except: pass gaborext = GaborWavelet() gaborext.eval() extractor = GaborVAE() extractor.load_state_dict(torch.load('checkpoints/gabor_step_i3_vae_mu/model_90.pth')) # extractor.load_state_dict(torch.load('checkpoints/gabor_kl_step_1_i3_3c/model_500.pth')) extractor.eval() model = GaborAE() # print(model) if args.start>1: model.load_state_dict(torch.load('checkpoints2/%s/model_%d.pth' % (args.outf, args.start-1))) # solver.load_state_dict(torch.load('checkpoints2/%s/optimizer_%d.pth' % (args.outf, args.start-1))) else: init_weights(model, init_type='kaiming') solver = optim.Adam(model.parameters(), lr=args.lr, betas=(0.5,0.9))#, weight_decay=1e-5) scheduler = get_scheduler(solver, args) train_writer = tensorboardX.SummaryWriter("./logs2/%s/"%args.outf) gaborext.cuda() extractor.cuda() model.cuda() for epoch in range(args.start, args.nepoch+1): train(epoch,args) with torch.no_grad(): test(epoch,args) scheduler.step()
out = (mu[0]+1)*127.5 cv2.imwrite(data[1][0].replace('data/','results/').replace('.jpg','.png'), cv2.cvtColor(out.cpu().numpy().transpose(1,2,0), cv2.COLOR_RGB2BGR)) # out = torch.clamp(inter[0]*200+200,0,400)*255//400 # cv2.imwrite('results/%s/%d-in.png'%(args.outf,i), (data[0,0].cpu().numpy()+1)*127.5) # cv2.imwrite('results/%s/%d-out.png'%(args.outf,i), cv2.cvtColor(out[:3,:,:].cpu().numpy().transpose(1,2,0), cv2.COLOR_RGB2BGR)) batch_size=args.batchSize # gaborext = GaborWavelet() # gaborext.eval() model = GaborAE() model.eval() try: os.mkdir('results/%s'%args.outf) except: pass # print(model) if args.start>1: model.load_state_dict(torch.load('checkpoints2/%s/model_%d.pth' % (args.outf, args.start))) # solver.load_state_dict(torch.load('checkpoints/%s/optimizer_%d.pth' % (args.outf, args.start-1))) else: init_weights(model, init_type='xavier') # gaborext.cuda() model.cuda() with torch.no_grad(): test(args)