`quark.onnx.graph_transformations.model_transformer_test`#

Module Contents#

Classes#

Functions#

quark.onnx.graph_transformations.model_transformer_test.generate_input_initializer(tensor_shape: List[int], tensor_dtype: type, input_name: str) → onnx.TensorProto#: Helper function to generate initializers for test inputs

class quark.onnx.graph_transformations.model_transformer_test.ModelTransformerTest(methodName='runTest')#

A class whose instances are single test cases.

By default, the test code itself should be placed in a method named ‘runTest’.

If the fixture may be used for many test cases, create as many test methods as are needed. When instantiating such a TestCase subclass, specify in the constructor arguments the name of the test method that the instance is to execute.

Test authors should subclass TestCase for their own tests. Construction and deconstruction of the test’s environment (‘fixture’) can be implemented by overriding the ‘setUp’ and ‘tearDown’ methods respectively.

If it is necessary to override the __init__ method, the base class __init__ method must always be called. It is important that subclasses should not change the signature of their __init__ method, since instances of the classes are instantiated automatically by parts of the framework in order to be run.

When subclassing TestCase, you can set these attributes: * failureException: determines which exception will be raised when

the instance’s assertion methods fail; test methods raising this exception will be deemed to have ‘failed’ rather than ‘errored’.

longMessage: determines whether long messages (including repr of
objects used in assert methods) will be printed on failure in addition to any explicit message passed.
maxDiff: sets the maximum length of a diff in failure messages
by assert methods using difflib. It is looked up as an instance attribute so can be configured by individual tests if required.

class ReplaceWholeModel#

Defines a transform to be applied to a onnx model graph.

A transform is a combination of ‘Find + Replace’ which describes how to find a pattern of nodes in a model, and what to replace those nodes with.

A pattern is described using OpTypePattern. The replacement function receives a NodeTree which contains the matched nodes and should return a NodeTree which contains the set of nodes which replaced the matched nodes.

pattern() → OpTypePattern#: Return the OpTypePattern to find in the model graph.

replacement(match_node: NodeTree) → NodeTree#

Generate a replacement sub-graph for the matched sub-graph.

The fundamental constraint of the replacement is that the replacement sub-graph should consume the same input tensors as the original sub-graph and also produce a final list of tensors which are same in number and shape as the original sub-graph. Not following this could crash model creation, or introduce bugs in the new model graph.

Parameters:: match_nodes – Matched NodeTree based on self.pattern().

class RemoveRelu#