Layers
PaddingMode
- tensorrt.PaddingMode
- Enumerates types of padding available in convolution, deconvolution and pooling layers.
Padding mode takes precedence if both
padding_mode
andpre_padding
are set.EXPLICIT* corresponds to explicit padding.SAME* implicitly calculates padding such that the output dimensions are the same as the input dimensions. For convolution and pooling, output dimensions are determined by ceil(input dimensions, stride).CAFFE* corresponds to symmetric padding.
Members:
EXPLICIT_ROUND_DOWN : Use explicit padding, rounding the output size down
EXPLICIT_ROUND_UP : Use explicit padding, rounding the output size up
SAME_UPPER : Use SAME padding, with
pre_padding
<=post_padding
SAME_LOWER : Use SAME padding, with
pre_padding
>=post_padding
CAFFE_ROUND_DOWN : Use CAFFE padding, rounding the output size down
CAFFE_ROUND_UP : Use CAFFE padding, rounding the output size up
IConvolutionLayer
- class tensorrt.IConvolutionLayer
A convolution layer in an
INetworkDefinition
.This layer performs a correlation operation between 3-dimensional filter with a 4-dimensional tensor to produce another 4-dimensional tensor.
An optional bias argument is supported, which adds a per-channel 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 zero-padded by this number of elements in the height and width directions. If the padding is asymmetric, this value corresponds to the pre-padding. Default: (0, 0)pre_padding –
DimsHW
The pre-padding. The start of input will be zero-padded by this number of elements in the height and width directions. Default: (0, 0)post_padding –
DimsHW
The post-padding. The end of input will be zero-padded by this number of elements in the height and width directions. Default: (0, 0)padding_mode –
PaddingMode
The padding mode. Padding mode takes precedence if bothIConvolutionLayer.padding_mode
and eitherIConvolutionLayer.pre_padding
orIConvolutionLayer.post_padding
are set.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 per-channel, so the number of weights (if non-zero) must be equal to the number of output feature maps.dilation –
DimsHW
The dilation for a convolution. Default: (1, 1)kernel_size_nd –
Dims
The multi-dimension kernel size of the convolution.stride_nd –
Dims
The multi-dimension stride of the convolution. Default: (1, …, 1)padding_nd –
Dims
The multi-dimension padding of the convolution. The input will be zero-padded by this number of elements in each dimension. If the padding is asymmetric, this value corresponds to the pre-padding. Default: (0, …, 0)dilation_nd –
Dims
The multi-dimension dilation for the convolution. Default: (1, …, 1)
IFullyConnectedLayer
- class tensorrt.IFullyConnectedLayer
A fully connected layer in an
INetworkDefinition
.This layer expects an input tensor of three or more non-batch 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} .
IActivationLayer
- tensorrt.ActivationType
The type of activation to perform.
Members:
RELU : Rectified Linear activation
SIGMOID : Sigmoid activation
TANH : Hyperbolic Tangent 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
SELU : Selu activation: f(x) = beta * x if x > 0, f(x) = beta * (alpha * exp(x) - alpha) if x <= 0
SOFTSIGN : Softsign activation: f(x) = x / (1 + abs(x))
SOFTPLUS : Softplus activation: f(x) = alpha * log(exp(beta * x) + 1)
CLIP : Clip activation: f(x) = max(alpha, min(beta, x))
HARD_SIGMOID : Hard sigmoid activation: f(x) = max(0, min(1, alpha * x + beta))
SCALED_TANH : Scaled Tanh activation: f(x) = alpha * tanh(beta * x)
THRESHOLDED_RELU : Thresholded Relu activation: f(x) = x if x > alpha, f(x) = 0 if x <= alpha
- class tensorrt.IActivationLayer
An Activation layer in an
INetworkDefinition
. This layer applies a per-element 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.
IPoolingLayer
- tensorrt.PoolingType
The type of pooling to perform in a pooling layer.
Members:
MAX : Maximum over elements
AVERAGE : Average over elements. If the tensor is padded, the count includes the padding
MAX_AVERAGE_BLEND : Blending between the max pooling and average pooling: (1-blendFactor)*maxPool + blendFactor*avgPool
- 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, 1)padding –
DimsHW
The padding for pooling. Default: (0, 0)pre_padding –
DimsHW
The pre-padding. The start of input will be zero-padded by this number of elements in the height and width directions. Default: (0, 0)post_padding –
DimsHW
The post-padding. The end of input will be zero-padded by this number of elements in the height and width directions. Default: (0, 0)padding_mode –
PaddingMode
The padding mode. Padding mode takes precedence if bothIPoolingLayer.padding_mode
and eitherIPoolingLayer.pre_padding
orIPoolingLayer.post_padding
are set.blend_factor –
float
The blending factor for the max_average_blend mode: \(max_average_blendPool = (1-blendFactor)*maxPool + blendFactor*avgPool\) .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: Truewindow_size_nd –
Dims
The multi-dimension window size for pooling.stride_nd –
Dims
The multi-dimension stride for pooling. Default: (1, …, 1)padding_nd –
Dims
The multi-dimension padding for pooling. Default: (0, …, 0)
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 [1e-5, 1e10].
