def main(args): # Model getter: specify dataset and depth of the network. model = pytorchcv_wrapper.resnet('cifar10', depth=20, pretrained=False) # Or get a more specific model. E.g. wide resnet, with depth 40 and growth # factor 8 for Cifar 10. # model = pytorchcv_wrapper.get_model("wrn40_8_cifar10", pretrained=False) # --- CONFIG device = torch.device(f"cuda:{args.cuda}" if torch.cuda.is_available() and args.cuda >= 0 else "cpu") device = "cpu" # --- TRANSFORMATIONS transform = transforms.Compose([ ToTensor(), transforms.Normalize((0.491, 0.482, 0.446), (0.247, 0.243, 0.261)) ]) # --- SCENARIO CREATION cifar_train = CIFAR10(root=expanduser("~") + "/.avalanche/data/cifar10/", train=True, download=True, transform=transform) cifar_test = CIFAR10(root=expanduser("~") + "/.avalanche/data/cifar10/", train=False, download=True, transform=transform) scenario = nc_benchmark( cifar_train, cifar_test, 5, task_labels=False, seed=1234, fixed_class_order=[i for i in range(10)]) # choose some metrics and evaluation method interactive_logger = InteractiveLogger() eval_plugin = EvaluationPlugin( accuracy_metrics( minibatch=True, epoch=True, experience=True, stream=True), loss_metrics(minibatch=True, epoch=True, experience=True, stream=True), forgetting_metrics(experience=True), loggers=[interactive_logger]) # CREATE THE STRATEGY INSTANCE (Naive, with Replay) cl_strategy = Naive(model, torch.optim.SGD(model.parameters(), lr=0.01), CrossEntropyLoss(), train_mb_size=100, train_epochs=1, eval_mb_size=100, device=device, plugins=[ReplayPlugin(mem_size=1000)], evaluator=eval_plugin ) # TRAINING LOOP print('Starting experiment...') results = [] for experience in scenario.train_stream: print("Start of experience ", experience.current_experience) cl_strategy.train(experience) print('Training completed') print('Computing accuracy on the whole test set') results.append(cl_strategy.eval(scenario.test_stream))
def main(args): model = SimpleMLP(hidden_size=args.hs) optimizer = torch.optim.SGD(model.parameters(), lr=args.lr) criterion = torch.nn.CrossEntropyLoss() # check if selected GPU is available or use CPU assert args.cuda == -1 or args.cuda >= 0, "cuda must be -1 or >= 0." device = torch.device( f"cuda:{args.cuda}" if torch.cuda.is_available() and args.cuda >= 0 else "cpu" ) print(f"Using device: {device}") # create Permuted MNIST scenario scenario = PermutedMNIST(n_experiences=4) interactive_logger = InteractiveLogger() eval_plugin = EvaluationPlugin( accuracy_metrics( minibatch=True, epoch=True, experience=True, stream=True ), loss_metrics(minibatch=True, epoch=True, experience=True, stream=True), forgetting_metrics(experience=True), loggers=[interactive_logger], ) # create strategy assert ( len(args.lambda_e) == 1 or len(args.lambda_e) == 5 ), "Lambda_e must be a non-empty list." lambda_e = args.lambda_e[0] if len(args.lambda_e) == 1 else args.lambda_e strategy = LFL( model, optimizer, criterion, lambda_e=lambda_e, train_epochs=args.epochs, device=device, train_mb_size=args.minibatch_size, evaluator=eval_plugin, ) # train on the selected scenario with the chosen strategy print("Starting experiment...") results = [] for train_batch_info in scenario.train_stream: print( "Start training on experience ", train_batch_info.current_experience ) strategy.train(train_batch_info, num_workers=0) print( "End training on experience ", train_batch_info.current_experience ) print("Computing accuracy on the test set") results.append(strategy.eval(scenario.test_stream[:]))
def main(args): model = SimpleMLP(hidden_size=args.hs) optimizer = torch.optim.SGD(model.parameters(), lr=args.lr) criterion = torch.nn.CrossEntropyLoss() # check if selected GPU is available or use CPU assert args.cuda == -1 or args.cuda >= 0, "cuda must be -1 or >= 0." device = torch.device(f"cuda:{args.cuda}" if torch.cuda.is_available() and args.cuda >= 0 else "cpu") print(f'Using device: {device}') # create scenario if args.scenario == 'pmnist': scenario = PermutedMNIST(n_experiences=args.permutations) elif args.scenario == 'smnist': scenario = SplitMNIST(n_experiences=5, return_task_id=False) else: raise ValueError("Wrong scenario name. Allowed pmnist, smnist.") # choose some metrics and evaluation method interactive_logger = InteractiveLogger() eval_plugin = EvaluationPlugin(accuracy_metrics(minibatch=True, epoch=True, experience=True, stream=True), loss_metrics(minibatch=True, epoch=True, experience=True, stream=True), forgetting_metrics(experience=True), loggers=[interactive_logger]) # create strategy strategy = EWC(model, optimizer, criterion, args.ewc_lambda, args.ewc_mode, decay_factor=args.decay_factor, train_epochs=args.epochs, device=device, train_mb_size=args.minibatch_size, evaluator=eval_plugin) # train on the selected scenario with the chosen strategy print('Starting experiment...') results = [] for experience in scenario.train_stream: print("Start training on experience ", experience.current_experience) strategy.train(experience) print("End training on experience", experience.current_experience) print('Computing accuracy on the test set') results.append(strategy.eval(scenario.test_stream[:]))
