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 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
} # Package the above stuff into dictionaries. mining_funcs = {"tuple_miner": miner} 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,
def test_metric_loss_only(self): cifar_resnet_folder = "temp_cifar_resnet_for_pytorch_metric_learning_test" dataset_folder = "temp_dataset_for_pytorch_metric_learning_test" model_folder = "temp_saved_models_for_pytorch_metric_learning_test" logs_folder = "temp_logs_for_pytorch_metric_learning_test" tensorboard_folder = "temp_tensorboard_for_pytorch_metric_learning_test" os.system( "git clone https://github.com/akamaster/pytorch_resnet_cifar10.git {}" .format(cifar_resnet_folder)) loss_fn = NTXentLoss() normalize_transform = transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]) train_transform = transforms.Compose([ transforms.RandomHorizontalFlip(), transforms.RandomCrop(32, 4), transforms.ToTensor(), normalize_transform, ]) eval_transform = transforms.Compose( [transforms.ToTensor(), normalize_transform]) assert not os.path.isdir(dataset_folder) assert not os.path.isdir(model_folder) assert not os.path.isdir(logs_folder) assert not os.path.isdir(tensorboard_folder) subset_idx = np.arange(10000) train_dataset = datasets.CIFAR100(dataset_folder, train=True, download=True, transform=train_transform) train_dataset_for_eval = datasets.CIFAR100(dataset_folder, train=True, download=True, transform=eval_transform) val_dataset = datasets.CIFAR100(dataset_folder, train=False, download=True, transform=eval_transform) train_dataset = torch.utils.data.Subset(train_dataset, subset_idx) train_dataset_for_eval = torch.utils.data.Subset( train_dataset_for_eval, subset_idx) val_dataset = torch.utils.data.Subset(val_dataset, subset_idx) for dtype in TEST_DTYPES: for splits_to_eval in [ None, [("train", ["train", "val"]), ("val", ["train", "val"])], ]: from temp_cifar_resnet_for_pytorch_metric_learning_test import resnet model = torch.nn.DataParallel(resnet.resnet20()) checkpoint = torch.load( "{}/pretrained_models/resnet20-12fca82f.th".format( cifar_resnet_folder), map_location=TEST_DEVICE, ) model.load_state_dict(checkpoint["state_dict"]) model.module.linear = c_f.Identity() if TEST_DEVICE == torch.device("cpu"): model = model.module model = model.to(TEST_DEVICE).type(dtype) optimizer = torch.optim.Adam( model.parameters(), lr=0.0002, weight_decay=0.0001, eps=1e-04, ) batch_size = 32 iterations_per_epoch = None if splits_to_eval is None else 1 model_dict = {"trunk": model} optimizer_dict = {"trunk_optimizer": optimizer} loss_fn_dict = {"metric_loss": loss_fn} sampler = MPerClassSampler( np.array(train_dataset.dataset.targets)[subset_idx], m=4, batch_size=32, length_before_new_iter=len(train_dataset), ) record_keeper, _, _ = logging_presets.get_record_keeper( logs_folder, tensorboard_folder) hooks = logging_presets.get_hook_container( record_keeper, primary_metric="precision_at_1") dataset_dict = { "train": train_dataset_for_eval, "val": val_dataset } tester = GlobalEmbeddingSpaceTester( end_of_testing_hook=hooks.end_of_testing_hook, accuracy_calculator=accuracy_calculator.AccuracyCalculator( include=("precision_at_1", "AMI"), k=1), data_device=TEST_DEVICE, dtype=dtype, dataloader_num_workers=32, ) end_of_epoch_hook = hooks.end_of_epoch_hook( tester, dataset_dict, model_folder, test_interval=1, patience=1, splits_to_eval=splits_to_eval, ) trainer = MetricLossOnly( models=model_dict, optimizers=optimizer_dict, batch_size=batch_size, loss_funcs=loss_fn_dict, mining_funcs={}, dataset=train_dataset, sampler=sampler, data_device=TEST_DEVICE, dtype=dtype, dataloader_num_workers=32, iterations_per_epoch=iterations_per_epoch, freeze_trunk_batchnorm=True, end_of_iteration_hook=hooks.end_of_iteration_hook, end_of_epoch_hook=end_of_epoch_hook, ) num_epochs = 3 trainer.train(num_epochs=num_epochs) best_epoch, best_accuracy = hooks.get_best_epoch_and_accuracy( tester, "val") if splits_to_eval is None: self.assertTrue(best_epoch == 3) self.assertTrue(best_accuracy > 0.2) accuracies, primary_metric_key = hooks.get_accuracies_of_best_epoch( tester, "val") accuracies = c_f.sqliteObjToDict(accuracies) self.assertTrue( accuracies[primary_metric_key][0] == best_accuracy) self.assertTrue(primary_metric_key == "precision_at_1_level0") best_epoch_accuracies = hooks.get_accuracies_of_epoch( tester, "val", best_epoch) best_epoch_accuracies = c_f.sqliteObjToDict( best_epoch_accuracies) self.assertTrue(best_epoch_accuracies[primary_metric_key][0] == best_accuracy) accuracy_history = hooks.get_accuracy_history(tester, "val") self.assertTrue(accuracy_history[primary_metric_key][ accuracy_history["epoch"].index(best_epoch)] == best_accuracy) loss_history = hooks.get_loss_history() if splits_to_eval is None: self.assertTrue( len(loss_history["metric_loss"]) == (len(sampler) / batch_size) * num_epochs) curr_primary_metric = hooks.get_curr_primary_metric( tester, "val") self.assertTrue(curr_primary_metric == accuracy_history[primary_metric_key][-1]) base_record_group_name = hooks.base_record_group_name(tester) self.assertTrue( base_record_group_name == "accuracies_normalized_GlobalEmbeddingSpaceTester_level_0") record_group_name = hooks.record_group_name(tester, "val") if splits_to_eval is None: self.assertTrue( record_group_name == "accuracies_normalized_GlobalEmbeddingSpaceTester_level_0_VAL_vs_self" ) else: self.assertTrue( record_group_name == "accuracies_normalized_GlobalEmbeddingSpaceTester_level_0_VAL_vs_TRAIN_and_VAL" ) shutil.rmtree(model_folder) shutil.rmtree(logs_folder) shutil.rmtree(tensorboard_folder) shutil.rmtree(cifar_resnet_folder) shutil.rmtree(dataset_folder)
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)
def train_app(cfg): print(cfg.pretty()) 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.__dict__[cfg.model.model_name](pretrained=cfg.model.pretrained) #resnet18(pretrained=True) #trunk = models.alexnet(pretrained=True) #trunk = models.resnet50(pretrained=True) #trunk = models.resnet152(pretrained=True) #trunk = models.wide_resnet50_2(pretrained=True) #trunk = EfficientNet.from_pretrained('efficientnet-b2') trunk_output_size = trunk.fc.in_features trunk.fc = Identity() trunk = torch.nn.DataParallel(trunk.to(device)) embedder = torch.nn.DataParallel(MLP([trunk_output_size, cfg.embedder.size]).to(device)) classifier = torch.nn.DataParallel(MLP([cfg.embedder.size, cfg.embedder.class_out_size])).to(device) # Set optimizers if cfg.optimizer.name == "sdg": trunk_optimizer = torch.optim.SGD(trunk.parameters(), lr=cfg.optimizer.lr, momentum=cfg.optimizer.momentum, weight_decay=cfg.optimizer.weight_decay) embedder_optimizer = torch.optim.SGD(embedder.parameters(), lr=cfg.optimizer.lr, momentum=cfg.optimizer.momentum, weight_decay=cfg.optimizer.weight_decay) classifier_optimizer = torch.optim.SGD(classifier.parameters(), lr=cfg.optimizer.lr, momentum=cfg.optimizer.momentum, weight_decay=cfg.optimizer.weight_decay) elif cfg.optimizer.name == "rmsprop": trunk_optimizer = torch.optim.RMSprop(trunk.parameters(), lr=cfg.optimizer.lr, momentum=cfg.optimizer.momentum, weight_decay=cfg.optimizer.weight_decay) embedder_optimizer = torch.optim.RMSprop(embedder.parameters(), lr=cfg.optimizer.lr, momentum=cfg.optimizer.momentum, weight_decay=cfg.optimizer.weight_decay) classifier_optimizer = torch.optim.RMSprop(classifier.parameters(), lr=cfg.optimizer.lr, momentum=cfg.optimizer.momentum, weight_decay=cfg.optimizer.weight_decay) # Set the datasets data_dir = os.environ["DATASET_FOLDER"]+"/"+cfg.dataset.data_dir print("Data dir: "+data_dir) train_dataset, val_dataset, val_samples_dataset = get_datasets(data_dir, cfg, mode=cfg.mode.type) print("Trainset: ",len(train_dataset), "Testset: ",len(val_dataset), "Samplesset: ",len(val_samples_dataset)) # Set the loss function if cfg.embedder_loss.name == "margin_loss": loss = losses.MarginLoss(margin=cfg.embedder_loss.margin,nu=cfg.embedder_loss.nu,beta=cfg.embedder_loss.beta) if cfg.embedder_loss.name == "triplet_margin": loss = losses.TripletMarginLoss(margin=cfg.embedder_loss.margin) if cfg.embedder_loss.name == "multi_similarity": loss = losses.MultiSimilarityLoss(alpha=cfg.embedder_loss.alpha, beta=cfg.embedder_loss.beta, base=cfg.embedder_loss.base) # Set the classification loss: classification_loss = torch.nn.CrossEntropyLoss() # Set the mining function if cfg.miner.name == "triplet_margin": #miner = miners.TripletMarginMiner(margin=0.2) miner = miners.TripletMarginMiner(margin=cfg.miner.margin) if cfg.miner.name == "multi_similarity": miner = miners.MultiSimilarityMiner(epsilon=cfg.miner.epsilon) #miner = miners.MultiSimilarityMiner(epsilon=0.05) batch_size = cfg.trainer.batch_size num_epochs = cfg.trainer.num_epochs iterations_per_epoch = cfg.trainer.iterations_per_epoch # Set the dataloader sampler sampler = samplers.MPerClassSampler(train_dataset.targets, m=4, length_before_new_iter=len(train_dataset)) # Package the above stuff into dictionaries. models = {"trunk": trunk, "embedder": embedder, "classifier": classifier} optimizers = {"trunk_optimizer": trunk_optimizer, "embedder_optimizer": embedder_optimizer, "classifier_optimizer": classifier_optimizer} loss_funcs = {"metric_loss": loss, "classifier_loss": classification_loss} mining_funcs = {"tuple_miner": miner} # We can specify loss weights if we want to. This is optional loss_weights = {"metric_loss": cfg.loss.metric_loss, "classifier_loss": cfg.loss.classifier_loss} schedulers = { #"metric_loss_scheduler_by_epoch": torch.optim.lr_scheduler.StepLR(classifier_optimizer, cfg.scheduler.step_size, gamma=cfg.scheduler.gamma), "embedder_scheduler_by_epoch": torch.optim.lr_scheduler.StepLR(embedder_optimizer, cfg.scheduler.step_size, gamma=cfg.scheduler.gamma), "classifier_scheduler_by_epoch": torch.optim.lr_scheduler.StepLR(classifier_optimizer, cfg.scheduler.step_size, gamma=cfg.scheduler.gamma), "trunk_scheduler_by_epoch": torch.optim.lr_scheduler.StepLR(embedder_optimizer, cfg.scheduler.step_size, gamma=cfg.scheduler.gamma), } experiment_name = "%s_model_%s_cl_%s_ml_%s_miner_%s_mix_ml_%02.2f_mix_cl_%02.2f_resize_%d_emb_size_%d_class_size_%d_opt_%s_lr_%02.2f_m_%02.2f_wd_%02.2f"%(cfg.dataset.name, cfg.model.model_name, "cross_entropy", cfg.embedder_loss.name, cfg.miner.name, cfg.loss.metric_loss, cfg.loss.classifier_loss, cfg.transform.transform_resize, cfg.embedder.size, cfg.embedder.class_out_size, cfg.optimizer.name, cfg.optimizer.lr, cfg.optimizer.momentum, cfg.optimizer.weight_decay) record_keeper, _, _ = logging_presets.get_record_keeper("logs/%s"%(experiment_name), "tensorboard/%s"%(experiment_name)) hooks = logging_presets.get_hook_container(record_keeper) dataset_dict = {"samples": val_samples_dataset, "val": val_dataset} model_folder = "example_saved_models/%s/"%(experiment_name) # Create the tester tester = OneShotTester( end_of_testing_hook=hooks.end_of_testing_hook, #size_of_tsne=20 ) #tester.embedding_filename=data_dir+"/embeddings_pretrained_triplet_loss_multi_similarity_miner.pkl" tester.embedding_filename=data_dir+"/"+experiment_name+".pkl" end_of_epoch_hook = hooks.end_of_epoch_hook(tester, dataset_dict, model_folder) trainer = trainers.TrainWithClassifier(models, optimizers, batch_size, loss_funcs, mining_funcs, train_dataset, sampler=sampler, lr_schedulers=schedulers, dataloader_num_workers = cfg.trainer.batch_size, loss_weights=loss_weights, end_of_iteration_hook=hooks.end_of_iteration_hook, end_of_epoch_hook=end_of_epoch_hook ) trainer.train(num_epochs=num_epochs) tester = OneShotTester()