IScaleLayer
- tensorrt.ScaleMode
Controls how scale is applied in a Scale layer.
Members:
UNIFORM : Identical coefficients across all elements of the tensor.
CHANNEL : Per-channel coefficients. The channel dimension is assumed to be the third to last dimension.
ELEMENTWISE : Elementwise coefficients.
- class tensorrt.IScaleLayer
A Scale layer in an
INetworkDefinition
.This layer applies a per-element computation to its input:
\(output = (input * scale + shift) ^ power\)
The coefficients can be applied on a per-tensor, per-channel, or per-element 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.
ISoftMaxLayer
- class tensorrt.ISoftMaxLayer
A Softmax layer in an
INetworkDefinition
.This layer applies a per-channel softmax to its input.
The output size is the same as the input size.
- Variables
axes –
int
The axis along which softmax is computed. Currently, only one axis can be set.
The axis is specified by setting the bit corresponding to the axis to 1, as a bit mask.For example, consider an NCHW tensor as input (three non-batch dimensions).In implicit mode :Bit 0 corresponds to the C dimension boolean.Bit 1 corresponds to the H dimension boolean.Bit 2 corresponds to the W dimension boolean.By default, softmax is performed on the axis which is the number of axes minus three. It is 0 if there are fewer than 3 non-batch axes. For example, if the input is NCHW, the default axis is C. If the input is NHW, then the default axis is H.
In explicit mode :Bit 0 corresponds to the N dimension boolean.Bit 1 corresponds to the C dimension boolean.Bit 2 corresponds to the H dimension boolean.Bit 3 corresponds to the W dimension boolean.By default, softmax is performed on the axis which is the number of axes minus three. It is 0 ifthere are fewer than 3 axes. For example, if the input is NCHW, the default axis is C. If the inputis NHW, then the default axis is N.For example, to perform softmax on axis R of a NPQRCHW input, set bit 2 with implicit batch mode,set bit 3 with explicit batch mode.On Xavier, this layer is not supported on DLA. Otherwise, the following constraints must be satisfied to execute this layer on DLA:
Axis must be one of the channel or spatial dimensions.
There are two classes of supported input sizes:
Non-axis, non-batch dimensions are all 1 and the axis dimension is at most 8192. This is the recommended case for using softmax since it is the most accurate.
At least one non-axis, non-batch dimension greater than 1 and the axis dimension is at most 1024. Note that in this case, there may be some approximation error as the axis dimension size approaches the upper bound. See the TensorRT Developer Guide for more details on the approximation error.
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 non-batch axes in the tensor minus three (e.g. for an NCHW input it would be 0), or 0 if there are fewer than 3 non-batch 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 zero-padded by this number of elements in the height and width directions. Padding is symmetric. Default: (0, 0)pre_padding –
DimsHW
The pre-padding. The start of input will be zero-padded by this number of elements in the height and width directions. Default: (0, 0)post_padding –
DimsHW
The post-padding. The end of input will be zero-padded by this number of elements in the height and width directions. Default: (0, 0)padding_mode –
PaddingMode
The padding mode. Padding mode takes precedence if bothIDeconvolutionLayer.padding_mode
and eitherIDeconvolutionLayer.pre_padding
orIDeconvolutionLayer.post_padding
are set.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: 1kernel –
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 per-feature-map, so the number of weights (if non-zero) must be equal to the number of output feature maps.kernel_size_nd –
Dims
The multi-dimension kernel size of the convolution.stride_nd –
Dims
The multi-dimension stride of the deconvolution. Default: (1, …, 1)padding_nd –
Dims
The multi-dimension padding of the deconvolution. The input will be zero-padded by this number of elements in each dimension. Padding is symmetric. Default: (0, …, 0)
IElementWiseLayer
- tensorrt.ElementWiseOperation
The binary operations that may be performed by an ElementWise layer.
Members:
SUM : Sum of the two elements
PROD : Product of the two elements
MAX : Max of the two elements
MIN : Min of the two elements
SUB : Subtract the second element from the first
DIV : Divide the first element by the second
POW : The first element to the power of the second element
FLOOR_DIV : Floor division of the first element by the second
AND : Logical AND of two elements
OR : Logical OR of two elements
XOR : Logical XOR of two elements
EQUAL : Check if two elements are equal
GREATER : Check if element in first tensor is greater than corresponding element in second tensor
LESS : Check if element in first tensor is less than corresponding element in second tensor
- class tensorrt.IElementWiseLayer
A elementwise layer in an
INetworkDefinition
.This layer applies a per-element 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 non-batch dimension axis to gather on. The axis must be less than the number of non-batch dimensions in the data input.num_elementwise_dims –
int
The number of leading dimensions of indices tensor to be handled elementwise. For GatherMode.DEFAULT, it must be 0 if there is an implicit batch dimension. It can be 0 or 1 if there is not an implicit batch dimension. For GatherMode::kND, it can be between 0 and one less than rank(data). For GatherMode::kELEMENT, it must be 0.mode –
GatherMode
The gather mode.
