|
||||
|
||||
auto results = func->get_results();
|
||||
std::vector<std::shared_ptr<ngraph::opset4::TensorIterator::OutputDescription>> ordered_out_descs(2);
|
||||
std::vector<std::shared_ptr<ngraph::opset5::TensorIterator::OutputDescription>> ordered_out_descs(2);
|
||||
for (const auto& output_desc : ti->get_output_descriptions()) {
|
||||
std::shared_ptr<opset4::Result> res = results[output_desc->m_body_value_index];
|
||||
std::shared_ptr<opset5::Result> res = results[output_desc->m_body_value_index];
|
||||
if (res->get_input_source_output(0) == pattern_map[unsqueeze]) {
|
||||
auto concat_output
|
||||
= std::dynamic_pointer_cast<ngraph::opset4::TensorIterator::ConcatOutputDescription>(output_desc);
|
||||
= std::dynamic_pointer_cast<ngraph::opset5::TensorIterator::ConcatOutputDescription>(output_desc);
|
||||
if (!concat_output)
|
||||
return false;
|
||||
|
||||
@ -263,22 +263,22 @@ ngraph::pass::ConvertTensorIteratorToRNNSequence::ConvertTensorIteratorToRNNSequ
|
||||
}
|
||||
}
|
||||
|
||||
const auto& rnn_cell = std::dynamic_pointer_cast<ngraph::opset4::RNNCell>(pattern_map[cell]);
|
||||
const auto& rnn_cell = std::dynamic_pointer_cast<ngraph::opset5::RNNCell>(pattern_map[cell]);
|
||||
|
||||
auto in_0 = ti->input_values()[ordered_in_descs[0]->m_input_index];
|
||||
if (slice_axis == 0) {
|
||||
auto order = ngraph::opset4::Constant::create(ngraph::element::i64, ngraph::Shape{3}, {1, 0, 2});
|
||||
in_0 = std::make_shared<ngraph::opset4::Transpose>(ti->input_values()[ordered_in_descs[0]->m_input_index], order);
|
||||
auto order = ngraph::opset5::Constant::create(ngraph::element::i64, ngraph::Shape{3}, {1, 0, 2});
|
||||
in_0 = std::make_shared<ngraph::opset5::Transpose>(ti->input_values()[ordered_in_descs[0]->m_input_index], order);
|
||||
}
|
||||
|
||||
auto axis_1 = ngraph::opset4::Constant::create(ngraph::element::i64, ngraph::Shape{1}, {1});
|
||||
auto in_1 = std::make_shared<ngraph::opset4::Unsqueeze>(ti->input_values()[ordered_in_descs[1]->m_input_index], axis_1);
|
||||
auto axis_1 = ngraph::opset5::Constant::create(ngraph::element::i64, ngraph::Shape{1}, {1});
|
||||
auto in_1 = std::make_shared<ngraph::opset5::Unsqueeze>(ti->input_values()[ordered_in_descs[1]->m_input_index], axis_1);
|
||||
|
||||
auto axis_2 = ngraph::opset4::Constant::create(ngraph::element::i64, ngraph::Shape{1}, {0});
|
||||
auto in_3 = std::make_shared<ngraph::opset4::Unsqueeze>(pattern_map[input_W]->output(0).get_node_shared_ptr(), axis_2);
|
||||
auto in_4 = std::make_shared<ngraph::opset4::Unsqueeze>(pattern_map[input_R]->output(0).get_node_shared_ptr(), axis_2);
|
||||
auto in_5 = std::make_shared<ngraph::opset4::Unsqueeze>(pattern_map[input_B]->output(0).get_node_shared_ptr(), axis_2);
|
||||
auto sequence = std::make_shared<op::v5::RNNSequence>(
|
||||
auto axis_2 = ngraph::opset5::Constant::create(ngraph::element::i64, ngraph::Shape{1}, {0});
|
||||
auto in_3 = std::make_shared<ngraph::opset5::Unsqueeze>(pattern_map[input_W]->output(0).get_node_shared_ptr(), axis_2);
|
||||
auto in_4 = std::make_shared<ngraph::opset5::Unsqueeze>(pattern_map[input_R]->output(0).get_node_shared_ptr(), axis_2);
|
||||
auto in_5 = std::make_shared<ngraph::opset5::Unsqueeze>(pattern_map[input_B]->output(0).get_node_shared_ptr(), axis_2);
|
||||
auto sequence = std::make_shared<opset5::RNNSequence>(
|
||||
in_0,
|
||||
in_1,
|
||||
seq_lengths,
|
||||
@ -292,14 +292,14 @@ ngraph::pass::ConvertTensorIteratorToRNNSequence::ConvertTensorIteratorToRNNSequ
|
||||
rnn_cell->get_activations_beta(),
|
||||
rnn_cell->get_clip());
|
||||
|
||||
auto axis_out = ngraph::opset4::Constant::create(ngraph::element::i64, ngraph::Shape{1}, {1});
|
||||
auto out_0 = std::make_shared<ngraph::opset4::Squeeze>(sequence->output(0), axis_out);
|
||||
auto out_1 = std::make_shared<ngraph::opset4::Squeeze>(sequence->output(1), axis_out);
|
||||
auto axis_out = ngraph::opset5::Constant::create(ngraph::element::i64, ngraph::Shape{1}, {1});
|
||||
auto out_0 = std::make_shared<ngraph::opset5::Squeeze>(sequence->output(0), axis_out);
|
||||
auto out_1 = std::make_shared<ngraph::opset5::Squeeze>(sequence->output(1), axis_out);
|
||||
|
||||
std::shared_ptr<Node> out = out_0;
|
||||
if (slice_axis == 0) {
|
||||
auto order = ngraph::opset4::Constant::create(ngraph::element::i64, ngraph::Shape{3}, {1, 0, 2});
|
||||
out = std::make_shared<ngraph::opset4::Transpose>(out_0, order);
|
||||
auto order = ngraph::opset5::Constant::create(ngraph::element::i64, ngraph::Shape{3}, {1, 0, 2});
|
||||
out = std::make_shared<ngraph::opset5::Transpose>(out_0, order);
|
||||
}
|
||||
|
||||
ngraph::NodeVector outputs = {out, out_1};
|
||||
@ -327,26 +327,26 @@ ngraph::pass::ConvertTensorIteratorToRNNSequence::ConvertTensorIteratorToRNNSequ
|
||||
|
||||
ngraph::pass::ConvertTensorIteratorToGRUSequence::ConvertTensorIteratorToGRUSequence() {
|
||||
auto tensor_iterator = std::make_shared<ngraph::pattern::op::Label>(ngraph::element::f32,
|
||||
ngraph::Shape{}, ngraph::pattern::has_class<ngraph::opset4::TensorIterator>());
|
||||
ngraph::Shape{}, ngraph::pattern::has_class<ngraph::opset5::TensorIterator>());
|
||||
ngraph::matcher_pass_callback callback = [this](pattern::Matcher &m) {
|
||||
auto ti = std::dynamic_pointer_cast<ngraph::opset4::TensorIterator>(m.get_match_root());
|
||||
auto ti = std::dynamic_pointer_cast<ngraph::opset5::TensorIterator>(m.get_match_root());
|
||||
if (!ti || !m_transformation_callback(ti))
|
||||
return false;
|
||||
|
||||
// create pattern
|
||||
auto data = std::make_shared<ngraph::opset4::Parameter>(ngraph::element::f32, ngraph::Shape{1, 1, 1});
|
||||
auto axis_squeeze = std::make_shared<ngraph::opset4::Constant>(ngraph::element::i64, ngraph::Shape{1}, 0);
|
||||
auto input_data = std::make_shared<ngraph::opset4::Squeeze>(data, axis_squeeze);
|
||||
auto data = std::make_shared<ngraph::opset5::Parameter>(ngraph::element::f32, ngraph::Shape{1, 1, 1});
|
||||
auto axis_squeeze = std::make_shared<ngraph::opset5::Constant>(ngraph::element::i64, ngraph::Shape{1}, 0);
|
||||
auto input_data = std::make_shared<ngraph::opset5::Squeeze>(data, axis_squeeze);
|
||||
|
||||
auto input_H_state = std::make_shared<ngraph::opset4::Parameter>(ngraph::element::f32, ngraph::Shape{1, 1});
|
||||
auto input_W = std::make_shared<ngraph::opset4::Constant>(ngraph::element::f32, ngraph::Shape{3, 1});
|
||||
auto input_R = std::make_shared<ngraph::opset4::Constant>(ngraph::element::f32, ngraph::Shape{3, 1});
|
||||
auto input_B = std::make_shared<ngraph::opset4::Constant>(ngraph::element::f32, ngraph::Shape{3});
|
||||
auto input_H_state = std::make_shared<ngraph::opset5::Parameter>(ngraph::element::f32, ngraph::Shape{1, 1});
|
||||
auto input_W = std::make_shared<ngraph::opset5::Constant>(ngraph::element::f32, ngraph::Shape{3, 1});
|
||||
auto input_R = std::make_shared<ngraph::opset5::Constant>(ngraph::element::f32, ngraph::Shape{3, 1});
|
||||
auto input_B = std::make_shared<ngraph::opset5::Constant>(ngraph::element::f32, ngraph::Shape{3});
|
||||
|
||||
auto cell = std::make_shared<ngraph::opset4::GRUCell>(input_data, input_H_state, input_W, input_R, input_B, 1);
|
||||
auto cell = std::make_shared<ngraph::opset5::GRUCell>(input_data, input_H_state, input_W, input_R, input_B, 1);
|
||||
|
||||
auto axis_unsqueeze = std::make_shared<ngraph::opset4::Constant>(ngraph::element::i64, ngraph::Shape{1}, 0);
|
||||
auto unsqueeze = std::make_shared<ngraph::opset4::Unsqueeze>(cell, axis_unsqueeze);
|
||||
auto axis_unsqueeze = std::make_shared<ngraph::opset5::Constant>(ngraph::element::i64, ngraph::Shape{1}, 0);
|
||||
auto unsqueeze = std::make_shared<ngraph::opset5::Unsqueeze>(cell, axis_unsqueeze);
|
||||
ngraph::pattern::Matcher matcher(unsqueeze);
|
||||
|
||||
bool match = false;
|
||||
@ -364,7 +364,7 @@ ngraph::pass::ConvertTensorIteratorToGRUSequence::ConvertTensorIteratorToGRUSequ
|
||||
auto pattern_map = matcher.get_pattern_map();
|
||||
|
||||
auto params = func->get_parameters();
|
||||
std::vector<std::shared_ptr<ngraph::opset4::TensorIterator::InputDescription>> ordered_in_descs(3);
|
||||
std::vector<std::shared_ptr<ngraph::opset5::TensorIterator::InputDescription>> ordered_in_descs(3);
|
||||
int64_t stride = 0, slice_axis = 0;
|
||||
size_t batch_size = 0;
|
||||
for (const auto& input_desc : ti->get_input_descriptions()) {
|
||||
@ -375,7 +375,7 @@ ngraph::pass::ConvertTensorIteratorToGRUSequence::ConvertTensorIteratorToGRUSequ
|
||||
return false;
|
||||
}
|
||||
auto slice_input
|
||||
= std::dynamic_pointer_cast<ngraph::opset4::TensorIterator::SliceInputDescription>(input_desc);
|
||||
= std::dynamic_pointer_cast<ngraph::opset5::TensorIterator::SliceInputDescription>(input_desc);
|
||||
if (!slice_input)
|
||||
return false;
|
||||
|
||||
@ -393,15 +393,15 @@ ngraph::pass::ConvertTensorIteratorToGRUSequence::ConvertTensorIteratorToGRUSequ
|
||||
}
|
||||
}
|
||||
|
||||
auto seq_lengths = ngraph::opset4::Constant::create(element::i32, Shape{batch_size}, {ti->get_num_iterations()});
|
||||
auto seq_lengths = ngraph::opset5::Constant::create(element::i32, Shape{batch_size}, {ti->get_num_iterations()});
|
||||
|
||||
auto results = func->get_results();
|
||||
std::vector<std::shared_ptr<ngraph::opset4::TensorIterator::OutputDescription>> ordered_out_descs(2);
|
||||
std::vector<std::shared_ptr<ngraph::opset5::TensorIterator::OutputDescription>> ordered_out_descs(2);
|
||||
for (const auto& output_desc : ti->get_output_descriptions()) {
|
||||
std::shared_ptr<opset4::Result> res = results[output_desc->m_body_value_index];
|
||||
std::shared_ptr<opset5::Result> res = results[output_desc->m_body_value_index];
|
||||
if (res->get_input_source_output(0) == pattern_map[unsqueeze]) {
|
||||
auto concat_output
|
||||
= std::dynamic_pointer_cast<ngraph::opset4::TensorIterator::ConcatOutputDescription>(output_desc);
|
||||
= std::dynamic_pointer_cast<ngraph::opset5::TensorIterator::ConcatOutputDescription>(output_desc);
|
||||
if (!concat_output)
|
||||
return false;
|
||||
|
||||
@ -414,22 +414,22 @@ ngraph::pass::ConvertTensorIteratorToGRUSequence::ConvertTensorIteratorToGRUSequ
|
||||
}
|
||||
}
|
||||
|
||||
const auto& rnn_cell = std::dynamic_pointer_cast<ngraph::opset4::GRUCell>(pattern_map[cell]);