def main(args): model = SimpleMLP(hidden_size=args.hs) optimizer = torch.optim.SGD(model.parameters(), lr=args.lr) criterion = torch.nn.CrossEntropyLoss() # check if selected GPU is available or use CPU assert args.cuda == -1 or args.cuda >= 0, "cuda must be -1 or >= 0." device = torch.device(f"cuda:{args.cuda}" if torch.cuda.is_available() and args.cuda >= 0 else "cpu") print(f'Using device: {device}') # create split scenario scenario = SplitMNIST(n_experiences=5, return_task_id=False) interactive_logger = InteractiveLogger() eval_plugin = EvaluationPlugin(accuracy_metrics(minibatch=True, epoch=True, experience=True, stream=True), loss_metrics(minibatch=True, epoch=True, experience=True, stream=True), forgetting_metrics(experience=True), loggers=[interactive_logger]) # create strategy assert len(args.lwf_alpha) == 1 or len(args.lwf_alpha) == 5,\ 'Alpha must be a non-empty list.' lwf_alpha = args.lwf_alpha[0] if len(args.lwf_alpha) == 1 \ else args.lwf_alpha strategy = LwF(model, optimizer, criterion, alpha=lwf_alpha, temperature=args.softmax_temperature, train_epochs=args.epochs, device=device, train_mb_size=args.minibatch_size, evaluator=eval_plugin) # train on the selected scenario with the chosen strategy print('Starting experiment...') results = [] for train_batch_info in scenario.train_stream: print("Start training on experience ", train_batch_info.current_experience) strategy.train(train_batch_info, num_workers=0) print("End training on experience ", train_batch_info.current_experience) print('Computing accuracy on the test set') results.append(strategy.eval(scenario.test_stream[:]))
def main(args): # --- CONFIG device = torch.device(f"cuda:{args.cuda}" if torch.cuda.is_available() and args.cuda >= 0 else "cpu") # --- SCENARIO CREATION scenario = SplitMNIST(n_experiences=10, seed=1234) # --------- # MODEL CREATION model = SimpleMLP(num_classes=scenario.n_classes) # choose some metrics and evaluation method interactive_logger = InteractiveLogger() eval_plugin = EvaluationPlugin( accuracy_metrics(minibatch=True, epoch=True, experience=True, stream=True), loss_metrics(minibatch=True, epoch=True, experience=True, stream=True), forgetting_metrics(experience=True), loggers=[interactive_logger], ) # CREATE THE STRATEGY INSTANCE (GenerativeReplay) cl_strategy = GenerativeReplay( model, torch.optim.Adam(model.parameters(), lr=0.001), CrossEntropyLoss(), train_mb_size=100, train_epochs=4, eval_mb_size=100, device=device, evaluator=eval_plugin, ) # TRAINING LOOP print("Starting experiment...") results = [] for experience in scenario.train_stream: print("Start of experience ", experience.current_experience) cl_strategy.train(experience) print("Training completed") print("Computing accuracy on the whole test set") results.append(cl_strategy.eval(scenario.test_stream))
def main(args): # Config device = torch.device(f"cuda:{args.cuda}" if torch.cuda.is_available() and args.cuda >= 0 else "cpu") # model model = MTSimpleMLP() # CL Benchmark Creation scenario = SplitMNIST(n_experiences=5, return_task_id=True) train_stream = scenario.train_stream test_stream = scenario.test_stream # Prepare for training & testing optimizer = Adam(model.parameters(), lr=0.01) criterion = CrossEntropyLoss() # choose some metrics and evaluation method interactive_logger = InteractiveLogger() eval_plugin = EvaluationPlugin( accuracy_metrics(minibatch=False, epoch=True, experience=True, stream=True), forgetting_metrics(experience=True), loggers=[interactive_logger], ) # Choose a CL strategy strategy = EWC( model=model, optimizer=optimizer, criterion=criterion, train_mb_size=128, train_epochs=3, eval_mb_size=128, device=device, evaluator=eval_plugin, ewc_lambda=0.4, ) # train and test loop for train_task in train_stream: strategy.train(train_task) strategy.eval(test_stream)
def run_base_online(experience, device, use_interactive_logger: bool = False): """ Runs OnlineNaive for one experience. """ # Create list of loggers to be used loggers = [] if use_interactive_logger: interactive_logger = InteractiveLogger() loggers.append(interactive_logger) # Evaluation plugin eval_plugin = EvaluationPlugin( accuracy_metrics(minibatch=True, epoch=True, experience=True, stream=True), loss_metrics(minibatch=True, epoch=True, experience=True, stream=True), forgetting_metrics(experience=True), loggers=loggers, ) # Model model = SimpleMLP(num_classes=10) # Create OnlineNaive strategy cl_strategy = OnlineNaive( model, torch.optim.SGD(model.parameters(), lr=0.01), CrossEntropyLoss(), num_passes=1, train_mb_size=1, device=device, evaluator=eval_plugin, ) start = time.time() print("Running OnlineNaive ...") cl_strategy.train(experience) end = time.time() duration = end - start return duration