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 sub-layer of the RNN layer will perform one of the following operations:
RELU
\(H[t] := ReLU(W[i].X[t] + R[i].H[t-1] + Wb[i] + Rb[i])\)
TANH
\(H[t] := tanh(W[i].X[t] + R[i].H[t-1] + Wb[i] + Rb[i])\)
LSTM
\(i[t] := sigmoid(W[i].X[t] + R[i].H[t-1] + Wb[i] + Rb[i])\)\(f[t] := sigmoid(W[f].X[t] + R[f].H[t-1] + Wb[f] + Rb[f])\)\(o[t] := sigmoid(W[o].X[t] + R[o].H[t-1] + Wb[o] + Rb[o])\)\(c[t] := tanh(W[c].X[t] + R[c].H[t-1] + Wb[c] + Rb[c])\)\(C[t] := f[t]*C[t-1] + i[t]*c[t]\)\(H[t] := o[t]*tanh(C[t])\)GRU
\(z[t] := sigmoid(W[z].X[t] + R[z].H[t-1] + Wb[z] + Rb[z])\)\(r[t] := sigmoid(W[r].X[t] + R[r].H[t-1] + Wb[r] + Rb[r])\)\(h[t] := tanh(W[h].X[t] + r[t]*(R[h].H[t-1] + Rb[h]) + Wb[h])\)\(H[t] := (1 - z[t])*h[t] + z[t]*H[t-1]\)Members:
RELU : Single gate RNN w/ ReLU activation
TANH : Single gate RNN w/ TANH activation
LSTM : Four-gate LSTM network w/o peephole connections
GRU : Three-gate network consisting of Gated Recurrent Units
- 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 left-multiplied 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:
LINEAR : Perform the normal matrix multiplication in the first recurrent layer
SKIP : No operation is performed on the first recurrent layer
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 left-multiplied 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:
INPUT : Input Gate
OUTPUT : Output Gate
FORGET : Forget Gate
UPDATE : Update Gate
RESET : Reset Gate
CELL : Cell Gate
HIDDEN : Hidden Gate
- class tensorrt.IRNNv2Layer
An RNN layer in an
INetworkDefinition
, version 2- Variables
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 RNNdata_length –
int
The length of the data being processed by the RNN for use in computing other values.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
]. Zero-length sequences are not supported. This tensor must be of typeint32
.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) numpy.ndarray
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) numpy.ndarray
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.
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
.
- 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.
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 row-major 2D matrix. For more information, see IRNNv2Layer::setWeights().
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:
EXP : Exponentiation
LOG : Log (base e)
SQRT : Square root
RECIP : Reciprocal
ABS : Absolute value
NEG : Negation
SIN : Sine
COS : Cosine
TAN : Tangent
SINH : Hyperbolic sine
COSH : Hyperbolic cosine
ASIN : Inverse sine
ACOS : Inverse cosine
ATAN : Inverse tangent
ASINH : Inverse hyperbolic sine
ACOSH : Inverse hyperbolic cosine
ATANH : Inverse hyperbolic tangent
CEIL : Ceiling
FLOOR : Floor
ERF : Gauss error function
NOT : Not
SIGN : Sign. If input > 0, output 1; if input < 0, output -1; if input == 0, output 0.
ROUND : Round to nearest even for float datatype.
- class tensorrt.IUnaryLayer
A unary layer in an
INetworkDefinition
.- Variables
op –
UnaryOperation
The unary operation for the layer. When running this layer on DLA, onlyUnaryOperation.ABS
is supported.
IReduceLayer
- tensorrt.ReduceOperation
The reduce operations that may be performed by a Reduce layer
Members:
SUM :
PROD :
MAX :
MIN :
AVG :
- 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.
IPaddingLayer
- class tensorrt.IPaddingLayer
A padding layer in an
INetworkDefinition
.- Variables
pre_padding –
DimsHW
The padding that is applied at the start of the tensor. Negative padding results in trimming the edge by the specified amount.post_padding –
DimsHW
The padding that is applied at the end of the tensor. Negative padding results in trimming the edge by the specified amountpre_padding_nd –
Dims
The padding that is applied at the start of the tensor. Negative padding results in trimming the edge by the specified amount. Only 2 dimensions currently supported.post_padding_nd –
Dims
The padding that is applied at the end of the tensor. Negative padding results in trimming the edge by the specified amount. Only 2 dimensions currently supported.
