app.py

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

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-3.onnx")

# Enter path to the ONNX model file

sym, arg_params, aux_params = import_model('googlenet-3.onnx')

Batch = namedtuple('Batch', ['data'])
def get_image(path, show=False):
    img = mx.image.imread(path)
    if img is None:
        return None
    if show:
        plt.imshow(img.asnumpy())
        plt.axis('off')
    return img
    
def preprocess(img):   
    transform_fn = transforms.Compose([
    transforms.Resize(256),
    transforms.CenterCrop(224),
    transforms.ToTensor(),
    transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
    ])
    img = transform_fn(img)
    img = img.expand_dims(axis=0)
    return img
    
def predict(path):
    img = get_image(path, show=True)
    img = preprocess(img)
    mod.forward(Batch([img]))
    # Take softmax to generate probabilities
    scores = mx.ndarray.softmax(mod.get_outputs()[0]).asnumpy()
    # print the top-5 inferences class
    scores = np.squeeze(scores)
    a = np.argsort(scores)[::-1]
    results = {}
    for i in a[0:5]:
        results[labels[i]] = float(scores[i])
    return results
        
 # Determine and set context
if len(mx.test_utils.list_gpus())==0:
    ctx = mx.cpu()
else:
    ctx = mx.gpu(0)
# Load module
mod = mx.mod.Module(symbol=sym, context=ctx, label_names=None)
mod.bind(for_training=False, data_shapes=[('data', (1,3,224,224))], 
         label_shapes=mod._label_shapes)
mod.set_params(arg_params, aux_params, allow_missing=True, allow_extra=True)

title="MobileNet"
description="MobileNet improves the state-of-the-art performance of mobile models on multiple tasks and benchmarks as well as across a spectrum of different model sizes. MobileNet is based on an inverted residual structure where the shortcut connections are between the thin bottleneck layers. The intermediate expansion layer uses lightweight depthwise convolutions to filter features as a source of non-linearity. Additionally, it removes non-linearities in the narrow layers in order to maintain representational power."

examples=[['catonnx.jpg']]
gr.Interface(predict,gr.inputs.Image(type='filepath'),"label",title=title,description=description,examples=examples).launch(enable_queue=True)