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pred_fake_pool = self.discriminate(input_label, fake_image, use_pool=True) loss_D_fake = self.criterionGAN(pred_fake_pool, False) pred_real = self.discriminate(input_label, real_image) loss_D_real = self.criterionGAN(pred_real, True) pred_fake = self.netD.forward(torch.cat((input_label, fake_image), dim=1)) loss_G_GAN = self.criterionGAN(pred_fake, True) In the last line,...
I noticed that it is very very slow in generating the h5 file using the preprocessing script. I tried to create a training set from mini_chembl which contains around 160000...
Hi, thank you very much for sharing the code. I read your paper, it is a great paper. However, using this repo doesn't seem to be able to reproduce the...
I read your paper which looks great. I tried to run according to the instruction, sample.py, using the sample.yml and the two model ckpts downloaded from google drive. I waited...
I followed the posebuster.ipynb provided in your repo, downloaded the weight file and downloaded the eval_set zip files. However, since the eval_set does not contain pdb file, so I downloaded...
Great work for the detailed comparison. I notice a small issue, maybe critical to the comparison between nnUNet and other SAM-based methods. The dice calculation in the code (single_GPU_train_finetune_noprompt.py) includes...