IParametricReLULayer
- class tensorrt.IParametricReLULayer
A parametric ReLU layer in an
INetworkDefinition
.This layer applies a parametric ReLU activation to an input tensor (first input), with slopes taken from a slopes tensor (second input). This can be viewed as a leaky ReLU operation where the negative slope differs from element to element (and can in fact be learned).
The slopes tensor must be unidirectional broadcastable to the input tensor: the rank of the two tensors must be the same, and all dimensions of the slopes tensor must either equal the input tensor or be 1. The output tensor has the same shape as the input tensor.
ISelectLayer
- class tensorrt.ISelectLayer
A select layer in an
INetworkDefinition
.This layer implements an element-wise ternary conditional operation. Wherever
condition
isTrue
, elements are taken from the first input, and wherevercondition
isFalse
, elements are taken from the second input.
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 Permutationreshape_dims –
Dims
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 Permutationzero_is_placeholder –
bool
The meaning of 0 in reshape dimensions. If true, then a 0 in the reshape dimensions denotes copying the corresponding dimension from the first input tensor. If false, then a 0 in the reshape dimensions denotes a zero-length dimension.
- set_input(self: tensorrt.tensorrt.IShuffleLayer, index: int, tensor: tensorrt.tensorrt.ITensor) None
Sets the input tensor for the given index. The index must be 0 for a static shuffle layer. A static shuffle layer is converted to a dynamic shuffle layer by calling
set_input()
with an index 1. A dynamic shuffle layer cannot be converted back to a static shuffle layer.For a dynamic shuffle layer, the values 0 and 1 are valid. The indices in the dynamic case are as follows:
Index
Description
0
Data or Shape tensor to be shuffled.
1
The dimensions for the reshape operation, as a 1D
int32
shape tensor.If this function is called with a value 1, then
num_inputs
changes from 1 to 2.- Parameters
index – The index of the input tensor.
tensor – The input tensor.
ISliceLayer
- class tensorrt.ISliceLayer
A slice layer in an
INetworkDefinition
.The slice layer has two variants, static and dynamic. Static slice specifies the start, size, and stride dimensions at layer creation time via
Dims
and can use the get/set accessor functions of theISliceLayer
. Dynamic slice specifies one or more of start, size or stride asITensor`s, by using :func:`ILayer.set_input
to add a second, third, or fourth input respectively. The correspondingDims
are used if an input is missing or null.An application can determine if the
ISliceLayer
has a dynamic output shape based on whether the size input (third input) is present and non-null.The slice layer selects for each dimension a start location from within the input tensor, and copies elements to the output tensor using the specified stride across the input tensor. Start, size, and stride tensors must be 1-D
int32
shape tensors if not specified viaDims
.An example of using slice on a tensor: input = {{0, 2, 4}, {1, 3, 5}} start = {1, 0} size = {1, 2} stride = {1, 2} output = {{1, 5}}
When the sliceMode is
SliceMode.CLAMP
orSliceMode.REFLECT
, for each input dimension, if its size is 0 then the corresponding output dimension must be 0 too.A slice layer can produce a shape tensor if the following conditions are met:
start
,size
, andstride
are build time constants, either as staticDims
or as constant input tensors.The number of elements in the output tensor does not exceed 2 *
Dims.MAX_DIMS
.
The input tensor is a shape tensor if the output is a shape tensor.
The following constraints must be satisfied to execute this layer on DLA: *
start
,size
, andstride
are build time constants, either as staticDims
or as constant input tensors. * sliceMode isSliceMode.DEFAULT
. * Strides are 1 for all dimensions. * Slicing is not performed on the first dimension * The input tensor has four dimensions- Variables
start –
Dims
The start offset.shape –
Dims
The output dimensions.stride –
Dims
The slicing stride.mode –
SliceMode
Controls howISliceLayer
handles out of bounds coordinates.
- set_input(self: tensorrt.tensorrt.ISliceLayer, index: int, tensor: tensorrt.tensorrt.ITensor) None
Sets the input tensor for the given index. The index must be 0 or 4 for a static slice layer. A static slice layer is converted to a dynamic slice layer by calling
set_input()
with an index between 1 and 3. A dynamic slice layer cannot be converted back to a static slice layer.The indices are as follows:
Index
Description
0
Data or Shape tensor to be sliced.
1
The start tensor to begin slicing, N-dimensional for Data, and 1-D for Shape.
2
The size tensor of the resulting slice, N-dimensional for Data, and 1-D for Shape.
3
The stride of the slicing operation, N-dimensional for Data, and 1-D for Shape.
