def train_epoch_end(config, compression_algo, net, epoch, iteration, epoch_size, lr_scheduler, optimizer, test_data_loader): test_freq_in_epochs = max(config.test_interval // epoch_size, 1) compression_algo.scheduler.epoch_step(epoch) if not isinstance(lr_scheduler, ReduceLROnPlateau): lr_scheduler.step(epoch) if epoch % test_freq_in_epochs == 0 and iteration != 0: if is_on_first_rank(config): print_statistics(compression_algo.statistics()) with torch.no_grad(): net.eval() mAP = test_net(net, config.device, test_data_loader, distributed=config.multiprocessing_distributed) if isinstance(lr_scheduler, ReduceLROnPlateau): lr_scheduler.step(mAP) net.train() if epoch > 0 and epoch % config.save_freq == 0 and is_on_first_rank(config): print('Saving state, iter:', iteration) checkpoint_file_path = osp.join(config.intermediate_checkpoints_path, "{}_{}.pth".format(config.model, iteration)) torch.save({ 'state_dict': net.state_dict(), 'optimizer': optimizer.state_dict(), 'iter': iteration, 'scheduler': compression_algo.scheduler.state_dict() }, str(checkpoint_file_path))
def test_can_print_stats(self, config_provider, model_provider): model = model_provider() config = config_provider() compression_algo = create_test_compression_algo(config, model) print_statistics(compression_algo.statistics())
def train(config, compression_algo, model, criterion, is_inception, lr_scheduler, model_name, optimizer, train_loader, train_sampler, val_loader): global best_acc1 for epoch in range(config.start_epoch, config.epochs): config.cur_epoch = epoch if config.distributed: train_sampler.set_epoch(epoch) lr_scheduler.step(epoch if not isinstance( lr_scheduler, ReduceLROnPlateau) else best_acc1) # train for one epoch train_epoch(train_loader, model, criterion, optimizer, compression_algo, epoch, config, is_inception) # compute compression algo statistics stats = compression_algo.statistics() acc1 = best_acc1 if epoch % config.test_every_n_epochs == 0: # evaluate on validation set acc1, _ = validate(val_loader, model, criterion, config) # remember best acc@1 and save checkpoint is_best = acc1 > best_acc1 best_acc1 = max(acc1, best_acc1) # update compression scheduler state at the end of the epoch compression_algo.scheduler.epoch_step() if is_main_process(): print_statistics(stats) checkpoint_path = osp.join(config.checkpoint_save_dir, get_name(config) + '_last.pth') checkpoint = { 'epoch': epoch + 1, 'arch': model_name, 'state_dict': model.state_dict(), 'best_acc1': best_acc1, 'optimizer': optimizer.state_dict(), 'scheduler': compression_algo.scheduler.state_dict() } torch.save(checkpoint, checkpoint_path) make_additional_checkpoints(checkpoint_path, is_best, epoch + 1, config) for key, value in stats.items(): if isinstance(value, (int, float)): config.tb.add_scalar( "compression/statistics/{0}".format(key), value, len(train_loader) * epoch)
def load_torch_model(config, cuda=False): weights = config.get('weights') model = load_model(config.model, pretrained=config.get('pretrained', True) if weights is None else False, num_classes=config.get('num_classes', 1000), model_params=config.get('model_params', {})) compression_algo, model = create_compressed_model(model, config) if weights: sd = torch.load(weights, map_location='cpu') load_state(model, sd) if cuda: model = model.cuda() model = torch.nn.DataParallel(model) print_statistics(compression_algo.statistics()) return model
def train_epoch_end(config, compression_algo, net, epoch, iteration, epoch_size, lr_scheduler, optimizer, test_data_loader, best_mAp): is_best = False test_freq_in_epochs = max(config.test_interval // epoch_size, 1) compression_algo.scheduler.epoch_step(epoch) if not isinstance(lr_scheduler, ReduceLROnPlateau): lr_scheduler.step(epoch) if epoch % test_freq_in_epochs == 0 and iteration != 0: if is_on_first_rank(config): print_statistics(compression_algo.statistics()) with torch.no_grad(): net.eval() mAP = test_net(net, config.device, test_data_loader, distributed=config.multiprocessing_distributed) if mAP > best_mAp: is_best = True best_mAp = mAP if config.metrics_dump is not None: write_metrics(mAP, config) if isinstance(lr_scheduler, ReduceLROnPlateau): lr_scheduler.step(mAP) net.train() if is_on_first_rank(config): checkpoint_file_path = osp.join(config.checkpoint_save_dir, "{}_last.pth".format(get_name(config))) torch.save( { 'state_dict': net.state_dict(), 'optimizer': optimizer.state_dict(), 'iter': iteration, 'scheduler': compression_algo.scheduler.state_dict() }, str(checkpoint_file_path)) make_additional_checkpoints(checkpoint_file_path, is_best=is_best, epoch=epoch + 1, config=config) return best_mAp