|
||||
const auto& rnn_cell = std::dynamic_pointer_cast<ngraph::opset5::GRUCell>(pattern_map[cell]);
|
||||
|
||||
auto in_0 = ti->input_values()[ordered_in_descs[0]->m_input_index];
|
||||
if (slice_axis == 0) {
|
||||
auto order = ngraph::opset4::Constant::create(ngraph::element::i64, ngraph::Shape{3}, {1, 0, 2});
|
||||
in_0 = std::make_shared<ngraph::opset4::Transpose>(ti->input_values()[ordered_in_descs[0]->m_input_index], order);
|
||||
auto order = ngraph::opset5::Constant::create(ngraph::element::i64, ngraph::Shape{3}, {1, 0, 2});
|
||||
in_0 = std::make_shared<ngraph::opset5::Transpose>(ti->input_values()[ordered_in_descs[0]->m_input_index], order);
|
||||
}
|
||||
|
||||
auto axis_1 = ngraph::opset4::Constant::create(ngraph::element::i64, ngraph::Shape{1}, {1});
|
||||
auto in_1 = std::make_shared<ngraph::opset4::Unsqueeze>(ti->input_values()[ordered_in_descs[1]->m_input_index], axis_1);
|
||||
auto axis_1 = ngraph::opset5::Constant::create(ngraph::element::i64, ngraph::Shape{1}, {1});
|
||||
auto in_1 = std::make_shared<ngraph::opset5::Unsqueeze>(ti->input_values()[ordered_in_descs[1]->m_input_index], axis_1);
|
||||
|
||||
auto axis_2 = ngraph::opset4::Constant::create(ngraph::element::i64, ngraph::Shape{1}, {0});
|
||||
auto in_3 = std::make_shared<ngraph::opset4::Unsqueeze>(pattern_map[input_W]->output(0).get_node_shared_ptr(), axis_2);
|
||||
auto in_4 = std::make_shared<ngraph::opset4::Unsqueeze>(pattern_map[input_R]->output(0).get_node_shared_ptr(), axis_2);
|
||||
auto in_5 = std::make_shared<ngraph::opset4::Unsqueeze>(pattern_map[input_B]->output(0).get_node_shared_ptr(), axis_2);
|
||||
auto sequence = std::make_shared<op::v5::GRUSequence>(
|
||||
auto axis_2 = ngraph::opset5::Constant::create(ngraph::element::i64, ngraph::Shape{1}, {0});
|
||||
auto in_3 = std::make_shared<ngraph::opset5::Unsqueeze>(pattern_map[input_W]->output(0).get_node_shared_ptr(), axis_2);
|
||||
auto in_4 = std::make_shared<ngraph::opset5::Unsqueeze>(pattern_map[input_R]->output(0).get_node_shared_ptr(), axis_2);
|
||||
auto in_5 = std::make_shared<ngraph::opset5::Unsqueeze>(pattern_map[input_B]->output(0).get_node_shared_ptr(), axis_2);
|
||||
auto sequence = std::make_shared<opset5::GRUSequence>(
|
||||
in_0,
|
||||
in_1,
|
||||
seq_lengths,
|
||||
@ -444,14 +444,14 @@ ngraph::pass::ConvertTensorIteratorToGRUSequence::ConvertTensorIteratorToGRUSequ
|
||||
rnn_cell->get_clip(),
|
||||
rnn_cell->get_linear_before_reset());
|
||||
|
||||
auto axis_out = ngraph::opset4::Constant::create(ngraph::element::i64, ngraph::Shape{1}, {1});
|
||||
auto out_0 = std::make_shared<ngraph::opset4::Squeeze>(sequence->output(0), axis_out);
|
||||
auto out_1 = std::make_shared<ngraph::opset4::Squeeze>(sequence->output(1), axis_out);
|
||||
auto axis_out = ngraph::opset5::Constant::create(ngraph::element::i64, ngraph::Shape{1}, {1});
|
||||
auto out_0 = std::make_shared<ngraph::opset5::Squeeze>(sequence->output(0), axis_out);
|
||||
auto out_1 = std::make_shared<ngraph::opset5::Squeeze>(sequence->output(1), axis_out);
|
||||
|
||||
std::shared_ptr<Node> out = out_0;
|
||||
if (slice_axis == 0) {
|
||||
auto order = ngraph::opset4::Constant::create(ngraph::element::i64, ngraph::Shape{3}, {1, 0, 2});
|
||||
out = std::make_shared<ngraph::opset4::Transpose>(out_0, order);
|
||||
auto order = ngraph::opset5::Constant::create(ngraph::element::i64, ngraph::Shape{3}, {1, 0, 2});
|
||||
out = std::make_shared<ngraph::opset5::Transpose>(out_0, order);
|
||||
}
|
||||
|
||||
ngraph::NodeVector outputs = {out, out_1};
|
||||
|
||||
@ -11,7 +11,7 @@
|
||||
#include <transformations/convert_opset1_to_legacy/convert_sequences_to_sequences_ie.hpp>
|
||||
|
||||
#include <ngraph/ops.hpp>
|
||||
#include <ngraph/opsets/opset4.hpp>
|
||||
#include <ngraph/opsets/opset5.hpp>
|
||||
#include <ngraph/function.hpp>
|
||||
#include <ngraph_ops/gru_sequence_ie.hpp>
|
||||
#include <ngraph_ops/rnn_sequence_ie.hpp>
|
||||
@ -25,7 +25,7 @@ using namespace testing;
|
||||
|
||||
TEST(TransformationTests, GRUSequenceConversionTest) {
|
||||
std::shared_ptr<ngraph::Function> f(nullptr), f_ref(nullptr);
|
||||
std::shared_ptr<ngraph::op::v5::GRUSequence> sequence;
|
||||
std::shared_ptr<ngraph::opset5::GRUSequence> sequence;
|
||||
|
||||
const size_t batch_size = 2;
|
||||
const size_t input_size = 3;
|
||||
@ -33,21 +33,21 @@ TEST(TransformationTests, GRUSequenceConversionTest) {
|
||||
const size_t gates_count = 3;
|
||||
const size_t num_directions = 1;
|
||||
{
|
||||
const auto X = std::make_shared<ngraph::opset4::Parameter>(ngraph::element::f32,
|
||||
const auto X = std::make_shared<ngraph::opset5::Parameter>(ngraph::element::f32,
|
||||
ngraph::Shape{batch_size, 1, input_size});
|
||||
const auto W =
|
||||
std::make_shared<ngraph::opset4::Constant>(ngraph::element::f32,
|
||||
std::make_shared<ngraph::opset5::Constant>(ngraph::element::f32,
|
||||
ngraph::Shape{num_directions, gates_count * hidden_size, input_size});
|
||||
const auto R =
|
||||
std::make_shared<ngraph::opset4::Constant>(ngraph::element::f32,
|
||||
std::make_shared<ngraph::opset5::Constant>(ngraph::element::f32,
|
||||
ngraph::Shape{num_directions, gates_count * hidden_size, hidden_size});
|
||||
const auto H_t = std::make_shared<ngraph::opset4::Parameter>(ngraph::element::f32,
|
||||
const auto H_t = std::make_shared<ngraph::opset5::Parameter>(ngraph::element::f32,
|
||||
ngraph::Shape{batch_size, num_directions, hidden_size});
|
||||
const auto B = std::make_shared<ngraph::opset4::Constant>(ngraph::element::f32,
|
||||
const auto B = std::make_shared<ngraph::opset5::Constant>(ngraph::element::f32,
|
||||
ngraph::Shape{num_directions, gates_count * hidden_size});
|
||||
|
||||
const auto seq_len = std::make_shared<ngraph::opset4::Constant>(ngraph::element::i32, ngraph::Shape{batch_size});
|
||||
sequence = std::make_shared<ngraph::op::v5::GRUSequence>(X, H_t, seq_len, W, R, B, hidden_size,
|
||||
const auto seq_len = std::make_shared<ngraph::opset5::Constant>(ngraph::element::i32, ngraph::Shape{batch_size});
|
||||
sequence = std::make_shared<ngraph::opset5::GRUSequence>(X, H_t, seq_len, W, R, B, hidden_size,
|
||||
ngraph::op::RecurrentSequenceDirection::FORWARD);
|
||||
sequence->set_friendly_name("test_sequence");
|
||||
|
||||
@ -60,26 +60,26 @@ TEST(TransformationTests, GRUSequenceConversionTest) {
|
||||
}
|
||||
|
||||
{
|
||||
const auto X = std::make_shared<ngraph::opset4::Parameter>(ngraph::element::f32,
|
||||
const auto X = std::make_shared<ngraph::opset5::Parameter>(ngraph::element::f32,
|
||||
ngraph::Shape{batch_size, 1, input_size});
|
||||
const auto W =
|
||||
std::make_shared<ngraph::opset4::Constant>(ngraph::element::f32,
|
||||
std::make_shared<ngraph::opset5::Constant>(ngraph::element::f32,
|
||||
ngraph::Shape{num_directions, gates_count * hidden_size, input_size});
|
||||
const auto R =
|
||||
std::make_shared<ngraph::opset4::Constant>(ngraph::element::f32,
|
||||
std::make_shared<ngraph::opset5::Constant>(ngraph::element::f32,
|
||||
ngraph::Shape{num_directions, gates_count * hidden_size, hidden_size});
|
||||
const auto H_t = std::make_shared<ngraph::opset4::Parameter>(ngraph::element::f32,
|
||||
const auto H_t = std::make_shared<ngraph::opset5::Parameter>(ngraph::element::f32,
|
||||
ngraph::Shape{batch_size, num_directions, hidden_size});
|
||||
const auto B = std::make_shared<ngraph::opset4::Constant>(ngraph::element::f32,
|
||||
const auto B = std::make_shared<ngraph::opset5::Constant>(ngraph::element::f32,
|
||||
ngraph::Shape{num_directions, gates_count * hidden_size});
|
||||
|
||||
const auto seq_len = std::make_shared<ngraph::opset4::Constant>(ngraph::element::i32, ngraph::Shape{batch_size}, 1);
|
||||
auto axis_1 = ngraph::opset4::Constant::create(ngraph::element::i64, ngraph::Shape{1}, {1});
|
||||
auto in_1 = std::make_shared<ngraph::opset4::Squeeze>(H_t, axis_1);
|
||||
auto concat = std::make_shared<ngraph::opset4::Concat>(ngraph::NodeVector({W, R}), 2);
|
||||
auto axis_2 = ngraph::opset4::Constant::create(ngraph::element::i64, ngraph::Shape{1}, {0});
|
||||
auto in_3 = std::make_shared<ngraph::opset4::Squeeze>(concat->output(0), axis_2);
|
||||
auto in_4 = std::make_shared<ngraph::opset4::Squeeze>(B, axis_2);
|
||||
const auto seq_len = std::make_shared<ngraph::opset5::Constant>(ngraph::element::i32, ngraph::Shape{batch_size}, 1);
|
||||
auto axis_1 = ngraph::opset5::Constant::create(ngraph::element::i64, ngraph::Shape{1}, {1});
|
||||
auto in_1 = std::make_shared<ngraph::opset5::Squeeze>(H_t, axis_1);
|
||||
auto concat = std::make_shared<ngraph::opset5::Concat>(ngraph::NodeVector({W, R}), 2);
|
||||
auto axis_2 = ngraph::opset5::Constant::create(ngraph::element::i64, ngraph::Shape{1}, {0});
|
||||
auto in_3 = std::make_shared<ngraph::opset5::Squeeze>(concat->output(0), axis_2);
|
||||
auto in_4 = std::make_shared<ngraph::opset5::Squeeze>(B, axis_2);
|
||||
auto sequence_ie = std::make_shared<ngraph::op::GRUSequenceIE>(X,
|
||||
in_1,
|
||||
seq_len, // this input is not supported
|
||||
@ -94,9 +94,9 @@ TEST(TransformationTests, GRUSequenceConversionTest) {
|
||||
sequence->get_linear_before_reset());
|
||||
sequence_ie->set_friendly_name("test_sequence");
|
||||
|
||||
auto unsqueeze_axis = ngraph::opset4::Constant::create(ngraph::element::i64, ngraph::Shape{1}, {1});
|
||||
auto unsqueeze_1 = std::make_shared<ngraph::opset4::Unsqueeze>(sequence_ie->output(0), unsqueeze_axis);
|
||||
auto unsqueeze_2 = std::make_shared<ngraph::opset4::Unsqueeze>(sequence_ie->output(1), unsqueeze_axis);
|
||||
auto unsqueeze_axis = ngraph::opset5::Constant::create(ngraph::element::i64, ngraph::Shape{1}, {1});
|
||||
auto unsqueeze_1 = std::make_shared<ngraph::opset5::Unsqueeze>(sequence_ie->output(0), unsqueeze_axis);
|
||||
auto unsqueeze_2 = std::make_shared<ngraph::opset5::Unsqueeze>(sequence_ie->output(1), unsqueeze_axis);
|
||||
f_ref = std::make_shared<ngraph::Function>(ngraph::NodeVector{unsqueeze_1}, ngraph::ParameterVector{X, H_t});
|
||||
}
|
||||
auto res = compare_functions(f, f_ref);
|
||||
@ -112,16 +112,16 @@ TEST(TransformationTests, RNNSequenceConversionTest) {
|
||||
const size_t num_directions = 1;
|
||||
const size_t batch_size = 2;
|
