ICudaEngine

class tensorrt.ICudaEngine

An ICudaEngine for executing inference on a built network.

The engine can be indexed with [] . When indexed in this way with an integer, it will return the corresponding binding name. When indexed with a string, it will return the corresponding binding index.

Variables:
  • num_bindings – int The number of binding indices.
  • max_batch_size – int The maximum batch size which can be used for inference.
  • num_layers – int The number of layers in the network. The number of layers in the network is not necessarily the number in the original INetworkDefinition, as layers may be combined or eliminated as the ICudaEngine is optimized. This value can be useful when building per-layer tables, such as when aggregating profiling data over a number of executions.
  • max_workspace_size – int The amount of workspace the ICudaEngine uses. The workspace size will be no greater than the value provided to the Builder when the ICudaEngine was built, and will typically be smaller. Workspace will be allocated for each IExecutionContext .
  • device_memory_size – int The amount of device memory required by an IExecutionContext .
  • refittable – bool Whether the engine can be refit.
  • name – str The name of the network associated with the engine. The name is set during network creation and is retrieved after building or deserialization.
  • num_optimization_profiles – The number of optimization profiles defined for this engine. This is always at least 1.
__del__(self: tensorrt.tensorrt.ICudaEngine) → None
__exit__(exc_type, exc_value, traceback)

Destroy this object, freeing all memory associated with it. This should be called to ensure that the object is cleaned up properly. Equivalent to invoking __del__()

__getitem__(*args, **kwargs)

Overloaded function.

  1. __getitem__(self: tensorrt.tensorrt.ICudaEngine, arg0: str) -> int
  2. __getitem__(self: tensorrt.tensorrt.ICudaEngine, arg0: int) -> str
__init__

Initialize self. See help(type(self)) for accurate signature.

__len__(self: tensorrt.tensorrt.ICudaEngine) → int
binding_is_input(*args, **kwargs)

Overloaded function.

  1. binding_is_input(self: tensorrt.tensorrt.ICudaEngine, index: int) -> bool

    Determine whether a binding is an input binding.

    index:The binding index.
    returns:True if the index corresponds to an input binding and the index is in range.

  2. binding_is_input(self: tensorrt.tensorrt.ICudaEngine, name: str) -> bool

    Determine whether a binding is an input binding.

    name:The name of the tensor corresponding to an engine binding.
    returns:True if the index corresponds to an input binding and the index is in range.
create_execution_context(self: tensorrt.tensorrt.ICudaEngine) → tensorrt.tensorrt.IExecutionContext

Create an IExecutionContext .

Returns:The newly created IExecutionContext .
create_execution_context_without_device_memory(self: tensorrt.tensorrt.ICudaEngine) → tensorrt.tensorrt.IExecutionContext

Create an IExecutionContext without any device memory allocated The memory for execution of this device context must be supplied by the application.

Returns:An IExecutionContext without device memory allocated.
get_binding_bytes_per_component(self: tensorrt.tensorrt.ICudaEngine, index: int) → int

Return the number of bytes per component of an element. The vector component size is returned if get_binding_vectorized_dim() != -1.

Parameters:index – The binding index.
get_binding_components_per_element(self: tensorrt.tensorrt.ICudaEngine, index: int) → int

Return the number of components included in one element.

The number of elements in the vectors is returned if get_binding_vectorized_dim() != -1.

Parameters:index – The binding index.
get_binding_dtype(*args, **kwargs)

Overloaded function.

  1. get_binding_dtype(self: tensorrt.tensorrt.ICudaEngine, index: int) -> tensorrt.tensorrt.DataType

    Determine the required data type for a buffer from its binding index.

    index:The binding index.
    Returns:The type of data in the buffer.

  2. get_binding_dtype(self: tensorrt.tensorrt.ICudaEngine, name: str) -> tensorrt.tensorrt.DataType

    Determine the required data type for a buffer from its binding index.

    name:The name of the tensor corresponding to an engine binding.
    Returns:The type of data in the buffer.
get_binding_format(self: tensorrt.tensorrt.ICudaEngine, index: int) → tensorrt.tensorrt.TensorFormat

Return the binding format.

Parameters:index – The binding index.
get_binding_format_desc(self: tensorrt.tensorrt.ICudaEngine, index: int) → str

Return the human readable description of the tensor format.

The description includes the order, vectorization, data type, strides, etc. For example:

Example 1: kCHW + FP32
“Row major linear FP32 format”
Example 2: kCHW2 + FP16
“Two wide channel vectorized row major FP16 format”
Example 3: kHWC8 + FP16 + Line Stride = 32
“Channel major FP16 format where C % 8 == 0 and H Stride % 32 == 0”
Parameters:index – The binding index.
get_binding_index(self: tensorrt.tensorrt.ICudaEngine, name: str) → int

Retrieve the binding index for a named tensor.

You can also use engine’s __getitem__() with engine[name]. When invoked with a str , this will return the corresponding binding index.

IExecutionContext.execute_async() and IExecutionContext.execute() require an array of buffers. Engine bindings map from tensor names to indices in this array. Binding indices are assigned at ICudaEngine build time, and take values in the range [0 … n-1] where n is the total number of inputs and outputs.

Parameters:name – The tensor name.
Returns:The binding index for the named tensor, or -1 if the name is not found.
get_binding_name(self: tensorrt.tensorrt.ICudaEngine, index: int) → str

Retrieve the name corresponding to a binding index.

You can also use engine’s __getitem__() with engine[index]. When invoked with an int , this will return the corresponding binding name.