4
Value for the
SliceMode.FILL
slice mode. Disallowed for other modes.If this function is called with a value greater than 0, then
num_inputs
changes from 1 to index + 1.- Parameters
index – The index of the input tensor.
tensor – The input tensor.
IShapeLayer
- class tensorrt.IShapeLayer
A shape layer in an
INetworkDefinition
. Used for getting the shape of a tensor. This class sets the output to a one-dimensional tensor with the dimensions of the input tensor.For example, if the input is a four-dimensional tensor (of any type) with dimensions [2,3,5,7], the output tensor is a one-dimensional
int32
tensor of length 4 containing the sequence 2, 3, 5, 7.
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 3840 are supported.axes –
TopKOperation
The axes along which to reduce.
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.
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
.Note: This layer does not support boolean types.
IResizeLayer
- tensorrt.ResizeMode
Various modes of resize in the resize layer.
Members:
NEAREST : 1D, 2D, and 3D nearest neighbor resizing.
LINEAR : Can handle linear, bilinear, trilinear resizing.
- class tensorrt.IResizeLayer
A resize layer in an
INetworkDefinition
.Resize layer can be used for resizing a N-D tensor.
Resize layer currently supports the following configurations:
ResizeMode.NEAREST - resizes innermost m dimensions of N-D, where 0 < m <= min(3, N) and N > 0.
ResizeMode.LINEAR - resizes innermost m dimensions of N-D, where 0 < m <= min(3, N) and N > 0.
Default resize mode is ResizeMode.NEAREST.
Resize layer provides two ways to resize tensor dimensions:
- Set output dimensions directly. It can be done for static as well as dynamic resize layer.
Static resize layer requires output dimensions to be known at build-time. Dynamic resize layer requires output dimensions to be set as one of the input tensors.
- Set scales for resize. Each output dimension is calculated as floor(input dimension * scale).
Only static resize layer allows setting scales where the scales are known at build-time.
If executing this layer on DLA, the following combinations of parameters are supported:
In NEAREST mode:
(ResizeCoordinateTransformation.ASYMMETRIC, ResizeSelector.FORMULA, ResizeRoundMode.FLOOR)
(ResizeCoordinateTransformation.HALF_PIXEL, ResizeSelector.FORMULA, ResizeRoundMode.HALF_DOWN)
(ResizeCoordinateTransformation.HALF_PIXEL, ResizeSelector.FORMULA, ResizeRoundMode.HALF_UP)
In LINEAR mode:
(ResizeCoordinateTransformation.HALF_PIXEL, ResizeSelector.FORMULA)
(ResizeCoordinateTransformation.HALF_PIXEL, ResizeSelector.UPPER)
- Variables
shape –
Dims
The output dimensions. Must to equal to input dimensions size.scales –
List[float]
List of resize scales. If executing this layer on DLA, there are three restrictions: 1.len(scales)
has to be exactly 4. 2. The first two elements in scales need to be exactly 1 (for unchanged batch and channel dimensions). 3. The last two elements in scales, representing the scale values along height and width dimensions, respectively, need to be integer values in the range of [1, 32] for NEAREST mode and [1, 4] for LINEAR. Example of DLA-supported scales: [1, 1, 2, 2].resize_mode –
ResizeMode
Resize mode can be Linear or Nearest.coordinate_transformation –
ResizeCoordinateTransformationDoc
Supported resize coordinate transformation modes are ALIGN_CORNERS, ASYMMETRIC and HALF_PIXEL.selector_for_single_pixel –
ResizeSelector
Supported resize selector modes are FORMULA and UPPER.nearest_rounding –
ResizeRoundMode
Supported resize Round modes are HALF_UP, HALF_DOWN, FLOOR and CEIL.
- set_input(self: tensorrt.tensorrt.IResizeLayer, index: int, tensor: tensorrt.tensorrt.ITensor) None
Sets the input tensor for the given index.
If index == 1 and num_inputs == 1, and there is no implicit batch dimension, in which case num_inputs changes to 2. Once such additional input is set, resize layer works in dynamic mode. When index == 1 and num_inputs == 1, the output dimensions are used from the input tensor, overriding the dimensions supplied by shape.
- Parameters
index – The index of the input tensor.
tensor – The input tensor.
ILoop
- class tensorrt.ILoop
Helper for creating a recurrent subgraph.
- Variables
name – The name of the loop. The name is used in error diagnostics.
- add_iterator(self: tensorrt.tensorrt.ILoop, tensor: tensorrt.tensorrt.ITensor, axis: int = 0, reverse: bool = False) tensorrt.tensorrt.IIteratorLayer
Return layer that subscripts tensor by loop iteration.
For reverse=false, this is equivalent to add_gather(tensor, I, 0) where I is a scalar tensor containing the loop iteration number. For reverse=true, this is equivalent to add_gather(tensor, M-1-I, 0) where M is the trip count computed from TripLimits of kind
COUNT
.- Parameters
tensor – The tensor to iterate over.
axis – The axis along which to iterate.
reverse – Whether to iterate in the reverse direction.
