def train(run_dir: str = './run',
datasets_dir: str = './data',
dataset: str = 'cifar10',
augmentation: bool = True,
validation: int = 0,
shuffle: bool = True,
arch: str = 'preact20',
optimizer: str = 'sgd',
epochs: Tuple[int, ...] = (1, 90, 45, 45),
learning_rates: Tuple[float, ...] = (0.01, 0.1, 0.01, 0.001),
momentum: float = 0.9,
weight_decay: float = 5e-4,
batch_size: int = 128,
eval_batch_size: int = 128,
cuda: bool = True,
device_ids: Tuple[int, ...] = tuple(range(cuda.device_count())),
num_workers: int = 0,
eval_num_workers: int = 0,
seed: Optional[int] = None,
checkpoint: str = 'best',
track_test_acc: bool = True):
"""
Train deep learning models (e.g., ResNet) on CIFAR10 and CIFAR100.
Parameters
----------
run_dir : str, default './run'
Path to log results and other artifacts.
datasets_dir : str, default './data'
Path to datasets.
dataset : str, default 'cifar10'
Dataset to use in experiment (i.e., CIFAR10 or CIFAR100)
augmentation : bool, default True
Add data augmentation (i.e., random crop and horizontal flip).
validation : int, default 0
Number of examples from training set to use for valdiation.
shuffle : bool, default True
Shuffle training data before splitting into training and validation.
arch : str, default 'preact20'
Model architecture. `preact20` is short for ResNet20 w/ Pre-Activation.
optimizer : str, default = 'sgd'
Optimizer for training.
epochs : Tuple[int, ...], default (1, 90, 45, 45)
Epochs for training. Each number corresponds to a learning rate below.
learning_rates : Tuple[float, ...], default (0.01, 0.1, 0.01, 0.001)
Learning rates for training. Each learning rate is used for the
corresponding number of epochs above.
momentum : float, default 0.9
Momentum for SGD.
weight_decay : float, default 5e-4
Weight decay for SGD.
batch_size : int, default 128
Minibatch size for training.
eval_batch_size : int, default 128
Minibatch size for evaluation (validation and testing).
cuda : bool, default True
Enable or disable use of available GPUs
device_ids : Tuple[int, ...], default True
GPU device ids to use.
num_workers : int, default 0
Number of data loading workers for training.
eval_num_workers : int, default 0
Number of data loading workers for evaluation.
seed : Optional[int], default None
Random seed for numpy, torch, and others. If None, a random int is
chosen and logged in the experiments config file.
checkpoint : str, default 'best'
Specify when to create a checkpoint for the model: only checkpoint the
best performing model on the validation data or the training data if
`validation == 0` ("best"), after every epoch ("all"), or only the last
epoch of each segment of the learning rate schedule ("last").
track_test_acc : bool, default True
Calculate performance of the models on the test data in addition or
instead of the validation dataset.'
Returns
-------
model : nn.Module
Trained model.
accuracies : Tuple[float, ...]
The best accuracies from the model on the train, dev, and test splits.
times : Tuple[timedelta, ...]
Time spent training or evaluating on the train, dev, and test splits.
"""
# Set seeds for reproducibility.
seed = utils.set_random_seed(seed)
# Capture all of the arguments to save alongside the results.
config = utils.capture_config(**locals())
# Create a unique timestamped directory for this experiment.
run_dir = utils.create_run_dir(run_dir, timestamp=config['timestamp'])
utils.save_config(config, run_dir)
# Update the computing arguments based on the runtime system.
use_cuda, device, device_ids, num_workers = utils.config_run_env(
cuda=cuda, device_ids=device_ids, num_workers=num_workers)
# Create the training dataset.
train_dataset = create_dataset(dataset,
datasets_dir,
train=True,
augmentation=augmentation)
# Create the test dataset.
test_dataset = None
if track_test_acc:
test_dataset = create_dataset(dataset,
datasets_dir,
train=False,
augmentation=False)
# Create data loaders
train_loader, dev_loader, test_loader = create_loaders(
train_dataset,
batch_size=batch_size,
eval_batch_size=eval_batch_size,
validation=validation,
run_dir=run_dir,
test_dataset=test_dataset,
use_cuda=use_cuda,
shuffle=shuffle,
num_workers=num_workers,
eval_num_workers=eval_num_workers)
