def main(opts): # Set parameters p = OrderedDict() # Parameters to include in report p['trainBatch'] = opts.batch # Training batch size testBatch = 1 # Testing batch size useTest = True # See evolution of the test set when training nTestInterval = opts.testInterval # Run on test set every nTestInterval epochs snapshot = 1 # Store a model every snapshot epochs p['nAveGrad'] = 1 # Average the gradient of several iterations p['lr'] = opts.lr # Learning rate p['wd'] = 5e-4 # Weight decay p['momentum'] = 0.9 # Momentum p['epoch_size'] = opts.step # How many epochs to change learning rate p['num_workers'] = opts.numworker model_path = opts.pretrainedModel backbone = 'xception' # Use xception or resnet as feature extractor nEpochs = opts.epochs max_id = 0 save_dir_root = os.path.join(os.path.dirname(os.path.abspath(__file__))) exp_name = os.path.dirname(os.path.abspath(__file__)).split('/')[-1] runs = glob.glob(os.path.join(save_dir_root, 'run', 'run_*')) for r in runs: run_id = int(r.split('_')[-1]) if run_id >= max_id: max_id = run_id + 1 # run_id = int(runs[-1].split('_')[-1]) + 1 if runs else 0 save_dir = os.path.join(save_dir_root, 'run', 'run_' + str(max_id)) # Device if (opts.device == "gpu"): use_cuda = torch.cuda.is_available() if (use_cuda == True): device = torch.device("cuda") #torch.cuda.set_device(args.gpu_ids[0]) print("実行デバイス :", device) print("GPU名 :", torch.cuda.get_device_name(device)) print("torch.cuda.current_device() =", torch.cuda.current_device()) else: print("can't using gpu.") device = torch.device("cpu") print("実行デバイス :", device) else: device = torch.device("cpu") print("実行デバイス :", device) # Network definition if backbone == 'xception': net_ = deeplab_xception_universal.deeplab_xception_end2end_3d( n_classes=20, os=16, hidden_layers=opts.hidden_layers, source_classes=7, middle_classes=18, ) elif backbone == 'resnet': # net_ = deeplab_resnet.DeepLabv3_plus(nInputChannels=3, n_classes=7, os=16, pretrained=True) raise NotImplementedError else: raise NotImplementedError modelName = 'deeplabv3plus-' + backbone + '-voc' + datetime.now().strftime( '%b%d_%H-%M-%S') criterion = ut.cross_entropy2d if gpu_id >= 0: # torch.cuda.set_device(device=gpu_id) net_.to(device) # net load weights if not model_path == '': x = torch.load(model_path) net_.load_state_dict_new(x) print('load pretrainedModel.') else: print('no pretrainedModel.') if not opts.loadmodel == '': x = torch.load(opts.loadmodel) net_.load_source_model(x) print('load model:', opts.loadmodel) else: print('no trained model load !!!!!!!!') print(net_) log_dir = os.path.join( save_dir, 'models', datetime.now().strftime('%b%d_%H-%M-%S') + '_' + socket.gethostname()) writer = SummaryWriter(log_dir=log_dir) writer.add_text('load model', opts.loadmodel, 1) writer.add_text('setting', sys.argv[0], 1) # Use the following optimizer optimizer = optim.SGD(net_.parameters(), lr=p['lr'], momentum=p['momentum'], weight_decay=p['wd']) composed_transforms_tr = transforms.Compose([ tr.RandomSized_new(opts.image_size), tr.Normalize_xception_tf(), tr.ToTensor_() ]) composed_transforms_ts = transforms.Compose( [tr.Normalize_xception_tf(), tr.ToTensor_()]) composed_transforms_ts_flip = transforms.Compose( [tr.HorizontalFlip(), tr.Normalize_xception_tf(), tr.ToTensor_()]) #all_train = cihp_pascal_atr.VOCSegmentation(split='train', transform=composed_transforms_tr, flip=True) #voc_val = pascal.VOCSegmentation(split='val', transform=composed_transforms_ts) #voc_val_flip = pascal.VOCSegmentation(split='val', transform=composed_transforms_ts_flip) all_train = cihp_pascal_atr.VOCSegmentation( cihp_dir="./data/datasets/CIHP_4w", split='train', transform=composed_transforms_tr, flip=True) #voc_val = pascal.VOCSegmentation(base_dir="./data/datasets/pascal", split='val', transform=composed_transforms_ts) #voc_val_flip = pascal.VOCSegmentation(base_dir="./data/datasets/pascal", split='val', transform=composed_transforms_ts_flip) num_cihp, num_pascal, num_atr = all_train.get_class_num() ss = sam.Sampler_uni(num_cihp, num_pascal, num_atr, opts.batch) # balance datasets based pascal ss_balanced = sam.Sampler_uni(num_cihp, num_pascal, num_atr, opts.batch, balance_id=1) trainloader = DataLoader(all_train, batch_size=p['trainBatch'], shuffle=False, num_workers=p['num_workers'], sampler=ss, drop_last=True) trainloader_balanced = DataLoader(all_train, batch_size=p['trainBatch'], shuffle=False, num_workers=p['num_workers'], sampler=ss_balanced, drop_last=True) #testloader = DataLoader(voc_val, batch_size=testBatch, shuffle=False, num_workers=p['num_workers']) #testloader_flip = DataLoader(voc_val_flip, batch_size=testBatch, shuffle=False, num_workers=p['num_workers']) num_img_tr = len(trainloader) num_img_balanced = len(trainloader_balanced) #num_img_ts = len(testloader) num_img_ts = 0 running_loss_tr = 0.0 running_loss_tr_atr = 0.0 running_loss_ts = 0.0 aveGrad = 0 global_step = 0 print("Training Network") net = torch.nn.DataParallel(net_) id_list = torch.LongTensor(range(opts.batch)) pascal_iter = int(num_img_tr // opts.batch) # Get graphs train_graph, test_graph = get_graphs(opts, device) adj1, adj2, adj3, adj4, adj5, adj6 = train_graph adj1_test, adj2_test, adj3_test, adj4_test, adj5_test, adj6_test = test_graph # Main Training and Testing Loop for epoch in range(resume_epoch, int(1.5 * nEpochs)): start_time = timeit.default_timer() if epoch % p['epoch_size'] == p['epoch_size'] - 1 and epoch < nEpochs: lr_ = ut.lr_poly(p['lr'], epoch, nEpochs, 0.9) optimizer = optim.SGD(net_.parameters(), lr=lr_, momentum=p['momentum'], weight_decay=p['wd']) print('(poly lr policy) learning rate: ', lr_) writer.add_scalar('data/lr_', lr_, epoch) elif epoch % p['epoch_size'] == p['epoch_size'] - 1 and epoch > nEpochs: lr_ = ut.lr_poly(p['lr'], epoch - nEpochs, int(0.5 * nEpochs), 0.9) optimizer = optim.SGD(net_.parameters(), lr=lr_, momentum=p['momentum'], weight_decay=p['wd']) print('(poly lr policy) learning rate: ', lr_) writer.add_scalar('data/lr_', lr_, epoch) net_.train() if epoch < nEpochs: for ii, sample_batched in enumerate(trainloader): inputs, labels = sample_batched['image'], sample_batched[ 'label'] dataset_lbl = sample_batched['pascal'][0].item() # Forward-Backward of the mini-batch inputs, labels = Variable(inputs, requires_grad=True), Variable(labels) global_step += 1 if gpu_id >= 0: inputs, labels = inputs.to(device), labels.to(device) if dataset_lbl == 0: # 0 is cihp -- target #print( "inputs.shape : ", inputs.shape ) # torch.Size([batch, 3, 512, 512]) #print( "adj1.shape : ", adj1.shape ) # torch.Size([1, 1, 20, 20]) #print( "adj2.shape : ", adj2.shape ) # torch.Size([1, 1, 7, 7]) #print( "adj3.shape : ", adj3.shape ) # torch.Size([1, 1, 20, 20]) _, outputs, _ = net.forward( None, input_target=inputs, input_middle=None, adj1_target=adj1, adj2_source=adj2, adj3_transfer_s2t=adj3, adj3_transfer_t2s=adj3.transpose(2, 3), adj4_middle=adj4, adj5_transfer_s2m=adj5.transpose(2, 3), adj6_transfer_t2m=adj6.transpose(2, 3), adj5_transfer_m2s=adj5, adj6_transfer_m2t=adj6, ) #print( "outputs.shape : ", outputs.shape ) # torch.Size([2, 20, 512, 512]) elif dataset_lbl == 1: # pascal is source outputs, _, _ = net.forward( inputs, input_target=None, input_middle=None, adj1_target=adj1, adj2_source=adj2, adj3_transfer_s2t=adj3, adj3_transfer_t2s=adj3.transpose(2, 3), adj4_middle=adj4, adj5_transfer_s2m=adj5.transpose(2, 3), adj6_transfer_t2m=adj6.transpose(2, 3), adj5_transfer_m2s=adj5, adj6_transfer_m2t=adj6, ) else: # atr _, _, outputs = net.forward( None, input_target=None, input_middle=inputs, adj1_target=adj1, adj2_source=adj2, adj3_transfer_s2t=adj3, adj3_transfer_t2s=adj3.transpose(2, 3), adj4_middle=adj4, adj5_transfer_s2m=adj5.transpose(2, 3), adj6_transfer_t2m=adj6.transpose(2, 3), adj5_transfer_m2s=adj5, adj6_transfer_m2t=adj6, ) # print(sample_batched['pascal']) # print(outputs.size(),) # print(labels) loss = criterion(outputs, labels, batch_average=True) running_loss_tr += loss.item() # Print stuff if ii % num_img_tr == (num_img_tr - 1): running_loss_tr = running_loss_tr / num_img_tr writer.add_scalar('data/total_loss_epoch', running_loss_tr, epoch) print('[Epoch: %d, numImages: %5d]' % (epoch, epoch)) print('Loss: %f' % running_loss_tr) running_loss_tr = 0 stop_time = timeit.default_timer() print("Execution time: " + str(stop_time - start_time) + "\n") # Backward the averaged gradient loss /= p['nAveGrad'] loss.backward() aveGrad += 1 # Update the weights once in p['nAveGrad'] forward passes if aveGrad % p['nAveGrad'] == 0: writer.add_scalar('data/total_loss_iter', loss.item(), global_step) if dataset_lbl == 0: writer.add_scalar('data/total_loss_iter_cihp', loss.item(), global_step) if dataset_lbl == 1: writer.add_scalar('data/total_loss_iter_pascal', loss.item(), global_step) if dataset_lbl == 2: writer.add_scalar('data/total_loss_iter_atr', loss.item(), global_step) optimizer.step() optimizer.zero_grad() # optimizer_gcn.step() # optimizer_gcn.zero_grad() aveGrad = 0 # Show 10 * 3 images results each epoch if ii % (num_img_tr // 10) == 0: # if ii % (num_img_tr // 4000) == 0: grid_image = make_grid(inputs[:3].clone().cpu().data, 3, normalize=True) writer.add_image('Image', grid_image, global_step) grid_image = make_grid(ut.decode_seg_map_sequence( torch.max(outputs[:3], 1)[1].detach().cpu().numpy()), 3, normalize=False, range=(0, 255)) writer.add_image('Predicted label', grid_image, global_step) grid_image = make_grid(ut.decode_seg_map_sequence( torch.squeeze(labels[:3], 1).detach().cpu().numpy()), 3, normalize=False, range=(0, 255)) writer.add_image('Groundtruth label', grid_image, global_step) print('step {} | loss is {}'.format(ii, loss.cpu().item()), flush=True) else: # Balanced the number of datasets for ii, sample_batched in enumerate(trainloader_balanced): inputs, labels = sample_batched['image'], sample_batched[ 'label'] dataset_lbl = sample_batched['pascal'][0].item() # Forward-Backward of the mini-batch inputs, labels = Variable(inputs, requires_grad=True), Variable(labels) global_step += 1 if gpu_id >= 0: inputs, labels = inputs.to(device), labels.to(device) if dataset_lbl == 0: # 0 is cihp -- target _, outputs, _ = net.forward( None, input_target=inputs, input_middle=None, adj1_target=adj1, adj2_source=adj2, adj3_transfer_s2t=adj3, adj3_transfer_t2s=adj3.transpose(2, 3), adj4_middle=adj4, adj5_transfer_s2m=adj5.transpose(2, 3), adj6_transfer_t2m=adj6.transpose(2, 3), adj5_transfer_m2s=adj5, adj6_transfer_m2t=adj6, ) elif dataset_lbl == 1: # pascal is source outputs, _, _ = net.forward( inputs, input_target=None, input_middle=None, adj1_target=adj1, adj2_source=adj2, adj3_transfer_s2t=adj3, adj3_transfer_t2s=adj3.transpose(2, 3), adj4_middle=adj4, adj5_transfer_s2m=adj5.transpose(2, 3), adj6_transfer_t2m=adj6.transpose(2, 3), adj5_transfer_m2s=adj5, adj6_transfer_m2t=adj6, ) else: # atr _, _, outputs = net.forward( None, input_target=None, input_middle=inputs, adj1_target=adj1, adj2_source=adj2, adj3_transfer_s2t=adj3, adj3_transfer_t2s=adj3.transpose(2, 3), adj4_middle=adj4, adj5_transfer_s2m=adj5.transpose(2, 3), adj6_transfer_t2m=adj6.transpose(2, 3), adj5_transfer_m2s=adj5, adj6_transfer_m2t=adj6, ) # print(sample_batched['pascal']) # print(outputs.size(),) # print(labels) loss = criterion(outputs, labels, batch_average=True) running_loss_tr += loss.item() # Print stuff if ii % num_img_balanced == (num_img_balanced - 1): running_loss_tr = running_loss_tr / num_img_balanced writer.add_scalar('data/total_loss_epoch', running_loss_tr, epoch) print('[Epoch: %d, numImages: %5d]' % (epoch, epoch)) print('Loss: %f' % running_loss_tr) running_loss_tr = 0 stop_time = timeit.default_timer() print("Execution time: " + str(stop_time - start_time) + "\n") # Backward the averaged gradient loss /= p['nAveGrad'] loss.backward() aveGrad += 1 # Update the weights once in p['nAveGrad'] forward passes if aveGrad % p['nAveGrad'] == 0: writer.add_scalar('data/total_loss_iter', loss.item(), global_step) if dataset_lbl == 0: writer.add_scalar('data/total_loss_iter_cihp', loss.item(), global_step) if dataset_lbl == 1: writer.add_scalar('data/total_loss_iter_pascal', loss.item(), global_step) if dataset_lbl == 2: writer.add_scalar('data/total_loss_iter_atr', loss.item(), global_step) optimizer.step() optimizer.zero_grad() aveGrad = 0 # Show 10 * 3 images results each epoch if ii % (num_img_balanced // 10) == 0: grid_image = make_grid(inputs[:3].clone().cpu().data, 3, normalize=True) writer.add_image('Image', grid_image, global_step) grid_image = make_grid(ut.decode_seg_map_sequence( torch.max(outputs[:3], 1)[1].detach().cpu().numpy()), 3, normalize=False, range=(0, 255)) writer.add_image('Predicted label', grid_image, global_step) grid_image = make_grid(ut.decode_seg_map_sequence( torch.squeeze(labels[:3], 1).detach().cpu().numpy()), 3, normalize=False, range=(0, 255)) writer.add_image('Groundtruth label', grid_image, global_step) print('loss is ', loss.cpu().item(), flush=True) # Save the model if (epoch % snapshot) == snapshot - 1: torch.save( net_.state_dict(), os.path.join(save_dir, 'models', modelName + '_epoch-' + str(epoch) + '.pth')) print("Save model at {}\n".format( os.path.join(save_dir, 'models', modelName + '_epoch-' + str(epoch) + '.pth'))) # One testing epoch """
