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@ulfhanebutte
ulfhanebutte authored Jun 9, 2023
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Expand Up @@ -11,74 +11,77 @@ Use 'python onnxinsight.py -h' to explore available features

Use 'python onnxinsight.py [--help] [--version] [--all] [--info] [--io] [--op] [--node_csv] [--shape] [--batch] [--simplify] onnx_model'

Given a valid ONNX model as input, performs per-node analysis of the model.
Given a valid ONNX model as input, the tool performs per-node analysis of the model.

To get more insight, try different arguments.

Required arguments:
onnx_model ONNX model file

onnx_model ONNX model file

Optional arguments:
-h, --help show this help message and exit

--version Show version and exit

--all Print all available tables
Print out model, inputs/outputs, operators tables

--info Print model general info table
Model Overview
-----------------------------------
| Model Components| Value |
-----------------------------------
| ir_version | **** |
-----------------------------------
| **** | **** |
-----------------------------------
| model_MACs | **** |
-----------------------------------
| model_params | **** |
-----------------------------------
Disclaimer: MACs (number of multiply and accumulate operations) account for Convolution, ConvTranspose
and MatMul layers assuming 4dim input tensors for Convolutions and 2dim for Gemm and
2, 3 and 4 dim input tensors for MatMul. This covers many networks, but may not cover all
A detailed layer by layer information is provied with --node_csv
Layer specific operator information can be used to verify the MACs and/or to calculate MACs
for other operators that perform MAC operations.

-h, --help show this help message and exit

--version Show version and exit

--all Print all available tables
Print out model, inputs/outputs, operators tables

--info Print model general info table
Model Overview
-----------------------------------
| Model Components| Value |
-----------------------------------
| ir_version | **** |
-----------------------------------
| **** | **** |
-----------------------------------
| model_MACs | **** |
-----------------------------------
| model_params | **** |
-----------------------------------
Disclaimer: MACs (number of multiply and accumulate operations) account for Convolution, ConvTranspose
and MatMul layers assuming 4dim input tensors for Convolutions and 2dim for Gemm and
2, 3 and 4 dim input tensors for MatMul. This covers many networks, but may not cover all
A detailed layer by layer information is provied with --node_csv
Layer specific operator information can be used to verify the MACs and/or to calculate MACs
for other operators that perform MAC operations.
--io Print model inputs/outputs info table
Model Inputs/outputs Overview
------------------------------------------
| ID | Name | Shape | Type | ByteSize|
------------------------------------------
| ** | **** | **** | *** | ***** |
------------------------------------------

--op Print model operators summary table
Model Operators Overview
--------------------------
| Op Name | # |
--------------------------
| **** | *** |
--------------------------
| Total | **** |
--------------------------

--node_csv Save model node info to CSV file
--io Print model inputs/outputs info table
Model Inputs/outputs Overview
------------------------------------------
| ID | Name | Shape | Type | ByteSize|
------------------------------------------
| ** | **** | **** | *** | ***** |
------------------------------------------

--op Print model operators summary table
Model Operators Overview
--------------------------
| Op Name | # |
--------------------------
| **** | *** |
--------------------------
| Total | **** |
--------------------------

--node_csv Save model node info to CSV file
Save each node "name", "type", "inputs", "output", "params", "macs", "attrs" information to CSV file

--shape Save shape of intermediate tensor into model
Run shape inference and save the model with shape information

--simplify Simplify model and save the simplified model
--shape Save shape of intermediate tensor into model
Run shape inference and save the model with shape information

--batch Change the batch size (experimental) changes first dimension of inputs and outputs
Does not support Reshape operator and other explicitly tensor shape changing operators
Feature relies on onnx shape propagation
For dynamic batch size models, this feature can be used to set the batch size to a fixed dimension,
and it is recomended to run the --simplify step afterwards.
An alternative tool to try may be the onnxruntime.tools.make_dynamic_shape_fixed
--simplify Simplify model and save the simplified model

Examples:
--batch Change the batch size (experimental) changes first dimension of inputs and outputs
Does not support Reshape operator and other explicitly tensor shape changing operators
Feature relies on onnx shape propagation
For dynamic batch size models, this feature can be used to set the batch size to a fixed dimension,
and it is recomended to run the --simplify step afterwards.
An alternative tool to try may be the onnxruntime.tools.make_dynamic_shape_fixed
# Examples:
1. python onnxinsight.py --all model.onnx
2. python onnxinsight.py --all --node_csv model.onnx
3. python onnxinsight.py --shape model.onnx

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