def main_worker(current_gpu, config): config.current_gpu = current_gpu config.distributed = config.execution_mode in (ExecutionMode.DISTRIBUTED, ExecutionMode.MULTIPROCESSING_DISTRIBUTED) if config.distributed: configure_distributed(config) if is_main_process(): configure_logging(config) print_args(config) print(config) config.device = get_device(config) dataset = get_dataset(config.dataset) color_encoding = dataset.color_encoding num_classes = len(color_encoding) weights = config.get('weights') model = load_model(config.model, pretrained=config.get('pretrained', True) if weights is None else False, num_classes=num_classes, model_params=config.get('model_params', {})) compression_algo, model = create_compressed_model(model, config) if weights: sd = torch.load(weights, map_location='cpu') load_state(model, sd) model, model_without_dp = prepare_model_for_execution(model, config) if config.distributed: compression_algo.distributed() resuming_checkpoint = config.resuming_checkpoint if resuming_checkpoint is not None: if not config.pretrained: # Load the previously saved model state model, _, _, _, _ = \ load_checkpoint(model, resuming_checkpoint, config.device, compression_scheduler=compression_algo.scheduler) if config.to_onnx is not None: compression_algo.export_model(config.to_onnx) print("Saved to", config.to_onnx) return if config.mode.lower() == 'test': print(model) model_parameters = filter(lambda p: p.requires_grad, model.parameters()) params = sum([np.prod(p.size()) for p in model_parameters]) print("Trainable argument count:{params}".format(params=params)) model = model.to(config.device) loaders, w_class = load_dataset(dataset, config) _, val_loader = loaders test(model, val_loader, w_class, color_encoding, config) print_statistics(compression_algo.statistics()) elif config.mode.lower() == 'train': loaders, w_class = load_dataset(dataset, config) train_loader, val_loader = loaders if not resuming_checkpoint: compression_algo.initialize(train_loader) model = \ train(model, model_without_dp, compression_algo, train_loader, val_loader, w_class, color_encoding, config) else: # Should never happen...but just in case it does raise RuntimeError( "\"{0}\" is not a valid choice for execution mode.".format( config.mode))
def train(model, model_without_dp, compression_algo, train_loader, val_loader, class_weights, class_encoding, config): print("\nTraining...\n") # Check if the network architecture is correct print(model) optim_config = config.get('optimizer', {}) optim_params = optim_config.get('optimizer_params', {}) lr = optim_params.get("lr", 1e-4) params_to_optimize, criterion = get_aux_loss_dependent_params(model_without_dp, class_weights, lr * 10, config) optimizer, lr_scheduler = make_optimizer(params_to_optimize, config) # Evaluation metric ignore_index = None ignore_unlabeled = config.get("ignore_unlabeled", True) if ignore_unlabeled and ('unlabeled' in class_encoding): ignore_index = list(class_encoding).index('unlabeled') metric = IoU(len(class_encoding), ignore_index=ignore_index) best_miou = -1 resuming_checkpoint = config.resuming_checkpoint # Optionally resume from a checkpoint if resuming_checkpoint is not None: model, optimizer, start_epoch, best_miou, _ = \ load_checkpoint( model, resuming_checkpoint, config.device, optimizer, compression_algo.scheduler) print("Resuming from model: Start epoch = {0} " "| Best mean IoU = {1:.4f}".format(start_epoch, best_miou)) config.start_epoch = start_epoch # Start Training train_obj = Train(model, train_loader, optimizer, criterion, compression_algo, metric, config.device, config.model) val_obj = Test(model, val_loader, criterion, metric, config.device, config.model) for epoch in