def net_prediction(img, comask, fomask, model): utils.prepare_data(img, comask, fomask) normalize = transforms.Normalize(mean=saliency.mean, std=saliency.std) transform = transforms.Compose([ transforms.ToTensor(), normalize, ]) img, comask, cont, fomask = utils.test_data_loader(transform) inputs = torch.cat((img, comask, cont, fomask), 0) inputs = torch.unsqueeze(inputs, dim=0) inputs = Variable(inputs.cuda(), volatile=True) mask = model(inputs) return mask
def net_prediction(img, mask, model): if img.size != mask.size: img = img.resize(mask.size) utils.prepare_test_data(img, mask) normalize = transforms.Normalize(mean=saliency.mean, std=saliency.std) transform = transforms.Compose([transforms.ToTensor(), normalize, ]) img_ref, mask_ref = utils.test_data_loader(transform) inputs = torch.cat((img_ref, mask_ref), 0) inputs = torch.unsqueeze(inputs, dim=0) inputs = Variable(inputs.cuda(), volatile=True) feature = model(inputs) # remove dimision 2,3 feature.squeeze_(dim=2) feature.squeeze_(dim=2) return feature