def main(args): # Device config device = torch.device(f"cuda:{args.cuda}" if torch.cuda.is_available() and args.cuda >= 0 else "cpu") print('device ', device) # --------- # --- TRANSFORMATIONS _mu = [0.485, 0.456, 0.406] # imagenet normalization _std = [0.229, 0.224, 0.225] transform = transforms.Compose([ transforms.Resize((224, 224)), transforms.ToTensor(), transforms.Normalize(mean=_mu, std=_std) ]) # --------- # --- SCENARIO CREATION scenario = CORe50(scenario=args.scenario, train_transform=transform, eval_transform=transform) # --------- eval_plugin = EvaluationPlugin( loss_metrics(epoch=True, experience=True, stream=True), accuracy_metrics(epoch=True, experience=True, stream=True), forgetting_metrics(experience=True, stream=True), loggers=[InteractiveLogger()] ) criterion = torch.nn.CrossEntropyLoss() model = SLDAResNetModel(device=device, arch='resnet18', imagenet_pretrained=args.imagenet_pretrained) # CREATE THE STRATEGY INSTANCE cl_strategy = StreamingLDA(model, criterion, args.feature_size, args.n_classes, eval_mb_size=args.batch_size, train_mb_size=args.batch_size, train_epochs=1, shrinkage_param=args.shrinkage, streaming_update_sigma=args.plastic_cov, device=device, evaluator=eval_plugin) warnings.warn( "The Deep SLDA example is not perfectly aligned with " "the paper implementation since it does not use a base " "initialization phase and instead starts streming from " "pre-trained weights.") # TRAINING LOOP print('Starting experiment...') for i, exp in enumerate(scenario.train_stream): # fit SLDA model to batch (one sample at a time) cl_strategy.train(exp) # evaluate model on test data cl_strategy.eval(scenario.test_stream)
def run_base(experience, device, use_interactive_logger: bool = False): """ Runs Naive (from BaseStrategy) for one experience. """ def create_sub_experience_list(experience): """Creates a list of sub-experiences from an experience. It returns a list of experiences, where each experience is a subset of the original experience. :param experience: single Experience. :return: list of Experience. """ # Shuffle the indices indices = torch.randperm(len(experience.dataset)) num_sub_exps = len(indices) mb_size = 1 sub_experience_list = [] for subexp_id in range(num_sub_exps): subexp_indices = indices[subexp_id * mb_size:(subexp_id + 1) * mb_size] sub_experience = copy.copy(experience) subexp_ds = AvalancheSubset(sub_experience.dataset, indices=subexp_indices) sub_experience.dataset = subexp_ds sub_experience_list.append(sub_experience) return sub_experience_list # Create list of loggers to be used loggers = [] if use_interactive_logger: interactive_logger = InteractiveLogger() loggers.append(interactive_logger) # Evaluation plugin eval_plugin = EvaluationPlugin( accuracy_metrics(minibatch=True, epoch=True, experience=True, stream=True), loss_metrics(minibatch=True, epoch=True, experience=True, stream=True), forgetting_metrics(experience=True), loggers=loggers, ) # Model model = SimpleMLP(num_classes=10) # Create OnlineNaive strategy cl_strategy = Naive( model, torch.optim.SGD(model.parameters(), lr=0.01), CrossEntropyLoss(), train_mb_size=1, device=device, evaluator=eval_plugin, ) start = time.time() sub_experience_list = create_sub_experience_list(experience) # !!! This is only for profiling purpose. This method may not work # in practice for dynamic modules since the model adaptation step # can go wrong. # Train for each sub-experience print("Running OnlineNaive ...") for i, sub_experience in enumerate(sub_experience_list): experience = sub_experience cl_strategy.train(experience) end = time.time() duration = end - start return duration
def main(args): # --- CONFIG device = torch.device(f"cuda:{args.cuda}" if torch.cuda.is_available() and args.cuda >= 0 else "cpu") # --------- # --- TRANSFORMATIONS train_transform = transforms.Compose([ RandomCrop(28, padding=4), ToTensor(), transforms.Normalize((0.1307, ), (0.3081, )) ]) test_transform = transforms.Compose( [ToTensor(), transforms.Normalize((0.1307, ), (0.3081, ))]) # --------- # --- SCENARIO CREATION mnist_train = MNIST(root=expanduser("~") + "/.avalanche/data/mnist/", train=True, download=True, transform=train_transform) mnist_test = MNIST(root=expanduser("~") + "/.avalanche/data/mnist/", train=False, download=True, transform=test_transform) scenario = nc_benchmark(mnist_train, mnist_test, 5, task_labels=False, seed=1234) # --------- # MODEL CREATION model = SimpleMLP(num_classes=scenario.n_classes) # DEFINE THE EVALUATION PLUGIN AND LOGGER # The evaluation plugin manages the metrics computation. # It takes as argument a list of metrics and a list of loggers. # The evaluation plugin calls the loggers to serialize the metrics # and save them in