# Layer Base Classes¶

## ITensor¶

tensorrt.TensorLocation

The physical location of the data.

Members:

DEVICE : Data is stored on the device.

HOST : Data is stored on the device.

tensorrt.TensorFormat

Format of the input/output tensors.

This enum is extended to be used by both plugins and reformat-free network I/O tensors.

For more information about data formats, see the topic “Data Format Description” located in the TensorRT Developer Guide (https://docs.nvidia.com/deeplearning/sdk/tensorrt-developer-guide/index.html).

Members:

LINEAR :

Row major linear format.

For a tensor with dimensions {N, C, H, W} or {numbers, channels, columns, rows}, the dimensional index corresponds to {3, 2, 1, 0} and thus the order is W major.

CHW2 :

Two wide channel vectorized row major format.

This format is bound to FP16. It is only available for dimensions >= 3.

For a tensor with dimensions {N, C, H, W}, the memory layout is equivalent to a C array with dimensions [N][(C+1)/2][H][W][2], with the tensor coordinates (n, c, h, w) mapping to array subscript [n][c/2][h][w][c%2].

HWC8 :

Eight channel format where C is padded to a multiple of 8.

This format is bound to FP16. It is only available for dimensions >= 3.

For a tensor with dimensions {N, H, W, C}, the memory layout is equivalent to the array with dimensions [N][H][W][(C+7)/8*8], with the tensor coordinates (n, h, w, c) mapping to array subscript [n][h][w][c].

CHW4 :

Four wide channel vectorized row major format. This format is bound to INT8. It is only available for dimensions >= 3.

For a tensor with dimensions {N, C, H, W}, the memory layout is equivalent to a C array with dimensions [N][(C+3)/4][H][W][4], with the tensor coordinates (n, c, h, w) mapping to array subscript [n][c/4][h][w][c%4].

CHW16 :

Sixteen wide channel vectorized row major format.

This format is bound to FP16. It is only available for dimensions >= 3.

For a tensor with dimensions {N, C, H, W}, the memory layout is equivalent to a C array with dimensions [N][(C+15)/16][H][W][16], with the tensor coordinates (n, c, h, w) mapping to array subscript [n][c/16][h][w][c%16].

CHW32 :

Thirty-two wide channel vectorized row major format.

This format is bound to INT8. It is only available for dimensions >= 3.

For a tensor with dimensions {N, C, H, W}, the memory layout is equivalent to a C array with dimensions [N][(C+31)/32][H][W][32], with the tensor coordinates (n, c, h, w) mapping to array subscript [n][c/32][h][w][c%32].

class tensorrt.ITensor

A tensor in an INetworkDefinition .

Variables: name – str The tensor name. For a network input, the name is assigned by the application. For tensors which are layer outputs, a default name is assigned consisting of the layer name followed by the index of the output in brackets. shape – Dims The shape of a tensor. For a network input the shape is assigned by the application. For a network output it is computed based on the layer parameters and the inputs to the layer. If a tensor size or a parameter is modified in the network, the shape of all dependent tensors will be recomputed. This call is only legal for network input tensors, since the shape of layer output tensors are inferred based on layer inputs and parameters. dtype – DataType The data type of a tensor. The type is unchanged if the type is invalid for the given tensor. If the tensor is a network input or output, then the tensor type cannot be tensorrt.int8 . broadcast_across_batch – bool Whether to enable broadcast of tensor across the batch. When a tensor is broadcast across a batch, it has the same value for every member in the batch. Memory is only allocated once for the single member. This method is only valid for network input tensors, since the flags of layer output tensors are inferred based on layer inputs and parameters. If this state is modified for a tensor in the network, the states of all dependent tensors will be recomputed. location – TensorLocation The storage location of a tensor. allowed_formats – TensorLocation The combination of supported TensorFormat. is_network_input – bool Whether the tensor is a network input. is_network_output – bool Whether the tensor is a network output. dynamic_range – Tuple[float, float] A tuple containing the [minimum, maximum] of the dynamic range, or None if the range was not set. is_shape – bool Whether the tensor is a shape tensor. allowed_formats – int The allowed set of format candidates. This should be in integer consisting of one or more TensorFormat s, combined via binary OR. For example, 1 << TensorFormats.CHW4 | 1 << TensorFormat.CHW32.
get_dynamic_range(self: tensorrt.tensorrt.ITensor) → float

Get dynamic range for the tensor. NOTE: It is suggested to use tensor.dynamic_range instead, which is a tuple including both the minimum and maximum of the dynamic range.