||||
std::shared_ptr<ngraph::Function> f(nullptr), f_ref(nullptr);
|
||||
std::shared_ptr<ngraph::op::v5::RNNSequence> sequence;
|
||||
std::shared_ptr<ngraph::opset5::RNNSequence> sequence;
|
||||
|
||||
{
|
||||
auto X = std::make_shared<ngraph::opset4::Parameter>(ngraph::element::f32, ngraph::Shape{batch_size, 1, 3});
|
||||
auto H = std::make_shared<ngraph::opset4::Parameter>(ngraph::element::f32, ngraph::Shape{batch_size, num_directions, 3});
|
||||
auto W = std::make_shared<ngraph::opset4::Constant>(ngraph::element::f32, ngraph::Shape{num_directions, 3, 3});
|
||||
auto R = std::make_shared<ngraph::opset4::Constant>(ngraph::element::f32, ngraph::Shape{num_directions, 3, 3});
|
||||
auto B = std::make_shared<ngraph::opset4::Constant>(ngraph::element::f32, ngraph::Shape{num_directions, 3});
|
||||
auto seq_len = std::make_shared<ngraph::opset4::Constant>(ngraph::element::f32, ngraph::Shape{2});
|
||||
sequence = std::make_shared<ngraph::op::v5::RNNSequence>(X, H, seq_len, W, R, B, hidden_size,
|
||||
auto X = std::make_shared<ngraph::opset5::Parameter>(ngraph::element::f32, ngraph::Shape{batch_size, 1, 3});
|
||||
auto H = std::make_shared<ngraph::opset5::Parameter>(ngraph::element::f32, ngraph::Shape{batch_size, num_directions, 3});
|
||||
auto W = std::make_shared<ngraph::opset5::Constant>(ngraph::element::f32, ngraph::Shape{num_directions, 3, 3});
|
||||
auto R = std::make_shared<ngraph::opset5::Constant>(ngraph::element::f32, ngraph::Shape{num_directions, 3, 3});
|
||||
auto B = std::make_shared<ngraph::opset5::Constant>(ngraph::element::f32, ngraph::Shape{num_directions, 3});
|
||||
auto seq_len = std::make_shared<ngraph::opset5::Constant>(ngraph::element::f32, ngraph::Shape{2});
|
||||
sequence = std::make_shared<ngraph::opset5::RNNSequence>(X, H, seq_len, W, R, B, hidden_size,
|
||||
ngraph::op::RecurrentSequenceDirection::FORWARD);
|
||||
sequence->set_friendly_name("test_sequence");
|
||||
|
||||
@ -134,18 +134,18 @@ TEST(TransformationTests, RNNSequenceConversionTest) {
|
||||
}
|
||||
|
||||
{
|
||||
auto X = std::make_shared<ngraph::opset4::Parameter>(ngraph::element::f32, ngraph::Shape{batch_size, 1, 3});
|
||||
auto H = std::make_shared<ngraph::opset4::Parameter>(ngraph::element::f32, ngraph::Shape{batch_size, num_directions, 3});
|
||||
auto W = std::make_shared<ngraph::opset4::Constant>(ngraph::element::f32, ngraph::Shape{num_directions, 3, 3});
|
||||
auto R = std::make_shared<ngraph::opset4::Constant>(ngraph::element::f32, ngraph::Shape{num_directions, 3, 3});
|
||||
auto B = std::make_shared<ngraph::opset4::Constant>(ngraph::element::f32, ngraph::Shape{num_directions, 3});
|
||||
auto seq_len = std::make_shared<ngraph::opset4::Constant>(ngraph::element::f32, ngraph::Shape{batch_size}, 1);
|
||||
auto axis_1 = ngraph::opset4::Constant::create(ngraph::element::i64, ngraph::Shape{1}, {1});
|
||||
auto in_1 = std::make_shared<ngraph::opset4::Squeeze>(H, axis_1);
|
||||
auto concat = std::make_shared<ngraph::opset4::Concat>(ngraph::NodeVector({W, R}), 2);
|
||||
auto axis_2 = ngraph::opset4::Constant::create(ngraph::element::i64, ngraph::Shape{1}, {0});
|
||||
auto in_3 = std::make_shared<ngraph::opset4::Squeeze>(concat->output(0), axis_2);
|
||||
auto in_4 = std::make_shared<ngraph::opset4::Squeeze>(B, axis_2);
|
||||
auto X = std::make_shared<ngraph::opset5::Parameter>(ngraph::element::f32, ngraph::Shape{batch_size, 1, 3});
|
||||
auto H = std::make_shared<ngraph::opset5::Parameter>(ngraph::element::f32, ngraph::Shape{batch_size, num_directions, 3});
|
||||
auto W = std::make_shared<ngraph::opset5::Constant>(ngraph::element::f32, ngraph::Shape{num_directions, 3, 3});
|
||||
auto R = std::make_shared<ngraph::opset5::Constant>(ngraph::element::f32, ngraph::Shape{num_directions, 3, 3});
|
||||
auto B = std::make_shared<ngraph::opset5::Constant>(ngraph::element::f32, ngraph::Shape{num_directions, 3});
|
||||
auto seq_len = std::make_shared<ngraph::opset5::Constant>(ngraph::element::f32, ngraph::Shape{batch_size}, 1);
|
||||
auto axis_1 = ngraph::opset5::Constant::create(ngraph::element::i64, ngraph::Shape{1}, {1});
|
||||
auto in_1 = std::make_shared<ngraph::opset5::Squeeze>(H, axis_1);
|
||||
auto concat = std::make_shared<ngraph::opset5::Concat>(ngraph::NodeVector({W, R}), 2);
|
||||
auto axis_2 = ngraph::opset5::Constant::create(ngraph::element::i64, ngraph::Shape{1}, {0});
|
||||
auto in_3 = std::make_shared<ngraph::opset5::Squeeze>(concat->output(0), axis_2);
|
||||
auto in_4 = std::make_shared<ngraph::opset5::Squeeze>(B, axis_2);
|
||||
auto sequence_ie = std::make_shared<ngraph::op::RNNSequenceIE>(X,
|
||||
in_1,
|
||||
seq_len,
|
||||
@ -158,9 +158,9 @@ TEST(TransformationTests, RNNSequenceConversionTest) {
|
||||
sequence->get_activations_beta(),
|
||||
sequence->get_clip());
|
||||
|
||||
auto unsqueeze_axis = ngraph::opset4::Constant::create(ngraph::element::i64, ngraph::Shape{1}, {1});
|
||||
auto unsqueeze_1 = std::make_shared<ngraph::opset4::Unsqueeze>(sequence_ie->output(0), unsqueeze_axis);
|
||||
auto unsqueeze_2 = std::make_shared<ngraph::opset4::Unsqueeze>(sequence_ie->output(1), unsqueeze_axis);
|
||||
auto unsqueeze_axis = ngraph::opset5::Constant::create(ngraph::element::i64, ngraph::Shape{1}, {1});
|
||||
auto unsqueeze_1 = std::make_shared<ngraph::opset5::Unsqueeze>(sequence_ie->output(0), unsqueeze_axis);
|
||||
auto unsqueeze_2 = std::make_shared<ngraph::opset5::Unsqueeze>(sequence_ie->output(1), unsqueeze_axis);
|
||||
sequence_ie->set_friendly_name("test_sequence");
|
||||
f_ref = std::make_shared<ngraph::Function>(ngraph::NodeVector{unsqueeze_1}, ngraph::ParameterVector{X, H});
|
||||
}
|
||||
@ -180,28 +180,28 @@ TEST(TransformationTests, LSTMSequenceConversionTest) {
|
||||
const size_t gates_count = 4;
|
||||
const size_t num_directions = 1;
|
||||
std::shared_ptr<ngraph::Function> f(nullptr), f_ref(nullptr);
|
||||
std::shared_ptr<ngraph::op::v5::LSTMSequence> sequence;
|
||||
std::shared_ptr<ngraph::opset5::LSTMSequence> sequence;
|
||||
{
|
||||
const auto X = std::make_shared<ngraph::opset4::Parameter>(ngraph::element::f32,
|
||||
const auto X = std::make_shared<ngraph::opset5::Parameter>(ngraph::element::f32,
|
||||
ngraph::Shape{batch_size, 10, input_size});
|
||||
const auto W =
|
||||
std::make_shared<ngraph::opset4::Constant>(ngraph::element::f32,
|
||||
std::make_shared<ngraph::opset5::Constant>(ngraph::element::f32,
|
||||
ngraph::Shape{num_directions,
|
||||
gates_count * hidden_size, input_size});
|
||||
const auto R =
|
||||
std::make_shared<ngraph::opset4::Constant>(ngraph::element::f32,
|
||||
std::make_shared<ngraph::opset5::Constant>(ngraph::element::f32,
|
||||
ngraph::Shape{num_directions,
|
||||
gates_count * hidden_size, hidden_size});
|
||||
const auto H_t = std::make_shared<ngraph::opset4::Parameter>(ngraph::element::f32,
|
||||
const auto H_t = std::make_shared<ngraph::opset5::Parameter>(ngraph::element::f32,
|
||||
ngraph::Shape{batch_size, num_directions,
|
||||
hidden_size});
|
||||
const auto C_t = std::make_shared<ngraph::opset4::Parameter>(ngraph::element::f32,
|
||||
const auto C_t = std::make_shared<ngraph::opset5::Parameter>(ngraph::element::f32,
|
||||
ngraph::Shape{batch_size, num_directions, hidden_size});
|
||||
const auto B = std::make_shared<ngraph::opset4::Constant>(ngraph::element::f32,
|
||||
const auto B = std::make_shared<ngraph::opset5::Constant>(ngraph::element::f32,
|
||||
ngraph::Shape{num_directions,
|
||||
gates_count * hidden_size});
|
||||
const auto seq_len = std::make_shared<ngraph::opset4::Constant>(ngraph::element::i32, ngraph::Shape{batch_size});
|
||||
sequence = std::make_shared<ngraph::op::v5::LSTMSequence>(X, H_t, C_t, seq_len, W, R, B, hidden_size,
|
||||
const auto seq_len = std::make_shared<ngraph::opset5::Constant>(ngraph::element::i32, ngraph::Shape{batch_size});
|
||||
sequence = std::make_shared<ngraph::opset5::LSTMSequence>(X, H_t, C_t, seq_len, W, R, B, hidden_size,
|
||||
ngraph::op::RecurrentSequenceDirection::FORWARD);
|
||||
sequence->set_friendly_name("test_sequence");
|
||||
|
||||
@ -214,33 +214,33 @@ TEST(TransformationTests, LSTMSequenceConversionTest) {
|
||||
}
|
||||
|
||||
{
|
||||
const auto X = std::make_shared<ngraph::opset4::Parameter>(ngraph::element::f32,
|
||||
const auto X = std::make_shared<ngraph::opset5::Parameter>(ngraph::element::f32,
|
||||
ngraph::Shape{batch_size, 10, input_size});
|
||||
const auto W =
|
||||
std::make_shared<ngraph::opset4::Constant>(ngraph::element::f32,
|
||||
std::make_shared<ngraph::opset5::Constant>(ngraph::element::f32,
|
||||
ngraph::Shape{num_directions,
|
||||
gates_count * hidden_size, input_size});
|
||||
const auto R =
|
||||
std::make_shared<ngraph::opset4::Constant>(ngraph::element::f32,
|
||||
std::make_shared<ngraph::opset5::Constant>(ngraph::element::f32,
|
||||
ngraph::Shape{num_directions,
|
||||
gates_count * hidden_size, hidden_size});
|
||||
const auto H_t = std::make_shared<ngraph::opset4::Parameter>(ngraph::element::f32,
|
||||
const auto H_t = std::make_shared<ngraph::opset5::Parameter>(ngraph::element::f32,
|
||||
ngraph::Shape{batch_size, num_directions, hidden_size});
|
||||
const auto C_t = std::make_shared<ngraph::opset4::Parameter>(ngraph::element::f32,
|
||||
const auto C_t = std::make_shared<ngraph::opset5::Parameter>(ngraph::element::f32,
|
||||
ngraph::Shape{batch_size, num_directions, hidden_size});
|
||||
const auto seq_lenghts = std::make_shared<ngraph::opset4::Constant>(ngraph::element::f32,
|
||||
const auto seq_lenghts = std::make_shared<ngraph::opset5::Constant>(ngraph::element::f32,
|
||||
ngraph::Shape{batch_size});
|
||||
const auto B = std::make_shared<ngraph::opset4::Constant>(ngraph::element::f32,
|
||||
const auto B = std::make_shared<ngraph::opset5::Constant>(ngraph::element::f32,
|
||||
ngraph::Shape{num_directions,
|
||||
gates_count * hidden_size});
|
||||
// const auto seq_len = std::make_shared<ngraph::opset4::Constant>(ngraph::element::i32, ngraph::Shape{1}, 1);
|
||||
auto axis_1 = ngraph::opset4::Constant::create(ngraph::element::i64, ngraph::Shape{1}, {1});
|
||||
auto in_1 = std::make_shared<ngraph::opset4::Squeeze>(H_t, axis_1);
|
||||
auto in_2 = std::make_shared<ngraph::opset4::Squeeze>(C_t, axis_1);
|
||||
