def vis_using_Colorize(indir_list, outdir): indir = indir_list[0] # outdir = os.path.join(os.path.split(indir)[0], "vis_labels") mkdir_if_not_exist(outdir) for one_file in tqdm(os.listdir(indir)): fullpath = os.path.join(indir, one_file) hard_to_see_img = m.imread(fullpath) # outputs = outputs[0, :19].data.max(0)[1] # outputs = outputs.view(1, outputs.size()[0], outputs.size()[1]) outputs = hard_to_see_img # TODO this should be fixed output = Colorize()(outputs) output = np.transpose(output.cpu().numpy(), (1, 2, 0)) img = Image.fromarray(output, "RGB") img = img.resize(hard_to_see_img.shape, Image.NEAREST) outfn = os.path.join(outdir, one_file) plt.savefig(outfn, transparent=True, bbox_inches='tight', pad_inches=0) img.save(outfn)
loss.backward() optimizer_seg.step() optimizer_feat.step() print "epoch is:[{}|{}],index is:[{}|{}],loss:{}".\ format(epoch,epoch_num,i,len(dataloader),loss) win = visutils.visualize_loss(epoch, loss.cpu().detach(), env, win) if epoch % 40 == 0: #save model torch.save(vgg16.state_dict(), '%s/vgg16_%03d.pkl' % (result_directory, epoch)) torch.save(Seg.state_dict(), '%s/Seg_%03d.pkl' % (result_directory, epoch)) #save result input = make_image_grid(img, mean, std) label = make_label_grid(labels.data) label = Colorize()(label).type(torch.FloatTensor) output = make_label_grid(torch.max(logits, dim=1)[1].data) output = Colorize()(output).type(torch.FloatTensor) vutils.save_image( label.cpu().detach(), '%s/labels_epoch_%03d.png' % (result_directory, epoch)) vutils.save_image( output.cpu().detach(), '%s/output_epoch_%03d.png' % (result_directory, epoch)) vutils.save_image(input.cpu().detach(), '%s/img_epoch_%03d.png' % (result_directory, epoch))