- Returns
The
IIteratorLayer
, orNone
if it could not be created.
- add_loop_output(self: tensorrt.tensorrt.ILoop, tensor: tensorrt.tensorrt.ITensor, kind: tensorrt.tensorrt.LoopOutput, axis: int = 0) tensorrt.tensorrt.ILoopOutputLayer
Make an output for this loop, based on the given tensor.
If
kind
isCONCATENATE
orREVERSE
, a second input specifying the concatenation dimension must be added via methodILoopOutputLayer.set_input()
.- Parameters
kind – The kind of loop output. See
LoopOutput
axis – The axis for concatenation (if using
kind
ofCONCATENATE
orREVERSE
).
- Returns
The added
ILoopOutputLayer
, orNone
if it could not be created.
- add_recurrence(self: tensorrt.tensorrt.ILoop, initial_value: tensorrt.tensorrt.ITensor) tensorrt.tensorrt.IRecurrenceLayer
Create a recurrence layer for this loop with initial_value as its first input.
- Parameters
initial_value – The initial value of the recurrence layer.
- Returns
The added
IRecurrenceLayer
, orNone
if it could not be created.
- add_trip_limit(self: tensorrt.tensorrt.ILoop, tensor: tensorrt.tensorrt.ITensor, kind: tensorrt.tensorrt.TripLimit) tensorrt.tensorrt.ITripLimitLayer
Add a trip-count limiter, based on the given tensor.
There may be at most one
COUNT
and oneWHILE
limiter for a loop. When both trip limits exist, the loop exits when the count is reached or condition is falsified. It is an error to not add at least one trip limiter.For
WHILE
, the input tensor must be the output of a subgraph that contains only layers that are notITripLimitLayer
,IIteratorLayer
orILoopOutputLayer
. AnyIRecurrenceLayer
s in the subgraph must belong to the same loop as theITripLimitLayer
. A trivial example of this rule is that the input to theWHILE
is the output of anIRecurrenceLayer
for the same loop.- Parameters
tensor – The input tensor. Must be available before the loop starts.
kind – The kind of trip limit. See
TripLimit
- Returns
The added
ITripLimitLayer
, orNone
if it could not be created.
ILoopBoundaryLayer
ITripLimitLayer
- tensorrt.TripLimit
Describes kinds of trip limits.
Members:
COUNT : Tensor is a scalar of type
int32
that contains the trip count.WHILE : Tensor is a scalar of type
bool
. Loop terminates when its value is false.
IRecurrenceLayer
- class tensorrt.IRecurrenceLayer
- set_input(self: tensorrt.tensorrt.IRecurrenceLayer, index: int, tensor: tensorrt.tensorrt.ITensor) None
Set the first or second input. If index==1 and the number of inputs is one, the input is appended. The first input specifies the initial output value, and must come from outside the loop. The second input specifies the next output value, and must come from inside the loop. The two inputs must have the same dimensions.
- Parameters
index – The index of the input to set.
tensor – The input tensor.
IIteratorLayer
- class tensorrt.IIteratorLayer
- Variables
axis – The axis to iterate over
reverse – For reverse=false, the layer is equivalent to add_gather(tensor, I, 0) where I is a scalar tensor containing the loop iteration number. For reverse=true, the layer is equivalent to add_gather(tensor, M-1-I, 0) where M is the trip count computed from TripLimits of kind
COUNT
. The default is reverse=false.
ILoopOutputLayer
- tensorrt.LoopOutput
Describes kinds of loop outputs.
Members:
LAST_VALUE : Output value is value of tensor for last iteration.
CONCATENATE : Output value is concatenation of values of tensor for each iteration, in forward order.
REVERSE : Output value is concatenation of values of tensor for each iteration, in reverse order.
- class tensorrt.ILoopOutputLayer
An
ILoopOutputLayer
is the sole way to get output from a loop.The first input tensor must be defined inside the loop; the output tensor is outside the loop. The second input tensor, if present, must be defined outside the loop.
If
kind
isLAST_VALUE
, a single input must be provided.If
kind
isCONCATENATE
orREVERSE
, a second input must be provided. The second input must be a scalar “shape tensor”, defined before the loop commences, that specifies the concatenation length of the output.The output tensor has j more dimensions than the input tensor, where j == 0 if
kind
isLAST_VALUE
j == 1 ifkind
isCONCATENATE
orREVERSE
.- Variables
axis – The contenation axis. Ignored if
kind
isLAST_VALUE
. For example, if the input tensor has dimensions [b,c,d], andkind
isCONCATENATE
, the output has four dimensions. Let a be the value of the second input. axis=0 causes the output to have dimensions [a,b,c,d]. axis=1 causes the output to have dimensions [b,a,c,d]. axis=2 causes the output to have dimensions [b,c,a,d]. axis=3 causes the output to have dimensions [b,c,d,a]. Default is axis is 0.kind – The kind of loop output. See
LoopOutput
- set_input(self: tensorrt.tensorrt.ILoopOutputLayer, index: int, tensor: tensorrt.tensorrt.ITensor) None
Like
ILayer.set_input()
, but additionally works if index==1,num_inputs`==1, in which case :attr:`num_inputs
changes to 2.