def main(opts): p = OrderedDict() # Parameters to include in report p['trainBatch'] = opts.batch # Training batch size testBatch = 1 # Testing batch size useTest = True # See evolution of the test set when training nTestInterval = opts.testInterval # Run on test set every nTestInterval epochs snapshot = 1 # Store a model every snapshot epochs p['nAveGrad'] = 1 # Average the gradient of several iterations p['lr'] = opts.lr # Learning rate p['lrFtr'] = 1e-5 p['lraspp'] = 1e-5 p['lrpro'] = 1e-5 p['lrdecoder'] = 1e-5 p['lrother'] = 1e-5 p['wd'] = 5e-4 # Weight decay p['momentum'] = 0.9 # Momentum p['epoch_size'] = opts.step # How many epochs to change learning rate p['num_workers'] = opts.numworker model_path = opts.pretrainedModel backbone = 'xception' # Use xception or resnet as feature extractor, nEpochs = opts.epochs max_id = 0 save_dir_root = os.path.join(os.path.dirname(os.path.abspath(__file__))) exp_name = os.path.dirname(os.path.abspath(__file__)).split('/')[-1] runs = glob.glob(os.path.join(save_dir_root, 'run_cihp', 'run_*')) for r in runs: run_id = int(r.split('_')[-1]) if run_id >= max_id: max_id = run_id + 1 save_dir = os.path.join(save_dir_root, 'run_cihp', 'run_' + str(max_id)) # Device if (opts.device == "gpu"): use_cuda = torch.cuda.is_available() if (use_cuda == True): device = torch.device("cuda") #torch.cuda.set_device(args.gpu_ids[0]) print("実行デバイス :", device) print("GPU名 :", torch.cuda.get_device_name(device)) print("torch.cuda.current_device() =", torch.cuda.current_device()) else: print("can't using gpu.") device = torch.device("cpu") print("実行デバイス :", device) else: device = torch.device("cpu") print("実行デバイス :", device) # Network definition if backbone == 'xception': net_ = deeplab_xception_transfer.deeplab_xception_transfer_projection_savemem( n_classes=opts.classes, os=16, hidden_layers=opts.hidden_layers, source_classes=7, ) elif backbone == 'resnet': # net_ = deeplab_resnet.DeepLabv3_plus(nInputChannels=3, n_classes=7, os=16, pretrained=True) raise NotImplementedError else: raise NotImplementedError modelName = 'deeplabv3plus-' + backbone + '-voc' + datetime.now().strftime( '%b%d_%H-%M-%S') criterion = util.cross_entropy2d if gpu_id >= 0: # torch.cuda.set_device(device=gpu_id) #net_.cuda() net_.to(device) # net load weights if not model_path == '': x = torch.load(model_path) net_.load_state_dict_new(x) print('load pretrainedModel:', model_path) else: print('no pretrainedModel.') if not opts.loadmodel == '': x = torch.load(opts.loadmodel) net_.load_source_model(x) print('load model:', opts.loadmodel) else: print('no model load !!!!!!!!') log_dir = os.path.join( save_dir, 'models', datetime.now().strftime('%b%d_%H-%M-%S') + '_' + socket.gethostname()) writer = SummaryWriter(log_dir=log_dir) writer.add_text('load model', opts.loadmodel, 1) writer.add_text('setting', sys.argv[0], 1) if opts.freezeBN: net_.freeze_bn() print(net_) # Use the following optimizer optimizer = optim.SGD(net_.parameters(), lr=p['lr'], momentum=p['momentum'], weight_decay=p['wd']) composed_transforms_tr = transforms.Compose([ tr.RandomSized_new(opts.image_size), tr.Normalize_xception_tf(), tr.ToTensor_() ]) composed_transforms_ts = transforms.Compose( [tr.Normalize_xception_tf(), tr.ToTensor_()]) composed_transforms_ts_flip = transforms.Compose( [tr.HorizontalFlip(), tr.Normalize_xception_tf(), tr.ToTensor_()]) #voc_train = cihp.VOCSegmentation(split='train', transform=composed_transforms_tr, flip=True) #voc_val = cihp.VOCSegmentation(split='val', transform=composed_transforms_ts) #voc_val_flip = cihp.VOCSegmentation(split='val', transform=composed_transforms_ts_flip) voc_train = cihp.VOCSegmentation(base_dir="../../data/datasets/CIHP_4w", split='train', transform=composed_transforms_tr, flip=True) voc_val = cihp.VOCSegmentation(base_dir="../../data/datasets/CIHP_4w", split='val', transform=composed_transforms_ts) voc_val_flip = cihp.VOCSegmentation(base_dir="../../data/datasets/CIHP_4w", split='val', transform=composed_transforms_ts_flip) trainloader = DataLoader(voc_train, batch_size=p['trainBatch'], shuffle=True, num_workers=p['num_workers'], drop_last=True) testloader = DataLoader(voc_val, batch_size=testBatch, shuffle=False, num_workers=p['num_workers']) testloader_flip = DataLoader(voc_val_flip, batch_size=testBatch, shuffle=False, num_workers=p['num_workers']) num_img_tr = len(trainloader) num_img_ts = len(testloader) running_loss_tr = 0.0 running_loss_ts = 0.0 aveGrad = 0 global_step = 0 print("Training Network") print("num_img_tr : ", num_img_tr) net = torch.nn.DataParallel(net_) train_graph, test_graph = get_graphs(opts, device) adj1, adj2, adj3 = train_graph # Main Training and Testing Loop for epoch in range(resume_epoch, nEpochs): start_time = timeit.default_timer() if epoch % p['epoch_size'] == p['epoch_size'] - 1: lr_ = util.lr_poly(p['lr'], epoch, nEpochs, 0.9) optimizer = optim.SGD(net_.parameters(), lr=lr_, momentum=p['momentum'], weight_decay=p['wd']) writer.add_scalar('data/lr_', lr_, epoch) print('(poly lr policy) learning rate: ', lr_) net.train() for ii, sample_batched in enumerate(trainloader): inputs, labels = sample_batched['image'], sample_batched['label'] # Forward-Backward of the mini-batch inputs, labels = Variable(inputs, requires_grad=True), Variable(labels) global_step += inputs.data.shape[0] if gpu_id >= 0: #inputs, labels = inputs.cuda(), labels.cuda() inputs, labels = inputs.to(device), labels.to(device) #print( "inputs.shape : ", inputs.shape ) # torch.Size([batch, 3, 512, 512]) #print( "adj1.shape : ", adj1.shape ) # torch.Size([8, 1, 20, 20]) #print( "adj2.shape : ", adj2.shape ) # torch.Size([8, 1, 20, 7]) #print( "adj3.shape : ", adj3.shape ) # torch.Size([8, 1, 7, 7]) outputs = net.forward(inputs, adj1, adj3, adj2) #print( "outputs.shape : ", outputs.shape ) # torch.Size([2, 20, 512, 512]) loss = criterion(outputs, labels, batch_average=True) running_loss_tr += loss.item() # Print stuff if ii % num_img_tr == (num_img_tr - 1): running_loss_tr = running_loss_tr / num_img_tr writer.add_scalar('data/total_loss_epoch', running_loss_tr, epoch) print('[Epoch: %d, numImages: %5d]' % (epoch, ii * p['trainBatch'] + inputs.data.shape[0])) print('Loss: %f' % running_loss_tr) running_loss_tr = 0 stop_time = timeit.default_timer() print("Execution time: " + str(stop_time - start_time) + "\n") # Backward the averaged gradient loss /= p['nAveGrad'] loss.backward() aveGrad += 1 # Update the weights once in p['nAveGrad'] forward passes if aveGrad % p['nAveGrad'] == 0: writer.add_scalar('data/total_loss_iter', loss.item(), ii + num_img_tr * epoch) optimizer.step() optimizer.zero_grad() aveGrad = 0 # Show 10 * 3 images results each epoch if ii % (num_img_tr // 10) == 0: # if ii % (num_img_tr // 4000) == 0: grid_image = make_grid(inputs[:3].clone().cpu().data, 3, normalize=True) writer.add_image('Image', grid_image, global_step) grid_image = make_grid(util.decode_seg_map_sequence( torch.max(outputs[:3], 1)[1].detach().cpu().numpy()), 3, normalize=False, range=(0, 255)) writer.add_image('Predicted label', grid_image, global_step) grid_image = make_grid(util.decode_seg_map_sequence( torch.squeeze(labels[:3], 1).detach().cpu().numpy()), 3, normalize=False, range=(0, 255)) writer.add_image('Groundtruth label', grid_image, global_step) print('loss is ', loss.cpu().item(), flush=True) # Save the model if (epoch % snapshot) == snapshot - 1: torch.save( net_.state_dict(), os.path.join(save_dir, 'models', modelName + '_epoch-' + str(epoch) + '.pth')) print("Save model at {}\n".format( os.path.join(save_dir, 'models', modelName + '_epoch-' + str(epoch) + '.pth'))) torch.cuda.empty_cache() # One testing epoch if useTest and epoch % nTestInterval == (nTestInterval - 1): val_cihp(net_, testloader=testloader, testloader_flip=testloader_flip, test_graph=test_graph, epoch=epoch, writer=writer, criterion=criterion, classes=opts.classes, device=device) torch.cuda.empty_cache()
def main(opts): p = OrderedDict() # Parameters to include in report p["trainBatch"] = opts.batch # Training batch size testBatch = 1 # Testing batch size useTest = True # See evolution of the test set when training nTestInterval = opts.testInterval # Run on test set every nTestInterval epochs snapshot = 1 # Store a model every snapshot epochs p["nAveGrad"] = 1 # Average the gradient of several iterations p["lr"] = opts.lr # Learning rate p["lrFtr"] = 1e-5 p["lraspp"] = 1e-5 p["lrpro"] = 1e-5 p["lrdecoder"] = 1e-5 p["lrother"] = 1e-5 p["wd"] = 5e-4 # Weight decay p["momentum"] = 0.9 # Momentum p["epoch_size"] = opts.step # How many epochs to change learning rate p["num_workers"] = opts.numworker model_path = opts.pretrainedModel backbone = "xception" # Use xception or resnet as feature extractor, nEpochs = opts.epochs max_id = 0 save_dir_root = os.path.join(os.path.dirname(os.path.abspath(__file__))) exp_name = os.path.dirname(os.path.abspath(__file__)).split("/")[-1] runs = glob.glob(os.path.join(save_dir_root, "run_cihp", "run_*")) for r in runs: run_id = int(r.split("_")[-1]) if run_id >= max_id: max_id = run_id + 1 save_dir = os.path.join(save_dir_root, "run_cihp", "run_" + str(max_id)) # Network definition if backbone == "xception": net_ = deeplab_xception_transfer.deeplab_xception_transfer_projection_savemem( n_classes=opts.classes, os=16, hidden_layers=opts.hidden_layers, source_classes=7, ) elif backbone == "resnet": # net_ = deeplab_resnet.DeepLabv3_plus(nInputChannels=3, n_classes=7, os=16, pretrained=True) raise NotImplementedError else: raise NotImplementedError modelName = ("deeplabv3plus-" + backbone + "-voc" + datetime.now().strftime("%b%d_%H-%M-%S")) criterion = util.cross_entropy2d if gpu_id >= 0: # torch.cuda.set_device(device=gpu_id) net_.cuda() # net load weights if not model_path == "": x = torch.load(model_path) net_.load_state_dict_new(x) print("load pretrainedModel:", model_path) else: print("no pretrainedModel.") if not opts.loadmodel == "": x = torch.load(opts.loadmodel) net_.load_source_model(x) print("load model:", opts.loadmodel) else: print("no model load !!!!!!!!") log_dir = os.path.join( save_dir, "models", datetime.now().strftime("%b%d_%H-%M-%S") + "_" + socket.gethostname(), ) writer = SummaryWriter(log_dir=log_dir) writer.add_text("load model", opts.loadmodel, 1) writer.add_text("setting", sys.argv[0], 1) if opts.freezeBN: net_.freeze_bn() # Use the following optimizer optimizer = optim.SGD(net_.parameters(), lr=p["lr"], momentum=p["momentum"], weight_decay=p["wd"]) composed_transforms_tr = transforms.Compose( [tr.RandomSized_new(512), tr.Normalize_xception_tf(), tr.ToTensor_()]) composed_transforms_ts = transforms.Compose( [tr.Normalize_xception_tf(), tr.ToTensor_()]) composed_transforms_ts_flip = transforms.Compose( [tr.HorizontalFlip(), tr.Normalize_xception_tf(), tr.ToTensor_()]) voc_train = cihp.VOCSegmentation(split="train", transform=composed_transforms_tr, flip=True) voc_val = cihp.VOCSegmentation(split="val", transform=composed_transforms_ts) voc_val_flip = cihp.VOCSegmentation(split="val", transform=composed_transforms_ts_flip) trainloader = DataLoader( voc_train, batch_size=p["trainBatch"], shuffle=True, num_workers=p["num_workers"], drop_last=True, ) testloader = DataLoader(voc_val, batch_size=testBatch, shuffle=False, num_workers=p["num_workers"]) testloader_flip = DataLoader(voc_val_flip, batch_size=testBatch, shuffle=False, num_workers=p["num_workers"]) num_img_tr = len(trainloader) num_img_ts = len(testloader) running_loss_tr = 0.0 running_loss_ts = 0.0 aveGrad = 0 global_step = 0 print("Training Network") net = torch.nn.DataParallel(net_) train_graph, test_graph = get_graphs(opts) adj1, adj2, adj3 = train_graph # Main Training and Testing Loop for epoch in range(resume_epoch, nEpochs): start_time = timeit.default_timer() if epoch % p["epoch_size"] == p["epoch_size"] - 1: lr_ = util.lr_poly(p["lr"], epoch, nEpochs, 0.9) optimizer = optim.SGD(net_.parameters(), lr=lr_, momentum=p["momentum"], weight_decay=p["wd"]) writer.add_scalar("data/lr_", lr_, epoch) print("(poly lr policy) learning rate: ", lr_) net.train() for ii, sample_batched in enumerate(trainloader): inputs, labels = sample_batched["image"], sample_batched["label"] # Forward-Backward of the mini-batch