auto concat = std::make_shared<ngraph::opset4::Concat>(ngraph::NodeVector({W, R}), 2);
|
||||
auto axis_2 = ngraph::opset4::Constant::create(ngraph::element::i64, ngraph::Shape{1}, {0});
|
||||
auto in_3 = std::make_shared<ngraph::opset4::Squeeze>(concat->output(0), axis_2);
|
||||
auto in_4 = std::make_shared<ngraph::opset4::Squeeze>(B, axis_2);
|
||||
// const auto seq_len = std::make_shared<ngraph::opset5::Constant>(ngraph::element::i32, ngraph::Shape{1}, 1);
|
||||
auto axis_1 = ngraph::opset5::Constant::create(ngraph::element::i64, ngraph::Shape{1}, {1});
|
||||
auto in_1 = std::make_shared<ngraph::opset5::Squeeze>(H_t, axis_1);
|
||||
auto in_2 = std::make_shared<ngraph::opset5::Squeeze>(C_t, axis_1);
|
||||
auto concat = std::make_shared<ngraph::opset5::Concat>(ngraph::NodeVector({W, R}), 2);
|
||||
auto axis_2 = ngraph::opset5::Constant::create(ngraph::element::i64, ngraph::Shape{1}, {0});
|
||||
auto in_3 = std::make_shared<ngraph::opset5::Squeeze>(concat->output(0), axis_2);
|
||||
auto in_4 = std::make_shared<ngraph::opset5::Squeeze>(B, axis_2);
|
||||
auto sequence_ie = std::make_shared<ngraph::op::LSTMSequenceIE>(X,
|
||||
in_1,
|
||||
in_2,
|
||||
@ -254,10 +254,10 @@ TEST(TransformationTests, LSTMSequenceConversionTest) {
|
||||
sequence->get_activations_beta(),
|
||||
sequence->get_clip());
|
||||
sequence_ie->set_friendly_name("test_sequence");
|
||||
auto unsqueeze_axis = ngraph::opset4::Constant::create(ngraph::element::i64, ngraph::Shape{1}, {1});
|
||||
auto unsqueeze_1 = std::make_shared<ngraph::opset4::Unsqueeze>(sequence_ie->output(0), unsqueeze_axis);
|
||||
auto unsqueeze_2 = std::make_shared<ngraph::opset4::Unsqueeze>(sequence_ie->output(1), unsqueeze_axis);
|
||||
auto unsqueeze_3 = std::make_shared<ngraph::opset4::Unsqueeze>(sequence_ie->output(2), unsqueeze_axis);
|
||||
auto unsqueeze_axis = ngraph::opset5::Constant::create(ngraph::element::i64, ngraph::Shape{1}, {1});
|
||||
auto unsqueeze_1 = std::make_shared<ngraph::opset5::Unsqueeze>(sequence_ie->output(0), unsqueeze_axis);
|
||||
auto unsqueeze_2 = std::make_shared<ngraph::opset5::Unsqueeze>(sequence_ie->output(1), unsqueeze_axis);
|
||||
auto unsqueeze_3 = std::make_shared<ngraph::opset5::Unsqueeze>(sequence_ie->output(2), unsqueeze_axis);
|
||||
f_ref = std::make_shared<ngraph::Function>(ngraph::NodeVector{unsqueeze_1},
|
||||
ngraph::ParameterVector{X, H_t, C_t});
|
||||
}
|
||||
|
||||
@ -11,7 +11,7 @@
|
||||
|
||||
#include <ngraph/pass/manager.hpp>
|
||||
#include <ngraph/function.hpp>
|
||||
#include <ngraph/opsets/opset4.hpp>
|
||||
#include <ngraph/opsets/opset5.hpp>
|
||||
#include <ngraph_ops/fully_connected.hpp>
|
||||
#include <transformations/tensor_iterator_transformations/convert_ti_to_sequences.h>
|
||||
#include <transformations/utils/utils.hpp>
|
||||
@ -25,34 +25,34 @@ using namespace ngraph;
|
||||
TEST(TransformationTests, ConvertTensorIteratorToLSTMSequence) {
|
||||
std::shared_ptr<ngraph::Function> f(nullptr), f_ref(nullptr);
|
||||
{
|
||||
auto X = std::make_shared<opset4::Parameter>(element::f32, Shape{1, 2, 16});
|
||||
auto Y = std::make_shared<opset4::Parameter>(element::f32, Shape{1, 128});
|
||||
auto Z = std::make_shared<opset4::Parameter>(element::f32, Shape{1, 128});
|
||||
auto X = std::make_shared<opset5::Parameter>(element::f32, Shape{1, 2, 16});
|
||||
auto Y = std::make_shared<opset5::Parameter>(element::f32, Shape{1, 128});
|
||||
auto Z = std::make_shared<opset5::Parameter>(element::f32, Shape{1, 128});
|
||||
|
||||
auto Xi = std::make_shared<opset4::Parameter>(element::f32, Shape{1, 1, 16});
|
||||
auto Yi = std::make_shared<opset4::Parameter>(element::f32, Shape{1, 128});
|
||||
auto Zi = std::make_shared<opset4::Parameter>(element::f32, Shape{1, 128});
|
||||
auto Xi = std::make_shared<opset5::Parameter>(element::f32, Shape{1, 1, 16});
|
||||
auto Yi = std::make_shared<opset5::Parameter>(element::f32, Shape{1, 128});
|
||||
auto Zi = std::make_shared<opset5::Parameter>(element::f32, Shape{1, 128});
|
||||
|
||||
// Body
|
||||
auto axis = ngraph::opset4::Constant::create(ngraph::element::i64, ngraph::Shape{1}, {1});
|
||||
auto squeeze = std::make_shared<opset4::Squeeze>(Xi, axis);
|
||||
auto axis = ngraph::opset5::Constant::create(ngraph::element::i64, ngraph::Shape{1}, {1});
|
||||
auto squeeze = std::make_shared<opset5::Squeeze>(Xi, axis);
|
||||
|
||||
auto w_val = std::vector<float>(512 * 16, 0);
|
||||
auto r_val = std::vector<float>(512 * 128, 0);
|
||||
auto b_val = std::vector<float>(512, 0);
|
||||
auto W = ngraph::opset4::Constant::create(ngraph::element::f32, ngraph::Shape{512, 16}, w_val);
|
||||
auto R = ngraph::opset4::Constant::create(ngraph::element::f32, ngraph::Shape{512, 128}, r_val);
|
||||
auto B = ngraph::opset4::Constant::create(ngraph::element::f32, ngraph::Shape{512}, b_val);
|
||||
auto W = ngraph::opset5::Constant::create(ngraph::element::f32, ngraph::Shape{512, 16}, w_val);
|
||||
auto R = ngraph::opset5::Constant::create(ngraph::element::f32, ngraph::Shape{512, 128}, r_val);
|
||||
auto B = ngraph::opset5::Constant::create(ngraph::element::f32, ngraph::Shape{512}, b_val);
|
||||
|
||||
auto lstm_cell = std::make_shared<opset4::LSTMCell>(squeeze, Yi, Zi, W, R, B, 128);
|
||||
auto res_1 = std::make_shared<opset4::Result>(lstm_cell);
|
||||
auto axis_unsqueeze = ngraph::opset4::Constant::create(ngraph::element::i64, ngraph::Shape{1}, {1});
|
||||
auto unsqueeze = std::make_shared<opset4::Unsqueeze>(lstm_cell, axis_unsqueeze);
|
||||
auto res_2 = std::make_shared<opset4::Result>(unsqueeze);
|
||||
auto lstm_cell = std::make_shared<opset5::LSTMCell>(squeeze, Yi, Zi, W, R, B, 128);
|
||||
auto res_1 = std::make_shared<opset5::Result>(lstm_cell);
|
||||
auto axis_unsqueeze = ngraph::opset5::Constant::create(ngraph::element::i64, ngraph::Shape{1}, {1});
|
||||
auto unsqueeze = std::make_shared<opset5::Unsqueeze>(lstm_cell, axis_unsqueeze);
|
||||
auto res_2 = std::make_shared<opset5::Result>(unsqueeze);
|
||||
auto body = std::make_shared<Function>(OutputVector{res_1, res_2},
|
||||
ParameterVector{Xi, Yi, Zi});
|
||||
|
||||
auto tensor_iterator = std::make_shared<opset4::TensorIterator>();
|
||||
auto tensor_iterator = std::make_shared<opset5::TensorIterator>();
|
||||
tensor_iterator->set_body(body);
|
||||
|
||||
tensor_iterator->set_invariant_input(Zi, Z);
|
||||
@ -62,8 +62,8 @@ TEST(TransformationTests, ConvertTensorIteratorToLSTMSequence) {
|
||||
auto out0 = tensor_iterator->get_iter_value(res_1, -1);
|
||||
auto out1 = tensor_iterator->get_concatenated_slices(res_2, 0, 1, 1, -1, 1);
|
||||
|
||||
auto res_ti_1 = std::make_shared<opset4::Result>(tensor_iterator->output(1));
|
||||
//auto res_ti_2 = std::make_shared<opset4::Result>(tensor_iterator->output(0));
|
||||
auto res_ti_1 = std::make_shared<opset5::Result>(tensor_iterator->output(1));
|
||||
//auto res_ti_2 = std::make_shared<opset5::Result>(tensor_iterator->output(0));
|
||||
f = std::make_shared<ngraph::Function>(ngraph::NodeVector{res_ti_1},
|
||||
ngraph::ParameterVector{X, Y, Z});
|
||||
|
||||
@ -76,34 +76,34 @@ TEST(TransformationTests, ConvertTensorIteratorToLSTMSequence) {
|
||||
}
|
||||
|
||||
{
|
||||
auto X = std::make_shared<opset4::Parameter>(element::f32, Shape{1, 2, 16});
|
||||
auto Y = std::make_shared<opset4::Parameter>(element::f32, Shape{1, 128});
|
||||
auto Z = std::make_shared<opset4::Parameter>(element::f32, Shape{1, 128});
|
||||
auto X = std::make_shared<opset5::Parameter>(element::f32, Shape{1, 2, 16});
|
||||
auto Y = std::make_shared<opset5::Parameter>(element::f32, Shape{1, 128});
|
||||
auto Z = std::make_shared<opset5::Parameter>(element::f32, Shape{1, 128});
|
||||
|
||||
auto w_val = std::vector<float>(512 * 16, 0);
|
||||
auto r_val = std::vector<float>(512 * 128, 0);
|
||||
auto b_val = std::vector<float>(512, 0);
|
||||
auto W = ngraph::opset4::Constant::create(ngraph::element::f32, ngraph::Shape{512, 16}, w_val);
|
||||
auto R = ngraph::opset4::Constant::create(ngraph::element::f32, ngraph::Shape{512, 128}, r_val);
|
||||
auto B = ngraph::opset4::Constant::create(ngraph::element::f32, ngraph::Shape{512}, b_val);
|
||||
auto W = ngraph::opset5::Constant::create(ngraph::element::f32, ngraph::Shape{512, 16}, w_val);
|
||||
auto R = ngraph::opset5::Constant::create(ngraph::element::f32, ngraph::Shape{512, 128}, r_val);
|
||||
auto B = ngraph::opset5::Constant::create(ngraph::element::f32, ngraph::Shape{512}, b_val);
|
||||
|
||||
auto axis_1 = ngraph::opset4::Constant::create(ngraph::element::i64, ngraph::Shape{1}, {1});
|
||||
auto in_1 = std::make_shared<ngraph::opset4::Unsqueeze>(Y, axis_1);
|
||||
auto in_2 = std::make_shared<ngraph::opset4::Unsqueeze>(Z, axis_1);
|
||||
auto axis_1 = ngraph::opset5::Constant::create(ngraph::element::i64, ngraph::Shape{1}, {1});
|
||||
auto in_1 = std::make_shared<ngraph::opset5::Unsqueeze>(Y, axis_1);
|
||||
auto in_2 = std::make_shared<ngraph::opset5::Unsqueeze>(Z, axis_1);
|
||||
|
||||
auto axis_2 = ngraph::opset4::Constant::create(ngraph::element::i64, ngraph::Shape{1}, {0});
|
||||
auto in_4 = std::make_shared<ngraph::opset4::Unsqueeze>(W, axis_2);
|
||||
auto in_5 = std::make_shared<ngraph::opset4::Unsqueeze>(R, axis_2);
|
||||
auto in_6 = std::make_shared<ngraph::opset4::Unsqueeze>(B, axis_2);
|
||||
auto axis_2 = ngraph::opset5::Constant::create(ngraph::element::i64, ngraph::Shape{1}, {0});
|
||||
auto in_4 = std::make_shared<ngraph::opset5::Unsqueeze>(W, axis_2);
|
||||
auto in_5 = std::make_shared<ngraph::opset5::Unsqueeze>(R, axis_2);
|
||||
auto in_6 = std::make_shared<ngraph::opset5::Unsqueeze>(B, axis_2);
|
||||
|
||||
auto seq_lengths = ngraph::opset4::Constant::create(element::i32, Shape{1}, {2});
|
||||
auto lstm_seq = std::make_shared<op::v5::LSTMSequence>(X, in_1, in_2, seq_lengths, in_4, in_5, in_6,
|
||||
auto seq_lengths = ngraph::opset5::Constant::create(element::i32, Shape{1}, {2});
|
||||
auto lstm_seq = std::make_shared<opset5::LSTMSequence>(X, in_1, in_2, seq_lengths, in_4, in_5, in_6,
|
||||
128, ngraph::op::RecurrentSequenceDirection::FORWARD);
|
||||
auto axis_out = ngraph::opset4::Constant::create(ngraph::element::i64, ngraph::Shape{1}, {1});
|
||||
auto out_0 = std::make_shared<ngraph::opset4::Squeeze>(lstm_seq->output(0), axis_out);