range(config.start_epoch, config.epochs): print(">>>> [Epoch: {0:d}] Training".format(epoch)) if config.distributed: train_loader.sampler.set_epoch(epoch) if not isinstance(lr_scheduler, ReduceLROnPlateau): lr_scheduler.step(epoch) epoch_loss, (iou, miou) = train_obj.run_epoch(config.print_step) compression_algo.scheduler.epoch_step() print(">>>> [Epoch: {0:d}] Avg. loss: {1:.4f} | Mean IoU: {2:.4f}". format(epoch, epoch_loss, miou)) if is_main_process(): config.tb.add_scalar("train/loss", epoch_loss, epoch) config.tb.add_scalar("train/mIoU", miou, epoch) config.tb.add_scalar("train/learning_rate", optimizer.param_groups[0]['lr'], epoch) config.tb.add_scalar("train/compression_loss", compression_algo.loss(), epoch) for key, value in compression_algo.statistics().items(): if isinstance(value, (int, float)): config.tb.add_scalar("compression/statistics/{0}".format(key), value, epoch) if (epoch + 1) % config.save_freq == 0 or epoch + 1 == config.epochs: print(">>>> [Epoch: {0:d}] Validation".format(epoch)) loss, (iou, miou) = val_obj.run_epoch(config.print_step) print(">>>> [Epoch: {0:d}] Avg. loss: {1:.4f} | Mean IoU: {2:.4f}". format(epoch, loss, miou)) if is_main_process(): config.tb.add_scalar("val/mIoU", miou, epoch) config.tb.add_scalar("val/loss", loss, epoch) for i, (key, class_iou) in enumerate(zip(class_encoding.keys(), iou)): config.tb.add_scalar("{}/mIoU_Cls{}_{}".format(config.dataset, i, key), class_iou, epoch) is_best = miou > best_miou best_miou = max(miou, best_miou) if isinstance(lr_scheduler, ReduceLROnPlateau): lr_scheduler.step(best_miou) # Print per class IoU on last epoch or if best iou if epoch + 1 == config.epochs or is_best: for key, class_iou in zip(class_encoding.keys(), iou): print("{0}: {1:.4f}".format(key, class_iou)) # Save the model if it's the best thus far if is_main_process(): checkpoint_path = save_checkpoint(model, optimizer, epoch + 1, best_miou, compression_algo.scheduler, config) make_additional_checkpoints(checkpoint_path, is_best, epoch + 1, config) print_statistics(compression_algo.statistics()) return model
def main_worker(current_gpu, config): config.current_gpu = current_gpu config.distributed = config.execution_mode in ( ExecutionMode.DISTRIBUTED, ExecutionMode.MULTIPROCESSING_DISTRIBUTED) if config.distributed: configure_distributed(config) config.device = get_device(config) if is_main_process(): configure_logging(config) print_args(config) if config.seed is not None: manual_seed(config.seed) cudnn.deterministic = True cudnn.benchmark = False # create model model_name = config['model'] weights = config.get('weights') model = load_model(model_name, pretrained=config.get('pretrained', True) if weights is None else False, num_classes=config.get('num_classes', 1000), model_params=config.get('model_params')) compression_algo, model = create_compressed_model(model, config) if weights: load_state(model, torch.load(weights, map_location='cpu')) model, _ = prepare_model_for_execution(model, config) if config.distributed: compression_algo.distributed() is_inception = 'inception' in model_name # define loss function (criterion) and optimizer criterion = nn.CrossEntropyLoss() criterion = criterion.to(config.device) params_to_optimize = get_parameter_groups(model, config) optimizer, lr_scheduler = make_optimizer(params_to_optimize, config) resuming_checkpoint = config.resuming_checkpoint best_acc1 = 0 # optionally resume from a checkpoint if resuming_checkpoint is not None: model, config, optimizer, compression_algo, best_acc1 = \ resume_from_checkpoint(resuming_checkpoint, model, config, optimizer, compression_algo) if config.to_onnx is not None: compression_algo.export_model(config.to_onnx) print("Saved to", config.to_onnx) return if config.execution_mode != ExecutionMode.CPU_ONLY: cudnn.benchmark = True # Data loading code train_loader, train_sampler, val_loader = create_dataloaders(config) if config.mode.lower() == 'test': print_statistics(compression_algo.statistics()) validate(val_loader, model, criterion, config) if config.mode.lower() == 'train': if not resuming_checkpoint: compression_algo.initialize(train_loader) train(config, compression_algo, model, criterion, is_inception, lr_scheduler, model_name, optimizer, train_loader, train_sampler, val_loader, best_acc1)
