def _train(self) -> Optional[float]: record_keeper, _, _ = logging_presets.get_record_keeper( "example_logs", "example_tensorboard") hooks = logging_presets.get_hook_container(record_keeper) dataset_dict = {"val": self.val_dataset} model_folder = "example_saved_models" def visualizer_hook(umapper, umap_embeddings, labels, split_name, keyname, *args): logging.info("UMAP plot for the {} split and label set {}".format( split_name, keyname)) label_set = np.unique(labels) num_classes = len(label_set) fig = plt.figure(figsize=(20, 15)) plt.gca().set_prop_cycle( cycler("color", [ plt.cm.nipy_spectral(i) for i in np.linspace(0, 0.9, num_classes) ])) for i in range(num_classes): idx = labels == label_set[i] plt.plot(umap_embeddings[idx, 0], umap_embeddings[idx, 1], ".", markersize=1) #plt.show() #plt.show(block=False) file_name = './plots/metric_{0}.png'.format(args[0]) plt.savefig(file_name, dpi=300) # # Create the tester tester = testers.GlobalEmbeddingSpaceTester( end_of_testing_hook=hooks.end_of_testing_hook, visualizer=umap.UMAP(), visualizer_hook=visualizer_hook, dataloader_num_workers=32) end_of_epoch_hook = hooks.end_of_epoch_hook(tester, dataset_dict, model_folder, test_interval=1, patience=200) trainer = trainers.MetricLossOnly( self.models_dict, self.optimizers, self._train_cfg.batch_per_gpu, self.loss_funcs, self.mining_funcs, #self._train_loader, self.train_set, sampler=self.sampler, dataloader_num_workers=self._train_cfg.workers - 1, end_of_iteration_hook=hooks.end_of_iteration_hook, end_of_epoch_hook=end_of_epoch_hook) #trainer.train(num_epochs=self._train_cfg.epochs) trainer.train(num_epochs=500)
def mk_tester(self, *args, **kwargs): return testers.GlobalEmbeddingSpaceTester( *args, dataloader_num_workers=4, data_device=self.tester_device if self.tester_device is not None else self.device, end_of_testing_hook=self.end_of_testing_hook, **kwargs, )
def get_testing_hooks(experiment_id, val_dataset, test_interval, patience): experiment_dir = os.path.join('experiment_logs', experiment_id) record_keeper, _, _ = logging_presets.get_record_keeper( experiment_dir, os.path.join('experiment_logs', 'tensorboard', experiment_id)) hooks = logging_presets.get_hook_container(record_keeper) dataset_dict = {"val": val_dataset} model_folder = experiment_dir def visualizer_hook(umapper, umap_embeddings, labels, split_name, keyname, *args): logging.info("UMAP plot for the {} split and label set {}".format( split_name, keyname)) label_set = np.unique(labels) num_classes = len(label_set) fig = plt.figure(figsize=(20, 15)) plt.gca().set_prop_cycle( cycler("color", [ plt.cm.nipy_spectral(i) for i in np.linspace(0, 0.9, num_classes) ])) for i in range(num_classes): idx = labels == label_set[i] plt.plot(umap_embeddings[idx, 0], umap_embeddings[idx, 1], ".", markersize=1) plt.show() writer = SummaryWriter(log_dir=os.path.join( 'experiment_logs', 'tensorboard', experiment_id)) writer.add_embedding(umap_embeddings, metadata=labels) writer.close() # Create the tester tester = testers.GlobalEmbeddingSpaceTester( end_of_testing_hook=hooks.end_of_testing_hook, visualizer=umap.UMAP(), visualizer_hook=visualizer_hook, dataloader_num_workers=6) end_of_epoch_hook = hooks.end_of_epoch_hook(tester, dataset_dict, model_folder, test_interval=test_interval, patience=patience) return end_of_epoch_hook, hooks.end_of_iteration_hook
