File size: 2,056 Bytes
ba65a7b 3d83121 f56da98 3d83121 ba65a7b 25d3510 ba65a7b 25d3510 3d83121 ba65a7b f56da98 ba65a7b f56da98 ba65a7b f56da98 ba65a7b a19ec39 ba65a7b f56da98 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 |
import mxnet as mx
import matplotlib.pyplot as plt
import numpy as np
from collections import namedtuple
from mxnet.gluon.data.vision import transforms
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/onnx/models/raw/main/vision/classification/inception_and_googlenet/googlenet/model/googlenet-9.onnx")
ort_session = ort.InferenceSession("googlenet-9.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[labels[i]]=float(outputs[0][0][i])
return results
title="GoogleNet"
description="GoogLeNet is the name of a convolutional neural network for classification, which competed in the ImageNet Large Scale Visual Recognition Challenge in 2014."
examples=[['catonnx.jpg']]
gr.Interface(predict,gr.inputs.Image(type='filepath'),"label",title=title,description=description,examples=examples).launch(enable_queue=True,debug=True) |