Layers¶
IConvolutionLayer¶

class
tensorrt.
IConvolutionLayer
¶ A convolution layer in an
INetworkDefinition
.This layer performs a correlation operation between 3dimensional filter with a 4dimensional tensor to produce another 4dimensional tensor.
An optional bias argument is supported, which adds a perchannel constant to each value in the output.
Variables:  kernel_size –
DimsHW
The HW kernel size of the convolution.  num_output_maps –
int
The number of output maps for the convolution.  stride –
DimsHW
The stride of the convolution. Default: (1, 1)  padding –
DimsHW
the padding of the convolution. The input will be zeropadded by this number of elements in the height and width directions. If the padding is asymmetric, this value corresponds to the prepadding. Default: (0, 0)  pre_padding –
DimsHW
the prepadding. The start of input will be zeropadded by this number of elements in the height and width directions. Default: (0, 0)  post_padding –
DimsHW
the prepadding. The end of input will be zeropadded by this number of elements in the height and width directions. Default: (0, 0)  num_groups –
int
The number of groups for a convolution. The input tensor channels are divided into this many groups, and a convolution is executed for each group, using a filter per group. The results of the group convolutions are concatenated to form the output. Note When using groups in int8 mode, the size of the groups (i.e. the channel count divided by the group count) must be a multiple of 4 for both input and output. Default: 1.  kernel –
Weights
The kernel weights for the convolution. The weights are specified as a contiguous array in GKCRS order, where G is the number of groups, K the number of output feature maps, C the number of input channels, and R and S are the height and width of the filter.  bias –
Weights
The bias weights for the convolution. Bias is optional. To omit bias, set this to an emptyWeights
object. The bias is applied perchannel, so the number of weights (if nonzero) must be equal to the number of output feature maps.  dilation –
DimsHW
The dilation for a convolution. Default: (1, 1)
 kernel_size –
IFullyConnectedLayer¶

class
tensorrt.
IFullyConnectedLayer
¶ A fully connected layer in an
INetworkDefinition
.This layer expects an input tensor of three or more nonbatch dimensions. The input is automatically reshaped into an MxV tensor X, where V is a product of the last three dimensions and M is a product of the remaining dimensions (where the product over 0 dimensions is defined as 1). For example:
 If the input tensor has shape {C, H, W}, then the tensor is reshaped into {1, C*H*W} .
 If the input tensor has shape {P, C, H, W}, then the tensor is reshaped into {P, C*H*W} .
The layer then performs:
\(Y := matmul(X, W^T) + bias\)
Where X is the MxV tensor defined above, W is the KxV weight tensor of the layer, and bias is a row vector size K that is broadcasted to MxK . K is the number of output channels, and configurable via
IFullyConnectedLayer.num_output_channels
. If bias is not specified, it is implicitly 0 .The MxK result Y is then reshaped such that the last three dimensions are {K, 1, 1} and the remaining dimensions match the dimensions of the input tensor. For example:
 If the input tensor has shape {C, H, W}, then the output tensor will have shape {K, 1, 1} .
 If the input tensor has shape {P, C, H, W}, then the output tensor will have shape {P, K, 1, 1} .
Variables:
IActivationLayer¶

tensorrt.
ActivationType
¶ The type of activation to perform.
Members:
RELU : Rectified Linear activation
SELU : Selu activation: f(x) = beta * x if x > 0, f(x) = beta * (alpha * exp(x)  alpha) if x <= 0
THRESHOLDED_RELU : Thresholded Relu activation: f(x) = x if x > alpha, f(x) = 0 if x <= alpha
TANH : Hyperbolic Tangent activation
SIGMOID : Sigmoid activation
LEAKY_RELU : Leaky Relu activation: f(x) = x if x >= 0, f(x) = alpha * x if x < 0
ELU : Elu activation: f(x) = x if x >= 0, f(x) = alpha * (exp(x)  1) if x < 0
SCALED_TANH : Scaled Tanh activation: f(x) = alpha * tanh(beta * x)
HARD_SIGMOID : Hard sigmoid activation: f(x) = max(0, min(1, alpha * x + beta))
CLIP : Clip activation: f(x) = max(alpha, min(beta, x))
SOFTSIGN : Softsign activation: f(x) = x / (1 + x)
SOFTPLUS : Softplus activation: f(x) = alpha * log(exp(beta * x) + 1)

