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import os | |
import sys | |
import numpy as np | |
from skimage.io import imread | |
from keras.applications.imagenet_utils import decode_predictions | |
from efficientnet import EfficientNetB0 | |
from efficientnet import center_crop_and_resize, preprocess_input | |
def test_efficientnet(): | |
# Load pretrained model | |
model = EfficientNetB0(weights='imagenet') | |
# preprocess input | |
image = imread('Giant_Panda_in_Beijing_Zoo_1.JPG') | |
image_size = model.input_shape[1] | |
x = center_crop_and_resize(image, image_size=image_size) | |
x = preprocess_input(x) | |
x = np.expand_dims(x, 0) | |
# make prediction and decode | |
y = model.predict(x) | |
actual = list(decode_predictions(y)[0][i][1] for i in range(len(decode_predictions(y)[0]))) | |
expected = [ | |
'giant_panda', 'ice_bear', 'lesser_panda', 'American_black_bear', 'brown_bear' | |
] | |
assert actual == expected |
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