ONNX Runtime is a high-performance inference engine for machine learning models in the Open Neural Network Exchange (ONNX) format. It is designed to optimize and accelerate the deployment of machine learning models across various platforms and devices. By supporting a wide range of hardware and software environments, ONNX Runtime enables developers to run models efficiently and effectively.
When using ONNX Runtime, you may encounter the following error message: ONNXRuntimeError: [ONNXRuntimeError] : 2 : INVALID_ARGUMENT : Invalid input shape. This error indicates that there is a mismatch between the shape of the input tensor provided to the model and the shape expected by the model.
During model inference, the process is interrupted by the error message, preventing the model from executing successfully. This can halt the deployment of your application or service.
The error INVALID_ARGUMENT : Invalid input shape occurs when the input tensor's dimensions do not align with the model's expected input dimensions. Each ONNX model specifies the shape of inputs it requires, and any deviation from this shape results in an error.
Input shapes are defined by the model architecture and are crucial for the model's operation. For example, a model trained on 224x224 images will expect inputs of this size. Providing inputs of a different size, such as 256x256, will trigger an invalid input shape error.
To fix the invalid input shape error, follow these steps:
Examine the model's input specifications to determine the expected input shape. This information is often available in the model documentation or can be extracted programmatically using tools like Netron, a viewer for neural network models. You can download Netron from Netron's official website.
Once you know the required input shape, modify your input data to match this shape. This may involve resizing images, reshaping arrays, or padding data. For image data, libraries like OpenCV or PIL can be used to resize images to the correct dimensions.
import cv2
# Example: Resize an image to 224x224
image = cv2.imread('input.jpg')
resized_image = cv2.resize(image, (224, 224))
After adjusting the input shape, rerun the model inference to ensure the error is resolved. If the error persists, double-check the input shape and model requirements.
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