onnxruntimequantizationquantize_static float16

The onnxruntime.quantization.quantize_static function is a method in the ONNX Runtime library that performs static quantization on a given model.

Static quantization is a technique used to reduce the memory footprint and improve the runtime performance of deep learning models. It involves converting the model's weights and activations from floating-point representation (e.g., float32) to a lower precision format (e.g., float16).

By using float16, the model's memory requirements are reduced by half compared to float32, which can be beneficial for deployment on memory-constrained devices such as mobile phones or edge devices. Additionally, lower precision calculations like float16 can be performed faster on hardware that supports accelerated operations for this format.

The onnxruntime.quantization.quantize_static function takes as input an ONNX model and applies static quantization to all the applicable tensors in the model. The function returns a quantized model that can be used for inference.

Here's an example of how to use the onnxruntime.quantization.quantize_static function in Python:

import onnxruntime.quantization as quantization

# Load the original ONNX model
original_model = onnx.load("original_model.onnx")

# Quantize the model
quantized_model = quantization.quantize_static(original_model, quantization_mode='QLinearOps')

# Save the quantized model
onnx.save(quantized_model, "quantized_model.onnx")

In this example, we first load the original ONNX model using the onnx.load function. Then, we apply static quantization using the onnxruntime.quantization.quantize_static function, specifying the quantization_mode as 'QLinearOps'. Finally, we save the quantized model using the onnx.save function