polygraphy.backend.onnx.runner.OnnxTfRunner Class Reference

Inheritance diagram for polygraphy.backend.onnx.runner.OnnxTfRunner:

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Public Member Functions
def	__init__ (self, model, name=None)
def	activate_impl (self)
def	deactivate_impl (self)
def	infer_impl (self, feed_dict)
def	get_input_metadata (self)
def	last_inference_time (self)
def	__enter__ (self)
def	__exit__ (self, exc_type, exc_value, traceback)
def	activate (self)
def	infer_impl (self)
def	infer (self, feed_dict)
def	deactivate (self)

Public Attributes
	tf_rep
	inference_time
	name
	is_active

Static Public Attributes
	RUNNER_COUNTS = defaultdict(int)

Private Attributes
	_model

Detailed Description

Runs an ONNX model using the TensorFlow backend for ONNX.

Constructor & Destructor Documentation

__init__()

def polygraphy.backend.onnx.runner.OnnxTfRunner.__init__	(		self,
			model,
			name = None
	)

Creates an ONNX-TF runner.

Reimplemented from polygraphy.backend.base.runner.BaseRunner.

Member Function Documentation

activate_impl()

def polygraphy.backend.onnx.runner.OnnxTfRunner.activate_impl

(

self

)

Implementation for runner activation. Derived classes should override this function
rather than ``activate()``.

Reimplemented from polygraphy.backend.base.runner.BaseRunner.

deactivate_impl()

def polygraphy.backend.onnx.runner.OnnxTfRunner.deactivate_impl

(

self

)

Implementation for runner deactivation. Derived classes should override this function
rather than ``deactivate()``.

Reimplemented from polygraphy.backend.base.runner.BaseRunner.

infer_impl() [1/2]

def polygraphy.backend.onnx.runner.OnnxTfRunner.infer_impl	(		self,
			feed_dict
	)

get_input_metadata()

def polygraphy.backend.onnx.runner.OnnxTfRunner.get_input_metadata

(

self

)

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().

Returns:
    TensorMetadata: Input names, shapes, and data types.

Reimplemented from polygraphy.backend.base.runner.BaseRunner.

last_inference_time()

def polygraphy.backend.base.runner.BaseRunner.last_inference_time (   self )

inherited

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

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

__enter__()

def polygraphy.backend.base.runner.BaseRunner.__enter__ (   self )

inherited

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__exit__()

def polygraphy.backend.base.runner.BaseRunner.__exit__ (   self,   exc_type,   exc_value,   traceback  )

inherited

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activate()

def polygraphy.backend.base.runner.BaseRunner.activate (   self )

inherited

Activate the runner for inference. This may involve allocating GPU buffers, for example.

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infer_impl() [2/2]

def polygraphy.backend.base.runner.BaseRunner.infer_impl (   self )

inherited

Implementation for runner inference. Derived classes should override this function
rather than ``infer()``

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infer()

def polygraphy.backend.base.runner.BaseRunner.infer (   self,   feed_dict  )

inherited

Runs inference using the provided feed_dict.

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

Returns:
    OrderedDict[str, numpy.ndarray]:
    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.copy(outputs)``.

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deactivate()

def polygraphy.backend.base.runner.BaseRunner.deactivate (   self )

inherited

Deactivate the runner.

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Member Data Documentation

_model

polygraphy.backend.onnx.runner.OnnxTfRunner._model

private

tf_rep

polygraphy.backend.onnx.runner.OnnxTfRunner.tf_rep

inference_time

polygraphy.backend.onnx.runner.OnnxTfRunner.inference_time

RUNNER_COUNTS

polygraphy.backend.base.runner.BaseRunner.RUNNER_COUNTS = defaultdict(int)

staticinherited

name

polygraphy.backend.base.runner.BaseRunner.name

inherited

is_active

polygraphy.backend.base.runner.BaseRunner.is_active

inherited

---

The documentation for this class was generated from the following file:

• backend/onnx/runner.py