class
tensorrt.
IActivationLayer
¶ An Activation layer in an
INetworkDefinition
. This layer applies a perelement activation function to its input. The output has the same shape as the input.Variables:  type –
ActivationType
The type of activation to be performed.  alpha –
float
The alpha parameter that is used by some parametric activations (LEAKY_RELU, ELU, SELU, SOFTPLUS, CLIP, HARD_SIGMOID, SCALED_TANH). Other activations ignore this parameter.  beta –
float
The beta parameter that is used by some parametric activations (SELU, SOFTPLUS, CLIP, HARD_SIGMOID, SCALED_TANH). Other activations ignore this parameter.
 type –
IPoolingLayer¶

tensorrt.
PoolingType
¶ The type of pooling to perform in a pooling layer.
Members:
MAX : Maximum over elements
MAX_AVERAGE_BLEND : Blending between the max pooling and average pooling: (1blendFactor)*maxPool + blendFactor*avgPool
AVERAGE : Average over elements. If the tensor is padded, the count includes the padding

class
tensorrt.
IPoolingLayer
¶ A Pooling layer in an
INetworkDefinition
. The layer applies a reduction operation within a window over the input.Variables:  type –
PoolingType
The type of pooling to be performed.  window_size –
DimsHW
The window size for pooling.  stride –
DimsHW
The stride for pooling. Default: 1  padding –
DimsHW
The padding for pooling. Default: 1  pre_padding –
DimsHW
the prepadding. The start of input will be zeropadded by this number of elements in the height and width directions. Default: (0, 0)  post_padding –
DimsHW
the prepadding. The end of input will be zeropadded by this number of elements in the height and width directions. Default: (0, 0)  blend_factor –
float
The blending factor for the max_average_blend mode: \(max_average_blendPool = (1blendFactor)*maxPool + blendFactor*avgPool\) .blend_factor
is a user value in [0,1] with the default value of 0.0. This value only applies for thePoolingType.MAX_AVERAGE_BLEND
mode.  average_count_excludes_padding –
bool
Whether average pooling uses as a denominator the overlap area between the window and the unpadded input. If this is not set, the denominator is the overlap between the pooling window and the padded input. Default: True
 type –
ILRNLayer¶

class
tensorrt.
ILRNLayer
¶ A LRN layer in an
INetworkDefinition
. The output size is the same as the input size.Variables:  window_size –
int
The LRN window size. The window size must be odd and in the range of [1, 15].  alpha –
float
The LRN alpha value. The valid range is [1e20, 1e20].  beta –
float
The LRN beta value. The valid range is [0.01, 1e5f].  k –
float
The LRN K value. The valid range is [1e5, 1e10].
 window_size –
IScaleLayer¶

tensorrt.
ScaleMode
¶ Controls how scale is applied in a Scale layer.
Members:
UNIFORM : Identical coefficients across all elements of the tensor.
ELEMENTWISE : Elementwise coefficients.
CHANNEL : Perchannel coefficients. The channel dimension is assumed to be the third to last dimension.

class
tensorrt.
IScaleLayer
¶ A Scale layer in an
INetworkDefinition
.This layer applies a perelement computation to its input:
\(output = (input * scale + shift) ^ power\)
The coefficients can be applied on a pertensor, perchannel, or perelement basis.
Note If the number of weights is 0, then a default value is used for shift, power, and scale. The default shift is 0, the default power is 1, and the default scale is 1.
The output size is the same as the input size.
Note The input tensor for this layer is required to have a minimum of 3 dimensions.
Variables:
ISoftMaxLayer¶