inputs, labels = Variable(inputs, requires_grad=True), Variable(labels) global_step += inputs.data.shape[0] if gpu_id >= 0: inputs, labels = inputs.cuda(), labels.cuda() outputs = net.forward(inputs, adj1, adj3, adj2) loss = criterion(outputs, labels, batch_average=True) running_loss_tr += loss.item() # Print stuff if ii % num_img_tr == (num_img_tr - 1): running_loss_tr = running_loss_tr / num_img_tr writer.add_scalar("data/total_loss_epoch", running_loss_tr, epoch) print("[Epoch: %d, numImages: %5d]" % (epoch, ii * p["trainBatch"] + inputs.data.shape[0])) print("Loss: %f" % running_loss_tr) running_loss_tr = 0 stop_time = timeit.default_timer() print("Execution time: " + str(stop_time - start_time) + "\n") # Backward the averaged gradient loss /= p["nAveGrad"] loss.backward() aveGrad += 1 # Update the weights once in p['nAveGrad'] forward passes if aveGrad % p["nAveGrad"] == 0: writer.add_scalar("data/total_loss_iter", loss.item(), ii + num_img_tr * epoch) optimizer.step() optimizer.zero_grad() aveGrad = 0 # Show 10 * 3 images results each epoch if ii % (num_img_tr // 10) == 0: grid_image = make_grid(inputs[:3].clone().cpu().data, 3, normalize=True) writer.add_image("Image", grid_image, global_step) grid_image = make_grid( util.decode_seg_map_sequence( torch.max(outputs[:3], 1)[1].detach().cpu().numpy()), 3, normalize=False, range=(0, 255), ) writer.add_image("Predicted label", grid_image, global_step) grid_image = make_grid( util.decode_seg_map_sequence( torch.squeeze(labels[:3], 1).detach().cpu().numpy()), 3, normalize=False, range=(0, 255), ) writer.add_image("Groundtruth label", grid_image, global_step) print("loss is ", loss.cpu().item(), flush=True) # Save the model if (epoch % snapshot) == snapshot - 1: torch.save( net_.state_dict(), os.path.join(save_dir, "models", modelName + "_epoch-" + str(epoch) + ".pth"), ) print("Save model at {}\n".format( os.path.join(save_dir, "models", modelName + "_epoch-" + str(epoch) + ".pth"))) torch.cuda.empty_cache() # One testing epoch if useTest and epoch % nTestInterval == (nTestInterval - 1): val_cihp( net_, testloader=testloader, testloader_flip=testloader_flip, test_graph=test_graph, epoch=epoch, writer=writer, criterion=criterion, classes=opts.classes, ) torch.cuda.empty_cache()
return _img, _target, type_lbl def __str__(self): return 'datasets(split=' + str(self.split) + ')' if __name__ == '__main__': from dataloaders import custom_transforms as tr from dataloaders.utils import decode_segmap from torch.utils.data import DataLoader from torchvision import transforms import matplotlib.pyplot as plt composed_transforms_tr = transforms.Compose([ # tr.RandomHorizontalFlip(), tr.RandomSized_new(512), tr.RandomRotate(15), tr.ToTensor_() ]) voc_train = VOCSegmentation(split='train', transform=composed_transforms_tr) dataloader = DataLoader(voc_train, batch_size=5, shuffle=True, num_workers=1) for ii, sample in enumerate(dataloader): if ii > 10: break
def main(opts): # Some of the settings are not used p = OrderedDict() # Parameters to include in report p['trainBatch'] = opts.batch # Training batch size testBatch = 1 # Testing batch size useTest = True # See evolution of the test set when training nTestInterval = opts.testInterval # Run on test set every nTestInterval epochs snapshot = 1 # Store a model every snapshot epochs p['nAveGrad'] = 1 # Average the gradient of several iterations p['lr'] = opts.lr # Learning rate p['lrFtr'] = 1e-5 p['lraspp'] = 1e-5 p['lrpro'] = 1e-5 p['lrdecoder'] = 1e-5 p['lrother'] = 1e-5 p['wd'] = 5e-4 # Weight decay p['momentum'] = 0.9 # Momentum p['epoch_size'] = opts.step # How many epochs to change learning rate p['num_workers'] = opts.numworker backbone = 'xception' # Use xception or resnet as feature extractor, nEpochs = opts.epochs resume_epoch = opts.resume_epoch max_id = 0 save_dir_root = os.path.join(os.path.dirname(os.path.abspath(__file__))) exp_name = os.path.dirname(os.path.abspath(__file__)).split('/')[-1] runs = glob.glob(os.path.join(save_dir_root, 'run_cihp', 'run_*')) for r in runs: run_id = int(r.split('_')[-1]) if run_id >= max_id: max_id = run_id + 1 save_dir = os.path.join(save_dir_root, 'run_cihp', 'run_' + str(max_id)) print(save_dir) # Network definition net_ = grapy_net.GrapyMutualLearning(os=16, hidden_layers=opts.hidden_graph_layers) modelName = 'deeplabv3plus-' + backbone + '-voc' + datetime.now().strftime('%b%d_%H-%M-%S') criterion = util.cross_entropy2d log_dir = os.path.join(save_dir, 'models', datetime.now().strftime('%b%d_%H-%M-%S') + '_' + socket.gethostname()) writer = SummaryWriter(log_dir=log_dir) writer.add_text('load model', opts.loadmodel, 1) writer.add_text('setting', sys.argv[0], 1) # Use the following optimizer optimizer = optim.SGD(net_.parameters(), lr=p['lr'], momentum=p['momentum'], weight_decay=p['wd']) composed_transforms_tr = transforms.Compose([ tr.RandomSized_new(512), tr.Normalize_xception_tf(), tr.ToTensor_()]) composed_transforms_ts = transforms.Compose([ tr.Normalize_xception_tf(), tr.ToTensor_()]) composed_transforms_ts_flip = transforms.Compose([ tr.HorizontalFlip(), tr.Normalize_xception_tf(), tr.ToTensor_()]) if opts.train_mode == 'cihp_pascal_atr': all_train = cihp_pascal_atr.VOCSegmentation(split='train', transform=composed_transforms_tr, flip=True) num_cihp, num_pascal, num_atr = all_train.get_class_num() voc_val = atr.VOCSegmentation(split='val', transform=composed_transforms_ts) voc_val_flip = atr.VOCSegmentation(split='val', transform=composed_transforms_ts_flip) ss = sam.Sampler_uni(num_cihp, num_pascal, num_atr, opts.batch) trainloader = DataLoader(all_train, batch_size=p['trainBatch'], shuffle=False, num_workers=18, sampler=ss, drop_last=True) elif opts.train_mode == 'cihp_pascal_atr_1_1_1': all_train = cihp_pascal_atr.VOCSegmentation(split='train', transform=composed_transforms_tr, flip=True) num_cihp, num_pascal, num_atr = all_train.get_class_num() voc_val = pascal.VOCSegmentation(split='val', transform=composed_transforms_ts) voc_val_flip = pascal.VOCSegmentation(split='val', transform=composed_transforms_ts_flip) ss_uni = sam.Sampler_uni(num_cihp, num_pascal, num_atr, opts.batch, balance_id=1) trainloader = DataLoader(all_train, batch_size=p['trainBatch'], shuffle=False, num_workers=1, sampler=ss_uni, drop_last=True) elif opts.train_mode == 'cihp': voc_train = cihp.VOCSegmentation(split='train', transform=composed_transforms_tr, flip=True) voc_val = cihp.VOCSegmentation(split='val', transform=composed_transforms_ts) voc_val_flip = cihp.VOCSegmentation(split='val', transform=composed_transforms_ts_flip) trainloader = DataLoader(voc_train, batch_size=p['trainBatch'], shuffle=True, num_workers=18, drop_last=True) elif opts.train_mode == 'pascal': # here we train without flip but test with flip voc_train = pascal_flip.VOCSegmentation(split='train', transform=composed_transforms_tr) voc_val = pascal.VOCSegmentation(split='val', transform=composed_transforms_ts) voc_val_flip = pascal.VOCSegmentation(split='val', transform=composed_transforms_ts_flip) trainloader = DataLoader(voc_train, batch_size=p['trainBatch'], shuffle=True, num_workers=18, drop_last=True) elif opts.train_mode == 'atr': # here we train without flip but test with flip voc_train = atr.VOCSegmentation(split='train', transform=composed_transforms_tr, flip=True) voc_val = atr.VOCSegmentation(split='val', transform=composed_transforms_ts) voc_val_flip = atr.VOCSegmentation(split='val', transform=composed_transforms_ts_flip) trainloader = DataLoader(voc_train, batch_size=p['trainBatch'], shuffle=True, num_workers=18, drop_last=True) else: raise NotImplementedError if not opts.loadmodel == '': x = torch.load(opts.loadmodel) net_.load_state_dict_new(x, strict=False) print('load model:', opts.loadmodel) else: print('no model load !!!!!!!!') if not opts.resume_model == '': x = torch.load(opts.resume_model) net_.load_state_dict(x) print('resume model:', opts.resume_model) else: print('we are not resuming from any model') # We only validate on pascal dataset to save time testloader = DataLoader(voc_val, batch_size=testBatch, shuffle=False, num_workers=3) testloader_flip = DataLoader(voc_val_flip, batch_size=testBatch, shuffle=False, num_workers=3) num_img_tr = len(trainloader) num_img_ts = len(testloader) # Set the category relations c1, c2, p1, p2, a1, a2 = [[0], [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19]],\ [[0], [1, 2, 4, 13], [5, 6, 7, 10, 11, 12], [3, 14, 15], [8, 9, 16, 17, 18, 19]], \ [[0], [1, 2, 3, 4, 5, 6]], [[0], [1], [2], [3, 4], [5, 6]], [[0], [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17]],\ [[0], [1, 2, 3, 11], [4, 5, 7, 8, 16, 17], [14, 15], [6, 9, 10, 12, 13]] net_.set_category_list(c1, c2, p1, p2, a1, a2) if gpu_id >= 0: # torch.cuda.set_device(device=gpu_id) net_.cuda() running_loss_tr = 0.0 running_loss_ts = 0.0 running_loss_tr_main = 0.0 running_loss_tr_aux = 0.0 aveGrad = 0 global_step = 0 miou = 0 cur_miou = 0 print("Training Network") net = torch.nn.DataParallel(net_) # Main Training and Testing Loop for epoch in range(resume_epoch, nEpochs): start_time = timeit.default_timer() if opts.poly: if epoch % p['epoch_size'] == p['epoch_size'] - 1: lr_ = util.lr_poly(p['lr'], epoch, nEpochs, 0.9) optimizer = optim.SGD(net_.parameters(), lr=lr_, momentum=p['momentum'], weight_decay=p['wd']) writer.add_scalar('data/lr_', lr_, epoch) print('(poly lr policy) learning rate: ', lr_) net.train() for ii, sample_batched in enumerate(trainloader): inputs, labels = sample_batched['image'], sample_batched['label'] # Forward-Backward of the mini-batch inputs, labels = Variable(inputs, requires_grad=True), Variable(labels) global_step += inputs.data.shape[0] if gpu_id >= 0: inputs, labels = inputs.cuda(), labels.cuda() if opts.train_mode == 'cihp_pascal_atr' or opts.train_mode == 'cihp_pascal_atr_1_1_1': num_dataset_lbl = sample_batched['pascal'][0].item() elif opts.train_mode == 'cihp': num_dataset_lbl = 0 elif opts.train_mode == 'pascal': num_dataset_lbl = 1 else: num_dataset_lbl = 2 outputs, outputs_aux = net.forward((inputs, num_dataset_lbl)) # print(inputs.shape, labels.shape, outputs.shape, outputs_aux.shape) loss_main = criterion(outputs, labels, batch_average=True) loss_aux = criterion(outputs_aux, labels, batch_average=True) loss = opts.beta_main * loss_main + opts.beta_aux * loss_aux running_loss_tr_main += loss_main.item() running_loss_tr_aux += loss_aux.item() running_loss_tr += loss.item() # Print stuff if ii % num_img_tr == (num_img_tr - 1): running_loss_tr = running_loss_tr / num_img_tr running_loss_tr_aux = running_loss_tr_aux / num_img_tr running_loss_tr_main = running_loss_tr_main / num_img_tr writer.add_scalar('data/total_loss_epoch', running_loss_tr, epoch) writer.add_scalars('data/scalar_group', {'loss': running_loss_tr_main, 'loss_aux': running_loss_tr_aux}, epoch) print('[Epoch: %d, numImages: %5d]' % (epoch, ii * p['trainBatch'] + inputs.data.shape[0])) print('Loss: %f' % running_loss_tr) running_loss_tr = 0 stop_time = timeit.default_timer() print("Execution time: " + str(stop_time - start_time) + "\n") # Backward the averaged gradient loss /= p['nAveGrad'] loss.backward() aveGrad += 1 # Update the weights once in p['nAveGrad'] forward passes if aveGrad % p['nAveGrad'] == 0: writer.add_scalar('data/total_loss_iter', loss.item(), ii + num_img_tr * epoch) if num_dataset_lbl == 0: writer.add_scalar('data/total_loss_iter_cihp', loss.item(), global_step) if num_dataset_lbl == 1: writer.add_scalar('data/total_loss_iter_pascal', loss.item(), global_step) if num_dataset_lbl == 2: writer.add_scalar('data/total_loss_iter_atr', loss.item(), global_step) optimizer.step() optimizer.zero_grad() aveGrad = 0 # Show 10 * 3 images results each # print(ii, (num_img_tr * 10), (ii % (num_img_tr * 10) == 0)) if ii % (num_img_tr * 10) == 0: grid_image = make_grid(inputs[:3].clone().cpu().data, 3, normalize=True) writer.add_image('Image', grid_image, global_step) grid_image = make_grid( util.decode_seg_map_sequence(torch.max(outputs[:3], 1)[1].detach().cpu().numpy()), 3, normalize=False, range=(0, 255)) writer.add_image('Predicted label', grid_image, global_step) grid_image = make_grid( util.decode_seg_map_sequence(torch.squeeze(labels[:3], 1).detach().cpu().numpy()), 3, normalize=False, range=(0, 255)) writer.add_image('Groundtruth label', grid_image, global_step) print('loss is ', loss.cpu().item(), flush=True) # Save the model # One testing epoch if useTest and epoch % nTestInterval == (nTestInterval - 1): cur_miou = validation(net_, testloader=testloader, testloader_flip=testloader_flip, classes=opts.classes, epoch=epoch, writer=writer, criterion=criterion, dataset=opts.train_mode) torch.cuda.empty_cache() if (epoch % snapshot) == snapshot - 1: torch.save(net_.state_dict(), os.path.join(save_dir, 'models', modelName + '_epoch' + '_current' + '.pth')) print("Save model at {}\n".format( os.path.join(save_dir, 'models', modelName + str(epoch) + '_epoch-' + str(epoch) + '.pth as our current model'))) if cur_miou > miou: miou = cur_miou torch.save(net_.state_dict(), os.path.join(save_dir, 'models', modelName + '_best' + '.pth')) print("Save model at {}\n".format( os.path.join(save_dir, 'models', modelName + '_epoch-' + str(epoch) + '.pth as our best model'))) torch.cuda.empty_cache()