loss_weights = { "metric_loss": 1, "synth_loss": 0.1, "g_adv_loss": 0.1, "g_hard_loss": 0.1, "g_reg_loss": 0.1 } record_keeper, _, _ = logging_presets.get_record_keeper( "example_logs", "example_tensorboard") hooks = logging_presets.get_hook_container(record_keeper) dataset_dict = {"val": val_dataset} model_folder = "example_saved_models" # Create the tester tester = testers.GlobalEmbeddingSpaceTester( end_of_testing_hook=hooks.end_of_testing_hook) end_of_epoch_hook = hooks.end_of_epoch_hook(tester, dataset_dict, model_folder) trainer = trainers.DeepAdversarialMetricLearning( models=models, optimizers=optimizers, batch_size=batch_size, loss_funcs=loss_funcs, mining_funcs=mining_funcs, iterations_per_epoch=iterations_per_epoch, dataset=train_dataset, loss_weights=loss_weights, sampler=sampler, end_of_iteration_hook=hooks.end_of_iteration_hook, end_of_epoch_hook=end_of_epoch_hook, metric_alone_epochs=0, g_alone_epochs=0,
logger=logging.getLogger()) constructors = MODEL_DEF.get(CONF, best_trial, param_gen) train_dataset, dev_dataset, train_sampler, batch_size = \ next(constructors["fold_generator"]()) trainer_kwargs = constructors["modules"]() # logging record_keeper, _, _ = logging_presets.get_record_keeper( csv_folder=os.path.join(args.log_dir, f"csv"), tensorboard_folder=os.path.join(args.log_dir, f"tensorboard")) hooks = logging_presets.get_hook_container(record_keeper) # tester tester = testers.GlobalEmbeddingSpaceTester( end_of_testing_hook=hooks.end_of_testing_hook, dataloader_num_workers=32) end_of_epoch_hook = hooks.end_of_epoch_hook(tester, {"val": dev_dataset}, os.path.join( args.log_dir, f"model"), test_interval=1, patience=args.patience) # train if args.trainer == "MetricLossOnly": trainer = trainers.MetricLossOnly( batch_size=batch_size, mining_funcs={}, dataset=train_dataset, sampler=train_sampler, dataloader_num_workers=32, end_of_iteration_hook=hooks.end_of_iteration_hook,
} loss_funcs = {"metric_loss": loss} mining_funcs = {"post_gradient_miner": miner} trainer = trainers.MetricLossOnly(models, optimizers, batch_size, loss_funcs, mining_funcs, iterations_per_epoch, train_dataset, record_keeper=record_keeper) trainer.train(num_epochs=num_epochs) ############################# ########## Testing ########## ############################# # The testing module requires faiss and scikit-learn # So if you don't have these, then this import will break from pytorch_metric_learning import testers tester = testers.GlobalEmbeddingSpaceTester(record_keeper=record_keeper) dataset_dict = {"train": train_dataset, "val": val_dataset} epoch = 2 tester.test(dataset_dict, epoch, trunk, embedder) if record_keeper is not None: record_keeper.pickler_and_csver.save_records()
def main(): device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu') print('Running on device: {}'.format(device)) # Data transformations trans_train = transforms.Compose([ transforms.RandomApply(transforms=[ transforms.AutoAugment(transforms.AutoAugmentPolicy.IMAGENET), # transforms.RandomPerspective(distortion_scale=0.6, p=1.0), transforms.RandomRotation(degrees=(0, 180)), transforms.RandomHorizontalFlip(), ]), np.float32, transforms.ToTensor(), fixed_image_standardization, ]) trans_val = transforms.Compose([ # transforms.CenterCrop(120), np.float32, transforms.ToTensor(), fixed_image_standardization, ]) train_dataset = datasets.ImageFolder(os.path.join(data_dir, "train_aligned"), transform=trans_train) val_dataset = datasets.ImageFolder(os.path.join(data_dir, "val_aligned"), transform=trans_val) # Prepare the model model = InceptionResnetV1(classify=False, pretrained="vggface2", dropout_prob=0.5).to(device) # for param in list(model.parameters())[:-8]: # param.requires_grad = False trunk_optimizer = torch.optim.SGD(model.parameters(), lr=LR) # Set the loss function loss = losses.ArcFaceLoss(len(train_dataset.classes), 512) # Package the above stuff into dictionaries. models = {"trunk": model} optimizers = {"trunk_optimizer": trunk_optimizer} loss_funcs = {"metric_loss": loss} mining_funcs = {} lr_scheduler = { "trunk_scheduler_by_plateau": torch.optim.lr_scheduler.ReduceLROnPlateau(trunk_optimizer) } # Create the tester record_keeper, _, _ = logging_presets.get_record_keeper( "logs", "tensorboard") hooks = logging_presets.get_hook_container(record_keeper) dataset_dict = {"val": val_dataset, "train": train_dataset} model_folder = "training_saved_models" def visualizer_hook(umapper, umap_embeddings, labels, split_name, keyname, *args): logging.info("UMAP plot for the {} split and label set {}".format( split_name, keyname)) label_set = np.unique(labels) num_classes = len(label_set) fig = plt.figure(figsize=(8, 7)) plt.gca().set_prop_cycle( cycler("color", [ plt.cm.nipy_spectral(i) for i in np.linspace(0, 0.9, num_classes) ])) for i in range(num_classes): idx = labels == label_set[i] plt.plot(umap_embeddings[idx, 0], umap_embeddings[idx, 1], ".", markersize=1) plt.show() tester = testers.GlobalEmbeddingSpaceTester( end_of_testing_hook=hooks.end_of_testing_hook, dataloader_num_workers=4, accuracy_calculator=AccuracyCalculator( include=['mean_average_precision_at_r'], k="max_bin_count")) end_of_epoch_hook = hooks.end_of_epoch_hook(tester, dataset_dict, model_folder, splits_to_eval=[('val', ['train'])]) # Create the trainer trainer = trainers.MetricLossOnly( models, optimizers, batch_size, loss_funcs, mining_funcs, train_dataset, lr_schedulers=lr_scheduler, dataloader_num_workers=8, end_of_iteration_hook=hooks.end_of_iteration_hook, end_of_epoch_hook=end_of_epoch_hook) trainer.train(num_epochs=num_epochs)
def train(train_data, test_data, save_model, num_epochs, lr, embedding_size, batch_size): device = torch.device("cuda" if torch.cuda.is_available() else "cpu") # Set trunk model and replace the softmax layer with an identity function trunk = torchvision.models.resnet18(pretrained=True) trunk_output_size = trunk.fc.in_features trunk.fc = common_functions.Identity() trunk = torch.nn.DataParallel(trunk.to(device)) # Set embedder model. This takes in the output of the trunk and outputs 64 dimensional embeddings embedder = torch.nn.DataParallel( MLP([trunk_output_size, embedding_size]).to(device)) # Set optimizers trunk_optimizer = torch.optim.Adam(trunk.parameters(), lr=lr / 10, weight_decay=0.0001) embedder_optimizer = torch.optim.Adam(embedder.parameters(), lr=lr, weight_decay=0.0001) # Set the loss function loss = losses.TripletMarginLoss(margin=0.1) # Set the mining function miner = miners.MultiSimilarityMiner(epsilon=0.1) # Set the dataloader sampler sampler = samplers.MPerClassSampler(train_data.targets, m=4, length_before_new_iter=len(train_data)) save_dir = os.path.join( save_model, ''.join(str(lr).split('.')) + '_' + str(batch_size) + '_' + str(embedding_size)) os.makedirs(save_dir, exist_ok=True) # Package the above stuff into dictionaries. models = {"trunk": trunk, "embedder": embedder} optimizers = { "trunk_optimizer": trunk_optimizer, "embedder_optimizer": embedder_optimizer } loss_funcs = {"metric_loss": loss} mining_funcs = {"tuple_miner": miner} record_keeper, _, _ = logging_presets.get_record_keeper( os.path.join(save_dir, "example_logs"), os.path.join(save_dir, "example_tensorboard")) hooks = logging_presets.get_hook_container(record_keeper) dataset_dict = {"val": test_data, "train": train_data} model_folder = "example_saved_models" def visualizer_hook(umapper, umap_embeddings, labels, split_name, keyname, *args): logging.info("UMAP plot for the {} split and label set {}".format( split_name, keyname)) label_set = np.unique(labels) num_classes = len(label_set) fig = plt.figure(figsize=(20, 15)) plt.title(str(split_name) + '_' + str(num_embeddings)) plt.gca().set_prop_cycle( cycler("color", [ plt.cm.nipy_spectral(i) for i in np.linspace(0, 0.9, num_classes) ])) for i in range(num_classes): idx = labels == label_set[i] plt.plot(umap_embeddings[idx, 0], umap_embeddings[idx, 1], ".", markersize=1) plt.show() # Create the