persistent memory or print them in the standard output. # log to text file text_logger = TextLogger(open('log.txt', 'a')) # print to stdout interactive_logger = InteractiveLogger() csv_logger = CSVLogger() eval_plugin = EvaluationPlugin( accuracy_metrics(minibatch=True, epoch=True, epoch_running=True, experience=True, stream=True), loss_metrics(minibatch=True, epoch=True, epoch_running=True, experience=True, stream=True), forgetting_metrics(experience=True, stream=True), bwt_metrics(experience=True, stream=True), forward_transfer_metrics(experience=True, stream=True), cpu_usage_metrics(minibatch=True, epoch=True, epoch_running=True, experience=True, stream=True), timing_metrics(minibatch=True, epoch=True, epoch_running=True, experience=True, stream=True), ram_usage_metrics(every=0.5, minibatch=True, epoch=True, experience=True, stream=True), gpu_usage_metrics(args.cuda, every=0.5, minibatch=True, epoch=True, experience=True, stream=True), disk_usage_metrics(minibatch=True, epoch=True, experience=True, stream=True), MAC_metrics(minibatch=True, epoch=True, experience=True), loggers=[interactive_logger, text_logger, csv_logger], collect_all=True) # collect all metrics (set to True by default) # CREATE THE STRATEGY INSTANCE (NAIVE) cl_strategy = Naive(model, SGD(model.parameters(), lr=0.001, momentum=0.9), CrossEntropyLoss(), train_mb_size=500, train_epochs=1, eval_mb_size=100, device=device, evaluator=eval_plugin, eval_every=1) # TRAINING LOOP print('Starting experiment...') results = [] for i, experience in enumerate(scenario.train_stream): print("Start of experience: ", experience.current_experience) print("Current Classes: ", experience.classes_in_this_experience) # train returns a dictionary containing last recorded value # for each metric. res = cl_strategy.train(experience, eval_streams=[scenario.test_stream]) print('Training completed') print('Computing accuracy on the whole test set') # test returns a dictionary with the last metric collected during # evaluation on that stream results.append(cl_strategy.eval(scenario.test_stream)) print(f"Test metrics:\n{results}") # Dict with all the metric curves, # only available when `collect_all` is True. # Each entry is a (x, metric value) tuple. # You can use this dictionary to manipulate the # metrics without avalanche. all_metrics = cl_strategy.evaluator.get_all_metrics() print(f"Stored metrics: {list(all_metrics.keys())}")
def main(args): model = SimpleMLP(hidden_size=args.hs) optimizer = torch.optim.SGD(model.parameters(), lr=args.lr) criterion = torch.nn.CrossEntropyLoss() # check if selected GPU is available or use CPU assert args.cuda == -1 or args.cuda >= 0, "cuda must be -1 or >= 0." device = torch.device(f"cuda:{args.cuda}" if torch.cuda.is_available() and args.cuda >= 0 else "cpu") print(f"Using device: {device}") # create scenario if args.scenario == "pmnist": scenario = PermutedMNIST(n_experiences=args.permutations) elif args.scenario == "smnist": mnist_train = MNIST( root=expanduser("~") + "/.avalanche/data/mnist/", train=True, download=True, transform=ToTensor(), ) mnist_test = MNIST( root=expanduser("~") + "/.avalanche/data/mnist/", train=False, download=True, transform=ToTensor(), ) scenario = nc_benchmark(mnist_train, mnist_test, 5, task_labels=False, seed=1234) else: raise ValueError("Wrong scenario name. Allowed pmnist, smnist.") # choose some metrics and evaluation method interactive_logger = InteractiveLogger() tensorboard_logger = TensorboardLogger() eval_plugin = EvaluationPlugin( accuracy_metrics(minibatch=True, epoch=True, experience=True, stream=True), loss_metrics(minibatch=True, epoch=True, experience=True, stream=True), forgetting_metrics(experience=True, stream=True), bwt_metrics(experience=True, stream=True), loggers=[interactive_logger, tensorboard_logger], ) # create strategy strategy = EWC( model, optimizer, criterion, args.ewc_lambda, args.ewc_mode, decay_factor=args.decay_factor, train_epochs=args.epochs, device=device, train_mb_size=args.minibatch_size, evaluator=eval_plugin, ) # train on the selected scenario with the chosen strategy print("Starting experiment...") results = [] for experience in scenario.train_stream: print("Start training on experience ", experience.current_experience) strategy.train(experience) print("End training on experience", experience.current_experience) print("Computing accuracy on the test set") results.append(strategy.eval(scenario.test_stream[:]))
