| # TFLite quantized inference example | |
| # | |
| # Based on: | |
| # https://www.tensorflow.org/lite/performance/post_training_integer_quant | |
| # https://www.tensorflow.org/lite/api_docs/java/org/tensorflow/lite/Tensor.QuantizationParams | |
| import numpy as np | |
| import tensorflow as tf | |
| # Location of tflite model file (float32 or int8 quantized) |
Scalable Vector Extensions (SVE) is ARM’s latest SIMD extension to their instruction set, which was announced back in 2016. A follow-up SVE2 extension was announced in 2019, designed to incorporate all functionality from ARM’s current primary SIMD extension, NEON (aka ASIMD).
Despite being announced 5 years ago, there is currently no generally available CPU which supports any form of SVE (which excludes the [Fugaku supercomputer](https://www.fujitsu.com/global/about/innovation/
First of all: YUV pixel formats and Recommended 8-Bit YUV Formats for Video Rendering. Chromium's source code contains good documentation about those formats too: chromium/src/media/base/video_types.h and chromium/src/media/base/video_frame.cc (search for RequiresEvenSizeAllocation(), NumPlanes() and those kinds of functions).
You can think of an image as a superposition of several planes (or layers in a more natural language). YUV formats have three planes: Y, U, and V.
Y is the luma plane, and can be seen as the image as grayscale. U and V are reffered to as the chroma planes, which are basically the colours. All the YUV formats have these three planes, and differ by the different orderings of them.