def init_net_D(args, state_dict=None): net_D = FCDiscriminator(cfg.DATASET.NUM_CLASSES) if args.distributed: net_D = torch.nn.SyncBatchNorm.convert_sync_batchnorm(net_D) if cfg.MODEL.DOMAIN_BN: net_D = DomainBN.convert_domain_batchnorm(net_D, num_domains=2) if state_dict is not None: try: net_D.load_state_dict(state_dict) except: net_D = DomainBN.convert_domain_batchnorm(net_D, num_domains=2) net_D.load_state_dict(state_dict) if cfg.TRAIN.FREEZE_BN: net_D.apply(freeze_BN) if torch.cuda.is_available(): net_D.cuda() if args.distributed: net_D = DistributedDataParallel(net_D, device_ids=[args.gpu]) else: net_D = torch.nn.DataParallel(net_D) return net_D
def main(): """Create the model and start the training.""" model_num = 0 # The number of model (for saving models) torch.manual_seed(args.random_seed) torch.cuda.manual_seed_all(args.random_seed) random.seed(args.random_seed) if not os.path.exists(args.snapshot_dir): os.makedirs(args.snapshot_dir) writer = SummaryWriter(log_dir=args.snapshot_dir) h, w = map(int, args.input_size.split(',')) input_size = (h, w) h, w = map(int, args.input_size_target.split(',')) input_size_target = (h, w) cudnn.enabled = True gpu = args.gpu cudnn.benchmark = True # init G if args.model == 'DeepLab': if args.training_option == 1: model = Res_Deeplab(num_classes=args.num_classes, num_layers=args.num_layers, dropout=args.dropout, after_layer=args.after_layer) elif args.training_option == 2: model = Res_Deeplab2(num_classes=args.num_classes) '''elif args.training_option == 3: model = Res_Deeplab50(num_classes=args.num_classes)''' if args.restore_from[:4] == 'http': saved_state_dict = model_zoo.load_url(args.restore_from) else: saved_state_dict = torch.load(args.restore_from) new_params = model.state_dict().copy() for k, v in saved_state_dict.items(): print(k) for k in new_params: print(k) for i in saved_state_dict: i_parts = i.split('.') if '.'.join(i_parts[args.i_parts_index:]) in new_params: print("Restored...") if args.not_restore_last == True: if not i_parts[ args.i_parts_index] == 'layer5' and not i_parts[ args.i_parts_index] == 'layer6': new_params['.'.join(i_parts[args.i_parts_index:] )] = saved_state_dict[i] else: new_params['.'.join( i_parts[args.i_parts_index:])] = saved_state_dict[i] model.load_state_dict(new_params) model.train() model.cuda(args.gpu) # init D model_D1 = FCDiscriminator(num_classes=args.num_classes, extra_layers=args.extra_discriminator_layers) model_D2 = FCDiscriminator(num_classes=args.num_classes, extra_layers=0) model_D1.train() model_D1.cuda(args.gpu) model_D2.train() model_D2.cuda(args.gpu) trainloader = data.DataLoader(sourceDataSet( args.data_dir, args.data_list, max_iters=args.num_steps * args.iter_size * args.batch_size, crop_size=input_size, random_rotate=False, random_flip=args.augment_1, random_lighting=args.augment_1, random_blur=args.augment_1, random_scaling=args.augment_1, mean=IMG_MEAN_SOURCE, ignore_label=args.ignore_label), batch_size=args.batch_size, shuffle=True, num_workers=args.num_workers, pin_memory=True) trainloader_iter = enumerate(trainloader) trainloader2 = data.DataLoader(sourceDataSet( args.data_dir2, args.data_list2, max_iters=args.num_steps * args.iter_size * args.batch_size, crop_size=input_size, random_rotate=False, random_flip=args.augment_2, random_lighting=args.augment_2, random_blur=args.augment_2, random_scaling=args.augment_2, mean=IMG_MEAN_SOURCE2, ignore_label=args.ignore_label), batch_size=args.batch_size, shuffle=True, num_workers=args.num_workers, pin_memory=True) trainloader_iter2 = enumerate(trainloader2) if args.num_of_targets > 1: IMG_MEAN_TARGET1 = np.array( (101.41694189393208, 89.68194541655483, 77.79408426901315), dtype=np.float32) # crowdai all BGR targetloader1 = data.DataLoader(isprsDataSet( args.data_dir_target1, args.data_list_target1, max_iters=args.num_steps * args.iter_size * args.batch_size, crop_size=input_size_target, scale=False, mean=IMG_MEAN_TARGET1, ignore_label=args.ignore_label), batch_size=args.batch_size, shuffle=True, num_workers=args.num_workers, pin_memory=True) targetloader_iter1 = enumerate(targetloader1) targetloader = data.DataLoader(isprsDataSet( args.data_dir_target, args.data_list_target, max_iters=args.num_steps * args.iter_size * args.batch_size, crop_size=input_size_target, scale=False, mean=IMG_MEAN_TARGET, ignore_label=args.ignore_label), batch_size=args.batch_size, shuffle=True, num_workers=args.num_workers, pin_memory=True) targetloader_iter = enumerate(targetloader) valloader = data.DataLoader(valDataSet(args.data_dir_val, args.data_list_val, crop_size=input_size_target, mean=IMG_MEAN_TARGET, scale=args.val_scale, mirror=False), batch_size=1, shuffle=False, pin_memory=True) # implement model.optim_parameters(args) to handle different models' lr setting optimizer = optim.SGD(model.optim_parameters(args), lr=args.learning_rate, momentum=args.momentum, weight_decay=args.weight_decay) optimizer.zero_grad() optimizer_D1 = optim.Adam(model_D1.parameters(), lr=args.learning_rate_D, betas=(0.9, 0.99)) optimizer_D1.zero_grad() optimizer_D2 = optim.Adam(model_D2.parameters(), lr=args.learning_rate_D, betas=(0.9, 0.99)) optimizer_D2.zero_grad() if args.weighted_loss == True: bce_loss = torch.nn.BCEWithLogitsLoss() else: bce_loss = torch.nn.BCEWithLogitsLoss() interp = nn.Upsample(size=(input_size[0], input_size[1]), mode='bilinear') interp_target = nn.Upsample(size=(input_size_target[0], input_size_target[1]), mode='bilinear') # labels for adversarial training source_label = 0 target_label = 1 # Which layers to freeze non_trainable(args.dont_train, model) # List saving all best 5 mIoU's best_mIoUs = [0.0, 0.0, 0.0, 0.0, 0.0] for i_iter in range(args.num_steps): loss_seg_value1 = 0 loss_adv_target_value1 = 0 loss_D_value1 = 0 loss_seg_value2 = 0 loss_adv_target_value2 = 0 loss_D_value2 = 0 optimizer.zero_grad() optimizer_D1.zero_grad() optimizer_D2.zero_grad() adjust_learning_rate(optimizer, i_iter) adjust_learning_rate_D(optimizer_D1, i_iter) adjust_learning_rate_D(optimizer_D2, i_iter) for sub_i in range(args.iter_size): ################################## train G ################################# # don't accumulate grads in D for param in model_D1.parameters(): param.requires_grad = False for param in model_D2.parameters(): param.requires_grad = False ################################## train with source ################################# while True: try: _, batch = next( trainloader_iter) # Cityscapes, only discriminator1 images, labels, _, train_name = batch images = Variable(images).cuda(args.gpu) _, batch = next( trainloader_iter2 ) # Main (airsim) discriminator2 and final output images2, labels2, size, train_name2 = batch images2 = Variable(images2).cuda(args.gpu) pred1, _ = model(images) pred1 = interp(pred1) _, pred2 = model(images2) pred2 = interp(pred2) loss_seg1 = loss_calc(pred1, labels, args.gpu, args.ignore_label, train_name, weights1) loss_seg2 = loss_calc(pred2, labels2, args.gpu, args.ignore_label, train_name2, weights2) loss = loss_seg2 + args.lambda_seg * loss_seg1 # proper normalization loss = loss / args.iter_size loss.backward() if isinstance(loss_seg1.data.cpu().numpy(), list): loss_seg_value1 += loss_seg1.data.cpu().numpy( )[0] / args.iter_size else: loss_seg_value1 += loss_seg1.data.cpu().numpy( ) / args.iter_size if isinstance(loss_seg2.data.cpu().numpy(), list): loss_seg_value2 += loss_seg2.data.cpu().numpy( )[0] / args.iter_size else: loss_seg_value2 += loss_seg2.data.cpu().numpy( ) / args.iter_size break except (RuntimeError, AssertionError, AttributeError): continue ################################################################################################### _, batch = next(targetloader_iter) images, _, _ = batch images = Variable(images).cuda(args.gpu) pred_target1, pred_target2 = model(images) if args.num_of_targets > 1: _, batch1 = next(targetloader_iter1) images1, _, _ = batch1 images1 = Variable(images1).cuda(args.gpu) pred_target1, _ = model(images1) pred_target1 = interp_target(pred_target1) pred_target2 = interp_target(pred_target2) ################################## train with target ################################# if args.adv_option == 1 or args.adv_option == 3: D_out1 = model_D1(F.softmax(pred_target1)) D_out2 = model_D2(F.softmax(pred_target2)) loss_adv_target1 = bce_loss( D_out1, Variable( torch.FloatTensor( D_out1.data.size()).fill_(source_label)).cuda( args.gpu)) loss_adv_target2 = bce_loss( D_out2, Variable( torch.FloatTensor( D_out2.data.size()).fill_(source_label)).cuda( args.gpu)) loss = args.lambda_adv_target1 * loss_adv_target1 + args.lambda_adv_target2 * loss_adv_target2 loss = loss / args.iter_size loss.backward() if isinstance(loss_adv_target1.data.cpu().numpy(), list): loss_adv_target_value1 += loss_adv_target1.data.cpu( ).numpy()[0] / args.iter_size else: loss_adv_target_value1 += loss_adv_target1.data.cpu( ).numpy() / args.iter_size if isinstance(loss_adv_target2.data.cpu().numpy(), list): loss_adv_target_value2 += loss_adv_target2.data.cpu( ).numpy()[0] / args.iter_size else: loss_adv_target_value2 += loss_adv_target2.data.cpu( ).numpy() / args.iter_size ################################################################################################### if args.adv_option == 2 or args.adv_option == 3: pred1, _ = model(images) pred1 = interp(pred1) _, pred2 = model(images2) pred2 = interp(pred2) '''pred1 = pred1.detach() pred2 = pred2.detach()''' D_out1 = model_D1(F.softmax(pred1, dim=1)) D_out2 = model_D2(F.softmax(pred2, dim=1)) loss_adv_target1 = bce_loss( D_out1, Variable( torch.FloatTensor( D_out1.data.size()).fill_(target_label)).cuda( args.gpu)) loss_adv_target2 = bce_loss( D_out2, Variable( torch.FloatTensor( D_out2.data.size()).fill_(target_label)).cuda( args.gpu)) loss = args.lambda_adv_target1 * loss_adv_target1 + args.lambda_adv_target2 * loss_adv_target2 loss = loss / args.iter_size loss.backward() if isinstance(loss_adv_target1.data.cpu().numpy(), list): loss_adv_target_value1 += loss_adv_target1.data.cpu( ).numpy()[0] / args.iter_size else: loss_adv_target_value1 += loss_adv_target1.data.cpu( ).numpy() / args.iter_size if isinstance(loss_adv_target2.data.cpu().numpy(), list): loss_adv_target_value2 += loss_adv_target2.data.cpu( ).numpy()[0] / args.iter_size else: loss_adv_target_value2 += loss_adv_target2.data.cpu( ).numpy() / args.iter_size ################################################################################################### ################################## train D ################################# # bring back requires_grad for param in model_D1.parameters(): param.requires_grad = True for param in model_D2.parameters(): param.requires_grad = True ################################## train with source ################################# pred1 = pred1.detach() pred2 = pred2.detach() D_out1 = model_D1(F.softmax(pred1)) D_out2 = model_D2(F.softmax(pred2)) loss_D1 = bce_loss( D_out1, Variable( torch.FloatTensor( D_out1.data.size()).fill_(source_label)).cuda( args.gpu)) loss_D2 = bce_loss( D_out2, Variable( torch.FloatTensor( D_out2.data.size()).fill_(source_label)).cuda( args.gpu)) loss_D1 = loss_D1 / args.iter_size / 2 loss_D2 = loss_D2 / args.iter_size / 2 loss_D1.backward() loss_D2.backward() if isinstance(loss_D1.data.cpu().numpy(), list): loss_D_value1 += loss_D1.data.cpu().numpy()[0] else: loss_D_value1 += loss_D1.data.cpu().numpy() if isinstance(loss_D2.data.cpu().numpy(), list): loss_D_value2 += loss_D2.data.cpu().numpy()[0] else: loss_D_value2 += loss_D2.data.cpu().numpy() ################################# train with target ################################# pred_target1 = pred_target1.detach() pred_target2 = pred_target2.detach() D_out1 = model_D1(F.softmax(pred_target1)) D_out2 = model_D2(F.softmax(pred_target2)) loss_D1 = bce_loss( D_out1, Variable( torch.FloatTensor( D_out1.data.size()).fill_(target_label)).cuda( args.gpu)) loss_D2 = bce_loss( D_out2, Variable( torch.FloatTensor( D_out2.data.size()).fill_(target_label)).cuda( args.gpu)) loss_D1 = loss_D1 / args.iter_size / 2 loss_D2 = loss_D2 / args.iter_size / 2 loss_D1.backward() loss_D2.backward() if isinstance(loss_D1.data.cpu().numpy(), list): loss_D_value1 += loss_D1.data.cpu().numpy()[0] else: loss_D_value1 += loss_D1.data.cpu().numpy() if isinstance(loss_D2.data.cpu().numpy(), list): loss_D_value2 += loss_D2.data.cpu().numpy()[0] else: loss_D_value2 += loss_D2.data.cpu().numpy() optimizer.step() optimizer_D1.step() optimizer_D2.step() if i_iter % args.save_pred_every == 0 and i_iter != 0: if model_num != args.num_models_keep: torch.save( model.state_dict(), osp.join(args.snapshot_dir, 'model_' + str(model_num) + '.pth')) torch.save( model_D1.state_dict(), osp.join(args.snapshot_dir, 'model_' + str(model_num) + '_D1.pth')) torch.save( model_D2.state_dict(), osp.join(args.snapshot_dir, 'model_' + str(model_num) + '_D2.pth')) model_num = model_num + 1 if model_num == args.num_models_keep: model_num = 0 # Validation if (i_iter % args.val_every == 0 and i_iter != 0) or i_iter == 1: mIoU = validation(valloader, model, interp_target, writer, i_iter, [37, 41, 10]) for i in range(0, len(best_mIoUs)): if best_mIoUs[i] < mIoU: torch.save( model.state_dict(), osp.join(args.snapshot_dir, 'bestmodel_' + str(i) + '.pth')) torch.save( model_D1.state_dict(), osp.join(args.snapshot_dir, 'bestmodel_' + str(i) + '_D1.pth')) torch.save( model_D2.state_dict(), osp.join(args.snapshot_dir, 'bestmodel_' + str(i) + '_D2.pth')) best_mIoUs.append(mIoU) print("Saved model at iteration %d as the best %d" % (i_iter, i)) best_mIoUs.sort(reverse=True) best_mIoUs = best_mIoUs[:5] break # Save for tensorboardx writer.add_scalar('loss_seg_value1', loss_seg_value1, i_iter) writer.add_scalar('loss_seg_value2', loss_seg_value2, i_iter) writer.add_scalar('loss_adv_target_value1', loss_adv_target_value1, i_iter) writer.add_scalar('loss_adv_target_value2', loss_adv_target_value2, i_iter) writer.add_scalar('loss_D_value1', loss_D_value1, i_iter) writer.add_scalar('loss_D_value2', loss_D_value2, i_iter) writer.close()
def main(): """Create the model and start the training.""" w, h = map(int, args.input_size.split(',')) input_size = (w, h) w, h = map(int, args.input_size_target.split(',')) input_size_target = (w, h) cudnn.enabled = True gpu = args.gpu # Create network if args.model == 'DeepLab': model = DeeplabMulti(num_classes=args.num_classes) if args.restore_from[:4] == 'http': saved_state_dict = model_zoo.load_url(args.restore_from) else: saved_state_dict = torch.load(args.restore_from) new_params = model.state_dict().copy() for i in saved_state_dict: # Scale.layer5.conv2d_list.3.weight i_parts = i.split('.') # print i_parts if args.num_classes != 19 and i_parts[1] != 'layer5': new_params['.'.join(i_parts[1:])] = saved_state_dict[i] # if args.num_classes !=19: # print i_parts model.load_state_dict(new_params) start_time = datetime.datetime.now().strftime('%m-%d_%H-%M') writer_dir = os.path.join("./logs/", args.name, start_time) writer = tensorboard.SummaryWriter(writer_dir) model.train() model.cuda(args.gpu) cudnn.benchmark = True # init D model_D1 = FCDiscriminator(num_classes=args.num_classes) model_D2 = FCDiscriminator(num_classes=args.num_classes) model_D1.train() model_D1.cuda(args.gpu) model_D2.train() model_D2.cuda(args.gpu) if not os.path.exists(args.snapshot_dir): os.makedirs(args.snapshot_dir) trainloader = data.DataLoader( MulitviewSegLoader( num_classes=args.num_classes, root=args.data_dir, number_views=2, view_idx=1, # max_iters=args.num_steps * args.iter_size * args.batch_size, # crop_size=input_size, # scale=args.random_scale, mirror=args.random_mirror, img_mean=IMG_MEAN), batch_size=args.batch_size, shuffle=True, num_workers=args.num_workers, pin_memory=True) trainloader_iter = iter(data_loader_cycle(trainloader)) targetloader = data.DataLoader( MulitviewSegLoader( root=args.data_dir_target, num_classes=args.num_classes, number_views=1, view_idx=0, # max_iters=args.num_steps * args.iter_size * args.batch_size, # crop_size=input_size_target, # scale=False, # mirror=args.random_mirror, img_mean=IMG_MEAN, # set=args.set ), batch_size=args.batch_size, shuffle=True, num_workers=args.num_workers, pin_memory=True) interp = nn.Upsample(size=(input_size[1], input_size[0]), mode='bilinear') interp_target = nn.Upsample(size=(input_size_target[1], input_size_target[0]), mode='bilinear') def mdl_val_func(x): return interp_target(model(x)[1]) targetloader_iter = iter(data_loader_cycle(targetloader)) val_loader = data.DataLoader( MulitviewSegLoader( root=args.data_dir_val, number_views=1, view_idx=0, num_classes=args.num_classes, # max_iters=args.num_steps * args.iter_size * args.batch_size, # crop_size=input_size_target, # scale=False, # mirror=args.random_mirror, img_mean=IMG_MEAN, # set=args.set ), batch_size=args.batch_size, shuffle=False, num_workers=args.num_workers, pin_memory=True) criterion = CrossEntropyLoss2d().cuda(args.gpu) valhelper = ValHelper(gpu=args.gpu, model=mdl_val_func, val_loader=val_loader, loss=criterion, writer=writer) # implement model.optim_parameters(args) to handle different models' lr setting optimizer = optim.SGD(model.optim_parameters(args), lr=args.learning_rate, momentum=args.momentum, weight_decay=args.weight_decay) optimizer.zero_grad() optimizer_D1 = optim.Adam(model_D1.parameters(), lr=args.learning_rate_D, betas=(0.9, 0.99)) optimizer_D1.zero_grad() optimizer_D2 = optim.Adam(model_D2.parameters(), lr=args.learning_rate_D, betas=(0.9, 0.99)) optimizer_D2.zero_grad() if args.gan == 'Vanilla': bce_loss = torch.nn.BCEWithLogitsLoss() elif args.gan == 'LS': bce_loss = torch.nn.MSELoss() # labels for adversarial training source_label = 0 target_label = 1 for i_iter in range(args.num_steps): loss_seg_value1 = 0 loss_adv_target_value1 = 0 loss_D_value1 = 0 loss_seg_value2 = 0 loss_adv_target_value2 = 0 loss_D_value2 = 0 optimizer.zero_grad() adjust_learning_rate(optimizer, i_iter) optimizer_D1.zero_grad() optimizer_D2.zero_grad() adjust_learning_rate_D(optimizer_D1, i_iter) adjust_learning_rate_D(optimizer_D2, i_iter) for sub_i in range(args.iter_size): # train G # don't accumulate grads in D for param in model_D1.parameters(): param.requires_grad = False for param in model_D2.parameters(): param.requires_grad = False # train with source batch = next(trainloader_iter) images, labels, *_ = batch images = Variable(images).cuda(args.gpu) pred1, pred2 = model(images) pred1 = interp(pred1) pred2 = interp(pred2) loss_seg1 = loss_calc(pred1, labels, args.gpu, criterion) loss_seg2 = loss_calc(pred2, labels, args.gpu, criterion) loss = loss_seg2 + args.lambda_seg * loss_seg1 # proper normalization loss = loss / args.iter_size loss.backward() loss_seg_value1 += loss_seg1.data.cpu().numpy() / args.iter_size loss_seg_value2 += loss_seg2.data.cpu().numpy() / args.iter_size # train with target batch = next(targetloader_iter) images, *_ = batch images = Variable(images).cuda(args.gpu) pred_target1, pred_target2 = model(images) pred_target1 = interp_target(pred_target1) pred_target2 = interp_target(pred_target2) D_out1 = model_D1(F.softmax(pred_target1)) D_out2 = model_D2(F.softmax(pred_target2)) loss_adv_target1 = bce_loss( D_out1, Variable( torch.FloatTensor( D_out1.data.size()).fill_(source_label)).cuda( args.gpu)) loss_adv_target2 = bce_loss( D_out2, Variable( torch.FloatTensor( D_out2.data.size()).fill_(source_label)).cuda( args.gpu)) loss = args.lambda_adv_target1 * loss_adv_target1 + args.lambda_adv_target2 * loss_adv_target2 loss = loss / args.iter_size loss.backward() loss_adv_target_value1 += loss_adv_target1.data.cpu().numpy( ) / args.iter_size loss_adv_target_value2 += loss_adv_target2.data.cpu().numpy( ) / args.iter_size # train D # bring back requires_grad for param in model_D1.parameters(): param.requires_grad = True for param in model_D2.parameters(): param.requires_grad = True # train with source pred1 = pred1.detach() pred2 = pred2.detach() D_out1 = model_D1(F.softmax(pred1)) D_out2 = model_D2(F.softmax(pred2)) loss_D1 = bce_loss( D_out1, Variable( torch.FloatTensor( D_out1.data.size()).fill_(source_label)).cuda( args.gpu)) loss_D2 = bce_loss( D_out2, Variable( torch.FloatTensor( D_out2.data.size()).fill_(source_label)).cuda( args.gpu)) loss_D1 = loss_D1 / args.iter_size / 2 loss_D2 = loss_D2 / args.iter_size / 2 loss_D1.backward() loss_D2.backward() loss_D_value1 += loss_D1.data.cpu().numpy() loss_D_value2 += loss_D2.data.cpu().numpy() # train with target pred_target1 = pred_target1.detach() pred_target2 = pred_target2.detach() D_out1 = model_D1(F.softmax(pred_target1)) D_out2 = model_D2(F.softmax(pred_target2)) loss_D1 = bce_loss( D_out1, Variable( torch.FloatTensor( D_out1.data.size()).fill_(target_label)).cuda( args.gpu)) loss_D2 = bce_loss( D_out2, Variable( torch.FloatTensor( D_out2.data.size()).fill_(target_label)).cuda( args.gpu)) loss_D1 = loss_D1 / args.iter_size / 2 loss_D2 = loss_D2 / args.iter_size / 2 loss_D1.backward() loss_D2.backward() loss_D_value1 += loss_D1.data.cpu().numpy() loss_D_value2 += loss_D2.data.cpu().numpy() optimizer.step() optimizer_D1.step() optimizer_D2.step() if i_iter % args.val_steps == 0 and i_iter: model.eval() log = valhelper.valid_epoch(i_iter) print('log: {}'.format(log)) model.train() if i_iter % 10 == 0 and i_iter: print('exp = {}'.format(args.snapshot_dir)) print( 'iter = {0:8d}/{1:8d}, loss_seg1 = {2:.3f} loss_seg2 = {3:.3f} loss_adv1 = {4:.3f}, loss_adv2 = {5:.3f} loss_D1 = {6:.3f} loss_D2 = {7:.3f}' .format(i_iter, args.num_steps, loss_seg_value1, loss_seg_value2, loss_adv_target_value1, loss_adv_target_value2, loss_D_value1, loss_D_value2)) writer.add_scalar(f'train/loss_seg_value1', loss_seg_value1, i_iter) writer.add_scalar(f'train/loss_seg_value2', loss_seg_value2, i_iter) writer.add_scalar(f'train/loss_adv_target_value1', loss_adv_target_value1, i_iter) writer.add_scalar(f'train/loss_D_value1', loss_D_value1, i_iter) writer.add_scalar(f'train/loss_D_value2', loss_D_value2, i_iter) if i_iter >= args.num_steps_stop - 1: print('save model ...') torch.save( model.state_dict(), osp.join(args.snapshot_dir, 'GTA5_' + str(args.num_steps_stop) + '.pth')) torch.save( model_D1.state_dict(), osp.join(args.snapshot_dir, 'GTA5_' + str(args.num_steps_stop) + '_D1.pth')) torch.save( model_D2.state_dict(), osp.join(args.snapshot_dir, 'GTA5_' + str(args.num_steps_stop) + '_D2.pth')) break if i_iter % args.save_pred_every == 0 and i_iter != 0: print('taking snapshot ...') torch.save( model.state_dict(), osp.join(args.snapshot_dir, 'GTA5_' + str(i_iter) + '.pth')) torch.save( model_D1.state_dict(), osp.join(args.snapshot_dir, 'GTA5_' + str(i_iter) + '_D1.pth')) torch.save( model_D2.state_dict(), osp.join(args.snapshot_dir, 'GTA5_' + str(i_iter) + '_D2.pth'))
def main(): """Create the model and start the training.""" h, w = map(int, args.input_size.split(',')) input_size = (h, w) h, w = map(int, args.input_size_target.split(',')) input_size_target = (h, w) #cudnn.enabled = True gpu = args.gpu # Create network #if args.model == 'DeepLab': # model = DeeplabMulti(num_classes=args.num_classes) # if args.restore_from[:4] == 'http' : # saved_state_dict = model_zoo.load_url(args.restore_from) # else: # saved_state_dict = torch.load(args.restore_from) # new_params = model.state_dict().copy() # for i in saved_state_dict: # # Scale.layer5.conv2d_list.3.weight # i_parts = i.split('.') # # print i_parts # if not args.num_classes == 19 or not i_parts[1] == 'layer5': # new_params['.'.join(i_parts[1:])] = saved_state_dict[i] # print i_parts # model.load_state_dict(new_params) #model.train() #model.cuda(args.gpu) #cudnn.benchmark = True # init D model_D = FCDiscriminator(num_classes=args.num_classes) model_D.train() model_D.cuda(0) #model_D1 = FCDiscriminator(num_classes=args.num_classes) #model_D2 = FCDiscriminator(num_classes=args.num_classes) #model_D1.train() #model_D1.cuda(args.gpu) #model_D2.train() #model_D2.cuda(args.gpu) if not os.path.exists(args.snapshot_dir): os.makedirs(args.snapshot_dir) gta_trainloader = data.DataLoader(GTA5DataSet( args.data_dir, args.data_list, args.num_classes, max_iters=args.num_steps * args.iter_size * args.batch_size, crop_size=input_size, scale=args.random_scale, mirror=args.random_mirror, mean=IMG_MEAN), batch_size=args.batch_size, shuffle=True, num_workers=args.num_workers, pin_memory=True) gta_trainloader_iter = enumerate(gta_trainloader) gta_valloader = data.DataLoader(GTA5DataSet(args.data_dir, args.valdata_list, args.num_classes, max_iters=None, crop_size=input_size, scale=args.random_scale, mirror=args.random_mirror, mean=IMG_MEAN), batch_size=args.batch_size, shuffle=True, num_workers=args.num_workers, pin_memory=True) gta_valloader_iter = enumerate(gta_valloader) cityscapes_targetloader = data.DataLoader(cityscapesDataSet( args.data_dir_target, args.data_list_target, args.num_classes, max_iters=args.num_steps * args.iter_size * args.batch_size, crop_size=input_size_target, scale=False, mirror=args.random_mirror, mean=IMG_MEAN, set='train'), batch_size=args.batch_size, shuffle=True, num_workers=args.num_workers, pin_memory=True) cityscapes_targetloader_iter = enumerate(cityscapes_targetloader) cityscapes_valtargetloader = data.DataLoader(cityscapesDataSet( args.data_dir_target, args.valdata_list_target, args.num_classes, max_iters=None, crop_size=input_size_target, scale=False, mirror=args.random_mirror, mean=IMG_MEAN, set='val'), batch_size=args.batch_size, shuffle=True, num_workers=args.num_workers, pin_memory=True) cityscapes_valtargetloader_iter = enumerate(cityscapes_valtargetloader) # implement model.optim_parameters(args) to handle different models' lr setting #optimizer = optim.SGD(model.optim_parameters(args), # lr=args.learning_rate, momentum=args.momentum, weight_decay=args.weight_decay) #optimizer.zero_grad() optimizer_D = optim.Adam(model_D.parameters(), lr=args.learning_rate_D, betas=(0.9, 0.99)) optimizer_D.zero_grad() #optimizer_D1 = optim.Adam(model_D1.parameters(), lr=args.learning_rate_D, betas=(0.9, 0.99)) #optimizer_D1.zero_grad() #optimizer_D2 = optim.Adam(model_D2.parameters(), lr=args.learning_rate_D, betas=(0.9, 0.99)) #optimizer_D2.zero_grad() bce_loss = torch.nn.BCEWithLogitsLoss() #interp = nn.Upsample(size=(input_size[1], input_size[0]), mode='bilinear') #interp_target = nn.Upsample(size=(input_size_target[1], input_size_target[0]), mode='bilinear') # labels for adversarial training source_label = 0 target_label = 1 for i_iter in range(args.num_steps): loss_D_value = 0 #loss_seg_value1 = 0 #loss_adv_target_value1 = 0 #loss_D_value1 = 0 #loss_seg_value2 = 0 #loss_adv_target_value2 = 0 #loss_D_value2 = 0 optimizer_D.zero_grad() adjust_learning_rate_D(optimizer_D, i_iter) _, batch = gta_trainloader_iter.next() images, labels, _, _ = batch size = labels.size() #print(size) #labels = Variable(labels) oneHot_size = (size[0], args.num_classes, size[2], size[3]) input_label = torch.FloatTensor(torch.Size(oneHot_size)).zero_() input_label = input_label.scatter_(1, labels.long(), 1.0) #print(input_label.size()) labels1 = Variable(input_label).cuda(0) #D_out1 = model_D(labels) #print(D_out1.data.size()) #loss_out1 = bce_loss(D_out1, Variable(torch.FloatTensor(D_out1.data.size()).fill_(source_label)).cuda(0)) _, batch = cityscapes_targetloader_iter.next() images, labels, _, _ = batch size = labels.size() #labels = Variable(labels) oneHot_size = (size[0], args.num_classes, size[2], size[3]) input_label = torch.FloatTensor(torch.Size(oneHot_size)).zero_() input_label = input_label.scatter_(1, labels.long(), 1.0) labels2 = Variable(input_label).cuda(0) #print(labels1.data.size()) #print(labels2.data.size()) labels = torch.cat((labels1, labels2), 0) #print(labels.data.size()) #D_out2 = model_D(labels) D_out = model_D(labels) #print(D_out.data.size()) target_size = D_out.data.size() target_labels1 = torch.FloatTensor( torch.Size((target_size[0] / 2, target_size[1], target_size[2], target_size[3]))).fill_(source_label) target_labels2 = torch.FloatTensor( torch.Size((target_size[0] / 2, target_size[1], target_size[2], target_size[3]))).fill_(target_label) target_labels = torch.cat((target_labels1, target_labels2), 0) target_labels = Variable(target_labels).cuda(0) #print(target_labels.data.size()) loss_out = bce_loss(D_out, target_labels) loss = loss_out / args.iter_size loss.backward() loss_D_value += loss_out.data.cpu().numpy()[0] / args.iter_size #print(loss_D_value) optimizer_D.step() #print('exp = {}'.format(args.snapshot_dir)) if i_iter % 100 == 0: print('iter = {0:8d}/{1:8d}, loss_D = {2:.3f}'.format( i_iter, args.num_steps, loss_D_value)) if i_iter >= args.num_steps_stop - 1: print('save model ...') torch.save( model_D.state_dict(), osp.join(args.snapshot_dir, 'Classify_' + str(args.num_steps) + '.pth')) break if i_iter % args.save_pred_every == 0 and i_iter != 0: print('taking snapshot ...') torch.save( model_D.state_dict(), osp.join(args.snapshot_dir, 'Classify_' + str(i_iter) + '.pth')) model_D.eval() loss_valD_value = 0 correct = 0 wrong = 0 for i, (images, labels, _, _) in enumerate(gta_valloader): #if i > 500: # break size = labels.size() #labels = Variable(labels) oneHot_size = (size[0], args.num_classes, size[2], size[3]) input_label = torch.FloatTensor( torch.Size(oneHot_size)).zero_() input_label = input_label.scatter_(1, labels.long(), 1.0) labels = Variable(input_label).cuda(0) D_out1 = model_D(labels) loss_out1 = bce_loss( D_out1, Variable( torch.FloatTensor( D_out1.data.size()).fill_(source_label)).cuda(0)) loss_valD_value += loss_out1.data.cpu().numpy()[0] correct = correct + (D_out1.data.cpu() < 0).sum() / 100 wrong = wrong + (D_out1.data.cpu() >= 0).sum() / 100 #accuracy = 1.0 * correct / (wrong + correct) #print('accuracy:%f' % accuracy) #print(correct) #print(wrong) for i, (images, labels, _, _) in enumerate(cityscapes_valtargetloader): #if i > 500: # break size = labels.size() #labels = Variable(labels) oneHot_size = (size[0], args.num_classes, size[2], size[3]) input_label = torch.FloatTensor( torch.Size(oneHot_size)).zero_() input_label = input_label.scatter_(1, labels.long(), 1.0) labels = Variable(input_label).cuda(0) D_out2 = model_D(labels) loss_out2 = bce_loss( D_out2, Variable( torch.FloatTensor( D_out2.data.size()).fill_(target_label)).cuda(0)) loss_valD_value += loss_out2.data.cpu().numpy()[0] wrong = wrong + (D_out2.data.cpu() < 0).sum() correct = correct + (D_out2.data.cpu() >= 0).sum() accuracy = 1.0 * correct / (wrong + correct) print('accuracy:%f' % accuracy) print('iter = {0:8d}/{1:8d}, loss_valD = {2:.3f}'.format( i_iter, args.num_steps, loss_valD_value)) model_D.train()
