Dynamic Input Shape Example - Onnx Typing CST Test
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Dynamic Input Shape Example — Onnx Code
Inference with dynamic input shapes in ONNX Runtime.
import onnxruntime as ort
import numpy as np
# Input with variable batch size
input_data = np.random.rand(5,4).astype(np.float32)
# Load model
session = ort.InferenceSession('dynamic_model.onnx')
input_name = session.get_inputs()[0].name
# Run inference
outputs = session.run(None, {input_name: input_data})
print('Dynamic input output:', outputs)Onnx Language Guide
ONNX (Open Neural Network Exchange) is an open-source format and ecosystem for representing machine learning models, enabling interoperability between frameworks like PyTorch, TensorFlow, and scikit-learn, and allowing deployment across diverse platforms.
Primary Use Cases
- ▸Exporting models from PyTorch, TensorFlow, or other frameworks
- ▸Cross-framework deployment without retraining
- ▸Hardware-accelerated inference on CPUs, GPUs, and specialized accelerators
- ▸Optimizing models with ONNX Runtime for production
- ▸Edge AI and mobile deployment of ML models
Notable Features
- ▸Framework-agnostic model format
- ▸Supports both deep learning and classical ML operators
- ▸ONNX Runtime for high-performance inference
- ▸Quantization and optimization tools for deployment
- ▸Extensible operator set for custom layers
Origin & Creator
ONNX was co-developed by Microsoft and Facebook in 2017 to unify model representation and interoperability between deep learning frameworks.
Industrial Note
ONNX is widely used in production pipelines where models need to be transferred between frameworks, optimized for inference, or deployed on resource-constrained devices like mobile phones or edge servers.