IFillLayer
- tensorrt.FillOperation
The tensor fill operations that may performed by an Fill layer.
Members:
LINSPACE : Generate evenly spaced numbers over a specified interval
RANDOM_UNIFORM : Generate a tensor with random values drawn from a uniform distribution
- class tensorrt.IFillLayer
A fill layer in an
INetworkDefinition
.- set_input(self: tensorrt.tensorrt.IFillLayer, index: int, tensor: tensorrt.tensorrt.ITensor) None
replace an input of this layer with a specific tensor.
Index
Description for kLINSPACE
0
Shape tensor, represents the output tensor’s dimensions.
1
Start, a scalar, represents the start value.
2
Delta, a 1D tensor, length equals to shape tensor’s nbDims, represents the delta value for each dimension.
Index
Description for kRANDOM_UNIFORM
0
Shape tensor, represents the output tensor’s dimensions.
1
Minimum, a scalar, represents the minimum random value.
2
Maximum, a scalar, represents the maximal random value.
- Parameters
index – the index of the input to modify.
tensor – the input tensor.
IQuantizeLayer
- class tensorrt.IQuantizeLayer
A Quantize layer in an
INetworkDefinition
.This layer accepts a floating-point data input tensor, and uses the scale and zeroPt inputs to
quantize the data to an 8-bit signed integer according to:
\(output = clamp(round(input / scale) + zeroPt)\)
Rounding type is rounding-to-nearest ties-to-even (https://en.wikipedia.org/wiki/Rounding#Round_half_to_even).
Clamping is in the range [-128, 127].
The first input (index 0) is the tensor to be quantized. The second (index 1) and third (index 2) are the scale and zero point respectively. Each of scale and zeroPt must be either a scalar, or a 1D tensor.
The zeroPt tensor is optional, and if not set, will be assumed to be zero. Its data type must be tensorrt.int8. zeroPt must only contain zero-valued coefficients, because only symmetric quantization is supported. The scale value must be either a scalar for per-tensor quantization, or a 1D tensor for per-axis quantization. The size of the 1-D scale tensor must match the size of the quantization axis. The size of the scale must match the size of the zeroPt.
The subgraph which terminates with the scale tensor must be a build-time constant. The same restrictions apply to the zeroPt. The output type, if constrained, must be constrained to tensorrt.int8. The input type, if constrained, must be constrained to tensorrt.float32 (FP16 input is not supported). The output size is the same as the input size.
IQuantizeLayer only supports tensorrt.float32 precision and will default to this precision during instantiation. IQuantizeLayer only supports tensorrt.int8 output.
- Variables
axis –
int
The axis along which quantization occurs. The quantization axis is in reference to the input tensor’s dimensions.
IDequantizeLayer
- class tensorrt.IDequantizeLayer
A Dequantize layer in an
INetworkDefinition
.This layer accepts a signed 8-bit integer input tensor, and uses the configured scale and zeroPt inputs to dequantize the input according to: \(output = (input - zeroPt) * scale\)
The first input (index 0) is the tensor to be quantized. The second (index 1) and third (index 2) are the scale and zero point respectively. Each of scale and zeroPt must be either a scalar, or a 1D tensor.
The zeroPt tensor is optional, and if not set, will be assumed to be zero. Its data type must be tensorrt.int8. zeroPt must only contain zero-valued coefficients, because only symmetric quantization is supported. The scale value must be either a scalar for per-tensor quantization, or a 1D tensor for per-axis quantization. The size of the 1-D scale tensor must match the size of the quantization axis. The size of the scale must match the size of the zeroPt.
The subgraph which terminates with the scale tensor must be a build-time constant. The same restrictions apply to the zeroPt. The output type, if constrained, must be constrained to tensorrt.int8. The input type, if constrained, must be constrained to tensorrt.float32 (FP16 input is not supported). The output size is the same as the input size.
IDequantizeLayer only supports tensorrt.int8 precision and will default to this precision during instantiation. IDequantizeLayer only supports tensorrt.float32 output.
- Variables
axis –
int
The axis along which dequantization occurs. The dequantization axis is in reference to the input tensor’s dimensions.
IScatterLayer
- class tensorrt.IScatterLayer
A Scatter layer as in
INetworkDefinition
. :ivar axis: axis to scatter on when using Scatter Element mode (ignored in ND mode) :ivar mode:ScatterMode
The operation mode of the scatter.