def main_worker(current_gpu, config): ################################# # Setup experiment environment ################################# config.current_gpu = current_gpu config.distributed = config.execution_mode in (ExecutionMode.DISTRIBUTED, ExecutionMode.MULTIPROCESSING_DISTRIBUTED) if config.distributed: configure_distributed(config) if is_on_first_rank(config): configure_logging(config) print_args(config) config.device = get_device(config) config.start_iter = 0 ########################## # Prepare metrics log file ########################## if config.metrics_dump and config.resuming_checkpoint is not None: avg = 0 metrics = {os.path.basename(config.resuming_checkpoint): avg} write_metrics(config, metrics) ################## # Prepare model ################## compression_algo, net = create_model(config) if config.distributed: config.batch_size //= config.ngpus_per_node config.workers //= config.ngpus_per_node compression_algo.distributed() ########################### # Criterion and optimizer ########################### params_to_optimize = get_parameter_groups(net, config) optimizer, lr_scheduler = make_optimizer(params_to_optimize, config) criterion = MultiBoxLoss( config, config['num_classes'], overlap_thresh=0.5, prior_for_matching=True, bkg_label=0, neg_mining=True, neg_pos=3, neg_overlap=0.5, encode_target=False, device=config.device ) ########################### # Load checkpoint ########################### resuming_checkpoint = config.resuming_checkpoint if resuming_checkpoint: print('Resuming training, loading {}...'.format(resuming_checkpoint)) checkpoint = torch.load(resuming_checkpoint, map_location='cpu') # use checkpoint itself in case of only state dict is saved # i.e. checkpoint is created with `torch.save(module.state_dict())` state_dict = checkpoint.get('state_dict', checkpoint) load_state(net, state_dict, is_resume=True) if config.mode.lower() == 'train' and config.to_onnx is None: compression_algo.scheduler.load_state_dict(checkpoint['scheduler']) optimizer.load_state_dict(checkpoint.get('optimizer', optimizer.state_dict())) config.start_iter = checkpoint.get('iter', 0) + 1 if config.to_onnx: compression_algo.export_model(config.to_onnx) print("Saved to {}".format(config.to_onnx)) return ########################### # Prepare data ########################### test_data_loader, train_data_loader = create_dataloaders(config) if config.mode.lower() == 'test': with torch.no_grad(): print_statistics(compression_algo.statistics()) net.eval() mAp = test_net(net, config.device, test_data_loader, distributed=config.distributed) if config.metrics_dump and config.resuming_checkpoint is not None: avg = mAp*100 metrics = {os.path.basename(config.resuming_checkpoint): round(avg, 2)} write_metrics(config, metrics) return if not resuming_checkpoint: compression_algo.initialize(train_data_loader) train(net, compression_algo, train_data_loader, test_data_loader, criterion, optimizer, config, lr_scheduler)
def train(net, compression_algo, train_data_loader, test_data_loader, criterion, optimizer, config, lr_scheduler): net.train() # loss counters loc_loss = 0 # epoch conf_loss = 0 epoch_size = len(train_data_loader) print('Training ', config.model, ' on ', train_data_loader.dataset.name, ' dataset...') batch_iterator = None t_start = time.time() print_statistics(compression_algo.statistics()) for iteration in range(config.start_iter, config['max_iter']): if (not batch_iterator) or (iteration % epoch_size == 0): # create batch iterator batch_iterator = iter(train_data_loader) epoch = iteration // epoch_size if iteration % epoch_size == 0: train_epoch_end(config, compression_algo, net, epoch, iteration, epoch_size, lr_scheduler, optimizer, test_data_loader) compression_algo.scheduler.step(iteration - config.start_iter) optimizer.zero_grad() batch_iterator, batch_loss, batch_loss_c, batch_loss_l, loss_comp = train_step( batch_iterator, compression_algo, config, criterion, net, train_data_loader ) optimizer.step() batch_loss_l = batch_loss_l / config.iter_size batch_loss_c = batch_loss_c / config.iter_size