Runners

Module: polygraphy.backend.onnxrt

class OnnxrtRunner(sess, name=None)

Bases: BaseRunner

Runs inference using an ONNX-Runtime inference session.

Parameters:

sess (Union[onnxruntime.InferenceSession, Callable() -> onnxruntime.InferenceSession]) – An ONNX-Runtime inference session or a callable that returns one.

infer_impl(feed_dict)

Implementation for running inference with ONNX-Runtime. Do not call this method directly - use infer() instead, which will forward unrecognized arguments to this method.

Parameters:

feed_dict (OrderedDict[str, Union[numpy.ndarray, torch.Tensor]]) – A mapping of input tensor names to corresponding input NumPy arrays or PyTorch tensors. If PyTorch tensors are provided in the feed_dict, then this function will return the outputs also as PyTorch tensors.

Returns:

A mapping of output tensor names to corresponding output NumPy arrays or PyTorch tensors.

Return type:

OrderedDict[str, Union[numpy.ndarray, torch.Tensor]]

__enter__()

Activate the runner for inference. For example, this may involve allocating CPU or GPU memory.

__exit__(exc_type, exc_value, traceback)

Deactivate the runner. For example, this may involve freeing CPU or GPU memory.

activate()

Activate the runner for inference. For example, this may involve allocating CPU or GPU memory.

Generally, you should use a context manager instead of manually activating and deactivating. For example:

with RunnerType(...) as runner:
    runner.infer(...)

deactivate()

Deactivate the runner. For example, this may involve freeing CPU or GPU memory.

Generally, you should use a context manager instead of manually activating and deactivating. For example:

with RunnerType(...) as runner:
    runner.infer(...)

get_input_metadata(use_numpy_dtypes=None)

Returns information about the inputs of the model. Shapes here may include dynamic dimensions, represented by None. Must be called only after activate() and before deactivate().

Parameters:

use_numpy_dtypes (bool) – [DEPRECATED] Whether to return NumPy data types instead of Polygraphy DataType s. This is provided to retain backwards compatibility. In the future, this parameter will be removed and Polygraphy DataType s will always be returned. These can be converted to NumPy data types by calling the numpy() method. Defaults to True.

Returns:

Input names, shapes, and data types.

Return type:

TensorMetadata

infer(feed_dict, check_inputs=True, *args, **kwargs)

Runs inference using the provided feed_dict.

Must be called only after activate() and before deactivate().

NOTE: Some runners may accept additional parameters in infer(). For details on these, see the documentation for their infer_impl() methods.

Parameters:

• feed_dict (OrderedDict[str, numpy.ndarray]) – A mapping of input tensor names to corresponding input NumPy arrays.

• check_inputs (bool) – Whether to check that the provided feed_dict includes the expected inputs with the expected data types and shapes. Disabling this may improve performance. Defaults to True.

inference_time

The time required to run inference in seconds.

Type:

float

Returns:

A mapping of output tensor names to their corresponding NumPy arrays.

IMPORTANT: Runners may reuse these output buffers. Thus, if you need to save outputs from multiple inferences, you should make a copy with copy.deepcopy(outputs).

Return type:

OrderedDict[str, numpy.ndarray]

last_inference_time()

Returns the total inference time in seconds required during the last call to infer().

Must be called only after activate() and before deactivate().

Returns:

The time in seconds, or None if runtime was not measured by the runner.

Return type:

float

is_active

Whether this runner has been activated, either via context manager, or by calling activate().

Type:

bool