File size: 2,060 Bytes
ef1897c 4ada375 ef1897c |
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/AK391/models/raw/main/vision/classification/caffenet/model/caffenet-12.onnx")
ort_session = ort.InferenceSession("caffenet-12.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="CaffeNet"
description="CaffeNet a variant of AlexNet. 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,debug=True) |