def main(): h, w = map(int, args.input_size.split(',')) input_size = (h, w) cudnn.enabled = True # create network model = Res_Deeplab(num_classes=args.num_classes) # load pretrained parameters if args.restore_from[:4] == 'http' : saved_state_dict = model_zoo.load_url(args.restore_from) else: saved_state_dict = torch.load(args.restore_from) # only copy the params that exist in currendt model (caffe-like) new_params = model.state_dict().copy() for name, param in new_params.items(): print (name) if name in saved_state_dict and param.size() == saved_state_dict[name].size(): new_params[name].copy_(saved_state_dict[name]) print('copy {}'.format(name)) model.load_state_dict(new_params) model.train() model=nn.DataParallel(model) model.cuda() cudnn.benchmark = True # init D model_D = FCDiscriminator(num_classes=args.num_classes) if args.restore_from_D is not None: model_D.load_state_dict(torch.load(args.restore_from_D)) model_D = nn.DataParallel(model_D) model_D.train() model_D.cuda() if not os.path.exists(args.snapshot_dir): os.makedirs(args.snapshot_dir) train_dataset = VOCDataSet(args.data_dir, args.data_list, crop_size=input_size, scale=args.random_scale, mirror=args.random_mirror, mean=IMG_MEAN) train_dataset_size = len(train_dataset) train_gt_dataset = VOCGTDataSet(args.data_dir, args.data_list, crop_size=input_size, scale=args.random_scale, mirror=args.random_mirror, mean=IMG_MEAN) if args.partial_data is None: trainloader = data.DataLoader(train_dataset, batch_size=args.batch_size, shuffle=True, num_workers=5, pin_memory=True) trainloader_gt = data.DataLoader(train_gt_dataset, batch_size=args.batch_size, shuffle=True, num_workers=5, pin_memory=True) else: #sample partial data partial_size = int(args.partial_data * train_dataset_size) if args.partial_id is not None: train_ids = pickle.load(open(args.partial_id)) print('loading train ids from {}'.format(args.partial_id)) else: train_ids = list(range(train_dataset_size)) np.random.shuffle(train_ids) pickle.dump(train_ids, open(osp.join(args.snapshot_dir, 'train_id.pkl'), 'wb')) train_sampler = data.sampler.SubsetRandomSampler(train_ids[:partial_size]) train_remain_sampler = data.sampler.SubsetRandomSampler(train_ids[partial_size:]) train_gt_sampler = data.sampler.SubsetRandomSampler(train_ids[:partial_size]) trainloader = data.DataLoader(train_dataset, batch_size=args.batch_size, sampler=train_sampler, num_workers=3, pin_memory=True) trainloader_remain = data.DataLoader(train_dataset, batch_size=args.batch_size, sampler=train_remain_sampler, num_workers=3, pin_memory=True) trainloader_gt = data.DataLoader(train_gt_dataset, batch_size=args.batch_size, sampler=train_gt_sampler, num_workers=3, pin_memory=True) trainloader_remain_iter = enumerate(trainloader_remain) trainloader_iter = enumerate(trainloader) trainloader_gt_iter = enumerate(trainloader_gt) # implement model.optim_parameters(args) to handle different models' lr setting # optimizer for segmentation network optimizer = optim.SGD(model.module.optim_parameters(args), lr=args.learning_rate, momentum=args.momentum,weight_decay=args.weight_decay) optimizer.zero_grad() # optimizer for discriminator network optimizer_D = optim.Adam(model_D.parameters(), lr=args.learning_rate_D, betas=(0.9,0.99)) optimizer_D.zero_grad() # loss/ bilinear upsampling bce_loss = BCEWithLogitsLoss2d() interp = nn.Upsample(size=(input_size[1], input_size[0]), mode='bilinear') if version.parse(torch.__version__) >= version.parse('0.4.0'): interp = nn.Upsample(size=(input_size[1], input_size[0]), mode='bilinear', align_corners=True) else: interp = nn.Upsample(size=(input_size[1], input_size[0]), mode='bilinear') # labels for adversarial training pred_label = 0 gt_label = 1 for i_iter in range(args.num_steps): loss_seg_value = 0 loss_adv_pred_value = 0 loss_D_value = 0 loss_semi_value = 0 loss_semi_adv_value = 0 optimizer.zero_grad() adjust_learning_rate(optimizer, i_iter) optimizer_D.zero_grad() adjust_learning_rate_D(optimizer_D, i_iter) for sub_i in range(args.iter_size): # train G # don't accumulate grads in D for param in model_D.parameters(): param.requires_grad = False # do semi first if (args.lambda_semi > 0 or args.lambda_semi_adv > 0 ) and i_iter >= args.semi_start_adv : try: _, batch = trainloader_remain_iter.next() except: trainloader_remain_iter = enumerate(trainloader_remain) _, batch = trainloader_remain_iter.next() # only access to img images, _, _, _ = batch images = Variable(images).cuda() pred = interp(model(images)) pred_remain = pred.detach() mask1=F.softmax(pred,dim=1).data.cpu().numpy() id2 = np.argmax(mask1, axis=1)#10, 321, 321) D_out = interp(model_D(F.softmax(pred,dim=1))) D_out_sigmoid = F.sigmoid(D_out).data.cpu().numpy().squeeze(axis=1) ignore_mask_remain = np.zeros(D_out_sigmoid.shape).astype(np.bool) loss_semi_adv = args.lambda_semi_adv * bce_loss(D_out, make_D_label(gt_label, ignore_mask_remain)) loss_semi_adv = loss_semi_adv/args.iter_size #loss_semi_adv.backward() loss_semi_adv_value += loss_semi_adv.data.cpu().numpy()[0]/args.lambda_semi_adv if args.lambda_semi <= 0 or i_iter < args.semi_start: loss_semi_adv.backward() loss_semi_value = 0 else: # produce ignore mask semi_ignore_mask = (D_out_sigmoid < args.mask_T) #print semi_ignore_mask.shape 10,321,321 map2 = np.zeros([pred.size()[0], id2.shape[1], id2.shape[2]]) for k in range(pred.size()[0]): for i in range(id2.shape[1]): for j in range(id2.shape[2]): map2[k][i][j] = mask1[k][id2[k][i][j]][i][j] semi_ignore_mask = (map2 < 0.999999) semi_gt = pred.data.cpu().numpy().argmax(axis=1) semi_gt[semi_ignore_mask] = 255 semi_ratio = 1.0 - float(semi_ignore_mask.sum())/semi_ignore_mask.size print('semi ratio: {:.4f}'.format(semi_ratio)) if semi_ratio == 0.0: loss_semi_value += 0 else: semi_gt = torch.FloatTensor(semi_gt) loss_semi = args.lambda_semi * loss_calc(pred, semi_gt) loss_semi = loss_semi/args.iter_size loss_semi_value += loss_semi.data.cpu().numpy()[0]/args.lambda_semi loss_semi += loss_semi_adv loss_semi.backward() else: loss_semi = None loss_semi_adv = None # train with source try: _, batch = trainloader_iter.next() except: trainloader_iter = enumerate(trainloader) _, batch = trainloader_iter.next() images, labels, _, _ = batch images = Variable(images).cuda() ignore_mask = (labels.numpy() == 255) pred = interp(model(images)) loss_seg = loss_calc(pred, labels) D_out = interp(model_D(F.softmax(pred,dim=1))) loss_adv_pred = bce_loss(D_out, make_D_label(gt_label, ignore_mask)) loss = loss_seg + args.lambda_adv_pred * loss_adv_pred # proper normalization loss = loss/args.iter_size loss.backward() loss_seg_value += loss_seg.data.cpu().numpy()[0]/args.iter_size loss_adv_pred_value += loss_adv_pred.data.cpu().numpy()[0]/args.iter_size # train D # bring back requires_grad for param in model_D.parameters(): param.requires_grad = True # train with pred pred = pred.detach() if args.D_remain: pred = torch.cat((pred, pred_remain), 0) ignore_mask = np.concatenate((ignore_mask,ignore_mask_remain), axis = 0) D_out = interp(model_D(F.softmax(pred,dim=1))) loss_D = bce_loss(D_out, make_D_label(pred_label, ignore_mask)) loss_D = loss_D/args.iter_size/2 loss_D.backward() loss_D_value += loss_D.data.cpu().numpy()[0] # train with gt # get gt labels try: _, batch = trainloader_gt_iter.next() except: trainloader_gt_iter = enumerate(trainloader_gt) _, batch = trainloader_gt_iter.next() _, labels_gt, _, _ = batch D_gt_v = Variable(one_hot(labels_gt)).cuda() ignore_mask_gt = (labels_gt.numpy() == 255) D_out = interp(model_D(D_gt_v)) loss_D = bce_loss(D_out, make_D_label(gt_label, ignore_mask_gt)) loss_D = loss_D/args.iter_size/2 loss_D.backward() loss_D_value += loss_D.data.cpu().numpy()[0] optimizer.step() optimizer_D.step() print('exp = {}'.format(args.snapshot_dir)) print('iter = {0:8d}/{1:8d}, loss_seg = {2:.3f}, loss_adv_p = {3:.3f}, loss_D = {4:.3f}, loss_semi = {5:.3f}, loss_semi_adv = {6:.3f}'.format(i_iter, args.num_steps, loss_seg_value, loss_adv_pred_value, loss_D_value, loss_semi_value, loss_semi_adv_value)) if i_iter >= args.num_steps-1: print( 'save model ...') torch.save(model.state_dict(),osp.join(args.snapshot_dir, 'VOC_'+os.path.abspath(__file__).split('/')[-1].split('.')[0]+'_'+str(args.num_steps)+'.pth')) torch.save(model_D.state_dict(),osp.join(args.snapshot_dir, 'VOC_'+os.path.abspath(__file__).split('/')[-1].split('.')[0]+'_'+str(args.num_steps)+'_D.pth')) break if i_iter % args.save_pred_every == 0 and i_iter!=0: print ('taking snapshot ...') torch.save(model.state_dict(),osp.join(args.snapshot_dir, 'VOC_'+os.path.abspath(__file__).split('/')[-1].split('.')[0]+'_'+str(i_iter)+'.pth')) torch.save(model_D.state_dict(),osp.join(args.snapshot_dir, 'VOC_'+os.path.abspath(__file__).split('/')[-1].split('.')[0]+'_'+str(i_iter)+'_D.pth')) end = timeit.default_timer() print(end-start,'seconds')
def main(): """Create the model and start the training.""" h, w = map(int, args.input_size.split(',')) input_size = (h, w) h, w = map(int, args.input_size_target.split(',')) input_size_target = (h, w) cudnn.enabled = True gpu = args.gpu # Create network if args.model == 'DeepLab': model = Res_Deeplab(num_classes=args.num_classes) if args.restore_from[:4] == 'http': saved_state_dict = model_zoo.load_url(args.restore_from) else: saved_state_dict = torch.load(args.restore_from) new_params = model.state_dict().copy() for i in saved_state_dict: # Scale.layer5.conv2d_list.3.weight i_parts = i.split('.') # print i_parts if not args.num_classes == 19 or not i_parts[1] == 'layer5': new_params['.'.join(i_parts[1:])] = saved_state_dict[i] # print i_parts model.load_state_dict(new_params) # model.load_state_dict(saved_state_dict) model.train() model.cuda(args.gpu) cudnn.benchmark = True # init D model_D1 = FCDiscriminator(num_classes=args.num_classes) model_D2 = FCDiscriminator(num_classes=args.num_classes) # # load discriminator params # saved_state_dict_D1 = torch.load(D1_RESTORE_FROM) # saved_state_dict_D2 = torch.load(D2_RESTORE_FROM) # model_D1.load_state_dict(saved_state_dict_D1) # model_D2.load_state_dict(saved_state_dict_D2) model_D1.train() model_D1.cuda(args.gpu) model_D2.train() model_D2.cuda(args.gpu) if not os.path.exists(args.snapshot_dir): os.makedirs(args.snapshot_dir) trainloader = data.DataLoader(synthiaDataSet( args.data_dir, args.data_list, max_iters=args.num_steps * args.iter_size * args.batch_size, crop_size=input_size, scale=args.random_scale, mirror=args.random_mirror, mean=IMG_MEAN), batch_size=args.batch_size, shuffle=True, num_workers=args.num_workers, pin_memory=True) trainloader_iter = enumerate(trainloader) targetloader = data.DataLoader(cityscapesDataSet( args.data_dir_target, args.data_list_target, max_iters=args.num_steps * args.iter_size * args.batch_size, crop_size=input_size_target, scale=False, mirror=args.random_mirror, mean=CITY_IMG_MEAN, set=args.set), batch_size=args.batch_size, shuffle=True, num_workers=args.num_workers, pin_memory=True) targetloader_iter = enumerate(targetloader) # implement model.optim_parameters(args) to handle different models' lr setting optimizer = optim.SGD(model.optim_parameters(args), lr=args.learning_rate, momentum=args.momentum, weight_decay=args.weight_decay) optimizer_D1 = optim.Adam(model_D1.parameters(), lr=args.learning_rate_D, betas=(0.7, 0.99)) optimizer_D2 = optim.Adam(model_D2.parameters(), lr=args.learning_rate_D, betas=(0.7, 0.99)) #BYZQ # opti_state_dict = torch.load(OPTI_RESTORE_FROM) # opti_state_dict_d1 = torch.load(OPTI_D1_RESTORE_FROM) # opti_state_dict_d2 = torch.load(OPTI_D2_RESTORE_FROM) # optimizer.load_state_dict(opti_state_dict) # optimizer_D1.load_state_dict(opti_state_dict_d1) # optimizer_D1.load_state_dict(opti_state_dict_d2) optimizer.zero_grad() optimizer_D1.zero_grad() optimizer_D2.zero_grad() bce_loss = torch.nn.BCEWithLogitsLoss() interp = nn.Upsample(size=(input_size[1], input_size[0]), mode='bilinear', align_corners=True) interp_target = nn.Upsample(size=(input_size_target[1], input_size_target[0]), mode='bilinear', align_corners=True) # labels for adversarial training source_label = 1 target_label = 0 mIoUs = [] i_iters = [] for i_iter in range(args.num_steps): if i_iter <= iter_start: continue loss_seg_value1 = 0 loss_adv_target_value1 = 0 loss_D_value1 = 0 loss_seg_value2 = 0 loss_adv_target_value2 = 0 loss_D_value2 = 0 optimizer.zero_grad() adjust_learning_rate(optimizer, i_iter) optimizer_D1.zero_grad() optimizer_D2.zero_grad() adjust_learning_rate_D(optimizer_D1, i_iter) adjust_learning_rate_D(optimizer_D2, i_iter) for sub_i in range(args.iter_size): # train G # don't accumulate grads in D for param in model_D1.parameters(): param.requires_grad = False for param in model_D2.parameters(): param.requires_grad = False # train with source _, batch = trainloader_iter.__next__() images, labels, _, _ = batch images = Variable(images).cuda(args.gpu) pred1, pred2 = model(images) pred1 = interp(pred1) pred2 = interp(pred2) loss_seg1 = loss_calc(pred1, labels, args.gpu) loss_seg2 = loss_calc(pred2, labels, args.gpu) loss = loss_seg2 + args.lambda_seg * loss_seg1 # proper normalization loss = loss / args.iter_size loss.backward() loss_seg_value1 += loss_seg1.data.cpu().numpy() / args.iter_size loss_seg_value2 += loss_seg2.data.cpu().numpy() / args.iter_size # train with target _, batch = targetloader_iter.__next__() images, _, name = batch images = Variable(images).cuda(args.gpu) pred_target1, pred_target2 = model(images) pred_target1 = interp_target(pred_target1) pred_target2 = interp_target(pred_target2) D_out1 = model_D1(F.softmax(pred_target1, dim=1)) D_out2 = model_D2(F.softmax(pred_target2, dim=1)) loss_adv_target1 = bce_loss( D_out1, Variable( torch.FloatTensor( D_out1.data.size()).fill_(source_label)).cuda( args.gpu)) loss_adv_target2 = bce_loss( D_out2, Variable( torch.FloatTensor( D_out2.data.size()).fill_(source_label)).cuda( args.gpu)) loss = args.lambda_adv_target1 * loss_adv_target1 + args.lambda_adv_target2 * loss_adv_target2 loss = loss / args.iter_size loss.backward() loss_adv_target_value1 += loss_adv_target1.data.cpu().numpy( ) / args.iter_size loss_adv_target_value2 += loss_adv_target2.data.cpu().numpy( ) / args.iter_size # train D # bring back requires_grad for param in model_D1.parameters(): param.requires_grad = True for param in model_D2.parameters(): param.requires_grad = True # train with source pred1 = pred1.detach() pred2 = pred2.detach() D_out1 = model_D1(F.softmax(pred1, dim=1)) D_out2 = model_D2(F.softmax(pred2, dim=1)) weight_s = float(D_out2.mean().data.cpu().numpy()) loss_D1 = bce_loss( D_out1, Variable( torch.FloatTensor( D_out1.data.size()).fill_(source_label)).cuda( args.gpu)) loss_D2 = bce_loss( D_out2, Variable( torch.FloatTensor( D_out2.data.size()).fill_(source_label)).cuda( args.gpu)) loss_D1 = loss_D1 / args.iter_size / 2 loss_D2 = loss_D2 / args.iter_size / 2 loss_D1.backward() loss_D2.backward() loss_D_value1 += loss_D1.data.cpu().numpy() loss_D_value2 += loss_D2.data.cpu().numpy() # train with target pred_target1 = pred_target1.detach() pred_target2 = pred_target2.detach() D_out1 = model_D1(F.softmax(pred_target1, dim=1)) D_out2 = model_D2(F.softmax(pred_target2, dim=1)) weight_t = float(D_out2.mean().data.cpu().numpy()) # if weight_b>0.5 and i_iter>500: # confidence_map = interp(D_out2).cpu().data[0][0].numpy() # name = name[0].split('/')[-1] # confidence_map=255*confidence_map # confidence_output=Image.fromarray(confidence_map.astype(np.uint8)) # confidence_output.save('./result/confid_map/%s.png' % (name.split('.')[0])) # zq=1 print(weight_s, weight_t) loss_D1 = bce_loss( D_out1, Variable( torch.FloatTensor( D_out1.data.size()).fill_(target_label)).cuda( args.gpu)) loss_D2 = bce_loss( D_out2, Variable( torch.FloatTensor( D_out2.data.size()).fill_(target_label)).cuda( args.gpu)) loss_D1 = loss_D1 / args.iter_size / 2 loss_D2 = loss_D2 / args.iter_size / 2 loss_D1.backward() loss_D2.backward() loss_D_value1 += loss_D1.data.cpu().numpy() loss_D_value2 += loss_D2.data.cpu().numpy() optimizer.step() optimizer_D1.step() optimizer_D2.step() # print('exp = {}'.format(args.snapshot_dir)) print( 'iter = {0:8d}/{1:8d}, loss_seg1 = {2:.3f} loss_seg2 = {3:.3f} loss_adv1 = {4:.3f}, loss_adv2 = {5:.3f} loss_D1 = {6:.3f} loss_D2 = {7:.3f}' .format(i_iter, args.num_steps, loss_seg_value1, loss_seg_value2, loss_adv_target_value1, loss_adv_target_value2, loss_D_value1, loss_D_value2)) if i_iter >= args.num_steps_stop - 1: print('save model ...') torch.save( model.state_dict(), osp.join(args.snapshot_dir, 'GTA5_' + str(args.num_steps) + '.pth')) torch.save( model_D1.state_dict(), osp.join(args.snapshot_dir, 'GTA5_' + str(args.num_steps) + '_D1.pth')) torch.save( model_D2.state_dict(), osp.join(args.snapshot_dir, 'GTA5_' + str(args.num_steps) + '_D2.pth')) break if i_iter % args.save_pred_every == 0 and i_iter != 0: print('taking snapshot ...') torch.save( model.state_dict(), osp.join(args.snapshot_dir, 'GTA5_' + str(i_iter) + '.pth')) torch.save( model_D1.state_dict(), osp.join(args.snapshot_dir, 'GTA5_' + str(i_iter) + '_D1.pth')) torch.save( model_D2.state_dict(), osp.join(args.snapshot_dir, 'GTA5_' + str(i_iter) + '_D2.pth')) torch.save( optimizer.state_dict(), osp.join(args.snapshot_dir, 'GTA5_' + str(i_iter) + '_optimizer.pth')) torch.save( optimizer_D1.state_dict(), osp.join(args.snapshot_dir, 'GTA5_' + str(i_iter) + '_optimizer_D1.pth')) torch.save( optimizer_D2.state_dict(), osp.join(args.snapshot_dir, 'GTA5_' + str(i_iter) + '_optimizer_D2.pth')) show_pred_sv_dir = pre_sv_dir.format(i_iter) mIoU = show_val(model.state_dict(), show_pred_sv_dir, gpu) mIoUs.append(str(round(np.nanmean(mIoU) * 100, 2))) i_iters.append(i_iter) print_i = 0 for miou in mIoUs: print('i{0}: {1}'.format(i_iters[print_i], miou)) print_i = print_i + 1
def main(): """Create the model and start the training.""" model_num = 0 torch.manual_seed(args.random_seed) torch.cuda.manual_seed_all(args.random_seed) random.seed(args.random_seed) h, w = map(int, args.input_size.split(',')) input_size = (h, w) h, w = map(int, args.input_size_target.split(',')) input_size_target = (h, w) cudnn.enabled = True gpu = args.gpu # Create network if args.model == 'DeepLab': if args.training_option == 1: model = Res_Deeplab(num_classes=args.num_classes, num_layers=args.num_layers) elif args.training_option == 2: model = Res_Deeplab2(num_classes=args.num_classes) if args.restore_from[:4] == 'http': saved_state_dict = model_zoo.load_url(args.restore_from) else: saved_state_dict = torch.load(args.restore_from) new_params = model.state_dict().copy() for k, v in saved_state_dict.items(): print(k) for k in new_params: print(k) for i in saved_state_dict: i_parts = i.split('.') if '.'.join(i_parts[args.i_parts_index:]) in new_params: print("Restored...") if args.not_restore_last == True: if not i_parts[ args.i_parts_index] == 'layer5' and not i_parts[ args.i_parts_index] == 'layer6': new_params['.'.join(i_parts[args.i_parts_index:] )] = saved_state_dict[i] else: new_params['.'.join( i_parts[args.i_parts_index:])] = saved_state_dict[i] model.load_state_dict(new_params) model.train() model.cuda(args.gpu) cudnn.benchmark = True writer = SummaryWriter(log_dir=args.snapshot_dir) # init D model_D1 = FCDiscriminator(num_classes=args.num_classes) model_D2 = FCDiscriminator(num_classes=args.num_classes) model_D1.train() model_D1.cuda(args.gpu) model_D2.train() model_D2.cuda(args.gpu) if not os.path.exists(args.snapshot_dir): os.makedirs(args.snapshot_dir) '''trainloader = data.DataLoader(sourceDataSet(args.data_dir, args.data_list, max_iters=args.num_steps * args.iter_size * args.batch_size, crop_size=input_size, scale=args.random_scale, mirror=args.random_mirror, mean=IMG_MEAN_SOURCE, ignore_label=args.ignore_label), batch_size=args.batch_size, shuffle=True, num_workers=args.num_workers, pin_memory=True)''' trainloader = data.DataLoader(sourceDataSet( args.data_dir, args.data_list, max_iters=args.num_steps * args.iter_size * args.batch_size, crop_size=input_size, random_rotate=args.augment_1, random_flip=args.augment_1, random_lighting=args.augment_1, mean=IMG_MEAN_SOURCE, ignore_label=args.ignore_label), batch_size=args.batch_size, shuffle=True, num_workers=args.num_workers, pin_memory=True) trainloader_iter = enumerate(trainloader) targetloader = data.DataLoader(isprsDataSet( args.data_dir_target, args.data_list_target, max_iters=args.num_steps * args.iter_size * args.batch_size, crop_size=input_size_target, scale=False, mirror=args.random_mirror, mean=IMG_MEAN_TARGET, ignore_label=args.ignore_label), batch_size=args.batch_size, shuffle=True, num_workers=args.num_workers, pin_memory=True) targetloader_iter = enumerate(targetloader) valloader = data.DataLoader(valDataSet(args.data_dir_val, args.data_list_val, crop_size=input_size_target, mean=IMG_MEAN_TARGET, scale=False, mirror=False), batch_size=1, shuffle=False, pin_memory=True) # implement model.optim_parameters(args) to handle different models' lr setting optimizer = optim.SGD(model.optim_parameters(args), lr=args.learning_rate, momentum=args.momentum, weight_decay=args.weight_decay) optimizer.zero_grad() optimizer_D1 = optim.Adam(model_D1.parameters(), lr=args.learning_rate_D, betas=(0.9, 0.99)) optimizer_D1.zero_grad() optimizer_D2 = optim.Adam(model_D2.parameters(), lr=args.learning_rate_D, betas=(0.9, 0.99)) optimizer_D2.zero_grad() bce_loss = torch.nn.BCEWithLogitsLoss() # EDITTED by me interp = nn.Upsample(size=(input_size[0], input_size[1]), mode='bilinear') interp_target = nn.Upsample(size=(input_size_target[0], input_size_target[1]), mode='bilinear') # labels for adversarial training source_label = 0 target_label = 1 # Which layers to freeze non_trainable(args.dont_train, model) for i_iter in range(args.num_steps): loss_seg_value1 = 0 loss_adv_target_value1 = 0 loss_D_value1 = 0 loss_seg_value2 = 0 loss_adv_target_value2 = 0 loss_D_value2 = 0 optimizer.zero_grad() adjust_learning_rate(optimizer, i_iter) optimizer_D1.zero_grad() optimizer_D2.zero_grad() adjust_learning_rate_D(optimizer_D1, i_iter) adjust_learning_rate_D(optimizer_D2, i_iter) for sub_i in range(args.iter_size): # train G # don't accumulate grads in D for param in model_D1.parameters(): param.requires_grad = False for param in model_D2.parameters(): param.requires_grad = False # train with source while True: try: _, batch = next(trainloader_iter) images, labels, _, train_name = batch #print(train_name) images = Variable(images).cuda(args.gpu) pred1, pred2 = model(images) pred1 = interp(pred1) pred2 = interp(pred2) # Save img '''if i_iter % 5 == 0: save_image_for_test(concatenate_side_by_side([images, labels, pred2]), i_iter)''' loss_seg1 = loss_calc(pred1, labels, args.gpu, args.ignore_label, train_name) loss_seg2 = loss_calc(pred2, labels, args.gpu, args.ignore_label, train_name) loss = loss_seg2 + args.lambda_seg * loss_seg1 # proper normalization loss = loss / args.iter_size loss.backward() if isinstance(loss_seg1.data.cpu().numpy(), list): loss_seg_value1 += loss_seg1.data.cpu().numpy( )[0] / args.iter_size else: loss_seg_value1 += loss_seg1.data.cpu().numpy( ) / args.iter_size if isinstance(loss_seg2.data.cpu().numpy(), list): loss_seg_value2 += loss_seg2.data.cpu().numpy( )[0] / args.iter_size else: loss_seg_value2 += loss_seg2.data.cpu().numpy( ) / args.iter_size break except (RuntimeError, AssertionError, AttributeError): continue if args.experiment == 1: # Which layers to freeze non_trainable('0', model) # train with target _, batch = next(targetloader_iter) images, _, _ = batch images = Variable(images).cuda(args.gpu) pred_target1, pred_target2 = model(images) pred_target1 = interp_target(pred_target1) pred_target2 = interp_target(pred_target2) #total_image2 = vutils.make_grid(torch.cat((images.cuda()), dim = 2),normalize=True, scale_each=True) #total_image2 = images.cuda() #, pred_target1.cuda(), pred_target2.cuda() D_out1 = model_D1(F.softmax(pred_target1)) D_out2 = model_D2(F.softmax(pred_target2)) loss_adv_target1 = bce_loss( D_out1, Variable( torch.FloatTensor( D_out1.data.size()).fill_(source_label)).cuda( args.gpu)) loss_adv_target2 = bce_loss( D_out2, Variable( torch.FloatTensor( D_out2.data.size()).fill_(source_label)).cuda( args.gpu)) loss = args.lambda_adv_target1 * loss_adv_target1 + args.lambda_adv_target2 * loss_adv_target2 loss = loss / args.iter_size loss.backward() if isinstance(loss_adv_target1.data.cpu().numpy(), list): loss_adv_target_value1 += loss_adv_target1.data.cpu().numpy( )[0] / args.iter_size else: loss_adv_target_value1 += loss_adv_target1.data.cpu().numpy( ) / args.iter_size if isinstance(loss_adv_target2.data.cpu().numpy(), list): loss_adv_target_value2 += loss_adv_target2.data.cpu().numpy( )[0] / args.iter_size else: loss_adv_target_value2 += loss_adv_target2.data.cpu().numpy( ) / args.iter_size if args.experiment == 1: # Which layers to freeze non_trainable(args.dont_train, model) # train D # bring back requires_grad for param in model_D1.parameters(): param.requires_grad = True for param in model_D2.parameters(): param.requires_grad = True # train with source pred1 = pred1.detach() pred2 = pred2.detach() D_out1 = model_D1(F.softmax(pred1)) D_out2 = model_D2(F.softmax(pred2)) loss_D1 = bce_loss( D_out1, Variable( torch.FloatTensor( D_out1.data.size()).fill_(source_label)).cuda( args.gpu)) loss_D2 = bce_loss( D_out2, Variable( torch.FloatTensor( D_out2.data.size()).fill_(source_label)).cuda( args.gpu)) loss_D1 = loss_D1 / args.iter_size / 2 loss_D2 = loss_D2 / args.iter_size / 2 loss_D1.backward() loss_D2.backward() if isinstance(loss_D1.data.cpu().numpy(), list): loss_D_value1 += loss_D1.data.cpu().numpy()[0] else: loss_D_value1 += loss_D1.data.cpu().numpy() if isinstance(loss_D2.data.cpu().numpy(), list): loss_D_value2 += loss_D2.data.cpu().numpy()[0] else: loss_D_value2 += loss_D2.data.cpu().numpy() # train with target pred_target1 = pred_target1.detach() pred_target2 = pred_target2.detach() D_out1 = model_D1(F.softmax(pred_target1)) D_out2 = model_D2(F.softmax(pred_target2)) loss_D1 = bce_loss( D_out1, Variable( torch.FloatTensor( D_out1.data.size()).fill_(target_label)).cuda( args.gpu)) loss_D2 = bce_loss( D_out2, Variable( torch.FloatTensor( D_out2.data.size()).fill_(target_label)).cuda( args.gpu)) loss_D1 = loss_D1 / args.iter_size / 2 loss_D2 = loss_D2 / args.iter_size / 2 loss_D1.backward() loss_D2.backward() if isinstance(loss_D1.data.cpu().numpy(), list): loss_D_value1 += loss_D1.data.cpu().numpy()[0] else: loss_D_value1 += loss_D1.data.cpu().numpy() if isinstance(loss_D2.data.cpu().numpy(), list): loss_D_value2 += loss_D2.data.cpu().numpy()[0] else: loss_D_value2 += loss_D2.data.cpu().numpy() optimizer.step() optimizer_D1.step() optimizer_D2.step() print('exp = {}'.format(args.snapshot_dir)) print( 'iter = {0:8d}/{1:8d}, loss_seg1 = {2:.3f} loss_seg2 = {3:.3f} loss_adv1 = {4:.3f}, loss_adv2 = {5:.3f} loss_D1 = {6:.3f} loss_D2 = {7:.3f}' .format(i_iter, args.num_steps, loss_seg_value1, loss_seg_value2, loss_adv_target_value1, loss_adv_target_value2, loss_D_value1, loss_D_value2)) if i_iter >= args.num_steps_stop - 1: #print ('save model ...') torch.save( model.state_dict(), osp.join(args.snapshot_dir, 'model_' + str(args.num_steps) + '.pth')) torch.save( model_D1.state_dict(), osp.join(args.snapshot_dir, 'model_' + str(args.num_steps) + '_D1.pth')) torch.save( model_D2.state_dict(), osp.join(args.snapshot_dir, 'model_' + str(args.num_steps) + '_D2.pth')) break if i_iter % args.save_pred_every == 0 and i_iter != 0: #print ('taking snapshot ...') '''torch.save(model.state_dict(), osp.join(args.snapshot_dir, 'model_' + str(i_iter) + '.pth')) torch.save(model_D1.state_dict(), osp.join(args.snapshot_dir, 'model_' + str(i_iter) + '_D1.pth')) torch.save(model_D2.state_dict(), osp.join(args.snapshot_dir, 'model_' + str(i_iter) + '_D2.pth'))''' if model_num != args.num_models_keep: torch.save( model.state_dict(), osp.join(args.snapshot_dir, 'model_' + str(model_num) + '.pth')) torch.save( model_D1.state_dict(), osp.join(args.snapshot_dir, 'model_' + str(model_num) + '_D1.pth')) torch.save( model_D2.state_dict(), osp.join(args.snapshot_dir, 'model_' + str(model_num) + '_D2.pth')) model_num = model_num + 1 if model_num == args.num_models_keep: model_num = 0 # Validation if (i_iter % args.val_every == 0 and i_iter != 0) or i_iter == 1: validation(valloader, model, interp_target, writer, i_iter, [37, 41, 10]) # Save for tensorboardx writer.add_scalar('loss_seg_value1', loss_seg_value1, i_iter) writer.add_scalar('loss_seg_value2', loss_seg_value2, i_iter) writer.add_scalar('loss_adv_target_value1', loss_adv_target_value1, i_iter) writer.add_scalar('loss_adv_target_value2', loss_adv_target_value2, i_iter) writer.add_scalar('loss_D_value1', loss_D_value1, i_iter) writer.add_scalar('loss_D_value2', loss_D_value2, i_iter) writer.close()
