Hi,

I’m using a pre-trained gluon resnet to do some inference. Predictions are different depending on the method I use to read images (mxnet or cv2). Is this expected? See below my pipeline. My input picture is this one https://pixabay.com/en/turtle-tortoise-swim-sea-turtle-863336/

```
import cv2
import mxnet as mx
from mxnet import image, nd
from mxnet.gluon.utils import download
from mxnet.gluon.model_zoo import vision as models
import numpy as np
net = models.resnet50_v2(pretrained=True)
def transform(data):
data = image.resize_short(data, 256)
data, _ = image.center_crop(data, (224,224))
data = data.transpose((2,0,1)).expand_dims(axis=0)
rgb_mean = nd.array([0.485, 0.456, 0.406]).reshape((1,3,1,1))
rgb_std = nd.array([0.229, 0.224, 0.225]).reshape((1,3,1,1))
return (data.astype('float32') / 255 - rgb_mean) / rgb_std
def top5cat(pic):
"""getting a classification text summary"""
prob = net(transform(pic)).softmax()
idx = prob.topk(k=5)[0]
for i in idx:
i = int(i.asscalar())
print('With prob = %.5f, it contains %s' % (
prob[0,i].asscalar(), text_labels[i]))
top5cat(image.imread(local + pic))
#returns this:
# With prob = 0.86020, it contains loggerhead, loggerhead turtle, Caretta caretta
# With prob = 0.13905, it contains leatherback turtle, leatherback, leathery turtle, Dermochelys coriacea
# With prob = 0.00057, it contains terrapin
# With prob = 0.00007, it contains mud turtle
# With prob = 0.00003, it contains starfish, sea star
top5cat(mx.nd.array(cv2.imread(local + pic)))
#returns this:
# With prob = 0.61446, it contains leatherback turtle, leatherback, leathery turtle, Dermochelys coriacea
# With prob = 0.28488, it contains loggerhead, loggerhead turtle, Caretta caretta
# With prob = 0.05506, it contains terrapin
# With prob = 0.01007, it contains starfish, sea star
# With prob = 0.00838, it contains mud turtle
```