def main(args): # --- CONFIG device = torch.device( f"cuda:{args.cuda}" if torch.cuda.is_available() and args.cuda >= 0 else "cpu" ) # --- SCENARIO CREATION scenario = SplitCIFAR100(n_experiences=20, return_task_id=True) config = {"scenario": "SplitCIFAR100"} # MODEL CREATION model = MTSimpleCNN() # choose some metrics and evaluation method loggers = [InteractiveLogger()] if args.wandb_project != "": wandb_logger = WandBLogger( project_name=args.wandb_project, run_name="LaMAML_" + config["scenario"], config=config, ) loggers.append(wandb_logger) eval_plugin = EvaluationPlugin( accuracy_metrics( minibatch=True, epoch=True, experience=True, stream=True ), loss_metrics(minibatch=True, epoch=True, experience=True, stream=True), forgetting_metrics(experience=True), loggers=loggers, ) # LAMAML STRATEGY rs_buffer = ReservoirSamplingBuffer(max_size=200) replay_plugin = ReplayPlugin( mem_size=200, batch_size=10, batch_size_mem=10, task_balanced_dataloader=False, storage_policy=rs_buffer, ) cl_strategy = LaMAML( model, torch.optim.SGD(model.parameters(), lr=0.1), CrossEntropyLoss(), n_inner_updates=5, second_order=True, grad_clip_norm=1.0, learn_lr=True, lr_alpha=0.25, sync_update=False, train_mb_size=10, train_epochs=10, eval_mb_size=100, device=device, plugins=[replay_plugin], evaluator=eval_plugin, ) # TRAINING LOOP print("Starting experiment...") results = [] for experience in scenario.train_stream: print("Start of experience ", experience.current_experience) cl_strategy.train(experience) print("Training completed") print("Computing accuracy on the whole test set") results.append(cl_strategy.eval(scenario.test_stream)) if args.wandb_project != "": wandb.finish()
def main(args): # --- CONFIG device = torch.device( f"cuda:{args.cuda}" if torch.cuda.is_available() and args.cuda >= 0 else "cpu" ) # --------- tr_ds = [ AvalancheTensorDataset( torch.randn(10, 3), torch.randint(0, 3, (10,)).tolist(), task_labels=torch.randint(0, 5, (10,)).tolist(), ) for _ in range(3) ] ts_ds = [ AvalancheTensorDataset( torch.randn(10, 3), torch.randint(0, 3, (10,)).tolist(), task_labels=torch.randint(0, 5, (10,)).tolist(), ) for _ in range(3) ] scenario = create_multi_dataset_generic_benchmark( train_datasets=tr_ds, test_datasets=ts_ds ) # --------- # MODEL CREATION model = SimpleMLP(num_classes=3, input_size=3) # DEFINE THE EVALUATION PLUGIN AND LOGGER # The evaluation plugin manages the metrics computation. # It takes as argument a list of metrics and a list of loggers. # The evaluation plugin calls the loggers to serialize the metrics # and save them in persistent memory or print them in the standard output. # log to text file text_logger = TextLogger(open("log.txt", "a")) # print to stdout interactive_logger = InteractiveLogger() csv_logger = CSVLogger() eval_plugin = EvaluationPlugin( accuracy_metrics( minibatch=True, epoch=True, epoch_running=True, experience=True, stream=True, ), loss_metrics( minibatch=True, epoch=True, epoch_running=True, experience=True, stream=True, ), forgetting_metrics(experience=True, stream=True), bwt_metrics(experience=True, stream=True), cpu_usage_metrics( minibatch=True, epoch=True, epoch_running=True, experience=True, stream=True, ), timing_metrics( minibatch=True, epoch=True, epoch_running=True, experience=True, stream=True, ), ram_usage_metrics( every=0.5, minibatch=True, epoch=True, experience=True, stream=True ), gpu_usage_metrics( args.cuda, every=0.5, minibatch=True, epoch=True, experience=True, stream=True, ), disk_usage_metrics( minibatch=True, epoch=True, experience=True, stream=True ), MAC_metrics(minibatch=True, epoch=True, experience=True), loggers=[interactive_logger, text_logger, csv_logger], collect_all=True, ) # collect all metrics (set to True by default) # CREATE THE STRATEGY INSTANCE (NAIVE) cl_strategy = Naive( model, SGD(model.parameters(), lr=0.001, momentum=0.9), CrossEntropyLoss(), train_mb_size=500, train_epochs=1, eval_mb_size=100, device=device, evaluator=eval_plugin, eval_every=1, ) # TRAINING LOOP print("Starting experiment...") results = [] for i, experience in enumerate(scenario.train_stream): print("Start of experience: ", experience.current_experience) print("Current Classes: ", experience.classes_in_this_experience) # train returns a dictionary containing last recorded value # for each metric. res = cl_strategy.train(experience, eval_streams=[scenario.test_stream]) print("Training completed") print("Computing accuracy on the whole test set") # test returns a dictionary with the last metric collected during # evaluation on that stream results.append(cl_strategy.eval(scenario.test_stream)) print(f"Test metrics:\n{results}") # Dict with all the metric curves, # only available when `collect_all` is True. # Each entry is a (x, metric value) tuple. # You can use this dictionary to manipulate the # metrics without avalanche. all_metrics = cl_strategy.evaluator.get_all_metrics() print(f"Stored metrics: {list(all_metrics.keys())}")