def main(opts): # Set parameters p = OrderedDict() # Parameters to include in report p["trainBatch"] = opts.batch # Training batch size testBatch = 1 # Testing batch size useTest = True # See evolution of the test set when training nTestInterval = opts.testInterval # Run on test set every nTestInterval epochs snapshot = 1 # Store a model every snapshot epochs p["nAveGrad"] = 1 # Average the gradient of several iterations p["lr"] = opts.lr # Learning rate p["wd"] = 5e-4 # Weight decay p["momentum"] = 0.9 # Momentum p["epoch_size"] = opts.step # How many epochs to change learning rate p["num_workers"] = opts.numworker model_path = opts.pretrainedModel backbone = "xception" # Use xception or resnet as feature extractor nEpochs = opts.epochs max_id = 0 save_dir_root = os.path.join(os.path.dirname(os.path.abspath(__file__))) exp_name = os.path.dirname(os.path.abspath(__file__)).split("/")[-1] runs = glob.glob(os.path.join(save_dir_root, "run", "run_*")) for r in runs: run_id = int(r.split("_")[-1]) if run_id >= max_id: max_id = run_id + 1 # run_id = int(runs[-1].split('_')[-1]) + 1 if runs else 0 save_dir = os.path.join(save_dir_root, "run", "run_" + str(max_id)) # Network definition if backbone == "xception": net_ = deeplab_xception_universal.deeplab_xception_end2end_3d( n_classes=20, os=16, hidden_layers=opts.hidden_layers, source_classes=7, middle_classes=18, ) elif backbone == "resnet": # net_ = deeplab_resnet.DeepLabv3_plus(nInputChannels=3, n_classes=7, os=16, pretrained=True) raise NotImplementedError else: raise NotImplementedError modelName = ( "deeplabv3plus-" + backbone + "-voc" + datetime.now().strftime("%b%d_%H-%M-%S") ) criterion = ut.cross_entropy2d if gpu_id >= 0: # torch.cuda.set_device(device=gpu_id) net_.cuda() # net load weights if not model_path == "": x = torch.load(model_path) net_.load_state_dict_new(x) print("load pretrainedModel.") else: print("no pretrainedModel.") if not opts.loadmodel == "": x = torch.load(opts.loadmodel) net_.load_source_model(x) print("load model:", opts.loadmodel) else: print("no trained model load !!!!!!!!") log_dir = os.path.join( save_dir, "models", datetime.now().strftime("%b%d_%H-%M-%S") + "_" + socket.gethostname(), ) writer = SummaryWriter(log_dir=log_dir) writer.add_text("load model", opts.loadmodel, 1) writer.add_text("setting", sys.argv[0], 1) # Use the following optimizer optimizer = optim.SGD( net_.parameters(), lr=p["lr"], momentum=p["momentum"], weight_decay=p["wd"] ) composed_transforms_tr = transforms.Compose( [tr.RandomSized_new(512), tr.Normalize_xception_tf(), tr.ToTensor_()] ) composed_transforms_ts = transforms.Compose( [tr.Normalize_xception_tf(), tr.ToTensor_()] ) composed_transforms_ts_flip = transforms.Compose( [tr.HorizontalFlip(), tr.Normalize_xception_tf(), tr.ToTensor_()] ) all_train = cihp_pascal_atr.VOCSegmentation( split="train", transform=composed_transforms_tr, flip=True ) voc_val = pascal.VOCSegmentation(split="val", transform=composed_transforms_ts) voc_val_flip = pascal.VOCSegmentation( split="val", transform=composed_transforms_ts_flip ) num_cihp, num_pascal, num_atr = all_train.get_class_num() ss = sam.Sampler_uni(num_cihp, num_pascal, num_atr, opts.batch) # balance datasets based pascal ss_balanced = sam.Sampler_uni( num_cihp, num_pascal, num_atr, opts.batch, balance_id=1 ) trainloader = DataLoader( all_train, batch_size=p["trainBatch"], shuffle=False, num_workers=p["num_workers"], sampler=ss, drop_last=True, ) trainloader_balanced = DataLoader( all_train, batch_size=p["trainBatch"], shuffle=False, num_workers=p["num_workers"], sampler=ss_balanced, drop_last=True, ) testloader = DataLoader( voc_val, batch_size=testBatch, shuffle=False, num_workers=p["num_workers"] ) testloader_flip = DataLoader( voc_val_flip, batch_size=testBatch, shuffle=False, num_workers=p["num_workers"] ) num_img_tr = len(trainloader) num_img_balanced = len(trainloader_balanced) num_img_ts = len(testloader) running_loss_tr = 0.0 running_loss_tr_atr = 0.0 running_loss_ts = 0.0 aveGrad = 0 global_step = 0 print("Training Network") net = torch.nn.DataParallel(net_) id_list = torch.LongTensor(range(opts.batch)) pascal_iter = int(num_img_tr // opts.batch) # Get graphs train_graph, test_graph = get_graphs(opts) adj1, adj2, adj3, adj4, adj5, adj6 = train_graph adj1_test, adj2_test, adj3_test, adj4_test, adj5_test, adj6_test = test_graph # Main Training and Testing Loop for epoch in range(resume_epoch, int(1.5 * nEpochs)): start_time = timeit.default_timer() if epoch % p["epoch_size"] == p["epoch_size"] - 1 and epoch < nEpochs: lr_ = ut.lr_poly(p["lr"], epoch, nEpochs, 0.9) optimizer = optim.SGD( net_.parameters(), lr=lr_, momentum=p["momentum"], weight_decay=p["wd"] ) print("(poly lr policy) learning rate: ", lr_) writer.add_scalar("data/lr_", lr_, epoch) elif epoch % p["epoch_size"] == p["epoch_size"] - 1 and epoch > nEpochs: lr_ = ut.lr_poly(p["lr"], epoch - nEpochs, int(0.5 * nEpochs), 0.9) optimizer = optim.SGD( net_.parameters(), lr=lr_, momentum=p["momentum"], weight_decay=p["wd"] ) print("(poly lr policy) learning rate: ", lr_) writer.add_scalar("data/lr_", lr_, epoch) net_.train() if epoch < nEpochs: for ii, sample_batched in enumerate(trainloader): inputs, labels = sample_batched["image"], sample_batched["label"] dataset_lbl = sample_batched["pascal"][0].item() # Forward-Backward of the mini-batch inputs, labels = Variable(inputs, requires_grad=True), Variable(labels) global_step += 1 if gpu_id >= 0: inputs, labels = inputs.cuda(), labels.cuda() if dataset_lbl == 0: # 0 is cihp -- target _, outputs, _ = net.forward( None, input_target=inputs, input_middle=None, adj1_target=adj1, adj2_source=adj2, adj3_transfer_s2t=adj3, adj3_transfer_t2s=adj3.transpose(2, 3), adj4_middle=adj4, adj5_transfer_s2m=adj5.transpose(2, 3), adj6_transfer_t2m=adj6.transpose(2, 