I would like to test the trained built-in VGG16 network in MxNet. The experiment is to feed the network with an image from ImageNet. Then, I would like to see whether the result is correct. However, the results are always error! Hi, how stupid the network is! Well, that cannot be true. I am stupid, and I must do something wrong. Can you help me?
from mxnet.gluon.model_zoo.vision import vgg16
from mxnet.image import color_normalize
import mxnet as mx
import numpy as np
import cv2
path=‘http://data.mxnet.io/models/imagenet-11k/’
data_dir = ‘F:/Temps/Models_tmp/’
k = ‘synset.txt’
#gluon.utils.download(path+k, data_dir+k)
img_dir = ‘F:/Temps/DataSets/ImageNet/’
img = cv2.imread(img_dir + ‘cat.jpg’)
img = mx.nd.array(img)
img,_ = mx.image.center_crop(img,(224,224))
img = img/255
img = color_normalize(img,mean=mx.nd.array([0.485, 0.456, 0.406]),std=mx.nd.array([0.229, 0.224, 0.225]))
img = mx.nd.transpose(img, axes=(2, 0, 1))
img = img.expand_dims(axis=0)
with open(data_dir + ‘synset.txt’, ‘r’) as f:
labels = [l.rstrip() for l in f]
aVGG = vgg16(pretrained=True,root=‘F:/Temps/Models_tmp/’)
features = aVGG.forward(img)
features = mx.ndarray.softmax(features)
features = features.asnumpy()
features = np.squeeze(features)
a = np.argsort(features)[::-1]
for i in a[0:5]:
print(‘probability=%f, class=%s’ %(features[i], labels[i]))