|
||||
auto out_1 = std::make_shared<ngraph::opset4::Squeeze>(lstm_seq->output(1), axis_out);
|
||||
auto out_2 = std::make_shared<ngraph::opset4::Squeeze>(lstm_seq->output(1), axis_out);
|
||||
auto res_ti_1 = std::make_shared<opset4::Result>(out_0);
|
||||
auto axis_out = ngraph::opset5::Constant::create(ngraph::element::i64, ngraph::Shape{1}, {1});
|
||||
auto out_0 = std::make_shared<ngraph::opset5::Squeeze>(lstm_seq->output(0), axis_out);
|
||||
auto out_1 = std::make_shared<ngraph::opset5::Squeeze>(lstm_seq->output(1), axis_out);
|
||||
auto out_2 = std::make_shared<ngraph::opset5::Squeeze>(lstm_seq->output(1), axis_out);
|
||||
auto res_ti_1 = std::make_shared<opset5::Result>(out_0);
|
||||
f_ref = std::make_shared<ngraph::Function>(ngraph::NodeVector{res_ti_1}, ngraph::ParameterVector{X, Y, Z});
|
||||
}
|
||||
|
||||
@ -114,32 +114,32 @@ TEST(TransformationTests, ConvertTensorIteratorToLSTMSequence) {
|
||||
TEST(TransformationTests, ConvertTensorIteratorToRNNSequence) {
|
||||
std::shared_ptr<ngraph::Function> f(nullptr), f_ref(nullptr);
|
||||
{
|
||||
auto X = std::make_shared<opset4::Parameter>(element::f32, Shape{1, 2, 16});
|
||||
auto Y = std::make_shared<opset4::Parameter>(element::f32, Shape{1, 128});
|
||||
auto X = std::make_shared<opset5::Parameter>(element::f32, Shape{1, 2, 16});
|
||||
auto Y = std::make_shared<opset5::Parameter>(element::f32, Shape{1, 128});
|
||||
|
||||
auto Xi = std::make_shared<opset4::Parameter>(element::f32, Shape{1, 1, 16});
|
||||
auto Yi = std::make_shared<opset4::Parameter>(element::f32, Shape{1, 128});
|
||||
auto Xi = std::make_shared<opset5::Parameter>(element::f32, Shape{1, 1, 16});
|
||||
auto Yi = std::make_shared<opset5::Parameter>(element::f32, Shape{1, 128});
|
||||
|
||||
// Body
|
||||
auto axis = ngraph::opset4::Constant::create(ngraph::element::i64, ngraph::Shape{1}, {1});
|
||||
auto squeeze = std::make_shared<opset4::Squeeze>(Xi, axis);
|
||||
auto axis = ngraph::opset5::Constant::create(ngraph::element::i64, ngraph::Shape{1}, {1});
|
||||
auto squeeze = std::make_shared<opset5::Squeeze>(Xi, axis);
|
||||
|
||||
auto w_val = std::vector<float>(128 * 16, 0);
|
||||
auto r_val = std::vector<float>(128 * 128, 0);
|
||||
auto b_val = std::vector<float>(128, 0);
|
||||
auto W = ngraph::opset4::Constant::create(ngraph::element::f32, ngraph::Shape{128, 16}, w_val);
|
||||
auto R = ngraph::opset4::Constant::create(ngraph::element::f32, ngraph::Shape{128, 128}, r_val);
|
||||
auto B = ngraph::opset4::Constant::create(ngraph::element::f32, ngraph::Shape{128}, b_val);
|
||||
auto W = ngraph::opset5::Constant::create(ngraph::element::f32, ngraph::Shape{128, 16}, w_val);
|
||||
auto R = ngraph::opset5::Constant::create(ngraph::element::f32, ngraph::Shape{128, 128}, r_val);
|
||||
auto B = ngraph::opset5::Constant::create(ngraph::element::f32, ngraph::Shape{128}, b_val);
|
||||
|
||||
auto rnn_cell = std::make_shared<opset4::RNNCell>(squeeze, Yi, W, R, B, 128);
|
||||
auto res_1 = std::make_shared<opset4::Result>(rnn_cell);
|
||||
auto axis_unsqueeze = ngraph::opset4::Constant::create(ngraph::element::i64, ngraph::Shape{1}, {1});
|
||||
auto unsqueeze = std::make_shared<opset4::Unsqueeze>(rnn_cell, axis_unsqueeze);
|
||||
auto res_2 = std::make_shared<opset4::Result>(unsqueeze);
|
||||
auto rnn_cell = std::make_shared<opset5::RNNCell>(squeeze, Yi, W, R, B, 128);
|
||||
auto res_1 = std::make_shared<opset5::Result>(rnn_cell);
|
||||
auto axis_unsqueeze = ngraph::opset5::Constant::create(ngraph::element::i64, ngraph::Shape{1}, {1});
|
||||
auto unsqueeze = std::make_shared<opset5::Unsqueeze>(rnn_cell, axis_unsqueeze);
|
||||
auto res_2 = std::make_shared<opset5::Result>(unsqueeze);
|
||||
auto body = std::make_shared<Function>(OutputVector{res_1, res_2},
|
||||
ParameterVector{Xi, Yi});
|
||||
|
||||
auto tensor_iterator = std::make_shared<opset4::TensorIterator>();
|
||||
auto tensor_iterator = std::make_shared<opset5::TensorIterator>();
|
||||
tensor_iterator->set_body(body);
|
||||
|
||||
tensor_iterator->set_sliced_input(Xi, X, 0, 1, 1, -1, 1);
|
||||
@ -148,8 +148,8 @@ TEST(TransformationTests, ConvertTensorIteratorToRNNSequence) {
|
||||
auto out0 = tensor_iterator->get_iter_value(res_1, -1);
|
||||
auto out1 = tensor_iterator->get_concatenated_slices(res_2, 0, 1, 1, -1, 1);
|
||||
|
||||
auto res_ti_1 = std::make_shared<opset4::Result>(tensor_iterator->output(1));
|
||||
//auto res_ti_2 = std::make_shared<opset4::Result>(tensor_iterator->output(0));
|
||||
auto res_ti_1 = std::make_shared<opset5::Result>(tensor_iterator->output(1));
|
||||
//auto res_ti_2 = std::make_shared<opset5::Result>(tensor_iterator->output(0));
|
||||
f = std::make_shared<ngraph::Function>(ngraph::NodeVector{res_ti_1},
|
||||
ngraph::ParameterVector{X, Y});
|
||||
|
||||
@ -162,31 +162,31 @@ TEST(TransformationTests, ConvertTensorIteratorToRNNSequence) {
|
||||
}
|
||||
|
||||
{
|
||||
auto X = std::make_shared<opset4::Parameter>(element::f32, Shape{1, 2, 16});
|
||||
auto Y = std::make_shared<opset4::Parameter>(element::f32, Shape{1, 128});
|
||||
auto X = std::make_shared<opset5::Parameter>(element::f32, Shape{1, 2, 16});
|
||||
auto Y = std::make_shared<opset5::Parameter>(element::f32, Shape{1, 128});
|
||||
|
||||
auto w_val = std::vector<float>(128 * 16, 0);
|
||||
auto r_val = std::vector<float>(128 * 128, 0);
|
||||
auto b_val = std::vector<float>(128, 0);
|
||||
auto W = ngraph::opset4::Constant::create(ngraph::element::f32, ngraph::Shape{128, 16}, w_val);
|
||||
auto R = ngraph::opset4::Constant::create(ngraph::element::f32, ngraph::Shape{128, 128}, r_val);
|
||||
auto B = ngraph::opset4::Constant::create(ngraph::element::f32, ngraph::Shape{128}, b_val);
|
||||
auto W = ngraph::opset5::Constant::create(ngraph::element::f32, ngraph::Shape{128, 16}, w_val);
|
||||
auto R = ngraph::opset5::Constant::create(ngraph::element::f32, ngraph::Shape{128, 128}, r_val);
|
||||
auto B = ngraph::opset5::Constant::create(ngraph::element::f32, ngraph::Shape{128}, b_val);
|
||||
|
||||
auto axis_1 = ngraph::opset4::Constant::create(ngraph::element::i64, ngraph::Shape{1}, {1});
|
||||
auto in_1 = std::make_shared<ngraph::opset4::Unsqueeze>(Y, axis_1);
|
||||
auto axis_1 = ngraph::opset5::Constant::create(ngraph::element::i64, ngraph::Shape{1}, {1});
|
||||
auto in_1 = std::make_shared<ngraph::opset5::Unsqueeze>(Y, axis_1);
|
||||
|
||||
auto axis_2 = ngraph::opset4::Constant::create(ngraph::element::i64, ngraph::Shape{1}, {0});
|
||||
auto in_3 = std::make_shared<ngraph::opset4::Unsqueeze>(W, axis_2);
|
||||
auto in_4 = std::make_shared<ngraph::opset4::Unsqueeze>(R, axis_2);
|
||||
auto in_5 = std::make_shared<ngraph::opset4::Unsqueeze>(B, axis_2);
|
||||
auto axis_2 = ngraph::opset5::Constant::create(ngraph::element::i64, ngraph::Shape{1}, {0});
|
||||
auto in_3 = std::make_shared<ngraph::opset5::Unsqueeze>(W, axis_2);
|
||||
auto in_4 = std::make_shared<ngraph::opset5::Unsqueeze>(R, axis_2);
|
||||
auto in_5 = std::make_shared<ngraph::opset5::Unsqueeze>(B, axis_2);
|
||||
|
||||
auto seq_lengths = ngraph::opset4::Constant::create(element::i32, Shape{1}, {2});
|
||||
auto rnn_sequence = std::make_shared<op::v5::RNNSequence>(X, in_1, seq_lengths, in_3, in_4, in_5,
|
||||
auto seq_lengths = ngraph::opset5::Constant::create(element::i32, Shape{1}, {2});
|
||||
auto rnn_sequence = std::make_shared<opset5::RNNSequence>(X, in_1, seq_lengths, in_3, in_4, in_5,
|
||||
128, ngraph::op::RecurrentSequenceDirection::FORWARD);
|
||||
auto axis_out = ngraph::opset4::Constant::create(ngraph::element::i64, ngraph::Shape{1}, {1});
|
||||
auto out_0 = std::make_shared<ngraph::opset4::Squeeze>(rnn_sequence->output(0), axis_out);
|
||||
auto out_1 = std::make_shared<ngraph::opset4::Squeeze>(rnn_sequence->output(1), axis_out);
|
||||
auto res_ti_1 = std::make_shared<opset4::Result>(out_0);
|
||||
auto axis_out = ngraph::opset5::Constant::create(ngraph::element::i64, ngraph::Shape{1}, {1});
|
||||
auto out_0 = std::make_shared<ngraph::opset5::Squeeze>(rnn_sequence->output(0), axis_out);
|
||||
auto out_1 = std::make_shared<ngraph::opset5::Squeeze>(rnn_sequence->output(1), axis_out);
|
||||
auto res_ti_1 = std::make_shared<opset5::Result>(out_0);
|
||||
f_ref = std::make_shared<ngraph::Function>(ngraph::NodeVector{res_ti_1}, ngraph::ParameterVector{X, Y});
|
||||
}
|
||||
|
||||
@ -197,32 +197,32 @@ TEST(TransformationTests, ConvertTensorIteratorToRNNSequence) {
|
||||
TEST(TransformationTests, ConvertTensorIteratorToGRUSequence) {
|
||||
std::shared_ptr<ngraph::Function> f(nullptr), f_ref(nullptr);
|
||||
{
|
||||
auto X = std::make_shared<opset4::Parameter>(element::f32, Shape{1, 2, 16});
|
||||
auto Y = std::make_shared<opset4::Parameter>(element::f32, Shape{1, 128});
|
||||
auto X = std::make_shared<opset5::Parameter>(element::f32, Shape{1, 2, 16});
|
||||
auto Y = std::make_shared<opset5::Parameter>(element::f32, Shape{1, 128});
|
||||
|
||||
auto Xi = std::make_shared<opset4::Parameter>(element::f32, Shape{1, 1, 16});
|
||||
auto Yi = std::make_shared<opset4::Parameter>(element::f32, Shape{1, 128});
|
||||
auto Xi = std::make_shared<opset5::Parameter>(element::f32, Shape{1, 1, 16});
|
||||
auto Yi = std::make_shared<opset5::Parameter>(element::f32, Shape{1, 128});
|
||||
|
||||
// Body
|
||||
auto axis = ngraph::opset4::Constant::create(ngraph::element::i64, ngraph::Shape{1}, {1});
|
||||
auto squeeze = std::make_shared<opset4::Squeeze>(Xi, axis);
|
||||
auto axis = ngraph::opset5::Constant::create(ngraph::element::i64, ngraph::Shape{1}, {1});
|
||||
auto squeeze = std::make_shared<opset5::Squeeze>(Xi, axis);
|
||||
|
||||
auto w_val = std::vector<float>(384 * 16, 0);
|
||||
auto r_val = std::vector<float>(384 * 128, 0);
|
||||
auto b_val = std::vector<float>(384, 0);
|
||||
auto W = ngraph::opset4::Constant::create(ngraph::element::f32, ngraph::Shape{384, 16}, w_val);
|
||||
auto R = ngraph::opset4::Constant::create(ngraph::element::f32, ngraph::Shape{384, 128}, r_val);
|
||||
auto B = ngraph::opset4::Constant::create(ngraph::element::f32, ngraph::Shape{384}, b_val);
|
||||
auto W = ngraph::opset5::Constant::create(ngraph::element::f32, ngraph::Shape{384, 16}, w_val);
|
||||
