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Transform Name | Description | Lossless? |
|---|---|---|
CVFlow / Default | Applies a combination of built-in and custom optimization passes to optimize the network for CVFlow compilation. The following transform passes will be applied (in order):
Usage: $ graph_surgery.py onnx -t Default | Yes |
BatchToChannel / Constrain3DConv | Fold batch dimension into channel dimension for the inputs of Conv/ConvTranspose operators with batch size greater than 1. Also takes care of dimension updates for the corresponding weight/bias tensors, as well as for associated BatchNormalization operators.
Usage: $ graph_surgery.py onnx -t BatchToChannel | Yes |
ConstantifyShapes | Uses Monte Carlo Sampling to convert eligible Shape, ConstantOfShape operators to constants. Typically used in conjunction with the FoldConstants transform. Usage: $ graph_surgery.py onnx -t ConstantifyShapes, FoldConstants | Yes |
Constrain2DSoftmax | Reshape inputs of Softmax nodes with rank > 2 to the coerced 2D shape Usage: $ graph_surgery.py onnx -t Constrain2DSoftmax | Yes |
Constrain2DMatMul | Optimize model to favor performing MatMul operations on tensors having significant rank no greater than 2.
Usage: $ graph_surgery.py onnx -t Constrain2DMatMul | Yes |
CutGraph | Splits and returns subgraph bounded by user-specified input and output tensors Usage: $ graph_surgery.py onnx -isrc "i:input0" -isrc "i:input1" -on output0,output1 -t CutGraph | No |
FlattenIO | Flatten statically-shaped primary input/output tensors
Usage: $ graph_surgery.py onnx -t FlattenIO | No |
FoldConstants | Fold sections of the network which evaluate to constants values. Typically used in conjunction with the ConstantifyShapes transform Usage: $ graph_surgery.py onnx -t ConstantifyShapes, FoldConstants | No |
FoldNoOps | Fold no-op operators by applying a combination of built-in and custom optimization passes. The following transform passes will be applied (in order):
Usage: $ graph_surgery.py onnx -t FoldNoOps | Yes |
FoldPassthroughWhere | Fold 'Where' nodes which have constant, homogeneous condition input tensors, essentially becoming passthrough operations Usage: $ graph_surgery.py onnx -t FoldPassthroughWhere | Yes |
FuseConsecutiveReshapes | Fuse consecutive Reshape nodes into the last Reshape node of the sequence Usage: $ graph_surgery.py onnx -t FuseConsecutiveReshapes | Yes |
FuseMaxPoolMaxUnpool | Fuse MaxPool-MaxUnpool nodes to ensure direct connection of indices tensor
Usage: $ graph_surgery.py onnx -t FuseMaxPoolMaxUnpool | Yes |
InferShapes | Infer (all) tensor shapes using built-in shape inference, supplemented by onnxruntime. Uses sampling to evaluate dynamic shapes Usage: $ graph_surgery.py onnx -t InferShapes | Yes |
ModNodeNames | Assigns names to all unnamed graph nodes in the form '<op_type>_<id>' Usage: $ graph_surgery.py onnx -t ModNodeNames | Yes |
NamespaceTensors | Prepends a dot-separated namespace to the names of all tensors except for primary inputs/outputs. If no namespace argument is given, defaults to the model producer's name.
Usage: $ graph_surgery.py onnx -t NamespaceTensors=<ns> | Yes |
RemoveZeroChannel | Remove entire channels that have been zeroed out by structured pruning from eligible Conv/ConvT-to-Conv/ConvT operator sequences.
Usage: $ graph_surgery.py onnx -t RemoveZeroChannel | Yes |
RenameTensors | Rename arbitrary tensors in the model.
Usage: $ graph_surgery.py onnx -t "RenameTensors(orig_name=new_name, ...)" | Yes |
ReplaceSubgraph | Replace subgraph(s) with alternative implementations, e.g. replacing operations with rank greater than 4 Usage: $ graph_surgery.py onnx -t ReplaceSubgraph=path/to/config.json | Yes |
SetIOShapes | Sets shape of primary input and output tensors to userspecified values, can be static or dynamic Usage: $ graph_surgery.py onnx -isrc "i:input0|is:1,3,224,224" -odst "o:output0|os:1,1000" -t SetIOShapes | Yes |
AsymmetricAdjust | Adjust the zero points of asymmetrically quantized operators to either 128 if it's uint8 or 0 if it's int8 and annotate the quantized operators with asymmetric zero-points to parse with 16-bit precision in onnxparser. Input '.ini' file to configure annotations and types of adjustment.
Usage: $ graph_surgery.py onnx -t AsymmetricAdjust=config.ini | Yes |