class
tensorrt.
ISoftMaxLayer
¶ A Softmax layer in an
INetworkDefinition
.This layer applies a perchannel softmax to its input.
The output size is the same as the input size.
Variables: axes – int
The axes along which softmax is computed. Currently, only one axis can be set. The axis is specified by setting the bit corresponding to the axis, after excluding the batch dimension, to 1. Let’s say we have an NCHW tensor as input (three nonbatch dimensions). Bit 0 corresponds to the C dimension boolean. Bit 1 corresponds to the H dimension boolean. Bit 2 corresponds to the W dimension boolean. For example, to perform softmax on axis R of a NPQRCHW input, set bit 2. By default, softmax is performed on the axis which is the number of nonbatch axes minus three. It is 0 if there are fewer than 3 nonbatch axes. For example, if the input is NCHW, the default axis is C. If the input is NHW, then the default axis is H.
IConcatenationLayer¶

class
tensorrt.
IConcatenationLayer
¶ A concatenation layer in an
INetworkDefinition
.The output channel size is the sum of the channel sizes of the inputs. The other output sizes are the same as the other input sizes, which must all match.
Variables: axis – int
The axis along which concatenation occurs. 0 is the major axis (excluding the batch dimension). The default is the number of nonbatch axes in the tensor minus three (e.g. for an NCHW input it would be 0), or 0 if there are fewer than 3 nonbatch axes.
IDeconvolutionLayer¶

class
tensorrt.
IDeconvolutionLayer
¶ A deconvolution layer in an
INetworkDefinition
.Variables:  kernel_size –
DimsHW
The HW kernel size of the convolution.  num_output_maps –
int
The number of output feature maps for the deconvolution.  stride –
DimsHW
the stride of the deconvolution. Default: (1, 1)  padding –
DimsHW
The padding of the deconvolution. The input will be zeropadded by this number of elements in the height and width directions. Padding is symmetric. Default: (0,0)  pre_padding –
DimsHW
the prepadding. The start of input will be zeropadded by this number of elements in the height and width directions. Default: (0, 0)  post_padding –
DimsHW
the prepadding. The end of input will be zeropadded by this number of elements in the height and width directions. Default: (0, 0)  num_groups –
int
The number of groups for a deconvolution. The input tensor channels are divided into this many groups, and a deconvolution is executed for each group, using a filter per group. The results of the group convolutions are concatenated to form the output. Note When using groups in int8 mode, the size of the groups (i.e. the channel count divided by the group count) must be a multiple of 4 for both input and output. Default: 1  kernel –
Weights
The kernel weights for the deconvolution. The weights are specified as a contiguous array in CKRS order, where C the number of input channels, K the number of output feature maps, and R and S are the height and width of the filter.  bias –
Weights
The bias weights for the deconvolution. Bias is optional. To omit bias, set this to an emptyWeights
object. The bias is applied perfeaturemap, so the number of weights (if nonzero) must be equal to the number of output feature maps.
 kernel_size –
IElementWiseLayer¶

tensorrt.
ElementWiseOperation
¶ The binary operations that may be performed by an ElementWise layer.
Members:
PROD : Product of the two elements
MAX : Max of the two elements
MIN : Min of the two elements
DIV : Divide the first element by the second
SUM : Sum of the two elements
SUB : Subtract the second element from the first
POW : The first element to the power of the second element

class
tensorrt.
IElementWiseLayer
¶ A elementwise layer in an
INetworkDefinition
.This layer applies a perelement binary operation between corresponding elements of two tensors.
The input dimensions of the two input tensors must be equal, and the output tensor is the same size as each input.
Variables: op – ElementWiseOperation
The binary operation for the layer.
IGatherLayer¶

class
tensorrt.
IGatherLayer
¶ A gather layer in an
INetworkDefinition
.Variables: axis – int
The nonbatch dimension axis to gather on. The axis must be less than the number of nonbatch dimensions in the data input.
RNN Layers¶

