About the weights of the loss function about ssn_superpixels

[varunjampani]

Hi,

Your interpretation of loss is correct. We use Euclidean loss on position features and 'Loss without Softmax' on segment labels. Lines 293 and 312 in https://github.com/NVlabs/ssn_superpixels/blob/master/create_net.py.

'LossWithoutSoftmax' layer is a custom layer I implemented which is a modified version of 'SoftmaxWithLoss' layer. SoftmaxWithLoss layer (http://caffe.berkeleyvision.org/doxygen/classcaffe_1_1SoftmaxWithLossLayer.html) does 'Softmax' followed by 'Multinomial logistic loss'. In 'LossWithoutSoftmax', we directly do multinomial logistic loss without Softmax. Please check whether there is softmax or not in the cross entropy loss you are using.

A student I am advising is also planning to do a pytorch implementation. I think, it would be good to co-ordinate these efforts in porting to pytorch. Would you be interested in co-ordinating? Can you email me if you are interested?

Thanks,
Varun

Thanks Varun,

I realized the implementation of l2 norm in caffe is actually different from pytorch, so just to confirm the loss function. Given a input size as b * c * h * w, the loss will be:

L = ^1e-5⁄ _(2N) ∑_i ^N ||x_1i - x_2i|| ² + ∑_i ^N q_i log (p_i)

where N = b * h * w,
x₁ is pos_pix_feat , x₂ is pos_recon_feat,
q is ori_label and p is rec_label . Is it correct?

It would be very nice to have a further decision. A email has been sent to [email protected].

[varunjampani]

The equations seem correct. I do not clearly remember if caffe uses 1/2N or 1/N for Eucledian loss.

I checked the caffe document, and I believe they use 1/2N for that.
http://caffe.berkeleyvision.org/tutorial/layers/euclideanloss.html

Thanks a lot for your kind reply!