INetworkDefinition

class tensorrt.INetworkDefinition

Represents a TensorRT Network from which the Builder can build an Engine

Variables

num_layers – int The number of layers in the network.
num_inputs – int The number of inputs of the network.
num_outputs – int The number of outputs of the network.
name – str The name of the network. This is used so that it can be associated with a built engine. The name must be at most 128 characters in length. TensorRT makes no use of this string except storing it as part of the engine so that it may be retrieved at runtime. A name unique to the builder will be generated by default.
has_implicit_batch_dimension – bool Whether the network was created with an implicit batch dimension. This is a network-wide property. Either all tensors in the network have an implicit batch dimension or none of them do. This is True when the INetworkDefinition is created with default flags: create_network(). To specify explicit batch, set the flag: create_network(flags=1 << int(tensorrt.NetworkDefinitionCreationFlag.EXPLICIT_BATCH)).
has_explicit_precision – bool True if and only if this INetworkDefinition was created with NetworkDefinitionCreationFlag.EXPLICIT_PRECISION set: create_network(flags=(1 << int(NetworkDefinitionCreationFlag.EXPLICIT_PRECISION))).
error_recorder – IErrorRecorder Application-implemented error reporting interface for TensorRT objects.

__del__(self: tensorrt.tensorrt.INetworkDefinition) → None

__exit__(exc_type, exc_value, traceback): Context managers are deprecated and have no effect. Objects are automatically freed when the reference count reaches 0.

__getitem__(self: tensorrt.tensorrt.INetworkDefinition, arg0: int) → tensorrt.tensorrt.ILayer

__init__(*args, **kwargs)

__len__(self: tensorrt.tensorrt.INetworkDefinition) → int

add_activation(self: tensorrt.tensorrt.INetworkDefinition, input: tensorrt.tensorrt.ITensor, type: tensorrt.tensorrt.ActivationType) → tensorrt.tensorrt.IActivationLayer

Add an activation layer to the network. See IActivationLayer for more information.

Parameters

input – The input tensor to the layer.
type – The type of activation function to apply.

Returns

The new activation layer, or None if it could not be created.

add_assertion(self: tensorrt.tensorrt.INetworkDefinition, condition: tensorrt.tensorrt.ITensor, message: str) → tensorrt.tensorrt.IAssertionLayer

Add a assertion layer. See IAssertionLayer for more information.

Parameters

condition – The condition tensor to the layer.
message – The message to print if the assertion fails.

Returns

The new assertion layer, or None if it could not be created.

add_cast(self: tensorrt.tensorrt.INetworkDefinition, input: tensorrt.tensorrt.ITensor, to_type: tensorrt.tensorrt.DataType) → tensorrt.tensorrt.ICastLayer

Add a cast layer. See ICastLayer for more information.

Parameters

input – The input tensor to the layer.
to_type – The data type the output tensor should be cast into.

Returns

The new cast layer, or None if it could not be created.

add_concatenation(self: tensorrt.tensorrt.INetworkDefinition, inputs: List[tensorrt.tensorrt.ITensor]) → tensorrt.tensorrt.IConcatenationLayer

Add a concatenation layer to the network. Note that all tensors must have the same dimension except for the Channel dimension. See IConcatenationLayer for more information.

Parameters: inputs – The input tensors to the layer.
Returns: The new concatenation layer, or None if it could not be created.

add_constant(self: tensorrt.tensorrt.INetworkDefinition, shape: tensorrt.tensorrt.Dims, weights: tensorrt.tensorrt.Weights) → tensorrt.tensorrt.IConstantLayer

Add a constant layer to the network. See IConstantLayer for more information.

Parameters

shape – The shape of the constant.
weights – The constant value, represented as weights.

Returns

The new constant layer, or None if it could not be created.

add_convolution(self: tensorrt.tensorrt.INetworkDefinition, input: tensorrt.tensorrt.ITensor, num_output_maps: int, kernel_shape: tensorrt.tensorrt.DimsHW, kernel: tensorrt.tensorrt.Weights, bias: tensorrt.tensorrt.Weights = None) → tensorrt.tensorrt.IConvolutionLayer

Add a 2D convolution layer to the network. See IConvolutionLayer for more information.

Parameters

input – The input tensor to the convolution.
num_output_maps – The number of output feature maps for the convolution.
kernel_shape – The dimensions of the convolution kernel.
kernel – The kernel weights for the convolution.
bias – The optional bias weights for the convolution.

