def get_loader(
dataset: GenericSSLDataset,
dataset_config: dict,
num_dataloader_workers: int,
pin_memory: bool,
multi_processing_method: str,
device: torch.device,
sampler_seed=0,
get_sampler=get_sampler,
worker_init_fn=set_dataloader_seeds,
):
"""
Get the dataloader for the given satasets and data split
Args:
dataset (GenericSSLDataset): the dataset object for which dataloader is constructed
dataset_config (dict): configuration of the dataset.
should be DATA.TRAIN or DATA.TEST settings
num_dataloader_workers (int): number of workers per gpu (or cpu) training
pin_memory (bool): whether to pin memory or not
multi_processing_method (str): method to use. options: forkserver | fork | spawn
sampler_seed (int): seed for the sampler. Should be identical per process
device (torch.device): training on cuda or cpu
get_sampler (get_sampler): function that is used to get the sampler
worker_init_fn (None): any function that should be executed during
initialization of dataloader workers
Returns:
Instance of Pytorch DataLoader. The dataloader is wrapped with
DataloaderAsyncGPUWrapper or DataloaderSyncGPUWrapper depending
on whether user wants to copy data to gpu async or not.
"""
# pytorch dataloader requires setting the multiprocessing type.
setup_multiprocessing_method(multi_processing_method)
# we don't need to set the rank, replicas as the Sampler already does so in
# it's init function
data_sampler = get_sampler(dataset, dataset_config, sampler_seed)
collate_function = get_collator(dataset_config["COLLATE_FUNCTION"],
dataset_config["COLLATE_FUNCTION_PARAMS"])
# Replace the worker_init_fn with a deterministic one when debugging
if dataset_config["USE_DEBUGGING_SAMPLER"]:
worker_init_fn = debugging_worker_init_fn
# Create the pytorch dataloader
dataloader = DataLoader(
dataset=dataset,
num_workers=num_dataloader_workers,
pin_memory=pin_memory,
shuffle=False,
batch_size=dataset_config["BATCHSIZE_PER_REPLICA"],
collate_fn=collate_function,
sampler=data_sampler,
drop_last=dataset_config["DROP_LAST"],
worker_init_fn=worker_init_fn,
)
# If the targeted device is CUDA, set up async device copy:
# - makes sure that samples are on device
# - overlap the copy with the previous batch computation.
if device.type == "cuda":
if dataset.cfg["DATA"]["ENABLE_ASYNC_GPU_COPY"]:
logging.info(
"Wrapping the dataloader to async device copies") # NOQA
dataloader = DataloaderAsyncGPUWrapper(dataloader)
else:
logging.info(
"Wrapping the dataloader to synchronous device copies") # NOQA
dataloader = DataloaderSyncGPUWrapper(dataloader)
else:
logging.warning("Selecting a CPU device")
return dataloader
def build_dataloader(
dataset: GenericSSLDataset,
dataset_config: dict,
num_dataloader_workers: int,
pin_memory: bool,
multi_processing_method: str,
device: torch.device,
sampler_seed=0,
get_sampler=get_sampler,
worker_init_fn=set_dataloader_seeds,
**kwargs,
):
"""
Get the dataloader for the given satasets and data split
Args:
dataset (GenericSSLDataset): the dataset object for which dataloader is constructed
dataset_config (dict): configuration of the dataset.
should be DATA.TRAIN or DATA.TEST settings
num_dataloader_workers (int): number of workers per gpu (or cpu) training
pin_memory (bool): whether to pin memory or not
multi_processing_method (str): method to use. options: forkserver | fork | spawn
sampler_seed (int): seed for the sampler. Should be identical per process
device (torch.device): training on cuda or cpu
get_sampler (get_sampler): function that is used to get the sampler
worker_init_fn (None): any function that should be executed during
initialization of dataloader workers
Returns:
Instance of Pytorch DataLoader. The dataloader is wrapped with
DataloaderAsyncGPUWrapper or DataloaderSyncGPUWrapper depending
on whether user wants to copy data to gpu async or not.
"""
# pytorch dataloader requires setting the multiprocessing type.
setup_multiprocessing_method(multi_processing_method)
# we don't need to set the rank, replicas as the Sampler already does so in
# it's init function
data_sampler = get_sampler(dataset, dataset_config, sampler_seed)
collate_function = get_collator(dataset_config["COLLATE_FUNCTION"],
dataset_config["COLLATE_FUNCTION_PARAMS"])
# Replace the worker_init_fn with a deterministic one when debugging
if dataset_config["USE_DEBUGGING_SAMPLER"]:
worker_init_fn = debugging_worker_init_fn
# Load the labels of the dataset before creating the data loader
# or else the load of files will happen on each data loader separately
# decreasing performance / hitting quota on data source
dataset.load_labels()
# Create the pytorch dataloader
dataloader = DataLoader(
dataset=dataset,
num_workers=num_dataloader_workers,
pin_memory=pin_memory,
shuffle=False,
batch_size=dataset_config["BATCHSIZE_PER_REPLICA"],
collate_fn=collate_function,
sampler=data_sampler,
drop_last=dataset_config["DROP_LAST"],
worker_init_fn=worker_init_fn,
)
enable_async_gpu_copy = dataset.cfg["DATA"]["ENABLE_ASYNC_GPU_COPY"]
dataloader = wrap_dataloader(dataloader, enable_async_gpu_copy, device)
return dataloader