About using pre-trained model from PyTorch

I want to use pre-trained model from PyTorch to train a faster-rcnn. And I see:

If you want to use pytorch pre-trained models, please remember to transpose images from BGR to RGB, and also use the same data transformer (minus mean and normalize) as used in pretrained model.

Does this mean I need to do the following two things?

1. Comment this line
2. Reverse this line

By the way, I didn’t see any code about normalizing the images with a stdv value. Is there a configuration option about this or do I need to add code in lib/roi_data_layer/minibatch.py?

Thank you!

[jwyang]

yes, the order of commands should be:

im /= 255. # Convert range to [0,1] im -= pixel_means # Minus mean im /= pixel_stdens # divide by stddev

you do not need to add “im = im[:, :, ::-1]” since im from scipy.misc.imread is already RGB.

To facilitate the transfer from caffe models to pytorch models, I will make some changes to the code. Stay tuned.

[jwyang]

Hi, @clavichord93 ,

Yes, to use pytorch pre-tained model, you need to make three changes:

1. convert image from BGR to RGB

2. normalize image range from [0, 255] to [0, 1]

3. use transformer:

normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])

to transform the image data.

Details can be found here: http://pytorch.org/docs/master/torchvision/models.html