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import mxnet as mx
import matplotlib.pyplot as plt
import numpy as np
from collections import namedtuple
from mxnet.gluon.data.vision import transforms
from mxnet.contrib.onnx.onnx2mx.import_model import import_model
import os
import gradio as gr
from PIL import Image
import imageio
import onnxruntime as ort
def get_image(path):
'''
Using path to image, return the RGB load image
'''
img = imageio.imread(path, pilmode='RGB')
return img
# Pre-processing function for ImageNet models using numpy
def preprocess(img):
'''
Preprocessing required on the images for inference with mxnet gluon
The function takes loaded image and returns processed tensor
'''
img = np.array(Image.fromarray(img).resize((224, 224))).astype(np.float32)
img[:, :, 0] -= 123.68
img[:, :, 1] -= 116.779
img[:, :, 2] -= 103.939
img[:,:,[0,1,2]] = img[:,:,[2,1,0]]
img = img.transpose((2, 0, 1))
img = np.expand_dims(img, axis=0)
return img
mx.test_utils.download('https://s3.amazonaws.com/model-server/inputs/kitten.jpg')
mx.test_utils.download('https://s3.amazonaws.com/onnx-model-zoo/synset.txt')
with open('synset.txt', 'r') as f:
labels = [l.rstrip() for l in f]
os.system("wget https://github.com/AK391/models/raw/main/vision/classification/alexnet/model/bvlcalexnet-7.onnx")
ort_session = ort.InferenceSession("bvlcalexnet-7.onnx")
def predict(path):
img_batch = preprocess(get_image(path))
outputs = ort_session.run(
None,
{"data_0": img_batch.astype(np.float32)},
)
a = np.argsort(-outputs[0].flatten())
results = {}
for i in a[0:5]:
results[label[i]]=outputs[0][0][i]
return results
title="AlexNet"
description="AlexNet is the name of a convolutional neural network for classification, which competed in the ImageNet Large Scale Visual Recognition Challenge in 2012."
examples=[['catonnx.jpg']]
gr.Interface(predict,gr.inputs.Image(type='filepath'),"label",title=title,description=description,examples=examples).launch(enable_queue=True) |