Supported Framework Layers

Caffe* Supported Layers

Standard Caffe* layers:

Layer Name in Caffe* Limitations
Axpy No
BN No
BatchNorm No
Bias No
Concat No
Convolution No
Deconvolution No
DetectionOutput No
Dropout Not needed for inference
Eltwise No
Flatten No
GlobalInput No
InnerProduct No
Input No
LRN No
Permute No
Pooling No
Power No
ROIPooling No
ReLU No
Reshape No
Scale No
ShuffleChannel No
Slice No
Softmax No
Tile No

MXNet* Supported Symbols

Standard MXNet* symbols:

Symbol Name in MXNet*Limitations
Activation supported "act_type" = "relu", "sigmoid" or "tanh"
BatchNorm No
Concat No
Convolution No
Crop "center_crop" = 1 is not supported
Custom Custom Layers in the Model Optimizer
Deconvolution No
DeformableConvolution No
DeformablePSROIPooling No
Dropout Not needed for inference
ElementWiseSum No
Embedding No
Flatten No
FullyConnected No
InstanceNorm No
L2Normalization only 4D input is supported
LRN No
LeakyReLU No
Pad No
Pooling No
ROIPooling No
ReLU No
Reshape Shape values equal to -2, -3 and -4 are not supported
ScaleShift No
SoftmaxActivation No
SoftmaxOutput No
Tile No
UpSampling No
Where No
_Plus No
_contrib_MultiBoxDetection "force_suppress" = 1 is not supported, non-default variances are not supported
_contrib_MultiBoxPrior No
_contrib_Proposal No
_copy Not needed for inference
_minus_scalar No
_mul_scalar No
_arange No
_contrib_AdaptiveAvgPooling2D Converted to the Average Pooling with fixed paddings
_maximum No
_minimum No
add_n No
broadcast_add No
broadcast_mul No
div_scalar No
elementwise_sub No
elemwise_add No
elemwise_mul No
exp No
expand_dims No
greater_scalar No
minus_scalar No
null Not needed for inference
repeat No
rnn No
rnn_param_concat No
sigmoid No
slice No
slice_axis No
slice_channel No
slice_like No
stack No
swapaxis No
tile No
transpose No
zeros No

TensorFlow* Supported Operations

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:

Operation Name in TensorFlow* Limitations
Add No
AddN No
ArgMax No
AvgPool No
BatchToSpaceND Supported in a pattern when converted to Convolution layer dilation attribute, Constant propagation
BiasAdd No
Bucketize CPU only
Cast No
Concat No
ConcatV2 No
Const No
Conv2D No
Conv2DBackpropInput No
Cos No
Cosh No
CropAndResize "method" = "bilinear" only
DepthToSpaceNo
DepthwiseConv2dNativeNo
Enter Supported only when it is fused to the TensorIterator layer
Equal No
Exit Supported only when it is fused to the TensorIterator layer
Exp No
ExpandDims No
ExperimentalSparseWeightedSum CPU only
ExtractImagePatches No
Fill No
FusedBatchNorm No
Gather No
GatherNd Supported if it can be replaced with Gather
GatherV2 No
Greater No
GreaterEqual No
Identity Not needed for shape inference
LRN No
Less No
Log1p No
LogicalAnd No
LogicalOr No
LogicalNot No
LoopCond Supported only when it is fused to the TensorIterator layer
MatMul No
Max No
MaxPool No
Maximum No
Mean No
Merge Supported only when it is fused to the TensorIterator layer
Min No
Minimum No
MirrorPad No
Mul No
Neg No
NextIteration Supported only when it is fused to the TensorIterator layer
NonMaxSuppressionV3 No
NonMaxSuppressionV4 No
NonMaxSuppressionV5 No
OneHot No
Pack No
Pad No
PadV2 No
Placeholder No
PlaceholderWithDefault No
Prod No
Range No
Rank No
RealDiv No
Relu No
Relu6 No
Reshape No
ResizeBilinear No
ResizeNearestNeighbor No
ResourceGatherNo
ReverseSequence No
Round No
Rsqrt No
Shape No
Sigmoid No
Sin No
Sinh No
Size No
Slice No
Softmax No
SpaceToBatchND Supported in a pattern when converted to Convolution layer dilation attribute, Constant propagation
SparseToDense CPU only
Split No
SplitV No
Sqrt No
Square No
SquaredDifference No
SquareNo
Squeeze The case when squeeze axis is not specified is not supported
StopGradient Not needed for shape inference
StridedSlice No
Sub No
Sum No
Swish No
Switch Control flow propagation
Tan No
Tanh No
TensorArrayGatherV3 Supported only when it is fused to the TensorIterator layer
TensorArrayReadV3 Supported only when it is fused to the TensorIterator layer
TensorArrayScatterV3 Supported only when it is fused to the TensorIterator layer
TensorArraySizeV3 Supported only when it is fused to the TensorIterator layer
TensorArrayV3 Supported only when it is fused to the TensorIterator layer
TensorArrayWriteV3 Supported only when it is fused to the TensorIterator layer
Tile No
TopkV2 No
Transpose No
Unpack No
ZerosLike No

