Standard Caffe* layers:
Number  Layer Name in Caffe*  Layer Name in the Intermediate Representation 

1  Input  Input 
2  GlobalInput  Input 
3  InnerProduct  FullyConnected 
4  Dropout  Ignored, does not appear in IR 
5  Convolution  Convolution 
6  Deconvolution  Deconvolution 
7  Pooling  Pooling 
8  BatchNorm  BatchNormalization 
9  LRN  Norm 
10  Power  Power 
11  ReLU  ReLU 
12  Scale  ScaleShift 
13  Concat  Concat 
14  Eltwise  Eltwise 
15  Flatten  Flatten 
16  Reshape  Reshape 
17  Slice  Slice 
18  Softmax  SoftMax 
19  Permute  Permute 
20  ROIPooling  ROIPooling 
21  Tile  Tile 
22  ShuffleChannel  Reshape + Split + Permute + Concat 
23  Axpy  ScaleShift + Eltwise 
24  BN  ScaleShift 
25  DetectionOutput  DetectionOutput 
26  StridedSlice  StridedSlice 
27  Bias  Eltwise(operation = sum) 
Standard MXNet* symbols:
Number  Symbol Name in MXNet*  Layer Name in the Intermediate Representation 

1  BatchNorm  BatchNormalization 
2  Crop  Crop 
3  ScaleShift  ScaleShift 
4  Pooling  Pooling 
5  SoftmaxOutput  SoftMax 
6  SoftmaxActivation  SoftMax 
7  null  Ignored, does not appear in IR 
8  Convolution  Convolution 
9  Deconvolution  Deconvolution 
10  Activation(act_type = relu)  ReLU 
11  ReLU  ReLU 
12  LeakyReLU  ReLU (negative_slope = 0.25) 
13  Concat  Concat 
14  elemwise_add  Eltwise(operation = sum) 
15  _Plus  Eltwise(operation = sum) 
16  Flatten  Flatten 
17  Reshape  Reshape 
18  FullyConnected  FullyConnected 
19  UpSampling  Resample 
20  transpose  Permute 
21  LRN  Norm 
22  L2Normalization  Normalize 
23  Dropout  Ignored, does not appear in IR 
24  _copy  Ignored, does not appear in IR 
25  _contrib_MultiBoxPrior  PriorBox 
26  _contrib_MultiBoxDetection  DetectionOutput 
27  broadcast_mul  ScaleShift 
28  sigmoid  sigmoid 
29  Activation (act_type = tanh)  Activation (operation = tanh) 
30  LeakyReLU (act_type = prelu)  PReLU 
31  LeakyReLU (act_type = elu)  Activation (operation = elu) 
32  elemwise_mul  Eltwise (operation = mul) 
33  add_n 
Eltwise (operation = sum) 
34  ElementWiseSum 
Eltwise (operation = sum) or ScaleShift 
35  _mul_scalar  Power 
36  broadcast_add  Eltwise (operation = sum) 
37  slice_axis  Crop 
38  Custom  Custom Layers in the Model Optimizer 
39  _minus_scalar  Power 
40  Pad  Pad 
41  _contrib_Proposal  Proposal 
42  ROIPooling  ROIPooling 
43  stack  Concat 
44  swapaxis  Permute 
45  zeros  Const 
45  rnn  TensorIterator 
46  rnn_param_concat  Concat 
47  slice_channel  Split 
48  _maximum  Eltwise(operation = max) 
49  _minimum  Power(scale=1) + Eltwise(operation = max) + Power(scale=1) 
50  InstanceNorm  scale * (x  mean) / sqrt(variance + epsilon) + B 
51  Embedding  Gather 
52  DeformableConvolution  DeformableConvolution 
53  DeformablePSROIPooling  PSROIPooling (method=deformable) 
54  Where  Select 
55  exp  Exp 
56  slice_like  Crop 
57  div_scalar  Power(power = 1) + Eltwise(operation = mul) 
58  minus_scalar  Eltwise(operation = sum) + Power(scale=1) 
59  greater_scalar  Eltwise(operation=Greater) 
60  elemtwise_sub  Eltwise(operation = sum) + Power(scale=1) 
61  expand_dims  Unsqueeze 
62  Tile  Tile 
63  _arange  Range 
64  repeat  Unsqueeze + Tile + Reshape 
Some TensorFlow* operations do not match to any Inference Engine layer, but are still supported by the Model Optimizer and can be used on constant propagation path. These layers are labeled 'Constant propagation' in the table.
Standard TensorFlow* operations:
Number  Operation Name in TensorFlow  Layer Name in the Intermediate Representation 

