chop.passes.transform.utils

chop.passes.transform.utils#

conv_bn_fusion_transform_pass#

chop.passes.graph.transforms.utils.conv_bn_fusion_transform_pass(graph, pass_args={})[source]#

Perform Conv-BN fusion on the given graph.

Parameters:

graph (MaseGraph) – a MaseGraph
pass_args (dict, optional) – this pass can take a string argument named “file_name”, defaults to None

Returns:

return a tuple of a MaseGraph and an empty dict (no additional info to return)

Return type:

tuple(MaseGraph, dict)

logicnets_fusion_transform_pass#

chop.passes.graph.transforms.utils.logicnets_fusion_transform_pass(graph, pass_args, **_)[source]#

onnx_annotate_transform_pass#

chop.passes.graph.transforms.utils.onnx_annotate_transform_pass(graph, pass_args)[source]#

Convert MaseGraph to ONNX and annotate the relevant layers with sparsity information. The code here is derived from Zhewen’s SparseCNN codebase: Yu-Zhewen/sparseCNN