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@juliensimon
Created April 14, 2017 22:57
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MXNet + Inception v3
import mxnet as mx
import numpy as np
import cv2
from collections import namedtuple
def loadInceptionv3():
sym, arg_params, aux_params = mx.model.load_checkpoint('Inception-BN', 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'])
model.forward(Batch([array]))
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():
model = loadInceptionv3()
cats = loadCategories()
return model, cats
m,c = init()
topn = predict("/tmp/kreator.jpeg",m,c,5)
print topn
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