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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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from mxnet.contrib.onnx.onnx2mx.import_model import import_model |
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import os |
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import gradio as gr |
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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/AK391/models/raw/main/vision/classification/alexnet/model/bvlcalexnet-7.onnx") |
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sym, arg_params, aux_params = import_model('bvlcalexnet-7.onnx') |
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Batch = namedtuple('Batch', ['data']) |
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def get_image(path, show=False): |
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img = mx.image.imread(path) |
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if img is None: |
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return None |
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if show: |
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plt.imshow(img.asnumpy()) |
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plt.axis('off') |
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return img |
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def preprocess(img): |
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transform_fn = transforms.Compose([ |
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transforms.Resize(256), |
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transforms.CenterCrop(224), |
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transforms.ToTensor(), |
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transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]) |
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]) |
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img = transform_fn(img) |
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img = img.expand_dims(axis=0) |
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return img |
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def predict(path): |
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img = get_image(path, show=True) |
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img = preprocess(img) |
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mod.forward(Batch([img])) |
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scores = mx.ndarray.softmax(mod.get_outputs()[0]).asnumpy() |
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scores = np.squeeze(scores) |
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a = np.argsort(scores)[::-1] |
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results = {} |
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for i in a[0:5]: |
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results[labels[i]] = float(scores[i]) |
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return results |
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if len(mx.test_utils.list_gpus())==0: |
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ctx = mx.cpu() |
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else: |
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ctx = mx.gpu(0) |
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mod = mx.mod.Module(symbol=sym, context=ctx,data_names=['data_0'],label_names=None) |
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mod.bind(for_training=False, data_shapes=[('data_0', (1,3,224,224))], |
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label_shapes=mod._label_shapes) |
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mod.set_params(arg_params, aux_params, allow_missing=True, allow_extra=True) |
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title="SqueezeNet" |
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description="SqueezeNet is a small CNN which achieves AlexNet level accuracy on ImageNet with 50x fewer parameters. SqueezeNet requires less communication across servers during distributed training, less bandwidth to export a new model from the cloud to an autonomous car and more feasible to deploy on FPGAs and other hardware with limited memory." |
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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) |