Kaldi* Supported Layers

Standard Kaldi* Layers:

Symbol Name in Kaldi*Limitations
addshift No
affinecomponent No
affinetransform No
clipgradientcomponent Not needed for inference
concat No
convolutional1dcomponent No
convolutionalcomponent No
copy No
Crop No
elementwiseproductcomponent No
fixedaffinecomponent No
linearcomponent No
logsoftmaxcomponent No
lstmnonlinearitycomponent No
lstmprojected No
lstmprojectedstreams No
maxpoolingcomponent No
naturalgradientaffinecomponent No
naturalgradientperelementscalecomponent No
noopcomponent Not needed for inference
normalizecomponent No
parallelcomponent No
pnormcomponent No
rectifiedlinearcomponent No
rescale No
sigmoid No
slice No
softmax No
softmaxComponent No
softsign No
splicecomponent No
tanhcomponent No

ONNX* Supported Operators

Standard ONNX* operators:

Symbol Name in ONNX*Limitations
Abs No
Acos No
Add No
Affine No
ArgMax No
Asin No
Atan No
AveragePool No
BatchMatMul No
BatchNormalization No
Cast No
Ceil No
Clip No
Concat No
Constant No
ConstantFill No
ConstantOfShape No
Conv No
ConvTranspose
Cos No
Cosh No
Crop No
DetectionOutput (Intel experimental) No
Div No
Dropout Not needed for inference
Elu No
Equal No
Erf No
Expand No
FakeQuantize (Intel experimental) No
Fill No
Flatten No
Floor No
GRU No
Gather No
GatherTree No
Gemm No
GlobalAveragePool No
GlobalMaxPool No
Greater No
GreaterEqual No
HardSigmoid No
Identity Not needed for inference
ImageScaler No
LRN No
LSTM Peepholes are not supported
LeakyRelu No
Less No
LessEqual No
Log No
LogicalAnd No
LogicalOr No
MatMul No
MaxPool No
Mul No
Neg No
NonMaxSuppression No
Not No
NotEqual No
OneHot No
Pad No
Pow No
PriorBox (Intel experimental) No
RNN No
Reciprocal No
ReduceMax No
ReduceMean No
ReduceMin No
ReduceProd No
ReduceSum No
Relu No
Reshape No
Resize Opset-10 version is supported
Select No
Shape No
Sigmoid No
Sign No
Sin No
Slice No
Softmax No
SpaceToDepth No
Sqrt No
Squeeze The case when squeeze axis is not specified is not supported
Sub No
Sum No
Tan No
Tanh No
TopK No
Transpose No
Unsqueeze No
Upsample No
Xor No