Open jannctu opened 5 years ago
@jannctu Thanks a lot to point it out. I would like to ask you if you have already tested the original implementation (caffe framework)? If so, I wonder if you noticed the difference of output for these two versions? For my side, I found that the original version gives a slightly better result...
hi @chenyuZha
This implementation very close to the original version. the only different is the cropping method and the upsampling (bilinear interpolation) implementation.
the cropping method, you can take a look in model.py on function crop and crop_caffe . the upsampling method, actually the original caffe implementation gives thw weight to upsample you can port them to pytorch. I hope it would make this pytorch better.
btw, I made a project for the original weights wrapper (except the upsampling and the crop). you may take a look here. https://github.com/jannctu/Rcf-Pytorch-Wrapper
thanks
@jannctu Thanks a lot I will check this.
hi @meteorshowers
Just want to inform you that there is small mistake on your implementation. File : models.py line : 141
weight_deconv5 = make_bilinear_weights(32, 1).cuda()
Based on the original code https://github.com/yun-liu/rcf/blob/master/examples/rcf/train_val.prototxt https://github.com/yun-liu/rcf/blob/master/examples/rcf/test.prototxtit should be
weight_deconv5 = make_bilinear_weights(16, 1).cuda()
it doesn't matter a lot, but if someone want to use the original pretrained model, it will produce significant different results.
Thank you