tensorrt.
RNNOperation
¶ The RNN operations that may be performed by an RNN layer.
Equation definitions
In the equations below, we use the following naming convention:
t := current time stepi := input gateo := output gatef := forget gatez := update gater := reset gatec := cell gateh := hidden gateg[t] denotes the output of gate g at timestep t, e.g.`f[t]` is the output of the forget gate f .X[t] := input tensor for timestep tC[t] := cell state for timestep tH[t] := hidden state for timestep tW[g] := W (input) parameter weight matrix for gate gR[g] := U (recurrent) parameter weight matrix for gate gWb[g] := W (input) parameter bias vector for gate gRb[g] := U (recurrent) parameter bias vector for gate gUnless otherwise specified, all operations apply pointwise to elements of each operand tensor.
ReLU(X) := max(X, 0)tanh(X) := hyperbolic tangent of Xsigmoid(X) := 1 / (1 + exp(X))exp(X) := e^XA.B denotes matrix multiplication of A and B .A*B denotes pointwise multiplication of A and B .Equations
Depending on the value of RNNOperation chosen, each sublayer of the RNN layer will perform one of the following operations:
RELU
\(H[t] := ReLU(W[i].X[t] + R[i].H[t1] + Wb[i] + Rb[i])\)
TANH
\(H[t] := tanh(W[i].X[t] + R[i].H[t1] + Wb[i] + Rb[i])\)
LSTM
\(i[t] := sigmoid(W[i].X[t] + R[i].H[t1] + Wb[i] + Rb[i])\)\(f[t] := sigmoid(W[f].X[t] + R[f].H[t1] + Wb[f] + Rb[f])\)\(o[t] := sigmoid(W[o].X[t] + R[o].H[t1] + Wb[o] + Rb[o])\)\(c[t] := tanh(W[c].X[t] + R[c].H[t1] + Wb[c] + Rb[c])\)\(C[t] := f[t]*C[t1] + i[t]*c[t]\)\(H[t] := o[t]*tanh(C[t])\)GRU
\(z[t] := sigmoid(W[z].X[t] + R[z].H[t1] + Wb[z] + Rb[z])\)\(r[t] := sigmoid(W[r].X[t] + R[r].H[t1] + Wb[r] + Rb[r])\)\(h[t] := tanh(W[h].X[t] + r[t]*(R[h].H[t1] + Rb[h]) + Wb[h])\)\(H[t] := (1  z[t])*h[t] + z[t]*H[t1]\)Members:
GRU : Threegate network consisting of Gated Recurrent Units
LSTM : Fourgate LSTM network w/o peephole connections
TANH : Single gate RNN w/ TANH activation
RELU : Single gate RNN w/ ReLU activation

tensorrt.
RNNDirection
¶ The RNN direction that may be performed by an RNN layer.
Members:
UNIDIRECTION : Network iterates from first input to last input
BIDIRECTION : Network iterates from first to last (and vice versa) and outputs concatenated

tensorrt.
RNNInputMode
¶ The RNN input modes that may occur with an RNN layer.
If the RNN is configured with
RNNInputMode.LINEAR
, then for each gate g in the first layer of the RNN, the input vector X[t] (length E) is leftmultiplied by the gate’s corresponding weight matrix W[g] (dimensions HxE) as usual, before being used to compute the gate output as described byRNNOperation
.If the RNN is configured with
RNNInputMode.SKIP
, then this initial matrix multiplication is “skipped” and W[g] is conceptually an identity matrix. In this case, the input vector X[t] must have length H (the size of the hidden state).Members:
SKIP : No operation is performed on the first recurrent layer
LINEAR : Perform the normal matrix multiplication in the first recurrent layer
IRNNLayer¶
Deprecated since version 4.0.