Returns: The absolute maximum of the dynamic range.
reset_dynamic_range(self: tensorrt.tensorrt.ITensor) → None

Undo the effect of setting the dynamic range.

set_dynamic_range(self: tensorrt.tensorrt.ITensor, min: float, max: float) → bool

Set dynamic range for the tensor. NOTE: It is suggested to use tensor.dynamic_range = (min, max) instead.

Parameters: min – Minimum of the dynamic range. max – Maximum of the dyanmic range. true if succeed in setting range. Otherwise false.

## ILayer¶

tensorrt.LayerType

Type of Layer

Members:

CONVOLUTION : Convolution layer

FULLY_CONNECTED : Fully connected layer

ACTIVATION : Activation layer

POOLING : Pooling layer

LRN : LRN layer

SCALE : Scale layer

SOFTMAX : Softmax layer

DECONVOLUTION : Deconvolution layer

CONCATENATION : Concatenation layer

ELEMENTWISE : Elementwise layer

PLUGIN : Plugin layer

RNN : RNN layer

UNARY : Unary layer

SHUFFLE : Shuffle layer

REDUCE : Reduce layer

TOPK : TopK layer

GATHER : Gather layer

MATRIX_MULTIPLY : Matrix multiply layer

RAGGED_SOFTMAX : Ragged softmax layer

CONSTANT : Constant layer

RNN_V2 : RNNv2 layer

IDENTITY : Identity layer

PLUGIN_V2 : PluginV2 layer

SLICE : Slice layer

SHAPE : Shape layer

PARAMETRIC_RELU : Parametric ReLU layer

RESIZE : Resize layer

class tensorrt.ILayer

Base class for all layer classes in an INetworkDefinition .

Variables: name – str The name of the layer. type – LayerType The type of the layer. num_inputs – int The number of inputs of the layer. num_outputs – int The number of outputs of the layer. precision – DataType The computation precision. precision_is_set – bool Whether the precision is set or not.
get_input(self: tensorrt.tensorrt.ILayer, index: int) → tensorrt.tensorrt.ITensor

Get the layer input corresponding to the given index.

Parameters: index – The index of the input tensor. The input tensor, or None if the index is out of range.
get_output(self: tensorrt.tensorrt.ILayer, index: int) → tensorrt.tensorrt.ITensor

Get the layer output corresponding to the given index.

Parameters: index – The index of the output tensor. The output tensor, or None if the index is out of range.
get_output_type(self: tensorrt.tensorrt.ILayer, index: int) → tensorrt.tensorrt.DataType

Get the output type of the layer.

Parameters: index – The index of the output tensor. The output precision. Default : DataType.FLOAT.
output_type_is_set(self: tensorrt.tensorrt.ILayer, index: int) → bool

Whether the output type has been set for this layer.

Parameters: index – The index of the output. Whether the output type has been explicitly set.
reset_output_type(self: tensorrt.tensorrt.ILayer, index: int) → None

Reset output type of this layer.

Parameters: index – The index of the output.
reset_precision(self: tensorrt.tensorrt.ILayer) → None

Reset the computation precision of the layer.

set_input(self: tensorrt.tensorrt.ILayer, index: int, tensor: tensorrt.tensorrt.ITensor) → None

Set the layer input corresponding to the given index.

Parameters: index – The index of the input tensor. tensor – The input tensor.
set_output_type(self: tensorrt.tensorrt.ILayer, index: int, dtype: tensorrt.tensorrt.DataType) → None

Constraint layer to generate output data with given type. Note that this method cannot be used to set the data type of the second output tensor of the topK layer. The data type of the second output tensor of the topK layer is always Int32.

Parameters: index – The index of the output tensor to set the type. dtype – DataType of the output.

## IOutputDimensionsFormula¶

class tensorrt.IOutputDimensionsFormula

Application-implemented interface to compute layer output sizes.

compute(self: tensorrt.tensorrt.IOutputDimensionsFormula, input_shape: tensorrt.tensorrt.DimsHW, kernel_shape: tensorrt.tensorrt.DimsHW, stride: tensorrt.tensorrt.DimsHW, padding: tensorrt.tensorrt.DimsHW, dilation: tensorrt.tensorrt.DimsHW, layer_name: str) → tensorrt.tensorrt.DimsHW

Application-implemented interface to compute the HW output dimensions of a layer from the layer input and parameters.

Parameters: input_shape – The input shape of the layer. kernel_shape – The kernel shape (or window size, for a pooling layer) parameter of the layer operation. stride – The stride parameter for the layer. padding – The padding parameter of the layer. dilation – The dilation parameter of the layer (only applicable to convolutions). layer_name – The name of the layer. The output size of the layer