auto R = ngraph::opset5::Constant::create(ngraph::element::f32, ngraph::Shape{384, 128}, r_val);
|
||||
auto B = ngraph::opset5::Constant::create(ngraph::element::f32, ngraph::Shape{384}, b_val);
|
||||
|
||||
auto gru_cell = std::make_shared<opset4::GRUCell>(squeeze, Yi, W, R, B, 128);
|
||||
auto res_1 = std::make_shared<opset4::Result>(gru_cell);
|
||||
auto axis_unsqueeze = ngraph::opset4::Constant::create(ngraph::element::i64, ngraph::Shape{1}, {1});
|
||||
auto unsqueeze = std::make_shared<opset4::Unsqueeze>(gru_cell, axis_unsqueeze);
|
||||
auto res_2 = std::make_shared<opset4::Result>(unsqueeze);
|
||||
auto gru_cell = std::make_shared<opset5::GRUCell>(squeeze, Yi, W, R, B, 128);
|
||||
auto res_1 = std::make_shared<opset5::Result>(gru_cell);
|
||||
auto axis_unsqueeze = ngraph::opset5::Constant::create(ngraph::element::i64, ngraph::Shape{1}, {1});
|
||||
auto unsqueeze = std::make_shared<opset5::Unsqueeze>(gru_cell, axis_unsqueeze);
|
||||
auto res_2 = std::make_shared<opset5::Result>(unsqueeze);
|
||||
auto body = std::make_shared<Function>(OutputVector{res_1, res_2},
|
||||
ParameterVector{Xi, Yi});
|
||||
|
||||
auto tensor_iterator = std::make_shared<opset4::TensorIterator>();
|
||||
auto tensor_iterator = std::make_shared<opset5::TensorIterator>();
|
||||
tensor_iterator->set_body(body);
|
||||
|
||||
tensor_iterator->set_sliced_input(Xi, X, 0, 1, 1, -1, 1);
|
||||
@ -231,8 +231,8 @@ TEST(TransformationTests, ConvertTensorIteratorToGRUSequence) {
|
||||
auto out0 = tensor_iterator->get_iter_value(res_1, -1);
|
||||
auto out1 = tensor_iterator->get_concatenated_slices(res_2, 0, 1, 1, -1, 1);
|
||||
|
||||
auto res_ti_1 = std::make_shared<opset4::Result>(tensor_iterator->output(1));
|
||||
//auto res_tRNNCelli_2 = std::make_shared<opset4::Result>(tensor_iterator->output(0));
|
||||
auto res_ti_1 = std::make_shared<opset5::Result>(tensor_iterator->output(1));
|
||||
//auto res_tRNNCelli_2 = std::make_shared<opset5::Result>(tensor_iterator->output(0));
|
||||
f = std::make_shared<ngraph::Function>(ngraph::NodeVector{res_ti_1},
|
||||
ngraph::ParameterVector{X, Y});
|
||||
|
||||
@ -245,31 +245,31 @@ TEST(TransformationTests, ConvertTensorIteratorToGRUSequence) {
|
||||
}
|
||||
|
||||
{
|
||||
auto X = std::make_shared<opset4::Parameter>(element::f32, Shape{1, 2, 16});
|
||||
auto Y = std::make_shared<opset4::Parameter>(element::f32, Shape{1, 128});
|
||||
auto X = std::make_shared<opset5::Parameter>(element::f32, Shape{1, 2, 16});
|
||||
auto Y = std::make_shared<opset5::Parameter>(element::f32, Shape{1, 128});
|
||||
|
||||
auto w_val = std::vector<float>(384 * 16, 0);
|
||||
auto r_val = std::vector<float>(384 * 128, 0);
|
||||
auto b_val = std::vector<float>(384, 0);
|
||||
auto W = ngraph::opset4::Constant::create(ngraph::element::f32, ngraph::Shape{384, 16}, w_val);
|
||||
auto R = ngraph::opset4::Constant::create(ngraph::element::f32, ngraph::Shape{384, 128}, r_val);
|
||||
auto B = ngraph::opset4::Constant::create(ngraph::element::f32, ngraph::Shape{384}, b_val);
|
||||
auto W = ngraph::opset5::Constant::create(ngraph::element::f32, ngraph::Shape{384, 16}, w_val);
|
||||
auto R = ngraph::opset5::Constant::create(ngraph::element::f32, ngraph::Shape{384, 128}, r_val);
|
||||
auto B = ngraph::opset5::Constant::create(ngraph::element::f32, ngraph::Shape{384}, b_val);
|
||||
|
||||
auto axis_1 = ngraph::opset4::Constant::create(ngraph::element::i64, ngraph::Shape{1}, {1});
|
||||
auto in_1 = std::make_shared<ngraph::opset4::Unsqueeze>(Y, axis_1);
|
||||
auto axis_1 = ngraph::opset5::Constant::create(ngraph::element::i64, ngraph::Shape{1}, {1});
|
||||
auto in_1 = std::make_shared<ngraph::opset5::Unsqueeze>(Y, axis_1);
|
||||
|
||||
auto axis_2 = ngraph::opset4::Constant::create(ngraph::element::i64, ngraph::Shape{1}, {0});
|
||||
auto in_3 = std::make_shared<ngraph::opset4::Unsqueeze>(W, axis_2);
|
||||
auto in_4 = std::make_shared<ngraph::opset4::Unsqueeze>(R, axis_2);
|
||||
auto in_5 = std::make_shared<ngraph::opset4::Unsqueeze>(B, axis_2);
|
||||
auto axis_2 = ngraph::opset5::Constant::create(ngraph::element::i64, ngraph::Shape{1}, {0});
|
||||
auto in_3 = std::make_shared<ngraph::opset5::Unsqueeze>(W, axis_2);
|
||||
auto in_4 = std::make_shared<ngraph::opset5::Unsqueeze>(R, axis_2);
|
||||
auto in_5 = std::make_shared<ngraph::opset5::Unsqueeze>(B, axis_2);
|
||||
|
||||
auto seq_lengths = ngraph::opset4::Constant::create(element::i32, Shape{1}, {2});
|
||||
auto gru_sequence = std::make_shared<op::v5::GRUSequence>(X, in_1, seq_lengths, in_3, in_4, in_5,
|
||||
auto seq_lengths = ngraph::opset5::Constant::create(element::i32, Shape{1}, {2});
|
||||
auto gru_sequence = std::make_shared<opset5::GRUSequence>(X, in_1, seq_lengths, in_3, in_4, in_5,
|
||||
128, ngraph::op::RecurrentSequenceDirection::FORWARD);
|
||||
auto axis_out = ngraph::opset4::Constant::create(ngraph::element::i64, ngraph::Shape{1}, {1});
|
||||
auto out_0 = std::make_shared<ngraph::opset4::Squeeze>(gru_sequence->output(0), axis_out);
|
||||
auto out_1 = std::make_shared<ngraph::opset4::Squeeze>(gru_sequence->output(1), axis_out);
|
||||
auto res_ti_1 = std::make_shared<opset4::Result>(out_0);
|
||||
auto axis_out = ngraph::opset5::Constant::create(ngraph::element::i64, ngraph::Shape{1}, {1});
|
||||
auto out_0 = std::make_shared<ngraph::opset5::Squeeze>(gru_sequence->output(0), axis_out);
|
||||
auto out_1 = std::make_shared<ngraph::opset5::Squeeze>(gru_sequence->output(1), axis_out);
|
||||
auto res_ti_1 = std::make_shared<opset5::Result>(out_0);
|
||||
f_ref = std::make_shared<ngraph::Function>(ngraph::NodeVector{res_ti_1}, ngraph::ParameterVector{X, Y});
|
||||
}
|
||||
|
||||
|
||||
@ -11,6 +11,7 @@
|
||||
#include <ngraph/opsets/opset2.hpp>
|
||||
#include <ngraph/opsets/opset3.hpp>
|
||||
#include <ngraph/opsets/opset4.hpp>
|
||||
#include <ngraph/opsets/opset5.hpp>
|
||||
|
||||
#include "ngraph_functions/utils/data_utils.hpp"
|
||||
|
||||
|
||||
@ -31,7 +31,7 @@ std::shared_ptr<ngraph::Node> makeGRU(const OutputVector& in,
|
||||
} else {
|
||||
std::vector<float> lenghts(in[0].get_shape()[0], in[0].get_shape()[1]);
|
||||
auto seq_lenghts = ngraph::builder::makeConstant(in[0].get_element_type(), constants[3], lenghts, false);
|
||||
return std::make_shared<ngraph::op::v5::GRUSequence>(in[0], in[1], seq_lenghts, W, R, B, hidden_size, direction,
|
||||
return std::make_shared<ngraph::opset5::GRUSequence>(in[0], in[1], seq_lenghts, W, R, B, hidden_size, direction,
|
||||
activations, activations_alpha, activations_beta, clip, linear_before_reset);
|
||||
}
|
||||
}
|
||||
|
||||
@ -29,7 +29,7 @@ std::shared_ptr<ngraph::Node> makeLSTM(const std::vector<ngraph::Output<Node>>&
|
||||
} else {
|
||||
std::vector<float> lenghts(in[0].get_shape()[0], in[0].get_shape()[1]);
|
||||
auto seq_lenghts = ngraph::builder::makeConstant(in[0].get_element_type(), constants[3], lenghts, false);
|
||||
return std::make_shared<ngraph::op::v5::LSTMSequence>(in[0], in[1], in[2], seq_lenghts, W, R, B, hidden_size, direction,
|
||||
return std::make_shared<ngraph::opset5::LSTMSequence>(in[0], in[1], in[2], seq_lenghts, W, R, B, hidden_size, direction,
|
||||
activations_alpha, activations_beta, activations, clip);
|
||||
}
|
||||
}
|
||||
|
||||
@ -29,7 +29,7 @@ std::shared_ptr<ngraph::Node> makeRNN(const OutputVector& in,
|
||||
} else {
|
||||
std::vector<float> lenghts(in[0].get_shape()[0], in[0].get_shape()[1]);
|
||||
auto seq_lenghts = ngraph::builder::makeConstant(in[0].get_element_type(), constants[3], lenghts, false);
|
||||
return std::make_shared<ngraph::op::v5::RNNSequence>(in[0], in[1], seq_lenghts, W, R, B, hidden_size, direction,
|
||||
return std::make_shared<ngraph::opset5::RNNSequence>(in[0], in[1], seq_lenghts, W, R, B, hidden_size, direction,
|
||||
activations, activations_alpha, activations_beta, clip);
|
||||
}
|
||||
}
|
||||
|
||||
@ -132,4 +132,5 @@ namespace ngraph
|
||||
const NGRAPH_API OpSet& get_opset2();
|
||||
const NGRAPH_API OpSet& get_opset3();
|
||||
const NGRAPH_API OpSet& get_opset4();
|
||||
const NGRAPH_API OpSet& get_opset5();
|
||||
}
|
||||
|
||||
29
ngraph/core/include/ngraph/opsets/opset5.hpp
Normal file
29
ngraph/core/include/ngraph/opsets/opset5.hpp
Normal file
@ -0,0 +1,29 @@
|
||||
//*****************************************************************************
|
||||
// Copyright 2017-2020 Intel Corporation
|
||||
//
|
||||
// Licensed under the Apache License, Version 2.0 (the "License");
|
||||
// you may not use this file except in compliance with the License.
|
||||
// You may obtain a copy of the License at
|
||||
//
|
||||
// http://www.apache.org/licenses/LICENSE-2.0
|
||||
//
|
||||
// Unless required by applicable law or agreed to in writing, software
|
||||
// distributed under the License is distributed on an "AS IS" BASIS,
|
||||
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
// See the License for the specific language governing permissions and
|
||||
// limitations under the License.
|
||||
//*****************************************************************************
|
||||
|
||||
#pragma once
|
||||
|
||||
#include "ngraph/ops.hpp"
|
||||
|
||||
namespace ngraph
|
||||
{
|
||||
namespace opset5
|
||||
{
|
||||
#define NGRAPH_OP(a, b) using b::a;
|
||||
#include "ngraph/opsets/opset5_tbl.hpp"
|
||||
#undef NGRAPH_OP
|
||||
}
|
||||
}
|
||||
169
ngraph/core/include/ngraph/opsets/opset5_tbl.hpp
Normal file
169
ngraph/core/include/ngraph/opsets/opset5_tbl.hpp
Normal file
@ -0,0 +1,169 @@
|
||||
//*****************************************************************************
|
||||
// Copyright 2017-2020 Intel Corporation
|
||||
//
|
||||
// Licensed under the Apache License, Version 2.0 (the "License");
|
||||
// you may not use this file except in compliance with the License.
|
||||
// You may obtain a copy of the License at
|
||||
//
|
||||
// http://www.apache.org/licenses/LICENSE-2.0
|
||||
//
|
||||
// Unless required by applicable law or agreed to in writing, software
|
||||
// distributed under the License is distributed on an "AS IS" BASIS,
|
||||
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
// See the License for the specific language governing permissions and
|
||||
// limitations under the License.