# Calculate the number of classes (e.g., 10 or 100) so the model has
# the right dimension for its output.
num_classes = len(set(train_dataset.targets)) # type: ignore
# Create the model and optimizer for training.
model, _optimizer = create_model_and_optimizer(
run_dir=run_dir,
arch=arch,
num_classes=num_classes,
optimizer=optimizer,
learning_rate=learning_rates[0],
momentum=momentum,
weight_decay=weight_decay)
# Create the loss criterion.
criterion = nn.CrossEntropyLoss()
# Move the model and loss to the appropriate devices.
model = model.to(device)
if use_cuda:
model = nn.DataParallel(model, device_ids=device_ids)
criterion = criterion.to(device)
# Run training.
return run_training(model=model,
optimizer=_optimizer,
criterion=criterion,
device=device,
train_loader=train_loader,
epochs=epochs,
learning_rates=learning_rates,
dev_loader=dev_loader,
test_loader=test_loader,
run_dir=run_dir,
checkpoint=checkpoint)
# Set seeds for reproducibility.
seed = utils.set_random_seed(seed)
# Capture all of the arguments to save alongside the results.
config = utils.capture_config(**locals())
if scale_learning_rates:
# For convenience, scale the learning rate for large-batch SGD
learning_rates = tuple(np.array(learning_rates) * (batch_size / 256))
config['scaled_learning_rates'] = learning_rates
if proxy_scale_learning_rates:
# For convenience, scale the learning rate for large-batch SGD
proxy_learning_rates = tuple(
np.array(proxy_learning_rates) *
(proxy_batch_size / 256)) # noqa: E501
config['proxy_scaled_learning_rates'] = proxy_learning_rates
# Create a unique timestamped directory for this experiment.
run_dir = utils.create_run_dir(run_dir, timestamp=config['timestamp'])
utils.save_config(config, run_dir)
# Update the computing arguments based on the runtime system.
use_cuda, device, device_ids, num_workers = utils.config_run_env(
cuda=cuda, device_ids=device_ids, num_workers=num_workers)
# Create the training dataset.
train_dataset = create_dataset(dataset,
datasets_dir,
train=True,
augmentation=augmentation)
# Verify there is enough training data for validation and
# the final selected subset.
validate_splits(train_dataset, validation, subset)
# Create the test dataset.
def train(run_dir: str = './run',
datasets_dir: str = './data',
dataset: str = 'imagenet',
augmentation: bool = True,
validation: int = 0,
shuffle: bool = True,
arch: str = 'resnet18',
optimizer: str = 'sgd',
epochs: Tuple[int, ...] = (1, 1, 1, 1, 1, 25, 30, 20, 20),
learning_rates: Tuple[float,
...] = (0.0167, 0.0333, 0.05, 0.0667, 0.0833,
0.1, 0.01, 0.001, 0.0001),
scale_learning_rates: bool = True,
momentum: float = 0.9,
weight_decay: float = 1e-4,
batch_size: int = 256,
eval_batch_size: int = 256,
fp16: bool = False,
label_smoothing: float = 0.1,
loss_scale: float = 256.0,
cuda: bool = True,
device_ids: Tuple[int, ...] = tuple(range(cuda.device_count())),
num_workers: int = 0,
eval_num_workers: int = 0,
seed: Optional[int] = None,
checkpoint: str = 'best',
track_test_acc: bool = True):
"""
Train deep learning models (e.g., ResNet) on ImageNet.
Parameters
----------
run_dir : str, default './run'
Path to log results and other artifacts.
datasets_dir : str, default './data'
Path to datasets.
dataset : str, default 'imagenet'
Dataset to use in experiment (unnecessary but kept for consistency)
augmentation : bool, default True
Add data augmentation (i.e., random crop and horizontal flip).
validation : int, default 0
Number of examples from training set to use for valdiation.
shuffle : bool, default True
Shuffle training data before splitting into training and validation.
arch : str, default 'resnet18'
Model architecture. `resnet18` is short for ResNet18. Other models are
pulled from `torchvision.models`.
optimizer : str, default = 'sgd'
Optimizer for training.
epochs : Tuple[int, ...], default (1, 1, 1, 1, 1, 25, 30, 20, 20)
Epochs for training. Each number corresponds to a learning rate below.