Returns

The new convolution layer, or None if it could not be created.

add_convolution_nd(self: tensorrt.tensorrt.INetworkDefinition, input: tensorrt.tensorrt.ITensor, num_output_maps: int, kernel_shape: tensorrt.tensorrt.Dims, kernel: tensorrt.tensorrt.Weights, bias: tensorrt.tensorrt.Weights = None) → tensorrt.tensorrt.IConvolutionLayer

Add a multi-dimension convolution layer to the network. See IConvolutionLayer for more information.

Parameters

input – The input tensor to the convolution.
num_output_maps – The number of output feature maps for the convolution.
kernel_shape – The dimensions of the convolution kernel.
kernel – The kernel weights for the convolution.
bias – The optional bias weights for the convolution.

Returns

The new convolution layer, or None if it could not be created.

add_deconvolution(self: tensorrt.tensorrt.INetworkDefinition, input: tensorrt.tensorrt.ITensor, num_output_maps: int, kernel_shape: tensorrt.tensorrt.DimsHW, kernel: tensorrt.tensorrt.Weights, bias: tensorrt.tensorrt.Weights = None) → tensorrt.tensorrt.IDeconvolutionLayer

Add a 2D deconvolution layer to the network. See IDeconvolutionLayer for more information.

Parameters

input – The input tensor to the layer.
num_output_maps – The number of output feature maps.
kernel_shape – The dimensions of the convolution kernel.
kernel – The kernel weights for the convolution.
bias – The optional bias weights for the convolution.

Returns

The new deconvolution layer, or None if it could not be created.

add_deconvolution_nd(self: tensorrt.tensorrt.INetworkDefinition, input: tensorrt.tensorrt.ITensor, num_output_maps: int, kernel_shape: tensorrt.tensorrt.Dims, kernel: tensorrt.tensorrt.Weights, bias: tensorrt.tensorrt.Weights = None) → tensorrt.tensorrt.IDeconvolutionLayer

Add a multi-dimension deconvolution layer to the network. See IDeconvolutionLayer for more information.

Parameters

input – The input tensor to the layer.
num_output_maps – The number of output feature maps.
kernel_shape – The dimensions of the convolution kernel.
kernel – The kernel weights for the convolution.
bias – The optional bias weights for the convolution.

Returns

The new deconvolution layer, or None if it could not be created.

add_dequantize(self: tensorrt.tensorrt.INetworkDefinition, input: tensorrt.tensorrt.ITensor, scale: tensorrt.tensorrt.ITensor) → tensorrt.tensorrt.IDequantizeLayer

Add a dequantization layer to the network. See IDequantizeLayer for more information.

Parameters

input – A tensor to quantize.
scale – A tensor with the scale coefficients.

Returns

The new dequantization layer, or None if it could not be created.

add_einsum(self: tensorrt.tensorrt.INetworkDefinition, inputs: List[tensorrt.tensorrt.ITensor], equation: str) → tensorrt.tensorrt.IEinsumLayer

Adds an Einsum layer to the network. See IEinsumLayer for more information.

Parameters

inputs – The input tensors to the layer.
equation – The Einsum equation of the layer.

Returns

the new Einsum layer, or None if it could not be created.

add_elementwise(self: tensorrt.tensorrt.INetworkDefinition, input1: tensorrt.tensorrt.ITensor, input2: tensorrt.tensorrt.ITensor, op: tensorrt.tensorrt.ElementWiseOperation) → tensorrt.tensorrt.IElementWiseLayer

Add an elementwise layer to the network. See IElementWiseLayer for more information.

Parameters

input1 – The first input tensor to the layer.
input2 – The second input tensor to the layer.
op – The binary operation that the layer applies.

The input tensors must have the same number of dimensions. For each dimension, their lengths must match, or one of them must be one. In the latter case, the tensor is broadcast along that axis.

The output tensor has the same number of dimensions as the inputs. For each dimension, its length is the maximum of the lengths of the corresponding input dimension.

Returns: The new element-wise layer, or None if it could not be created.

add_fill(self: tensorrt.tensorrt.INetworkDefinition, shape: tensorrt.tensorrt.Dims, op: tensorrt.tensorrt.FillOperation) → tensorrt.tensorrt.IFillLayer

Add a fill layer. See IFillLayer for more information.