|
||||
//*****************************************************************************
|
||||
|
||||
#ifndef NGRAPH_OP
|
||||
#warning "NGRAPH_OP not defined"
|
||||
#define NGRAPH_OP(x, y)
|
||||
#endif
|
||||
|
||||
NGRAPH_OP(Abs, ngraph::op::v0)
|
||||
NGRAPH_OP(Acos, ngraph::op::v0)
|
||||
NGRAPH_OP(Add, ngraph::op::v1)
|
||||
NGRAPH_OP(Asin, ngraph::op::v0)
|
||||
NGRAPH_OP(Atan, ngraph::op::v0)
|
||||
NGRAPH_OP(AvgPool, ngraph::op::v1)
|
||||
NGRAPH_OP(BatchNormInference, ngraph::op::v0)
|
||||
NGRAPH_OP(BinaryConvolution, ngraph::op::v1)
|
||||
NGRAPH_OP(Broadcast, ngraph::op::v3)
|
||||
NGRAPH_OP(Bucketize, ngraph::op::v3)
|
||||
NGRAPH_OP(CTCGreedyDecoder, ngraph::op::v0)
|
||||
NGRAPH_OP(Ceiling, ngraph::op::v0)
|
||||
NGRAPH_OP(Clamp, ngraph::op::v0)
|
||||
NGRAPH_OP(Concat, ngraph::op::v0)
|
||||
NGRAPH_OP(Constant, ngraph::op)
|
||||
NGRAPH_OP(Convert, ngraph::op::v0)
|
||||
NGRAPH_OP(ConvertLike, ngraph::op::v1)
|
||||
NGRAPH_OP(Convolution, ngraph::op::v1)
|
||||
NGRAPH_OP(ConvolutionBackpropData, ngraph::op::v1)
|
||||
NGRAPH_OP(Cos, ngraph::op::v0)
|
||||
NGRAPH_OP(Cosh, ngraph::op::v0)
|
||||
NGRAPH_OP(CumSum, ngraph::op::v0)
|
||||
NGRAPH_OP(DeformableConvolution, ngraph::op::v1)
|
||||
NGRAPH_OP(DeformablePSROIPooling, ngraph::op::v1)
|
||||
NGRAPH_OP(DepthToSpace, ngraph::op::v0)
|
||||
NGRAPH_OP(DetectionOutput, ngraph::op::v0)
|
||||
NGRAPH_OP(Divide, ngraph::op::v1)
|
||||
NGRAPH_OP(Elu, ngraph::op::v0)
|
||||
NGRAPH_OP(Erf, ngraph::op::v0)
|
||||
NGRAPH_OP(Equal, ngraph::op::v1)
|
||||
NGRAPH_OP(Exp, ngraph::op::v0)
|
||||
NGRAPH_OP(ExtractImagePatches, ngraph::op::v3)
|
||||
NGRAPH_OP(FakeQuantize, ngraph::op::v0)
|
||||
NGRAPH_OP(Floor, ngraph::op::v0)
|
||||
NGRAPH_OP(FloorMod, ngraph::op::v1)
|
||||
NGRAPH_OP(Gather, ngraph::op::v1)
|
||||
NGRAPH_OP(GatherTree, ngraph::op::v1)
|
||||
NGRAPH_OP(Greater, ngraph::op::v1)
|
||||
NGRAPH_OP(GreaterEqual, ngraph::op::v1)
|
||||
NGRAPH_OP(GroupConvolution, ngraph::op::v1)
|
||||
NGRAPH_OP(GroupConvolutionBackpropData, ngraph::op::v1)
|
||||
NGRAPH_OP(GRN, ngraph::op::v0)
|
||||
NGRAPH_OP(HardSigmoid, ngraph::op::v0)
|
||||
NGRAPH_OP(Less, ngraph::op::v1)
|
||||
NGRAPH_OP(LessEqual, ngraph::op::v1)
|
||||
NGRAPH_OP(Log, ngraph::op::v0)
|
||||
NGRAPH_OP(LogicalAnd, ngraph::op::v1)
|
||||
NGRAPH_OP(LogicalNot, ngraph::op::v1)
|
||||
NGRAPH_OP(LogicalOr, ngraph::op::v1)
|
||||
NGRAPH_OP(LogicalXor, ngraph::op::v1)
|
||||
NGRAPH_OP(LRN, ngraph::op::v0)
|
||||
NGRAPH_OP(LSTMCell, ngraph::op::v4)
|
||||
NGRAPH_OP(MatMul, ngraph::op::v0)
|
||||
NGRAPH_OP(MaxPool, ngraph::op::v1)
|
||||
NGRAPH_OP(Maximum, ngraph::op::v1)
|
||||
NGRAPH_OP(Minimum, ngraph::op::v1)
|
||||
NGRAPH_OP(Mod, ngraph::op::v1)
|
||||
NGRAPH_OP(Multiply, ngraph::op::v1)
|
||||
NGRAPH_OP(MVN, ngraph::op::v0)
|
||||
NGRAPH_OP(Negative, ngraph::op::v0)
|
||||
NGRAPH_OP(NormalizeL2, ngraph::op::v0)
|
||||
NGRAPH_OP(NotEqual, ngraph::op::v1)
|
||||
NGRAPH_OP(OneHot, ngraph::op::v1)
|
||||
NGRAPH_OP(PRelu, ngraph::op::v0)
|
||||
NGRAPH_OP(PSROIPooling, ngraph::op::v0)
|
||||
NGRAPH_OP(Pad, ngraph::op::v1)
|
||||
NGRAPH_OP(Parameter, ngraph::op::v0)
|
||||
NGRAPH_OP(Power, ngraph::op::v1)
|
||||
NGRAPH_OP(PriorBox, ngraph::op::v0)
|
||||
NGRAPH_OP(PriorBoxClustered, ngraph::op::v0)
|
||||
NGRAPH_OP(Proposal, ngraph::op::v4)
|
||||
NGRAPH_OP(Range, ngraph::op::v4)
|
||||
NGRAPH_OP(Relu, ngraph::op::v0)
|
||||
NGRAPH_OP(ReduceMax, ngraph::op::v1)
|
||||
NGRAPH_OP(ReduceLogicalAnd, ngraph::op::v1)
|
||||
NGRAPH_OP(ReduceLogicalOr, ngraph::op::v1)
|
||||
NGRAPH_OP(ReduceMean, ngraph::op::v1)
|
||||
NGRAPH_OP(ReduceMin, ngraph::op::v1)
|
||||
NGRAPH_OP(ReduceProd, ngraph::op::v1)
|
||||
NGRAPH_OP(ReduceSum, ngraph::op::v1)
|
||||
NGRAPH_OP(RegionYolo, ngraph::op::v0)
|
||||
NGRAPH_OP(ReorgYolo, ngraph::op::v0)
|
||||
NGRAPH_OP(Reshape, ngraph::op::v1)
|
||||
NGRAPH_OP(Result, ngraph::op::v0)
|
||||
NGRAPH_OP(ReverseSequence, ngraph::op::v0)
|
||||
NGRAPH_OP(ROIPooling, ngraph::op::v0)
|
||||
NGRAPH_OP(ScatterNDUpdate, ngraph::op::v3)
|
||||
NGRAPH_OP(Select, ngraph::op::v1)
|
||||
NGRAPH_OP(Selu, ngraph::op::v0)
|
||||
NGRAPH_OP(Sign, ngraph::op::v0)
|
||||
NGRAPH_OP(Sigmoid, ngraph::op::v0)
|
||||
NGRAPH_OP(Sin, ngraph::op::v0)
|
||||
NGRAPH_OP(Sinh, ngraph::op::v0)
|
||||
NGRAPH_OP(Softmax, ngraph::op::v1)
|
||||
NGRAPH_OP(Sqrt, ngraph::op::v0)
|
||||
NGRAPH_OP(SpaceToDepth, ngraph::op::v0)
|
||||
NGRAPH_OP(Split, ngraph::op::v1)
|
||||
NGRAPH_OP(SquaredDifference, ngraph::op::v0)
|
||||
NGRAPH_OP(Squeeze, ngraph::op::v0)
|
||||
NGRAPH_OP(StridedSlice, ngraph::op::v1)
|
||||
NGRAPH_OP(Subtract, ngraph::op::v1)
|
||||
NGRAPH_OP(Tan, ngraph::op::v0)
|
||||
NGRAPH_OP(Tanh, ngraph::op::v0)
|
||||
NGRAPH_OP(TensorIterator, ngraph::op::v0)
|
||||
NGRAPH_OP(Tile, ngraph::op::v0)
|
||||
NGRAPH_OP(Transpose, ngraph::op::v1)
|
||||
NGRAPH_OP(Unsqueeze, ngraph::op::v0)
|
||||
NGRAPH_OP(VariadicSplit, ngraph::op::v1)
|
||||
|
||||
// New operations added in opset2
|
||||
NGRAPH_OP(Gelu, ngraph::op::v0)
|
||||
NGRAPH_OP(BatchToSpace, ngraph::op::v1)
|
||||
NGRAPH_OP(SpaceToBatch, ngraph::op::v1)
|
||||
|
||||
// New operations added in opset3
|
||||
NGRAPH_OP(EmbeddingBagPackedSum, ngraph::op::v3)
|
||||
NGRAPH_OP(EmbeddingSegmentsSum, ngraph::op::v3)
|
||||
NGRAPH_OP(EmbeddingBagOffsetsSum, ngraph::op::v3)
|
||||
NGRAPH_OP(GRUCell, ngraph::op::v3)
|
||||
NGRAPH_OP(NonZero, ngraph::op::v3)
|
||||
NGRAPH_OP(RNNCell, ngraph::op::v0)
|
||||
NGRAPH_OP(ROIAlign, ngraph::op::v3)
|
||||
NGRAPH_OP(ScatterElementsUpdate, ngraph::op::v3)
|
||||
NGRAPH_OP(ScatterUpdate, ngraph::op::v3)
|
||||
NGRAPH_OP(ShuffleChannels, ngraph::op::v0)
|
||||
NGRAPH_OP(ShapeOf, ngraph::op::v3)
|
||||
NGRAPH_OP(Assign, ngraph::op::v3)
|
||||
NGRAPH_OP(ReadValue, ngraph::op::v3)
|
||||
NGRAPH_OP(TopK, ngraph::op::v3)
|
||||
|
||||
// New operations added in opset4
|
||||
NGRAPH_OP(Acosh, ngraph::op::v3)
|
||||
NGRAPH_OP(Asinh, ngraph::op::v3)
|
||||
NGRAPH_OP(Atanh, ngraph::op::v3)
|
||||
NGRAPH_OP(CTCLoss, ngraph::op::v4)
|
||||
NGRAPH_OP(HSwish, ngraph::op::v4)
|
||||
NGRAPH_OP(Interpolate, ngraph::op::v4)
|
||||
NGRAPH_OP(Mish, ngraph::op::v4)
|
||||
NGRAPH_OP(NonMaxSuppression, ngraph::op::v4)
|
||||
NGRAPH_OP(ReduceL1, ngraph::op::v4)
|
||||
NGRAPH_OP(ReduceL2, ngraph::op::v4)
|
||||
NGRAPH_OP(SoftPlus, ngraph::op::v4)
|
||||
NGRAPH_OP(Swish, ngraph::op::v4)
|
||||
|
||||
// New operations added in opset5
|
||||
NGRAPH_OP(LSTMSequence, ngraph::op::v5)
|
||||
NGRAPH_OP(GRUSequence, ngraph::op::v5)
|
||||
NGRAPH_OP(RNNSequence, ngraph::op::v5)
|
||||
@ -119,3 +119,22 @@ const ngraph::OpSet& ngraph::get_opset4()
|
||||
}
|
||||
return opset;
|
||||
}
|
||||
|
||||
const ngraph::OpSet& ngraph::get_opset5()
|
||||
{
|
||||
static std::mutex init_mutex;
|
||||
static bool opset_is_initialized = false;
|
||||
static OpSet opset;
|
||||
if (!opset_is_initialized)
|
||||
{
|
||||
std::lock_guard<std::mutex> guard(init_mutex);
|
||||
if (!opset_is_initialized)
|
||||
{
|
||||
#define NGRAPH_OP(NAME, NAMESPACE) opset.insert<NAMESPACE::NAME>();
|
||||
#include "ngraph/opsets/opset5_tbl.hpp"
|
||||
#undef NGRAPH_OP
|
||||
opset_is_initialized = true;
|
||||
}
|
||||
}
|
||||
return opset;
|
||||
}
|
||||
@ -21,6 +21,7 @@
|
||||
#include "ngraph/opsets/opset1.hpp"
|
||||
#include "ngraph/opsets/opset3.hpp"
|
||||
#include "ngraph/opsets/opset4.hpp"
|
||||
#include "ngraph/opsets/opset5.hpp"
|
||||
|
||||
#include "util/visitor.hpp"
|
||||
|
||||
@ -1099,7 +1100,7 @@ TEST(attributes, lstm_cell_op)
|
||||
|
||||
TEST(attributes, lstm_sequence_op)
|
||||
{
|
||||
FactoryRegistry<Node>::get().register_factory<op::v5::LSTMSequence>();
|
||||
FactoryRegistry<Node>::get().register_factory<opset5::LSTMSequence>();
|
||||
|
||||
const size_t batch_size = 4;
|
||||
const size_t num_directions = 2;
|
||||
@ -1126,7 +1127,7 @@ TEST(attributes, lstm_sequence_op)
|
||||
const std::vector<std::string> activations = {"tanh", "sigmoid", "tanh"};
|
||||
const float clip_threshold = 0.5f;
|
||||
|
||||
const auto lstm_sequence = make_shared<op::v5::LSTMSequence>(X,
|
||||
const auto lstm_sequence = make_shared<opset5::LSTMSequence>(X,
|
||||
initial_hidden_state,
|
||||
initial_cell_state,
|
||||
sequence_lengths,
|
||||
@ -1140,7 +1141,7 @@ TEST(attributes, lstm_sequence_op)
|
||||
activations,
|
||||
clip_threshold);
|
||||
NodeBuilder builder(lstm_sequence);
|
||||
auto g_lstm_sequence = as_type_ptr<op::v5::LSTMSequence>(builder.create());
|
||||
auto g_lstm_sequence = as_type_ptr<opset5::LSTMSequence>(builder.create());
|
||||
|
||||
EXPECT_EQ(g_lstm_sequence->get_hidden_size(), lstm_sequence->get_hidden_size());
|
||||
EXPECT_EQ(g_lstm_sequence->get_activations(), lstm_sequence->get_activations());
|
||||
|
||||
@ -16,7 +16,7 @@
|
||||
|
||||
#include "gtest/gtest.h"
|
||||
#include "ngraph/ngraph.hpp"
|
||||
#include "ngraph/opsets/opset4.hpp"
|
||||
#include "ngraph/opsets/opset5.hpp"
|
||||
#include "util/type_prop.hpp"
|
||||
|
||||
using namespace std;
|
||||
@ -31,20 +31,20 @@ TEST(type_prop, gru_sequence_forward)
|
||||
const size_t hidden_size = 128;
|
||||
|
||||
const auto X =
|
||||
make_shared<opset4::Parameter>(element::f32, Shape{batch_size, seq_length, input_size});
|
||||
const auto initial_hidden_state = make_shared<opset4::Parameter>(
|
||||
make_shared<opset5::Parameter>(element::f32, Shape{batch_size, seq_length, input_size});
|
||||
const auto initial_hidden_state = make_shared<opset5::Parameter>(
|
||||
element::f32, Shape{batch_size, num_directions, hidden_size});
|
||||
const auto sequence_lengths = make_shared<op::Parameter>(element::i32, Shape{batch_size});
|
||||
const auto W = make_shared<opset4::Parameter>(
|
||||
const auto W = make_shared<opset5::Parameter>(
|
||||
element::f32, Shape{num_directions, 3 * hidden_size, input_size});
|
||||
const auto R = make_shared<opset4::Parameter>(
|
||||
const auto R = make_shared<opset5::Parameter>(
|
||||
element::f32, Shape{num_directions, 3 * hidden_size, hidden_size});
|
||||
const auto B =
|
||||
make_shared<opset4::Parameter>(element::f32, Shape{num_directions, 3 * hidden_size});
|
||||
make_shared<opset5::Parameter>(element::f32, Shape{num_directions, 3 * hidden_size});
|
||||
|
||||
const auto direction = op::RecurrentSequenceDirection::FORWARD;
|
||||
|
||||
const auto sequence = make_shared<op::v5::GRUSequence>(
|
||||
const auto sequence = make_shared<opset5::GRUSequence>(
|
||||
X, initial_hidden_state, sequence_lengths, W, R, B, hidden_size, direction);
|
||||
|
||||
EXPECT_EQ(sequence->get_hidden_size(), hidden_size);
|
||||
|
||||
@ -16,7 +16,7 @@
|
||||
|
||||
#include "gtest/gtest.h"
|
||||
#include "ngraph/ngraph.hpp"
|
||||
#include "ngraph/opsets/opset4.hpp"
|
||||
#include "ngraph/opsets/opset5.hpp"
|
||||
#include "util/type_prop.hpp"
|
||||
|
||||
// suppress FusedOp deprecation warnings
|
||||
@ -41,7 +41,7 @@ struct recurrent_sequence_parameters
|
||||
//
|
||||
// Create and initialize default input test tensors.
