Use of int8 and float32 as data types in model built using MXNet

In this recipe, we will see what happens when you use int8 and float32 as data types in the model built using MXNet

Recipe Objective: What happens when you use int8 and float32 as data types in the model built using MXNet?

This recipe explains what happens when you use int8 and float32 as a data type in the model building using MXNet.

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Step 1: Importing library

Let us first import the necessary libraries. We'll import mxnet, NumPy, gluoncv, and matplotlib.pyplot.

import numpy as np import mxnet as mx import gluoncv as gcv import matplotlib.pyplot as plt

Step 2: Loading The Sample Image

We'll be downloading a sample image of a dog from the URL "https://raw.githubusercontent.com/zhreshold/mxnet-ssd/master/data/demo/dog.jpg"

image_url = 'https://raw.githubusercontent.com/zhreshold/mxnet-ssd/master/data/demo/dog.jpg' image_path='dog.jpg' gcv.utils.download(url=image_url,path=image_path) img = mx.image.imread('dog.jpg')

Step 3: Processing

We'll be defining a utility function that will be used for the augmentation visualization. We have loaded the image with numpy.int8 datatype because for some functions, for example, swapexe don't compute on int type, so we have converted int18 to float32.

def plot(arr): imshow((arr.clip(0, 255)/255).asnumpy()) from matplotlib.pyplot import imshow img = image.astype("float32") plot(img)

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