Parameters

dimensions – The output tensor dimensions.
op – The fill operation that the layer applies.

Returns

The new fill layer, or None if it could not be created.

add_fully_connected(self: tensorrt.tensorrt.INetworkDefinition, input: tensorrt.tensorrt.ITensor, num_outputs: int, kernel: tensorrt.tensorrt.Weights, bias: tensorrt.tensorrt.Weights = None) → tensorrt.tensorrt.IFullyConnectedLayer

Add a fully connected layer to the network. See IFullyConnectedLayer for more information.

Parameters

input – The input tensor to the layer.
num_outputs – The number of outputs of the layer.
kernel – The kernel weights for the convolution.
bias – The optional bias weights for the convolution.

Returns

The new fully connected layer, or None if it could not be created.

add_gather(self: tensorrt.tensorrt.INetworkDefinition, input: tensorrt.tensorrt.ITensor, indices: tensorrt.tensorrt.ITensor, axis: int) → tensorrt.tensorrt.IGatherLayer

Add a gather layer to the network. See IGatherLayer for more information.

Parameters

input – The tensor to gather values from.
indices – The tensor to get indices from to populate the output tensor.
axis – The non-batch dimension axis in the data tensor to gather on.

Returns

The new gather layer, or None if it could not be created.

add_gather_v2(self: tensorrt.tensorrt.INetworkDefinition, input: tensorrt.tensorrt.ITensor, indices: tensorrt.tensorrt.ITensor, mode: tensorrt.tensorrt.GatherMode) → tensorrt.tensorrt.IGatherLayer

Add a gather layer to the network. See IGatherLayer for more information.

Parameters

input – The tensor to gather values from.
indices – The tensor to get indices from to populate the output tensor.
mode – The gather mode.

Returns

The new gather layer, or None if it could not be created.

add_grid_sample(self: tensorrt.tensorrt.INetworkDefinition, input: tensorrt.tensorrt.ITensor, grid: tensorrt.tensorrt.ITensor) → tensorrt.tensorrt.IGridSampleLayer

Creates a GridSample layer with a trt.InterpolationMode.LINEAR, unaligned corners, and trt.SampleMode.FILL for 4d-shape input tensors. See IGridSampleLayer for more information.

Parameters

input – The input tensor to the layer.
grid – The grid tensor to the layer.

Variables

interpolation_mode – class:InterpolationMode The interpolation mode to use in the layer. Default is LINEAR.
align_corners – class:bool the align mode to use in the layer. Default is False.
padding_mode – SampleMode The padding mode to use in the layer. Default is FILL.

Returns

The new grid sample layer, or None if it could not be created.

add_identity(self: tensorrt.tensorrt.INetworkDefinition, input: tensorrt.tensorrt.ITensor) → tensorrt.tensorrt.IIdentityLayer

Add an identity layer. See IIdentityLayer for more information.

Parameters: input – The input tensor to the layer.
Returns: The new identity layer, or None if it could not be created.

add_if_conditional(self: tensorrt.tensorrt.INetworkDefinition) → tensorrt.tensorrt.IIfConditional

Adds an if-conditional to the network, which provides a way to specify subgraphs that will be conditionally executed using lazy evaluation. See IIfConditional for more information.

Returns: The new if-condtional, or None if it could not be created.

add_input(self: tensorrt.tensorrt.INetworkDefinition, name: str, dtype: tensorrt.tensorrt.DataType, shape: tensorrt.tensorrt.Dims) → tensorrt.tensorrt.ITensor

Adds an input to the network.

Parameters

name – The name of the tensor.
dtype – The data type of the tensor. Currently, tensorrt.int8 is not supported for inputs.
shape – The dimensions of the tensor. The total volume must be less than 2^30 elements.

Returns

The newly added Tensor.

add_loop(self: tensorrt.tensorrt.INetworkDefinition) → tensorrt.tensorrt.ILoop

Adds a loop to the network, which provides a way to specify a recurrent subgraph. See ILoop for more information.

Returns: The new loop layer, or None if it could not be created.

add_lrn(self: tensorrt.tensorrt.INetworkDefinition, input: tensorrt.tensorrt.ITensor, window: int, alpha: float, beta: float, k: float) → tensorrt.tensorrt.ILRNLayer

Add a LRN layer to the network. See ILRNLayer for more information.

