Deep Learning AMI
Developer Guide

Use Apache MXNet for Inference with a ResNet 50 Model

How to Use a Pre-Trained MXNet Model with the Symbol API for Image Inference with MXNet

    • (Option for Python 3) - Activate the Python 3 MXNet environment:

      $ source activate mxnet_p36
    • (Option for Python 2) - Activate the Python 2 MXNet environment:

      $ source activate mxnet_p27
  1. The remaining steps assume you are using the mxnet_p36 environment.

  2. Use a your preferred text editor to create a script that has the following content. This script will download the ResNet-50 model files (resnet-50-0000.params and resnet-50-symbol.json) and labels list (synset.txt), download a cat image to get a prediction result from the pre-trained model, then look this up in the result in labels list, returning a prediction result.

    import mxnet as mx import numpy as np path='http://data.mxnet.io/models/imagenet/' [mx.test_utils.download(path+'resnet/50-layers/resnet-50-0000.params'), mx.test_utils.download(path+'resnet/50-layers/resnet-50-symbol.json'), mx.test_utils.download(path+'synset.txt')] ctx = mx.cpu() with open('synset.txt', 'r') as f: labels = [l.rstrip() for l in f] sym, args, aux = mx.model.load_checkpoint('resnet-50', 0) fname = mx.test_utils.download('https://github.com/dmlc/web-data/blob/master/mxnet/doc/tutorials/python/predict_image/cat.jpg?raw=true') img = mx.image.imread(fname) # convert into format (batch, RGB, width, height) img = mx.image.imresize(img, 224, 224) # resize img = img.transpose((2, 0, 1)) # Channel first img = img.expand_dims(axis=0) # batchify img = img.astype(dtype='float32') args['data'] = img softmax = mx.nd.random_normal(shape=(1,)) args['softmax_label'] = softmax exe = sym.bind(ctx=ctx, args=args, aux_states=aux, grad_req='null') exe.forward() prob = exe.outputs[0].asnumpy() # print the top-5 prob = np.squeeze(prob) a = np.argsort(prob)[::-1] for i in a[0:5]: print('probability=%f, class=%s' %(prob[i], labels[i]))
  3. Then run the script, and you should see a result as follows:

    probability=0.418679, class=n02119789 kit fox, Vulpes macrotis probability=0.293495, class=n02119022 red fox, Vulpes vulpes probability=0.029321, class=n02120505 grey fox, gray fox, Urocyon cinereoargenteus probability=0.026230, class=n02124075 Egyptian cat probability=0.022557, class=n02085620 Chihuahua