IIfConditional
- class tensorrt.IIfConditional
Helper for constructing conditionally-executed subgraphs.
An If-conditional conditionally executes (lazy evaluation) part of the network according to the following pseudo-code:
If condition is true Then: output = trueSubgraph(trueInputs); Else: output = falseSubgraph(falseInputs); Emit output
Condition is a 0D boolean tensor (representing a scalar). trueSubgraph represents a network subgraph that is executed when condition is evaluated to True. falseSubgraph represents a network subgraph that is executed when condition is evaluated to False.
The following constraints apply to If-conditionals: - Both the trueSubgraph and falseSubgraph must be defined. - The number of output tensors in both subgraphs is the same. - The type and shape of each output tensor from true/false subgraphs are the same.
- add_input(self: tensorrt.tensorrt.IIfConditional, input: tensorrt.tensorrt.ITensor) tensorrt.tensorrt.IIfConditionalInputLayer
Make an input for this if-conditional, based on the given tensor.
- Parameters
input – An input to the conditional that can be used by either or both of the conditional’s subgraphs.
- add_output(self: tensorrt.tensorrt.IIfConditional, true_subgraph_output: tensorrt.tensorrt.ITensor, false_subgraph_output: tensorrt.tensorrt.ITensor) tensorrt.tensorrt.IIfConditionalOutputLayer
Make an output for this if-conditional, based on the given tensors.
Each output layer of the if-conditional represents a single output of either the true-subgraph or the false-subgraph of the if-conditional, depending on which subgraph was executed.
- Parameters
true_subgraph_output – The output of the subgraph executed when this conditional’s condition input evaluates to true.
false_subgraph_output – The output of the subgraph executed when this conditional’s condition input evaluates to false.
- Returns
The
IIfConditionalOutputLayer
, orNone
if it could not be created.
- set_condition(self: tensorrt.tensorrt.IIfConditional, condition: tensorrt.tensorrt.ITensor) tensorrt.tensorrt.IConditionLayer
Set the condition tensor for this If-Conditional construct.
The
condition
tensor must be a 0D data tensor (scalar) with typebool
.- Parameters
condition – The condition tensor that will determine which subgraph to execute.
- Returns
The
IConditionLayer
, orNone
if it could not be created.
IConditionLayer
- class tensorrt.IConditionLayer
Describes the boolean condition of an if-conditional.
IIfConditionalOutputLayer
- class tensorrt.IIfConditionalOutputLayer
Describes kinds of if-conditional outputs.
IIfConditionalInputLayer
- class tensorrt.IIfConditionalInputLayer
Describes kinds of if-conditional inputs.
IEinsumLayer
- class tensorrt.IEinsumLayer
An Einsum layer in an
INetworkDefinition
.This layer implements a summation over the elements of the inputs along dimensions specified by the equation parameter, based on the Einstein summation convention. The layer can have one or more inputs of rank >= 0. All the inputs must be of same data type. This layer supports all TensorRT data types except
bool
. There is one output tensor of the same type as the input tensors. The shape of output tensor is determined by the equation.The equation specifies ASCII lower-case letters for each dimension in the inputs in the same order as the dimensions, separated by comma for each input. The dimensions labeled with the same subscript must match or be broadcastable. Repeated subscript labels in one input take the diagonal. Repeating a label across multiple inputs means that those axes will be multiplied. Omitting a label from the output means values along those axes will be summed. In implicit mode, the indices which appear once in the expression will be part of the output in increasing alphabetical order. In explicit mode, the output can be controlled by specifying output subscript labels by adding an arrow (‘->’) followed by subscripts for the output. For example, “ij,jk->ik” is equivalent to “ij,jk”. Ellipsis (‘…’) can be used in place of subscripts to broadcast the dimensions. See the TensorRT Developer Guide for more details on equation syntax.
Many common operations can be expressed using the Einsum equation. For example: Matrix Transpose: ij->ji Sum: ij-> Matrix-Matrix Multiplication: ik,kj->ij Dot Product: i,i-> Matrix-Vector Multiplication: ik,k->i Batch Matrix Multiplication: ijk,ikl->ijl Batch Diagonal: …ii->…i
Note that TensorRT does not support ellipsis or diagonal operations.
- Variables
equation –
str
The Einsum equation of the layer. The equation is a comma-separated list of subscript labels, where each label refers to a dimension of the corresponding tensor.
IAssertionLayer
- class tensorrt.IAssertionLayer
An assertion layer in an
INetworkDefinition
.This layer implements assertions. The input must be a boolean shape tensor. If any element of it is
False
, a build-time or run-time error occurs. Asserting equality of input dimensions may help the optimizer.- Variables
message –
string
Message to print if the assertion fails.