model_loss = (batch_loss_l + batch_loss_c) / config.iter_size batch_loss = batch_loss / config.iter_size loc_loss += batch_loss_l.item() conf_loss += batch_loss_c.item() ########################### # Logging ########################### if is_on_first_rank(config): config.tb.add_scalar("train/loss_l", batch_loss_l.item(), iteration) config.tb.add_scalar("train/loss_c", batch_loss_c.item(), iteration) config.tb.add_scalar("train/loss", batch_loss.item(), iteration) checkpoint_file_path = osp.join(config.checkpoint_save_dir, "{}_last.pth".format(get_name(config))) torch.save({ 'state_dict': net.state_dict(), 'optimizer': optimizer.state_dict(), 'iter': config['max_iter'], 'scheduler': compression_algo.scheduler.state_dict() }, str(checkpoint_file_path)) make_additional_checkpoints(checkpoint_file_path, is_best=True, epoch=epoch + 1, config=config) if iteration % config.print_freq == 0: t_finish = time.time() t_elapsed = t_finish - t_start t_start = time.time() print('{}: iter {} epoch {} || Loss: {:.4} || Time {:.4}s || lr: {} || CR loss: {}'.format( config.rank, iteration, epoch, model_loss.item(), t_elapsed, optimizer.param_groups[0]['lr'], loss_comp.item() if isinstance(loss_comp, torch.Tensor) else loss_comp ))
def main_worker_binarization(current_gpu, config): config.current_gpu = current_gpu config.distributed = config.execution_mode in ( ExecutionMode.DISTRIBUTED, ExecutionMode.MULTIPROCESSING_DISTRIBUTED) if config.distributed: configure_distributed(config) config.device = get_device(config) if is_main_process(): configure_logging(config) print_args(config) if config.seed is not None: manual_seed(config.seed) cudnn.deterministic = True cudnn.benchmark = False # create model model_name = config['model'] weights = config.get('weights') model = load_model(model_name, pretrained=config.get('pretrained', True) if weights is None else False, num_classes=config.get('num_classes', 1000), model_params=config.get('model_params')) original_model = copy.deepcopy(model) compression_algo, model = create_compressed_model(model, config) if not isinstance(compression_algo, Binarization): raise RuntimeError( "The binarization sample worker may only be run with the binarization algorithm!" ) if weights: load_state(model, torch.load(weights, map_location='cpu')) model, _ = prepare_model_for_execution(model, config) original_model.to(config.device) if config.distributed: compression_algo.distributed() is_inception = 'inception' in model_name # define loss function (criterion) and optimizer criterion = nn.CrossEntropyLoss() criterion = criterion.to(config.device) params_to_optimize = model.parameters() compression_config = config['compression'] binarization_config = compression_config if isinstance( compression_config, dict) else compression_config[0] optimizer = get_binarization_optimizer(params_to_optimize, binarization_config) optimizer_scheduler = BinarizationOptimizerScheduler( optimizer, binarization_config) kd_loss_calculator = KDLossCalculator(original_model) resuming_checkpoint = config.resuming_checkpoint best_acc1 = 0 # optionally resume from a checkpoint if resuming_checkpoint is not None: model, config, optimizer, optimizer_scheduler, kd_loss_calculator, compression_algo, best_acc1 = \ resume_from_checkpoint(resuming_checkpoint, model, config, optimizer, optimizer_scheduler, kd_loss_calculator, compression_algo) if config.to_onnx is not None: compression_algo.export_model(config.to_onnx) print("Saved to", config.to_onnx) return if config.execution_mode != ExecutionMode.CPU_ONLY: cudnn.benchmark = True # Data loading code train_loader, train_sampler, val_loader = create_dataloaders(config) if config.mode.lower() == 'test': print_statistics(compression_algo.statistics()) validate(val_loader, model, criterion, config) if config.mode.lower() == 'train': if not resuming_checkpoint: compression_algo.initialize(train_loader) batch_multiplier = (binarization_config.get("params", {})).get( "batch_multiplier", 1) train_bin(config, compression_algo, model, criterion, is_inception, optimizer_scheduler, model_name, optimizer, train_loader, train_sampler, val_loader, kd_loss_calculator, batch_multiplier, best_acc1)