learning_rates : Tuple[float, ...], default (
0.0167, 0.0333, 0.05, 0.0667, 0.0833, 0.1, 0.01, 0.001, 0.0001)
Learning rates for training. Each learning rate is used for the
corresponding number of epochs above.
scale_learning_rates : bool, default True
Scale learning rates above based on (`batch_size / 256`). Mainly for
convenience with large minibatch training.
momentum : float, default 0.9
Momentum for SGD.
weight_decay : float, default 1e-4
Weight decay for SGD.
batch_size : int, default 256
Minibatch size for training.
eval_batch_size : int, default 256
Minibatch size for evaluation (validation and testing).
fp16 : bool, default False
Use mixed precision training.
label_smoothing : float, default 0.1
Amount to smooth labels for loss.
loss_scale : float, default 256
Amount to scale loss for mixed precision training.
cuda : bool, default True
Enable or disable use of available GPUs
device_ids : Tuple[int, ...], default True
GPU device ids to use.
num_workers : int, default 0
Number of data loading workers for training.
eval_num_workers : int, default 0
Number of data loading workers for evaluation.
seed : Optional[int], default None
Random seed for numpy, torch, and others. If None, a random int is
chosen and logged in the experiments config file.
checkpoint : str, default 'best'
Specify when to create a checkpoint for the model: only checkpoint the
best performing model on the validation data or the training data if
`validation == 0` ("best"), after every epoch ("all"), or only the last
epoch of each segment of the learning rate schedule ("last").
track_test_acc : bool, default True
Calculate performance of the models on the test data in addition or
instead of the validation dataset.'
Returns
-------
model : nn.Module
Trained model.
accuracies : Tuple[float, ...]
The best accuracies from the model on the train, dev, and test splits.
times : Tuple[timedelta, ...]
Time spent training or evaluating on the train, dev, and test splits.
"""
# Set seeds for reproducibility.
seed = utils.set_random_seed(seed)
# Capture all of the arguments to save alongside the results.
config = utils.capture_config(**locals())
if scale_learning_rates:
# For convenience, scale the learning rate for large-batch SGD
learning_rates = tuple(np.array(learning_rates) * (batch_size / 256))
config['scaled_learning_rates'] = learning_rates
# Create a unique timestamped directory for this experiment.
run_dir = utils.create_run_dir(run_dir, timestamp=config['timestamp'])
utils.save_config(config, run_dir)
# Update the computing arguments based on the runtime system.
use_cuda, device, device_ids, num_workers = utils.config_run_env(
cuda=cuda, device_ids=device_ids, num_workers=num_workers)
# Create the training dataset.
train_dataset = create_dataset(dataset,
datasets_dir,
train=True,
augmentation=augmentation)
# Create the test dataset.
test_dataset = None
if track_test_acc:
test_dataset = create_dataset(dataset,
datasets_dir,
train=False,
augmentation=False)
# Create data loaders
train_loader, dev_loader, test_loader = create_loaders(
train_dataset,
batch_size=batch_size,
eval_batch_size=eval_batch_size,
validation=validation,
run_dir=run_dir,
test_dataset=test_dataset,
use_cuda=use_cuda,
shuffle=shuffle,
num_workers=num_workers,
eval_num_workers=eval_num_workers)
# Calculate the number of classes (e.g., 1000) so the model has
# the right dimension for its output.
num_classes = 1_000 # type: ignore
# Create the model and optimizer for training.
model, _optimizer = create_model_and_optimizer(
run_dir=run_dir,
arch=arch,
num_classes=num_classes,
optimizer=optimizer,
learning_rate=learning_rates[0],
momentum=momentum,
weight_decay=weight_decay)
# Create the loss criterion.
criterion = _LabelSmoothing(label_smoothing)
# Move the model and loss to the appropriate devices.
model = model.to(device)
criterion = criterion.to(device)
if fp16:
from apex import amp # avoid dependency unless necessary.
model, _optimizer = amp.initialize(model,
_optimizer,
loss_scale=loss_scale)
if use_cuda:
model = nn.DataParallel(model, device_ids=device_ids)
# Run training.
return run_training(model=model,
optimizer=_optimizer,
criterion=criterion,
device=device,
train_loader=train_loader,
epochs=epochs,
learning_rates=learning_rates,
dev_loader=dev_loader,
test_loader=test_loader,
fp16=fp16,
run_dir=run_dir,
checkpoint=checkpoint)