def load_datasets(self):
logger.info("Loading datasets")
self.dataset_loader = MultiDataModule(self.config)
self.train_loader = self.dataset_loader.train_dataloader()
self.val_loader = self.dataset_loader.val_dataloader()
self.test_loader = self.dataset_loader.test_dataloader()
def load_datasets(self) -> None:
logger.info("Loading datasets")
data_module = MultiDataModule(self.config)
self.data_module = data_module
self.train_loader = data_module.train_dataloader()
self.val_loader = data_module.val_dataloader()
self.test_loader = data_module.test_dataloader()
class MMFTrainer(
TrainerCallbackHookMixin,
TrainerTrainingLoopMixin,
TrainerDeviceMixin,
TrainerEvaluationLoopMixin,
TrainerReportingMixin,
TrainerProfilingMixin,
BaseTrainer,
):
def __init__(self, config: DictConfig):
super().__init__(config)
def load(self):
super().load()
self.load_fp16_scaler()
# Callbacks
self.on_init_start()
# Parallize model
self.parallelize_model()
# Callbacks
self.on_init_end()
def configure_callbacks(self):
self.checkpoint_callback = CheckpointCallback(self.config, self)
self.early_stop_callback = EarlyStoppingCallback(self.config, self)
self.logistics_callback = LogisticsCallback(self.config, self)
self.lr_scheduler_callback = LRSchedulerCallback(self.config, self)
# Add callbacks for execution during events
self.callbacks.append(self.lr_scheduler_callback)
# checkpoint_callback needs to be called after lr_scheduler_callback so that
# lr_scheduler_callback._scheduler.step() happens before saving checkpoints
# (otherwise the saved last_epoch in scheduler would be wrong)
self.callbacks.append(self.checkpoint_callback)
self.callbacks.append(self.logistics_callback)
def load_datasets(self):
logger.info("Loading datasets")
self.dataset_loader = MultiDataModule(self.config)
self.train_loader = self.dataset_loader.train_dataloader()
self.val_loader = self.dataset_loader.val_dataloader()
self.test_loader = self.dataset_loader.test_dataloader()
def load_model(self):
logger.info("Loading model")
if self.config.model in self.config.model_config:
attributes = self.config.model_config[self.config.model]
else:
warnings.warn(f"Model {self.config.model}'s config not present. " +
"Continuing with empty config")
attributes = OmegaConf.create()
# Easy way to point to config for other model
if isinstance(attributes, str):
attributes = self.config.model_config[attributes]
with omegaconf.open_dict(attributes):
attributes.model = self.config.model
self.model = build_model(attributes)
self.model = self.model.to(self.device)
def load_optimizer(self):
logger.info("Loading optimizer")
self.optimizer = build_optimizer(self.model, self.config)
def load_metrics(self) -> None:
logger.info("Loading metrics")
metrics = self.config.evaluation.get("metrics", [])
self.metrics = Metrics(metrics)
self.metrics_params = self.metrics.required_params
def load_fp16_scaler(self):
if self.training_config.fp16:
assert (torch.__version__ >=
"1.6"), "Using fp16 requires torch version >- 1.6"
assert self.device != torch.device(
"cpu"), "fp16 cannot be used on cpu"
set_torch_grad_scaler = True
if self.training_config.fp16 and self.distributed:
try:
from fairscale.optim.oss import OSS
from fairscale.optim.grad_scaler import ShardedGradScaler
if isinstance(self.optimizer, OSS):
self.scaler = ShardedGradScaler()
set_torch_grad_scaler = False
logger.info("Using FairScale ShardedGradScaler")
except ImportError:
logger.info("Using Pytorch AMP GradScaler")
if set_torch_grad_scaler:
self.scaler = torch.cuda.amp.GradScaler(
enabled=self.training_config.fp16)
def train(self):
logger.info("===== Model =====")
logger.info(self.model)
print_model_parameters(self.model)
if "train" not in self.run_type:
self.inference()
return
self.on_train_start()
self.training_loop()
self.on_train_end()
self.inference()
self.dataset_loader.teardown()
def inference(self):
dataset_type = []
if "val" in self.run_type:
dataset_type.append("val")
if any(rt in self.run_type for rt in ["inference", "test", "predict"]):
dataset_type.append("test")
for dataset in dataset_type:
if self.config.evaluation.predict:
self.on_prediction_start()
self.prediction_loop(dataset)
self.on_prediction_end()
else:
self.on_test_start()
logger.info(f"Starting inference on {dataset} set")
report, meter = self.evaluation_loop(dataset, use_tqdm=True)
self.on_test_end(report=report, meter=meter)
def load_datasets(self) -> None:
logger.info("Loading datasets")
data_module = MultiDataModule(self.config)
self.data_module = data_module