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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 | |
| 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/squeezenet/model/squeezenet1.1-7.onnx") | |
| # Enter path to the ONNX model file | |
| sym, arg_params, aux_params = import_model('squeezenet1.1-7.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="SqueezeNet" | |
| 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." | |
| examples=[['catonnx.jpg']] | |
| gr.Interface(predict,gr.inputs.Image(type='filepath'),"label",title=title,description=description,examples=examples).launch(enable_queue=True) |