1  Transpose  Permute 
2  LRN  Norm 
3  Split  Split 
4  SplitV  Split 
5  FusedBatchNorm  ScaleShift (can be fused into Convolution or FullyConnected) 
6  Relu6  Clamp 
7  DepthwiseConv2dNative  Convolution 
8  ExpandDims  Unsqueeze 
9  Slice  Split 
10  ConcatV2  Concat 
11  MatMul  FullyConnected 
12  Pack  Reshapes and Concat 
13  StridedSlice  StridedSlice or Split 
14  Prod  Constant propagation 
15  Const  Const 
16  Tile  Tile 
17  Placeholder  Input 
18  Pad  Fused into Convolution or Pooling layers (not supported as single operation) 
19  Conv2D  Convolution 
20  Conv2DBackpropInput  Deconvolution 
21  Identity  Ignored, does not appear in the IR 
22  Add  Eltwise(operation = sum) or ScaleShift 
23  Mul  Eltwise(operation = mul) 
24  Maximum  Eltwise(operation = max) 
25  Rsqrt  Power(power=0.5) 
26  Neg  Power(scale=1) 
27  Sub  Eltwise(operation = sum) + Power(scale=1) 
28  Relu  ReLU 
29  AvgPool  Pooling (pool_method=avg) 
30  MaxPool  Pooling (pool_method=max) 
31  Mean  Pooling (pool_method = avg) (sequential reduce dimensions are supported only) 
32  RandomUniform  Not supported 
33  BiasAdd  Fused or converted to ScaleShift 
34  Reshape  Reshape 
35  Squeeze  Squeeze 
36  Shape  Constant propagation (or layer generation if the "keep_shape_ops" command line parameter has been specified) 
37  Softmax  SoftMax 
38  SpaceToBatchND  Supported in a pattern when converted to Convolution layer dilation attribute, Constant propagation 
39  BatchToSpaceND  Supported in a pattern when converted to Convolution layer dilation attribute, Constant propagation 
40  StopGradient  Ignored, does not appear in IR 
41  Square  Constant propagation 
42  Sum  Pool(pool_method = avg) + Eltwise(operation = mul) 
43  Range  Constant propagation 
44  CropAndResize  ROIPooling (if the the method is 'bilinear') 
45  ArgMax  ArgMax 
46  DepthToSpace  Reshape + Permute + Reshape (works for CPU only because of 6D tensors) 
47  ExtractImagePatches  ReorgYolo 
48  ResizeBilinear  Interp 
49  ResizeNearestNeighbor  Resample 
50  Unpack  Split + Reshape (removes dimension being unpacked) if the number of parts is equal to size along given axis 
51  AddN  Several Eltwises 
52  Concat  Concat 
53  Minimum  Power(scale=1) + Eltwise(operation = max) + Power(scale=1) 
54  TopkV2  TopK 
55  RealDiv  Power(power = 1) and Eltwise(operation = mul) 
56  SquaredDifference  Power(scale = 1) + Eltwise(operation = sum) + Power(power = 2) 
57  Gather  Gather 
58  GatherV2  Gather 
59  ResourceGather  Gather 
60  Sqrt  Power(power=0.5) 
61  Square  Power(power=2) 
62  Pad  Pad 
63  PadV2  Pad 
64  MirrorPad  Pad 
65  ReverseSequence  ReverseSequence 
66  ZerosLike  Constant propagation 
67  Fill  Broadcast 
68  Cast  Cast to the following data types are removed from the graph float32, double, int32, int64 
69  Enter  Supported only when it is fused to the TensorIterator layer 
70  Exit  Supported only when it is fused to the TensorIterator layer 
71  LoopCond  Supported only when it is fused to the TensorIterator layer 
72  Merge  Supported only when it is fused to the TensorIterator layer 
73  NextIteration  Supported only when it is fused to the TensorIterator layer 
74  TensorArrayGatherV3  Supported only when it is fused to the TensorIterator layer 
75  TensorArrayReadV3  Supported only when it is fused to the TensorIterator layer 
76  TensorArrayScatterV3  Supported only when it is fused to the TensorIterator layer 
77  TensorArraySizeV3  Supported only when it is fused to the TensorIterator layer 
78  TensorArrayV3  Supported only when it is fused to the TensorIterator layer 
79  TensorArrayWriteV3  Supported only when it is fused to the TensorIterator layer 
80  Equal  Eltwise(operation = equal) 
81  Exp  Eltwise(operation = exp) 
82  Greater  Eltwise(operation = greater) 
83  GreaterEqual  Eltwise(operation = greater_equal) 
84  Less  Eltwise(operation = less) 
85  LogicalAnd  Eltwise(operation = logical_and) 
86  Min  Constant propagation 
87  Max  Reshape + Pooling (pool_method=max) + Reshape 
88  GatherNd  Supported if it can be replaced with Gather 
89  PlaceholderWithDefault  Const 
90  Rank  Constant propagation 
91  Round  Constant propagation 
92  Sigmoid  Activation(operation = sigmoid) 
93  Size  Constant propagation 
94  Switch  Control flow propagation 
94  Swish  Mul(x, Sigmoid(x)) 
95  Log1p  Power(power=1, scale=1, shift=1.0) + Log 
96  NonMaxSuppressionV3  NonMaxSuppression 
97  NonMaxSuppressionV4  NonMaxSuppression 
98  NonMaxSuppressionV5  NonMaxSuppression 
Standard Kaldi* Layers:
Number  Layer Name in Kaldi*  Layer name in the Intermediate Representation 

