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]
