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87 lines
3.5 KiB
Markdown
87 lines
3.5 KiB
Markdown
---
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title: Make dynamic input shape fixed
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descriptions:
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parent: ORT Mobile Model Export Helpers
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grand_parent: Deploy on Mobile
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nav_order: 2
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---
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# Making dynamic input shapes fixed
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{: .no_toc }
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If a model can potentially be used with NNAPI or CoreML as reported by the [model usability checker](./model-usability-checker.md), it may benefit from making the input shapes 'fixed'.
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This is because NNAPI does not support dynamic input shapes and CoreML may have better performance with fixed input shapes.
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For example, often models have a dynamic batch size so that training is more efficient. In mobile scenarios the batch generally has a size of 1.
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Making the batch size dimension 'fixed' by setting it to 1 may allow NNAPI to run the model.
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The helper can be used to update specific dimensions, or the entire input shape.
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## Contents
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{: .no_toc }
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* TOC placeholder
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{:toc}
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## Usage
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```
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python -m onnxruntime.tools.make_dynamic_shape_fixed -h
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usage: make_dynamic_shape_fixed.py:make_dynamic_shape_fixed_helper [-h] [--dim_param DIM_PARAM] [--dim_value DIM_VALUE] [--input_name INPUT_NAME] [--input_shape INPUT_SHAPE] input_model output_model
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Assign a fixed value to a dim_param or input shape. Provide either dim_param and dim_value or input_name and input_shape.
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positional arguments:
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input_model Provide path to ONNX model to update.
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output_model Provide path to write updated ONNX model to.
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optional arguments:
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-h, --help show this help message and exit
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--dim_param DIM_PARAM
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Symbolic parameter name. Provide dim_value if specified.
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--dim_value DIM_VALUE
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Value to replace dim_param with in the model. Must be > 0.
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--input_name INPUT_NAME
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Model input name to replace shape of. Provide input_shape if specified.
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--input_shape INPUT_SHAPE
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Shape to use for input_shape. Provide comma separated list for the shape. All values must be > 0. e.g. --input_shape 1,3,256,256
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```
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To determine the update required by the model, it's generally helpful to view the model in [Netron](https://netron.app/) to inspect the inputs.
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## Making a symbolic dimension fixed
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Here is an example model, viewed using Netron, with a symbolic dimension called 'batch' for the batch size in 'input:0'.
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We will update that to use the fixed value of 1.
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```
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python -m onnxruntime.tools.make_dynamic_shape_fixed --dim_param batch --dim_value 1 model.onnx model.fixed.onnx
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```
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After replacement you should see that the shape for 'input:0' is now 'fixed' with a value of [1, 36, 36, 3]
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## Making an input shape fixed
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Here is an example model that has unnamed dynamic dimensions for the 'x' input. Netron represents these with '?'.
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As there is no name for the dimension, we need to update the shape using the `--input_shape` option.
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```
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python -m onnxruntime.tools.make_dynamic_shape_fixed --input_name x --input_shape 1,3,960,960 model.onnx model.fixed.onnx
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```
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After replacement you should see that the shape for 'x' is now 'fixed' with a value of [1, 3, 960, 960]
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