Parameters

input – The input tensor to the layer.
window – The size of the window.
alpha – The alpha value for the LRN computation.
beta – The beta value for the LRN computation.
k – The k value for the LRN computation.

Returns

The new LRN layer, or None if it could not be created.

add_matrix_multiply(self: tensorrt.tensorrt.INetworkDefinition, input0: tensorrt.tensorrt.ITensor, op0: tensorrt.tensorrt.MatrixOperation, input1: tensorrt.tensorrt.ITensor, op1: tensorrt.tensorrt.MatrixOperation) → tensorrt.tensorrt.IMatrixMultiplyLayer

Add a matrix multiply layer to the network. See IMatrixMultiplyLayer for more information.

Parameters

input0 – The first input tensor (commonly A).
op0 – Whether to treat input0 as matrices, transposed matrices, or vectors.
input1 – The second input tensor (commonly B).
op1 – Whether to treat input1 as matrices, transposed matrices, or vectors.

Returns

The new matrix multiply layer, or None if it could not be created.

add_nms(self: tensorrt.tensorrt.INetworkDefinition, boxes: tensorrt.tensorrt.ITensor, scores: tensorrt.tensorrt.ITensor, max_output_boxes_per_class: tensorrt.tensorrt.ITensor) → tensorrt.tensorrt.INMSLayer

Add a non-maximum suppression layer to the network. See INMSLayer for more information.

Parameters

boxes – The input boxes tensor to the layer.
scores – The input scores tensor to the layer.
max_output_boxes_per_class – The maxOutputBoxesPerClass tensor to the layer.

Variables

bounding_box_format – BoundingBoxFormat The bounding box format used by the layer. Default is CORNER_PAIRS.
topk_box_limit – int The maximum number of filtered boxes considered for selection per batch item. Default is 2000 for SM 5.3 and 6.2 devices, and 5000 otherwise. The TopK box limit must be less than or equal to {2000 for SM 5.3 and 6.2 devices, 5000 otherwise}.

Returns

The new NMS layer, or None if it could not be created.

add_non_zero(self: tensorrt.tensorrt.INetworkDefinition, input: tensorrt.tensorrt.ITensor) → tensorrt.tensorrt.INonZeroLayer

Adds an NonZero layer to the network. See INonZeroLayer for more information.

Parameters: input – The input tensor to the layer.
Returns: the new NonZero layer, or None if it could not be created.

add_normalization(self: tensorrt.tensorrt.INetworkDefinition, input: tensorrt.tensorrt.ITensor, scale: tensorrt.tensorrt.ITensor, bias: tensorrt.tensorrt.ITensor, axesMask: int) → tensorrt.tensorrt.INormalizationLayer

Adds a Normalization layer to the network. See Normalization for more information.

Parameters

input – The input tensor to the layer.
scale – The scale tensor used to scale the normalized output.
bias – The bias tensor used to scale the normalized output.
axesMask – The axes on which to perform mean calculations. The bit in position i of bitmask axes corresponds to explicit dimension i of the result. E.g., the least significant bit corresponds to the first explicit dimension and the next to least significant bit corresponds to the second explicit dimension.

Returns

the new Normalization layer, or None if it could not be created.

add_one_hot(self: tensorrt.tensorrt.INetworkDefinition, indices: tensorrt.tensorrt.ITensor, values: tensorrt.tensorrt.ITensor, depth: tensorrt.tensorrt.ITensor, axis: int) → tensorrt.tensorrt.IOneHotLayer

Add a OneHot layer to the network. See IOneHotLayer for more information.

Parameters

indices – The tensor to get indices from to populate the output tensor.
values – The tensor to get off (cold) value and on (hot) value
depth – The tensor to get depth (number of classes) of one-hot encoding
axis – The axis to append the one-hot encoding to

Returns

The new OneHot layer, or None if it could not be created.

add_padding(self: tensorrt.tensorrt.INetworkDefinition, input: tensorrt.tensorrt.ITensor, pre_padding: tensorrt.tensorrt.DimsHW, post_padding: tensorrt.tensorrt.DimsHW) → tensorrt.tensorrt.IPaddingLayer

Add a 2D padding layer to the network. See IPaddingLayer for more information.

Parameters

input – The input tensor to the layer.
pre_padding – The padding to apply to the start of the tensor.
post_padding – The padding to apply to the end of the tensor.