tester tester = testers.GlobalEmbeddingSpaceTester( end_of_testing_hook=hooks.end_of_testing_hook, visualizer=umap.UMAP(), visualizer_hook=visualizer_hook, dataloader_num_workers=32, accuracy_calculator=AccuracyCalculator(k="max_bin_count")) end_of_epoch_hook = hooks.end_of_epoch_hook(tester, dataset_dict, model_folder, test_interval=1, patience=1) trainer = trainers.MetricLossOnly( models, optimizers, batch_size, loss_funcs, mining_funcs, train_data, sampler=sampler, dataloader_num_workers=32, end_of_iteration_hook=hooks.end_of_iteration_hook, end_of_epoch_hook=end_of_epoch_hook) trainer.train(num_epochs=num_epochs) if save_model is not None: torch.save(models["trunk"].state_dict(), os.path.join(save_dir, 'trunk.pth')) torch.save(models["embedder"].state_dict(), os.path.join(save_dir, 'embedder.pth')) print('Model saved in ', save_dir)
def objective(trial): param_gen = ParameterGenerator(trial, CONF["_fix_params"], logger=logger) # Average results of multiple folds. print("New parameter.") metrics = [] constructors = MODEL_DEF.get(CONF, trial, param_gen) for i_fold, (train_dataset, dev_dataset, train_sampler, batch_size) in enumerate(constructors["fold_generator"]()): print(f"Fold {i_fold}") trainer_kwargs = constructors["modules"]() # logging record_keeper, _, _ = logging_presets.get_record_keeper( csv_folder=os.path.join(args.log_dir, f"trial_{trial.number}_{i_fold}_csv"), tensorboard_folder=os.path.join( args.log_dir, f"trial_{trial.number}_{i_fold}_tensorboard")) hooks = logging_presets.get_hook_container(record_keeper) # tester tester = testers.GlobalEmbeddingSpaceTester( end_of_testing_hook=hooks.end_of_testing_hook, dataloader_num_workers=args.n_test_loader) end_of_epoch_hook = hooks.end_of_epoch_hook( tester, {"val": dev_dataset}, os.path.join(args.log_dir, f"trial_{trial.number}_{i_fold}_model"), test_interval=1, patience=args.patience) CHECKPOINT_FN = os.path.join( args.log_dir, f"trial_{trial.number}_{i_fold}_last.pth") def actual_end_of_epoch_hook(trainer): continue_training = end_of_epoch_hook(trainer) torch.save( ({k: m.state_dict() for k, m in trainer.models.items()}, {k: m.state_dict() for k, m in trainer.optimizers.items()}, {k: m.state_dict() for k, m in trainer.loss_funcs.items()}, trainer.epoch), CHECKPOINT_FN) return continue_training # train if args.trainer == "MetricLossOnly": trainer = trainers.MetricLossOnly( batch_size=batch_size, mining_funcs={}, dataset=train_dataset, sampler=train_sampler, dataloader_num_workers=args.n_train_loader, end_of_iteration_hook=hooks.end_of_iteration_hook, end_of_epoch_hook=actual_end_of_epoch_hook, **trainer_kwargs) elif args.trainer == "TrainWithClassifier": trainer = trainers.TrainWithClassifier( batch_size=batch_size, mining_funcs={}, dataset=train_dataset, sampler=train_sampler, dataloader_num_workers=args.n_train_loader, end_of_iteration_hook=hooks.end_of_iteration_hook, end_of_epoch_hook=actual_end_of_epoch_hook, **trainer_kwargs) while True: start_epoch = 1 if os.path.exists(CHECKPOINT_FN): model_dicts, optimizer_dicts, loss_dicts, last_epoch = \ torch.load(CHECKPOINT_FN) for k, d in model_dicts.items(): trainer.models[k].load_state_dict(d) for k, d in optimizer_dicts.items(): trainer.optimizers[k].load_state_dict(d) for k, d in loss_dicts.items(): trainer.loss_funcs[k].load_state_dict(d) start_epoch = last_epoch + 1 logger.critical(f"Start from old epoch: {last_epoch + 1}") try: trainer.train(num_epochs=args.max_epoch, start_epoch=start_epoch) except Exception as err: logger.critical(f"Error: {err}") if not args.ignore_error: break else: raise err else: break rslt = hooks.get_accuracy_history( tester, "val", metrics=["mean_average_precision_at_r"]) metrics.append(max(rslt["mean_average_precision_at_r_level0"])) return np.mean(metrics)