def main(): """Create the model and start the training.""" gpu_id_2 = 3 gpu_id_1 = 2 w, h = map(int, args.input_size.split(',')) input_size = (w, h) w, h = map(int, args.input_size_target.split(',')) input_size_target = (w, h) cudnn.enabled = True # gpu = args.gpu # Create network if args.model == 'DeepLab': model = DeeplabMulti(num_classes=args.num_classes) if args.restore_from[:4] == 'http': print("from url") saved_state_dict = model_zoo.load_url(args.restore_from) else: print("from restore") saved_state_dict = torch.load(args.restore_from) saved_state_dict = torch.load( 'snapshots/GTA2Cityscapes_multi_54/GTA5_10000.pth') model.load_state_dict(saved_state_dict) new_params = model.state_dict().copy() for i in saved_state_dict: # Scale.layer5.conv2d_list.3.weight i_parts = i.split('.') # print i_parts if not args.num_classes == 19 or not i_parts[1] == 'layer5': new_params['.'.join(i_parts[1:])] = saved_state_dict[i] # print i_parts # model.load_state_dict(new_params) model.train() model.cuda(gpu_id_2) cudnn.benchmark = True # init D model_D1 = FCDiscriminator(num_classes=args.num_classes) #model_D2 = model_D1 model_D2 = FCDiscriminator(num_classes=args.num_classes) model_D1.train() model_D1.cuda(gpu_id_1) model_D2.train() model_D2.cuda(gpu_id_1) if not os.path.exists(args.snapshot_dir): os.makedirs(args.snapshot_dir) trainloader = data.DataLoader(GTA5DataSet(args.data_dir, args.data_list, max_iters=args.num_steps * args.iter_size * args.batch_size, crop_size=input_size, scale=args.random_scale, mirror=args.random_mirror, mean=IMG_MEAN), batch_size=args.batch_size, shuffle=True, num_workers=args.num_workers, pin_memory=True) trainloader_iter = enumerate(trainloader) _, batch_last = trainloader_iter.next() targetloader = data.DataLoader(cityscapesDataSet( args.data_dir_target, args.data_list_target, max_iters=args.num_steps * args.iter_size * args.batch_size, crop_size=input_size_target, scale=False, mirror=args.random_mirror, mean=IMG_MEAN, set=args.set), batch_size=args.batch_size, shuffle=True, num_workers=args.num_workers, pin_memory=True) # print(args.num_steps * args.iter_size * args.batch_size, trainloader.__len__()) targetloader_iter = enumerate(targetloader) _, batch_last_target = targetloader_iter.next() # for i in range(200): # _, batch = targetloader_iter.__next__() # exit() # implement model.optim_parameters(args) to handle different models' lr setting optimizer = optim.SGD(model.optim_parameters(args), lr=args.learning_rate, momentum=args.momentum, weight_decay=args.weight_decay) optimizer.zero_grad() optimizer_D1 = optim.Adam(model_D1.parameters(), lr=args.learning_rate_D, betas=(0.9, 0.99)) optimizer_D1.zero_grad() optimizer_D2 = optim.Adam(model_D2.parameters(), lr=args.learning_rate_D, betas=(0.9, 0.99)) optimizer_D2.zero_grad() bce_loss = torch.nn.MSELoss() def upsample_(input_): return nn.functional.interpolate(input_, size=(input_size[1], input_size[0]), mode='bilinear', align_corners=False) def upsample_target(input_): return nn.functional.interpolate(input_, size=(input_size_target[1], input_size_target[0]), mode='bilinear', align_corners=False) interp = upsample_ interp_target = upsample_target # labels for adversarial training source_label = 1 target_label = -1 mix_label = 0 for i_iter in range(10000, args.num_steps): loss_seg_value1 = 0 loss_adv_target_value1 = 0 loss_D_value1 = 0 loss_seg_value2 = 0 loss_adv_target_value2 = 0 loss_D_value2 = 0 optimizer.zero_grad() adjust_learning_rate(optimizer, i_iter) optimizer_D1.zero_grad() optimizer_D2.zero_grad() adjust_learning_rate_D(optimizer_D1, i_iter) adjust_learning_rate_D(optimizer_D2, i_iter) for sub_i in range(args.iter_size): # train G # don't accumulate grads in D for param in model_D1.parameters(): param.requires_grad = False for param in model_D2.parameters(): param.requires_grad = False def result_model(batch, interp_): images, labels, _, name = batch images = Variable(images).cuda(gpu_id_2) labels = Variable(labels.long()).cuda(gpu_id_1) pred1, pred2 = model(images) pred1 = interp_(pred1) pred2 = interp_(pred2) pred1_ = pred1.cuda(gpu_id_1) pred2_ = pred2.cuda(gpu_id_1) return pred1_, pred2_, labels # train with source # _, batch = trainloader_iter.next() _, batch = trainloader_iter.next() _, batch_target = targetloader_iter.next() pred1, pred2, labels = result_model(batch, interp) loss_seg1 = loss_calc(pred1, labels, gpu_id_1) loss_seg2 = loss_calc(pred2, labels, gpu_id_1) loss = loss_seg2 + args.lambda_seg * loss_seg1 loss = loss / args.iter_size loss.backward() loss_seg_1 = loss_seg1.data.cpu().numpy() / args.iter_size loss_seg_2 = loss_seg2.data.cpu().numpy() / args.iter_size # print(loss_seg_1, loss_seg_2) pred1, pred2, labels = result_model(batch_target, interp_target) loss_seg1 = loss_calc(pred1, labels, gpu_id_1) loss_seg2 = loss_calc(pred2, labels, gpu_id_1) loss = loss_seg2 + args.lambda_seg * loss_seg1 loss = loss / args.iter_size loss.backward() loss_seg_value1 += loss_seg1.data.cpu().numpy() / args.iter_size loss_seg_value2 += loss_seg2.data.cpu().numpy() / args.iter_size # output = pred2.cpu().data[0].numpy() # real_lab = labels.cpu().data[0].numpy() # output = output.transpose(1,2,0) # print(real_lab.shape, output.shape) # output = np.asarray(np.argmax(output, axis=2), dtype=np.uint8) # output_col = colorize_mask(output) # real_lab_col = colorize_mask(real_lab) # output = Image.fromarray(output) # # name[0].split('/')[-1] # # print('result/train_seg_result/' + name[0][len(name[0])-23:len(name[0])-4] + '_color.png') # output_col.save('result/train_seg_result/' + name[0].split('/')[-1] + '_color.png') # real_lab_col.save('result/train_seg_result/' + name[0].split('/')[-1] + '_real.png') # print(loss_seg_value1, loss_seg_value2) # if i_iter == 100: # exit() # else: # break # train with target #_, batch = targetloader_iter.next() # images, _, _ = target_batch # images_target = Variable(images_target).cuda(gpu_id_2) pred1_last_target, pred2_last_target, labels_last_target = result_model( batch_last_target, interp_target) pred1_target, pred2_target, labels_target = result_model( batch_target, interp_target) pred1_target_D = F.softmax((pred1_target), dim=1) pred2_target_D = F.softmax((pred2_target), dim=1) pred1_last_target_D = F.softmax((pred1_last_target), dim=1) pred2_last_target_D = F.softmax((pred2_last_target), dim=1) fake1_D = torch.cat((pred1_target_D, pred1_last_target_D), dim=1) fake2_D = torch.cat((pred2_target_D, pred2_last_target_D), dim=1) D_out_fake_1 = model_D1(fake1_D) D_out_fake_2 = model_D2(fake1_D) loss_adv_fake1 = bce_loss( D_out_fake_1, Variable( torch.FloatTensor(D_out_fake_1.data.size()).fill_( source_label)).cuda(gpu_id_1)) loss_adv_fake2 = bce_loss( D_out_fake_2, Variable( torch.FloatTensor(D_out_fake_2.data.size()).fill_( source_label)).cuda(gpu_id_1)) loss_adv_target1 = loss_adv_fake1 loss_adv_target2 = loss_adv_fake2 loss = args.lambda_adv_target1 * loss_adv_target1.cuda( gpu_id_1) + args.lambda_adv_target2 * loss_adv_target2.cuda( gpu_id_1) loss = loss / args.iter_size loss.backward() pred1, pred2, labels = result_model(batch, interp) pred1_target, pred2_target, labels_target = result_model( batch_target, interp_target) pred1_target_D = F.softmax((pred1_target), dim=1) pred2_target_D = F.softmax((pred2_target), dim=1) pred1_D = F.softmax((pred1), dim=1) pred2_D = F.softmax((pred2), dim=1) mix1_D = torch.cat((pred1_target_D, pred1_D), dim=1) mix2_D = torch.cat((pred2_target_D, pred2_D), dim=1) D_out_mix_1 = model_D1(mix1_D) D_out_mix_2 = model_D2(mix2_D) # D_out1 = D_out1.cuda(gpu_id_1) # D_out2 = D_out2.cuda(gpu_id_1) loss_adv_mix1 = bce_loss( D_out_mix_1, Variable( torch.FloatTensor(D_out_mix_1.data.size()).fill_( source_label)).cuda(gpu_id_1)) loss_adv_mix2 = bce_loss( D_out_mix_2, Variable( torch.FloatTensor(D_out_mix_2.data.size()).fill_( source_label)).cuda(gpu_id_1)) loss_adv_target1 = loss_adv_mix1 * 2 loss_adv_target2 = loss_adv_mix2 * 2 loss = args.lambda_adv_target1 * loss_adv_target1.cuda( gpu_id_1) + args.lambda_adv_target2 * loss_adv_target2.cuda( gpu_id_1) loss = loss / args.iter_size loss.backward() loss_adv_target_value1 += loss_adv_target1.data.cpu().numpy( ) / args.iter_size loss_adv_target_value2 += loss_adv_target2.data.cpu().numpy( ) / args.iter_size # train D # bring back requires_grad for param in model_D1.parameters(): param.requires_grad = True for param in model_D2.parameters(): param.requires_grad = True pred1_last, pred2_last, labels_last = result_model( batch_last, interp) # train with source pred1 = pred1.detach().cuda(gpu_id_1) pred2 = pred2.detach().cuda(gpu_id_1) pred1_target = pred1_target.detach().cuda(gpu_id_1) pred2_target = pred2_target.detach().cuda(gpu_id_1) pred1_last = pred1_last.detach().cuda(gpu_id_1) pred2_last = pred2_last.detach().cuda(gpu_id_1) pred1_D = F.softmax((pred1), dim=1) pred2_D = F.softmax((pred2), dim=1) pred1_last_D = F.softmax((pred1_last), dim=1) pred2_last_D = F.softmax((pred2_last), dim=1) pred1_target_D = F.softmax((pred1_target), dim=1) pred2_target_D = F.softmax((pred2_target), dim=1) real1_D = torch.cat((pred1_D, pred1_last_D), dim=1) real2_D = torch.cat((pred2_D, pred2_last_D), dim=1) mix1_D_ = torch.cat((pred1_last_D, pred1_target_D), dim=1) mix2_D_ = torch.cat((pred2_last_D, pred2_target_D), dim=1) D_out1_real = model_D1(real1_D) D_out2_real = model_D2(real2_D) D_out1_mix = model_D1(mix1_D_) D_out2_mix = model_D2(mix2_D_) # D_out1 = D_out1.cuda(gpu_id_1) # D_out2 = D_out2.cuda(gpu_id_1) loss_D1 = bce_loss( D_out1_real, Variable( torch.FloatTensor(D_out1_real.data.size()).fill_( source_label)).cuda(gpu_id_1)) loss_D2 = bce_loss( D_out2_real, Variable( torch.FloatTensor(D_out2_real.data.size()).fill_( source_label)).cuda(gpu_id_1)) loss_D3 = bce_loss( D_out1_mix, Variable( torch.FloatTensor(D_out1_mix.data.size()).fill_( mix_label)).cuda(gpu_id_1)) loss_D4 = bce_loss( D_out2_mix, Variable( torch.FloatTensor(D_out2_mix.data.size()).fill_( mix_label)).cuda(gpu_id_1)) loss_D1 = (loss_D1 + loss_D3) / args.iter_size / 2 loss_D2 = (loss_D2 + loss_D4) / args.iter_size / 2 loss_D1.backward() loss_D2.backward() loss_D_value1 += loss_D1.data.cpu().numpy() loss_D_value2 += loss_D2.data.cpu().numpy() # train with target pred1 = pred1.detach().cuda(gpu_id_1) pred2 = pred2.detach().cuda(gpu_id_1) pred1_target = pred1_target.detach().cuda(gpu_id_1) pred2_target = pred2_target.detach().cuda(gpu_id_1) pred1_last_target = pred1_last_target.detach().cuda(gpu_id_1) pred2_last_target = pred2_last_target.detach().cuda(gpu_id_1) pred1_D = F.softmax((pred1), dim=1) pred2_D = F.softmax((pred2), dim=1) pred1_last_target_D = F.softmax((pred1_last_target), dim=1) pred2_last_target_D = F.softmax((pred2_last_target), dim=1) pred1_target_D = F.softmax((pred1_target), dim=1) pred2_target_D = F.softmax((pred2_target), dim=1) fake1_D_ = torch.cat((pred1_target_D, pred1_target_D), dim=1) fake2_D_ = torch.cat((pred2_target_D, pred2_target_D), dim=1) mix1_D__ = torch.cat((pred1_D, pred1_last_target_D), dim=1) mix2_D__ = torch.cat((pred2_D, pred2_last_target_D), dim=1) # pred_target1 = pred_target1.detach().cuda(gpu_id_1) # pred_target2 = pred_target2.detach().cuda(gpu_id_1) D_out1 = model_D1(fake1_D_) D_out2 = model_D2(fake2_D_) D_out3 = model_D1(mix1_D__) D_out4 = model_D2(mix2_D__) # D_out1 = D_out1.cuda(gpu_id_1) # D_out2 = D_out2.cuda(gpu_id_1) loss_D1 = bce_loss( D_out1, Variable( torch.FloatTensor(D_out1.data.size()).fill_( target_label)).cuda(gpu_id_1)) loss_D2 = bce_loss( D_out2, Variable( torch.FloatTensor(D_out2.data.size()).fill_( target_label)).cuda(gpu_id_1)) loss_D3 = bce_loss( D_out3, Variable( torch.FloatTensor( D_out3.data.size()).fill_(mix_label)).cuda(gpu_id_1)) loss_D4 = bce_loss( D_out4, Variable( torch.FloatTensor( D_out4.data.size()).fill_(mix_label)).cuda(gpu_id_1)) loss_D1 = (loss_D1 + loss_D3) / args.iter_size / 2 loss_D2 = (loss_D2 + loss_D4) / args.iter_size / 2 loss_D1.backward() loss_D2.backward() batch_last, batch_last_target = batch, batch_target loss_D_value1 += loss_D1.data.cpu().numpy() loss_D_value2 += loss_D2.data.cpu().numpy() optimizer.step() optimizer_D1.step() optimizer_D2.step() print('exp = {}'.format(args.snapshot_dir)) print( 'iter = {0:8d}/{1:8d}, loss_seg1 = {2:.3f} loss_seg2 = {3:.3f} loss_adv1 = {4:.3f}, loss_adv2 = {5:.3f} loss_D1 = {6:.3f} loss_D2 = {7:.3f}' .format(i_iter, args.num_steps, loss_seg_value1, loss_seg_value2, loss_adv_target_value1, loss_adv_target_value2, loss_D_value1, loss_D_value2)) if i_iter >= args.num_steps_stop - 1: print('save model ...') torch.save( model.state_dict(), osp.join(args.snapshot_dir, 'GTA5_' + str(args.num_steps_stop) + '.pth')) torch.save( model_D1.state_dict(), osp.join(args.snapshot_dir, 'GTA5_' + str(args.num_steps_stop) + '_D1.pth')) torch.save( model_D2.state_dict(), osp.join(args.snapshot_dir, 'GTA5_' + str(args.num_steps_stop) + '_D2.pth')) break if i_iter % args.save_pred_every == 0: print('taking snapshot ...') torch.save( model.state_dict(), osp.join(args.snapshot_dir, 'GTA5_' + str(i_iter) + '.pth')) torch.save( model_D1.state_dict(), osp.join(args.snapshot_dir, 'GTA5_' + str(i_iter) + '_D1.pth')) torch.save( model_D2.state_dict(), osp.join(args.snapshot_dir, 'GTA5_' + str(i_iter) + '_D2.pth'))
def main(args): """Create the model and start the training.""" mkdir_check(args.snapshot_dir) writer = SummaryWriter(log_dir=os.path.join(args.snapshot_dir, 'tb-logs')) h, w = map(int, args.src_input_size.split(',')) src_input_size = (h, w) h, w = map(int, args.tgt_input_size.split(',')) tgt_input_size = (h, w) cudnn.enabled = True gpu = args.gpu # Create network if args.model == 'DeepLab': model = DeeplabMulti(num_classes=args.num_classes) if args.restore_from[:4] == 'http' : saved_state_dict = model_zoo.load_url(args.restore_from) else: saved_state_dict = torch.load(args.restore_from) new_params = model.state_dict().copy() for i in saved_state_dict: # Scale.layer5.conv2d_list.3.weight i_parts = i.split('.') # print i_parts if not args.num_classes == 19 or not i_parts[1] == 'layer5': new_params['.'.join(i_parts[1:])] = saved_state_dict[i] # print i_parts model.load_state_dict(new_params) model.train() model.cuda(args.gpu) cudnn.benchmark = True # init D model_D2 = FCDiscriminator(num_classes=19) model_D2.train() model_D2.cuda(args.gpu) if not os.path.exists(args.snapshot_dir): os.makedirs(args.snapshot_dir) trainloader = data.DataLoader( ListDataSet(args.src_data_dir, args.src_img_list, args.src_lbl_list, max_iters=args.num_steps * args.iter_size * args.batch_size, crop_size=src_input_size, mean=IMG_MEAN), batch_size=args.batch_size, shuffle=True, num_workers=args.num_workers, pin_memory=True) trainloader_iter = enumerate(trainloader) targetloader = data.DataLoader( ListDataSet(args.tgt_data_dir, args.tgt_img_list, args.tgt_lbl_list, max_iters=args.num_steps * args.iter_size * args.batch_size, crop_size=tgt_input_size, mean=IMG_MEAN), batch_size=args.batch_size, shuffle=True, num_workers=args.num_workers, pin_memory=True) targetloader_nolabel = data.DataLoader( ListDataSet(args.tgt_data_dir, args.tgt_img_nolabel_list, None, max_iters=args.num_steps * args.iter_size * args.batch_size, crop_size=tgt_input_size, mean=IMG_MEAN), batch_size=args.batch_size, shuffle=True, num_workers=args.num_workers, pin_memory=True) targetloader_iter = enumerate(targetloader) targetloader_nolabel_iter = enumerate(targetloader_nolabel) # implement model.optim_parameters(args) to handle different models' lr setting optimizer = optim.SGD(model.optim_parameters(args), lr=args.learning_rate, momentum=args.momentum, weight_decay=args.weight_decay) optimizer.zero_grad() optimizer_D2 = optim.Adam(model_D2.parameters(), lr=args.learning_rate_D, betas=(0.9, 0.99)) optimizer_D2.zero_grad() bce_loss = torch.nn.BCEWithLogitsLoss() interp = nn.Upsample(size=(src_input_size[1], src_input_size[0]), mode='bilinear', align_corners=True) interp_target = nn.Upsample(size=(tgt_input_size[1], tgt_input_size[0]), mode='bilinear', align_corners=True) # labels for adversarial training source_label = 0 target_label = 1 for i_iter in range(args.num_steps): loss_seg_value2 = 0 loss_tgt_seg_value2 = 0 loss_adv_target_value2 = 0 loss_D_value2 = 0 optimizer.zero_grad() adjust_learning_rate(args, optimizer, i_iter) optimizer_D2.zero_grad() adjust_learning_rate_D(args, optimizer_D2, i_iter) for sub_i in range(args.iter_size): # train G # don't accumulate grads in D for param in model_D2.parameters(): param.requires_grad = False # train with source _, batch = trainloader_iter.__next__() images, labels, _, _ = batch images = Variable(images).cuda(args.gpu) pred1, pred2 = model(images) #pred1 = interp(pred1) pred2 = interp(pred2) #loss_seg1 = loss_calc(pred1, labels, args.gpu) loss_seg2 = loss_calc(pred2, labels, args.gpu) loss = loss_seg2 #+ args.lambda_seg * loss_seg1 # proper normalization loss = loss / args.iter_size loss.backward() loss_seg_value2 += loss_seg2.data.cpu().numpy() / args.iter_size # train with target seg _, batch = targetloader_iter.__next__() images, labels, _, _ = batch images = Variable(images).cuda(args.gpu) pred_target1, pred_target2 = model(images) #pred_target1 = interp_target(pred_target1) pred_target2 = interp_target(pred_target2) #loss_tgt_seg1 = loss_calc(pred_target1, labels, args.gpu) loss_tgt_seg2 = loss_calc(pred_target2, labels, args.gpu) loss = loss_tgt_seg2 #+ args.lambda_seg * loss_tgt_seg1 # proper normalization loss = loss / args.iter_size loss.backward(retain_graph=True) loss_tgt_seg_value2 += loss_tgt_seg2.data.cpu().numpy() / args.iter_size # train with target_nolabel adv _, batch = targetloader_nolabel_iter.__next__() images, _, _, _ = batch images = Variable(images).cuda(args.gpu) pred_target1, pred_target2 = model(images) #pred_target1 = interp_target(pred_target1) pred_target2 = interp_target(pred_target2) D_out2 = model_D2(F.softmax(pred_target2, dim=-1)) loss_adv_target2 = bce_loss(D_out2, Variable(torch.FloatTensor(D_out2.data.size()).fill_(source_label)).cuda( args.gpu)) loss = args.lambda_adv_target2 * loss_adv_target2 loss = loss / args.iter_size loss.backward() loss_adv_target_value2 += loss_adv_target2.data.cpu().numpy() / args.iter_size # train D # bring back requires_grad for param in model_D2.parameters(): param.requires_grad = True # train with source pred2 = pred2.detach() D_out2 = model_D2(F.softmax(pred2, dim=-1)) loss_D2 = bce_loss(D_out2, Variable(torch.FloatTensor(D_out2.data.size()).fill_(source_label)).cuda(args.gpu)) loss_D2 = loss_D2 / args.iter_size / 2 loss_D2.backward() loss_D_value2 += loss_D2.data.cpu().numpy() # train with target pred_target2 = pred_target2.detach() D_out2 = model_D2(F.softmax(pred_target2, dim=-1)) loss_D2 = bce_loss(D_out2, Variable(torch.FloatTensor(D_out2.data.size()).fill_(target_label)).cuda(args.gpu)) loss_D2 = loss_D2 / args.iter_size / 2 loss_D2.backward() loss_D_value2 += loss_D2.data.cpu().numpy() optimizer.step() optimizer_D2.step() print( 'iter = {:5d}/{:8d}, loss_seg2 = {:.3f} loss_tgt_seg2 = {:.3f} loss_adv2 = {:.3f} loss_D2 = {:.3f}'.format( i_iter, args.num_steps_stop, loss_seg_value2, loss_tgt_seg_value2, loss_adv_target_value2, loss_D_value2)) writer.add_scalars('loss/seg', { 'src2': loss_seg_value2, 'tgt2': loss_tgt_seg_value2, }, i_iter) writer.add_scalar('loss/adv', loss_adv_target_value2, i_iter) writer.add_scalar('loss/d', loss_D_value2, i_iter) if i_iter >= args.num_steps_stop - 1: print('save model ...') torch.save(model.state_dict(), osp.join(args.snapshot_dir, 'model_{}.pth'.format(i_iter))) torch.save(model_D2.state_dict(), osp.join(args.snapshot_dir, 'model_d_{}.pth'.format(i_iter))) break if i_iter % args.save_pred_every == 0 and i_iter != 0: print('taking snapshot ...') torch.save(model.state_dict(), osp.join(args.snapshot_dir, 'model_{}.pth'.format(i_iter))) torch.save(model_D2.state_dict(), osp.join(args.snapshot_dir, 'model_d_{}.pth'.format(i_iter)))
def main(): h, w = map(int, args.input_size.split(',')) input_size = (h, w) cudnn.enabled = True gpu = args.gpu np.random.seed(args.random_seed) # create network model = Res_Deeplab(num_classes=args.num_classes) # load pretrained parameters if args.restore_from[:4] == 'http': saved_state_dict = model_zoo.load_url(args.restore_from) else: saved_state_dict = torch.load(args.restore_from) # only copy the params that exist in current model (caffe-like) new_params = model.state_dict().copy() for name, param in new_params.items(): print(name) if name in saved_state_dict and param.size( ) == saved_state_dict[name].size(): new_params[name].copy_(saved_state_dict[name]) print('copy {}'.format(name)) model.load_state_dict(new_params) model.train() model.cuda(args.gpu) # init D model_D = FCDiscriminator(num_classes=args.num_classes) if args.restore_from_D is not None: model_D.load_state_dict(torch.load(args.restore_from_D)) model_D.train() model_D.cuda(args.gpu) if not os.path.exists(args.snapshot_dir): os.makedirs(args.snapshot_dir) # load dataset train_dataset = VOCDataSet(args.data_dir, args.data_list, crop_size=input_size, scale=args.random_scale, mirror=args.random_mirror, mean=IMG_MEAN) train_dataset_size = len(train_dataset) train_gt_dataset = VOCGTDataSet(args.data_dir, args.data_list, crop_size=input_size, scale=args.random_scale, mirror=args.random_mirror, mean=IMG_MEAN) if args.partial_data is None: trainloader = data.DataLoader(train_dataset, batch_size=args.batch_size, shuffle=True, num_workers=5, pin_memory=True) trainloader_gt = data.DataLoader(train_gt_dataset, batch_size=args.batch_size, shuffle=True, num_workers=5, pin_memory=True) else: #sample partial data partial_size = int(args.partial_data * train_dataset_size) if args.partial_id is not None: train_ids = pickle.load(open(args.partial_id)) print('loading train ids from {}'.format(args.partial_id)) else: train_ids = np.arange(train_dataset_size) np.random.shuffle(train_ids) pickle.dump(train_ids, open(osp.join(args.snapshot_dir, 'train_id.pkl'), 'wb')) # labeled data train_sampler = data.sampler.SubsetRandomSampler( train_ids[:partial_size]) train_gt_sampler = data.sampler.SubsetRandomSampler( train_ids[:partial_size]) trainloader = data.DataLoader(train_dataset, batch_size=args.batch_size, sampler=train_sampler, num_workers=3, pin_memory=True) trainloader_gt = data.DataLoader(train_gt_dataset, batch_size=args.batch_size, sampler=train_gt_sampler, num_workers=3, pin_memory=True) # unlabeled data train_remain_sampler = data.sampler.SubsetRandomSampler( train_ids[partial_size:]) trainloader_remain = data.DataLoader(train_dataset, batch_size=args.batch_size, sampler=train_remain_sampler, num_workers=3, pin_memory=True) trainloader_remain_iter = enumerate(trainloader_remain) trainloader_iter = enumerate(trainloader) trainloader_gt_iter = enumerate(trainloader_gt) # implement model.optim_parameters(args) to handle different models' lr setting # optimizer for segmentation network optimizer = optim.SGD(model.optim_parameters(args), lr=args.learning_rate, momentum=args.momentum, weight_decay=args.weight_decay) optimizer.zero_grad() # optimizer for discriminator network optimizer_D = optim.Adam(model_D.parameters(), lr=args.learning_rate_D, betas=(0.9, 0.99)) optimizer_D.zero_grad() # loss/bilinear upsampling bce_loss = BCEWithLogitsLoss2d() interp = nn.Upsample(size=(input_size[1], input_size[0]), mode='bilinear') if version.parse(torch.__version__) >= version.parse('0.4.0'): interp = nn.Upsample(size=(input_size[1], input_size[0]), mode='bilinear', align_corners=True) else: interp = nn.Upsample(size=(input_size[1], input_size[0]), mode='bilinear') # labels for adversarial training pred_label = 0 gt_label = 1 for i_iter in range(args.num_steps): loss_seg_value = 0 loss_adv_pred_value = 0 loss_D_value = 0 loss_D_ul_value = 0 loss_semi_value = 0 loss_semi_adv_value = 0 optimizer.zero_grad() adjust_learning_rate(optimizer, i_iter) optimizer_D.zero_grad() adjust_learning_rate_D(optimizer_D, i_iter) # creating 2nd discriminator as a copy of the 1st one if i_iter == args.discr_split: model_D_ul = FCDiscriminator(num_classes=args.num_classes) model_D_ul.load_state_dict(net_D.state_dict()) model_D_ul.train() model_D_ul.cuda(args.gpu) optimizer_D_ul = optim.Adam(model_D_ul.parameters(), lr=args.learning_rate_D, betas=(0.9, 0.99)) # start training 2nd discriminator after specified number of steps if i_iter >= args.discr_split: optimizer_D_ul.zero_grad() adjust_learning_rate_D(optimizer_D_ul, i_iter) for sub_i in range(args.iter_size): # train Segmentation # don't accumulate grads in D for param in model_D.parameters(): param.requires_grad = False # don't accumulate grads in D_ul, in case split has already been made if i_iter >= args.discr_split: for param in model_D_ul.parameters(): param.requires_grad = False # do semi-supervised training first if args.lambda_semi_adv > 0 and i_iter >= args.semi_start_adv: try: _, batch = trainloader_remain_iter.next() except: trainloader_remain_iter = enumerate(trainloader_remain) _, batch = trainloader_remain_iter.next() # only access to img images, _, _, _ = batch images = Variable(images).cuda(args.gpu) pred = interp(model(images)) pred_remain = pred.detach() # choose discriminator depending on the iteration if i_iter >= args.discr_split: D_out = interp(model_D_ul(F.softmax(pred))) else: D_out = interp(model_D(F.softmax(pred))) D_out_sigmoid = F.sigmoid(D_out).data.cpu().numpy().squeeze( axis=1) ignore_mask_remain = np.zeros(D_out_sigmoid.shape).astype( np.bool) # adversarial loss loss_semi_adv = args.lambda_semi_adv * bce_loss( D_out, make_D_label(gt_label, ignore_mask_remain, args.gpu)) loss_semi_adv = loss_semi_adv / args.iter_size # true loss value without multiplier loss_semi_adv_value += loss_semi_adv.data.cpu().numpy( ) / args.lambda_semi_adv loss_semi_adv.backward() else: loss_semi = None loss_semi_adv = None # train with labeled images try: _, batch = trainloader_iter.next() except: trainloader_iter = enumerate(trainloader) _, batch = trainloader_iter.next() images, labels, _, _ = batch images = Variable(images).cuda(args.gpu) ignore_mask = (labels.numpy() == 255) pred = interp(model(images)) D_out = interp(model_D(F.softmax(pred))) # computing loss loss_seg = loss_calc(pred, labels, args.gpu) loss_adv_pred = bce_loss( D_out, make_D_label(gt_label, ignore_mask, args.gpu)) loss = loss_seg + args.lambda_adv_pred * loss_adv_pred # proper normalization loss = loss / args.iter_size loss.backward() loss_seg_value += loss_seg.data.cpu().numpy() / args.iter_size loss_adv_pred_value += loss_adv_pred.data.cpu().numpy( ) / args.iter_size # train D and D_ul # bring back requires_grad for param in model_D.parameters(): param.requires_grad = True if i_iter >= args.discr_split: for param in model_D_ul.parameters(): param.requires_grad = True # train D with pred pred = pred.detach() # before split, traing D with both labeled and unlabeled if args.D_remain and i_iter < args.discr_split and ( args.lambda_semi > 0 or args.lambda_semi_adv > 0): pred = torch.cat((pred, pred_remain), 0) ignore_mask = np.concatenate((ignore_mask, ignore_mask_remain), axis=0) D_out = interp(model_D(F.softmax(pred))) loss_D = bce_loss(D_out, make_D_label(pred_label, ignore_mask, args.gpu)) loss_D = loss_D / args.iter_size / 2 loss_D.backward() loss_D_value += loss_D.data.cpu().numpy() # train D_ul with pred on unlabeled if i_iter >= args.discr_split and (args.lambda_semi > 0 or args.lambda_semi_adv > 0): D_ul_out = interp(model_D_ul(F.softmax(pred_remain))) loss_D_ul = bce_loss( D_ul_out, make_D_label(pred_label, ignore_mask_remain, args.gpu)) loss_D_ul = loss_D_ul / args.iter_size / 2 loss_D_ul.backward() loss_D_ul_value += loss_D_ul.data.cpu().numpy() # get gt labels try: _, batch = trainloader_gt_iter.next() except: trainloader_gt_iter = enumerate(trainloader_gt) _, batch = trainloader_gt_iter.next() images_gt, labels_gt, _, _ = batch images_gt = Variable(images_gt).cuda(args.gpu) with torch.no_grad(): pred_l = interp(model(images_gt)) # train D with gt D_gt_v = Variable(one_hot(labels_gt)).cuda(args.gpu) ignore_mask_gt = (labels_gt.numpy() == 255) D_out = interp(model_D(D_gt_v)) loss_D = bce_loss(D_out, make_D_label(gt_label, ignore_mask_gt, args.gpu)) loss_D = loss_D / args.iter_size / 2 loss_D.backward() loss_D_value += loss_D.data.cpu().numpy() # train D_ul with pseudo_gt (gt are substituted for pred) if i_iter >= args.discr_split: D_ul_out = interp(model_D_ul(F.softmax(pred_l))) loss_D_ul = bce_loss( D_ul_out, make_D_label(gt_label, ignore_mask_gt, args.gpu)) loss_D_ul = loss_D_ul / args.iter_size / 2 loss_D_ul.backward() loss_D_ul_value += loss_D_ul.data.cpu().numpy() optimizer.step() optimizer_D.step() if i_iter >= args.discr_split: optimizer_D_ul.step() print('exp = {}'.format(args.snapshot_dir)) print( 'iter = {0:8d}/{1:8d}, loss_seg = {2:.3f}, loss_adv_p = {3:.3f}, loss_D = {4:.3f}, loss_D_ul={5:.3f}, loss_semi = {6:.3f}, loss_semi_adv = {7:.3f}' .format(i_iter, args.num_steps, loss_seg_value, loss_adv_pred_value, loss_D_value, loss_D_ul_value, loss_semi_value, loss_semi_adv_value)) if i_iter >= args.num_steps - 1: print('save model ...') torch.save( net.state_dict(), osp.join( args.snapshot_dir, 'VOC_' + str(args.num_steps) + '_' + str(args.random_seed) + '.pth')) torch.save( net_D.state_dict(), osp.join( args.snapshot_dir, 'VOC_' + str(args.num_steps) + '_' + str(args.random_seed) + '_D.pth')) break if i_iter % args.save_pred_every == 0 and i_iter != 0: print('taking snapshot ...') torch.save( net.state_dict(), osp.join( args.snapshot_dir, 'VOC_' + str(i_iter) + '_' + str(args.random_seed) + '.pth')) torch.save( net_D.state_dict(), osp.join( args.snapshot_dir, 'VOC_' + str(i_iter) + '_' + str(args.random_seed) + '_D.pth')) end = timeit.default_timer() print(end - start, 'seconds')
def main(): """Create the model and start the training.""" h, w = map(int, args.input_size.split(',')) input_size = (h, w) h, w = map(int, args.input_size_target.split(',')) input_size_target = (h, w) cudnn.enabled = True gpu = args.gpu # Create network if args.model == 'DeepLab': model = Res_Deeplab(num_classes=args.num_classes) if args.restore_from[:4] == 'http': saved_state_dict = model_zoo.load_url(args.restore_from) elif args.restore_from[:4] == 'https': saved_state_dict = model_zoo.load_url(args.restore_from) else: saved_state_dict = torch.load(args.restore_from) new_params = model.state_dict().copy() for i in saved_state_dict: # Scale.layer5.conv2d_list.3.weight i_parts = i.split('.') # print i_parts if not args.num_classes == 19 or not i_parts[1] == 'layer5': print('.'.join(i_parts[1:]), saved_state_dict[i]) # print i_parts print("Key new") print(new_params.keys()) print("your model new") print(saved_state_dict.keys()) model.load_state_dict(saved_state_dict) model.train() model.cuda(args.gpu) cudnn.benchmark = True # init D model_D1 = FCDiscriminator(num_classes=args.num_classes) model_D2 = FCDiscriminator(num_classes=args.num_classes) model_D1.train() model_D1.cuda(args.gpu) model_D2.train() model_D2.cuda(args.gpu) if not os.path.exists(args.snapshot_dir): os.makedirs(args.snapshot_dir) trainloader = data.DataLoader(SynthiaDataSet( args.data_dir, args.data_list, max_iters=args.num_steps * args.iter_size * args.batch_size, crop_size=input_size, scale=args.random_scale, mirror=args.random_mirror, mean=IMG_MEAN), batch_size=args.batch_size, shuffle=True, num_workers=args.num_workers, pin_memory=True) trainloader_iter = enumerate(trainloader) targetloader = data.DataLoader(cityscapesDataSet( args.data_dir_target, args.data_list_target, max_iters=args.num_steps * args.iter_size * args.batch_size, crop_size=input_size_target, scale=False, mirror=args.random_mirror, mean=IMG_MEAN, set=args.set), batch_size=args.batch_size, shuffle=True, num_workers=args.num_workers, pin_memory=True) targetloader_iter = enumerate(targetloader) # implement model.optim_parameters(args) to handle different models' lr setting optimizer = optim.SGD(model.optim_parameters(args), lr=args.learning_rate, momentum=args.momentum, weight_decay=args.weight_decay) optimizer.zero_grad() optimizer_D1 = optim.Adam(model_D1.parameters(), lr=args.learning_rate_D, betas=(0.9, 0.99)) optimizer_D1.zero_grad() optimizer_D2 = optim.Adam(model_D2.parameters(), lr=args.learning_rate_D, betas=(0.9, 0.99)) optimizer_D2.zero_grad() bce_loss = torch.nn.BCEWithLogitsLoss() interp = nn.Upsample(size=(input_size[1], input_size[0]), mode='bilinear') interp_target = nn.Upsample(size=(input_size_target[1], input_size_target[0]), mode='bilinear') # labels for adversarial training source_label = 0 target_label = 1 for i_iter in range(args.num_steps): loss_seg_value1 = 0 loss_adv_target_value1 = 0 loss_D_value1 = 0 loss_seg_value2 = 0 loss_adv_target_value2 = 0 loss_D_value2 = 0 optimizer.zero_grad() adjust_learning_rate(optimizer, i_iter) optimizer_D1.zero_grad() optimizer_D2.zero_grad() adjust_learning_rate_D(optimizer_D1, i_iter) adjust_learning_rate_D(optimizer_D2, i_iter) for sub_i in range(args.iter_size): # train G # don't accumulate grads in D for param in model_D1.parameters(): param.requires_grad = False for param in model_D2.parameters(): param.requires_grad = False # train with source _, batch = next(trainloader_iter) images, labels, _, _ = batch images = Variable(images).cuda(args.gpu) pred1, pred2 = model(images) pred1 = interp(pred1) pred2 = interp(pred2) loss_seg1 = loss_calc(pred1, labels, args.gpu) loss_seg2 = loss_calc(pred2, labels, args.gpu) loss = loss_seg2 + args.lambda_seg * loss_seg1 # proper normalization loss = loss / args.iter_size loss.backward() loss_seg_value1 += loss_seg1.data.cpu().numpy()[0] / args.iter_size loss_seg_value2 += loss_seg2.data.cpu().numpy()[0] / args.iter_size # train with target _, batch = next(targetloader_iter) images, _, _ = batch images = Variable(images).cuda(args.gpu) pred_target1, pred_target2 = model(images) pred_target1 = interp_target(pred_target1) pred_target2 = interp_target(pred_target2) D_out1 = model_D1(F.softmax(pred_target1)) D_out2 = model_D2(F.softmax(pred_target2)) loss_adv_target1 = bce_loss( D_out1, Variable( torch.FloatTensor( D_out1.data.size()).fill_(source_label)).cuda( args.gpu)) loss_adv_target2 = bce_loss( D_out2, Variable( torch.FloatTensor( D_out2.data.size()).fill_(source_label)).cuda( args.gpu)) loss = args.lambda_adv_target1 * loss_adv_target1 + args.lambda_adv_target2 * loss_adv_target2 loss = loss / args.iter_size loss.backward() loss_adv_target_value1 += loss_adv_target1.data.cpu().numpy( )[0] / args.iter_size loss_adv_target_value2 += loss_adv_target2.data.cpu().numpy( )[0] / args.iter_size # train D # bring back requires_grad for param in model_D1.parameters(): param.requires_grad = True for param in model_D2.parameters(): param.requires_grad = True # train with source pred1 = pred1.detach() pred2 = pred2.detach() D_out1 = model_D1(F.softmax(pred1)) D_out2 = model_D2(F.softmax(pred2)) loss_D1 = bce_loss( D_out1, Variable( torch.FloatTensor( D_out1.data.size()).fill_(source_label)).cuda( args.gpu)) loss_D2 = bce_loss( D_out2, Variable( torch.FloatTensor( D_out2.data.size()).fill_(source_label)).cuda( args.gpu)) loss_D1 = loss_D1 / args.iter_size / 2 loss_D2 = loss_D2 / args.iter_size / 2 loss_D1.backward() loss_D2.backward() loss_D_value1 += loss_D1.data.cpu().numpy()[0] loss_D_value2 += loss_D2.data.cpu().numpy()[0] # train with target pred_target1 = pred_target1.detach() pred_target2 = pred_target2.detach() D_out1 = model_D1(F.softmax(pred_target1)) D_out2 = model_D2(F.softmax(pred_target2)) loss_D1 = bce_loss( D_out1, Variable( torch.FloatTensor( D_out1.data.size()).fill_(target_label)).cuda( args.gpu)) loss_D2 = bce_loss( D_out2, Variable( torch.FloatTensor( D_out2.data.size()).fill_(target_label)).cuda( args.gpu)) loss_D1 = loss_D1 / args.iter_size / 2 loss_D2 = loss_D2 / args.iter_size / 2 loss_D1.backward() loss_D2.backward() loss_D_value1 += loss_D1.data.cpu().numpy()[0] loss_D_value2 += loss_D2.data.cpu().numpy()[0] optimizer.step() optimizer_D1.step() optimizer_D2.step() print('exp = {}'.format(args.snapshot_dir)) print( 'iter = {0:8d}/{1:8d}, loss_seg1 = {2:.3f} loss_seg2 = {3:.3f} loss_adv1 = {4:.3f}, loss_adv2 = {5:.3f} loss_D1 = {6:.3f} loss_D2 = {7:.3f}' .format(i_iter, args.num_steps, loss_seg_value1, loss_seg_value2, loss_adv_target_value1, loss_adv_target_value2, loss_D_value1, loss_D_value2)) if i_iter >= args.num_steps_stop - 1: print('save model ...') torch.save( model.state_dict(), osp.join(args.snapshot_dir, 'Synthia_' + str(args.num_steps) + '.pth')) torch.save( model_D1.state_dict(), osp.join(args.snapshot_dir, 'Synthia_' + str(args.num_steps) + '_D1.pth')) torch.save( model_D2.state_dict(), osp.join(args.snapshot_dir, 'Synthia_' + str(args.num_steps) + '_D2.pth')) break if i_iter % args.save_pred_every == 0 and i_iter != 0: print('taking snapshot ...') torch.save( model.state_dict(), osp.join(args.snapshot_dir, 'Synthia_' + str(i_iter) + '.pth')) torch.save( model_D1.state_dict(), osp.join(args.snapshot_dir, 'Synthia_' + str(i_iter) + '_D1.pth')) torch.save( model_D2.state_dict(), osp.join(args.snapshot_dir, 'Synthia_' + str(i_iter) + '_D2.pth'))
def main(): # 将参数的input_size 映射到整数,并赋值,从字符串转换到整数二元组 h, w = map(int, args.input_size.split(',')) input_size = (h, w) cudnn.enabled = False gpu = args.gpu # create network model = Res_Deeplab(num_classes=args.num_classes) # load pretrained parameters if args.restore_from[:4] == 'http': saved_state_dict = model_zoo.load_url(args.restore_from) else: saved_state_dict = torch.load(args.restore_from) # only copy the params that exist in current model (caffe-like) # 确保模型中参数的格式与要加载的参数相同 # 返回一个字典,保存着module的所有状态(state);parameters和persistent buffers都会包含在字典中,字典的key就是parameter和buffer的 names。 new_params = model.state_dict().copy() for name, param in new_params.items(): # print (name) if name in saved_state_dict and param.size( ) == saved_state_dict[name].size(): new_params[name].copy_(saved_state_dict[name]) # print('copy {}'.format(name)) model.load_state_dict(new_params) # 设置为训练模式 model.train() cudnn.benchmark = True model.cuda(gpu) # init D model_D = FCDiscriminator(num_classes=args.num_classes) if args.restore_from_D is not None: model_D.load_state_dict(torch.load(args.restore_from_D)) model_D.train() model_D.cuda(gpu) if not os.path.exists(args.snapshot_dir): os.makedirs(args.snapshot_dir) train_dataset = VOCDataSet(args.data_dir, args.data_list, crop_size=input_size, scale=args.random_scale, mirror=args.random_mirror, mean=IMG_MEAN) train_dataset_size = len(train_dataset) train_gt_dataset = VOCGTDataSet(args.data_dir, args.data_list, crop_size=input_size, scale=args.random_scale, mirror=args.random_mirror, mean=IMG_MEAN) if args.partial_data is None: trainloader = data.DataLoader(train_dataset, batch_size=args.batch_size, shuffle=True, num_workers=5, pin_memory=True) trainloader_gt = data.DataLoader(train_gt_dataset, batch_size=args.batch_size, shuffle=True, num_workers=5, pin_memory=True) else: # sample partial data partial_size = int(args.partial_data * train_dataset_size) if args.partial_id is not None: train_ids = pickle.load(open(args.partial_id)) print('loading train ids from {}'.format(args.partial_id)) else: train_ids = list(range(train_dataset_size)) # ? np.random.shuffle(train_ids) pickle.dump(train_ids, open(osp.join(args.snapshot_dir, 'train_id.pkl'), 'wb')) # 写入文件 train_sampler = data.sampler.SubsetRandomSampler( train_ids[:partial_size]) train_remain_sampler = data.sampler.SubsetRandomSampler( train_ids[partial_size:]) train_gt_sampler = data.sampler.SubsetRandomSampler( train_ids[:partial_size]) trainloader = data.DataLoader(train_dataset, batch_size=args.batch_size, sampler=train_sampler, num_workers=3, pin_memory=True) trainloader_remain = data.DataLoader(train_dataset, batch_size=args.batch_size, sampler=train_remain_sampler, num_workers=3, pin_memory=True) trainloader_gt = data.DataLoader(train_gt_dataset, batch_size=args.batch_size, sampler=train_gt_sampler, num_workers=3, pin_memory=True) trainloader_remain_iter = enumerate(trainloader_remain) trainloader_iter = enumerate(trainloader) trainloader_gt_iter = enumerate(trainloader_gt) # implement model.optim_parameters(args) to handle different models' lr setting # optimizer for segmentation network optimizer = optim.SGD(model.optim_parameters(args), lr=args.learning_rate, momentum=args.momentum, weight_decay=args.weight_decay) optimizer.zero_grad() # optimizer for discriminator network optimizer_D = optim.Adam(model_D.parameters(), lr=args.learning_rate_D, betas=(0.9, 0.99)) optimizer_D.zero_grad() # loss/ bilinear upsampling bce_loss = BCEWithLogitsLoss2d() interp = nn.Upsample(size=(input_size[1], input_size[0]), mode='bilinear') # ??? # labels for adversarial training pred_label = 0 gt_label = 1 for i_iter in range(args.num_steps): print("Iter:", i_iter) loss_seg_value = 0 loss_adv_pred_value = 0 loss_D_value = 0 loss_semi_value = 0 optimizer.zero_grad() adjust_learning_rate(optimizer, i_iter) optimizer_D.zero_grad() adjust_learning_rate_D(optimizer_D, i_iter) for sub_i in range(args.iter_size): # train G # don't accumulate grads in D for param in model_D.parameters(): param.requires_grad = False # do semi first if args.lambda_semi > 0 and i_iter >= args.semi_start: try: _, batch = next(trainloader_remain_iter) except: trainloader_remain_iter = enumerate(trainloader_remain) _, batch = next(trainloader_remain_iter) # only access to img images, _, _, _ = batch images = Variable(images).cuda(gpu) # images = Variable(images).cpu() pred = interp(model(images)) D_out = interp(model_D(F.softmax(pred))) D_out_sigmoid = F.sigmoid(D_out).data.cpu().numpy().squeeze( axis=1) # produce ignore mask semi_ignore_mask = (D_out_sigmoid < args.mask_T) semi_gt = pred.data.cpu().numpy().argmax(axis=1) semi_gt[semi_ignore_mask] = 255 semi_ratio = 1.0 - float( semi_ignore_mask.sum()) / semi_ignore_mask.size print('semi ratio: {:.4f}'.format(semi_ratio)) if semi_ratio == 0.0: loss_semi_value += 0 else: semi_gt = torch.FloatTensor(semi_gt) loss_semi = args.lambda_semi * loss_calc( pred, semi_gt, args.gpu) loss_semi = loss_semi / args.iter_size loss_semi.backward() loss_semi_value += loss_semi.data.cpu().numpy( )[0] / args.lambda_semi else: loss_semi = None # train with source try: _, batch = next(trainloader_iter) except: trainloader_iter = enumerate(trainloader) _, batch = next(trainloader_iter) images, labels, _, _ = batch images = Variable(images).cuda(gpu) # images = Variable(images).cpu() ignore_mask = (labels.numpy() == 255) pred = interp(model(images)) loss_seg = loss_calc(pred, labels, args.gpu) D_out = interp(model_D(F.softmax(pred))) loss_adv_pred = bce_loss(D_out, make_D_label(gt_label, ignore_mask)) loss = loss_seg + args.lambda_adv_pred * loss_adv_pred # proper normalization loss = loss / args.iter_size loss.backward() loss_seg_value += loss_seg.data.cpu().numpy()[0] / args.iter_size loss_adv_pred_value += loss_adv_pred.data.cpu().numpy( )[0] / args.iter_size # train D # bring back requires_grad for param in model_D.parameters(): param.requires_grad = True # train with pred pred = pred.detach() D_out = interp(model_D(F.softmax(pred))) loss_D = bce_loss(D_out, make_D_label(pred_label, ignore_mask)) loss_D = loss_D / args.iter_size / 2 loss_D.backward() loss_D_value += loss_D.data.cpu().numpy()[0] # train with gt # get gt labels try: _, batch = next(trainloader_gt_iter) except: trainloader_gt_iter = enumerate(trainloader_gt) _, batch = next(trainloader_gt_iter) _, labels_gt, _, _ = batch D_gt_v = Variable(one_hot(labels_gt)).cuda(args.gpu) # D_gt_v = Variable(one_hot(labels_gt)).cpu() ignore_mask_gt = (labels_gt.numpy() == 255) D_out = interp(model_D(D_gt_v)) loss_D = bce_loss(D_out, make_D_label(gt_label, ignore_mask_gt)) loss_D = loss_D / args.iter_size / 2 loss_D.backward() loss_D_value += loss_D.data.cpu().numpy()[0] optimizer.step() optimizer_D.step() print('exp = {}'.format(args.snapshot_dir)) print( 'iter = {0:8d}/{1:8d}, loss_seg = {2:.3f}, loss_adv_p = {3:.3f}, loss_D = {4:.3f}, loss_semi = {5:.3f}' .format(i_iter, args.num_steps, loss_seg_value, loss_adv_pred_value, loss_D_value, loss_semi_value)) if i_iter >= args.num_steps - 1: print('save model ...') torch.save( model.state_dict(), osp.join(args.snapshot_dir, 'VOC_' + str(args.num_steps) + '.pth')) torch.save( model_D.state_dict(), osp.join(args.snapshot_dir, 'VOC_' + str(args.num_steps) + '_D.pth')) break if i_iter % args.save_pred_every == 0 and i_iter != 0: print('taking snapshot ...') torch.save( model.state_dict(), osp.join(args.snapshot_dir, 'VOC_' + str(i_iter) + '.pth')) torch.save( model_D.state_dict(), osp.join(args.snapshot_dir, 'VOC_' + str(i_iter) + '_D.pth')) end = timeit.default_timer() print(end - start, 'seconds')
def train(gpu, args): """Create the model and start the training.""" rank = args.nr * args.num_gpus + gpu if gpu == 1: gpu = 3 dist.init_process_group(backend="nccl", world_size=args.world_size, rank=rank) if args.batch_size == 1 and args.use_bn is True: raise Exception torch.autograd.set_detect_anomaly(True) torch.manual_seed(args.torch_seed) torch.cuda.manual_seed(args.cuda_seed) torch.cuda.set_device(gpu) w, h = map(int, args.input_size.split(',')) input_size = (w, h) w, h = map(int, args.input_size_target.split(',')) input_size_target = (w, h) cudnn.enabled = True gpu = gpu criterion = DiceBCELoss() # criterion = nn.CrossEntropyLoss(ignore_index=253) # Create network if args.model == 'DeepLab': model = DeeplabMulti(num_classes=args.num_classes) if args.restore_from is None: pass elif args.restore_from[:4] == 'http': saved_state_dict = model_zoo.load_url(args.restore_from) elif args.restore_from is not None: saved_state_dict = torch.load(args.restore_from) model.load_state_dict(saved_state_dict) print("Loaded state dicts for model") # if args.restore_from is not None: # new_params = model.state_dict().copy() # for i in saved_state_dict: # # Scale.layer5.conv2d_list.3.weight # i_parts = i.split('.') # # print i_parts # if not args.num_classes == 19 or not i_parts[1] == 'layer5': # new_params['.'.join(i_parts[1:])] = saved_state_dict[i] # # print i_parts # model.load_state_dict(new_params) if not args.no_logging: if not os.path.isdir(args.log_dir): os.mkdir(args.log_dir) log_dir = os.path.join(args.log_dir, args.exp_dir) if not os.path.isdir(log_dir): os.mkdir(log_dir) if args.exp_name == "": exp_name = datetime.datetime.now().strftime("%H%M%S-%Y%m%d") else: exp_name = args.exp_name log_dir = os.path.join(log_dir, exp_name) writer = SummaryWriter(log_dir) model.train() # model.cuda(gpu) model = model.cuda(device=gpu) if args.num_gpus > 0 or torch.cuda.device_count() > 0: model = DistributedDataParallel(model, device_ids=[gpu], find_unused_parameters=True) torch.backends.cudnn.deterministic = True torch.backends.cudnn.benchmark = False # cudnn.benchmark = True # init D model_D1 = FCDiscriminator(num_classes=args.num_classes) model_D2 = FCDiscriminator(num_classes=args.num_classes) start_epoch = 0 if "http" not in args.restore_from and args.restore_from is not None: root, extension = args.restore_from.strip().split(".") D1pth = root + "_D1." + extension D2pth = root + "_D2." + extension saved_state_dict = torch.load(D1pth) model_D1.load_state_dict(saved_state_dict) saved_state_dict = torch.load(D2pth) model_D2.load_state_dict(saved_state_dict) start_epoch = int(re.findall(r'[\d]+', root)[-1]) print("Loaded state dict for models D1 and D2") model_D1.train() # model_D1.cuda(gpu) model_D2.train() # model_D2.cuda(gpu) model_D1 = model_D1.cuda(device=gpu) model_D2 = model_D2.cuda(device=gpu) if args.num_gpus > 0 or torch.cuda.device_count() > 0: model_D1 = DistributedDataParallel(model_D1, device_ids=[gpu], find_unused_parameters=True) model_D2 = DistributedDataParallel(model_D2, device_ids=[gpu], find_unused_parameters=True) if not os.path.exists(args.snapshot_dir): os.makedirs(args.snapshot_dir) train_dataset = SyntheticSmokeTrain(args={}, dataset_limit=args.num_steps * args.iter_size * args.batch_size, image_shape=input_size, dataset_mean=IMG_MEAN) train_sampler = DistributedSampler(train_dataset, num_replicas=args.world_size, rank=rank, shuffle=True) trainloader = data.DataLoader(train_dataset, batch_size=args.batch_size, num_workers=args.num_workers, pin_memory=True, sampler=train_sampler) # trainloader = data.DataLoader( # GTA5DataSet(args.data_dir, args.data_list, max_iters=args.num_steps * args.iter_size * args.batch_size, # crop_size=input_size, # scale=args.random_scale, mirror=args.random_mirror, mean=IMG_MEAN), # batch_size=args.batch_size, shuffle=True, num_workers=args.num_workers, pin_memory=True) trainloader_iter = enumerate(trainloader) print("Length of train dataloader: ", len(trainloader)) target_dataset = SimpleSmokeVal(args={}, image_size=input_size_target, dataset_mean=IMG_MEAN) target_sampler = DistributedSampler(target_dataset, num_replicas=args.world_size, rank=rank, shuffle=True) targetloader = data.DataLoader(target_dataset, batch_size=args.batch_size, num_workers=args.num_workers, pin_memory=True, sampler=target_sampler) # targetloader = data.DataLoader(cityscapesDataSet(args.data_dir_target, args.data_list_target, # max_iters=args.num_steps * args.iter_size * args.batch_size, # crop_size=input_size_target, # scale=False, mirror=args.random_mirror, mean=IMG_MEAN, # set=args.set), # batch_size=args.batch_size, shuffle=True, num_workers=args.num_workers, # pin_memory=True) targetloader_iter = enumerate(targetloader) print("Length of train dataloader: ", len(targetloader)) # implement model.optim_parameters(args) to handle different models' lr setting optimizer = optim.SGD(model.module.optim_parameters(args), lr=args.learning_rate, momentum=args.momentum, weight_decay=args.weight_decay) optimizer.zero_grad() optimizer_D1 = optim.Adam(model_D1.parameters(), lr=args.learning_rate_D, betas=(0.9, 0.99)) optimizer_D1.zero_grad() optimizer_D2 = optim.Adam(model_D2.parameters(), lr=args.learning_rate_D, betas=(0.9, 0.99)) optimizer_D2.zero_grad() if args.gan == 'Vanilla': bce_loss = torch.nn.BCEWithLogitsLoss() elif args.gan == 'LS': bce_loss = torch.nn.MSELoss() interp = nn.Upsample(size=(input_size[1], input_size[0]), mode='bilinear') interp_target = nn.Upsample(size=(input_size_target[1], input_size_target[0]), mode='bilinear') # labels for adversarial training source_label = 0 target_label = 1 for i_iter in range(start_epoch, args.num_steps): loss_seg_value1 = 0 loss_adv_target_value1 = 0 loss_D_value1 = 0 loss_seg_value2 = 0 loss_adv_target_value2 = 0 loss_D_value2 = 0 optimizer.zero_grad() adjust_learning_rate(optimizer, i_iter) optimizer_D1.zero_grad() optimizer_D2.zero_grad() adjust_learning_rate_D(optimizer_D1, i_iter) adjust_learning_rate_D(optimizer_D2, i_iter) for sub_i in range(args.iter_size): # train G # don't accumulate grads in D for param in model_D1.parameters(): param.requires_grad = False for param in model_D2.parameters(): param.requires_grad = False # train with source # try: _, batch = next(trainloader_iter) #.next() # except StopIteration: # trainloader = data.DataLoader( # SyntheticSmokeTrain(args={}, dataset_limit=args.num_steps * args.iter_size * args.batch_size, # image_shape=input_size, dataset_mean=IMG_MEAN), # batch_size=args.batch_size, shuffle=True, num_workers=args.num_workers, pin_memory=True) # trainloader_iter = iter(trainloader) # _, batch = next(trainloader_iter) images, labels, _, _ = batch images = Variable(images).cuda(gpu) # print("Shape of labels", labels.shape) # print("Are labels all zero? ") # for i in range(labels.shape[0]): # print("{}: All zero? {}".format(i, torch.all(labels[i]==0))) # print("{}: All 255? {}".format(i, torch.all(labels[i]==255))) # print("{}: Mean = {}".format(i, torch.mean(labels[i]))) pred1, pred2 = model(images) # print("Pred1 and Pred2 original size: {}, {}".format(pred1.shape, pred2.shape)) pred1 = interp(pred1) pred2 = interp(pred2) # print("Pred1 and Pred2 upsampled size: {}, {}".format(pred1.shape, pred2.shape)) # for pred, name in zip([pred1, pred2], ['pred1', 'pred2']): # print(name) # for i in range(pred.shape[0]): # print("{}: All zero? {}".format(i, torch.all(pred[i]==0))) # print("{}: All 255? {}".format(i, torch.all(pred[i]==255))) # print("{}: Mean = {}".format(i, torch.mean(pred[i]))) loss_seg1 = loss_calc(pred1, labels, gpu, criterion) loss_seg2 = loss_calc(pred2, labels, gpu, criterion) loss = loss_seg2 + args.lambda_seg * loss_seg1 # proper normalization loss = loss / args.iter_size loss.backward() # print("Seg1 loss: ",loss_seg1, args.iter_size) # print("Seg2 loss: ",loss_seg2, args.iter_size) loss_seg_value1 += loss_seg1.data.cpu().item() / args.iter_size loss_seg_value2 += loss_seg2.data.cpu().item() / args.iter_size # train with target # try: _, batch = next(targetloader_iter) #.next() # except StopIteration: # targetloader = data.DataLoader( # SimpleSmokeVal(args = {}, image_size=input_size_target, dataset_mean=IMG_MEAN), # batch_size=args.batch_size, shuffle=True, num_workers=args.num_workers, # pin_memory=True) # targetloader_iter = iter(targetloader) # _, batch = next(targetloader_iter) images, _, _ = batch images = Variable(images).cuda(gpu) pred_target1, pred_target2 = model(images) pred_target1 = interp_target(pred_target1) pred_target2 = interp_target(pred_target2) D_out1 = model_D1(F.softmax(pred_target1, dim=1)) D_out2 = model_D2(F.softmax(pred_target2, dim=1)) loss_adv_target1 = bce_loss( D_out1, Variable( torch.FloatTensor( D_out1.data.size()).fill_(source_label)).cuda(gpu)) loss_adv_target2 = bce_loss( D_out2, Variable( torch.FloatTensor( D_out2.data.size()).fill_(source_label)).cuda(gpu)) loss = args.lambda_adv_target1 * loss_adv_target1 + args.lambda_adv_target2 * loss_adv_target2 loss = loss / args.iter_size loss.backward() loss_adv_target_value1 += loss_adv_target1.data.cpu().item( ) / args.iter_size loss_adv_target_value2 += loss_adv_target2.data.cpu().item( ) / args.iter_size # train D # bring back requires_grad for param in model_D1.parameters(): param.requires_grad = True for param in model_D2.parameters(): param.requires_grad = True # train with source pred1 = pred1.detach() pred2 = pred2.detach() D_out1 = model_D1(F.softmax(pred1, dim=1)) D_out2 = model_D2(F.softmax(pred2, dim=1)) loss_D1 = bce_loss( D_out1, Variable( torch.FloatTensor( D_out1.data.size()).fill_(source_label)).cuda(gpu)) loss_D2 = bce_loss( D_out2, Variable( torch.FloatTensor( D_out2.data.size()).fill_(source_label)).cuda(gpu)) loss_D1 = loss_D1 / args.iter_size / 2 loss_D2 = loss_D2 / args.iter_size / 2 loss_D1.backward() loss_D2.backward() loss_D_value1 += loss_D1.data.cpu().item() loss_D_value2 += loss_D2.data.cpu().item() # train with target pred_target1 = pred_target1.detach() pred_target2 = pred_target2.detach() D_out1 = model_D1(F.softmax(pred_target1, dim=1)) D_out2 = model_D2(F.softmax(pred_target2, dim=1)) loss_D1 = bce_loss( D_out1, Variable( torch.FloatTensor( D_out1.data.size()).fill_(target_label)).cuda(gpu)) loss_D2 = bce_loss( D_out2, Variable( torch.FloatTensor( D_out2.data.size()).fill_(target_label)).cuda(gpu)) loss_D1 = loss_D1 / args.iter_size / 2 loss_D2 = loss_D2 / args.iter_size / 2 loss_D1.backward() loss_D2.backward() loss_D_value1 += loss_D1.data.cpu().item() loss_D_value2 += loss_D2.data.cpu().item() optimizer.step() optimizer_D1.step() optimizer_D2.step() print('exp = {}'.format(args.snapshot_dir)) print( 'iter = {0:8d}/{1:8d}, loss_seg1 = {2:.3f} loss_seg2 = {3:.3f} loss_adv1 = {4:.3f}, loss_adv2 = {5:.3f} loss_D1 = {6:.3f} loss_D2 = {7:.3f}' .format(i_iter, args.num_steps, loss_seg_value1, loss_seg_value2, loss_adv_target_value1, loss_adv_target_value2, loss_D_value1, loss_D_value2)) writer.add_scalar(f'loss/train/segmentation/1', loss_seg_value1, i_iter) writer.add_scalar(f'loss/train/segmentation/2', loss_seg_value2, i_iter) writer.add_scalar(f'loss/train/adversarial/1', loss_adv_target_value1, i_iter) writer.add_scalar(f'loss/train/adversarial/2', loss_adv_target_value2, i_iter) writer.add_scalar(f'loss/train/domain/1', loss_D_value1, i_iter) writer.add_scalar(f'loss/train/domain/2', loss_D_value2, i_iter) if i_iter >= args.num_steps_stop - 1: print('save model ...') torch.save( model.state_dict(), osp.join( args.snapshot_dir, 'smoke_cross_entropy_multigpu_' + str(args.num_steps_stop) + '.pth')) torch.save( model_D1.state_dict(), osp.join( args.snapshot_dir, 'smoke_cross_entropy_multigpu_' + str(args.num_steps_stop) + '_D1.pth')) torch.save( model_D2.state_dict(), osp.join( args.snapshot_dir, 'smoke_cross_entropy_multigpu_' + str(args.num_steps_stop) + '_D2.pth')) break if i_iter % args.save_pred_every == 0 and i_iter != 0: print('taking snapshot ...') torch.save( model.state_dict(), osp.join( args.snapshot_dir, 'smoke_cross_entropy_multigpu_' + str(i_iter) + '.pth')) torch.save( model_D1.state_dict(), osp.join( args.snapshot_dir, 'smoke_cross_entropy_multigpu_' + str(i_iter) + '_D1.pth')) torch.save( model_D2.state_dict(), osp.join( args.snapshot_dir, 'smoke_cross_entropy_multigpu_' + str(i_iter) + '_D2.pth')) writer.flush()
def main(): """Create the model and start the training.""" w, h = map(int, args.input_size.split(',')) input_size = (w, h) w, h = map(int, args.input_size_target.split(',')) input_size_target = (w, h) cudnn.enabled = True gpu = args.gpu criterion = DiceBCELoss() # criterion = nn.CrossEntropyLoss(ignore_index=253) # Create network if args.model == 'DeepLab': model = DeeplabMulti(num_classes=args.num_classes) if args.restore_from is None: pass elif args.restore_from[:4] == 'http': saved_state_dict = model_zoo.load_url(args.restore_from) elif args.restore_from is not None: saved_state_dict = torch.load(args.restore_from) if args.restore_from is not None: new_params = model.state_dict().copy() for i in saved_state_dict: # Scale.layer5.conv2d_list.3.weight i_parts = i.split('.') # print i_parts if not args.num_classes == 19 or not i_parts[1] == 'layer5': new_params['.'.join(i_parts[1:])] = saved_state_dict[i] # print i_parts model.load_state_dict(new_params) if not args.no_logging: if not os.path.isdir(args.log_dir): os.mkdir(args.log_dir) log_dir = os.path.join(args.log_dir, args.exp_dir) if not os.path.isdir(log_dir): os.mkdir(log_dir) if args.exp_name == "": exp_name = datetime.datetime.now().strftime("%H%M%S-%Y%m%d") else: exp_name = args.exp_name log_dir = os.path.join(log_dir, exp_name) writer = SummaryWriter(log_dir) model.train() model.cuda(args.gpu) cudnn.benchmark = True # init D model_D1 = FCDiscriminator(num_classes=args.num_classes) model_D2 = FCDiscriminator(num_classes=args.num_classes) model_D1.train() model_D1.cuda(args.gpu) model_D2.train() model_D2.cuda(args.gpu) if not os.path.exists(args.snapshot_dir): os.makedirs(args.snapshot_dir) trainloader = data.DataLoader(SyntheticSmokeTrain( args={}, dataset_limit=args.num_steps * args.iter_size * args.batch_size, image_shape=input_size, dataset_mean=IMG_MEAN), batch_size=args.batch_size, shuffle=True, num_workers=args.num_workers, pin_memory=True) trainloader_iter = enumerate(trainloader) print("Length of train dataloader: ", len(trainloader)) targetloader = data.DataLoader(SimpleSmokeVal(args={}, image_size=input_size_target, dataset_mean=IMG_MEAN), batch_size=args.batch_size, shuffle=True, num_workers=args.num_workers, pin_memory=True) targetloader_iter = enumerate(targetloader) print("Length of train dataloader: ", len(targetloader)) # implement model.optim_parameters(args) to handle different models' lr setting optimizer = optim.SGD(model.optim_parameters(args), lr=args.learning_rate, momentum=args.momentum, weight_decay=args.weight_decay) optimizer.zero_grad() optimizer_D1 = optim.Adam(model_D1.parameters(), lr=args.learning_rate_D, betas=(0.9, 0.99)) optimizer_D1.zero_grad() optimizer_D2 = optim.Adam(model_D2.parameters(), lr=args.learning_rate_D, betas=(0.9, 0.99)) optimizer_D2.zero_grad() if args.gan == 'Vanilla': bce_loss = torch.nn.BCEWithLogitsLoss() # bce_loss_all = torch.nn.BCEWithLogitsLoss(reduction='none') elif args.gan == 'LS': bce_loss = torch.nn.MSELoss() # bce_loss_all = torch.nn.MSELoss(reduction='none') interp = nn.Upsample(size=(input_size[1], input_size[0]), mode='bilinear', align_corners=True) interp_target = nn.Upsample(size=(input_size_target[1], input_size_target[0]), mode='bilinear', align_corners=True) # interp_domain = nn.Upsample(size=(input_size_target[1], input_size_target[0]), mode='bilinear', align_corners=True) # labels for adversarial training source_label = 0 target_label = 1 for i_iter in range(args.num_steps): loss_seg_value1 = 0 loss_adv_target_value1 = 0 loss_D_value1 = 0 loss_seg_value2 = 0 loss_adv_target_value2 = 0 loss_D_value2 = 0 optimizer.zero_grad() adjust_learning_rate(optimizer, i_iter) optimizer_D1.zero_grad() optimizer_D2.zero_grad() adjust_learning_rate_D(optimizer_D1, i_iter) adjust_learning_rate_D(optimizer_D2, i_iter) for sub_i in range(args.iter_size): # train G # don't accumulate grads in D for param in model_D1.parameters(): param.requires_grad = False for param in model_D2.parameters(): param.requires_grad = False for param in interp_domain.parameters(): param.requires_grad = False # train with source # try: _, batch = next(trainloader_iter) #.next() # except StopIteration: # trainloader = data.DataLoader( # SyntheticSmokeTrain(args={}, dataset_limit=args.num_steps * args.iter_size * args.batch_size, # image_shape=input_size, dataset_mean=IMG_MEAN), # batch_size=args.batch_size, shuffle=True, num_workers=args.num_workers, pin_memory=True) # trainloader_iter = iter(trainloader) # _, batch = next(trainloader_iter) images, labels, _, _ = batch images = Variable(images).cuda(args.gpu) # print("Shape of labels", labels.shape) # print("Are labels all zero? ") # for i in range(labels.shape[0]): # print("{}: All zero? {}".format(i, torch.all(labels[i]==0))) # print("{}: All 255? {}".format(i, torch.all(labels[i]==255))) # print("{}: Mean = {}".format(i, torch.mean(labels[i]))) pred1, pred2 = model(images) # print("Pred1 and Pred2 original size: {}, {}".format(pred1.shape, pred2.shape)) pred1 = interp(pred1) pred2 = interp(pred2) # print("Pred1 and Pred2 upsampled size: {}, {}".format(pred1.shape, pred2.shape)) # for pred, name in zip([pred1, pred2], ['pred1', 'pred2']): # print(name) # for i in range(pred.shape[0]): # print("{}: All zero? {}".format(i, torch.all(pred[i]==0))) # print("{}: All 255? {}".format(i, torch.all(pred[i]==255))) # print("{}: Mean = {}".format(i, torch.mean(pred[i]))) loss_seg1 = loss_calc(pred1, labels, args.gpu, criterion) loss_seg2 = loss_calc(pred2, labels, args.gpu, criterion) loss = loss_seg2 + args.lambda_seg * loss_seg1 # proper normalization loss = loss / args.iter_size loss.backward() # print("Seg1 loss: ",loss_seg1, args.iter_size) # print("Seg2 loss: ",loss_seg2, args.iter_size) loss_seg_value1 += loss_seg1.detach().data.cpu().item( ) / args.iter_size loss_seg_value2 += loss_seg2.detach().data.cpu().item( ) / args.iter_size # train with target # try: _, batch = next(targetloader_iter) #.next() # except StopIteration: # targetloader = data.DataLoader( # SimpleSmokeVal(args = {}, image_size=input_size_target, dataset_mean=IMG_MEAN), # batch_size=args.batch_size, shuffle=True, num_workers=args.num_workers, # pin_memory=True) # targetloader_iter = iter(targetloader) # _, batch = next(targetloader_iter) images, _, _ = batch images = Variable(images).cuda(args.gpu) pred_target1, pred_target2 = model(images) pred_target1 = interp_target(pred_target1) pred_target2 = interp_target(pred_target2) D_out1 = model_D1(F.softmax(pred_target1, dim=1)) D_out2 = model_D2(F.softmax(pred_target2, dim=1)) # w1 = torch.argmax(pred_target1.detach(), dim=1) # w2 = torch.argmax(pred_target2.detach(), dim=1) min_class1 = sorted([(k, v) for k, v in Counter(w1.ravel()).items()], key=lambda x: x[1])[0][0] min_class2 = sorted([(k, v) for k, v in Counter(w2.ravel()).items()], key=lambda x: x[1])[0][0] # m1 = torch.where(w1==min_class1) # m1c = torch.where(w1!=min_class1) # w1[m1] = 11 # w1[m1c] = 1 # m2 = torch.where(w2==min_class2) # m2c = torch.where(w2!=min_class2) # w2[m2] = 11 # w2[m2c] = 1 # D_out1 = interp_domain(D_out1) # D_out2 = interp_domain(D_out2) loss_adv_target1 = bce_loss( D_out1, Variable( torch.FloatTensor( D_out1.data.size()).fill_(source_label)).cuda( args.gpu)) loss_adv_target2 = bce_loss( D_out2, Variable( torch.FloatTensor( D_out2.data.size()).fill_(source_label)).cuda( args.gpu)) loss = args.lambda_adv_target1 * loss_adv_target1 + args.lambda_adv_target2 * loss_adv_target2 loss = loss / args.iter_size loss.backward() loss_adv_target_value1 += loss_adv_target1.detach().data.cpu( ).item() / args.iter_size loss_adv_target_value2 += loss_adv_target2.detach().data.cpu( ).item() / args.iter_size # train D # bring back requires_grad for param in model_D1.parameters(): param.requires_grad = True for param in model_D2.parameters(): param.requires_grad = True # train with source pred1 = pred1.detach() pred2 = pred2.detach() D_out1 = model_D1(F.softmax(pred1, dim=1)) D_out2 = model_D2(F.softmax(pred2, dim=1)) loss_D1 = bce_loss( D_out1, Variable( torch.FloatTensor( D_out1.data.size()).fill_(source_label)).cuda( args.gpu)) loss_D2 = bce_loss( D_out2, Variable( torch.FloatTensor( D_out2.data.size()).fill_(source_label)).cuda( args.gpu)) loss_D1 = loss_D1 / args.iter_size / 2 loss_D2 = loss_D2 / args.iter_size / 2 loss_D1.backward() loss_D2.backward() loss_D_value1 += loss_D1.detach().data.cpu().item() loss_D_value2 += loss_D2.detach().data.cpu().item() # train with target pred_target1 = pred_target1.detach() pred_target2 = pred_target2.detach() D_out1 = model_D1(F.softmax(pred_target1, dim=1)) D_out2 = model_D2(F.softmax(pred_target2, dim=1)) loss_D1 = bce_loss( D_out1, Variable( torch.FloatTensor( D_out1.data.size()).fill_(target_label)).cuda( args.gpu)) loss_D2 = bce_loss( D_out2, Variable( torch.FloatTensor( D_out2.data.size()).fill_(target_label)).cuda( args.gpu)) loss_D1 = loss_D1 / args.iter_size / 2 loss_D2 = loss_D2 / args.iter_size / 2 loss_D1.backward() loss_D2.backward() loss_D_value1 += loss_D1.detach().data.cpu().item() loss_D_value2 += loss_D2.detach().data.cpu().item() optimizer.step() optimizer_D1.step() optimizer_D2.step() print('exp = {}'.format(args.snapshot_dir)) print( 'iter = {0:8d}/{1:8d}, loss_seg1 = {2:.3f} loss_seg2 = {3:.3f} loss_adv1 = {4:.3f}, loss_adv2 = {5:.3f} loss_D1 = {6:.3f} loss_D2 = {7:.3f}' .format(i_iter, args.num_steps, loss_seg_value1, loss_seg_value2, loss_adv_target_value1, loss_adv_target_value2, loss_D_value1, loss_D_value2)) writer.add_scalar(f'loss/train/segmentation/1', loss_seg_value1, i_iter) writer.add_scalar(f'loss/train/segmentation/2', loss_seg_value2, i_iter) writer.add_scalar(f'loss/train/adversarial/1', loss_adv_target_value1, i_iter) writer.add_scalar(f'loss/train/adversarial/2', loss_adv_target_value2, i_iter) writer.add_scalar(f'loss/train/domain/1', loss_D_value1, i_iter) writer.add_scalar(f'loss/train/domain/2', loss_D_value2, i_iter) if i_iter >= args.num_steps_stop - 1: print('save model ...') torch.save( model.state_dict(), osp.join(args.snapshot_dir, 'lmda_adv_0.1_' + str(args.num_steps_stop) + '.pth')) torch.save( model_D1.state_dict(), osp.join( args.snapshot_dir, 'lmda_adv_0.1_' + str(args.num_steps_stop) + '_D1.pth')) torch.save( model_D2.state_dict(), osp.join( args.snapshot_dir, 'lmda_adv_0.1_' + str(args.num_steps_stop) + '_D2.pth')) break if i_iter % args.save_pred_every == 0 and i_iter != 0: print('taking snapshot ...') torch.save( model.state_dict(), osp.join(args.snapshot_dir, 'lmda_adv_0.1_' + str(i_iter) + '.pth')) torch.save( model_D1.state_dict(), osp.join(args.snapshot_dir, 'lmda_adv_0.1_' + str(i_iter) + '_D1.pth')) torch.save( model_D2.state_dict(), osp.join(args.snapshot_dir, 'lmda_adv_0.1_' + str(i_iter) + '_D2.pth')) writer.flush()
def main(): h, w = map(int, args.input_size.split(',')) input_size = (h, w) cudnn.enabled = True gpu = args.gpu # create network model = DeeplabMulti(num_classes=args.num_classes) #model = Res_Deeplab(num_classes=args.num_classes) # load pretrained parameters if args.restore_from[:4] == 'http': saved_state_dict = model_zoo.load_url(args.restore_from) else: saved_state_dict = torch.load(args.restore_from, map_location='cuda:0') # only copy the params that exist in current model (caffe-like) new_params = model.state_dict().copy() for name, param in new_params.items(): print(name) if name in saved_state_dict and param.size( ) == saved_state_dict[name].size(): new_params[name].copy_(saved_state_dict[name]) print('copy {}'.format(name)) model.load_state_dict(new_params) model.train() model.cuda(args.gpu) #summary(model,(3,7,7)) cudnn.benchmark = True # init D model_D = FCDiscriminator(num_classes=args.num_classes) if args.restore_from_D is not None: model_D.load_state_dict(torch.load(args.restore_from_D)) model_D.train() model_D.cuda(args.gpu) #summary(model_D, (21,321,321)) #quit() if not os.path.exists(args.snapshot_dir): os.makedirs(args.snapshot_dir) train_dataset = cityscapesDataSet(max_iters=args.num_steps * args.iter_size * args.batch_size, scale=args.random_scale) train_dataset_size = len(train_dataset) train_gt_dataset = cityscapesDataSet(max_iters=args.num_steps * args.iter_size * args.batch_size, scale=args.random_scale) if args.partial_data is None: trainloader = data.DataLoader(train_dataset, batch_size=args.batch_size, shuffle=True, num_workers=args.num_workers, pin_memory=True) trainloader_gt = data.DataLoader(train_gt_dataset, batch_size=args.batch_size, shuffle=True, num_workers=5, pin_memory=True) else: # sample partial data partial_size = int(args.partial_data * train_dataset_size) if args.partial_id is not None: train_ids = pickle.load(open(args.partial_id)) print('loading train ids from {}'.format(args.partial_id)) else: train_ids = list(range(train_dataset_size)) np.random.shuffle(train_ids) pickle.dump(train_ids, open(osp.join(args.snapshot_dir, 'train_id.pkl'), 'wb')) train_sampler = data.sampler.SubsetRandomSampler( train_ids[:partial_size]) train_remain_sampler = data.sampler.SubsetRandomSampler( train_ids[partial_size:]) train_gt_sampler = data.sampler.SubsetRandomSampler( train_ids[:partial_size]) trainloader = data.DataLoader(train_dataset, sampler=train_sampler, batch_size=args.batch_size, shuffle=False, num_workers=args.num_workers, pin_memory=True) trainloader_remain = data.DataLoader(train_dataset, sampler=train_remain_sampler, batch_size=args.batch_size, shuffle=False, num_workers=args.num_workers, pin_memory=True) trainloader_gt = data.DataLoader(train_gt_dataset, sampler=train_gt_sampler, batch_size=args.batch_size, shuffle=False, num_workers=args.num_workers, pin_memory=True) trainloader_remain_iter = enumerate(trainloader) trainloader_iter = enumerate(trainloader) trainloader_gt_iter = enumerate(trainloader_gt) # implement model.optim_parameters(args) to handle different models' lr setting # optimizer for segmentation network optimizer = optim.SGD(model.optim_parameters(args), lr=args.learning_rate, momentum=args.momentum, weight_decay=args.weight_decay) optimizer.zero_grad() # optimizer for discriminator network optimizer_D = optim.Adam(model_D.parameters(), lr=args.learning_rate_D, betas=(0.9, 0.99)) optimizer_D.zero_grad() # loss/ bilinear upsampling bce_loss = BCEWithLogitsLoss2d() interp = nn.Upsample(size=(input_size[1], input_size[0]), mode='bilinear') if version.parse(torch.__version__) >= version.parse('0.4.0'): interp = nn.Upsample(size=(input_size[1], input_size[0]), mode='bilinear', align_corners=True) else: interp = nn.Upsample(size=(input_size[1], input_size[0]), mode='bilinear') # labels for adversarial training pred_label = 0 gt_label = 1 for i_iter in range(args.num_steps): loss_seg_value = 0 loss_adv_pred_value = 0 loss_D_value = 0 loss_semi_value = 0 loss_semi_adv_value = 0 loss_laplacian = 0 optimizer.zero_grad() adjust_learning_rate(optimizer, i_iter) optimizer_D.zero_grad() adjust_learning_rate_D(optimizer_D, i_iter) for sub_i in range(args.iter_size): # train G # don't accumulate grads in D for param in model_D.parameters(): param.requires_grad = False # do semi first if (args.lambda_semi > 0 or args.lambda_semi_adv > 0 ) and i_iter >= args.semi_start_adv: try: _, batch = trainloader_remain_iter.__next__() except: trainloader_remain_iter = enumerate(trainloader) _, batch = trainloader_remain_iter.__next__() # only access to img images, _, _, _ = batch images = Variable(images).cuda(args.gpu) try: pred = interp(model(images)) except RuntimeError as exception: if "out of memory" in str(exception): print("WARNING: out of memory") if hasattr(torch.cuda, 'empty_cache'): torch.cuda.empty_cache() else: raise exception pred_remain = pred.detach() D_out = interp(model_D(F.softmax(pred))) D_out_sigmoid = F.sigmoid(D_out).data.cpu().numpy().squeeze( axis=1) ignore_mask_remain = np.zeros(D_out_sigmoid.shape).astype( np.bool) loss_semi_adv = args.lambda_semi_adv * bce_loss( D_out, make_D_label(gt_label, ignore_mask_remain)) loss_semi_adv = loss_semi_adv / args.iter_size #loss_semi_adv.backward() loss_semi_adv_value += loss_semi_adv.data.cpu().numpy( ) / args.lambda_semi_adv if args.lambda_semi <= 0 or i_iter < args.semi_start: loss_semi_adv.backward() loss_semi_value = 0 else: # produce ignore mask semi_ignore_mask = (D_out_sigmoid < args.mask_T) semi_gt = pred.data.cpu().numpy().argmax(axis=1) semi_gt[semi_ignore_mask] = 255 semi_ratio = 1.0 - float( semi_ignore_mask.sum()) / semi_ignore_mask.size print('semi ratio: {:.4f}'.format(semi_ratio)) if semi_ratio == 0.0: loss_semi_value += 0 else: semi_gt = torch.FloatTensor(semi_gt) loss_semi = args.lambda_semi * loss_calc( pred, semi_gt, args.gpu) loss_semi = loss_semi / args.iter_size loss_semi_value += loss_semi.data.cpu().numpy( ) / args.lambda_semi loss_semi += loss_semi_adv loss_semi.backward() else: loss_semi = None loss_semi_adv = None # train with source try: _, batch = trainloader_iter.__next__() except: trainloader_iter = enumerate(trainloader) _, batch = trainloader_iter.__next__() images, labels, _, _ = batch images = Variable(images).cuda(args.gpu) ignore_mask = (labels.numpy() == 255) try: pred = interp(model(images)) except RuntimeError as exception: if "out of memory" in str(exception): print("WARNING: out of memory") if hasattr(torch.cuda, 'empty_cache'): torch.cuda.empty_cache() else: raise exception for i in range(1): imagess = torch.zeros(1280, 720).cuda() for j in range(19): try: imagess += pred[i, j, :, :].reshape(1280, 720) except IndexError: pass try: label = labels[i, :, :].reshape(1280, 720).cuda() except IndexError: pass imagess = torch.from_numpy( cv2.Laplacian(imagess.cpu().detach().numpy(), -1)).cuda() labell = torch.from_numpy( cv2.Laplacian(label.cpu().detach().numpy(), -1)).cuda() imagess = imagess.reshape(1, 1, 1280, 720) labell = labell.reshape(1, 1, 1280, 720) l = bce_loss(imagess, labell) loss_laplacian = l loss_seg = loss_calc(pred, labels, args.gpu) D_out = interp(model_D(F.softmax(pred))) loss_adv_pred = bce_loss(D_out, make_D_label(gt_label, ignore_mask)) loss = loss_seg + args.lambda_adv_pred * loss_adv_pred - loss_laplacian # proper normalization loss = loss / args.iter_size loss.backward() loss_seg_value += loss_seg.data.cpu().numpy() / args.iter_size loss_adv_pred_value += loss_adv_pred.data.cpu().numpy( ) / args.iter_size # train D # bring back requires_grad for param in model_D.parameters(): param.requires_grad = True # train with pred pred = pred.detach() if args.D_remain: pred = torch.cat((pred, pred_remain), 0) ignore_mask = np.concatenate((ignore_mask, ignore_mask_remain), axis=0) D_out = interp(model_D(F.softmax(pred))) loss_D = bce_loss(D_out, make_D_label(pred_label, ignore_mask)) loss_D = loss_D / args.iter_size / 2 loss_D.backward() loss_D_value += loss_D.data.cpu().numpy() # train with gt # get gt labels try: _, batch = trainloader_gt_iter.__next__() except: trainloader_gt_iter = enumerate(trainloader_gt) _, batch = trainloader_gt_iter.__next__() _, labels_gt, _, _ = batch D_gt_v = Variable(one_hot(labels_gt)).cuda(args.gpu) ignore_mask_gt = (labels_gt.numpy() == 255) D_out = interp(model_D(D_gt_v)) loss_D = bce_loss(D_out, make_D_label(gt_label, ignore_mask_gt)) loss_D = loss_D / args.iter_size / 2 loss_D.backward() loss_D_value += loss_D.data.cpu().numpy() optimizer.step() optimizer_D.step() print('exp = {}'.format(args.snapshot_dir)) print( 'iter = {0:8d}/{1:8d}, loss_seg = {2:.3f}, loss_adv_p = {3:.3f}, loss_D = {4:.3f}, loss_semi = {5:.3f}, loss_semi_adv = {6:.3f}, loss_laplacian = {7:.3f}' .format(i_iter, args.num_steps, loss_seg_value, loss_adv_pred_value, loss_D_value, loss_semi_value, loss_semi_adv_value, loss_laplacian)) if i_iter >= args.num_steps - 1: print('save model ...') torch.save( model.state_dict(), osp.join(args.snapshot_dir, 'CITY_' + str(args.num_steps) + '.pth')) torch.save( model_D.state_dict(), osp.join(args.snapshot_dir, 'CITY_' + str(args.num_steps) + '_D.pth')) break if i_iter % args.save_pred_every == 0 and i_iter != 0: print('taking snapshot ...') torch.save( model.state_dict(), osp.join(args.snapshot_dir, 'CITY_' + str(i_iter) + '.pth')) torch.save( model_D.state_dict(), osp.join(args.snapshot_dir, 'CITY_' + str(i_iter) + '_D.pth')) #torch.cuda.empty_cache() end = timeit.default_timer() print(end - start, 'seconds')
def main(): # 将参数的input_size 映射到整数,并赋值,从字符串转换到整数二元组 h, w = map(int, args.input_size.split(',')) input_size = (h, w) cudnn.enabled = False gpu = args.gpu # create network model = Res_Deeplab(num_classes=args.num_classes) # load pretrained parameters if args.restore_from[:4] == 'http': saved_state_dict = model_zoo.load_url(args.restore_from) else: saved_state_dict = torch.load(args.restore_from) # only copy the params that exist in current model (caffe-like) # 确保模型中参数的格式与要加载的参数相同 # 返回一个字典,保存着module的所有状态(state);parameters和persistent buffers都会包含在字典中,字典的key就是parameter和buffer的 names。 new_params = model.state_dict().copy() for name, param in new_params.items(): # print (name) if name in saved_state_dict and param.size() == saved_state_dict[name].size(): new_params[name].copy_(saved_state_dict[name]) # print('copy {}'.format(name)) model.load_state_dict(new_params) # 设置为训练模式 model.train() cudnn.benchmark = True model.cuda(gpu) # init D model_D = FCDiscriminator(num_classes=args.num_classes) if args.restore_from_D is not None: model_D.load_state_dict(torch.load(args.restore_from_D)) model_D.train() model_D.cuda(gpu) if not os.path.exists(args.snapshot_dir): os.makedirs(args.snapshot_dir) train_dataset = VOCDataSet(args.data_dir, args.data_list, crop_size=input_size, scale=args.random_scale, mirror=args.random_mirror, mean=IMG_MEAN) train_dataset_size = len(train_dataset) train_gt_dataset = VOCGTDataSet(args.data_dir, args.data_list, crop_size=input_size, scale=args.random_scale, mirror=args.random_mirror, mean=IMG_MEAN) if args.partial_data is None: trainloader = data.DataLoader(train_dataset, batch_size=args.batch_size, shuffle=True, num_workers=5, pin_memory=True) trainloader_gt = data.DataLoader(train_gt_dataset, batch_size=args.batch_size, shuffle=True, num_workers=5, pin_memory=True) else: # sample partial data partial_size = int(args.partial_data * train_dataset_size) if args.partial_id is not None: train_ids = pickle.load(open(args.partial_id)) print('loading train ids from {}'.format(args.partial_id)) else: train_ids = list(range(train_dataset_size)) # ? np.random.shuffle(train_ids) pickle.dump(train_ids, open(osp.join(args.snapshot_dir, 'train_id.pkl'), 'wb')) # 写入文件 train_sampler = data.sampler.SubsetRandomSampler(train_ids[:partial_size]) train_remain_sampler = data.sampler.SubsetRandomSampler(train_ids[partial_size:]) train_gt_sampler = data.sampler.SubsetRandomSampler(train_ids[:partial_size]) trainloader = data.DataLoader(train_dataset, batch_size=args.batch_size, sampler=train_sampler, num_workers=3, pin_memory=True) trainloader_remain = data.DataLoader(train_dataset, batch_size=args.batch_size, sampler=train_remain_sampler, num_workers=3, pin_memory=True) trainloader_gt = data.DataLoader(train_gt_dataset, batch_size=args.batch_size, sampler=train_gt_sampler, num_workers=3, pin_memory=True) trainloader_remain_iter = enumerate(trainloader_remain) trainloader_iter = enumerate(trainloader) trainloader_gt_iter = enumerate(trainloader_gt) # implement model.optim_parameters(args) to handle different models' lr setting # optimizer for segmentation network optimizer = optim.SGD(model.optim_parameters(args), lr=args.learning_rate, momentum=args.momentum, weight_decay=args.weight_decay) optimizer.zero_grad() # optimizer for discriminator network optimizer_D = optim.Adam(model_D.parameters(), lr=args.learning_rate_D, betas=(0.9, 0.99)) optimizer_D.zero_grad() # loss/ bilinear upsampling bce_loss = BCEWithLogitsLoss2d() interp = nn.Upsample(size=(input_size[1], input_size[0]), mode='bilinear') # ??? # labels for adversarial training pred_label = 0 gt_label = 1 for i_iter in range(args.num_steps): print("Iter:", i_iter) loss_seg_value = 0 loss_adv_pred_value = 0 loss_D_value = 0 loss_semi_value = 0 optimizer.zero_grad() adjust_learning_rate(optimizer, i_iter) optimizer_D.zero_grad() adjust_learning_rate_D(optimizer_D, i_iter) for sub_i in range(args.iter_size): # train G # don't accumulate grads in D for param in model_D.parameters(): param.requires_grad = False # do semi first if args.lambda_semi > 0 and i_iter >= args.semi_start: try: _, batch = next(trainloader_remain_iter) except: trainloader_remain_iter = enumerate(trainloader_remain) _, batch = next(trainloader_remain_iter) # only access to img images, _, _, _ = batch images = Variable(images).cuda(gpu) # images = Variable(images).cpu() pred = interp(model(images)) D_out = interp(model_D(F.softmax(pred))) D_out_sigmoid = F.sigmoid(D_out).data.cpu().numpy().squeeze(axis=1) # produce ignore mask semi_ignore_mask = (D_out_sigmoid < args.mask_T) semi_gt = pred.data.cpu().numpy().argmax(axis=1) semi_gt[semi_ignore_mask] = 255 semi_ratio = 1.0 - float(semi_ignore_mask.sum()) / semi_ignore_mask.size print('semi ratio: {:.4f}'.format(semi_ratio)) if semi_ratio == 0.0: loss_semi_value += 0 else: semi_gt = torch.FloatTensor(semi_gt) loss_semi = args.lambda_semi * loss_calc(pred, semi_gt, args.gpu) loss_semi = loss_semi / args.iter_size loss_semi.backward() loss_semi_value += loss_semi.data.cpu().numpy()[0] / args.lambda_semi else: loss_semi = None # train with source try: _, batch = next(trainloader_iter) except: trainloader_iter = enumerate(trainloader) _, batch = next(trainloader_iter) images, labels, _, _ = batch images = Variable(images).cuda(gpu) # images = Variable(images).cpu() ignore_mask = (labels.numpy() == 255) pred = interp(model(images)) loss_seg = loss_calc(pred, labels, args.gpu) D_out = interp(model_D(F.softmax(pred))) loss_adv_pred = bce_loss(D_out, make_D_label(gt_label, ignore_mask)) loss = loss_seg + args.lambda_adv_pred * loss_adv_pred # proper normalization loss = loss / args.iter_size loss.backward() loss_seg_value += loss_seg.data.cpu().numpy()[0] / args.iter_size loss_adv_pred_value += loss_adv_pred.data.cpu().numpy()[0] / args.iter_size # train D # bring back requires_grad for param in model_D.parameters(): param.requires_grad = True # train with pred pred = pred.detach() D_out = interp(model_D(F.softmax(pred))) loss_D = bce_loss(D_out, make_D_label(pred_label, ignore_mask)) loss_D = loss_D / args.iter_size / 2 loss_D.backward() loss_D_value += loss_D.data.cpu().numpy()[0] # train with gt # get gt labels try: _, batch = next(trainloader_gt_iter) except: trainloader_gt_iter = enumerate(trainloader_gt) _, batch = next(trainloader_gt_iter) _, labels_gt, _, _ = batch D_gt_v = Variable(one_hot(labels_gt)).cuda(args.gpu) # D_gt_v = Variable(one_hot(labels_gt)).cpu() ignore_mask_gt = (labels_gt.numpy() == 255) D_out = interp(model_D(D_gt_v)) loss_D = bce_loss(D_out, make_D_label(gt_label, ignore_mask_gt)) loss_D = loss_D / args.iter_size / 2 loss_D.backward() loss_D_value += loss_D.data.cpu().numpy()[0] optimizer.step() optimizer_D.step() print('exp = {}'.format(args.snapshot_dir)) print( 'iter = {0:8d}/{1:8d}, loss_seg = {2:.3f}, loss_adv_p = {3:.3f}, loss_D = {4:.3f}, loss_semi = {5:.3f}'.format( i_iter, args.num_steps, loss_seg_value, loss_adv_pred_value, loss_D_value, loss_semi_value)) if i_iter >= args.num_steps - 1: print('save model ...') torch.save(model.state_dict(), osp.join(args.snapshot_dir, 'VOC_' + str(args.num_steps) + '.pth')) torch.save(model_D.state_dict(), osp.join(args.snapshot_dir, 'VOC_' + str(args.num_steps) + '_D.pth')) break if i_iter % args.save_pred_every == 0 and i_iter != 0: print('taking snapshot ...') torch.save(model.state_dict(), osp.join(args.snapshot_dir, 'VOC_' + str(i_iter) + '.pth')) torch.save(model_D.state_dict(), osp.join(args.snapshot_dir, 'VOC_' + str(i_iter) + '_D.pth')) end = timeit.default_timer() print(end - start, 'seconds')
def main(): """Create the model and start the training.""" w, h = map(int, args.input_size_source.split(',')) input_size_source = (w, h) w, h = map(int, args.input_size_target.split(',')) input_size_target = (w, h) cudnn.enabled = True # Create network if args.model == 'ResNet': model = Res_Deeplab(num_classes=args.num_classes) if args.restore_from[:4] == 'http': saved_state_dict = model_zoo.load_url(args.restore_from) else: saved_state_dict = torch.load(args.restore_from) new_params = model.state_dict().copy() for i in saved_state_dict: # Scale.layer5.conv2d_list.3.weight i_parts = i.split('.') # print i_parts if not args.num_classes == 19 or not i_parts[1] == 'layer5': new_params['.'.join(i_parts[1:])] = saved_state_dict[i] # print i_parts model.load_state_dict(new_params) # model.load_state_dict(saved_state_dict) elif args.model == 'VGG': model = DeeplabVGG(num_classes=args.num_classes, pretrained=True, vgg16_caffe_path=args.restore_from) # saved_state_dict = torch.load(args.restore_from) # model.load_state_dict(saved_state_dict) optimizer = optim.SGD(model.optim_parameters(args), lr=args.learning_rate, momentum=args.momentum, weight_decay=args.weight_decay) optimizer.zero_grad() model.train() model.cuda(args.gpu) cudnn.benchmark = True #Discrimintator setting model_D = FCDiscriminator(num_classes=args.num_classes) model_D.train() model_D.cuda(args.gpu) optimizer_D = optim.Adam(model_D.parameters(), lr=args.learning_rate_D, betas=(0.9, 0.99)) optimizer_D.zero_grad() bce_loss = torch.nn.BCEWithLogitsLoss() # labels for adversarial training source_adv_label = 0 target_adv_label = 1 #Dataloader if not os.path.exists(args.snapshot_dir): os.makedirs(args.snapshot_dir) trainloader = data.DataLoader(GTA5DataSet(args.translated_data_dir, args.data_list, max_iters=args.num_steps * args.iter_size * args.batch_size, crop_size=input_size_source, scale=args.random_scale, mirror=args.random_mirror, mean=IMG_MEAN), batch_size=args.batch_size, shuffle=True, num_workers=args.num_workers, pin_memory=True) trainloader_iter = enumerate(trainloader) style_trainloader = data.DataLoader(GTA5DataSet( args.stylized_data_dir, args.data_list, max_iters=args.num_steps * args.iter_size * args.batch_size, crop_size=input_size_source, scale=args.random_scale, mirror=args.random_mirror, mean=IMG_MEAN), batch_size=args.batch_size, shuffle=True, num_workers=args.num_workers, pin_memory=True) style_trainloader_iter = enumerate(style_trainloader) if STAGE == 1: targetloader = data.DataLoader(cityscapesDataSet( args.data_dir_target, args.data_list_target, max_iters=args.num_steps * args.iter_size * args.batch_size, crop_size=input_size_target, mean=IMG_MEAN, set=args.set), batch_size=args.batch_size, shuffle=True, num_workers=args.num_workers, pin_memory=True) targetloader_iter = enumerate(targetloader) else: #Dataloader for self-training targetloader = data.DataLoader(cityscapesDataSetLabel( args.data_dir_target, args.data_list_target, max_iters=args.num_steps * args.iter_size * args.batch_size, crop_size=input_size_target, mean=IMG_MEAN, set=args.set, label_folder='Path to generated pseudo labels'), batch_size=args.batch_size, shuffle=True, num_workers=args.num_workers, pin_memory=True) targetloader_iter = enumerate(targetloader) interp = nn.Upsample(size=(input_size_source[1], input_size_source[0]), mode='bilinear', align_corners=True) interp_target = nn.Upsample(size=(input_size_target[1], input_size_target[0]), mode='bilinear', align_corners=True) # load checkpoint model, model_D, optimizer, start_iter = load_checkpoint( model, model_D, optimizer, filename=args.snapshot_dir + 'checkpoint_' + CHECKPOINT + '.pth.tar') for i_iter in range(start_iter, args.num_steps): optimizer.zero_grad() adjust_learning_rate(optimizer, i_iter) optimizer_D.zero_grad() adjust_learning_rate_D(optimizer_D, i_iter) #train segementation network # don't accumulate grads in D for param in model_D.parameters(): param.requires_grad = False # train with source if STAGE == 1: if i_iter % 2 == 0: _, batch = next(trainloader_iter) else: _, batch = next(style_trainloader_iter) else: _, batch = next(trainloader_iter) image_source, label, _, _ = batch image_source = Variable(image_source).cuda(args.gpu) pred_source = model(image_source) pred_source = interp(pred_source) loss_seg_source = loss_calc(pred_source, label, args.gpu) loss_seg_source_value = loss_seg_source.item() loss_seg_source.backward() if STAGE == 2: # train with target _, batch = next(targetloader_iter) image_target, target_label, _, _ = batch image_target = Variable(image_target).cuda(args.gpu) pred_target = model(image_target) pred_target = interp_target(pred_target) #target segmentation loss loss_seg_target = loss_calc(pred_target, target_label, gpu=args.gpu) loss_seg_target.backward() # optimize optimizer.step() if STAGE == 1: # train with target _, batch = next(targetloader_iter) image_target, _, _ = batch image_target = Variable(image_target).cuda(args.gpu) pred_target = model(image_target) pred_target = interp_target(pred_target) #output-level adversarial training D_output_target = model_D(F.softmax(pred_target)) loss_adv = bce_loss( D_output_target, Variable( torch.FloatTensor(D_output_target.data.size()).fill_( source_adv_label)).cuda(args.gpu)) loss_adv = loss_adv * args.lambda_adv loss_adv.backward() #train discriminator for param in model_D.parameters(): param.requires_grad = True pred_source = pred_source.detach() pred_target = pred_target.detach() D_output_source = model_D(F.softmax(pred_source)) D_output_target = model_D(F.softmax(pred_target)) loss_D_source = bce_loss( D_output_source, Variable( torch.FloatTensor(D_output_source.data.size()).fill_( source_adv_label)).cuda(args.gpu)) loss_D_target = bce_loss( D_output_target, Variable( torch.FloatTensor(D_output_target.data.size()).fill_( target_adv_label)).cuda(args.gpu)) loss_D_source = loss_D_source / 2 loss_D_target = loss_D_target / 2 loss_D_source.backward() loss_D_target.backward() #optimize optimizer_D.step() print('exp = {}'.format(args.snapshot_dir)) print('iter = {0:8d}/{1:8d}, loss_seg_source = {2:.5f}'.format( i_iter, args.num_steps, loss_seg_source_value)) if i_iter % args.save_pred_every == 0: print('taking snapshot ...') state = { 'iter': i_iter, 'model': model.state_dict(), 'model_D': model_D.state_dict(), 'optimizer': optimizer.state_dict() } torch.save( state, osp.join(args.snapshot_dir, 'checkpoint_' + str(i_iter) + '.pth.tar')) torch.save( model.state_dict(), osp.join(args.snapshot_dir, 'GTA5_' + str(i_iter) + '.pth')) torch.save( model_D.state_dict(), osp.join(args.snapshot_dir, 'GTA5_D_' + str(i_iter) + '.pth')) cityscapes_eval_dir = osp.join(args.cityscapes_eval_dir, str(i_iter)) if not os.path.exists(cityscapes_eval_dir): os.makedirs(cityscapes_eval_dir) eval(osp.join(args.snapshot_dir, 'GTA5_' + str(i_iter) + '.pth'), cityscapes_eval_dir, i_iter) iou19, iou13, iou = compute_mIoU(cityscapes_eval_dir, i_iter) outputfile = open(args.output_file, 'a') outputfile.write( str(i_iter) + '\t' + str(iou19) + '\t' + str(iou.replace('\n', ' ')) + '\n') outputfile.close()
def main(): """Create the model and start the training.""" w, h = map(int, args.input_size.split(',')) input_size = (w, h) w, h = map(int, args.input_size_target.split(',')) input_size_target = (w, h) h, w = map(int, args.com_size.split(',')) com_size = (h, w) cudnn.enabled = True gpu = args.gpu torch.cuda.set_device(args.gpu) ############################ #validation data testloader = data.DataLoader(cityscapesDataSet(args.data_dir_target, args.data_list_target_val, crop_size=input_size_target, mean=IMG_MEAN, scale=False, mirror=False, set=args.set_val), batch_size=1, shuffle=False, pin_memory=True) with open('./dataset/cityscapes_list/info.json', 'r') as fp: info = json.load(fp) mapping = np.array(info['label2train'], dtype=np.int) label_path_list = './dataset/cityscapes_list/label.txt' gt_imgs = open(label_path_list, 'r').read().splitlines() gt_imgs = [ osp.join('./data/Cityscapes/data/gtFine/val', x) for x in gt_imgs ] interp_val = nn.UpsamplingBilinear2d(size=(com_size[1], com_size[0])) ############################ # Create network # if args.model == 'DeepLab': # model = Res_Deeplab(num_classes=args.num_classes) # if args.restore_from[:4] == 'http' : # saved_state_dict = model_zoo.load_url(args.restore_from) # else: # saved_state_dict = torch.load(args.restore_from, map_location=lambda storage, loc: storage.cuda(args.gpu)) # # new_params = model.state_dict().copy() # for i in saved_state_dict: # # Scale.layer5.conv2d_list.3.weight # i_parts = i.split('.') # # print i_parts # if not args.num_classes == 19 or not i_parts[1] == 'layer5': # new_params['.'.join(i_parts[1:])] = saved_state_dict[i] # # print i_parts # model.load_state_dict(new_params) if args.model == 'DeepLab': model = Res_Deeplab(num_classes=args.num_classes) saved_state_dict = torch.load( args.restore_from, map_location=lambda storage, loc: storage.cuda(args.gpu)) model.load_state_dict(saved_state_dict) model.train() model.cuda(args.gpu) cudnn.benchmark = True # init D model_D1 = FCDiscriminator(num_classes=args.num_classes) model_D2 = FCDiscriminator(num_classes=args.num_classes) model_D1.train() model_D1.cuda(args.gpu) model_D2.train() model_D2.cuda(args.gpu) if not os.path.exists(args.snapshot_dir): os.makedirs(args.snapshot_dir) trainloader = data.DataLoader(GTA5DataSet(args.data_dir, args.data_list, max_iters=args.num_steps * args.iter_size * args.batch_size, crop_size=input_size, scale=args.random_scale, mirror=args.random_mirror, mean=IMG_MEAN), batch_size=args.batch_size, shuffle=True, num_workers=args.num_workers, pin_memory=True) trainloader_iter = enumerate(trainloader) targetloader = data.DataLoader(cityscapesDataSet( args.data_dir_target, args.data_list_target, max_iters=args.num_steps * args.iter_size * args.batch_size, crop_size=input_size_target, scale=False, mirror=args.random_mirror, mean=IMG_MEAN, set=args.set), batch_size=args.batch_size, shuffle=True, num_workers=args.num_workers, pin_memory=True) targetloader_iter = enumerate(targetloader) # implement model.optim_parameters(args) to handle different models' lr setting optimizer = optim.SGD(model.optim_parameters(args), lr=args.learning_rate, momentum=args.momentum, weight_decay=args.weight_decay) optimizer.zero_grad() optimizer_D1 = optim.Adam(model_D1.parameters(), lr=args.learning_rate_D, betas=(0.9, 0.99)) optimizer_D1.zero_grad() optimizer_D2 = optim.Adam(model_D2.parameters(), lr=args.learning_rate_D, betas=(0.9, 0.99)) optimizer_D2.zero_grad() bce_loss = torch.nn.BCEWithLogitsLoss() interp = nn.Upsample(size=(input_size[1], input_size[0]), mode='bilinear') interp_target = nn.Upsample(size=(input_size_target[1], input_size_target[0]), mode='bilinear') # labels for adversarial training source_label = 0 target_label = 1 AvePool = torch.nn.AvgPool2d(kernel_size=(512, 1024)) for i_iter in range(args.num_steps): model.train() loss_lse_target_two_value = 0 loss_lse_target_value = 0 loss_seg_value1 = 0 loss_adv_target_value1 = 0 loss_D_value1 = 0 loss_seg_value2 = 0 loss_adv_target_value2 = 0 loss_D_value2 = 0 optimizer.zero_grad() adjust_learning_rate(optimizer, i_iter) optimizer_D1.zero_grad() optimizer_D2.zero_grad() adjust_learning_rate_D(optimizer_D1, i_iter) adjust_learning_rate_D(optimizer_D2, i_iter) for sub_i in range(args.iter_size): # train G # don't accumulate grads in D for param in model_D1.parameters(): param.requires_grad = False for param in model_D2.parameters(): param.requires_grad = False # train with source _, batch = next(trainloader_iter) images, labels, class_label_source, mask_weakly, _, name = batch images = Variable(images).cuda(args.gpu) pred1, pred2 = model(images) pred1 = interp(pred1) pred2 = interp(pred2) loss_seg1 = loss_calc(pred1, labels, args.gpu) loss_seg2 = loss_calc(pred2, labels, args.gpu) loss = loss_seg2 + args.lambda_seg * loss_seg1 # proper normalization loss = loss / args.iter_size loss.backward() loss_seg_value1 += loss_seg1.data.item() / args.iter_size loss_seg_value2 += loss_seg2.data.item() / args.iter_size # train with target _, batch = next(targetloader_iter) images, class_label, _, _ = batch images = Variable(images).cuda(args.gpu) pred_target1, pred_target2 = model(images) pred_target1 = interp_target(pred_target1) pred_target2 = interp_target(pred_target2) class_label_target_lse = class_label.type(torch.FloatTensor) exp_target = torch.min( torch.exp(1 * pred_target2), Variable(torch.exp(torch.tensor(40.0))).cuda(args.gpu)) lse = (1.0 / 1) * torch.log(AvePool(exp_target)) loss_lse_target = bce_loss( lse, Variable(class_label_target_lse.reshape(lse.size())).cuda( args.gpu)) # exp_target = torch.min(torch.exp(1*pred_target1), Variable(torch.exp(torch.tensor(40.0))).cuda(args.gpu)) # lse = (1.0/1) * torch.log(AvePool(exp_target)) # loss_lse_target_two = bce_loss(lse, Variable(class_label_target_lse.reshape(lse.size())).cuda(args.gpu)) D_out1 = model_D1(F.softmax(pred_target1)) D_out2 = model_D2(F.softmax(pred_target2)) loss_adv_target1 = bce_loss( D_out1, Variable( torch.FloatTensor( D_out1.data.size()).fill_(source_label)).cuda( args.gpu)) loss_adv_target2 = bce_loss( D_out2, Variable( torch.FloatTensor( D_out2.data.size()).fill_(source_label)).cuda( args.gpu)) loss = args.lambda_adv_target1 * loss_adv_target1 + args.lambda_adv_target2 * loss_adv_target2 + 0.2 * loss_lse_target # + 0.02 * loss_lse_target_two loss = loss / args.iter_size loss.backward() loss_adv_target_value1 += loss_adv_target1.data.item( ) / args.iter_size loss_adv_target_value2 += loss_adv_target2.data.item( ) / args.iter_size loss_lse_target_value += loss_lse_target.data.item( ) / args.iter_size # loss_lse_target_two_value += loss_lse_target_two.data.item() / args.iter_size # train D # bring back requires_grad for param in model_D1.parameters(): param.requires_grad = True for param in model_D2.parameters(): param.requires_grad = True # train with source pred1 = pred1.detach() pred2 = pred2.detach() D_out1 = model_D1(F.softmax(pred1)) D_out2 = model_D2(F.softmax(pred2)) loss_D1 = bce_loss( D_out1, Variable( torch.FloatTensor( D_out1.data.size()).fill_(source_label)).cuda( args.gpu)) loss_D2 = bce_loss( D_out2, Variable( torch.FloatTensor( D_out2.data.size()).fill_(source_label)).cuda( args.gpu)) loss_D1 = loss_D1 / args.iter_size / 2 loss_D2 = loss_D2 / args.iter_size / 2 loss_D1.backward() loss_D2.backward() loss_D_value1 += loss_D1.data.item() loss_D_value2 += loss_D2.data.item() # train with target pred_target1 = pred_target1.detach() pred_target2 = pred_target2.detach() D_out1 = model_D1(F.softmax(pred_target1)) D_out2 = model_D2(F.softmax(pred_target2)) loss_D1 = bce_loss( D_out1, Variable( torch.FloatTensor( D_out1.data.size()).fill_(target_label)).cuda( args.gpu)) loss_D2 = bce_loss( D_out2, Variable( torch.FloatTensor( D_out2.data.size()).fill_(target_label)).cuda( args.gpu)) loss_D1 = loss_D1 / args.iter_size / 2 loss_D2 = loss_D2 / args.iter_size / 2 loss_D1.backward() loss_D2.backward() loss_D_value1 += loss_D1.data.item() loss_D_value2 += loss_D2.data.item() optimizer.step() optimizer_D1.step() optimizer_D2.step() del D_out1, D_out2, pred1, pred2, pred_target1, pred_target2, images, labels print('exp = {}'.format(args.snapshot_dir)) print( 'iter = {0:8d}/{1:8d}, loss_seg1 = {2:.3f} loss_seg2 = {3:.3f} loss_adv1 = {4:.3f}, loss_adv2 = {5:.3f} loss_D1 = {6:.3f} loss_D2 = {7:.3f} loss_lse_target = {8:.3f} loss_lse_target2 = {9:.3f}' .format(i_iter, args.num_steps, loss_seg_value1, loss_seg_value2, loss_adv_target_value1, loss_adv_target_value2, loss_D_value1, loss_D_value2, loss_lse_target_value, loss_lse_target_two_value)) if i_iter >= args.num_steps_stop - 1: print('save model ...') torch.save( model.state_dict(), osp.join(args.snapshot_dir, 'GTA5_' + str(args.num_steps_stop) + '.pth')) torch.save( model_D1.state_dict(), osp.join(args.snapshot_dir, 'GTA5_' + str(args.num_steps_stop) + '_D1.pth')) torch.save( model_D2.state_dict(), osp.join(args.snapshot_dir, 'GTA5_' + str(args.num_steps_stop) + '_D2.pth')) break if i_iter % args.save_pred_every == 0 and i_iter != 0: print('taking snapshot ...') torch.save(model.state_dict(), osp.join(args.snapshot_dir, 'GTA5' + '.pth')) torch.save(model_D2.state_dict(), osp.join(args.snapshot_dir, 'GTA5_' + '_D2.pth')) torch.save(model_D1.state_dict(), osp.join(args.snapshot_dir, 'GTA5_' + '_D1.pth')) hist = np.zeros((19, 19)) # model.cuda(0) model.eval() f = open(args.results_dir, 'a') for index, batch in enumerate(testloader): print(index) image, _, _, name = batch output1, output2 = model( Variable(image, volatile=True).cuda(args.gpu)) pred = interp_val(output2) pred = pred[0].permute(1, 2, 0) pred = torch.max(pred, 2)[1].byte() pred_cpu = pred.data.cpu().numpy() del pred, output1, output2 label = Image.open(gt_imgs[index]) label = np.array(label.resize(com_size, Image.NEAREST)) label = label_mapping(label, mapping) hist += fast_hist(label.flatten(), pred_cpu.flatten(), 19) # model.cuda(args.gpu) mIoUs = per_class_iu(hist) mIoU = round(np.nanmean(mIoUs) * 100, 2) print(mIoU) f.write('i_iter:{:d}, miou:{:0.5f} \n'.format(i_iter, mIoU)) f.close()
def main(): """Create the model and start the evaluation process.""" args = get_arguments() if not os.path.exists(args.save): os.makedirs(args.save) gpu0 = args.gpu model = Res_Deeplab(num_classes=args.num_classes) if args.restore_from[:4] == 'http': saved_state_dict = model_zoo.load_url(args.restore_from) else: saved_state_dict = torch.load(args.restore_from) model.load_state_dict(saved_state_dict) model.eval() model.cuda(gpu0) #===========load discriminator model====begin=== model_d2 = FCDiscriminator(num_classes=args.num_classes) d2_state_dict = torch.load(args.dis_restore_from) model_d2.load_state_dict(d2_state_dict) model_d2.eval() model_d2.cuda(gpu0) #===========load discriminator model====end=== testloader = data.DataLoader(cityscapesDataSet(args.data_dir, args.data_list, crop_size=(1024, 512), mean=IMG_MEAN, scale=False, mirror=False, set=args.set), batch_size=1, shuffle=False, pin_memory=True) interp = nn.Upsample(size=(1024, 2048), mode='bilinear', align_corners=True) # interp_target = nn.Upsample(size=(input_size_target[1], input_size_target[0]), mode='bilinear') out_values = [] fine_out_values = [] retrain_list = [] file = open(CITYS_RETRAIN_TXT, 'w') for index, batch in enumerate(testloader): if index % 20 == 0: print('%d processd of %d' % (index, len(testloader))) image, _, name = batch output1, output2 = model(Variable(image, volatile=True).cuda(gpu0)) ini_output = interp(output2) d2_out1 = model_d2(F.softmax(ini_output, dim=1)) #.cpu().data[0].numpy() out_valu = d2_out1.mean() out_valu_img = np.array([[name[0]], out_valu.cpu().data.numpy()]) out_values.extend(out_valu_img) if out_valu.cpu().data.numpy() > 0.64: fine_out_valu_img = np.array([[name[0]], out_valu.cpu().data.numpy()]) fine_out_values.extend(fine_out_valu_img) file.write(name[0] + '\n') output = interp(output2).cpu().data[0].numpy() output = output.transpose(1, 2, 0) output = np.asarray(np.argmax(output, axis=2), dtype=np.uint8) output_col = colorize_mask(output) name = name[0].split('/')[-1] # output_col.save('%s/%s_color.png' % (args.save, name.split('.')[0])) output_col.save('%s/%s.png' % (args.save, name.split('.')[0])) # print('its confidence value is %f' % out_valu) # plt.imshow(output_col) # plt.title(str(out_valu)) # plt.show() # output = Image.fromarray(output) # output.save('%s/%s' % (args.save, name)) out_values = np.array(out_values) np.save(CITYS_VALUES_SV_PATH, out_values) np.save(CITYS_FINE_VALUES_SV_PATH, fine_out_values) file.close()
def main(): h, w = map(int, args.input_size.split(',')) input_size = (h, w) cudnn.enabled = True gpu = args.gpu # create network model = Res_Deeplab(num_classes=args.num_classes) # load pretrained parameters if args.restore_from[:4] == 'http': saved_state_dict = model_zoo.load_url(args.restore_from) else: saved_state_dict = torch.load(args.restore_from) # only copy the params that exist in current model (caffe-like) new_params = model.state_dict().copy() for name, param in new_params.items(): print(name) if name in saved_state_dict and param.size( ) == saved_state_dict[name].size(): new_params[name].copy_(saved_state_dict[name]) print('copy {}'.format(name)) model.load_state_dict(new_params) model.train() model.cuda(args.gpu) cudnn.benchmark = True # init D model_D = FCDiscriminator(num_classes=args.num_classes) if args.restore_from_D is not None: model_D.load_state_dict(torch.load(args.restore_from_D)) model_D.train() model_D.cuda(args.gpu) if not os.path.exists(args.snapshot_dir): os.makedirs(args.snapshot_dir) train_dataset = VOCDataSet(args.data_dir, args.data_list, crop_size=input_size, scale=args.random_scale, mirror=args.random_mirror, mean=IMG_MEAN) train_dataset_size = len(train_dataset) train_gt_dataset = VOCGTDataSet(args.data_dir, args.data_list, crop_size=input_size, scale=args.random_scale, mirror=args.random_mirror, mean=IMG_MEAN) if args.partial_data is None: trainloader = data.DataLoader(train_dataset, batch_size=args.batch_size, shuffle=True, num_workers=16, pin_memory=True) trainloader_gt = data.DataLoader(train_gt_dataset, batch_size=args.batch_size, shuffle=True, num_workers=16, pin_memory=True) else: #sample partial data partial_size = int(args.partial_data * train_dataset_size) if args.partial_id is not None: train_ids = pickle.load(open(args.partial_id)) print('loading train ids from {}'.format(args.partial_id)) else: train_ids = np.arange(train_dataset_size) np.random.shuffle(train_ids) pickle.dump(train_ids, open(osp.join(args.snapshot_dir, 'train_id.pkl'), 'wb')) train_sampler_all = data.sampler.SubsetRandomSampler(train_ids) train_gt_sampler_all = data.sampler.SubsetRandomSampler(train_ids) train_sampler = data.sampler.SubsetRandomSampler( train_ids[:partial_size]) train_remain_sampler = data.sampler.SubsetRandomSampler( train_ids[partial_size:]) train_gt_sampler = data.sampler.SubsetRandomSampler( train_ids[:partial_size]) trainloader_all = data.DataLoader(train_dataset, batch_size=args.batch_size, sampler=train_sampler_all, num_workers=16, pin_memory=True) trainloader_gt_all = data.DataLoader(train_gt_dataset, batch_size=args.batch_size, sampler=train_gt_sampler_all, num_workers=16, pin_memory=True) trainloader = data.DataLoader(train_dataset, batch_size=args.batch_size, sampler=train_sampler, num_workers=16, pin_memory=True) trainloader_remain = data.DataLoader(train_dataset, batch_size=args.batch_size, sampler=train_remain_sampler, num_workers=16, pin_memory=True) trainloader_gt = data.DataLoader(train_gt_dataset, batch_size=args.batch_size, sampler=train_gt_sampler, num_workers=16, pin_memory=True) trainloader_remain_iter = iter(trainloader_remain) trainloader_all_iter = iter(trainloader_all) trainloader_iter = iter(trainloader) trainloader_gt_iter = iter(trainloader_gt) # implement model.optim_parameters(args) to handle different models' lr setting # optimizer for segmentation network optimizer = optim.SGD(model.optim_parameters(args), lr=args.learning_rate, momentum=args.momentum, weight_decay=args.weight_decay) optimizer.zero_grad() # optimizer for discriminator network optimizer_D = optim.Adam(model_D.parameters(), lr=args.learning_rate_D, betas=(0.9, 0.99)) optimizer_D.zero_grad() # loss/ bilinear upsampling bce_loss = BCEWithLogitsLoss2d() interp = nn.Upsample(size=(input_size[1], input_size[0]), mode='bilinear') # labels for adversarial training pred_label = 0 gt_label = 1 #y_real_, y_fake_ = Variable(torch.ones(args.batch_size, 1).cuda()), Variable(torch.zeros(args.batch_size, 1).cuda()) for i_iter in range(args.num_steps): loss_seg_value = 0 loss_adv_pred_value = 0 loss_D_value = 0 loss_fm_value = 0 loss_value = 0 optimizer.zero_grad() adjust_learning_rate(optimizer, i_iter) optimizer_D.zero_grad() adjust_learning_rate_D(optimizer_D, i_iter) for sub_i in range(args.iter_size): # train G # don't accumulate grads in D for param in model_D.parameters(): param.requires_grad = False # train with source try: batch = next(trainloader_iter) except: trainloader_iter = iter(trainloader) batch = next(trainloader_iter) images, labels, _, _ = batch images = Variable(images).cuda(args.gpu) #ignore_mask = (labels.numpy() == 255) pred = interp(model(images)) loss_seg = loss_calc(pred, labels, args.gpu) loss_seg.backward() loss_seg_value += loss_seg.data.cpu().numpy()[0] / args.iter_size if i_iter >= args.adv_start: #fm loss calc try: batch = next(trainloader_all_iter) except: trainloader_iter = iter(trainloader_all) batch = next(trainloader_all_iter) images, labels, _, _ = batch images = Variable(images).cuda(args.gpu) #ignore_mask = (labels.numpy() == 255) pred = interp(model(images)) _, D_out_y_pred = model_D(F.softmax(pred)) trainloader_gt_iter = iter(trainloader_gt) batch = next(trainloader_gt_iter) _, labels_gt, _, _ = batch D_gt_v = Variable(one_hot(labels_gt)).cuda(args.gpu) #ignore_mask_gt = (labels_gt.numpy() == 255) _, D_out_y_gt = model_D(D_gt_v) fm_loss = torch.mean( torch.abs( torch.mean(D_out_y_gt, 0) - torch.mean(D_out_y_pred, 0))) loss = loss_seg + args.lambda_fm * fm_loss # proper normalization fm_loss.backward() #loss_seg_value += loss_seg.data.cpu().numpy()[0]/args.iter_size loss_fm_value += fm_loss.data.cpu().numpy()[0] / args.iter_size loss_value += loss.data.cpu().numpy()[0] / args.iter_size # train D # bring back requires_grad for param in model_D.parameters(): param.requires_grad = True # train with pred pred = pred.detach() D_out_z, _ = model_D(F.softmax(pred)) y_fake_ = Variable(torch.zeros(D_out_z.size(0), 1).cuda()) loss_D_fake = criterion(D_out_z, y_fake_) # train with gt # get gt labels _, labels_gt, _, _ = batch D_gt_v = Variable(one_hot(labels_gt)).cuda(args.gpu) #ignore_mask_gt = (labels_gt.numpy() == 255) D_out_z_gt, _ = model_D(D_gt_v) #D_out = interp(D_out_x) y_real_ = Variable(torch.ones(D_out_z_gt.size(0), 1).cuda()) loss_D_real = criterion(D_out_z_gt, y_real_) loss_D = loss_D_fake + loss_D_real loss_D.backward() loss_D_value += loss_D.data.cpu().numpy()[0] optimizer.step() optimizer_D.step() print('exp = {}'.format(args.snapshot_dir)) print('iter = {0:8d}/{1:8d}, loss_seg = {2:.3f}, loss_D = {3:.3f}'. format(i_iter, args.num_steps, loss_seg_value, loss_D_value)) print('fm_loss: ', loss_fm_value, ' g_loss: ', loss_value) if i_iter >= args.num_steps - 1: print('save model ...') torch.save( model.state_dict(), osp.join(args.snapshot_dir, 'VOC_' + str(args.num_steps) + '.pth')) torch.save( model_D.state_dict(), osp.join(args.snapshot_dir, 'VOC_' + str(args.num_steps) + '_D.pth')) break if i_iter % args.save_pred_every == 0 and i_iter != 0: print('taking snapshot ...') torch.save( model.state_dict(), osp.join(args.snapshot_dir, 'VOC_' + str(i_iter) + '.pth')) torch.save( model_D.state_dict(), osp.join(args.snapshot_dir, 'VOC_' + str(i_iter) + '_D.pth')) end = timeit.default_timer() print(end - start, 'seconds')
def main(): h, w = map(int, args.input_size.split(',')) input_size = (h, w) cudnn.enabled = True device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') # initialize parameters num_steps = args.num_steps batch_size = args.batch_size lr = args.lr save_cp = args.save_cp img_scale = args.scale val_percent = args.val / 100 # data input dataset = BasicDataset(IMG_DIRECTORY, MASK_DIRECTORY, img_scale) n_val = int(len(dataset) * val_percent) n_train = len(dataset) - n_val train, val = random_split(dataset, [n_train, n_val]) tcga_dataset = UnlabeledDataset(TCGA_DIRECTORY) n_unlabeled = len(tcga_dataset) # create network logger = logging.getLogger() logger.setLevel(logging.INFO) #logger.addHandler(logging.StreamHandler()) logging.info('Using device %s' % str(device)) logging.info('Network %s' % args.mod) logging.info('''Starting training: Num_steps: %.2f Batch size: %.2f Learning rate: %.4f_transform Training size: %.0f Validation size: %.0f Unlabeled size: %.0f Checkpoints: %s Device: %s Scale: %.2f ''' % (num_steps, batch_size, lr, n_train, n_val, n_unlabeled, str(save_cp), str(device.type), img_scale)) if args.mod == 'unet': net = UNet(n_channels=3, n_classes=NUM_CLASSES) print('channels = %d , classes = %d' % (net.n_channels, net.n_classes)) elif args.mod == 'modified_unet': net = modified_UNet(n_channels=3, n_classes=NUM_CLASSES) print('channels = %d , classes = %d' % (net.n_channels, net.n_classes)) elif args.mod == 'deeplabv3': net = DeepLabV3(nclass=NUM_CLASSES, pretrained_base=False) print('channels = 3 , classes = %d' % net.nclass) elif args.mod == 'deeplabv3plus': net = DeepLabV3Plus(nclass=NUM_CLASSES, pretrained_base=False) print('channels = 3 , classes = %d' % net.nclass) elif args.mod == 'nestedunet': net = NestedUNet(nclass=NUM_CLASSES, deep_supervision=False) print('channels = 3 , classes = %d' % net.nlass) elif args.mod == 'inception3': net = Inception3(n_classes=4, inception_blocks=None, init_weights=True, bilinear=True) print('channels = 3 , classes = %d' % net.n_classes) net.to(device=device) net.train() cudnn.benchmark = True # init D model_D = FCDiscriminator(num_classes=args.num_classes) if args.restore_from_D is not None: model_D.load_state_dict(torch.load(args.restore_from_D)) model_D.train() model_D.cuda() if not os.path.exists(args.snapshot_dir): os.makedirs(args.snapshot_dir) if args.semi_train is None: train_loader = DataLoader(train, batch_size=batch_size, shuffle=True, num_workers=8, pin_memory=True) val_loader = DataLoader(val, batch_size=batch_size, shuffle=False, num_workers=8, pin_memory=True) else: #read unlabeled data and labeled data train_loader = DataLoader(train, batch_size=batch_size, shuffle=True, num_workers=4, pin_memory=True) val_loader = DataLoader(val, batch_size=batch_size, shuffle=False, num_workers=4, pin_memory=True) trainloader_remain = DataLoader(tcga_dataset, batch_size=batch_size, shuffle=True, num_workers=4, pin_memory=True) #trainloader_gt = data.DataLoader(train_gt_dataset, #batch_size=args.batch_size, sampler=train_gt_sampler, num_workers=3, pin_memory=True) trainloader_remain_iter = enumerate(trainloader_remain) trainloader_iter = enumerate(train_loader) # implement model.optim_parameters(args) to handle different models' lr setting # optimizer for segmentation network #optimizer = optim.SGD(net.optim_parameters(args), #lr=args.learning_rate, momentum=args.momentum,weight_decay=args.weight_decay) optimizer = optim.Adam(net.parameters(), lr=args.lr, weight_decay=args.weight_decay) optimizer.zero_grad() scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, 10000, eta_min=1e-6, last_epoch=-1) # optimizer for discriminator network optimizer_D = optim.Adam(model_D.parameters(), lr=args.learning_rate_D, betas=(0.9, 0.99)) #optimizer_D = optim.SGD(model_D.parameters(), lr=args.learning_rate_D, momentum=args.momentum,weight_decay=args.weight_decay) optimizer_D.zero_grad() # loss/ bilinear upsampling bce_loss = BCEWithLogitsLoss2d() interp = nn.Upsample(size=(input_size[1], input_size[0]), mode='bilinear', align_corners=True) ''' if version.parse(torch.__version__) >= version.parse('0.4.0'): interp = nn.Upsample(size=(input_size[1], input_size[0]), mode='bilinear', align_corners=True) else: interp = nn.Upsample(size=(input_size[1], input_size[0]), mode='bilinear') ''' # labels for adversarial training pred_label = 0 gt_label = 1 for i_iter in range(args.num_steps): best_acc = 0 loss_seg_value = 0 loss_adv_pred_value = 0 loss_D_value = 0 loss_semi_value = 0 loss_semi_adv_value = 0 optimizer.zero_grad() #adjust_learning_rate(optimizer, i_iter) optimizer_D.zero_grad() adjust_learning_rate_D(optimizer_D, i_iter) for sub_i in range(args.iter_size): # train G # don't accumulate grads in D for param in model_D.parameters(): param.requires_grad = False for param in net.parameters(): param.requires_grad = True # do semi first if (args.lambda_semi > 0 or args.lambda_semi_adv > 0 ) and i_iter >= args.semi_start_adv: try: _, batch = trainloader_remain_iter.__next__() except: trainloader_remain_iter = enumerate(trainloader_remain) _, batch = trainloader_remain_iter.__next__() # only access to img images = batch['image'] images = images.type(torch.FloatTensor) images = Variable(images).cuda() pred = net(images) pred_remain = pred.detach() D_out = interp(model_D(F.softmax(pred, dim=1))) D_out_sigmoid = torch.sigmoid( D_out).data.cpu().numpy().squeeze(axis=1) #D_out_sigmoid = torch.sigmoid(D_out).data.cpu().numpy() #ignore_mask_remain = np.zeros(D_out_sigmoid.shape).astype(np.bool) targetr = Variable(torch.ones(D_out.shape)) targetr = Variable(torch.FloatTensor(targetr)).cuda() loss_semi_adv = args.lambda_semi_adv * bce_loss(D_out, targetr) loss_semi_adv = loss_semi_adv / args.iter_size #loss_semi_adv.backward() #loss_semi_adv_value += loss_semi_adv.data.cpu().numpy()[0]/args.lambda_semi_adv loss_semi_adv_value += loss_semi_adv.cpu().detach().numpy( ).item() / args.lambda_semi_adv if args.lambda_semi <= 0 or i_iter < args.semi_start: loss_semi_adv.backward() loss_semi_value = 0 else: # produce ignore mask semi_ignore_mask = (D_out_sigmoid < args.mask_T) semi_gt = pred.data.cpu().numpy().argmax(axis=1) semi_gt[semi_ignore_mask] = 255 semi_ratio = 1.0 - float( semi_ignore_mask.sum()) / semi_ignore_mask.size print('semi ratio: {:.4f}'.format(semi_ratio)) if semi_ratio == 0.0: loss_semi_value += 0 else: semi_gt = torch.FloatTensor(semi_gt) loss_semi = args.lambda_semi * loss_calc(pred, semi_gt) loss_semi = loss_semi / args.iter_size loss_semi_value += loss_semi.cpu().detach().numpy( ).item() / args.lambda_semi loss_semi += loss_semi_adv loss_semi.backward() else: loss_semi = None loss_semi_adv = None # train with source try: _, batch = trainloader_iter.__next__() except: trainloader_iter = enumerate(train_loader) _, batch = trainloader_iter.__next__() images = batch['image'] labels = batch['mask'] images = images.to(device=device, dtype=torch.float32) labels = labels.to(device=device, dtype=torch.long) labels = labels.squeeze(1) ignore_mask = (labels.cpu().numpy() == 255) #pred = interp(net(images)) pred = net(images) criterion = nn.CrossEntropyLoss() loss_seg = criterion(pred, labels) #loss_seg = loss_calc(pred, labels) D_out = interp(model_D(F.softmax(pred, dim=1))) targetr = Variable(torch.ones(D_out.shape)) targetr = Variable(torch.FloatTensor(targetr)).cuda() #loss_adv_pred = bce_loss(D_out, targetr) if i_iter > args.semi_start_adv: loss_adv_pred = bce_loss(D_out, targetr) loss = loss_seg + args.lambda_adv_pred * loss_adv_pred loss_adv_pred_value += loss_adv_pred.cpu().detach().numpy( ).item() / args.iter_size else: loss = loss_seg # proper normalization loss = loss / args.iter_size loss.backward() optimizer.step() loss_seg_value += loss_seg.cpu().detach().numpy().item( ) / args.iter_size #loss_adv_pred_value += loss_adv_pred.cpu().detach().numpy().item()/args.iter_size # train D # bring back requires_grad if i_iter > args.semi_start_adv and i_iter % 3 == 0: for param in net.parameters(): param.requires_grad = False for param in model_D.parameters(): param.requires_grad = True # train with pred pred = pred.detach() if args.D_remain: pred = torch.cat((pred, pred_remain), 0) #ignore_mask = np.concatenate((ignore_mask,ignore_mask_remain), axis = 0) D_out = interp(model_D(F.softmax(pred, dim=1))) #targetf = Variable(torch.zeros(D_out.shape)) targetf = 0.1 * np.random.rand(D_out.shape[0], D_out.shape[1], D_out.shape[2], D_out.shape[3]) targetf = Variable(torch.FloatTensor(targetf)).cuda() loss_D = bce_loss(D_out, targetf) loss_D = loss_D / args.iter_size / 2 loss_D.backward() loss_D_value += loss_D.data.cpu().detach().numpy().item() # train with gt # get gt labels try: _, batch = trainloader_iter.__next__() except: trainloader_iter = enumerate(train_loader) _, batch = trainloader_iter.__next__() labels_gt = batch['mask'] D_gt_v = Variable(one_hot(labels_gt)).cuda() ignore_mask_gt = (labels_gt.numpy() == 255).squeeze(axis=1) D_out = interp(model_D(D_gt_v)) #targetr = Variable(torch.ones(D_out.shape)) targetr = 0.1 * np.random.rand(D_out.shape[0], D_out.shape[1], D_out.shape[2], D_out.shape[3]) + 0.9 targetr = Variable(torch.FloatTensor(targetr)).cuda() loss_D = bce_loss(D_out, targetr) loss_D = loss_D / args.iter_size / 2 loss_D.backward() optimizer_D.step() loss_D_value += loss_D.cpu().detach().numpy().item() scheduler.step() print( 'iter = {0:8d}/{1:8d}, loss_seg = {2:.3f}, loss_adv_p = {3:.3f}, loss_D = {4:.3f}, loss_semi = {5:.3f}, loss_semi_adv = {6:.3f}' .format(i_iter, args.num_steps, loss_seg_value, loss_adv_pred_value, loss_D_value, loss_semi_value, loss_semi_adv_value)) ''' if i_iter >= args.num_steps-1: print 'save model ...' torch.save(model.state_dict(),osp.join(args.snapshot_dir, 'VOC_'+str(args.num_steps)+'.pth')) torch.save(model_D.state_dict(),osp.join(args.snapshot_dir, 'VOC_'+str(args.num_steps)+'_D.pth')) break if i_iter % args.save_pred_every == 0 and i_iter!=0: print 'taking snapshot ...' torch.save(model.state_dict(),osp.join(args.snapshot_dir, 'VOC_'+str(i_iter)+'.pth')) torch.save(model_D.state_dict(),osp.join(args.snapshot_dir, 'VOC_'+str(i_iter)+'_D.pth')) ''' # save checkpoints if save_cp and (i_iter % 1000) == 0 and (i_iter != 0): try: os.mkdir(DIR_CHECKPOINTS) logging.info('Created checkpoint directory') except OSError: pass torch.save(net.state_dict(), DIR_CHECKPOINTS + 'i_iter_%d.pth' % (i_iter + 1)) logging.info('Checkpoint %d saved !' % (i_iter + 1)) if (i_iter % 1000 == 0) and (i_iter != 0): val_score, accuracy, dice_avr, dice_panck, dice_nuclei, dice_lcell = eval_net( net, val_loader, device, n_val) logging.info('Validation cross entropy: {}'.format(val_score)) if accuracy > best_acc: best_acc = accuracy result_file = open('result.txt', 'a', encoding='utf-8') result_file.write('best_acc = ' + str(best_acc) + '\n' + 'iter = ' + str(i_iter) + '\n') result_file.close
def main(): """Create the model and start the training.""" h, w = map(int, args.input_size.split(',')) input_size = (h, w) h, w = map(int, args.input_size_target.split(',')) input_size_target = (h, w) cudnn.enabled = True gpu = args.gpu # Create network if args.model == 'DeepLab': model = Res_Deeplab(num_classes=args.num_classes) if args.restore_from[:4] == 'http' : saved_state_dict = model_zoo.load_url(args.restore_from) else: saved_state_dict = torch.load(args.restore_from) new_params = model.state_dict().copy() for i in saved_state_dict: # Scale.layer5.conv2d_list.3.weight i_parts = i.split('.') # print i_parts if not args.num_classes == 19 or not i_parts[1] == 'layer5': new_params['.'.join(i_parts[1:])] = saved_state_dict[i] # print i_parts #model.load_state_dict(new_params) if CONTINUE_FLAG==1: model.load_state_dict(saved_state_dict) model.train() model.cuda(args.gpu) cudnn.benchmark = True # init D model_D_a = FCDiscriminator(num_classes=256) # need to check model_D1 = FCDiscriminator(num_classes=args.num_classes) model_D2 = FCDiscriminator(num_classes=args.num_classes) if CONTINUE_FLAG==1: d1_saved_state_dict = torch.load(D1_RESTORE_FROM) d2_saved_state_dict = torch.load(D2_RESTORE_FROM) model_D1.load_state_dict(d1_saved_state_dict) model_D2.load_state_dict(d2_saved_state_dict) model_D_a.train() model_D_a.cuda(args.gpu) model_D1.train() model_D1.cuda(args.gpu) model_D2.train() model_D2.cuda(args.gpu) if not os.path.exists(args.snapshot_dir): os.makedirs(args.snapshot_dir) trainloader = data.DataLoader( synthiaDataSet(args.data_dir, args.data_list, max_iters=args.num_steps * args.iter_size * args.batch_size, crop_size=input_size, scale=args.random_scale, mirror=args.random_mirror, mean=IMG_MEAN), batch_size=args.batch_size, shuffle=False, num_workers=args.num_workers, pin_memory=True) trainloader_iter = enumerate(trainloader) targetloader = data.DataLoader(cityscapesDataSet(args.data_dir_target, args.data_list_target, max_iters=args.num_steps * args.iter_size * args.batch_size, crop_size=input_size_target, scale=False, mirror=args.random_mirror, mean=IMG_MEAN, set=args.set), batch_size=args.batch_size, shuffle=True, num_workers=args.num_workers, pin_memory=True) targetloader_iter = enumerate(targetloader) # implement model.optim_parameters(args) to handle different models' lr setting optimizer = optim.SGD(model.optim_parameters(args), lr=args.learning_rate, momentum=args.momentum, weight_decay=args.weight_decay) optimizer.zero_grad() optimizer_D_a = optim.Adam(model_D_a.parameters(), lr=args.learning_rate_D, betas=(0.9, 0.99)) optimizer_D_a.zero_grad() optimizer_D1 = optim.Adam(model_D1.parameters(), lr=args.learning_rate_D, betas=(0.9, 0.99)) optimizer_D1.zero_grad() optimizer_D2 = optim.Adam(model_D2.parameters(), lr=args.learning_rate_D, betas=(0.9, 0.99)) optimizer_D2.zero_grad() bce_loss = torch.nn.BCEWithLogitsLoss() # interp = nn.Upsample(size=(input_size[1], input_size[0]), mode='bilinear') # interp_target = nn.Upsample(size=(input_size_target[1], input_size_target[0]), mode='bilinear') # labels for adversarial training source_label = 0 target_label = 1 mIoUs = [] for i_iter in range(continue_start_iter+1,args.num_steps): loss_seg_value1 = 0 loss_adv_target_value1 = 0 loss_D_value1 = 0 loss_seg_value2 = 0 loss_adv_target_value2 = 0 loss_D_value2 = 0 optimizer.zero_grad() adjust_learning_rate(optimizer, i_iter) optimizer_D_a.zero_grad() optimizer_D1.zero_grad() optimizer_D2.zero_grad() adjust_learning_rate_D(optimizer_D_a, i_iter) adjust_learning_rate_D(optimizer_D1, i_iter) adjust_learning_rate_D(optimizer_D2, i_iter) for sub_i in range(args.iter_size): # train G # don't accumulate grads in D for param in model_D_a.parameters(): param.requires_grad = False for param in model_D1.parameters(): param.requires_grad = False for param in model_D2.parameters(): param.requires_grad = False # train with source _, batch = trainloader_iter.__next__() images, labels, _, _ = batch images = Variable(images).cuda(args.gpu) pred_a, pred1, pred2 = model(images) pred1=nn.functional.interpolate(pred1,size=(input_size[1], input_size[0]), mode='bilinear',align_corners=True) pred2 = nn.functional.interpolate(pred2, size=(input_size[1], input_size[0]), mode='bilinear', align_corners=True) # pred1 = interp(pred1) # pred2 = interp(pred2) loss_seg1 = loss_calc(pred1, labels, args.gpu) loss_seg2 = loss_calc(pred2, labels, args.gpu) loss = loss_seg2 + args.lambda_seg * loss_seg1 # proper normalization loss = loss / args.iter_size loss.backward() loss_seg_value1 += loss_seg1.data.cpu().numpy() / args.iter_size loss_seg_value2 += loss_seg2.data.cpu().numpy() / args.iter_size # train with target _, batch = targetloader_iter.__next__() images, _, _ = batch images = Variable(images).cuda(args.gpu) lambda_wtight = (80000 - i_iter) / 80000 if lambda_wtight > 0: pred_target_a, _, _ = model(images) D_out_a = model_D_a(pred_target_a) loss_adv_target_a = bce_loss(D_out_a, Variable(torch.FloatTensor(D_out_a.data.size()).fill_(source_label)).cuda( args.gpu)) loss_adv_target_a=LAMBDA_ADV_TARGET_A *loss_adv_target_a loss_adv_target_a = loss_adv_target_a / args.iter_size loss_adv_target_a.backward() _, pred_target1, pred_target2 = model(images) pred_target1 = nn.functional.interpolate(pred_target1,size=(input_size_target[1], input_size_target[0]), mode='bilinear',align_corners=True) pred_target2 = nn.functional.interpolate(pred_target2, size=(input_size_target[1], input_size_target[0]), mode='bilinear', align_corners=True) D_out1 = model_D1(F.softmax(pred_target1,dim=1)) D_out2 = model_D2(F.softmax(pred_target2,dim=1)) loss_adv_target1 = bce_loss(D_out1, Variable(torch.FloatTensor(D_out1.data.size()).fill_(source_label)).cuda( args.gpu)) loss_adv_target2 = bce_loss(D_out2, Variable(torch.FloatTensor(D_out2.data.size()).fill_(source_label)).cuda( args.gpu)) loss = args.lambda_adv_target1 * loss_adv_target1 + args.lambda_adv_target2 * loss_adv_target2 loss = loss / args.iter_size loss.backward() loss_adv_target_value1 += loss_adv_target1.data.cpu().numpy() / args.iter_size loss_adv_target_value2 += loss_adv_target2.data.cpu().numpy() / args.iter_size # train D # bring back requires_grad for param in model_D_a.parameters(): param.requires_grad = True for param in model_D1.parameters(): param.requires_grad = True for param in model_D2.parameters(): param.requires_grad = True # train with source if lambda_wtight > 0: pred_a=pred_a.detach() D_out_a = model_D_a(pred_a) loss_D_a = bce_loss(D_out_a, Variable(torch.FloatTensor(D_out_a.data.size()).fill_(source_label)).cuda(args.gpu)) loss_D_a = loss_D_a / args.iter_size / 2 loss_D_a.backward() pred1 = pred1.detach() pred2 = pred2.detach() D_out1 = model_D1(F.softmax(pred1,dim=1)) D_out2 = model_D2(F.softmax(pred2,dim=1)) loss_D1 = bce_loss(D_out1, Variable(torch.FloatTensor(D_out1.data.size()).fill_(source_label)).cuda(args.gpu)) loss_D2 = bce_loss(D_out2, Variable(torch.FloatTensor(D_out2.data.size()).fill_(source_label)).cuda(args.gpu)) loss_D1 = loss_D1 / args.iter_size / 2 loss_D2 = loss_D2 / args.iter_size / 2 loss_D1.backward() loss_D2.backward() loss_D_value1 += loss_D1.data.cpu().numpy() loss_D_value2 += loss_D2.data.cpu().numpy() # train with target if lambda_wtight > 0: pred_target_a=pred_target_a.detach() D_out_a = model_D_a(pred_target_a) loss_D_a = bce_loss(D_out_a, Variable(torch.FloatTensor(D_out_a.data.size()).fill_(target_label)).cuda(args.gpu)) loss_D_a = loss_D_a / args.iter_size / 2 loss_D_a.backward() pred_target1 = pred_target1.detach() pred_target2 = pred_target2.detach() D_out1 = model_D1(F.softmax(pred_target1,dim=1)) D_out2 = model_D2(F.softmax(pred_target2,dim=1)) loss_D1 = bce_loss(D_out1, Variable(torch.FloatTensor(D_out1.data.size()).fill_(target_label)).cuda(args.gpu)) loss_D2 = bce_loss(D_out2, Variable(torch.FloatTensor(D_out2.data.size()).fill_(target_label)).cuda(args.gpu)) loss_D1 = loss_D1 / args.iter_size / 2 loss_D2 = loss_D2 / args.iter_size / 2 loss_D1.backward() loss_D2.backward() loss_D_value1 += loss_D1.data.cpu().numpy() loss_D_value2 += loss_D2.data.cpu().numpy() optimizer.step() optimizer_D_a.step() optimizer_D1.step() optimizer_D2.step() print('exp = {}'.format(args.snapshot_dir)) print( 'iter = {0:8d}/{1:8d}, loss_seg1 = {2:.3f} loss_seg2 = {3:.3f} loss_adv1 = {4:.3f}, loss_adv2 = {5:.3f} loss_D1 = {6:.3f} loss_D2 = {7:.3f}'.format( i_iter, args.num_steps, loss_seg_value1, loss_seg_value2, loss_adv_target_value1, loss_adv_target_value2, loss_D_value1, loss_D_value2)) if i_iter >= args.num_steps_stop - 1: print('save model ...') torch.save(model.state_dict(), osp.join(args.snapshot_dir, 'GTA5_' + str(args.num_steps) + '.pth')) torch.save(model_D1.state_dict(), osp.join(args.snapshot_dir, 'GTA5_' + str(args.num_steps) + '_D1.pth')) torch.save(model_D2.state_dict(), osp.join(args.snapshot_dir, 'GTA5_' + str(args.num_steps) + '_D2.pth')) show_val(model.state_dict(), LAMBDA_ADV_TARGET_A ,i_iter) break if i_iter % args.save_pred_every == 0 and i_iter != 0: print('taking snapshot ...') torch.save(model.state_dict(), osp.join(args.snapshot_dir, 'GTA5_' + str(i_iter) + '.pth')) torch.save(model_D1.state_dict(), osp.join(args.snapshot_dir, 'GTA5_' + str(i_iter) + '_D1.pth')) torch.save(model_D2.state_dict(), osp.join(args.snapshot_dir, 'GTA5_' + str(i_iter) + '_D2.pth')) mIoU=show_val(model.state_dict(), LAMBDA_ADV_TARGET_A ,i_iter) mIoUs.append(str(round(np.nanmean(mIoU) * 100, 2))) for miou in mIoUs: print(miou)
def main(): h, w = map(int, args.input_size.split(',')) input_size = (h, w) cudnn.enabled = True gpu = args.gpu # create network model = Res_Deeplab(num_classes=args.num_classes) # load pretrained parameters (weights) if args.restore_from[:4] == 'http': saved_state_dict = model_zoo.load_url( args.restore_from ) ## http://vllab1.ucmerced.edu/~whung/adv-semi-seg/resnet101COCO-41f33a49.pth else: saved_state_dict = torch.load(args.restore_from) #checkpoint = torch.load(args.restore_from)_ # only copy the params that exist in current model (caffe-like) new_params = model.state_dict().copy() # state_dict() is current model for name, param in new_params.items(): #print (name) # 'conv1.weight, name:param(value), dict if name in saved_state_dict and param.size( ) == saved_state_dict[name].size(): new_params[name].copy_(saved_state_dict[name]) #print('copy {}'.format(name)) model.load_state_dict(new_params) #model.load_state_dict(checkpoint['state_dict']) #optimizer.load_state_dict(args.checkpoint['optim_dict']) model.train( ) # https://pytorch.org/docs/stable/nn.html, Sets the module in training mode. model.cuda(args.gpu) ## cudnn.benchmark = True # This flag allows you to enable the inbuilt cudnn auto-tuner to find the best algorithm to use for your hardware # init D model_D = FCDiscriminator(num_classes=args.num_classes) #args.restore_from_D = 'snapshots/linear2/VOC_25000_D.pth' if args.restore_from_D is not None: # None model_D.load_state_dict(torch.load(args.restore_from_D)) # checkpoint_D = torch.load(args.restore_from_D) # model_D.load_state_dict(checkpoint_D['state_dict']) # optimizer_D.load_state_dict(checkpoint_D['optim_dict']) model_D.train() model_D.cuda(args.gpu) if USECALI: model_cali = ModelWithTemperature(model, model_D) model_cali.cuda(args.gpu) if not os.path.exists(args.snapshot_dir): os.makedirs(args.snapshot_dir) random.seed(args.random_seed) np.random.seed(args.random_seed) torch.manual_seed(args.random_seed) torch.cuda.manual_seed(args.random_seed) train_dataset = VOCDataSet(args.data_dir, args.data_list, crop_size=input_size, scale=args.random_scale, mirror=args.random_mirror, mean=IMG_MEAN) train_dataset_remain = VOCDataSet(args.data_dir, args.data_list_remain, crop_size=input_size, scale=args.random_scale, mirror=args.random_mirror, mean=IMG_MEAN) train_dataset_size = len(train_dataset) train_dataset_size_remain = len(train_dataset_remain) print train_dataset_size print train_dataset_size_remain train_gt_dataset = VOCGTDataSet(args.data_dir, args.data_list, crop_size=input_size, scale=args.random_scale, mirror=args.random_mirror, mean=IMG_MEAN) if args.partial_data is None: #if not partial, load all trainloader = data.DataLoader(train_dataset, batch_size=args.batch_size, shuffle=True, num_workers=5, pin_memory=True) trainloader_gt = data.DataLoader(train_gt_dataset, batch_size=args.batch_size, shuffle=True, num_workers=5, pin_memory=True) else: #sample partial data #args.partial_data = 0.125 partial_size = int(args.partial_data * train_dataset_size) if args.partial_id is not None: train_ids = pickle.load(open(args.partial_id)) print('loading train ids from {}'.format(args.partial_id)) else: #args.partial_id is none train_ids = range(train_dataset_size) train_ids_remain = range(train_dataset_size_remain) np.random.shuffle(train_ids) #shuffle! np.random.shuffle(train_ids_remain) pickle.dump(train_ids, open(osp.join(args.snapshot_dir, 'train_id.pkl'), 'wb')) #randomly suffled ids #sampler train_sampler = data.sampler.SubsetRandomSampler( train_ids[:]) # 0~1/8, train_remain_sampler = data.sampler.SubsetRandomSampler( train_ids_remain[:]) train_gt_sampler = data.sampler.SubsetRandomSampler(train_ids[:]) # train_sampler = data.sampler.SubsetRandomSampler(train_ids[:partial_size]) # 0~1/8 # train_remain_sampler = data.sampler.SubsetRandomSampler(train_ids[partial_size:]) # used as unlabeled, 7/8 # train_gt_sampler = data.sampler.SubsetRandomSampler(train_ids[:partial_size]) #train loader trainloader = data.DataLoader( train_dataset, batch_size=args.batch_size, sampler=train_sampler, num_workers=3, pin_memory=True) # multi-process data loading trainloader_remain = data.DataLoader(train_dataset_remain, batch_size=args.batch_size, sampler=train_remain_sampler, num_workers=3, pin_memory=True) # trainloader_remain = data.DataLoader(train_dataset, # batch_size=args.batch_size, sampler=train_remain_sampler, num_workers=3, # pin_memory=True) trainloader_gt = data.DataLoader(train_gt_dataset, batch_size=args.batch_size, sampler=train_gt_sampler, num_workers=3, pin_memory=True) trainloader_remain_iter = enumerate(trainloader_remain) trainloader_iter = enumerate(trainloader) trainloader_gt_iter = enumerate(trainloader_gt) # implement model.optim_parameters(args) to handle different models' lr setting # optimizer for segmentation network # model.optim_paramters(args) = list(dict1, dict2), dict1 >> 'lr' and 'params' # print(type(model.optim_parameters(args)[0]['params'])) # generator #print(model.state_dict()['coeff'][0]) #confirmed optimizer = optim.SGD(model.optim_parameters(args), lr=args.learning_rate, momentum=args.momentum, weight_decay=args.weight_decay) #optimizer.add_param_group({"params":model.coeff}) # assign new coefficient to the optimizer #print(len(optimizer.param_groups)) optimizer.zero_grad() # optimizer for discriminator network optimizer_D = optim.Adam(model_D.parameters(), lr=args.learning_rate_D, betas=(0.9, 0.99)) optimizer_D.zero_grad() #initialize if USECALI: optimizer_cali = optim.LBFGS([model_cali.temperature], lr=0.01, max_iter=50) optimizer_cali.zero_grad() nll_criterion = BCEWithLogitsLoss().cuda() # BCE!! ece_criterion = ECELoss().cuda() # loss/ bilinear upsampling bce_loss = BCEWithLogitsLoss2d() interp = nn.Upsample( size=(input_size[1], input_size[0]), mode='bilinear' ) # okay it automatically change to functional.interpolate # 321, 321 if version.parse(torch.__version__) >= version.parse('0.4.0'): #0.4.1 interp = nn.Upsample(size=(input_size[1], input_size[0]), mode='bilinear', align_corners=True) else: interp = nn.Upsample(size=(input_size[1], input_size[0]), mode='bilinear') # labels for adversarial training pred_label = 0 gt_label = 1 semi_ratio_sum = 0 semi_sum = 0 loss_seg_sum = 0 loss_adv_sum = 0 loss_vat_sum = 0 l_seg_sum = 0 l_vat_sum = 0 l_adv_sum = 0 logits_list = [] labels_list = [] #https: // towardsdatascience.com / understanding - pytorch -with-an - example - a - step - by - step - tutorial - 81fc5f8c4e8e for i_iter in range(args.num_steps): loss_seg_value = 0 # L_seg loss_adv_pred_value = 0 # 0.01 L_adv loss_D_value = 0 # L_D loss_semi_value = 0 # 0.1 L_semi loss_semi_adv_value = 0 # 0.001 L_adv loss_vat_value = 0 optimizer.zero_grad() adjust_learning_rate(optimizer, i_iter) #changing lr by iteration optimizer_D.zero_grad() adjust_learning_rate_D(optimizer_D, i_iter) for sub_i in range(args.iter_size): ###################### train G!!!########################### ############################################################ # don't accumulate grads in D for param in model_D.parameters( ): # <class 'torch.nn.parameter.Parameter'>, convolution weights param.requires_grad = False # do not update gradient of D (freeze) while G ######### do unlabeled first!! 0.001 L_adv + 0.1 L_semi ############### # lambda_semi, lambda_adv for unlabeled if (args.lambda_semi > 0 or args.lambda_semi_adv > 0 ) and i_iter >= args.semi_start_adv: try: _, batch = trainloader_remain_iter.next( ) #remain = unlabeled print(trainloader_remain_iter.next()) except: trainloader_remain_iter = enumerate( trainloader_remain) # impose counters _, batch = trainloader_remain_iter.next() # only access to img images, _, _, _ = batch # <class 'torch.Tensor'> images = Variable(images).cuda( args.gpu) # <class 'torch.Tensor'> pred = interp( model(images)) # S(X), pred <class 'torch.Tensor'> pred_remain = pred.detach( ) #use detach() when attempting to remove a tensor from a computation graph, will be used for D # https://discuss.pytorch.org/t/clone-and-detach-in-v0-4-0/16861 # The difference is that detach refers to only a given variable on which it's called. # torch.no_grad affects all operations taking place within the with statement. >> for context, # requires_grad is for tensor # pred >> (8,21,321,321), L_adv D_out = interp( model_D(F.softmax(pred)) ) # D(S(X)), confidence, 8,1,321,321, not detached, there was not dim D_out_sigmoid = F.sigmoid(D_out).data.cpu().numpy().squeeze( axis=1) # (8,321,321) 0~1 # 0.001 L_adv!!!! ignore_mask_remain = np.zeros(D_out_sigmoid.shape).astype( np.bool) # no ignore_mask for unlabeled adv loss_semi_adv = args.lambda_semi_adv * bce_loss( D_out, make_D_label(gt_label, ignore_mask_remain)) #gt_label =1, # -log(D(S(X))) loss_semi_adv = loss_semi_adv / args.iter_size #normalization loss_semi_adv_value += loss_semi_adv.data.cpu().numpy( ) / args.lambda_semi_adv ##--- visualization, pred(8,21,321,321), D_out_sigmoid(8,321,321) """ if i_iter % 1000 == 0: vpred = pred.transpose(1, 2).transpose(2, 3).contiguous() # (8,321,321,21) vpred = vpred.view(-1, 21) # (8*321*321, 21) vlogsx = F.log_softmax(vpred) # torch.Tensor vsemi_gt = pred.data.cpu().numpy().argmax(axis=1) vsemi_gt = Variable(torch.FloatTensor(vsemi_gt).long()).cuda(gpu) vlogsx = vlogsx.gather(1, vsemi_gt.view(-1, 1)) sx = F.softmax(vpred).gather(1, vsemi_gt.view(-1, 1)) vD_out_sigmoid = Variable(torch.FloatTensor(D_out_sigmoid)).cuda(gpu).view(-1, 1) vlogsx = (vlogsx*(2.5*vD_out_sigmoid+0.5)) vlogsx = -vlogsx.squeeze(dim=1) sx = sx.squeeze(dim=1) vD_out_sigmoid = vD_out_sigmoid.squeeze(dim=1) dsx = vD_out_sigmoid.data.cpu().detach().numpy() vlogsx = vlogsx.data.cpu().detach().numpy() sx = sx.data.cpu().detach().numpy() plt.clf() plt.figure(figsize=(15, 5)) plt.subplot(131) plt.ylim(0, 0.004) plt.scatter(dsx, vlogsx, s = 0.1) # variable requires grad cannot call numpy >> detach plt.xlabel('D(S(X))') plt.ylabel('Loss_Semi per Pixel') plt.subplot(132) plt.scatter(dsx, vlogsx, s = 0.1) # variable requires grad cannot call numpy >> detach plt.xlabel('D(S(X))') plt.ylabel('Loss_Semi per Pixel') plt.subplot(133) plt.scatter(dsx, sx, s=0.1) plt.xlabel('D(S(X))') plt.ylabel('S(x)') plt.savefig('/home/eungyo/AdvSemiSeg/plot/' + str(i_iter) + '.png') """ if args.lambda_semi <= 0 or i_iter < args.semi_start: loss_semi_adv.backward() loss_semi_value = 0 else: semi_gt = pred.data.cpu().numpy().argmax( axis=1 ) # pred=S(X) ((8,21,321,321)), semi_gt is not one-hot, 8,321,321 #(8, 321, 321) if not USECALI: semi_ignore_mask = ( D_out_sigmoid < args.mask_T ) # both (8,321,321) 0~1threshold!, numpy semi_gt[ semi_ignore_mask] = 255 # Yhat, ignore pixel becomes 255 semi_ratio = 1.0 - float(semi_ignore_mask.sum( )) / semi_ignore_mask.size # ignored pixels / H*W print('semi ratio: {:.4f}'.format(semi_ratio)) if semi_ratio == 0.0: loss_semi_value += 0 else: semi_gt = torch.FloatTensor(semi_gt) confidence = torch.FloatTensor( D_out_sigmoid) ## added, only pred is on cuda loss_semi = args.lambda_semi * weighted_loss_calc( pred, semi_gt, args.gpu, confidence) else: semi_ratio = 1 semi_gt = (torch.FloatTensor(semi_gt)) # (8,321,321) confidence = torch.FloatTensor( F.sigmoid( model_cali.temperature_scale(D_out.view( -1))).data.cpu().numpy()) # (8*321*321,) loss_semi = args.lambda_semi * calibrated_loss_calc( pred, semi_gt, args.gpu, confidence, accuracies, n_bin ) # L_semi = Yhat * log(S(X)) # loss_calc(pred, semi_gt, args.gpu) # pred(8,21,321,321) if semi_ratio != 0: loss_semi = loss_semi / args.iter_size loss_semi_value += loss_semi.data.cpu().numpy( ) / args.lambda_semi if args.method == 'vatent' or args.method == 'vat': #v_loss = vat_loss(model, images, pred, eps=args.epsilon[i]) # R_vadv weighted_v_loss = weighted_vat_loss( model, images, pred, confidence, eps=args.epsilon) if args.method == 'vatent': #v_loss += entropy_loss(pred) # R_cent (conditional entropy loss) weighted_v_loss += weighted_entropy_loss( pred, confidence) v_loss = weighted_v_loss / args.iter_size loss_vat_value += v_loss.data.cpu().numpy() loss_semi_adv += args.alpha * v_loss loss_vat_sum += loss_vat_value if i_iter % 100 == 0 and sub_i == 4: l_vat_sum = loss_vat_sum / 100 if i_iter == 0: l_vat_sum = l_vat_sum * 100 loss_vat_sum = 0 loss_semi += loss_semi_adv loss_semi.backward( ) # 0.001 L_adv + 0.1 L_semi, backward == back propagation else: loss_semi = None loss_semi_adv = None ###########train with source (labeled data)############### L_ce + 0.01 * L_adv try: _, batch = trainloader_iter.next() except: trainloader_iter = enumerate(trainloader) # safe coding _, batch = trainloader_iter.next() #counter, batch images, labels, _, _ = batch # also get labels images(8,321,321) images = Variable(images).cuda(args.gpu) ignore_mask = ( labels.numpy() == 255 ) # ignored pixels == 255 >> 1, yes ignored mask for labeled data pred = interp(model(images)) # S(X), 8,21,321,321 loss_seg = loss_calc(pred, labels, args.gpu) # -Y*logS(X)= L_ce, not detached if USED: softsx = F.softmax(pred, dim=1) D_out = interp(model_D(softsx)) # D(S(X)), L_adv loss_adv_pred = bce_loss( D_out, make_D_label( gt_label, ignore_mask)) # both 8,1,321,321, gt_label = 1 # L_adv = -log(D(S(X)), make_D_label is all 1 except ignored_region loss = loss_seg + args.lambda_adv_pred * loss_adv_pred if USECALI: if (args.lambda_semi > 0 or args.lambda_semi_adv > 0 ) and i_iter >= args.semi_start_adv: with torch.no_grad(): _, prediction = torch.max(softsx, 1) labels_mask = ( (labels > 0) * (labels != 255)) | (prediction.data.cpu() > 0) labels = labels[labels_mask] prediction = prediction[labels_mask] fake_mask = (labels.data.cpu().numpy() != prediction.data.cpu().numpy()) real_label = make_conf_label( 1, fake_mask ) # (10*321*321, ) 0 or 1 (fake or real) logits = D_out.squeeze(dim=1) logits = logits[labels_mask] logits_list.append(logits) # initialize labels_list.append(real_label) if (i_iter * args.iter_size * args.batch_size + sub_i + 1) % train_dataset_size == 0: logits = torch.cat(logits_list).cuda( ) # overall 5000 images in val, #logits >> 5000,100, (1464*321*321,) labels = torch.cat(labels_list).cuda() before_temperature_nll = nll_criterion( logits, labels).item() ####modify before_temperature_ece, _, _ = ece_criterion( logits, labels) # (1464*321*321,) before_temperature_ece = before_temperature_ece.item( ) print('Before temperature - NLL: %.3f, ECE: %.3f' % (before_temperature_nll, before_temperature_ece)) def eval(): loss_cali = nll_criterion( model_cali.temperature_scale(logits), labels) loss_cali.backward() return loss_cali optimizer_cali.step( eval) # just one backward >> not 50 iterations after_temperature_nll = nll_criterion( model_cali.temperature_scale(logits), labels).item() after_temperature_ece, accuracies, n_bin = ece_criterion( model_cali.temperature_scale(logits), labels) after_temperature_ece = after_temperature_ece.item( ) print('Optimal temperature: %.3f' % model_cali.temperature.item()) print( 'After temperature - NLL: %.3f, ECE: %.3f' % (after_temperature_nll, after_temperature_ece)) logits_list = [] labels_list = [] else: loss = loss_seg # proper normalization loss = loss / args.iter_size loss.backward() loss_seg_sum += loss_seg / args.iter_size if USED: loss_adv_sum += loss_adv_pred if i_iter % 100 == 0 and sub_i == 4: l_seg_sum = loss_seg_sum / 100 if USED: l_adv_sum = loss_adv_sum / 100 if i_iter == 0: l_seg_sum = l_seg_sum * 100 l_adv_sum = l_adv_sum * 100 loss_seg_sum = 0 loss_adv_sum = 0 loss_seg_value += loss_seg.data.cpu().numpy() / args.iter_size if USED: loss_adv_pred_value += loss_adv_pred.data.cpu().numpy( ) / args.iter_size ##################### train D!!!########################### ########################################################### # bring back requires_grad if USED: for param in model_D.parameters(): param.requires_grad = True # before False. ############# train with pred S(X)############# labeled + unlabeled pred = pred.detach( ) #orginally only use labeled data, freeze S(X) when train D, # We do train D with the unlabeled data. But the difference is quite small if args.D_remain: #default true pred = torch.cat( (pred, pred_remain), 0 ) # pred_remain(unlabeled S(x)) is detached 16,21,321,321 ignore_mask = np.concatenate( (ignore_mask, ignore_mask_remain), axis=0) # 16,321,321 D_out = interp( model_D(F.softmax(pred, dim=1)) ) # D(S(X)) 16,1,321,321 # softmax(pred,dim=1) for 0.4, not nessesary loss_D = bce_loss(D_out, make_D_label(pred_label, ignore_mask)) # pred_label = 0 # -log(1-D(S(X))) loss_D = loss_D / args.iter_size / 2 # iter_size = 1, /2 because there is G and D loss_D.backward() loss_D_value += loss_D.data.cpu().numpy() ################## train with gt################### only labeled #VOCGT and VOCdataset can be reduced to one dataset in this repo. # get gt labels Y #print "before train gt" try: print(trainloader_gt_iter.next()) # len 732 _, batch = trainloader_gt_iter.next() except: trainloader_gt_iter = enumerate(trainloader_gt) _, batch = trainloader_gt_iter.next() #print "train with gt?" _, labels_gt, _, _ = batch D_gt_v = Variable(one_hot(labels_gt)).cuda(args.gpu) #one_hot ignore_mask_gt = (labels_gt.numpy() == 255 ) # same as ignore_mask (8,321,321) #print "finish" D_out = interp(model_D(D_gt_v)) # D(Y) loss_D = bce_loss(D_out, make_D_label(gt_label, ignore_mask_gt)) # log(D(Y)) loss_D = loss_D / args.iter_size / 2 loss_D.backward() loss_D_value += loss_D.data.cpu().numpy() optimizer.step() if USED: optimizer_D.step() print('exp = {}'.format(args.snapshot_dir)) #snapshot print( 'iter = {0:8d}/{1:8d}, loss_seg = {2:.3f}, loss_adv_p = {3:.3f}, loss_D = {4:.6f}, loss_semi = {5:.6f}, loss_semi_adv = {6:.3f}, loss_vat = {7: .5f}' .format(i_iter, args.num_steps, loss_seg_value, loss_adv_pred_value, loss_D_value, loss_semi_value, loss_semi_adv_value, loss_vat_value)) # L_ce L_adv for labeled L_D L_semi L_adv for unlabeled #loss_adv should be inversely proportional to the loss_D if they are seeing the same data. # loss_adv_p is essentially the inverse loss of loss_D. We expect them to achieve a good balance during the adversarial training # loss_D is around 0.2-0.5 >> good if i_iter >= args.num_steps - 1: print('save model ...') torch.save( model.state_dict(), osp.join(args.snapshot_dir, 'VOC_' + str(args.num_steps) + '.pth')) torch.save( model_D.state_dict(), osp.join(args.snapshot_dir, 'VOC_' + str(args.num_steps) + '_D.pth')) #torch.save(state, osp.join(args.snapshot_dir, 'VOC_' + str(i_iter) + '.pth.tar')) #torch.save(state_D, osp.join(args.snapshot_dir, 'VOC_' + str(i_iter) + '_D.pth.tar')) break if i_iter % 100 == 0 and sub_i == 4: #loss_seg_value wdata = "iter = {0:8d}/{1:8d}, loss_seg = {2:.3f}, loss_adv_p = {3:.3f}, loss_D = {4:.6f}, loss_semi = {5:.8f}, loss_semi_adv = {6:.3f}, l_vat_sum = {7: .5f}, loss_label = {8: .4}\n".format( i_iter, args.num_steps, l_seg_sum, l_adv_sum, loss_D_value, loss_semi_value, loss_semi_adv_value, l_vat_sum, l_seg_sum + 0.01 * l_adv_sum) #wdata2 = "{0:8d} {1:s} {2:s} {3:s} {4:s} {5:s} {6:s} {7:s} {8:s}\n".format(i_iter,str(model.coeff[0])[8:14],str(model.coeff[1])[8:14],str(model.coeff[2])[8:14],str(model.coeff[3])[8:14],str(model.coeff[4])[8:14],str(model.coeff[5])[8:14],str(model.coeff[6])[8:14],str(model.coeff[7])[8:14]) if i_iter == 0: f2 = open("/home/eungyo/AdvSemiSeg/snapshots/log.txt", 'w') f2.write(wdata) f2.close() #f3 = open("/home/eungyo/AdvSemiSeg/snapshots/coeff.txt", 'w') #f3.write(wdata2) #f3.close() else: f1 = open("/home/eungyo/AdvSemiSeg/snapshots/log.txt", 'a') f1.write(wdata) f1.close() #f4 = open("/home/eungyo/AdvSemiSeg/snapshots/coeff.txt", 'a') #f4.write(wdata2) #f4.close() if i_iter % args.save_pred_every == 0 and i_iter != 0: # 5000 print('taking snapshot ...') #state = {'epoch':i_iter, 'state_dict':model.state_dict(),'optim_dict':optimizer.state_dict()} #state_D = {'epoch':i_iter, 'state_dict': model_D.state_dict(), 'optim_dict': optimizer_D.state_dict()} #torch.save(state, osp.join(args.snapshot_dir, 'VOC_' + str(i_iter) + '.pth.tar')) #torch.save(state_D, osp.join(args.snapshot_dir, 'VOC_' + str(i_iter) + '_D.pth.tar')) torch.save( model.state_dict(), osp.join(args.snapshot_dir, 'VOC_' + str(i_iter) + '.pth')) torch.save( model_D.state_dict(), osp.join(args.snapshot_dir, 'VOC_' + str(i_iter) + '_D.pth')) end = timeit.default_timer() print(end - start, 'seconds')
def main(): """Create the model and start the training.""" h, w = map(int, args.input_size.split(',')) input_size = (h, w) h, w = map(int, args.input_size_target.split(',')) input_size_target = (h, w) cudnn.enabled = True from pytorchgo.utils.pytorch_utils import set_gpu set_gpu(args.gpu) # Create network if args.model == 'DeepLab': logger.info("adopting Deeplabv2 base model..") model = Res_Deeplab(num_classes=args.num_classes, multi_scale=False) if args.restore_from[:4] == 'http': saved_state_dict = model_zoo.load_url(args.restore_from) else: saved_state_dict = torch.load(args.restore_from) new_params = model.state_dict().copy() for i in saved_state_dict: # Scale.layer5.conv2d_list.3.weight i_parts = i.split('.') # print i_parts if not args.num_classes == 19 or not i_parts[1] == 'layer5': new_params['.'.join(i_parts[1:])] = saved_state_dict[i] # print i_parts model.load_state_dict(new_params) optimizer = optim.SGD(model.optim_parameters(args), lr=args.learning_rate, momentum=args.momentum, weight_decay=args.weight_decay) elif args.model == "FCN8S": logger.info("adopting FCN8S base model..") from pytorchgo.model.MyFCN8s import MyFCN8s model = MyFCN8s(n_class=NUM_CLASSES) vgg16 = torchfcn.models.VGG16(pretrained=True) model.copy_params_from_vgg16(vgg16) optimizer = optim.SGD(model.parameters(), lr=args.learning_rate, momentum=args.momentum, weight_decay=args.weight_decay) else: raise ValueError model.train() model.cuda() cudnn.benchmark = True # init D model_D1 = FCDiscriminator(num_classes=args.num_classes) model_D2 = FCDiscriminator(num_classes=args.num_classes) model_D1.train() model_D1.cuda() model_D2.train() model_D2.cuda() if SOURCE_DATA == "GTA5": trainloader = data.DataLoader(GTA5DataSet( args.data_dir, args.data_list, max_iters=args.num_steps * args.iter_size * args.batch_size, crop_size=input_size, scale=args.random_scale, mirror=args.random_mirror, mean=IMG_MEAN), batch_size=args.batch_size, shuffle=True, num_workers=args.num_workers, pin_memory=True) trainloader_iter = enumerate(trainloader) elif SOURCE_DATA == "SYNTHIA": trainloader = data.DataLoader(SynthiaDataSet( args.data_dir, args.data_list, LABEL_LIST_PATH, max_iters=args.num_steps * args.iter_size * args.batch_size, crop_size=input_size, scale=args.random_scale, mirror=args.random_mirror, mean=IMG_MEAN), batch_size=args.batch_size, shuffle=True, num_workers=args.num_workers, pin_memory=True) trainloader_iter = enumerate(trainloader) else: raise ValueError targetloader = data.DataLoader(cityscapesDataSet( max_iters=args.num_steps * args.iter_size * args.batch_size, crop_size=input_size_target, scale=False, mirror=args.random_mirror, mean=IMG_MEAN, set=args.set), batch_size=args.batch_size, shuffle=True, num_workers=args.num_workers, pin_memory=True) targetloader_iter = enumerate(targetloader) # implement model.optim_parameters(args) to handle different models' lr setting optimizer.zero_grad() optimizer_D1 = optim.Adam(model_D1.parameters(), lr=args.learning_rate_D, betas=(0.9, 0.99)) optimizer_D1.zero_grad() optimizer_D2 = optim.Adam(model_D2.parameters(), lr=args.learning_rate_D, betas=(0.9, 0.99)) optimizer_D2.zero_grad() bce_loss = torch.nn.BCEWithLogitsLoss() interp = nn.Upsample(size=(input_size[1], input_size[0]), mode='bilinear') interp_target = nn.Upsample(size=(input_size_target[1], input_size_target[0]), mode='bilinear') # labels for adversarial training source_label = 0 target_label = 1 best_mIoU = 0 model_summary([model, model_D1, model_D2]) optimizer_summary([optimizer, optimizer_D1, optimizer_D2]) for i_iter in tqdm(range(args.num_steps_stop), total=args.num_steps_stop, desc="training"): loss_seg_value1 = 0 loss_adv_target_value1 = 0 loss_D_value1 = 0 loss_seg_value2 = 0 loss_adv_target_value2 = 0 loss_D_value2 = 0 optimizer.zero_grad() lr = adjust_learning_rate(optimizer, i_iter) optimizer_D1.zero_grad() optimizer_D2.zero_grad() lr_D1 = adjust_learning_rate_D(optimizer_D1, i_iter) lr_D2 = adjust_learning_rate_D(optimizer_D2, i_iter) for sub_i in range(args.iter_size): ######################### train G # don't accumulate grads in D for param in model_D1.parameters(): param.requires_grad = False for param in model_D2.parameters(): param.requires_grad = False # train with source _, batch = trainloader_iter.next() images, labels, _, _ = batch images = Variable(images).cuda() pred2 = model(images) pred2 = interp(pred2) loss_seg2 = loss_calc(pred2, labels) loss = loss_seg2 # proper normalization loss = loss / args.iter_size loss.backward() loss_seg_value2 += loss_seg2.data.cpu().numpy()[0] / args.iter_size # train with target _, batch = targetloader_iter.next() images, _, _, _ = batch images = Variable(images).cuda() pred_target2 = model(images) pred_target2 = interp_target(pred_target2) D_out2 = model_D2(F.softmax(pred_target2)) loss_adv_target2 = bce_loss( D_out2, Variable( torch.FloatTensor( D_out2.data.size()).fill_(source_label)).cuda()) loss = args.lambda_adv_target2 * loss_adv_target2 loss = loss / args.iter_size loss.backward() loss_adv_target_value2 += loss_adv_target2.data.cpu().numpy( )[0] / args.iter_size ################################## train D # bring back requires_grad for param in model_D1.parameters(): param.requires_grad = True for param in model_D2.parameters(): param.requires_grad = True # train with source pred2 = pred2.detach() D_out2 = model_D2(F.softmax(pred2)) loss_D2 = bce_loss( D_out2, Variable( torch.FloatTensor( D_out2.data.size()).fill_(source_label)).cuda()) loss_D2 = loss_D2 / args.iter_size / 2 loss_D2.backward() loss_D_value2 += loss_D2.data.cpu().numpy()[0] # train with target pred_target2 = pred_target2.detach() D_out2 = model_D2(F.softmax(pred_target2)) loss_D2 = bce_loss( D_out2, Variable( torch.FloatTensor( D_out2.data.size()).fill_(target_label)).cuda()) loss_D2 = loss_D2 / args.iter_size / 2 loss_D2.backward() loss_D_value2 += loss_D2.data.cpu().numpy()[0] optimizer.step() optimizer_D1.step() optimizer_D2.step() if i_iter % 100 == 0: logger.info( 'iter = {}/{},loss_seg1 = {:.3f} loss_seg2 = {:.3f} loss_adv1 = {:.3f}, loss_adv2 = {:.3f} loss_D1 = {:.3f} loss_D2 = {:.3f}, lr={:.7f}, lr_D={:.7f}, best miou16= {:.5f}' .format(i_iter, args.num_steps_stop, loss_seg_value1, loss_seg_value2, loss_adv_target_value1, loss_adv_target_value2, loss_D_value1, loss_D_value2, lr, lr_D1, best_mIoU)) if i_iter % args.save_pred_every == 0 and i_iter != 0: logger.info("saving snapshot.....") cur_miou16 = proceed_test(model, input_size) is_best = True if best_mIoU < cur_miou16 else False if is_best: best_mIoU = cur_miou16 torch.save( { 'iteration': i_iter, 'optim_state_dict': optimizer.state_dict(), 'optim_D1_state_dict': optimizer_D1.state_dict(), 'optim_D2_state_dict': optimizer_D2.state_dict(), 'model_state_dict': model.state_dict(), 'model_D1_state_dict': model_D1.state_dict(), 'model_D2_state_dict': model_D2.state_dict(), 'best_mean_iu': cur_miou16, }, osp.join(logger.get_logger_dir(), 'checkpoint.pth.tar')) if is_best: import shutil shutil.copy( osp.join(logger.get_logger_dir(), 'checkpoint.pth.tar'), osp.join(logger.get_logger_dir(), 'model_best.pth.tar')) if i_iter >= args.num_steps_stop - 1: break
def main(): """Create the model and start the training.""" w, h = map(int, args.input_size.split(',')) input_size = (w, h) w, h = map(int, args.input_size_target.split(',')) input_size_target = (w, h) cudnn.enabled = True gpu = args.gpu tau = torch.ones(1) * args.tau tau = tau.cuda(args.gpu) # Create network if args.model == 'DeepLab': model = DeeplabMulti(num_classes=args.num_classes) if args.restore_from[:4] == 'http': saved_state_dict = model_zoo.load_url(args.restore_from) else: saved_state_dict = torch.load(args.restore_from) new_params = model.state_dict().copy() for i in saved_state_dict: # Scale.layer5.conv2d_list.3.weight i_parts = i.split('.') # print i_parts if not args.num_classes == 19 or not i_parts[1] == 'layer5': new_params['.'.join(i_parts[1:])] = saved_state_dict[i] # print i_parts model.load_state_dict(new_params, False) elif args.model == 'DeepLabVGG': model = DeeplabVGG(pretrained=True, num_classes=args.num_classes) model.train() model.cuda(args.gpu) cudnn.benchmark = True # init D model_D1 = FCDiscriminator(num_classes=args.num_classes) model_D2 = FCDiscriminator(num_classes=args.num_classes) model_D1.train() model_D1.cuda(args.gpu) model_D2.train() model_D2.cuda(args.gpu) if not os.path.exists(args.snapshot_dir): os.makedirs(args.snapshot_dir) mean = [0.485, 0.456, 0.406] std = [0.229, 0.224, 0.225] weak_transform = transforms.Compose([ # transforms.RandomCrop(32, 4), # transforms.RandomRotation(30), # transforms.Resize(1024), transforms.ToTensor(), # transforms.Normalize(mean, std), # RandomCrop(768) ]) target_transform = transforms.Compose([ # transforms.RandomCrop(32, 4), # transforms.RandomRotation(30), # transforms.Normalize(mean, std) # transforms.Resize(1024), # transforms.ToTensor(), # RandomCrop(768) ]) label_set = GTA5( root=args.data_dir, num_cls=19, split='all', remap_labels=True, transform=weak_transform, target_transform=target_transform, scale=input_size, # crop_transform=RandomCrop(int(768*(args.scale/1024))), ) unlabel_set = Cityscapes( root=args.data_dir_target, split=args.set, remap_labels=True, transform=weak_transform, target_transform=target_transform, scale=input_size_target, # crop_transform=RandomCrop(int(768*(args.scale/1024))), ) test_set = Cityscapes( root=args.data_dir_target, split='val', remap_labels=True, transform=weak_transform, target_transform=target_transform, scale=input_size_target, # crop_transform=RandomCrop(768) ) label_loader = data.DataLoader(label_set, batch_size=args.batch_size, shuffle=True, num_workers=args.num_workers, pin_memory=False) unlabel_loader = data.DataLoader(unlabel_set, batch_size=args.batch_size, shuffle=True, num_workers=args.num_workers, pin_memory=False) test_loader = data.DataLoader(test_set, batch_size=2, shuffle=False, num_workers=args.num_workers, pin_memory=False) # implement model.optim_parameters(args) to handle different models' lr setting optimizer = optim.SGD(model.optim_parameters(args), lr=args.learning_rate, momentum=args.momentum, weight_decay=args.weight_decay) optimizer_D1 = optim.Adam(model_D1.parameters(), lr=args.learning_rate_D, betas=(0.9, 0.99)) optimizer_D2 = optim.Adam(model_D2.parameters(), lr=args.learning_rate_D, betas=(0.9, 0.99)) [model, model_D2, model_D2], [optimizer, optimizer_D1, optimizer_D2 ] = amp.initialize([model, model_D2, model_D2], [optimizer, optimizer_D1, optimizer_D2], opt_level="O1", num_losses=7) optimizer.zero_grad() optimizer_D1.zero_grad() optimizer_D2.zero_grad() if args.gan == 'Vanilla': bce_loss = torch.nn.BCEWithLogitsLoss() elif args.gan == 'LS': bce_loss = torch.nn.MSELoss() interp = Interpolate(size=(input_size[1], input_size[0]), mode='bilinear', align_corners=True) interp_target = Interpolate(size=(input_size_target[1], input_size_target[0]), mode='bilinear', align_corners=True) interp_test = Interpolate(size=(input_size_target[1], input_size_target[0]), mode='bilinear', align_corners=True) # interp_test = Interpolate(size=(1024, 2048), mode='bilinear', align_corners=True) normalize_transform = transforms.Compose([ torchvision.transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]), ]) # labels for adversarial training source_label = 0 target_label = 1 max_mIoU = 0 total_loss_seg_value1 = [] total_loss_adv_target_value1 = [] total_loss_D_value1 = [] total_loss_con_value1 = [] total_loss_seg_value2 = [] total_loss_adv_target_value2 = [] total_loss_D_value2 = [] total_loss_con_value2 = [] hist = np.zeros((num_cls, num_cls)) # for i_iter in range(args.num_steps): for i_iter, (batch, batch_un) in enumerate( zip(roundrobin_infinite(label_loader), roundrobin_infinite(unlabel_loader))): loss_seg_value1 = 0 loss_adv_target_value1 = 0 loss_D_value1 = 0 loss_con_value1 = 0 loss_seg_value2 = 0 loss_adv_target_value2 = 0 loss_D_value2 = 0 loss_con_value2 = 0 optimizer.zero_grad() adjust_learning_rate(optimizer, i_iter) optimizer_D1.zero_grad() optimizer_D2.zero_grad() adjust_learning_rate_D(optimizer_D1, i_iter) adjust_learning_rate_D(optimizer_D2, i_iter) # train G # don't accumulate grads in D for param in model_D1.parameters(): param.requires_grad = False for param in model_D2.parameters(): param.requires_grad = False # train with source images, labels = batch images_orig = images images = transform_batch(images, normalize_transform) images = Variable(images).cuda(args.gpu) pred1, pred2 = model(images) pred1 = interp(pred1) pred2 = interp(pred2) loss_seg1 = loss_calc(pred1, labels, args.gpu) loss_seg2 = loss_calc(pred2, labels, args.gpu) loss = loss_seg2 + args.lambda_seg * loss_seg1 # proper normalization loss = loss / args.iter_size with amp.scale_loss(loss, optimizer, loss_id=0) as scaled_loss: scaled_loss.backward() # loss.backward() loss_seg_value1 += loss_seg1.data.cpu().numpy() / args.iter_size loss_seg_value2 += loss_seg2.data.cpu().numpy() / args.iter_size # train with target images_tar, labels_tar = batch_un images_tar_orig = images_tar images_tar = transform_batch(images_tar, normalize_transform) images_tar = Variable(images_tar).cuda(args.gpu) pred_target1, pred_target2 = model(images_tar) pred_target1 = interp_target(pred_target1) pred_target2 = interp_target(pred_target2) D_out1 = model_D1(F.softmax(pred_target1, dim=1)) D_out2 = model_D2(F.softmax(pred_target2, dim=1)) loss_adv_target1 = bce_loss( D_out1, Variable( torch.FloatTensor( D_out1.data.size()).fill_(source_label)).cuda(args.gpu)) loss_adv_target2 = bce_loss( D_out2, Variable( torch.FloatTensor( D_out2.data.size()).fill_(source_label)).cuda(args.gpu)) loss = args.lambda_adv_target1 * loss_adv_target1 + args.lambda_adv_target2 * loss_adv_target2 loss = loss / args.iter_size with amp.scale_loss(loss, optimizer, loss_id=1) as scaled_loss: scaled_loss.backward() # loss.backward() loss_adv_target_value1 += loss_adv_target1.data.cpu().numpy( ) / args.iter_size loss_adv_target_value2 += loss_adv_target2.data.cpu().numpy( ) / args.iter_size # train with consistency loss # unsupervise phase policies = RandAugment().get_batch_policy(args.batch_size) rand_p1 = np.random.random(size=args.batch_size) rand_p2 = np.random.random(size=args.batch_size) random_dir = np.random.choice([-1, 1], size=[args.batch_size, 2]) images_aug = aug_batch_tensor(images_tar_orig, policies, rand_p1, rand_p2, random_dir) images_aug_orig = images_aug images_aug = transform_batch(images_aug, normalize_transform) images_aug = Variable(images_aug).cuda(args.gpu) pred_target_aug1, pred_target_aug2 = model(images_aug) pred_target_aug1 = interp_target(pred_target_aug1) pred_target_aug2 = interp_target(pred_target_aug2) pred_target1 = pred_target1.detach() pred_target2 = pred_target2.detach() max_pred1, psuedo_label1 = torch.max(F.softmax(pred_target1, dim=1), 1) max_pred2, psuedo_label2 = torch.max(F.softmax(pred_target2, dim=1), 1) psuedo_label1 = psuedo_label1.cpu().numpy().astype(np.float32) psuedo_label1_thre = psuedo_label1.copy() psuedo_label1_thre[(max_pred1 < tau).cpu().numpy().astype( np.bool)] = 255 # threshold to don't care psuedo_label1_thre = aug_batch_numpy(psuedo_label1_thre, policies, rand_p1, rand_p2, random_dir) psuedo_label2 = psuedo_label2.cpu().numpy().astype(np.float32) psuedo_label2_thre = psuedo_label2.copy() psuedo_label2_thre[(max_pred2 < tau).cpu().numpy().astype( np.bool)] = 255 # threshold to don't care psuedo_label2_thre = aug_batch_numpy(psuedo_label2_thre, policies, rand_p1, rand_p2, random_dir) psuedo_label1_thre = Variable(psuedo_label1_thre).cuda(args.gpu) psuedo_label2_thre = Variable(psuedo_label2_thre).cuda(args.gpu) if (psuedo_label1_thre != 255).sum().cpu().numpy() > 0: # nll_loss doesn't support empty tensors loss_con1 = loss_calc(pred_target_aug1, psuedo_label1_thre, args.gpu) loss_con_value1 += loss_con1.data.cpu().numpy() / args.iter_size else: loss_con1 = torch.tensor(0.0, requires_grad=True).cuda(args.gpu) if (psuedo_label2_thre != 255).sum().cpu().numpy() > 0: # nll_loss doesn't support empty tensors loss_con2 = loss_calc(pred_target_aug2, psuedo_label2_thre, args.gpu) loss_con_value2 += loss_con2.data.cpu().numpy() / args.iter_size else: loss_con2 = torch.tensor(0.0, requires_grad=True).cuda(args.gpu) loss = args.lambda_con * loss_con1 + args.lambda_con * loss_con2 # proper normalization loss = loss / args.iter_size with amp.scale_loss(loss, optimizer, loss_id=2) as scaled_loss: scaled_loss.backward() # loss.backward() # train D # bring back requires_grad for param in model_D1.parameters(): param.requires_grad = True for param in model_D2.parameters(): param.requires_grad = True # train with source pred1 = pred1.detach() pred2 = pred2.detach() D_out1 = model_D1(F.softmax(pred1, dim=1)) D_out2 = model_D2(F.softmax(pred2, dim=1)) loss_D1 = bce_loss( D_out1, Variable( torch.FloatTensor( D_out1.data.size()).fill_(source_label)).cuda(args.gpu)) loss_D2 = bce_loss( D_out2, Variable( torch.FloatTensor( D_out2.data.size()).fill_(source_label)).cuda(args.gpu)) loss_D1 = loss_D1 / args.iter_size / 2 loss_D2 = loss_D2 / args.iter_size / 2 with amp.scale_loss(loss_D1, optimizer_D1, loss_id=3) as scaled_loss: scaled_loss.backward() # loss_D1.backward() with amp.scale_loss(loss_D2, optimizer_D2, loss_id=4) as scaled_loss: scaled_loss.backward() # loss_D2.backward() loss_D_value1 += loss_D1.data.cpu().numpy() loss_D_value2 += loss_D2.data.cpu().numpy() # train with target pred_target1 = pred_target1.detach() pred_target2 = pred_target2.detach() D_out1 = model_D1(F.softmax(pred_target1, dim=1)) D_out2 = model_D2(F.softmax(pred_target2, dim=1)) loss_D1 = bce_loss( D_out1, Variable( torch.FloatTensor( D_out1.data.size()).fill_(target_label)).cuda(args.gpu)) loss_D2 = bce_loss( D_out2, Variable( torch.FloatTensor( D_out2.data.size()).fill_(target_label)).cuda(args.gpu)) loss_D1 = loss_D1 / args.iter_size / 2 loss_D2 = loss_D2 / args.iter_size / 2 with amp.scale_loss(loss_D1, optimizer_D1, loss_id=5) as scaled_loss: scaled_loss.backward() # loss_D1.backward() with amp.scale_loss(loss_D2, optimizer_D2, loss_id=6) as scaled_loss: scaled_loss.backward() # loss_D2.backward() loss_D_value1 += loss_D1.data.cpu().numpy() loss_D_value2 += loss_D2.data.cpu().numpy() optimizer.step() optimizer_D1.step() optimizer_D2.step() print('exp = {}'.format(args.snapshot_dir)) print( 'iter = {0:8d}/{1:8d}, loss_seg1 = {2:.3f} loss_seg2 = {3:.3f} loss_adv1 = {4:.3f}, loss_adv2 = {5:.3f} loss_D1 = {6:.3f} loss_D2 = {7:.3f}, loss_con1 = {8:.3f}, loss_con2 = {9:.3f}' .format(i_iter, args.num_steps, loss_seg_value1, loss_seg_value2, loss_adv_target_value1, loss_adv_target_value2, loss_D_value1, loss_D_value2, loss_con_value1, loss_con_value2)) total_loss_seg_value1.append(loss_seg_value1) total_loss_adv_target_value1.append(loss_adv_target_value1) total_loss_D_value1.append(loss_D_value1) total_loss_con_value1.append(loss_con_value1) total_loss_seg_value2.append(loss_seg_value2) total_loss_adv_target_value2.append(loss_adv_target_value2) total_loss_D_value2.append(loss_D_value2) total_loss_con_value2.append(loss_con_value2) hist += fast_hist( labels.cpu().numpy().flatten().astype(int), torch.argmax(pred2, dim=1).cpu().numpy().flatten().astype(int), num_cls) if i_iter % 10 == 0: print('({}/{})'.format(i_iter + 1, int(args.num_steps))) acc_overall, acc_percls, iu, fwIU = result_stats(hist) mIoU = np.mean(iu) per_class = [[classes[i], acc] for i, acc in list(enumerate(iu))] per_class = np.array(per_class).flatten() print( ('per cls IoU :' + ('\n{:>14s} : {}') * 19).format(*per_class)) print('mIoU : {:0.2f}'.format(np.mean(iu))) print('fwIoU : {:0.2f}'.format(fwIU)) print('pixel acc : {:0.2f}'.format(acc_overall)) per_class = [[classes[i], acc] for i, acc in list(enumerate(acc_percls))] per_class = np.array(per_class).flatten() print( ('per cls acc :' + ('\n{:>14s} : {}') * 19).format(*per_class)) avg_train_acc = acc_overall avg_train_loss_seg1 = np.mean(total_loss_seg_value1) avg_train_loss_adv1 = np.mean(total_loss_adv_target_value1) avg_train_loss_dis1 = np.mean(total_loss_D_value1) avg_train_loss_con1 = np.mean(total_loss_con_value1) avg_train_loss_seg2 = np.mean(total_loss_seg_value2) avg_train_loss_adv2 = np.mean(total_loss_adv_target_value2) avg_train_loss_dis2 = np.mean(total_loss_D_value2) avg_train_loss_con2 = np.mean(total_loss_con_value2) print('avg_train_acc :', avg_train_acc) print('avg_train_loss_seg1 :', avg_train_loss_seg1) print('avg_train_loss_adv1 :', avg_train_loss_adv1) print('avg_train_loss_dis1 :', avg_train_loss_dis1) print('avg_train_loss_con1 :', avg_train_loss_con1) print('avg_train_loss_seg2 :', avg_train_loss_seg2) print('avg_train_loss_adv2 :', avg_train_loss_adv2) print('avg_train_loss_dis2 :', avg_train_loss_dis2) print('avg_train_loss_con2 :', avg_train_loss_con2) writer['train'].add_scalar('log/mIoU', mIoU, i_iter) writer['train'].add_scalar('log/acc', avg_train_acc, i_iter) writer['train'].add_scalar('log1/loss_seg', avg_train_loss_seg1, i_iter) writer['train'].add_scalar('log1/loss_adv', avg_train_loss_adv1, i_iter) writer['train'].add_scalar('log1/loss_dis', avg_train_loss_dis1, i_iter) writer['train'].add_scalar('log1/loss_con', avg_train_loss_con1, i_iter) writer['train'].add_scalar('log2/loss_seg', avg_train_loss_seg2, i_iter) writer['train'].add_scalar('log2/loss_adv', avg_train_loss_adv2, i_iter) writer['train'].add_scalar('log2/loss_dis', avg_train_loss_dis2, i_iter) writer['train'].add_scalar('log2/loss_con', avg_train_loss_con2, i_iter) hist = np.zeros((num_cls, num_cls)) total_loss_seg_value1 = [] total_loss_adv_target_value1 = [] total_loss_D_value1 = [] total_loss_con_value1 = [] total_loss_seg_value2 = [] total_loss_adv_target_value2 = [] total_loss_D_value2 = [] total_loss_con_value2 = [] fig = plt.figure(figsize=(15, 15)) labels = labels[0].cpu().numpy().astype(np.float32) ax = fig.add_subplot(331) ax.imshow(print_palette(Image.fromarray(labels).convert('L'))) ax.axis("off") ax.set_title('labels') ax = fig.add_subplot(337) images = images_orig[0].cpu().numpy().transpose((1, 2, 0)) # images += IMG_MEAN ax.imshow(images) ax.axis("off") ax.set_title('datas') _, pred2 = torch.max(pred2, dim=1) pred2 = pred2[0].cpu().numpy().astype(np.float32) ax = fig.add_subplot(334) ax.imshow(print_palette(Image.fromarray(pred2).convert('L'))) ax.axis("off") ax.set_title('predicts') labels_tar = labels_tar[0].cpu().numpy().astype(np.float32) ax = fig.add_subplot(332) ax.imshow(print_palette(Image.fromarray(labels_tar).convert('L'))) ax.axis("off") ax.set_title('tar_labels') ax = fig.add_subplot(338) ax.imshow(images_tar_orig[0].cpu().numpy().transpose((1, 2, 0))) ax.axis("off") ax.set_title('tar_datas') _, pred_target2 = torch.max(pred_target2, dim=1) pred_target2 = pred_target2[0].cpu().numpy().astype(np.float32) ax = fig.add_subplot(335) ax.imshow(print_palette( Image.fromarray(pred_target2).convert('L'))) ax.axis("off") ax.set_title('tar_predicts') print(policies[0], 'p1', rand_p1[0], 'p2', rand_p2[0], 'random_dir', random_dir[0]) psuedo_label2_thre = psuedo_label2_thre[0].cpu().numpy().astype( np.float32) ax = fig.add_subplot(333) ax.imshow( print_palette( Image.fromarray(psuedo_label2_thre).convert('L'))) ax.axis("off") ax.set_title('psuedo_labels') ax = fig.add_subplot(339) ax.imshow(images_aug_orig[0].cpu().numpy().transpose((1, 2, 0))) ax.axis("off") ax.set_title('aug_datas') _, pred_target_aug2 = torch.max(pred_target_aug2, dim=1) pred_target_aug2 = pred_target_aug2[0].cpu().numpy().astype( np.float32) ax = fig.add_subplot(336) ax.imshow( print_palette(Image.fromarray(pred_target_aug2).convert('L'))) ax.axis("off") ax.set_title('aug_predicts') # plt.show() writer['train'].add_figure('image/', fig, global_step=i_iter, close=True) if i_iter % 500 == 0: loss1 = [] loss2 = [] for test_i, batch in enumerate(test_loader): images, labels = batch images_orig = images images = transform_batch(images, normalize_transform) images = Variable(images).cuda(args.gpu) pred1, pred2 = model(images) pred1 = interp_test(pred1) pred1 = pred1.detach() pred2 = interp_test(pred2) pred2 = pred2.detach() loss_seg1 = loss_calc(pred1, labels, args.gpu) loss_seg2 = loss_calc(pred2, labels, args.gpu) loss1.append(loss_seg1.item()) loss2.append(loss_seg2.item()) hist += fast_hist( labels.cpu().numpy().flatten().astype(int), torch.argmax(pred2, dim=1).cpu().numpy().flatten().astype(int), num_cls) print('test') fig = plt.figure(figsize=(15, 15)) labels = labels[-1].cpu().numpy().astype(np.float32) ax = fig.add_subplot(311) ax.imshow(print_palette(Image.fromarray(labels).convert('L'))) ax.axis("off") ax.set_title('labels') ax = fig.add_subplot(313) ax.imshow(images_orig[-1].cpu().numpy().transpose((1, 2, 0))) ax.axis("off") ax.set_title('datas') _, pred2 = torch.max(pred2, dim=1) pred2 = pred2[-1].cpu().numpy().astype(np.float32) ax = fig.add_subplot(312) ax.imshow(print_palette(Image.fromarray(pred2).convert('L'))) ax.axis("off") ax.set_title('predicts') # plt.show() writer['test'].add_figure('test_image/', fig, global_step=i_iter, close=True) acc_overall, acc_percls, iu, fwIU = result_stats(hist) mIoU = np.mean(iu) per_class = [[classes[i], acc] for i, acc in list(enumerate(iu))] per_class = np.array(per_class).flatten() print( ('per cls IoU :' + ('\n{:>14s} : {}') * 19).format(*per_class)) print('mIoU : {:0.2f}'.format(mIoU)) print('fwIoU : {:0.2f}'.format(fwIU)) print('pixel acc : {:0.2f}'.format(acc_overall)) per_class = [[classes[i], acc] for i, acc in list(enumerate(acc_percls))] per_class = np.array(per_class).flatten() print( ('per cls acc :' + ('\n{:>14s} : {}') * 19).format(*per_class)) avg_test_loss1 = np.mean(loss1) avg_test_loss2 = np.mean(loss2) avg_test_acc = acc_overall print('avg_test_loss2 :', avg_test_loss1) print('avg_test_loss1 :', avg_test_loss2) print('avg_test_acc :', avg_test_acc) writer['test'].add_scalar('log1/loss_seg', avg_test_loss1, i_iter) writer['test'].add_scalar('log2/loss_seg', avg_test_loss2, i_iter) writer['test'].add_scalar('log/acc', avg_test_acc, i_iter) writer['test'].add_scalar('log/mIoU', mIoU, i_iter) hist = np.zeros((num_cls, num_cls)) if i_iter >= args.num_steps_stop - 1: print('save model ...') torch.save( model.state_dict(), osp.join(args.snapshot_dir, 'GTA5_' + str(args.num_steps_stop) + '.pth')) torch.save( model_D1.state_dict(), osp.join(args.snapshot_dir, 'GTA5_' + str(args.num_steps_stop) + '_D1.pth')) torch.save( model_D2.state_dict(), osp.join(args.snapshot_dir, 'GTA5_' + str(args.num_steps_stop) + '_D2.pth')) break if max_mIoU < mIoU: max_mIoU = mIoU torch.save( model.state_dict(), osp.join(args.snapshot_dir, 'GTA5_' + 'best_iter' + '.pth')) torch.save( model_D1.state_dict(), osp.join(args.snapshot_dir, 'GTA5_' + 'best_iter' + '_D1.pth')) torch.save( model_D2.state_dict(), osp.join(args.snapshot_dir, 'GTA5_' + 'best_iter' + '_D2.pth'))
def main(): """Create the model and start the training.""" w, h = map(int, args.input_size.split(',')) input_size = (w, h) w, h = map(int, args.input_size_target.split(',')) input_size_target = (w, h) cudnn.enabled = True gpu = args.gpu # Create network if args.model == 'DeepLab': model = DeeplabMulti(num_classes=args.num_classes) if args.restore_from[:4] == 'http': saved_state_dict = model_zoo.load_url(args.restore_from) else: saved_state_dict = torch.load(args.restore_from) new_params = model.state_dict().copy() for i in saved_state_dict: # Scale.layer5.conv2d_list.3.weight i_parts = i.split('.') # print i_parts if not args.num_classes == 19 or not i_parts[1] == 'layer5': new_params['.'.join(i_parts[1:])] = saved_state_dict[i] # print i_parts model.load_state_dict(new_params) model.train() model.cuda(args.gpu) cudnn.benchmark = True if not os.path.exists(args.snapshot_dir): os.makedirs(args.snapshot_dir) trainloader = data.DataLoader(GTA5DataSet(args.data_dir, args.data_list, max_iters=args.num_steps * args.iter_size * args.batch_size, crop_size=input_size, scale=args.random_scale, mirror=args.random_mirror, mean=IMG_MEAN), batch_size=args.batch_size, shuffle=True, num_workers=args.num_workers, pin_memory=True) trainloader_iter = enumerate(trainloader) targetloader = data.DataLoader(cityscapesDataSet( args.data_dir_target, args.data_list_target, max_iters=args.num_steps * args.iter_size * args.batch_size, crop_size=input_size_target, scale=False, mirror=args.random_mirror, mean=IMG_MEAN, set=args.set), batch_size=args.batch_size, shuffle=True, num_workers=args.num_workers, pin_memory=True) targetloader_iter = enumerate(targetloader) # Implemented by Bongjoon Hyun model_D1 = FCDiscriminator(num_classes=args.num_classes) model_D2 = FCDiscriminator(num_classes=args.num_classes) model_D1.train() model_D1.cuda(args.gpu) model_D2.train() model_D2.cuda(args.gpu) # # Implemented by Bongjoon Hyun optimizer = optim.SGD(model.optim_parameters(args), lr=args.learning_rate, momentum=args.momentum, weight_decay=args.weight_decay) optimizer.zero_grad() optimizer_D1 = optim.Adam(model_D1.parameters(), lr=args.learning_rate_D, betas=(0.9, 0.99)) optimizer_D1.zero_grad() optimizer_D2 = optim.Adam(model_D2.parameters(), lr=args.learning_rate_D, betas=(0.9, 0.99)) optimizer_D2.zero_grad() # if args.gan == 'Vanilla': bce_loss = torch.nn.BCEWithLogitsLoss() elif args.gan == 'LS': # Implemented by Bongjoon Hyun bce_loss = torch.nn.MSELoss() # interp = nn.Upsample(size=(input_size[1], input_size[0]), mode='bilinear') interp_target = nn.Upsample(size=(input_size_target[1], input_size_target[0]), mode='bilinear') # labels for adversarial training source_label = 0 target_label = 1 for i_iter in range(args.num_steps): loss_seg_value1 = 0 loss_adv_target_value1 = 0 loss_D_value1 = 0 loss_seg_value2 = 0 loss_adv_target_value2 = 0 loss_D_value2 = 0 optimizer.zero_grad() adjust_learning_rate(optimizer, i_iter) optimizer_D1.zero_grad() optimizer_D2.zero_grad() adjust_learning_rate_D(optimizer_D1, i_iter) adjust_learning_rate_D(optimizer_D2, i_iter) for sub_i in range(args.iter_size): # Implemented by Bongjoon Hyun for param in model_D1.parameters(): param.requires_grad = False for param in model_D2.parameters(): param.requires_grad = False # train with source _, batch = next(trainloader_iter) images, labels, _, _ = batch images = Variable(images).cuda(args.gpu) pred1, pred2 = model(images) pred1 = interp(pred1) pred2 = interp(pred2) loss_seg1 = loss_calc(pred1, labels, args.gpu) loss_seg2 = loss_calc(pred2, labels, args.gpu) loss = (loss_seg2 + args.lambda_seg * loss_seg1) / args.iter_size loss.backward() loss_seg_value1 += loss_seg1.data.cpu().numpy() / args.iter_size loss_seg_value2 += loss_seg2.data.cpu().numpy() / args.iter_size _, batch = next(targetloader_iter) images, _, _ = batch images = Variable(images).cuda(args.gpu) pred_target1, pred_target2 = model(images) pred_target1 = interp_target(pred_target1) pred_target2 = interp_target(pred_target2) D1_out = model_D1(F.softmax(pred_target1)) D2_out = model_D2(F.softmax(pred_target2)) labels_source1 = Variable( torch.FloatTensor( D1_out.data.size()).fill_(source_label)).cuda(args.gpu) labels_source2 = Variable( torch.FloatTensor( D2_out.data.size()).fill_(source_label)).cuda(args.gpu) loss_adv_target1 = bce_loss(D1_out, labels_source1) loss_adv_target2 = bce_loss(D2_out, labels_source2) loss = args.lambda_adv_target1 * loss_adv_target1 + \ args.lambda_adv_target2 * loss_adv_target2 loss = loss / args.iter_size loss.backward() loss_adv_target_value1 += loss_adv_target1.data.cpu().numpy( ) / args.iter_size loss_adv_target_value2 += loss_adv_target2.data.cpu().numpy( ) / args.iter_size for param in model_D1.parameters(): param.requires_grad = True for param in model_D2.parameters(): param.requires_grad = True pred1 = pred1.detach() pred2 = pred2.detach() D1_out = model_D1(F.softmax(pred1)) D2_out = model_D2(F.softmax(pred2)) labels_source1 = Variable( torch.FloatTensor( D1_out.data.size()).fill_(source_label)).cuda(args.gpu) labels_source2 = Variable( torch.FloatTensor( D2_out.data.size()).fill_(source_label)).cuda(args.gpu) loss_D1 = bce_loss(D1_out, labels_source1) / args.iter_size / 2 loss_D2 = bce_loss(D2_out, labels_source2) / args.iter_size / 2 loss_D1.backward() loss_D2.backward() loss_D_value1 += loss_D1.data.cpu().numpy() loss_D_value2 += loss_D2.data.cpu().numpy() pred_target1 = pred_target1.detach() pred_target2 = pred_target2.detach() D1_out = model_D1(F.softmax(pred_target1)) D2_out = model_D2(F.softmax(pred_target2)) labels_target1 = Variable( torch.FloatTensor( D1_out.data.size()).fill_(target_label)).cuda(args.gpu) labels_target2 = Variable( torch.FloatTensor( D2_out.data.size()).fill_(target_label)).cuda(args.gpu) loss_D1 = bce_loss(D1_out, labels_target1) / args.iter_size / 2 loss_D2 = bce_loss(D2_out, labels_target2) / args.iter_size / 2 loss_D1.backward() loss_D2.backward() loss_D_value1 += loss_D1.data.cpu().numpy() loss_D_value2 += loss_D2.data.cpu().numpy() # optimizer.step() optimizer_D1.step() optimizer_D2.step() print('exp = {}'.format(args.snapshot_dir)) print( 'iter = {0:8d}/{1:8d}, loss_seg1 = {2:.3f} loss_seg2 = {3:.3f} loss_adv1 = {4:.3f}, loss_adv2 = {5:.3f} loss_D1 = {6:.3f} loss_D2 = {7:.3f}' .format(i_iter, args.num_steps, loss_seg_value1, loss_seg_value2, loss_adv_target_value1, loss_adv_target_value2, loss_D_value1, loss_D_value2)) if i_iter >= args.num_steps_stop - 1: print 'save model ...' torch.save( model.state_dict(), osp.join(args.snapshot_dir, 'GTA5_' + str(args.num_steps_stop) + '.pth')) torch.save( model_D1.state_dict(), osp.join(args.snapshot_dir, 'GTA5_' + str(args.num_steps_stop) + '_D1.pth')) torch.save( model_D2.state_dict(), osp.join(args.snapshot_dir, 'GTA5_' + str(args.num_steps_stop) + '_D2.pth')) break if i_iter % args.save_pred_every == 0 and i_iter != 0: print 'taking snapshot ...' torch.save( model.state_dict(), osp.join(args.snapshot_dir, 'GTA5_' + str(i_iter) + '.pth')) torch.save( model_D1.state_dict(), osp.join(args.snapshot_dir, 'GTA5_' + str(i_iter) + '_D1.pth')) torch.save( model_D2.state_dict(), osp.join(args.snapshot_dir, 'GTA5_' + str(i_iter) + '_D2.pth'))
def main(): h, w = list(map(int, args.input_size.split(','))) # 321, 321 input_size = (h, w) cudnn.enabled = True # create network model = Res_Deeplab(num_classes=args.num_classes) # num_classes = 21 # load pretrained parameters if args.restore_from[:4] == 'http': saved_state_dict = model_zoo.load_url(args.restore_from) else: saved_state_dict = torch.load(args.restore_from) # only copy the params that exist in current model (caffe-like) new_params = model.state_dict().copy() for name, param in list(new_params.items()): # print(name) if name in saved_state_dict and param.size() == saved_state_dict[name].size(): new_params[name].copy_(saved_state_dict[name]) # print(('copy {}'.format(name))) model.load_state_dict(new_params) model.train() model.cuda(args.gpu) cudnn.benchmark = True # init D model_D = FCDiscriminator(num_classes=args.num_classes) # num_classes = 21,全卷积判别模型 if args.restore_from_D is not None: model_D.load_state_dict(torch.load(args.restore_from_D)) model_D.train() model_D.cuda(args.gpu) if not os.path.exists(args.snapshot_dir): os.makedirs(args.snapshot_dir) if not os.path.exists('logs/'): os.makedirs('logs/') now_time = datetime.now().strftime('%Y-%m-%d-%H:%M:%S') log_file = 'logs/' + now_time + '.txt' file = open(log_file, 'w') # 保存loss train_dataset = VOCDataSet(args.data_dir, args.data_list, args.label_list, crop_size=input_size, scale=args.random_scale, mirror=args.random_mirror, mean=IMG_MEAN) train_dataset_size = len(train_dataset) train_gt_dataset = VOCGTDataSet(args.data_dir, args.data_list, args.label_list, crop_size=input_size, scale=args.random_scale, mirror=args.random_mirror, mean=IMG_MEAN) if args.partial_data is None: # 使用全部数据 trainloader = data.DataLoader(train_dataset, batch_size=args.batch_size, shuffle=True, # batch_size = 10 num_workers=5, pin_memory=True) trainloader_gt = data.DataLoader(train_gt_dataset, batch_size=args.batch_size, shuffle=True, num_workers=5, pin_memory=True) else: # sample partial data 部分数据 partial_size = int(args.partial_data * train_dataset_size) if args.partial_id is not None: train_ids = pickle.load(open(args.partial_id)) print(('loading train ids from {}'.format(args.partial_id))) else: train_ids = list(range(train_dataset_size)) np.random.shuffle(train_ids) pickle.dump(train_ids, open(osp.join(args.snapshot_dir, 'train_id.pkl'), 'wb')) # 将train_ids写入train_id.pkl train_sampler = data.sampler.SubsetRandomSampler(train_ids[:partial_size]) train_remain_sampler = data.sampler.SubsetRandomSampler(train_ids[partial_size:]) train_gt_sampler = data.sampler.SubsetRandomSampler(train_ids[:partial_size]) trainloader = data.DataLoader(train_dataset, # 数据集中采样输入 batch_size=args.batch_size, sampler=train_sampler, num_workers=3, pin_memory=True) trainloader_remain = data.DataLoader(train_dataset, batch_size=args.batch_size, sampler=train_remain_sampler, num_workers=3, pin_memory=True) trainloader_gt = data.DataLoader(train_gt_dataset, batch_size=args.batch_size, sampler=train_gt_sampler, num_workers=3, pin_memory=True) trainloader_remain_iter = enumerate(trainloader_remain) trainloader_iter = enumerate(trainloader) trainloader_gt_iter = enumerate(trainloader_gt) # implement model.optim_parameters(args) to handle different models' lr setting # optimizer for segmentation network optimizer = optim.SGD(model.optim_parameters(args), lr=args.learning_rate, momentum=args.momentum, weight_decay=args.weight_decay) optimizer.zero_grad() # optimizer for discriminator network optimizer_D = optim.Adam(model_D.parameters(), lr=args.learning_rate_D, betas=(0.9, 0.99)) optimizer_D.zero_grad() # loss/ bilinear upsampling bce_loss = BCEWithLogitsLoss2d() # interp = nn.Upsample(size=(input_size[1], input_size[0]), mode='bilinear') if version.parse(torch.__version__) >= version.parse('0.4.0'): interp = nn.Upsample(size=(input_size[1], input_size[0]), mode='bilinear', align_corners=True) else: interp = nn.Upsample(size=(input_size[1], input_size[0]), mode='bilinear') # labels for adversarial training pred_label = 0 gt_label = 1 best_loss = 1 best_epoch = 0 for i_iter in range(args.num_steps): # num_steps = 20000 loss_seg_value = 0 loss_adv_pred_value = 0 loss_D_value = 0 loss_semi_value = 0 loss_semi_adv_value = 0 optimizer.zero_grad() adjust_learning_rate(optimizer, i_iter) optimizer_D.zero_grad() adjust_learning_rate_D(optimizer_D, i_iter) for sub_i in range(args.iter_size): # iter_size = 1 # train G # don't accumulate grads in D for param in model_D.parameters(): param.requires_grad = False # do semi first if (args.lambda_semi > 0 or args.lambda_semi_adv > 0) and i_iter >= args.semi_start_adv: try: _, batch = next(trainloader_remain_iter) except: trainloader_remain_iter = enumerate(trainloader_remain) _, batch = next(trainloader_remain_iter) # only access to img 无标签数据 images, _, _, _, _ = batch images = Variable(images).cuda(args.gpu) pred = interp(model(images)) pred_remain = pred.detach() # 返回一个新的Variable,不具有grade D_out = interp(model_D(F.softmax(pred))) D_out_sigmoid = F.sigmoid(D_out).data.cpu().numpy().squeeze(axis=1) ignore_mask_remain = np.zeros(D_out_sigmoid.shape).astype(np.bool) loss_semi_adv = args.lambda_semi_adv * bce_loss(D_out, make_D_label(gt_label, ignore_mask_remain)) loss_semi_adv = loss_semi_adv/args.iter_size # loss_semi_adv.backward() # print('bug,', loss_semi_adv.data.cpu().numpy()) # loss_semi_adv_value += loss_semi_adv.data.cpu().numpy()[0]/args.lambda_semi_adv loss_semi_adv_value += loss_semi_adv.data.cpu().numpy()/args.lambda_semi_adv if args.lambda_semi <= 0 or i_iter < args.semi_start: loss_semi_adv.backward() loss_semi_value = 0 else: # produce ignore mask semi_ignore_mask = (D_out_sigmoid < args.mask_T) # mask_T = 0.2,阈值 semi_gt = pred.data.cpu().numpy().argmax(axis=1) # 返回维度为1上的最大值的下标 semi_gt[semi_ignore_mask] = 255 semi_ratio = 1.0 - float(semi_ignore_mask.sum())/semi_ignore_mask.size # 被忽略的点占的比重 print(('semi ratio: {:.4f}'.format(semi_ratio))) if semi_ratio == 0.0: loss_semi_value += 0 else: semi_gt = torch.FloatTensor(semi_gt) loss_semi = args.lambda_semi * loss_calc(pred, semi_gt, args.gpu) loss_semi = loss_semi/args.iter_size # loss_semi_value += loss_semi.data.cpu().numpy()[0]/args.lambda_semi loss_semi_value += loss_semi.data.cpu().numpy()/args.lambda_semi loss_semi += loss_semi_adv loss_semi.backward() else: loss_semi = None loss_semi_adv = None # train with source try: _, batch = next(trainloader_iter) except: trainloader_iter = enumerate(trainloader) _, batch = next(trainloader_iter) images, labels, _, _, _ = batch # 有标签数据 images = Variable(images).cuda(args.gpu) ignore_mask = (labels.numpy() == 255) pred = interp(model(images)) # interp上采样 loss_seg = loss_calc(pred, labels, args.gpu) # 语义分割的cross entropy loss # loss_seg_NLL = loss_NLL(pred, labels, args.gpu) # 语义分割的NLLLoss D_out = interp(model_D(F.softmax(pred))) # 得到判别模型输出的判别图 loss_adv_pred = bce_loss(D_out, make_D_label(gt_label, ignore_mask)) loss = loss_seg + args.lambda_adv_pred * loss_adv_pred # proper normalization loss = loss/args.iter_size loss.backward() # loss_seg_value += loss_seg.data.cpu().numpy()[0]/args.iter_size # loss_adv_pred_value += loss_adv_pred.data.cpu().numpy()[0]/args.iter_size loss_seg_value += loss_seg.data.cpu().numpy()/args.iter_size # loss_seg_value += loss_seg_NLL.data.cpu().numpy()/args.iter_size loss_adv_pred_value += loss_adv_pred.data.cpu().numpy()/args.iter_size # train D # bring back requires_grad for param in model_D.parameters(): param.requires_grad = True # train with pred pred = pred.detach() if args.D_remain: pred = torch.cat((pred, pred_remain), 0) ignore_mask = np.concatenate((ignore_mask,ignore_mask_remain), axis=0) D_out = interp(model_D(F.softmax(pred))) loss_D = bce_loss(D_out, make_D_label(pred_label, ignore_mask)) loss_D = loss_D/args.iter_size/2 loss_D.backward() # loss_D_value += loss_D.data.cpu().numpy()[0] loss_D_value += loss_D.data.cpu().numpy() # train with gt # get gt labels try: _, batch = next(trainloader_gt_iter) except: trainloader_gt_iter = enumerate(trainloader_gt) _, batch = next(trainloader_gt_iter) _, labels_gt, _, _, _ = batch D_gt_v = Variable(one_hot(labels_gt)).cuda(args.gpu) # 每个类别一张label图,batch * class * h * w ignore_mask_gt = (labels_gt.numpy() == 255) D_out = interp(model_D(D_gt_v)) # ground_truth输入判别模型 loss_D = bce_loss(D_out, make_D_label(gt_label, ignore_mask_gt)) loss_D = loss_D/args.iter_size/2 loss_D.backward() # loss_D_value += loss_D.data.cpu().numpy()[0] loss_D_value += loss_D.data.cpu().numpy() optimizer.step() optimizer_D.step() print(('exp = {}'.format(args.snapshot_dir))) print(('iter = {0:8d}/{1:8d}, loss_seg = {2:.3f}, loss_adv_p = {3:.3f}, loss_D = {4:.3f}, ' 'loss_semi = {5:.3f}, loss_semi_adv = {6:.3f}'.format(i_iter, args.num_steps, loss_seg_value, loss_adv_pred_value, loss_D_value, loss_semi_value, loss_semi_adv_value))) file.write('{0} {1} {2} {3} {4}\n'.format(loss_seg_value, loss_adv_pred_value, loss_D_value, loss_semi_value, loss_semi_adv_value)) if loss_seg_value < best_loss: # 保存最优模型,删除次优模型 # print('loss:', loss_seg_value, 'best:', best_loss) torch.save(model.state_dict(), osp.join(args.snapshot_dir, 'VOC_epoch_{0}_seg_loss_{1}.pth'.format(i_iter+1, loss_seg_value))) torch.save(model_D.state_dict(), osp.join(args.snapshot_dir, 'VOC_epoch_{0}_seg_loss_{1}_D.pth'.format(i_iter+1, loss_seg_value))) delete_models(best_epoch + 1, best_loss) best_loss = loss_seg_value best_epoch = i_iter if i_iter >= args.num_steps-1: # num_step = 20000 print('save model ...') torch.save(model.state_dict(), osp.join(args.snapshot_dir, 'VOC_'+str(args.num_steps)+'.pth')) torch.save(model_D.state_dict(), osp.join(args.snapshot_dir, 'VOC_'+str(args.num_steps)+'_D.pth')) break if i_iter % args.save_pred_every == 0 and i_iter != 0: # save_pred_every = 5000 print('taking snapshot ...') torch.save(model.state_dict(),osp.join(args.snapshot_dir, 'VOC_'+str(i_iter)+'.pth')) torch.save(model_D.state_dict(),osp.join(args.snapshot_dir, 'VOC_'+str(i_iter)+'_D.pth')) end = timeit.default_timer() print(end-start, 'seconds') file.close()