OpenVINO™ provides the following methods for runtime model reshaping:
InferenceEngine::CNNNetwork::reshape method updates input shapes and propagates them down to the outputs of the model through all intermediate layers.
- Starting with the 2021.1 release, the Model Optimizer converts topologies keeping shape-calculating sub-graphs by default, which enables correct shape propagation during reshaping in most cases.
- Older versions of IRs are not guaranteed to reshape successfully. Please regenerate them with the Model Optimizer of the latest version of OpenVINO™.
- If an ONNX model does not have a fully defined input shape and the model was imported with the ONNX importer, reshape the model before loading it to the plugin.
Set a new batch dimension value with the
The meaning of a model batch may vary depending on the model design. This method does not deduce batch placement for inputs from the model architecture. It assumes that the batch is placed at the zero index in the shape for all inputs and uses the
InferenceEngine::CNNNetwork::reshape method to propagate updated shapes through the model.
The method transforms the model before a new shape propagation to relax a hard-coded batch dimension in the model, if any.
InferenceEngine::CNNNetwork::setBatchSize method is a high-level API method that wraps the
InferenceEngine::CNNNetwork::reshape method call and works for trivial models from the batch placement standpoint. Use
InferenceEngine::CNNNetwork::reshape for other models.
InferenceEngine::CNNNetwork::setBatchSize method for models with a non-zero index batch placement or for models with inputs that do not have a batch dimension may lead to undefined behaviour.
You can change input shapes multiple times using the
InferenceEngine::CNNNetwork::setBatchSize methods in any order. If a model has a hard-coded batch dimension, use
InferenceEngine::CNNNetwork::setBatchSize first to change the batch, then call
InferenceEngine::CNNNetwork::reshape to update other dimensions, if needed.
Inference Engine takes three kinds of a model description as an input, which are converted into an
InferenceEngine::CNNNetwork keeps an
ngraph::Function object with the model description internally. The object should have fully defined input shapes to be successfully loaded to the Inference Engine plugins. To resolve undefined input dimensions of a model, call the
CNNNetwork::reshape method providing new input shapes before loading to the Inference Engine plugin.
Run the following code right after
InferenceEngine::CNNNetwork creation to explicitly check for model input names and shapes:
To feed input data of a shape that is different from the model input shape, reshape the model first.
Practically, some models are not ready to be reshaped. In this case, a new input shape cannot be set with the Model Optimizer or the
Operation semantics may impose restrictions on input shapes of the operation. Shape collision during shape propagation may be a sign that a new shape does not satisfy the restrictions. Changing the model input shape may result in intermediate operations shape collision.
Examples of such operations:
Const second input cannot be resized by spatial dimensions due to operation semantics
Model structure and logic should not change significantly after model reshaping.
Pooling operation with the fixed kernel size [H, W]. During spatial reshape, having the input of the shape [N, C, H1, W1], Pooling with the fixed kernel size [H, W] returns the output of the shape [N, C, H2, W2], where H2 and W2 are commonly not equal to
1. It breaks the classification model structure. For example, publicly available Inception family models from TensorFlow* have this issue.
pipeline.config file. For details, refer to the Tensorflow Object Detection API models resizing techniques.
The primary method of the feature is
InferenceEngine::CNNNetwork::reshape. It gets new input shapes and propagates it from input to output for all intermediates layers of the given network. The method takes
InferenceEngine::ICNNNetwork::InputShapes - a map of pairs: name of input data and its dimension.
The algorithm for resizing network is the following:
1) Collect the map of input names and shapes from Intermediate Representation (IR) using helper method
2) Set new input shapes
3) Call reshape
Here is a code example:
Shape Inference feature is used in Smart Classroom Demo.
Inference Engine provides a special mechanism that allows to add the support of shape inference for custom operations. This mechanism is described in the Extensibility documentation