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import mxnet as mx |
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import matplotlib.pyplot as plt |
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import numpy as np |
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from collections import namedtuple |
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from mxnet.gluon.data.vision import transforms |
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import os |
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import gradio as gr |
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from PIL import Image |
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import imageio |
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import onnxruntime as ort |
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def get_image(path): |
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''' |
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Using path to image, return the RGB load image |
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''' |
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img = imageio.imread(path, pilmode='RGB') |
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return img |
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def preprocess(img): |
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''' |
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Preprocessing required on the images for inference with mxnet gluon |
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The function takes loaded image and returns processed tensor |
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''' |
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img = np.array(Image.fromarray(img).resize((224, 224))).astype(np.float32) |
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img[:, :, 0] -= 123.68 |
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img[:, :, 1] -= 116.779 |
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img[:, :, 2] -= 103.939 |
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img[:,:,[0,1,2]] = img[:,:,[2,1,0]] |
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img = img.transpose((2, 0, 1)) |
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img = np.expand_dims(img, axis=0) |
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return img |
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mx.test_utils.download('https://s3.amazonaws.com/model-server/inputs/kitten.jpg') |
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mx.test_utils.download('https://s3.amazonaws.com/onnx-model-zoo/synset.txt') |
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with open('synset.txt', 'r') as f: |
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labels = [l.rstrip() for l in f] |
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os.system("wget https://github.com/onnx/models/raw/main/vision/classification/inception_and_googlenet/googlenet/model/googlenet-9.onnx") |
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ort_session = ort.InferenceSession("googlenet-9.onnx") |
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def predict(path): |
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img_batch = preprocess(get_image(path)) |
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outputs = ort_session.run( |
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None, |
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{"data_0": img_batch.astype(np.float32)}, |
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) |
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a = np.argsort(-outputs[0].flatten()) |
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results = {} |
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for i in a[0:5]: |
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results[labels[i]]=float(outputs[0][0][i]) |
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return results |
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title="GoogleNet" |
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description="GoogLeNet is the name of a convolutional neural network for classification, which competed in the ImageNet Large Scale Visual Recognition Challenge in 2014." |
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examples=[['catonnx.jpg']] |
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gr.Interface(predict,gr.inputs.Image(type='filepath'),"label",title=title,description=description,examples=examples).launch(enable_queue=True,debug=True) |
