def lightning(): return pl.Classification( train_dataloader=pl.DataLoader(train_dataset, batch_size=100), val_dataloaders=pl.DataLoader(test_dataset, batch_size=100), max_epochs=1, limit_train_batches=0.1, # for faster training progress_bar_refresh_rate=progress_bar_refresh_rate)
def test_mnist(): _reset() transform = transforms.Compose([transforms.ToTensor(), transforms.Normalize((0.1307,), (0.3081,))]) train_dataset = nni.trace(MNIST)(root='data/mnist', train=True, download=True, transform=transform) test_dataset = nni.trace(MNIST)(root='data/mnist', train=False, download=True, transform=transform) lightning = pl.Classification(train_dataloader=pl.DataLoader(train_dataset, batch_size=100), val_dataloaders=pl.DataLoader(test_dataset, batch_size=100), max_epochs=2, limit_train_batches=0.25, # for faster training progress_bar_refresh_rate=progress_bar_refresh_rate) lightning._execute(MNISTModel) assert _get_final_result() > 0.7 _reset()
def _test_searchspace_on_dataset(searchspace, dataset='cifar10', arch=None): _reset() # dataset supports cifar10 and imagenet model, mutators = extract_mutation_from_pt_module(searchspace) if arch is None: model = try_mutation_until_success(model, mutators, 10) arch = { mut.mutator.label: _unpack_if_only_one(mut.samples) for mut in model.history } print('Selected model:', arch) with fixed_arch(arch): model = model.python_class(**model.python_init_params) if dataset == 'cifar10': train_data = FakeData(size=200, image_size=(3, 32, 32), num_classes=10, transform=transforms.ToTensor()) valid_data = FakeData(size=200, image_size=(3, 32, 32), num_classes=10, transform=transforms.ToTensor()) elif dataset == 'imagenet': train_data = FakeData(size=200, image_size=(3, 224, 224), num_classes=1000, transform=transforms.ToTensor()) valid_data = FakeData(size=200, image_size=(3, 224, 224), num_classes=1000, transform=transforms.ToTensor()) train_dataloader = pl.DataLoader(train_data, batch_size=4, shuffle=True) valid_dataloader = pl.DataLoader(valid_data, batch_size=6) evaluator = pl.Classification( train_dataloader=train_dataloader, val_dataloaders=valid_dataloader, export_onnx=False, max_epochs=1, limit_train_batches=2, limit_val_batches=3, ) evaluator.fit(model) # cleanup to avoid affecting later test cases _reset()
def get_mnist_evaluator(): transform = transforms.Compose([ transforms.Resize((32, 32)), transforms.ToTensor(), transforms.Normalize((0.1307,), (0.3081,)) ]) train_dataset = nni.trace(MNIST)('data/mnist', download=True, train=True, transform=transform) train_loader = pl.DataLoader(train_dataset, 64) valid_dataset = nni.trace(MNIST)('data/mnist', download=True, train=False, transform=transform) valid_loader = pl.DataLoader(valid_dataset, 64) return pl.Classification( train_dataloader=train_loader, val_dataloaders=valid_loader, limit_train_batches=20, limit_val_batches=20, max_epochs=1 )
train_transform = transforms.Compose([ transforms.RandomCrop(32, padding=4), transforms.RandomHorizontalFlip(), transforms.ToTensor(), transforms.Normalize((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010)), ]) valid_transform = transforms.Compose([ transforms.ToTensor(), transforms.Normalize((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010)), ]) train_dataset = serialize(CIFAR10, root='data/cifar10', train=True, download=True, transform=train_transform) test_dataset = serialize(CIFAR10, root='data/cifar10', train=False, download=True, transform=valid_transform) trainer = pl.Classification(train_dataloader=pl.DataLoader(train_dataset, batch_size=100), val_dataloaders=pl.DataLoader(test_dataset, batch_size=100), max_epochs=1, limit_train_batches=0.2) simple_strategy = strategy.Random() exp = RetiariiExperiment(base_model, trainer, [], simple_strategy) exp_config = RetiariiExeConfig('remote') exp_config.experiment_name = 'darts_search' exp_config.trial_concurrency = 2 exp_config.max_trial_number = 10 exp_config.trial_gpu_number = 1 exp_config.training_service.use_active_gpu = True exp_config.training_service.reuse_mode = True exp_config.training_service.gpu_indices = [0, 1, 2]
]) train_dataset = serialize(CIFAR10, root='data/cifar10', train=True, download=True, transform=train_transform) test_dataset = serialize(CIFAR10, root='data/cifar10', train=False, download=True, transform=valid_transform) trainer = pl.Classification(train_dataloader=pl.DataLoader(train_dataset, batch_size=100), val_dataloaders=pl.DataLoader(test_dataset, batch_size=100), max_epochs=1, limit_train_batches=0.2, progress_bar_refresh_rate=0) simple_strategy = strategy.Random() exp = RetiariiExperiment(base_model, trainer, [], simple_strategy) exp_config = RetiariiExeConfig('local') exp_config.experiment_name = 'darts_search' exp_config.trial_concurrency = 2 exp_config.max_trial_number = 10 exp_config.trial_gpu_number = 1 exp_config.training_service.use_active_gpu = True exp_config.training_service.gpu_indices = [1, 2]
]) train_dataset = serialize(CIFAR10, root='data/cifar10', train=True, download=True, transform=train_transform) test_dataset = serialize(CIFAR10, root='data/cifar10', train=False, download=True, transform=valid_transform) trainer = pl.Classification(train_dataloader=pl.DataLoader(train_dataset, batch_size=100), val_dataloaders=pl.DataLoader(test_dataset, batch_size=100), max_epochs=1, limit_train_batches=0.2, enable_progress_bar=False) simple_strategy = strategy.Random() exp = RetiariiExperiment(base_model, trainer, [], simple_strategy) exp_config = RetiariiExeConfig('local') exp_config.experiment_name = 'darts_search' exp_config.trial_concurrency = 2 exp_config.max_trial_number = 10 exp_config.trial_gpu_number = 1 exp_config.training_service.use_active_gpu = True exp_config.training_service.gpu_indices = [1, 2]