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TensorRT 에서 지원되는 Operators 는 다음과 같다. (2020.04.29 기준)

 

 

Caffe

 

  • BatchNormalization
  • BNLL
  • Clip 
  • Concatenation
  • Convolution
  • Crop
  • Deconvolution
  • Dropout
  • ElementWise
  • ELU
  • InnerProduct
  • Input
  • LeakyReLU
  • LRN
  • Permute
  • Pooling
  • Power
  • Reduction
  • ReLU, TanH, and Sigmoid
  • Reshape
  • SoftMax
  • Scale

Clip : When using the Clip operation, Caffe users must serialize their layers using ditcaffe.pb.h instead of caffe.pb.h in order to import the layer into TensorRT.

 

 

 

 

 

 

TensorFlow

 

  • Add, Sub, Mul, Div, Minimum and Maximum
  • ArgMax
  • ArgMin
  • AvgPool
  • BiasAdd
  • Clip
  • ConcatV2
  • Const
  • Conv2D
  • ConvTranspose2D
  • DepthwiseConv2dNative
  • Elu
  • ExpandDims
  • FusedBatchNorm
  • Identity
  • LeakyReLU
  • MaxPool
  • Mean
  • Negative, Abs, Sqrt, Recip, Rsqrt, Pow, Exp and Log
  • Pad is supported if followed by one of these TensorFlow layers: Conv2D, DepthwiseConv2dNative, MaxPool, and AvgPool.
  • Placeholder
  • ReLU, TanH, and Sigmoid
  • Relu6
  • Reshape
  • Sin, Cos, Tan, Asin, Acos, Atan, Sinh, Cosh, Asinh, Acosh, Atanh, Ceil and Floor
  • Selu
  • Slice
  • SoftMax
  • Note: If the input to a TensorFlow SoftMax op is not NHWC, TensorFlow will automatically insert a transpose layer with a non-constant permutation, causing the UFF converter to fail. It is therefore advisable to manually transpose SoftMax inputs to NHWC using a constant permutation.
  • Softplus
  • Softsign
  • Transpose

 

 

 

 

ONNX

  • Abs
  • Acos
  • Acosh
  • And
  • Asin
  • Asinh
  • Atan
  • Atanh
  • Add
  • ArgMax
  • ArgMin
  • AveragePool
  • BatchNormalization
  • Cast
  • Ceil
  • Clip
  • Concat
  • Constant
  • ConstantOfShape
  • Conv
  • ConvTranspose
  • Cos
  • Cosh
  • DepthToSpace
  • DequantizeLinear
  • Div
  • Dropout
  • Elu
  • Equal
  • Erf
  • Exp
  • Expand
  • Flatten
  • Floor
  • Gather
  • Gemm
  • GlobalAveragePool
  • GlobalMaxPool
  • Greater
  • GRU
  • HardSigmoid
  • Identity
  • ImageScaler
  • InstanceNormalization
  • LRN
  • LeakyRelU
  • Less
  • Log
  • LogSoftmax
  • Loop
  • LRN
  • LSTM
  • MatMul
  • Max
  • MaxPool
  • Mean
  • Min
  • Mul
  • Neg
  • Not
  • Or
  • Pad
  • ParametricSoftplus
  • Pow
  • PRelu
  • QuantizeLinear
  • RandomUniform
  • RandomUniformLike
  • Range
  • Reciprocal
  • ReduceL1
  • ReduceL2
  • ReduceLogSum
  • ReduceLogSumExp
  • ReduceMax
  • ReduceMean
  • ReduceMin
  • ReduceProd
  • ReduceSum
  • ReduceSumSquare
  • Relu
  • Reshape
  • Resize
  • RNN
  • ScaledTanh
  • Scan
  • Selu
  • Shape
  • Sigmoid
  • Sin
  • Sinh
  • Size
  • Slice
  • Softmax
  • Softplus
  • Softsign
  • SpaceToDepth
  • Split
  • Sqrt
  • Squeeze
  • Sub
  • Sum
  • Tan
  • Tanh
  • ThresholdedRelu
  • Tile
  • TopK
  • Transpose
  • Unsqueeze
  • Upsample
  • Where

 

 

참고자료 : https://docs.nvidia.com/deeplearning/sdk/tensorrt-support-matrix/index.html#fntarg_12

 

TensorRT Support Matrix :: NVIDIA Deep Learning SDK Documentation

These support matrices provide a look into the supported platforms, features, and hardware capabilities of the TensorRT 7.1.0 Early Access (EA) APIs, parsers, and layers. For previously released TensorRT documentation, see TensorRT Archives.

docs.nvidia.com

 

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