3), adj5_transfer_m2s=adj5, adj6_transfer_m2t=adj6, ) elif dataset_lbl == 1: # pascal is source outputs, _, _ = net.forward( inputs, input_target=None, input_middle=None, adj1_target=adj1, adj2_source=adj2, adj3_transfer_s2t=adj3, adj3_transfer_t2s=adj3.transpose(2, 3), adj4_middle=adj4, adj5_transfer_s2m=adj5.transpose(2, 3), adj6_transfer_t2m=adj6.transpose(2, 3), adj5_transfer_m2s=adj5, adj6_transfer_m2t=adj6, ) else: # atr _, _, outputs = net.forward( None, input_target=None, input_middle=inputs, adj1_target=adj1, adj2_source=adj2, adj3_transfer_s2t=adj3, adj3_transfer_t2s=adj3.transpose(2, 3), adj4_middle=adj4, adj5_transfer_s2m=adj5.transpose(2, 3), adj6_transfer_t2m=adj6.transpose(2, 3), adj5_transfer_m2s=adj5, adj6_transfer_m2t=adj6, ) # print(sample_batched['pascal']) # print(outputs.size(),) # print(labels) loss = criterion(outputs, labels, batch_average=True) running_loss_tr += loss.item() # Print stuff if ii % num_img_tr == (num_img_tr - 1): running_loss_tr = running_loss_tr / num_img_tr writer.add_scalar("data/total_loss_epoch", running_loss_tr, epoch) print("[Epoch: %d, numImages: %5d]" % (epoch, epoch)) print("Loss: %f" % running_loss_tr) running_loss_tr = 0 stop_time = timeit.default_timer() print("Execution time: " + str(stop_time - start_time) + "\n") # Backward the averaged gradient loss /= p["nAveGrad"] loss.backward() aveGrad += 1 # Update the weights once in p['nAveGrad'] forward passes if aveGrad % p["nAveGrad"] == 0: writer.add_scalar("data/total_loss_iter", loss.item(), global_step) if dataset_lbl == 0: writer.add_scalar( "data/total_loss_iter_cihp", loss.item(), global_step ) if dataset_lbl == 1: writer.add_scalar( "data/total_loss_iter_pascal", loss.item(), global_step ) if dataset_lbl == 2: writer.add_scalar( "data/total_loss_iter_atr", loss.item(), global_step ) optimizer.step() optimizer.zero_grad() # optimizer_gcn.step() # optimizer_gcn.zero_grad() aveGrad = 0 # Show 10 * 3 images results each epoch if ii % (num_img_tr // 10) == 0: grid_image = make_grid( inputs[:3].clone().cpu().data, 3, normalize=True ) writer.add_image("Image", grid_image, global_step) grid_image = make_grid( ut.decode_seg_map_sequence( torch.max(outputs[:3], 1)[1].detach().cpu().numpy() ), 3, normalize=False, range=(0, 255), ) writer.add_image("Predicted label", grid_image, global_step) grid_image = make_grid( ut.decode_seg_map_sequence( torch.squeeze(labels[:3], 1).detach().cpu().numpy() ), 3, normalize=False, range=(0, 255), ) writer.add_image("Groundtruth label", grid_image, global_step) print("loss is ", loss.cpu().item(), flush=True) else: # Balanced the number of datasets for ii, sample_batched in enumerate(trainloader_balanced): inputs, labels = sample_batched["image"], sample_batched["label"] dataset_lbl = sample_batched["pascal"][0].item() # Forward-Backward of the mini-batch inputs, labels = Variable(inputs, requires_grad=True), Variable(labels) global_step += 1 if gpu_id >= 0: inputs, labels = inputs.cuda(), labels.cuda() if dataset_lbl == 0: # 0 is cihp -- target _, outputs, _ = net.forward( None, input_target=inputs, input_middle=None, adj1_target=adj1, adj2_source=adj2, adj3_transfer_s2t=adj3, adj3_transfer_t2s=adj3.transpose(2, 3), adj4_middle=adj4, adj5_transfer_s2m=adj5.transpose(2, 3), adj6_transfer_t2m=adj6.transpose(2, 3), adj5_transfer_m2s=adj5, adj6_transfer_m2t=adj6, ) elif dataset_lbl == 1: # pascal is source outputs, _, _ = net.forward( inputs, input_target=None, input_middle=None, adj1_target=adj1, adj2_source=adj2, adj3_transfer_s2t=adj3, adj3_transfer_t2s=adj3.transpose(2, 3), adj4_middle=adj4, adj5_transfer_s2m=adj5.transpose(2, 3), adj6_transfer_t2m=adj6.transpose(2, 3), adj5_transfer_m2s=adj5, adj6_transfer_m2t=adj6, ) else: # atr _, _, outputs = net.forward( None, input_target=None, input_middle=inputs, adj1_target=adj1, adj2_source=adj2, adj3_transfer_s2t=adj3, adj3_transfer_t2s=adj3.transpose(2, 3), adj4_middle=adj4, adj5_transfer_s2m=adj5.transpose(2, 3), adj6_transfer_t2m=adj6.transpose(2, 3), adj5_transfer_m2s=adj5, adj6_transfer_m2t=adj6, ) # print(sample_batched['pascal']) # print(outputs.size(),) # print(labels) loss = criterion(outputs, labels, batch_average=True) running_loss_tr += loss.item() # Print stuff if ii % num_img_balanced == (num_img_balanced - 1): running_loss_tr = running_loss_tr / num_img_balanced writer.add_scalar("data/total_loss_epoch", running_loss_tr, epoch) print("[Epoch: %d, numImages: %5d]" % (epoch, epoch)) print("Loss: %f" % running_loss_tr) running_loss_tr = 0 stop_time = timeit.default_timer() print("Execution time: " + str(stop_time - start_time) + "\n") # Backward the averaged gradient loss /= p["nAveGrad"] loss.backward() aveGrad += 1 # Update the weights once in p['nAveGrad'] forward passes if aveGrad % p["nAveGrad"] == 0: writer.add_scalar("data/total_loss_iter", loss.item(), global_step) if dataset_lbl == 0: writer.add_scalar( "data/total_loss_iter_cihp", loss.item(), global_step ) if dataset_lbl == 1: writer.add_scalar( "data/total_loss_iter_pascal", loss.item(), global_step ) if dataset_lbl == 2: writer.add_scalar( "data/total_loss_iter_atr", loss.item(), global_step ) optimizer.step() optimizer.zero_grad() aveGrad = 0 # Show 10 * 3 images results each epoch if ii % (num_img_balanced // 10) == 0: grid_image = make_grid( inputs[:3].clone().cpu().data, 3, normalize=True ) writer.add_image("Image", grid_image, global_step) grid_image = make_grid( ut.decode_seg_map_sequence( torch.max(outputs[:3], 1)[1].detach().cpu().numpy() ), 3, normalize=False, range=(0, 255), ) writer.add_image("Predicted label", grid_image, global_step) grid_image = make_grid( ut.decode_seg_map_sequence( torch.squeeze(labels[:3], 1).detach().cpu().numpy() ), 3, normalize=False, range=(0, 255), ) writer.add_image("Groundtruth label", grid_image, global_step) print("loss is ", loss.cpu().item(), flush=True) # Save the model if (epoch % snapshot) == snapshot - 1: torch.save( net_.state_dict(), os.path.join( save_dir, "models", modelName + "_epoch-" + str(epoch) + ".pth" ), ) print( "Save model at {}\n".format( os.path.join( save_dir, "models", modelName + "_epoch-" + str(epoch) + ".pth" ) ) ) # One testing epoch if useTest and epoch % nTestInterval == (nTestInterval - 1): val_pascal( net_=net_, testloader=testloader, testloader_flip=testloader_flip, test_graph=test_graph, criterion=criterion, epoch=epoch, writer=writer, )