Returns

The new padding layer, or None if it could not be created.

add_padding_nd(self: tensorrt.tensorrt.INetworkDefinition, input: tensorrt.tensorrt.ITensor, pre_padding: tensorrt.tensorrt.Dims, post_padding: tensorrt.tensorrt.Dims) → tensorrt.tensorrt.IPaddingLayer

Add a multi-dimensional padding layer to the network. See IPaddingLayer for more information.

Parameters

input – The input tensor to the layer.
pre_padding – The padding to apply to the start of the tensor.
post_padding – The padding to apply to the end of the tensor.

Returns

The new padding layer, or None if it could not be created.

add_parametric_relu(self: tensorrt.tensorrt.INetworkDefinition, input: tensorrt.tensorrt.ITensor, slopes: tensorrt.tensorrt.ITensor) → tensorrt.tensorrt.IParametricReLULayer

Add a parametric ReLU layer. See IParametricReLULayer for more information.

Parameters

input – The input tensor to the layer.
slopes – The slopes tensor (input elements are multiplied with the slopes where the input is negative).

Returns

The new parametric ReLU layer, or None if it could not be created.

add_plugin_v2(self: tensorrt.tensorrt.INetworkDefinition, inputs: List[tensorrt.tensorrt.ITensor], plugin: tensorrt.tensorrt.IPluginV2) → tensorrt.tensorrt.IPluginV2Layer

Add a plugin layer to the network using an IPluginV2 interface. See IPluginV2 for more information.

Parameters

inputs – The input tensors to the layer.
plugin – The layer plugin.

Returns

The new plugin layer, or None if it could not be created.

add_pooling(self: tensorrt.tensorrt.INetworkDefinition, input: tensorrt.tensorrt.ITensor, type: tensorrt.tensorrt.PoolingType, window_size: tensorrt.tensorrt.DimsHW) → tensorrt.tensorrt.IPoolingLayer

Add a 2D pooling layer to the network. See IPoolingLayer for more information.

Parameters

input – The input tensor to the layer.
type – The type of pooling to apply.
window_size – The size of the pooling window.

Returns

The new pooling layer, or None if it could not be created.

add_pooling_nd(self: tensorrt.tensorrt.INetworkDefinition, input: tensorrt.tensorrt.ITensor, type: tensorrt.tensorrt.PoolingType, window_size: tensorrt.tensorrt.Dims) → tensorrt.tensorrt.IPoolingLayer

Add a multi-dimension pooling layer to the network. See IPoolingLayer for more information.

Parameters

input – The input tensor to the layer.
type – The type of pooling to apply.
window_size – The size of the pooling window.

Returns

The new pooling layer, or None if it could not be created.

add_quantize(self: tensorrt.tensorrt.INetworkDefinition, input: tensorrt.tensorrt.ITensor, scale: tensorrt.tensorrt.ITensor) → tensorrt.tensorrt.IQuantizeLayer

Add a quantization layer to the network. See IQuantizeLayer for more information.

Parameters

input – A tensor to quantize.
scale – A tensor with the scale coefficients.

Returns

The new quantization layer, or None if it could not be created.

add_ragged_softmax(self: tensorrt.tensorrt.INetworkDefinition, input: tensorrt.tensorrt.ITensor, bounds: tensorrt.tensorrt.ITensor) → tensorrt.tensorrt.IRaggedSoftMaxLayer

Add a ragged softmax layer to the network. See IRaggedSoftMaxLayer for more information.

Parameters

input – The ZxS input tensor.
bounds – The Zx1 bounds tensor.

Returns

The new ragged softmax layer, or None if it could not be created.

add_reduce(self: tensorrt.tensorrt.INetworkDefinition, input: tensorrt.tensorrt.ITensor, op: tensorrt.tensorrt.ReduceOperation, axes: int, keep_dims: bool) → tensorrt.tensorrt.IReduceLayer

Add a reduce layer to the network. See IReduceLayer for more information.

Parameters

input – The input tensor to the layer.
op – The reduction operation to perform.
axes – The reduction dimensions. The bit in position i of bitmask axes corresponds to explicit dimension i of the result. E.g., the least significant bit corresponds to the first explicit dimension and the next to least significant bit corresponds to the second explicit dimension.
keep_dims – The boolean that specifies whether or not to keep the reduced dimensions in the output of the layer.