root = ".." # log to Tensorboard tb_logger = TensorboardLogger(root) # log to text file text_logger = TextLogger(open(f"{root}/log.txt", "w")) # print to stdout interactive_logger = InteractiveLogger() eval_plugin = EvaluationPlugin( accuracy_metrics(minibatch=True, epoch=True, experience=True, stream=True), loss_metrics(minibatch=True, epoch=True, experience=True, stream=True), timing_metrics(epoch=True, epoch_running=True), forgetting_metrics(experience=True, stream=True), cpu_usage_metrics(experience=True), confusion_matrix_metrics(num_classes=NUM_CLASSES[data_name], save_image=False, stream=True), disk_usage_metrics(minibatch=True, epoch=True, experience=True, stream=True), loggers=[interactive_logger, text_logger, tb_logger], ) num_epoch = 70 for eval_mode in EVALUATION_PROTOCOLS: for mode in ["feature", "image"]: if eval_mode == "streaming":
def main(args): # --- CONFIG device = torch.device(f"cuda:{args.cuda}" if torch.cuda.is_available() and args.cuda >= 0 else "cpu") # --------- # --- TRANSFORMATIONS train_transform = transforms.Compose([ RandomCrop(28, padding=4), ToTensor(), transforms.Normalize((0.1307, ), (0.3081, )), ]) test_transform = transforms.Compose( [ToTensor(), transforms.Normalize((0.1307, ), (0.3081, ))]) # --------- # --- SCENARIO CREATION mnist_train = MNIST( root=expanduser("~") + "/.avalanche/data/mnist/", train=True, download=True, transform=train_transform, ) mnist_test = MNIST( root=expanduser("~") + "/.avalanche/data/mnist/", train=False, download=True, transform=test_transform, ) scenario = nc_benchmark(mnist_train, mnist_test, 5, task_labels=False, seed=1234) # --------- # MODEL CREATION model = SimpleMLP(num_classes=scenario.n_classes) interactive_logger = InteractiveLogger() wandb_logger = WandBLogger(project_name=args.project, run_name=args.run, config=vars(args)) eval_plugin = EvaluationPlugin( accuracy_metrics( minibatch=True, epoch=True, epoch_running=True, experience=True, stream=True, ), loss_metrics( minibatch=True, epoch=True, epoch_running=True, experience=True, stream=True, ), forgetting_metrics(experience=True, stream=True), confusion_matrix_metrics(stream=True, wandb=True, class_names=[str(i) for i in range(10)]), cpu_usage_metrics(minibatch=True, epoch=True, experience=True, stream=True), timing_metrics(minibatch=True, epoch=True, experience=True, stream=True), ram_usage_metrics(every=0.5, minibatch=True, epoch=True, experience=True, stream=True), gpu_usage_metrics( args.cuda, every=0.5, minibatch=True, epoch=True, experience=True, stream=True, ), disk_usage_metrics(minibatch=True, epoch=True, experience=True, stream=True), MAC_metrics(minibatch=True, epoch=True, experience=True), loggers=[interactive_logger, wandb_logger], ) # CREATE THE STRATEGY INSTANCE (NAIVE) cl_strategy = Naive( model, SGD(model.parameters(), lr=0.001, momentum=0.9), CrossEntropyLoss(), train_mb_size=100, train_epochs=4, eval_mb_size=100, device=device, evaluator=eval_plugin, ) # TRAINING LOOP print("Starting experiment...") results = [] for experience in scenario.train_stream: print("Start of experience: ", experience.current_experience) print("Current Classes: ", experience.classes_in_this_experience) cl_strategy.train(experience) print("Training completed") print("Computing accuracy on the whole test set") results.append(cl_strategy.eval(scenario.test_stream))
def setUpClass(cls) -> None: torch.manual_seed(0) np.random.seed(0) random.seed(0) n_samples_per_class = 100 datasets = [] for i in range(3): dataset = make_classification(n_samples=3 * n_samples_per_class, n_classes=3, n_features=3, n_informative=3, n_redundant=0) X = torch.from_numpy(dataset[0]).float() y = torch.from_numpy(dataset[1]).long() train_X, test_X, train_y, test_y = train_test_split(X, y, train_size=0.5, shuffle=True, stratify=y) datasets.append((train_X, train_y, test_X, test_y)) tr_ds = [ AvalancheTensorDataset( tr_X, tr_y, dataset_type=AvalancheDatasetType.CLASSIFICATION, task_labels=torch.randint(0, 3, (150, )).tolist()) for tr_X, tr_y, _, _ in datasets ] ts_ds = [ AvalancheTensorDataset( ts_X, ts_y, dataset_type=AvalancheDatasetType.CLASSIFICATION, task_labels=torch.randint(0, 3, (150, )).tolist()) for _, _, ts_X, ts_y in datasets ] benchmark = dataset_benchmark(train_datasets=tr_ds, test_datasets=ts_ds) model = SimpleMLP(num_classes=3, input_size=3) f = open('log.txt', 'w') text_logger = TextLogger(f) eval_plugin = EvaluationPlugin( accuracy_metrics(minibatch=True, epoch=True, epoch_running=True, experience=True, stream=True, trained_experience=True), loss_metrics(minibatch=True, epoch=True, epoch_running=True, experience=True, stream=True), forgetting_metrics(experience=True, stream=True), confusion_matrix_metrics(num_classes=3, save_image=False, normalize='all', stream=True), bwt_metrics(experience=True, stream=True), forward_transfer_metrics(experience=True, stream=True), cpu_usage_metrics(minibatch=True, epoch=True, epoch_running=True, experience=True, stream=True), timing_metrics(minibatch=True, epoch=True, epoch_running=True, experience=True, stream=True), ram_usage_metrics(every=0.5, minibatch=True, epoch=True, experience=True, stream=True), disk_usage_metrics(minibatch=True, epoch=True, experience=True, stream=True), MAC_metrics(minibatch=True, epoch=True, experience=True), loggers=[text_logger], collect_all=True) # collect