# Using the Reshape Inference Feature¶

## Introduction (C++)¶

C++

OpenVINO™ provides two methods for runtime model reshaping: setting a new input shape and setting a new batch dimension value.

### Set a new input shape with the reshape() method¶

The InferenceEngine::CNNNetwork::reshape method updates input shapes and propagates them down to the outputs of the model through all intermediate layers.

NOTES :

• 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 setBatchSize() method¶

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.

Use InferenceEngine::CNNNetwork::reshape instead of InferenceEngine::CNNNetwork::setBatchSize to set new input shapes for the model if the model has one of the following:

• Multiple inputs with different zero-index dimension meanings

• Input without a batch dimension

• 0D, 1D, or 3D shape

The 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.

Using the 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::reshape and 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 object:

1. Intermediate Representation (IR) through InferenceEngine::Core::ReadNetwork

2. ONNX model through InferenceEngine::Core::ReadNetwork

3. nGraph function through the constructor of InferenceEngine::CNNNetwork

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 Inference Engine plugins. To resolve undefined input dimensions of a model, call the CNNNetwork::reshape method to provide 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:

CNNNetwork network = ... // read IR / ONNX model or create from nGraph::Function explicitly
const auto parameters = network.getFunction()->get_parameters();
for (const auto & parameter : parameters) {
std::cout << "name: " << parameter->get_friendly_name() << " shape: " << parameter->get_partial_shape() << std::endl;
if (parameter->get_partial_shape().is_dynamic())
std::cout << "ATTENTION: Input shape is not fully defined. Use the CNNNetwork::reshape method to resolve it." << std::endl;
}

To feed input data of a shape that is different from the model input shape, reshape the model first.

Once the input shape of InferenceEngine::CNNNetwork is set, call the InferenceEngine::Core::LoadNetwork method to get an InferenceEngine::ExecutableNetwork object for inference with updated shapes.

There are other approaches to reshape the model during the stage of IR generation or nGraph::Function creation.

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 InferenceEngine::CNNNetwork::reshape method.

### Usage of Reshape Method¶

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 InferenceEngine::CNNNetwork::getInputShapes

2. Set new input shapes

3. Call reshape

Here is a code example:

InferenceEngine::Core core;
// ------------- 0. Read IR and image ----------------------------------------------
// ---------------------------------------------------------------------------------

// ------------- 1. Collect the map of input names and shapes from IR---------------
auto input_shapes = network.getInputShapes();
// ---------------------------------------------------------------------------------

// ------------- 2. Set new input shapes -------------------------------------------
std::string input_name;
InferenceEngine::SizeVector input_shape;
std::tie(input_name, input_shape) = *input_shapes.begin(); // let's consider first input only
input_shape[0] = batch_size; // set batch size to the first input dimension
input_shape[2] = image.rows; // changes input height to the image one
input_shape[3] = image.cols; // changes input width to the image one
input_shapes[input_name] = input_shape;
// ---------------------------------------------------------------------------------

// ------------- 3. Call reshape ---------------------------------------------------
network.reshape(input_shapes);
// ---------------------------------------------------------------------------------

//...

std::string device = "CPU";
// ---------------------------------------------------------------------------------

The Shape Inference feature is used in Smart Classroom Demo.

### Troubleshooting Reshape Errors¶

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:

• Reshape operation with a hard-coded output shape value

• MatMul operation with the Const second input cannot be resized by spatial dimensions due to operation semantics

Model structure and logic should not change significantly after model reshaping.

• The Global Pooling operation is commonly used to reduce output feature map of classification models output. Having the input of the shape [N, C, H, W], Global Pooling returns the output of the shape [N, C, 1, 1]. Model architects usually express Global Pooling with the help of the 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.

• Changing the model input shape may significantly affect its accuracy. For example, Object Detection models from TensorFlow have resizing restrictions by design. To keep the model valid after the reshape, choose a new input shape that satisfies conditions listed in the pipeline.config file. For details, refer to the Tensorflow Object Detection API models resizing techniques.

### Extensibility¶

The Inference Engine provides a special mechanism that allows adding support of shape inference for custom operations. This mechanism is described in the Extensibility documentation

## Introduction (Python)¶

Python

OpenVINO™ provides the following methods for runtime model reshaping:

• Set a new input shape with the IENetwork.reshape method.

The IENetwork.reshape method updates input shapes and propagates them down to the outputs of the model through all intermediate layers.

NOTES :

• 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 IENetwork.batch_size method.

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 IENetwork.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.

Use IENetwork.reshape rather than IENetwork.batch_size to set new input shapes for the model if the model has:

• Multiple inputs with different zero-index dimension meanings

• Input without a batch dimension

• 0D, 1D, or 3D shape

The IENetwork.batch_size method is a high-level API method that wraps the IENetwork.reshape method call and works for trivial models from the batch placement standpoint. Use IENetwork.reshape for other models.

Using the IENetwork.batch_size 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 IENetwork.reshape and IENetwork.batch_size methods in any order. If a model has a hard-coded batch dimension, use IENetwork.batch_size first to change the batch, then call IENetwork.reshape to update other dimensions, if needed.

Inference Engine takes three kinds of a model description as an input, which are converted into an IENetwork object:

1. Intermediate Representation (IR) through IECore.read_network

2. ONNX model through IECore.read_network

3. nGraph function through the constructor of IENetwork

IENetwork 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 IENetwork.reshape method providing new input shapes before loading to the Inference Engine plugin.

Run the following code right after IENetwork 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.

Once the input shape of IENetwork is set, call the IECore.load_network method to get an ExecutableNetwork object for inference with updated shapes.

There are other approaches to reshape the model during the stage of IR generation or nGraph function creation.

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 IENetwork.reshape method.

### Troubleshooting Reshape Errors¶

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:

• Reshape operation with a hard-coded output shape value

• MatMul operation with the Const second input cannot be resized by spatial dimensions due to operation semantics

A model’s structure and logic should not significantly change after model reshaping.

• The Global Pooling operation is commonly used to reduce output feature map of classification models output. Having the input of the shape [N, C, H, W], Global Pooling returns the output of the shape [N, C, 1, 1]. Model architects usually express Global Pooling with the help of the 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.

• Changing the model input shape may significantly affect its accuracy. For example, Object Detection models from TensorFlow have resizing restrictions by design. To keep the model valid after the reshape, choose a new input shape that satisfies conditions listed in the pipeline.config file. For details, refer to the Tensorflow Object Detection API models resizing techniques.

### Usage of the Reshape Method¶

The primary method of the feature is IENetwork.reshape. It gets new input shapes and propagates it from input to output for all intermediates layers of the given network. Use IENetwork.input_info to get names of input_layers and .tensor_desc.dims to get the current network input shape.

The following code example shows how to reshape a model to the size of an input image.

import cv2
import numpy as np
from openvino.inference_engine import IECore

ie = IECore()

# Read an input image and transpose input to NCWH
input_image = image.transpose((2, 0, 1))
input_image = np.expand_dims(input_image, axis=0)

# Load the model and get input info
# Note that this model must support arbitrary input shapes
input_layer = next(iter(net.input_info))
print(f"Input shape: {net.input_info[input_blob].tensor_desc.dims}")

# Call reshape
net.reshape({input_layer: input_image.shape})
print(f"New input shape: {net.input_info[input_blob].tensor_desc.dims}")

# Load the model to the device and proceed with inference
exec_net = ie.load_network(network=net, device_name="CPU")

### Extensibility¶

The Inference Engine provides a special mechanism that allows adding support of shape inference for custom operations. This mechanism is described in the Extensibility documentation