Returns

The new reduce layer, or None if it could not be created.

add_resize(self: tensorrt.tensorrt.INetworkDefinition, input: tensorrt.tensorrt.ITensor) → tensorrt.tensorrt.IResizeLayer

Add a resize layer. See IResizeLayer for more information.

Parameters: input – The input tensor to the layer.
Returns: The new resize layer, or None if it could not be created.

add_reverse_sequence(self: tensorrt.tensorrt.INetworkDefinition, input: tensorrt.tensorrt.ITensor, sequence_lens: tensorrt.tensorrt.ITensor) → tensorrt.tensorrt.IReverseSequenceLayer

Adds a ReverseSequence layer to the network. See IReverseSequenceLayer for more information.

Parameters

input – The input tensor to the layer.
sequence_lens – 1D tensor specifying lengths of sequences to reverse in a batch. The length of sequence_lens must be equal to the size of the dimension in input specified by batch_axis.

Returns

the new ReverseSequence layer, or None if it could not be created.

add_rnn_v2(self: tensorrt.tensorrt.INetworkDefinition, input: tensorrt.tensorrt.ITensor, layer_count: int, hidden_size: int, max_seq_length: int, op: tensorrt.tensorrt.RNNOperation) → tensorrt.tensorrt.IRNNv2Layer

Add an RNNv2 layer to the network. See IRNNv2Layer for more information.

Add an layer_count deep RNN layer to the network with hidden_size internal states that can take a batch with fixed or variable sequence lengths.

Parameters

input – The input tensor to the layer (see below).
layer_count – The number of layers in the RNN.
hidden_size – Size of the internal hidden state for each layer.
max_seq_length – Maximum sequence length for the input.
op – The type of RNN to execute.

By default, the layer is configured with RNNDirection.UNIDIRECTION and RNNInputMode.LINEAR . To change these settings, set IRNNv2Layer.direction and IRNNv2Layer.input_mode .

Weights and biases for the added layer should be set using IRNNv2Layer.set_weights_for_gate() and IRNNv2Layer.set_bias_for_gate() prior to building an engine using this network.

The input tensors must be of the type float32 or float16 . The layout of the weights is row major and must be the same datatype as the input tensor. weights contain 8 matrices and bias contains 8 vectors.

See IRNNv2Layer.set_weights_for_gate() and IRNNv2Layer.set_bias_for_gate() for details on the required input format for weights and bias .

The input ITensor should contain zero or more index dimensions {N1, …, Np}, followed by two dimensions, defined as follows:

S_max is the maximum allowed sequence length (number of RNN iterations)

E specifies the embedding length (unless RNNInputMode.SKIP is set, in which case it should match IRNNv2Layer.hidden_size ).

By default, all sequences in the input are assumed to be size max_seq_length . To provide explicit sequence lengths for each input sequence in the batch, set IRNNv2Layer.seq_lengths .

The RNN layer outputs up to three tensors.

The first output tensor is the output of the final RNN layer across all timesteps, with dimensions {N1, …, Np, S_max, H}:

N1..Np are the index dimensions specified by the input tensor
S_max is the maximum allowed sequence length (number of RNN iterations)
H is an output hidden state (equal to IRNNv2Layer.hidden_size or 2x IRNNv2Layer.hidden_size )

The second tensor is the final hidden state of the RNN across all layers, and if the RNN is an LSTM (i.e. IRNNv2Layer.op is RNNOperation.LSTM ), then the third tensor is the final cell state of the RNN across all layers. Both the second and third output tensors have dimensions {N1, …, Np, L, H}:

N1..Np are the index dimensions specified by the input tensor
L is the number of layers in the RNN, equal to IRNNv2Layer.num_layers
H is the hidden state for each layer, equal to IRNNv2Layer.hidden_size if getDirection is RNNDirection.UNIDIRECTION, and 2x IRNNv2Layer.hidden_size otherwise.

Returns: The new RNNv2 layer, or None if it could not be created.

add_scale(self: tensorrt.tensorrt.INetworkDefinition, input: tensorrt.tensorrt.ITensor, mode: tensorrt.tensorrt.ScaleMode, shift: tensorrt.tensorrt.Weights = None, scale: tensorrt.tensorrt.Weights = None, power: tensorrt.tensorrt.Weights = None) → tensorrt.tensorrt.IScaleLayer

Add a scale layer to the network. See IScaleLayer for more information.

