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}, the W axis always has unit stride, and the stride of every other axis is at least the the product of of the next dimension times the next stride. the strides are the same as for a C array with dimensions [N][C][H][W].
- 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, C, H, W}, the memory layout is equivalent to the array with dimensions [N][H][W][(C+7)/8*8], with the tensor coordinates (n, c, h, w) 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 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].
- DHWC8 :
Eight channel format where C is padded to a multiple of 8.
This format is bound to FP16, and it is only available for dimensions >= 4.
For a tensor with dimensions {N, C, D, H, W}, the memory layout is equivalent to an array with dimensions [N][D][H][W][(C+7)/8*8], with the tensor coordinates (n, c, d, h, w) mapping to array subscript [n][d][h][w][c].
- CDHW32 :
Thirty-two wide channel vectorized row major format with 3 spatial dimensions.
This format is bound to FP16 and INT8. It is only available for dimensions >= 4.
For a tensor with dimensions {N, C, D, H, W}, the memory layout is equivalent to a C array with dimensions [N][(C+31)/32][D][H][W][32], with the tensor coordinates (n, d, c, h, w) mapping to array subscript [n][c/32][d][h][w][c%32].
- HWC :
Non-vectorized channel-last format. This format is bound to FP32 and is only available for dimensions >= 3.
- DLA_LINEAR :
DLA planar format. Row major format. The stride for stepping along the H axis is rounded up to 64 bytes.
This format is bound to FP16/Int8 and 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][H][roundUp(W, 64/elementSize)] where elementSize is 2 for FP16 and 1 for Int8, with the tensor coordinates (n, c, h, w) mapping to array subscript [n][c][h][w].
- DLA_HWC4 :
DLA image format. channel-last format. C can only be 1, 3, 4. If C == 3 it will be rounded to 4. The stride for stepping along the H axis is rounded up to 32 bytes.
This format is bound to FP16/Int8 and is only available for dimensions >= 3.
For a tensor with dimensions {N, C, H, W}, with C’ is 1, 4, 4 when C is 1, 3, 4 respectively, the memory layout is equivalent to a C array with dimensions [N][H][roundUp(W, 32/C’/elementSize)][C’] where elementSize is 2 for FP16 and 1 for Int8, C’ is the rounded C. The tensor coordinates (n, c, h, w) maps to array subscript [n][h][w][c].
- HWC16 :
Sixteen channel format where C is padded to a multiple of 16. 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 the array with dimensions [N][H][W][(C+15)/16*16], with the tensor coordinates (n, c, h, w) mapping to array subscript [n][h][w][c].
- 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.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.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, orNone
if the range was not set.is_shape –
bool
Whether the tensor is a shape tensor.allowed_formats –
int
The allowed set of TensorFormat candidates. This should be an integer consisting of one or moreTensorFormat
s, combined via bitwise OR after bit shifting. For example,1 << int(TensorFormats.CHW4) | 1 << int(TensorFormat.CHW32)
.
- 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.
- Returns
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
UNARY : Unary layer
PADDING : Padding 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
TRIP_LIMIT : Loop Trip limit layer
RECURRENCE : Loop Recurrence layer
ITERATOR : Loop Iterator layer
LOOP_OUTPUT : Loop output layer
SELECT : Select layer
ASSERTION : Assertion layer
FILL : Fill layer
QUANTIZE : Quantize layer
DEQUANTIZE : Dequantize layer
CONDITION : If-conditional Condition layer
CONDITIONAL_INPUT : If-conditional input layer
CONDITIONAL_OUTPUT : If-conditional output layer
SCATTER : Scatter layer
EINSUM : Einsum layer
- class tensorrt.ILayer
Base class for all layer classes in an
INetworkDefinition
.- Variables
- 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.
- Returns
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.
- Returns
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.
- Returns
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.
- Returns
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.