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 thisINetworkDefinition
was created withNetworkDefinitionCreationFlag.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_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_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_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. SeeIPluginV2
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_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 withhidden_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
andRNNInputMode.LINEAR
. To change these settings, setIRNNv2Layer.direction
andIRNNv2Layer.input_mode
.Weights and biases for the added layer should be set using
IRNNv2Layer.set_weights_for_gate()
andIRNNv2Layer.set_bias_for_gate()
prior to building an engine using this network.The input tensors must be of the type
float32
orfloat16
. The layout of the weights is row major and must be the same datatype as the input tensor.weights
contain 8 matrices andbias
contains 8 vectors.See
IRNNv2Layer.set_weights_for_gate()
andIRNNv2Layer.set_bias_for_gate()
for details on the required input format forweights
andbias
.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 (unlessRNNInputMode.SKIP
is set, in which case it should matchIRNNv2Layer.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, setIRNNv2Layer.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 tensorS_max is the maximum allowed sequence length (number of RNN iterations)H is an output hidden state (equal toIRNNv2Layer.hidden_size
or 2xIRNNv2Layer.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
isRNNOperation.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 tensorL is the number of layers in the RNN, equal toIRNNv2Layer.num_layers
H is the hidden state for each layer, equal toIRNNv2Layer.hidden_size
if getDirection isRNNDirection.UNIDIRECTION
, and 2xIRNNv2Layer.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 operationTopKOperation.MIN
.Currently only values of K up to 1024 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.
- 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
tensorrt.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.