Parameters

input – The input tensor to the layer. This tensor is required to have a minimum of 3 dimensions.
mode – The scaling mode.
shift – The shift value.
scale – The scale value.
power – The power value.

If the weights are available, then the size of weights are dependent on the ScaleMode. For UNIFORM, the number of weights is equal to 1. For CHANNEL, the number of weights is equal to the channel dimension. For ELEMENTWISE, the number of weights is equal to the volume of the input.

Returns: The new scale layer, or None if it could not be created.

add_scale_nd(self: tensorrt.tensorrt.INetworkDefinition, input: tensorrt.tensorrt.ITensor, mode: tensorrt.tensorrt.ScaleMode, shift: tensorrt.tensorrt.Weights = None, scale: tensorrt.tensorrt.Weights = None, power: tensorrt.tensorrt.Weights = None, channel_axis: int) → tensorrt.tensorrt.IScaleLayer

Add a multi-dimension scale layer to the network. See IScaleLayer for more information.

Parameters

input – The input tensor to the layer. This tensor is required to have a minimum of 3 dimensions.
mode – The scaling mode.
shift – The shift value.
scale – The scale value.
power – The power value.
channel_axis – The channel dimension axis.

If the weights are available, then the size of weights are dependent on the ScaleMode. For UNIFORM, the number of weights is equal to 1. For CHANNEL, the number of weights is equal to the channel dimension. For ELEMENTWISE, the number of weights is equal to the volume of the input.

Returns: The new scale layer, or None if it could not be created.

add_scatter(self: tensorrt.tensorrt.INetworkDefinition, data: tensorrt.tensorrt.ITensor, indices: tensorrt.tensorrt.ITensor, updates: tensorrt.tensorrt.ITensor, mode: tensorrt.tensorrt.ScatterMode) → tensorrt.tensorrt.IScatterLayer

Add a scatter layer to the network. See IScatterLayer for more information.

Parameters

data – The tensor to get default values from.
indices – The tensor to get indices from to populate the output tensor.
updates – The tensor to get values from to populate the output tensor.
mode – operation mode see IScatterLayer for more info

Returns

The new Scatter layer, or None if it could not be created.

add_select(self: tensorrt.tensorrt.INetworkDefinition, condition: tensorrt.tensorrt.ITensor, then_input: tensorrt.tensorrt.ITensor, else_input: tensorrt.tensorrt.ITensor) → tensorrt.tensorrt.ISelectLayer

Add a select layer. See ISelectLayer for more information.

Parameters

condition – The condition tensor to the layer.
then_input – The then input tensor to the layer.
else_input – The else input tensor to the layer.

Returns

The new select layer, or None if it could not be created.

add_shape(self: tensorrt.tensorrt.INetworkDefinition, input: tensorrt.tensorrt.ITensor) → tensorrt.tensorrt.IShapeLayer

Add a shape layer to the network. See IShapeLayer for more information.

Parameters: input – The input tensor to the layer.
Returns: The new shape layer, or None if it could not be created.

add_shuffle(self: tensorrt.tensorrt.INetworkDefinition, input: tensorrt.tensorrt.ITensor) → tensorrt.tensorrt.IShuffleLayer

Add a shuffle layer to the network. See IShuffleLayer for more information.

Parameters: input – The input tensor to the layer.
Returns: The new shuffle layer, or None if it could not be created.

add_slice(self: tensorrt.tensorrt.INetworkDefinition, input: tensorrt.tensorrt.ITensor, start: tensorrt.tensorrt.Dims, shape: tensorrt.tensorrt.Dims, stride: tensorrt.tensorrt.Dims) → tensorrt.tensorrt.ISliceLayer

Add a slice layer to the network. See ISliceLayer for more information.

Parameters

input – The input tensor to the layer.
start – The start offset.
shape – The output shape.
stride – The slicing stride. Positive, negative, zero stride values, and combinations of them in different dimensions are allowed.

Returns

The new slice layer, or None if it could not be created.

add_softmax(self: tensorrt.tensorrt.INetworkDefinition, input: tensorrt.tensorrt.ITensor) → tensorrt.tensorrt.ISoftMaxLayer

Add a softmax layer to the network. See ISoftMaxLayer for more information.

Parameters: input – The input tensor to the layer.
Returns: The new softmax layer, or None if it could not be created.

add_topk(self: tensorrt.tensorrt.INetworkDefinition, input: tensorrt.tensorrt.ITensor, op: tensorrt.tensorrt.TopKOperation, k: int, axes: int) → tensorrt.tensorrt.ITopKLayer

Add a TopK layer to the network. See ITopKLayer for more information.

The TopK layer has two outputs of the same dimensions. The first contains data values, the second contains index positions for the values. Output values are sorted, largest first for operation TopKOperation.MAX and smallest first for operation TopKOperation.MIN .

Currently only values of K up to 3840 are supported.

Parameters

input – The input tensor to the layer.
op – Operation to perform.
k – Number of elements to keep.
axes – The reduction dimensions. The bit in position i of bitmask axes corresponds to explicit dimension i of the result. E.g., the least significant bit corresponds to the first explicit dimension and the next to least significant bit corresponds to the second explicit dimension. Currently axes must specify exactly one dimension, and it must be one of the last four dimensions.

Returns

The new TopK layer, or None if it could not be created.

add_unary(self: tensorrt.tensorrt.INetworkDefinition, input: tensorrt.tensorrt.ITensor, op: tensorrt.tensorrt.UnaryOperation) → tensorrt.tensorrt.IUnaryLayer

Add a unary layer to the network. See IUnaryLayer for more information.

Parameters

input – The input tensor to the layer.
op – The operation to apply.

Returns

The new unary layer, or None if it could not be created.

property builder

The builder from which this INetworkDefinition was created.

See IBuilder for more information.

get_input(self: tensorrt.tensorrt.INetworkDefinition, index: int) → tensorrt.tensorrt.ITensor

Get the input tensor specified by the given index.

Parameters: index – The index of the input tensor.
Returns: The tensor, or None if it is out of range.

get_layer(self: tensorrt.tensorrt.INetworkDefinition, index: int) → tensorrt.tensorrt.ILayer

Get the layer specified by the given index.

Parameters: index – The index of the layer.
Returns: The layer, or None if it is out of range.

get_output(self: tensorrt.tensorrt.INetworkDefinition, index: int) → tensorrt.tensorrt.ITensor

Get the output tensor specified by the given index.

Parameters: index – The index of the output tensor.
Returns: The tensor, or None if it is out of range.

mark_output(self: tensorrt.tensorrt.INetworkDefinition, tensor: tensorrt.tensorrt.ITensor) → None

Mark a tensor as an output.

Parameters: tensor – The tensor to mark.

mark_output_for_shapes(self: tensorrt.tensorrt.INetworkDefinition, tensor: tensorrt.tensorrt.ITensor) → bool

Enable tensor’s value to be computed by IExecutionContext.get_shape_binding().

Parameters: tensor – The tensor to unmark as an output tensor. The tensor must be of type int32 and have no more than one dimension.
Returns: True if successful, False if tensor is already marked as an output.

remove_tensor(self: tensorrt.tensorrt.INetworkDefinition, tensor: tensorrt.tensorrt.ITensor) → None

Remove a tensor from the network.

Parameters: tensor – The tensor to remove

It is illegal to remove a tensor that is the input or output of a layer. if this method is called with such a tensor, a warning will be emitted on the log and the call will be ignored.

set_weights_name(self: tensorrt.tensorrt.INetworkDefinition, weights: tensorrt.tensorrt.Weights, name: str) → bool

Associate a name with all current uses of the given weights.

The name must be set after the Weights are used in the network. Lookup is associative. The name applies to all Weights with matching type, value pointer, and count. If Weights with a matching value pointer, but different type or count exists in the network, an error message is issued, the name is rejected, and return false. If the name has already been used for other weights, return false. None causes the weights to become unnamed, i.e. clears any previous name.

Parameters

weights – The weights to be named.
name – The name to associate with the weights.

Returns

true on success.

unmark_output(self: tensorrt.tensorrt.INetworkDefinition, tensor: tensorrt.tensorrt.ITensor) → None

Unmark a tensor as a network output.

Parameters: tensor – The tensor to unmark as an output tensor.

unmark_output_for_shapes(self: tensorrt.tensorrt.INetworkDefinition, tensor: tensorrt.tensorrt.ITensor) → bool

Undo mark_output_for_shapes() .

Parameters: tensor – The tensor to unmark as an output tensor.
Returns: True if successful, False if tensor is not marked as an output.