all metrics (set to True by default) cl_strategy = BaseStrategy(model, SGD(model.parameters(), lr=0.001, momentum=0.9), CrossEntropyLoss(), train_mb_size=2, train_epochs=2, eval_mb_size=2, device=DEVICE, evaluator=eval_plugin, eval_every=1) for i, experience in enumerate(benchmark.train_stream): cl_strategy.train(experience, eval_streams=[benchmark.test_stream], shuffle=False) cl_strategy.eval(benchmark.test_stream) cls.all_metrics = cl_strategy.evaluator.get_all_metrics() f.close() # # Uncomment me to regenerate the reference metrics. Make sure # # the old tests were passing for all unchanged metrics # with open(os.path.join(pathlib.Path(__file__).parent.absolute(), # 'target_metrics', # 'tpp.pickle'), 'wb') as f: # pickle.dump(dict(cls.all_metrics), f, # protocol=4) with open( os.path.join( pathlib.Path(__file__).parent.absolute(), 'target_metrics', 'tpp.pickle'), 'rb') as f: cls.ref = pickle.load(f)
def main(args): # --- CONFIG device = torch.device(f"cuda:{args.cuda}" if torch.cuda.is_available() and args.cuda >= 0 else "cpu") # --------- # --- TRANSFORMATIONS train_transform = transforms.Compose([ Resize(224), ToTensor(), transforms.Normalize((0.1307, ), (0.3081, )) ]) test_transform = transforms.Compose([ Resize(224), ToTensor(), transforms.Normalize((0.1307, ), (0.3081, )) ]) # --------- # --- SCENARIO CREATION scenario = SplitCIFAR10(5, train_transform=train_transform, eval_transform=test_transform) # --------- # MODEL CREATION model = MobilenetV1() adapt_classification_layer(model, scenario.n_classes, bias=False) # DEFINE THE EVALUATION PLUGIN AND LOGGER my_logger = TensorboardLogger(tb_log_dir="logs", tb_log_exp_name="logging_example") # print to stdout interactive_logger = InteractiveLogger() evaluation_plugin = EvaluationPlugin( accuracy_metrics(minibatch=True, epoch=True, experience=True, stream=True), loss_metrics(minibatch=True, epoch=True, experience=True, stream=True), forgetting_metrics(experience=True), loggers=[my_logger, interactive_logger], ) # CREATE THE STRATEGY INSTANCE (NAIVE with the Synaptic Intelligence plugin) cl_strategy = SynapticIntelligence( model, Adam(model.parameters(), lr=0.001), CrossEntropyLoss(), si_lambda=0.0001, train_mb_size=128, train_epochs=4, eval_mb_size=128, device=device, evaluator=evaluation_plugin, ) # TRAINING LOOP print("Starting experiment...") results = [] for experience in scenario.train_stream: print("Start of experience: ", experience.current_experience) print("Current Classes: ", experience.classes_in_this_experience) cl_strategy.train(experience) print("Training completed") print("Computing accuracy on the whole test set") results.append(cl_strategy.eval(scenario.test_stream))
def setUpClass(cls) -> None: torch.manual_seed(0) np.random.seed(0) random.seed(0) n_samples_per_class = 100 dataset = make_classification(n_samples=6 * n_samples_per_class, n_classes=6, n_features=4, n_informative=4, n_redundant=0) X = torch.from_numpy(dataset[0]).float() y = torch.from_numpy(dataset[1]).long() train_X, test_X, train_y, test_y = train_test_split(X, y, train_size=0.5, shuffle=True, stratify=y) tr_d = TensorDataset(train_X, train_y) ts_d = TensorDataset(test_X, test_y) benchmark = nc_benchmark(train_dataset=tr_d, test_dataset=ts_d, n_experiences=3, task_labels=True, shuffle=False, seed=0) model = SimpleMLP(input_size=4, num_classes=benchmark.n_classes) f = open('log.txt', 'w') text_logger = TextLogger(f) eval_plugin = EvaluationPlugin( accuracy_metrics(minibatch=True, epoch=True, epoch_running=True, experience=True, stream=True), loss_metrics(minibatch=True, epoch=True, epoch_running=True, experience=True, stream=True), forgetting_metrics(experience=True, stream=True, task=True), confusion_matrix_metrics(num_classes=6, save_image=False, normalize='all', stream=True), bwt_metrics(experience=True, stream=True, task=True), cpu_usage_metrics(minibatch=True, epoch=True, epoch_running=True, experience=True, stream=True), timing_metrics(minibatch=True, epoch=True, epoch_running=True, experience=True, stream=True), ram_usage_metrics(every=0.5, minibatch=True, epoch=True, experience=True, stream=True), disk_usage_metrics(minibatch=True, epoch=True, experience=True, stream=True), MAC_metrics(minibatch=True, epoch=True, experience=True), loggers=[text_logger], collect_all=True) # collect all metrics (set to True by default) cl_strategy = BaseStrategy(model, SGD(model.parameters(), lr=0.001, momentum=0.9), CrossEntropyLoss(), train_mb_size=10, train_epochs=2, eval_mb_size=10, device=DEVICE, evaluator=eval_plugin, eval_every=1) for i, experience in enumerate(benchmark.train_stream): cl_strategy.train(experience, eval_streams=[benchmark.test_stream[i]], shuffle=False) cl_strategy.eval(benchmark.test_stream) cls.all_metrics = cl_strategy.evaluator.get_all_metrics() f.close() # with open(os.path.join(pathlib.Path(__file__).parent.absolute(), # 'target_metrics', # 'mt.pickle'), 'wb') as f: # pickle.dump(dict(cls.all_metrics), f, # protocol=pickle.HIGHEST_PROTOCOL) with open( os.path.join( pathlib.Path(__file__).parent.absolute(), 'target_metrics', 'mt.pickle'), 'rb') as f: cls.ref = pickle.load(f)