1  AddShift  Will be fused or converted to ScaleShift 
2  AffineComponent  FullyConnected 
3  AffineTransform  FullyConnected 
4  ConvolutionalComponent  Convolution 
5  Convolutional1DComponent  Convolution 
6  FixedAffineComponent  FullyConnected 
7  LstmProjected 

8  LstmProjectedStreams  The same as for LstmProjected 
9  MaxPoolingComponent  Pooling (pool_method = max) 
10  NormalizeComponent  ScaleShift 
11  RectifiedLinearComponent  ReLU 
12  ParallelComponent 

13  Rescale  Will be fused or converted to ScaleShift 
14  Sigmoid  Activation (operation = sigmoid) 
15  Softmax  Softmax 
16  SoftmaxComponent  Softmax 
17  SpliceComponent 

18  TanhComponent  Activation (operation = tanh) 
Standard ONNX* operators:
Number  Operator name in ONNX*  Layer type in the Intermediate Representation 

1  Add  Eltwise(operation = sum) (added 'axis' support) or ScaleShift 
2  AveragePool  Pooling (pool_method=avg) 
3  BatchNormalization  ScaleShift (can be fused into Convlution or FC) 
4  Concat  Concat 
5  Constant  Const 
6  Conv  Convolution 
7  ConvTranspose  Deconvolution (added auto_pad and output_shape attributes support)) 
8  Div  Eltwise(operation = mul)>Power 
9  Dropout  Ignored, does not apeear in IR 
10  Elu  Activation (ELU) 
11  Flatten  Reshape 
12  Gemm  FullyConnected or GEMM depending on inputs 
13  GlobalAveragePool  Pooling (pool_method=avg) 
14  Identity  Ignored, does not appear in IR 
15  LRN  Norm 
16  LeakyRelu  ReLU 
17  MatMul  FullyConnected 
17  MaxPool  Pooling (pool_method=max) 
19  Mul  Eltwise(operation = mul) (added axis support) 
20  Relu  ReLU 
21  Reshape  Reshape 
22  Shape  Constant propagation 
23  Softmax  SoftMax 
24  Squeeze  Squeeze 
25  Sub  Power>Eltwise(operation = sum) 
26  Sum  Eltwise(operation = sum) 
27  Transpose  Permute 
28  Unsqueeze  Unsqueeze 
29  Upsample  Resample 
30  ImageScaler  ScaleShift 
31  Affine  ScaleShift 
32  Reciprocal  Power(power=1) 
33  Crop  Split 
34  Tanh  Activation (operation = tanh) 
35  Sigmoid  Activation (operation = sigmoid) 
36  Pow  Power 
37  ConvTranspose  
38  Gather  Gather 
39  ConstantFill  Constant propagation 
40  ReduceMean  Reshape + Pooling(pool_method=avg) + Reshape (sequential reduce dimensions are supported only) 
41  ReduceSum  Reshape + Pooling(pool_method=avg) + Power(scale=reduce_dim_size) + Reshape (sequential reduce dimensions are supported only) 
42  Gather  Gather 
43  Gemm  GEMM 
44  GlobalMaxPool  Pooling (pool_method=max) 
45  Neg  Power(scale=1) 
46  Pad  Pad 
47  ArgMax  ArgMax 
48  Clip  Clamp 
49  DetectionOutput (Intel experimental)  DetectionOutputONNX 
50  PriorBox (Intel experimental)  PriorBoxONNX 
51  RNN  TensorIterator(with RNNCell in a body) 
52  GRU  TensorIterator(with GRUCell in a body) 
53  LSTM  TensorIterator(with LSTMCell in a body) 
54  FakeQuantize (Intel experimental)  FakeQuantize 
55  Erf  Erf 
56  BatchMatMul  GEMM 
57  SpaceToDepth  Reshape + Permute + Reshape 
58  Fill  Broadcast 
59  Select  Select 
60  OneHot  OneHot 
61  TopK  TopK 
62  GatherTree  GatherTree 
63  LogicalAnd  Eltwise(operation = LogicalAnd) 
64  LogicalOr  Eltwise(operation = LogicalOr) 
65  Equal  Eltwise(operation = Equal) 
66  NotEqual  Eltwise(operation = NotEqual) 
67  Less  Eltwise(operation = Less) 
68  LessEqual  Eltwise(operation = LessEqual) 
69  Greater  Eltwise(operation = Greater) 
70  GreaterEqual  Eltwise(operation = GreaterEqual) 
71  ConstantOfShape  Broadcast 
72  Expand  Broadcast 
73  Not  Activation (operation = not) 
74  ReduceMin  ReduceMin 
75  NonMaxSuppression  NonMaxSuppression 
76  Floor  Activation (operation = floor) 
77  Slice  Split or StridedSlice 
78  NonMaxSuppression  NonMaxSuppression 