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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 | |
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/densenet-121/model/densenet-9.onnx") | |
ort_session = ort.InferenceSession("densenet-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="DenseNet-121" | |
description="DenseNet-121 is a convolutional neural network for classification." | |
examples=[['apple.jpg']] | |
gr.Interface(predict,gr.inputs.Image(type='filepath'),"label",title=title,description=description,examples=examples).launch(enable_queue=True,debug=True) | |