def main(args): # --- CONFIG device = torch.device(f"cuda:{args.cuda}" if torch.cuda.is_available() and args.cuda >= 0 else "cpu") n_batches = 5 # --------- # --- TRANSFORMATIONS train_transform = transforms.Compose([ RandomCrop(28, padding=4), ToTensor(), transforms.Normalize((0.1307, ), (0.3081, )), ]) test_transform = transforms.Compose( [ToTensor(), transforms.Normalize((0.1307, ), (0.3081, ))]) # --------- # --- SCENARIO CREATION mnist_train = MNIST( root=expanduser("~") + "/.avalanche/data/mnist/", train=True, download=True, transform=train_transform, ) mnist_test = MNIST( root=expanduser("~") + "/.avalanche/data/mnist/", train=False, download=True, transform=test_transform, ) scenario = nc_benchmark(mnist_train, mnist_test, n_batches, task_labels=False, seed=1234) # --------- # MODEL CREATION model = SimpleMLP(num_classes=scenario.n_classes) # choose some metrics and evaluation method interactive_logger = InteractiveLogger() eval_plugin = EvaluationPlugin( accuracy_metrics(minibatch=True, epoch=True, experience=True, stream=True), loss_metrics(minibatch=True, epoch=True, experience=True, stream=True), forgetting_metrics(experience=True), loggers=[interactive_logger], ) # CREATE THE STRATEGY INSTANCE (ONLINE-NAIVE) cl_strategy = OnlineNaive( model, torch.optim.Adam(model.parameters(), lr=0.001), CrossEntropyLoss(), num_passes=1, train_mb_size=1, eval_mb_size=32, device=device, evaluator=eval_plugin, ) # TRAINING LOOP print("Starting experiment...") results = [] for experience in scenario.train_stream: print("Start of experience ", experience.current_experience) cl_strategy.train(experience) print("Training completed") print("Computing accuracy on the whole test set") results.append(cl_strategy.eval(scenario.test_stream))
def setUpClass(cls) -> None: torch.manual_seed(0) np.random.seed(0) random.seed(0) n_samples_per_class = 100 dataset = make_classification( n_samples=6 * n_samples_per_class, n_classes=6, n_features=4, n_informative=4, n_redundant=0, ) X = torch.from_numpy(dataset[0]).float() y = torch.from_numpy(dataset[1]).long() train_X, test_X, train_y, test_y = train_test_split(X, y, train_size=0.5, shuffle=True, stratify=y) tr_d = TensorDataset(train_X, train_y) ts_d = TensorDataset(test_X, test_y) benchmark = nc_benchmark( train_dataset=tr_d, test_dataset=ts_d, n_experiences=3, task_labels=True, shuffle=False, seed=0, ) model = SimpleMLP(input_size=4, num_classes=benchmark.n_classes) f = open("log.txt", "w") text_logger = TextLogger(f) eval_plugin = EvaluationPlugin( accuracy_metrics( minibatch=True, epoch=True, epoch_running=True, experience=True, stream=True, trained_experience=True, ), loss_metrics( minibatch=True, epoch=True, epoch_running=True, experience=True, stream=True, ), forgetting_metrics(experience=True, stream=True), confusion_matrix_metrics(num_classes=6, save_image=False, normalize="all", stream=True), bwt_metrics(experience=True, stream=True), forward_transfer_metrics(experience=True, stream=True), cpu_usage_metrics( minibatch=True, epoch=True, epoch_running=True, experience=True, stream=True, ), timing_metrics( minibatch=True, epoch=True, epoch_running=True, experience=True, stream=True, ), ram_usage_metrics( every=0.5, minibatch=True, epoch=True, experience=True, stream=True, ), disk_usage_metrics(minibatch=True, epoch=True, experience=True, stream=True), MAC_metrics(minibatch=True, epoch=True, experience=True), loggers=[text_logger], collect_all=True, ) # collect all metrics (set to True by default) cl_strategy = BaseStrategy( model, SGD(model.parameters(), lr=0.001, momentum=0.9), CrossEntropyLoss(), train_mb_size=10, train_epochs=2, eval_mb_size=10, device=DEVICE, evaluator=eval_plugin, eval_every=1, ) for i, experience in enumerate(benchmark.train_stream): cl_strategy.train(experience, eval_streams=[benchmark.test_stream], shuffle=False) cl_strategy.eval(benchmark.test_stream) cls.all_metrics = cl_strategy.evaluator.get_all_metrics() f.close() # Set the environment variable UPDATE_METRICS to True to update # the pickle file with target values. # Make sure the old tests were passing for all unchanged metrics if UPDATE_METRICS: with open( os.path.join( pathlib.Path(__file__).parent.absolute(), "target_metrics", "mt.pickle", ), "wb", ) as f: pickle.dump(dict(cls.all_metrics), f, protocol=4) with open( os.path.join( pathlib.Path(__file__).parent.absolute(), "target_metrics", "mt.pickle", ), "rb", ) as f: cls.ref = pickle.load(f)