class
tensorrt.
IRNNLayer
¶ An RNN layer in an
INetworkDefinition
.This layer applies an RNN operation on the inputs.
Deprecated This interface is superseded by IRNNv2Layer.
Variables:  num_layers –
int
The number of layers in the RNN.  hidden_size –
int
The size of the hidden layers.  max_seq_length –
int
The sequence length. This is the maximum number of input tensors that the RNN can process at once.  op –
RNNOperation
The operation of the RNN layer.  input_mode –
RNNInputMode
The input mode of the RNN layer.  direction –
RNNDirection
the direction of the RNN layer. The direction determines if the RNN is run as a unidirectional(left to right) or bidirectional(left to right and right to left). In theRNNDirection.BIDIRECTION
case the output is concatenated together, resulting in output size of 2xhidden_size
.  weights –
Weights
The weight parameters for the RNN. For more information, see IRNNLayer::setWeights().  bias –
Weights
The bias parameter vector for the RNN layer. For more information see IRNNLayer::setBias().  data_length –
int
The length of the data being processed by the RNN for use in computing other values.  hidden_state –
ITensor
the initial hidden state of the RNN with the provided hidden ITensor. The layout for hidden is a linear layout of a 3D matrix: C  The number of layers in the RNN, it must matchnum_layers
. H  The number of minibatches for each time sequence. W  The size of the per layer hidden states, it must matchhidden_size
. The amount of space required is doubled ifdirection
isRNNDirection.BIDIRECTION
with the bidirectional states coming after the unidirectional states. If not specified, then the initial hidden state is set to zero.  cell_state –
ITensor
the initial cell state of the RNN with the provided cell ITensor. The layout for cell is a linear layout of a 3D matrix: C  The number of layers in the RNN, it must matchnum_layers
. H  The number of minibatches for each time sequence. W  The size of the per layer hidden states, it must matchhidden_size
. The amount of space required is doubled ifdirection
isRNNDirection.BIDIRECTION
with the bidirectional states coming after the unidirectional states. If not specified, then the initial cell state is set to zero. The cell state only affects LSTM RNN’s.
 num_layers –
IRNNv2Layer¶

tensorrt.
RNNGateType
¶ The RNN input modes that may occur with an RNN layer.
If the RNN is configured with
RNNInputMode.LINEAR
, then for each gate g in the first layer of the RNN, the input vector X[t] (length E) is leftmultiplied by the gate’s corresponding weight matrix W[g] (dimensions HxE) as usual, before being used to compute the gate output as described byRNNOperation
.If the RNN is configured with
RNNInputMode.SKIP
, then this initial matrix multiplication is “skipped” and W[g] is conceptually an identity matrix. In this case, the input vector X[t] must have length H (the size of the hidden state).Members:
HIDDEN : Hidden Gate
INPUT : Input Gate
FORGET : Forget Gate
RESET : Reset Gate
UPDATE : Update Gate
OUTPUT : Output Gate
CELL : Cell Gate

class
tensorrt.
IRNNv2Layer
¶ An RNN layer in an
INetworkDefinition
, version 2Variables:  num_layers –
int
The layer count of the RNN.  hidden_size –
int
The hidden size of the RNN.  max_seq_length –
int
The maximum sequence length of the RNN  data_length –
int
The layer count of the RNN.  seq_lengths –
ITensor
Individual sequence lengths in the batch with theITensor
provided. Theseq_lengths
ITensor
should be a {N1, …, Np} tensor, where N1..Np are the index dimensions of the input tensor to the RNN. Ifseq_lengths
is not specified, then the RNN layer assumes all sequences are sizemax_seq_length
. All sequence lengths inseq_lengths
should be in the range [1,max_seq_length
]. Zerolength sequences are not supported. This tensor must be of type int32.  op –
RNNOperation
The operation of the RNN layer.  input_mode –
int
The input mode of the RNN layer.  direction –
int
The direction of the RNN layer.  hidden_state –
ITensor
the initial hidden state of the RNN with the providedhidden_state
ITensor
. Thehidden_state
ITensor
should have the dimensions {N1, …, Np, L, H}, where: N1..Np are the index dimensions specified by the input tensor L is the number of layers in the RNN, equal tonum_layers
H is the hidden state for each layer, equal tohidden_size
ifdirection
isRNNDirection.UNIDIRECTION
, and 2xhidden_size
otherwise.  cell_state –
ITensor
The initial cell state of the LSTM with the providedcell_state
ITensor
. Thecell_state
ITensor
should have the dimensions {N1, …, Np, L, H}, where: N1..Np are the index dimensions specified by the input tensor L is the number of layers in the RNN, equal tonum_layers
H is the hidden state for each layer, equal tohidden_size
ifdirection
isRNNDirection.UNIDIRECTION
, and 2xhidden_size
otherwise. It is an error to set this on an RNN layer that is not configured withRNNOperation.LSTM
.

get_bias_for_gate
(self: tensorrt.tensorrt.IRNNv2Layer, layer_index: int, gate: tensorrt.tensorrt.RNNGateType, is_w: bool) → array¶ Get the bias parameters for an individual gate in the RNN.
Parameters:  layer_index – The index of the layer that contains this gate.
 gate – The name of the gate within the RNN layer.
 is_w – True if the bias parameters are for the input bias Wb[g] and false if they are for the recurrent input bias Rb[g].
Returns: The bias parameters.