This is the reverse mapping to that provided by get_binding_index() .

Parameters:index – The binding index.
Returns:The name corresponding to the binding index.
get_binding_shape(*args, **kwargs)

Overloaded function.

  1. get_binding_shape(self: tensorrt.tensorrt.ICudaEngine, index: int) -> tensorrt.tensorrt.Dims

    Get the shape of a binding.

    index:The binding index.
    Returns:The shape of the binding if the index is in range, otherwise Dims()

  2. get_binding_shape(self: tensorrt.tensorrt.ICudaEngine, name: str) -> tensorrt.tensorrt.Dims

    Get the shape of a binding.

    name:The name of the tensor corresponding to an engine binding.
    Returns:The shape of the binding if the tensor is present, otherwise Dims()
get_binding_vectorized_dim(self: tensorrt.tensorrt.ICudaEngine, index: int) → int

Return the dimension index that the buffer is vectorized.

Specifically -1 is returned if scalars per vector is 1.

Parameters:index – The binding index.
get_location(*args, **kwargs)

Overloaded function.

  1. get_location(self: tensorrt.tensorrt.ICudaEngine, index: int) -> tensorrt.tensorrt.TensorLocation

    Get location of binding. This lets you know whether the binding should be a pointer to device or host memory.

    index:The binding index.
    returns:The location of the bound tensor with given index.

  2. get_location(self: tensorrt.tensorrt.ICudaEngine, name: str) -> tensorrt.tensorrt.TensorLocation

    Get location of binding. This lets you know whether the binding should be a pointer to device or host memory.

    name:The name of the tensor corresponding to an engine binding.
    returns:The location of the bound tensor with given index.
get_profile_shape(*args, **kwargs)

Overloaded function.

  1. get_profile_shape(self: tensorrt.tensorrt.ICudaEngine, profile_index: int, binding: int) -> List[tensorrt.tensorrt.Dims]

    Get the minimum/optimum/maximum dimensions for a particular binding under an optimization profile.

    arg profile_index:
     The index of the profile.
    arg binding:The binding index or name.
    returns:A List[Dims] of length 3, containing the minimum, optimum, and maximum shapes, in that order.

  2. get_profile_shape(self: tensorrt.tensorrt.ICudaEngine, profile_index: int, binding: str) -> List[tensorrt.tensorrt.Dims]

    Get the minimum/optimum/maximum dimensions for a particular binding under an optimization profile.

    arg profile_index:
     The index of the profile.
    arg binding:The binding index or name.
    returns:A List[Dims] of length 3, containing the minimum, optimum, and maximum shapes, in that order.
get_profile_shape_input(*args, **kwargs)

Overloaded function.

  1. get_profile_shape_input(self: tensorrt.tensorrt.ICudaEngine, profile_index: int, binding: int) -> List[List[int]]

    Get minimum/optimum/maximum values for an input shape binding under an optimization profile. If the specified binding is not an input shape binding, an exception is raised.

    arg profile_index:
     The index of the profile.
    arg binding:The binding index or name.
    returns:A List[List[int]] of length 3, containing the minimum, optimum, and maximum values, in that order. If the values have not been set yet, an empty list is returned.

  2. get_profile_shape_input(self: tensorrt.tensorrt.ICudaEngine, profile_index: int, binding: str) -> List[List[int]]

    Get minimum/optimum/maximum values for an input shape binding under an optimization profile. If the specified binding is not an input shape binding, an exception is raised.

    arg profile_index:
     The index of the profile.
    arg binding:The binding index or name.
    returns:A List[List[int]] of length 3, containing the minimum, optimum, and maximum values, in that order. If the values have not been set yet, an empty list is returned.
is_execution_binding(self: tensorrt.tensorrt.ICudaEngine, binding: int) → bool

Returns True if tensor is required for execution phase, false otherwise.

For example, if a network uses an input tensor with binding i ONLY as the reshape dimensions for an IShuffleLayer , then is_execution_binding(i) == False, and a binding of 0 can be supplied for it when calling IExecutionContext.execute() or IExecutionContext.execute_async() .

Parameters:binding – The binding index.
is_shape_binding(self: tensorrt.tensorrt.ICudaEngine, binding: int) → bool

Returns True if tensor is required as input for shape calculations or output from them.

TensorRT evaluates a network in two phases:

  1. Compute shape information required to determine memory allocation requirements and validate that runtime sizes make sense.
  2. Process tensors on the device.

Some tensors are required in phase 1. These tensors are called “shape tensors”, and always have type tensorrt.int32 and no more than one dimension. These tensors are not always shapes themselves, but might be used to calculate tensor shapes for phase 2.

is_shape_binding() returns true if the tensor is a required input or an output computed in phase 1. is_execution_binding() returns true if the tensor is a required input or an output computed in phase 2.

For example, if a network uses an input tensor with binding i as an input to an IElementWiseLayer that computes the reshape dimensions for an IShuffleLayer , is_shape_binding(i) == True

It’s possible to have a tensor be required by both phases. For instance, a tensor can be used as a shape in an IShuffleLayer and as the indices for an IGatherLayer collecting floating-point data.

It’s also possible to have a tensor required by neither phase that shows up in the engine’s inputs. For example, if an input tensor is used only as an input to an IShapeLayer , only its shape matters and its values are irrelevant.

Parameters:binding – The binding index.
serialize(self: tensorrt.tensorrt.ICudaEngine) → tensorrt.tensorrt.IHostMemory

Serialize the network to a stream.

Returns:An IHostMemory object containing the serialized ICudaEngine .