MXNet + 3 CNNs

mxnet_example3.py

import mxnet as mx
import numpy as np
import cv2,sys,time
from collections import namedtuple

def loadModel(modelname):
        t1 = time.time()
        sym, arg_params, aux_params = mx.model.load_checkpoint(modelname, 0)
        t2 = time.time()
        t = 1000*(t2-t1)
        print("Loaded in %2.2f milliseconds" % t)
        arg_params['prob_label'] = mx.nd.array([0])
        mod = mx.mod.Module(symbol=sym)
        mod.bind(for_training=False, data_shapes=[('data', (1,3,224,224))])
        mod.set_params(arg_params, aux_params)
        return mod

def loadCategories():
        synsetfile = open('synset.txt', 'r')
        synsets = []
        for l in synsetfile:
                synsets.append(l.rstrip())
        return synsets

def prepareNDArray(filename):
        img = cv2.imread(filename)
        img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
        img = cv2.resize(img, (224, 224,))
        img = np.swapaxes(img, 0, 2)
        img = np.swapaxes(img, 1, 2)
        img = img[np.newaxis, :]
        return mx.nd.array(img)
      
def predict(filename, model, categories, n):
        array = prepareNDArray(filename)
        Batch = namedtuple('Batch', ['data'])
        t1 = time.time()
        model.forward(Batch([array]))
        t2 = time.time()
        t = 1000*(t2-t1)
        print("Predicted in %2.2f millsecond" % t)
        prob = model.get_outputs()[0].asnumpy()
        prob = np.squeeze(prob)
        sortedprobindex = np.argsort(prob)[::-1]
        topn = []
        for i in sortedprobindex[0:n]:
                topn.append((prob[i], categories[i]))
        return topn

def init(modelname):
        model = loadModel(modelname)
        cats = loadCategories()
        return model, cats

vgg16,c = init("vgg16")
resnet152,c = init("resnet-152")
inceptionv3,c = init("Inception-BN")

filename = sys.argv[1]

print ("*** VGG16")
print predict(filename,vgg16,c,5)
print ("*** ResNet-152")
print predict(filename,resnet152,c,5)
print ("*** Inception v3")
print predict(filename,inceptionv3,c,5)

I seem to have trouble with the vgg model. Searching google points to this: apache/mxnet#5779
Where a user suggested: "A probable remedy could be to just supply a dummy variable with the name 'softmax_label'"
But i'm not familiar enough with MXNet yet to make that change. Can you help?

Here's the output:
[02:32:46] src/nnvm/legacy_json_util.cc:190: Loading symbol saved by previous version v0.8.0. Attempting to upgrade...
[02:32:46] src/nnvm/legacy_json_util.cc:198: Symbol successfully upgraded!
Loaded in 384.57 microseconds
/Users/itsme/.virtualenvs/mxnet/lib/python2.7/site-packages/mxnet/module/base_module.py:52: UserWarning: You created Module with Module(..., label_names=['softmax_label']) but input with name 'softmax_label' is not found in symbol.list_arguments(). Did you mean one of:
data
prob_label
warnings.warn(msg)
/Users/itsme/.virtualenvs/mxnet/lib/python2.7/site-packages/mxnet/module/base_module.py:64: UserWarning: Data provided by label_shapes don't match names specified by label_names ([] vs. ['softmax_label'])
warnings.warn(msg)
Traceback (most recent call last):
File "anymodel.py", line 55, in
vgg16,c = init("vgg16")
File "anymodel.py", line 50, in init
model = loadModel(modelname)
File "anymodel.py", line 14, in loadModel
mod.set_params(arg_params, aux_params)
File "/Users/itsme/.virtualenvs/mxnet/lib/python2.7/site-packages/mxnet/module/module.py", line 309, in set_params
allow_missing=allow_missing, force_init=force_init)
File "/Users/itsme/.virtualenvs/mxnet/lib/python2.7/site-packages/mxnet/module/module.py", line 274, in init_params
_impl(desc, arr, arg_params)
File "/Users/itsme/.virtualenvs/mxnet/lib/python2.7/site-packages/mxnet/module/module.py", line 265, in _impl
raise RuntimeError("%s is not presented" % name)
RuntimeError: prob_label is not presented
(mxnet) galaga:pretrained-models itsme$