|
||||
//
|
||||
shared_ptr<op::v5::LSTMSequence>
|
||||
shared_ptr<opset5::LSTMSequence>
|
||||
lstm_seq_tensor_initialization(const recurrent_sequence_parameters& param)
|
||||
{
|
||||
auto batch_size = param.batch_size;
|
||||
@ -52,20 +52,20 @@ shared_ptr<op::v5::LSTMSequence>
|
||||
auto et = param.et;
|
||||
|
||||
const auto X =
|
||||
make_shared<opset4::Parameter>(et, PartialShape{batch_size, seq_length, input_size});
|
||||
make_shared<opset5::Parameter>(et, PartialShape{batch_size, seq_length, input_size});
|
||||
const auto initial_hidden_state =
|
||||
make_shared<opset4::Parameter>(et, PartialShape{batch_size, num_directions, hidden_size});
|
||||
make_shared<opset5::Parameter>(et, PartialShape{batch_size, num_directions, hidden_size});
|
||||
const auto initial_cell_state =
|
||||
make_shared<opset4::Parameter>(et, PartialShape{batch_size, num_directions, hidden_size});
|
||||
const auto sequence_lengths = make_shared<opset4::Parameter>(et, PartialShape{batch_size});
|
||||
const auto W = make_shared<opset4::Parameter>(
|
||||
make_shared<opset5::Parameter>(et, PartialShape{batch_size, num_directions, hidden_size});
|
||||
const auto sequence_lengths = make_shared<opset5::Parameter>(et, PartialShape{batch_size});
|
||||
const auto W = make_shared<opset5::Parameter>(
|
||||
et, PartialShape{num_directions, hidden_size * 4, input_size});
|
||||
const auto R = make_shared<opset4::Parameter>(
|
||||
const auto R = make_shared<opset5::Parameter>(
|
||||
et, PartialShape{num_directions, hidden_size * 4, hidden_size});
|
||||
const auto B =
|
||||
make_shared<opset4::Parameter>(et, PartialShape{num_directions, hidden_size * 4});
|
||||
make_shared<opset5::Parameter>(et, PartialShape{num_directions, hidden_size * 4});
|
||||
|
||||
const auto lstm_sequence = make_shared<op::v5::LSTMSequence>();
|
||||
const auto lstm_sequence = make_shared<opset5::LSTMSequence>();
|
||||
|
||||
lstm_sequence->set_argument(0, X);
|
||||
lstm_sequence->set_argument(1, initial_hidden_state);
|
||||
@ -87,22 +87,22 @@ TEST(type_prop, lstm_sequence_forward)
|
||||
const size_t hidden_size = 128;
|
||||
|
||||
const auto X =
|
||||
make_shared<opset4::Parameter>(element::f32, Shape{batch_size, seq_length, input_size});
|
||||
const auto initial_hidden_state = make_shared<opset4::Parameter>(
|
||||
make_shared<opset5::Parameter>(element::f32, Shape{batch_size, seq_length, input_size});
|
||||
const auto initial_hidden_state = make_shared<opset5::Parameter>(
|
||||
element::f32, Shape{batch_size, num_directions, hidden_size});
|
||||
const auto initial_cell_state = make_shared<opset4::Parameter>(
|
||||
const auto initial_cell_state = make_shared<opset5::Parameter>(
|
||||
element::f32, Shape{batch_size, num_directions, hidden_size});
|
||||
const auto sequence_lengths = make_shared<opset4::Parameter>(element::i32, Shape{batch_size});
|
||||
const auto W = make_shared<opset4::Parameter>(
|
||||
const auto sequence_lengths = make_shared<opset5::Parameter>(element::i32, Shape{batch_size});
|
||||
const auto W = make_shared<opset5::Parameter>(
|
||||
element::f32, Shape{num_directions, 4 * hidden_size, input_size});
|
||||
const auto R = make_shared<opset4::Parameter>(
|
||||
const auto R = make_shared<opset5::Parameter>(
|
||||
element::f32, Shape{num_directions, 4 * hidden_size, hidden_size});
|
||||
const auto B =
|
||||
make_shared<opset4::Parameter>(element::f32, Shape{num_directions, 4 * hidden_size});
|
||||
make_shared<opset5::Parameter>(element::f32, Shape{num_directions, 4 * hidden_size});
|
||||
|
||||
const auto lstm_direction = op::RecurrentSequenceDirection::FORWARD;
|
||||
|
||||
const auto lstm_sequence = make_shared<op::v5::LSTMSequence>(X,
|
||||
const auto lstm_sequence = make_shared<opset5::LSTMSequence>(X,
|
||||
initial_hidden_state,
|
||||
initial_cell_state,
|
||||
sequence_lengths,
|
||||
@ -139,25 +139,25 @@ TEST(type_prop, lstm_sequence_bidirectional)
|
||||
const size_t hidden_size = 256;
|
||||
|
||||
const auto X =
|
||||
make_shared<opset4::Parameter>(element::f32, Shape{batch_size, seq_length, input_size});
|
||||
const auto initial_hidden_state = make_shared<opset4::Parameter>(
|
||||
make_shared<opset5::Parameter>(element::f32, Shape{batch_size, seq_length, input_size});
|
||||
const auto initial_hidden_state = make_shared<opset5::Parameter>(
|
||||
element::f32, Shape{batch_size, num_directions, hidden_size});
|
||||
const auto initial_cell_state = make_shared<opset4::Parameter>(
|
||||
const auto initial_cell_state = make_shared<opset5::Parameter>(
|
||||
element::f32, Shape{batch_size, num_directions, hidden_size});
|
||||
const auto sequence_lengths = make_shared<opset4::Parameter>(element::i32, Shape{batch_size});
|
||||
const auto W = make_shared<opset4::Parameter>(
|
||||
const auto sequence_lengths = make_shared<opset5::Parameter>(element::i32, Shape{batch_size});
|
||||
const auto W = make_shared<opset5::Parameter>(
|
||||
element::f32, Shape{num_directions, 4 * hidden_size, input_size});
|
||||
const auto R = make_shared<opset4::Parameter>(
|
||||
const auto R = make_shared<opset5::Parameter>(
|
||||
element::f32, Shape{num_directions, 4 * hidden_size, hidden_size});
|
||||
const auto B =
|
||||
make_shared<opset4::Parameter>(element::f32, Shape{num_directions, 4 * hidden_size});
|
||||
make_shared<opset5::Parameter>(element::f32, Shape{num_directions, 4 * hidden_size});
|
||||
|
||||
const auto lstm_direction = op::v5::LSTMSequence::direction::BIDIRECTIONAL;
|
||||
const auto lstm_direction = opset5::LSTMSequence::direction::BIDIRECTIONAL;
|
||||
const std::vector<float> activations_alpha = {2.7, 7.0, 32.367};
|
||||
const std::vector<float> activations_beta = {0.0, 5.49, 6.0};
|
||||
const std::vector<std::string> activations = {"tanh", "sigmoid", "sigmoid"};
|
||||
|
||||
const auto lstm_sequence = make_shared<op::v5::LSTMSequence>(X,
|
||||
const auto lstm_sequence = make_shared<opset5::LSTMSequence>(X,
|
||||
initial_hidden_state,
|
||||
initial_cell_state,
|
||||
sequence_lengths,
|
||||
@ -170,7 +170,7 @@ TEST(type_prop, lstm_sequence_bidirectional)
|
||||
activations_beta,
|
||||
activations);
|
||||
EXPECT_EQ(lstm_sequence->get_hidden_size(), hidden_size);
|
||||
EXPECT_EQ(lstm_sequence->get_direction(), op::v5::LSTMSequence::direction::BIDIRECTIONAL);
|
||||
EXPECT_EQ(lstm_sequence->get_direction(), opset5::LSTMSequence::direction::BIDIRECTIONAL);
|
||||
EXPECT_EQ(lstm_sequence->get_activations_alpha(), activations_alpha);
|
||||
EXPECT_EQ(lstm_sequence->get_activations_beta(), activations_beta);
|
||||
EXPECT_EQ(lstm_sequence->get_activations()[0], "tanh");
|
||||
@ -328,7 +328,7 @@ TEST(type_prop, lstm_sequence_invalid_input_dimension)
|
||||
param.et = element::f32;
|
||||
|
||||
auto lstm_sequence = lstm_seq_tensor_initialization(param);
|
||||
auto invalid_rank0_tensor = make_shared<opset4::Parameter>(param.et, PartialShape{});
|
||||
auto invalid_rank0_tensor = make_shared<opset5::Parameter>(param.et, PartialShape{});
|
||||
|
||||
// Validate invalid rank0 tensor for all inputs: X, initial_hidden_state, initial_cell_state W,
|
||||
// R, B
|
||||
@ -352,7 +352,7 @@ TEST(type_prop, lstm_sequence_invalid_input_dynamic_rank)
|
||||
param.hidden_size = 256;
|
||||
param.et = element::f32;
|
||||
|
||||
auto check_dynamic_lstm = [](const shared_ptr<op::v5::LSTMSequence>& lstm) -> bool {
|
||||
auto check_dynamic_lstm = [](const shared_ptr<opset5::LSTMSequence>& lstm) -> bool {
|
||||
return lstm->output(0).get_partial_shape() == PartialShape::dynamic() &&
|
||||
lstm->output(1).get_partial_shape() == PartialShape::dynamic() &&
|
||||
lstm->output(2).get_partial_shape() == PartialShape::dynamic() &&
|
||||
@ -361,7 +361,7 @@ TEST(type_prop, lstm_sequence_invalid_input_dynamic_rank)
|
||||
|
||||
auto lstm_sequence = lstm_seq_tensor_initialization(param);
|
||||
auto invalid_dynamic_tensor =
|
||||
make_shared<opset4::Parameter>(param.et, PartialShape::dynamic(Rank::dynamic()));
|
||||
make_shared<opset5::Parameter>(param.et, PartialShape::dynamic(Rank::dynamic()));
|
||||
|
||||
// Validate invalid dynamic tensor for all inputs: X, initial_hidden_state, initial_cell_state
|
||||
// W, R, B
|
||||
|
||||
@ -16,7 +16,7 @@
|
||||
|
||||
#include "gtest/gtest.h"
|
||||
#include "ngraph/ngraph.hpp"
|
||||
#include "ngraph/opsets/opset4.hpp"
|
||||
#include "ngraph/opsets/opset5.hpp"
|
||||
#include "util/type_prop.hpp"
|
||||
|
||||
using namespace std;
|
||||
@ -31,20 +31,20 @@ TEST(type_prop, rnn_sequence_forward)
|
||||
const size_t hidden_size = 128;
|
||||
|
||||
const auto X =
|
||||
make_shared<opset4::Parameter>(element::f32, Shape{batch_size, seq_length, input_size});
|
||||
const auto initial_hidden_state = make_shared<opset4::Parameter>(
|
||||
make_shared<opset5::Parameter>(element::f32, Shape{batch_size, seq_length, input_size});
|
||||
const auto initial_hidden_state = make_shared<opset5::Parameter>(
|
||||
element::f32, Shape{batch_size, num_directions, hidden_size});
|
||||
const auto sequence_lengths = make_shared<op::Parameter>(element::i32, Shape{batch_size});
|
||||
|
||||
const auto W = make_shared<opset4::Parameter>(element::f32,
|
||||
const auto W = make_shared<opset5::Parameter>(element::f32,
|
||||
Shape{num_directions, hidden_size, input_size});
|
||||
const auto R = make_shared<opset4::Parameter>(element::f32,
|
||||
const auto R = make_shared<opset5::Parameter>(element::f32,
|
||||
Shape{num_directions, hidden_size, hidden_size});
|
||||
const auto B = make_shared<opset4::Parameter>(element::f32, Shape{num_directions, hidden_size});
|
||||
const auto B = make_shared<opset5::Parameter>(element::f32, Shape{num_directions, hidden_size});
|
||||
|
||||
const auto direction = op::RecurrentSequenceDirection::FORWARD;
|
||||
|
||||
const auto sequence = make_shared<op::v5::RNNSequence>(
|
||||
const auto sequence = make_shared<opset5::RNNSequence>(
|
||||
X, initial_hidden_state, sequence_lengths, W, R, B, hidden_size, direction);
|
||||
|
||||
EXPECT_EQ(sequence->get_hidden_size(), hidden_size);
|
||||
|
||||
Loading…
Reference in New Issue
Block a user