get_weights_for_gate
(self: tensorrt.tensorrt.IRNNv2Layer, layer_index: int, gate: tensorrt.tensorrt.RNNGateType, is_w: bool) → array¶ Get the weight parameters for an individual gate in the RNN.
Parameters:  layer_index – The index of the layer that contains this gate.
 gate – The name of the gate within the RNN layer.
 is_w – True if the weight parameters are for the input matrix W[g] and false if they are for the recurrent input matrix R[g].
Returns: The weight parameters.

set_bias_for_gate
(self: tensorrt.tensorrt.IRNNv2Layer, layer_index: int, gate: tensorrt.tensorrt.RNNGateType, is_w: bool, bias: tensorrt.tensorrt.Weights) → None¶ Set the bias parameters for an individual gate in the RNN.
Parameters:  layer_index – The index of the layer that contains this gate. Refer to
IRNNLayer.weights
for a description of the layer index.  gate – The name of the gate within the RNN layer. The gate name must correspond to one of the gates used by this layer’s
RNNOperation
.  is_w – True if the bias parameters are for the input bias Wb[g] and false if they are for the recurrent input bias Rb[g]. See
RNNOperation
for equations showing how these bias vectors are used in the RNN gate.  bias – The weight structure holding the bias parameters, which should be an array of size
hidden_size
.
 layer_index – The index of the layer that contains this gate. Refer to

set_weights_for_gate
(self: tensorrt.tensorrt.IRNNv2Layer, layer_index: int, gate: tensorrt.tensorrt.RNNGateType, is_w: bool, weights: tensorrt.tensorrt.Weights) → None¶ Set the weight parameters for an individual gate in the RNN.
Parameters:  layer_index – The index of the layer that contains this gate. Refer to
IRNNLayer.weights
for a description of the layer index.  gate – The name of the gate within the RNN layer. The gate name must correspond to one of the gates used by this layer’s
RNNOperation
.  is_w – True if the weight parameters are for the input matrix W[g] and false if they are for the recurrent input matrix R[g]. See
RNNOperation
for equations showing how these matrices are used in the RNN gate.  weights – The weight structure holding the weight parameters, which are stored as a rowmajor 2D matrix. Refer to
IRNNLayer.weights
for documentation on the expected dimensions of this matrix.
 layer_index – The index of the layer that contains this gate. Refer to
 num_layers –
IPluginLayer¶

class
tensorrt.
IPluginLayer
¶ A plugin layer in an
INetworkDefinition
.Variables: plugin – IPlugin
The plugin for the layer.
IPluginV2Layer¶

class
tensorrt.
IPluginV2Layer
¶ A plugin layer in an
INetworkDefinition
.Variables: plugin – IPluginV2
The plugin for the layer.
IUnaryLayer¶

tensorrt.
UnaryOperation
¶ The unary operations that may be performed by a Unary layer.
Members:
ATAN : Inverse tangent
ASIN : Inverse sine
SINH : Hyperbolic sine
SIN : Sine
ABS : Absolute value
ACOS : Inverse cosine
FLOOR : Floor
COSH : Hyperbolic cosine
RECIP : Reciprocal
LOG : Log (base e)
ASINH : Inverse hyperbolic sine
ACOSH : Inverse hyperbolic cosine
ATANH : Inverse hyperbolic tangent
COS : Cosine
CEIL : Ceiling
SQRT : Square root
EXP : Exponentiation
TAN : Tangent
NEG : Negation

class
tensorrt.
IUnaryLayer
¶ A unary layer in an
INetworkDefinition
.Variables: op – UnaryOperation
The unary operation for the layer.
IReduceLayer¶

tensorrt.
ReduceOperation
¶ The reduce operations that may be performed by a Reduce layer
Members:
PROD :
MAX :
AVG :
SUM :
MIN :

