How to do the prediction via pretrained model?

[Chao Pan]

This is my script to load the pretrained model and make prediction. However, it is not able to recognize the apparently pretty or ugly image, so I am thinking maybe my input is not right.
There are several places that I am not quite sure:

1. Is the input RGB or BGR?
2. The input scale should be 0~255 rather than 0-1, right?
3. The AVA1_mean file is (3, 256, 256), should I crop it to (227, 277, 3) and subtract that from each image?
   If possible, can anyone post a script about how to correctly read image, load model and make prediction? It is much appreciated.

import numpy as np
from PIL import Image

def preprocess_image(fp, ava1mean):
    im = Image.open(fp).convert("RGB")
    im = im.resize([227, 227])
    im = np.asarray(im).astype(np.float32) # 227, 227, 3
    if len(im.shape) != 3:
        raise Exception
    # im = im[:, :, ::-1] shall we convert RGB -> BGR?
    im -= ava1mean
    return im # 227, 227, 3


ava1mean = np.load("../ILGnet/mean/AVA1_mean.npy") # 3, 256, 256
ava1mean = ava1mean.transpose(1, 2, 0)[14:241,14:241,:] # 227, 227, 3

inputs = [preprocess_image("../ugly.jpg", ava1mean)]
classifier = caffe.Classifier("deploy2.prototxt", "ILGnet-AVA1.caffemodel",
                              image_dims=[227, 227])

print(classifier.predict(inputs, True))```

[The victory-lab of Besti]

I think your suggestion is necessary for our project.The script of prediction will be upload soon.

[Chao Pan]

That will be awesome! Thanks in advance and looking forward to that!

[horiken4]

I'd like that script, too!

[horiken4]

Is test.py the script of prediction sample?