Inferring resnet18 classification etlt model with python

Please modify the preprocessing. It will fix the issue. The standalone inference result will be the same as tlt-infer. Main change is that ‘RGB’->‘BGR’ and Zero-center by mean pixel.
Add below.

from keras.applications.imagenet_utils import preprocess_input

And change

return np.asarray(image.resize((w, h), Image.ANTIALIAS)).transpose([2, 0, 1]).astype(trt.nptype(trt.float32)).ravel()

to

return preprocess_input(np.asarray(image.resize((w, h), Image.ANTIALIAS)).transpose([2, 0, 1]).astype(trt.nptype(trt.float32)), mode=‘caffe’, data_format=‘channels_first’).ravel()

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Hi @Morganh ,
Thank you so much… It worked… The results are now same with tlt-infer and standalone script…

@jazeel.jk hii
hey after running your script on my classification model I am getting one class always and also 0 positive negative values like this .


Any idea?
Thanks

@senbhaskar26 ,
Yes, that script was made for testing my model… i had 2 classes “positive” and “negative”… you can edit with the name of your classes…

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thanks got it working for my model. @jazeel.jk

have you tried in which you can get the accuracy at last where you can check the total accuracy of your model for all different classes.

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