class
tensorrt.
IReduceLayer
¶ A reduce layer in an
INetworkDefinition
.Variables:  op –
ReduceOperation
The reduce operation for the layer.  axes –
int
The axes over which to reduce.  keep_dims –
bool
Specifies whether or not to keep the reduced dimensions for the layer.
 op –
IPaddingLayer¶

class
tensorrt.
IPaddingLayer
¶ A padding layer in an
INetworkDefinition
.Variables:
IShuffleLayer¶

class
tensorrt.
Permutation
(*args, **kwargs)¶ The elements of the permutation. The permutation is applied as outputDimensionIndex = permutation[inputDimensionIndex], so to permute from CHW order to HWC order, the required permutation is [1, 2, 0], and to permute from HWC to CHW, the required permutation is [2, 0, 1].
It supports iteration and indexing and is implicitly convertible to/from Python iterables (like
tuple
orlist
). Therefore, you can use those classes in place ofPermutation
.Overloaded function.
 __init__(self: tensorrt.tensorrt.Permutation) > None
 __init__(self: tensorrt.tensorrt.Permutation, arg0: List[int]) > None

class
tensorrt.
IShuffleLayer
¶ A shuffle layer in an
INetworkDefinition
.This class shuffles data by applying in sequence: a transpose operation, a reshape operation and a second transpose operation. The dimension types of the output are those of the reshape dimension.
Variables:  first_transpose –
Permutation
The permutation applied by the first transpose operation. Default: Identity Permutation  reshape_dims –
Permutation
The reshaped dimensions. Two special values can be used as dimensions. Value 0 copies the corresponding dimension from input. This special value can be used more than once in the dimensions. If number of reshape dimensions is less than input, 0s are resolved by aligning the most significant dimensions of input. Value 1 infers that particular dimension by looking at input and rest of the reshape dimensions. Note that only a maximum of one dimension is permitted to be specified as 1. The product of the new dimensions must be equal to the product of the old.  second_transpose –
Permutation
The permutation applied by the second transpose operation. Default: Identity Permutation
 first_transpose –
ISliceLayer¶

class
tensorrt.
ISliceLayer
¶ A slice layer in an
INetworkDefinition
.Variables:
ITopKLayer¶

tensorrt.
TopKOperation
¶ The operations that may be performed by a TopK layer
Members:
MAX : Maximum of the elements
MIN : Minimum of the elements

class
tensorrt.
ITopKLayer
¶ A TopK layer in an
INetworkDefinition
.Variables:  op –
TopKOperation
The operation for the layer.  k –
TopKOperation
the k value for the layer. Currently only values up to 25 are supported.  axes –
TopKOperation
The axes along which to reduce.
 op –
IMatrixMultiplyLayer¶

tensorrt.
MatrixOperation
¶ The matrix operations that may be performed by a Matrix layer
Members:
NONE :
TRANSPOSE : Transpose each matrix
VECTOR : Treat operand as collection of vectors

class
tensorrt.
IMatrixMultiplyLayer
¶ A matrix multiply layer in an
INetworkDefinition
.Let A be op(getInput(0)) and B be op(getInput(1)) where op(x) denotes the corresponding MatrixOperation.
When A and B are matrices or vectors, computes the inner product A * B:
matrix * matrix > matrixmatrix * vector > vectorvector * matrix > vectorvector * vector > scalarInputs of higher rank are treated as collections of matrices or vectors. The output will be a corresponding collection of matrices, vectors, or scalars.
Variables:  op0 –
MatrixOperation
How to treat the first input.  op1 –
MatrixOperation
How to treat the second input.
 op0 –
IRaggedSoftMaxLayer¶

class
tensorrt.
IRaggedSoftMaxLayer
¶ A ragged softmax layer in an
INetworkDefinition
.This layer takes a ZxS input tensor and an additional Zx1 bounds tensor holding the lengths of the Z sequences.
This layer computes a softmax across each of the Z sequences.
The output tensor is of the same size as the input tensor.
IIdentityLayer¶

class
tensorrt.
IIdentityLayer
¶ A layer that represents the identity function.
If tensor precision is explicitly specified, it can be used to transform from one precision to another.
IConstantLayer¶

class
tensorrt.
IConstantLayer
¶ A constant layer in an
INetworkDefinition
.Variables: