def main(): """Create the model and start the training.""" with open(args.config) as f: config = yaml.load(f) for k, v in config['common'].items(): setattr(args, k, v) mkdirs(osp.join("logs/"+args.exp_name)) logger = create_logger('global_logger', "logs/" + args.exp_name + '/log.txt') logger.info('{}'.format(args)) ############################## for key, val in vars(args).items(): logger.info("{:16} {}".format(key, val)) logger.info("random_scale {}".format(args.random_scale)) logger.info("is_training {}".format(args.is_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) print(type(input_size_target[1])) cudnn.enabled = True args.snapshot_dir = args.snapshot_dir + args.exp_name tb_logger = SummaryWriter("logs/"+args.exp_name) ############################## #validation data h, w = map(int, args.input_size_test.split(',')) input_size_test = (h,w) h, w = map(int, args.com_size.split(',')) com_size = (h, w) h, w = map(int, args.input_size_crop.split(',')) input_size_crop = h,w h,w = map(int, args.input_size_target_crop.split(',')) input_size_target_crop = h,w test_normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]) test_transform = transforms.Compose([ transforms.Resize((input_size_test[1], input_size_test[0])), transforms.ToTensor(), test_normalize]) valloader = data.DataLoader(cityscapesDataSet( args.data_dir_target, args.data_list_target_val, crop_size=input_size_test, set='train', transform=test_transform),num_workers=args.num_workers, 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_val = args.label_path_list_val label_path_list_test = args.label_path_list_test label_path_list_test = './dataset/cityscapes_list/label.txt' gt_imgs_val = open(label_path_list_val, 'r').read().splitlines() gt_imgs_val = [osp.join(args.data_dir_target_val, x) for x in gt_imgs_val] testloader = data.DataLoader(cityscapesDataSet( args.data_dir_target, args.data_list_target_test, crop_size=input_size_test, set='val', transform=test_transform), num_workers=args.num_workers, batch_size=1, shuffle=False, pin_memory=True) gt_imgs_test = open(label_path_list_test ,'r').read().splitlines() gt_imgs_test = [osp.join(args.data_dir_target_test, x) for x in gt_imgs_test] name_classes = np.array(info['label'], dtype=np.str) interp_val = nn.Upsample(size=(com_size[1], com_size[0]),mode='bilinear', align_corners=True) #### #build model #### builder = ModelBuilder() net_encoder = builder.build_encoder( arch=args.arch_encoder, fc_dim=args.fc_dim, weights=args.weights_encoder) net_decoder = builder.build_decoder( arch=args.arch_decoder, fc_dim=args.fc_dim, num_class=args.num_classes, weights=args.weights_decoder, use_aux=True) model = SegmentationModule( net_encoder, net_decoder, args.use_aux) if args.num_gpus > 1: model = torch.nn.DataParallel(model) patch_replication_callback(model) model.cuda() nets = (net_encoder, net_decoder, None, None) optimizers = create_optimizer(nets, args) cudnn.enabled=True cudnn.benchmark=True model.train() mean=[0.485, 0.456, 0.406] std=[0.229, 0.224, 0.225] source_normalize = transforms_seg.Normalize(mean=mean, std=std) mean_mapping = [0.485, 0.456, 0.406] mean_mapping = [item * 255 for item in mean_mapping] if not os.path.exists(args.snapshot_dir): os.makedirs(args.snapshot_dir) source_transform = transforms_seg.Compose([ transforms_seg.Resize([input_size[1], input_size[0]]), segtransforms.RandScale((args.scale_min, args.scale_max)), #segtransforms.RandRotate((args.rotate_min, args.rotate_max), padding=mean_mapping, ignore_label=args.ignore_label), #segtransforms.RandomGaussianBlur(), segtransforms.RandomHorizontalFlip(), segtransforms.Crop([input_size_crop[1], input_size_crop[0]], crop_type='rand', padding=mean_mapping, ignore_label=args.ignore_label), transforms_seg.ToTensor(), source_normalize]) target_normalize = transforms_seg.Normalize(mean=mean, std=std) target_transform = transforms_seg.Compose([ transforms_seg.Resize([input_size_target[1], input_size_target[0]]), segtransforms.RandScale((args.scale_min, args.scale_max)), #segtransforms.RandRotate((args.rotate_min, args.rotate_max), padding=mean_mapping, ignore_label=args.ignore_label), #segtransforms.RandomGaussianBlur(), segtransforms.RandomHorizontalFlip(), segtransforms.Crop([input_size_target_crop[1], input_size_target_crop[0]],crop_type='rand', padding=mean_mapping, ignore_label=args.ignore_label), transforms_seg.ToTensor(), target_normalize]) 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, transform = source_transform), batch_size=args.batch_size, shuffle=True, num_workers=1, pin_memory=True) trainloader_iter = enumerate(trainloader) targetloader = data.DataLoader(fake_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, set=args.set, transform=target_transform), batch_size=args.batch_size, shuffle=True, num_workers=1, pin_memory=True) targetloader_iter = enumerate(targetloader) # implement model.optim_parameters(args) to handle different models' lr setting criterion_seg = torch.nn.CrossEntropyLoss(ignore_index=255,reduce=False) interp_target = nn.Upsample(size=(input_size_target[1], input_size_target[0]), align_corners=True, mode='bilinear') # labels for adversarial training source_label = 0 target_label = 1 optimizer_encoder, optimizer_decoder, optimizer_disc, optimizer_reconst = optimizers batch_time = AverageMeter(10) loss_seg_value1 = AverageMeter(10) is_best_test = True best_mIoUs = 0 loss_seg_value2 = AverageMeter(10) loss_balance_value = AverageMeter(10) loss_pseudo_value = AverageMeter(10) bounding_num = AverageMeter(10) pseudo_num = AverageMeter(10) for i_iter in range(args.num_steps): # train G # don't accumulate grads in D end = time.time() _, batch = trainloader_iter.__next__() images, labels, _ = batch images = Variable(images).cuda(async=True) labels = Variable(labels).cuda(async=True) seg, aux_seg, loss_seg2, loss_seg1 = model(images, labels) loss_seg2 = torch.mean(loss_seg2) loss_seg1 = torch.mean(loss_seg1) loss = loss_seg2+args.lambda_seg*loss_seg1 #logger.info(loss_seg1.data.cpu().numpy()) loss_seg_value2.update(loss_seg2.data.cpu().numpy()) # train with target optimizer_encoder.zero_grad() optimizer_decoder.zero_grad() loss.backward() optimizer_encoder.step() optimizer_decoder.step() del seg, loss_seg2 _, batch = targetloader_iter.__next__() with torch.no_grad(): images, labels, _ = batch images = Variable(images).cuda(async=True) result = model(images, None) del result batch_time.update(time.time() - end) remain_iter = args.num_steps - i_iter remain_time = remain_iter * batch_time.avg t_m, t_s = divmod(remain_time, 60) t_h, t_m = divmod(t_m, 60) remain_time = '{:02d}:{:02d}:{:02d}'.format(int(t_h), int(t_m), int(t_s)) adjust_learning_rate(optimizer_encoder, i_iter, args.lr_encoder, args) adjust_learning_rate(optimizer_decoder, i_iter, args.lr_decoder, args) if i_iter % args.print_freq == 0: lr_encoder = optimizer_encoder.param_groups[0]['lr'] lr_decoder = optimizer_decoder.param_groups[0]['lr'] logger.info('exp = {}'.format(args.snapshot_dir)) logger.info('Iter = [{0}/{1}]\t' 'Time = {batch_time.avg:.3f}\t' 'loss_seg1 = {loss_seg1.avg:4f}\t' 'loss_seg2 = {loss_seg2.avg:.4f}\t' 'lr_encoder = {lr_encoder:.8f} lr_decoder = {lr_decoder:.8f}'.format( i_iter, args.num_steps, batch_time=batch_time, loss_seg1=loss_seg_value1, loss_seg2=loss_seg_value2, lr_encoder=lr_encoder, lr_decoder=lr_decoder)) logger.info("remain_time: {}".format(remain_time)) if not tb_logger is None: tb_logger.add_scalar('loss_seg_value1', loss_seg_value1.avg, i_iter) tb_logger.add_scalar('loss_seg_value2', loss_seg_value2.avg, i_iter) tb_logger.add_scalar('lr', lr_encoder, i_iter) ##### #save image result if i_iter % args.save_pred_every == 0 and i_iter != 0: logger.info('taking snapshot ...') model.eval() val_time = time.time() hist = np.zeros((19,19)) for index, batch in tqdm(enumerate(valloader)): with torch.no_grad(): image, name = batch output2, _ = model(Variable(image).cuda(), None) pred = interp_val(output2) del output2 pred = pred.cpu().data[0].numpy() pred = pred.transpose(1, 2, 0) pred = np.asarray(np.argmax(pred, axis=2), dtype=np.uint8) label = np.array(Image.open(gt_imgs_val[index])) #label = np.array(label.resize(com_size, Image. label = label_mapping(label, mapping) #logger.info(label.shape) hist += fast_hist(label.flatten(), pred.flatten(), 19) mIoUs = per_class_iu(hist) for ind_class in range(args.num_classes): logger.info('===>' + name_classes[ind_class] + ':\t' + str(round(mIoUs[ind_class] * 100, 2))) tb_logger.add_scalar(name_classes[ind_class] + '_mIoU', mIoUs[ind_class], i_iter) mIoUs = round(np.nanmean(mIoUs) *100, 2) if mIoUs >= best_mIoUs: is_best_test = True best_mIoUs = mIoUs else: is_best_test = False logger.info("current mIoU {}".format(mIoUs)) logger.info("best mIoU {}".format(best_mIoUs)) tb_logger.add_scalar('val mIoU', mIoUs, i_iter) tb_logger.add_scalar('val mIoU', mIoUs, i_iter) net_encoder, net_decoder, net_disc, net_reconst = nets save_checkpoint(net_encoder, 'encoder', i_iter, args, is_best_test) save_checkpoint(net_decoder, 'decoder', i_iter, args, is_best_test) model.train()
def main(): """Create the model and start the training.""" with open(args.config) as f: config = yaml.load(f) for k, v in config['common'].items(): setattr(args, k, v) mkdirs(osp.join("logs/"+args.exp_name)) logger = create_logger('global_logger', "logs/" + args.exp_name + '/log.txt') logger.info('{}'.format(args)) ############################## for key, val in vars(args).items(): logger.info("{:16} {}".format(key, val)) logger.info("random_scale {}".format(args.random_scale)) logger.info("is_training {}".format(args.is_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) print(type(input_size_target[1])) cudnn.enabled = True args.snapshot_dir = args.snapshot_dir + args.exp_name tb_logger = SummaryWriter("logs/"+args.exp_name) ############################## #validation data local_array = np.load("local.npy") local_array = local_array[:,:,:19] local_array = local_array / local_array.sum(2).reshape(512, 1024, 1) local_array = local_array.transpose(2,0,1) local_array = torch.from_numpy(local_array) local_array = local_array.view(1, 19, 512, 1024) h, w = map(int, args.input_size_test.split(',')) input_size_test = (h,w) h, w = map(int, args.com_size.split(',')) com_size = (h, w) h, w = map(int, args.input_size_crop.split(',')) input_size_crop = h,w h,w = map(int, args.input_size_target_crop.split(',')) input_size_target_crop = h,w mean=[0.485, 0.456, 0.406] std=[0.229, 0.224, 0.225] normalize_module = transforms_seg.Normalize(mean=mean, std=std) test_normalize = transforms.Normalize(mean=mean, std=std) test_transform = transforms.Compose([ transforms.Resize((input_size_test[1], input_size_test[0])), transforms.ToTensor(), test_normalize]) valloader = data.DataLoader(cityscapesDataSet( args.data_dir_target, args.data_list_target_val, crop_size=input_size_test, set='train', transform=test_transform),num_workers=args.num_workers, 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_val = args.label_path_list_val gt_imgs_val = open(label_path_list_val, 'r').read().splitlines() gt_imgs_val = [osp.join(args.data_dir_target_val, x) for x in gt_imgs_val] name_classes = np.array(info['label'], dtype=np.str) interp_val = nn.Upsample(size=(com_size[1], com_size[0]),mode='bilinear', align_corners=True) #### #build model #### builder = ModelBuilder() net_encoder = builder.build_encoder( arch=args.arch_encoder, fc_dim=args.fc_dim, weights=args.weights_encoder) net_decoder = builder.build_decoder( arch=args.arch_decoder, fc_dim=args.fc_dim, num_class=args.num_classes, weights=args.weights_decoder, use_aux=True) weighted_softmax = pd.read_csv("weighted_loss.txt", header=None) weighted_softmax = weighted_softmax.values weighted_softmax = torch.from_numpy(weighted_softmax) weighted_softmax = weighted_softmax / torch.sum(weighted_softmax) weighted_softmax = weighted_softmax.cuda().float() model = SegmentationModule( net_encoder, net_decoder, args.use_aux) if args.num_gpus > 1: model = torch.nn.DataParallel(model) patch_replication_callback(model) model.cuda() nets = (net_encoder, net_decoder, None, None) optimizers = create_optimizer(nets, args) cudnn.enabled=True cudnn.benchmark=True model.train() mean_mapping = [0.485, 0.456, 0.406] mean_mapping = [item * 255 for item in mean_mapping] if not os.path.exists(args.snapshot_dir): os.makedirs(args.snapshot_dir) source_transform = transforms_seg.Compose([ transforms_seg.Resize([input_size[1], input_size[0]]), #segtransforms.RandScale((0.75, args.scale_max)), #segtransforms.RandRotate((args.rotate_min, args.rotate_max), padding=mean_mapping, ignore_label=args.ignore_label), #segtransforms.RandomGaussianBlur(), #segtransforms.RandomHorizontalFlip(), #segtransforms.Crop([input_size_crop[1], input_size_crop[0]], crop_type='rand', padding=mean_mapping, ignore_label=args.ignore_label), transforms_seg.ToTensor(), normalize_module]) target_transform = transforms_seg.Compose([ transforms_seg.Resize([input_size_target[1], input_size_target[0]]), #segtransforms.RandScale((0.75, args.scale_max)), #segtransforms.RandRotate((args.rotate_min, args.rotate_max), padding=mean_mapping, ignore_label=args.ignore_label), #segtransforms.RandomGaussianBlur(), #segtransforms.RandomHorizontalFlip(), #segtransforms.Crop([input_size_target_crop[1], input_size_target_crop[0]],crop_type='rand', padding=mean_mapping, ignore_label=args.ignore_label), transforms_seg.ToTensor(), normalize_module]) 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, transform = source_transform), batch_size=args.batch_size, shuffle=True, num_workers=5, pin_memory=True) trainloader_iter = enumerate(trainloader) targetloader = data.DataLoader(fake_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, set=args.set, transform=target_transform), batch_size=args.batch_size, shuffle=True, num_workers=5, pin_memory=True) targetloader_iter = enumerate(targetloader) # implement model.optim_parameters(args) to handle different models' lr setting criterion_seg = torch.nn.CrossEntropyLoss(ignore_index=255,reduce=False) criterion_pseudo = torch.nn.BCEWithLogitsLoss(reduce=False).cuda() bce_loss = torch.nn.BCEWithLogitsLoss().cuda() criterion_reconst = torch.nn.L1Loss().cuda() criterion_soft_pseudo = torch.nn.MSELoss(reduce=False).cuda() criterion_box = torch.nn.CrossEntropyLoss(ignore_index=255, reduce=False) interp = nn.Upsample(size=(input_size[1], input_size[0]),align_corners=True, mode='bilinear') interp_target = nn.Upsample(size=(input_size_target[1], input_size_target[0]), align_corners=True, mode='bilinear') # labels for adversarial training source_label = 0 target_label = 1 optimizer_encoder, optimizer_decoder, optimizer_disc, optimizer_reconst = optimizers batch_time = AverageMeter(10) loss_seg_value1 = AverageMeter(10) best_mIoUs = 0 best_test_mIoUs = 0 loss_seg_value2 = AverageMeter(10) loss_reconst_source_value = AverageMeter(10) loss_reconst_target_value = AverageMeter(10) loss_balance_value = AverageMeter(10) loss_eq_att_value = AverageMeter(10) loss_pseudo_value = AverageMeter(10) bounding_num = AverageMeter(10) pseudo_num = AverageMeter(10) loss_bbx_att_value = AverageMeter(10) for i_iter in range(args.num_steps): # train G # don't accumulate grads in D end = time.time() _, batch = trainloader_iter.__next__() images, labels, _ = batch images = Variable(images).cuda(async=True) labels = Variable(labels).cuda(async=True) results = model(images, labels) loss_seg2 = results[-2] loss_seg1 = results[-1] loss_seg2 = torch.mean(loss_seg2) loss_seg1 = torch.mean(loss_seg1) loss = args.lambda_trade_off*(loss_seg2+args.lambda_seg * loss_seg1) # proper normalization #logger.info(loss_seg1.data.cpu().numpy()) loss_seg_value2.update(loss_seg2.data.cpu().numpy()) optimizer_encoder.zero_grad() optimizer_decoder.zero_grad() loss.backward() optimizer_encoder.step() optimizer_decoder.step() _, batch = targetloader_iter.__next__() images, fake_labels, _ = batch images = Variable(images).cuda(async=True) fake_labels = Variable(fake_labels, requires_grad=False).cuda() results = model(images, None) target_seg = results[0] conf_tea, pseudo_label = torch.max(nn.functional.softmax(target_seg), dim=1) pseudo_label = pseudo_label.detach() # pseudo label hard loss_pseudo = criterion_seg(target_seg, pseudo_label) fake_mask = (fake_labels!=255).float().detach() conf_mask = torch.gt(conf_tea, args.conf_threshold).float().detach() loss_pseudo = loss_pseudo * conf_mask.detach() * fake_mask.detach() loss_pseudo = loss_pseudo.view(-1) loss_pseudo = loss_pseudo[loss_pseudo!=0] #loss_pseudo = torch.sum(loss_pseudo * conf_mask.detach() * fake_mask.detach()) predict_class_mean = torch.mean(nn.functional.softmax(target_seg), dim=0).mean(1).mean(1) equalise_cls_loss = robust_binary_crossentropy(predict_class_mean, weighted_softmax) #equalise_cls_loss = torch.mean(equalise_cls_loss)* args.num_classes * torch.sum(conf_mask * fake_mask) / float(input_size_crop[0] * input_size_crop[1] * args.batch_size) # new equalise_cls_loss equalise_cls_loss = torch.mean(equalise_cls_loss) #loss=args.lambda_balance * equalise_cls_loss #bbx attention loss_bbx_att = [] loss_eq_att = [] for box_idx, box_size in enumerate(args.box_size): pooling = torch.nn.AvgPool2d(box_size) pooling_result_i = pooling(target_seg) local_i = pooling(local_array).float().cuda() pooling_conf_mask, pooling_pseudo = torch.max(nn.functional.softmax(pooling_result_i), dim=1) pooling_conf_mask = torch.gt(pooling_conf_mask, args.conf_threshold).float().detach() fake_mask_i = pooling(fake_labels.unsqueeze(1).float()) fake_mask_i = fake_mask_i.squeeze(1) fake_mask_i = (fake_mask_i!=255).float().detach() loss_bbx_att_i = criterion_seg(pooling_result_i, pooling_pseudo) loss_bbx_att_i = loss_bbx_att_i * pooling_conf_mask * fake_mask_i loss_bbx_att_i = loss_bbx_att_i.view(-1) loss_bbx_att_i = loss_bbx_att_i[loss_bbx_att_i!=0] loss_bbx_att.append(loss_bbx_att_i) pooling_result_i = pooling_result_i.mean(0).unsqueeze(0) equalise_cls_loss_i = robust_binary_crossentropy(nn.functional.softmax(pooling_result_i), local_i) equalise_cls_loss_i = equalise_cls_loss_i.mean(1) equalise_cls_loss_i = equalise_cls_loss_i * pooling_conf_mask * fake_mask_i equalise_cls_loss_i = equalise_cls_loss_i.view(-1) equalise_cls_loss_i = equalise_cls_loss_i[equalise_cls_loss_i!=0] loss_eq_att.append(equalise_cls_loss_i) if len(args.box_size) > 0: if args.merge_1x1: loss_bbx_att.append(loss_pseudo) loss_bbx_att = torch.cat(loss_bbx_att, dim=0) bounding_num.update(loss_bbx_att.size(0) / float(560*480*args.batch_size)) loss_bbx_att = torch.mean(loss_bbx_att) loss_eq_att = torch.cat(loss_eq_att, dim=0) loss_eq_att = torch.mean(loss_eq_att) loss_eq_att_value.update(loss_eq_att.item()) else: loss_bbx_att = torch.mean(loss_pseudo) loss_eq_att = 0 pseudo_num.update(loss_pseudo.size(0) / float(560*480*args.batch_size)) loss_pseudo = torch.mean(loss_pseudo) if not args.merge_1x1: loss += args.lambda_pseudo * loss_pseudo loss = args.lambda_balance * equalise_cls_loss if not isinstance(loss_bbx_att, list): loss += args.lambda_pseudo * loss_bbx_att loss += args.lambda_eq * loss_eq_att loss_pseudo_value.update(loss_pseudo.item()) loss_balance_value.update(equalise_cls_loss.item()) optimizer_encoder.zero_grad() optimizer_decoder.zero_grad() loss.backward() optimizer_encoder.step() optimizer_decoder.step() #optimizer_disc.step() #loss_target_disc_value.update(loss_target_disc.data.cpu().numpy()) batch_time.update(time.time() - end) remain_iter = args.num_steps - i_iter remain_time = remain_iter * batch_time.avg t_m, t_s = divmod(remain_time, 60) t_h, t_m = divmod(t_m, 60) remain_time = '{:02d}:{:02d}:{:02d}'.format(int(t_h), int(t_m), int(t_s)) if i_iter == args.decrease_lr: adjust_learning_rate(optimizer_encoder, i_iter, args.lr_encoder, args) adjust_learning_rate(optimizer_decoder, i_iter, args.lr_decoder, args) if i_iter % args.print_freq == 0: lr_encoder = optimizer_encoder.param_groups[0]['lr'] lr_decoder = optimizer_decoder.param_groups[0]['lr'] logger.info('exp = {}'.format(args.snapshot_dir)) logger.info('Iter = [{0}/{1}]\t' 'Time = {batch_time.avg:.3f}\t' 'loss_seg1 = {loss_seg1.avg:4f}\t' 'loss_seg2 = {loss_seg2.avg:.4f}\t' 'loss_reconst_source = {loss_reconst_source.avg:.4f}\t' 'loss_bbx_att = {loss_bbx_att.avg:.4f}\t' 'loss_reconst_target = {loss_reconst_target.avg:.4f}\t' 'loss_pseudo = {loss_pseudo.avg:.4f}\t' 'loss_eq_att = {loss_eq_att.avg:.4f}\t' 'loss_balance = {loss_balance.avg:.4f}\t' 'bounding_num = {bounding_num.avg:.4f}\t' 'pseudo_num = {pseudo_num.avg:4f}\t' 'lr_encoder = {lr_encoder:.8f} lr_decoder = {lr_decoder:.8f}'.format( i_iter, args.num_steps, batch_time=batch_time, loss_seg1=loss_seg_value1, loss_seg2=loss_seg_value2, loss_pseudo=loss_pseudo_value, loss_bbx_att = loss_bbx_att_value, bounding_num = bounding_num, loss_eq_att = loss_eq_att_value, pseudo_num = pseudo_num, loss_reconst_source=loss_reconst_source_value, loss_balance=loss_balance_value, loss_reconst_target=loss_reconst_target_value, lr_encoder=lr_encoder, lr_decoder=lr_decoder)) logger.info("remain_time: {}".format(remain_time)) if not tb_logger is None: tb_logger.add_scalar('loss_seg_value1', loss_seg_value1.avg, i_iter) tb_logger.add_scalar('loss_seg_value2', loss_seg_value2.avg, i_iter) tb_logger.add_scalar('bounding_num', bounding_num.avg, i_iter) tb_logger.add_scalar('pseudo_num', pseudo_num.avg, i_iter) tb_logger.add_scalar('loss_pseudo', loss_pseudo_value.avg, i_iter) tb_logger.add_scalar('lr', lr_encoder, i_iter) tb_logger.add_scalar('loss_balance', loss_balance_value.avg, i_iter) ##### #save image result if i_iter % args.save_pred_every == 0 and i_iter != 0: logger.info('taking snapshot ...') model.eval() val_time = time.time() hist = np.zeros((19,19)) # f = open(args.result_dir, 'a') # for index, batch in tqdm(enumerate(testloader)): # with torch.no_grad(): # image, name = batch # results = model(Variable(image).cuda(), None) # output2 = results[0] # pred = interp_val(output2) # del output2 # pred = pred.cpu().data[0].numpy() # pred = pred.transpose(1, 2, 0) # pred = np.asarray(np.argmax(pred, axis=2), dtype=np.uint8) # label = np.array(Image.open(gt_imgs_val[index])) # #label = np.array(label.resize(com_size, Image. # label = label_mapping(label, mapping) # #logger.info(label.shape) # hist += fast_hist(label.flatten(), pred.flatten(), 19) # mIoUs = per_class_iu(hist) # for ind_class in range(args.num_classes): # logger.info('===>' + name_classes[ind_class] + ':\t' + str(round(mIoUs[ind_class] * 100, 2))) # tb_logger.add_scalar(name_classes[ind_class] + '_mIoU', mIoUs[ind_class], i_iter) # logger.info(mIoUs) # tb_logger.add_scalar('val mIoU', mIoUs, i_iter) # tb_logger.add_scalar('val mIoU', mIoUs, i_iter) # f.write('i_iter:{:d},\tmiou:{:0.3f} \n'.format(i_iter, mIoUs)) # f.close() # if mIoUs > best_mIoUs: is_best = True # best_mIoUs = mIoUs #test validation model.eval() val_time = time.time() hist = np.zeros((19,19)) # f = open(args.result_dir, 'a') for index, batch in tqdm(enumerate(valloader)): with torch.no_grad(): image, name = batch results = model(Variable(image).cuda(), None) output2 = results[0] pred = interp_val(output2) del output2 pred = pred.cpu().data[0].numpy() pred = pred.transpose(1, 2, 0) pred = np.asarray(np.argmax(pred, axis=2), dtype=np.uint8) label = np.array(Image.open(gt_imgs_val[index])) #label = np.array(label.resize(com_size, Image. label = label_mapping(label, mapping) #logger.info(label.shape) hist += fast_hist(label.flatten(), pred.flatten(), 19) mIoUs = per_class_iu(hist) for ind_class in range(args.num_classes): logger.info('===>' + name_classes[ind_class] + ':\t' + str(round(mIoUs[ind_class] * 100, 2))) tb_logger.add_scalar(name_classes[ind_class] + '_mIoU', mIoUs[ind_class], i_iter) mIoUs = round(np.nanmean(mIoUs) *100, 2) is_best_test = False logger.info(mIoUs) tb_logger.add_scalar('test mIoU', mIoUs, i_iter) if mIoUs > best_test_mIoUs: best_test_mIoUs = mIoUs is_best_test = True # logger.info("best mIoU {}".format(best_mIoUs)) logger.info("best test mIoU {}".format(best_test_mIoUs)) net_encoder, net_decoder, net_disc, net_reconst = nets save_checkpoint(net_encoder, 'encoder', i_iter, args, is_best_test) save_checkpoint(net_decoder, 'decoder', i_iter, args, is_best_test) is_best_test = False model.train()