def load(self, **opts):
self.opts = opts
self._process_datasets()
self.datasets = []
self.builders = []
available_datasets = self._get_available_datasets()
self.total_length = 0
self.per_dataset_lengths = []
self.num_datasets = 0
for dataset in self.given_datasets:
if dataset in available_datasets:
builder_class = registry.get_builder_class(dataset)
if builder_class is None:
print("No builder class found for %s." % dataset)
continue
builder_instance = builder_class()
if dataset in self.opts["dataset_attributes"]:
attributes = self.opts["dataset_attributes"][dataset]
else:
self.writer.write(
"Dataset %s is missing from "
"dataset_attributes in config." % dataset,
"error",
)
sys.exit(1)
dataset_type = self.opts.get("dataset_type", "train")
if is_main_process():
builder_instance.build(dataset_type, attributes)
synchronize()
dataset_instance = builder_instance.load(dataset_type, attributes)
self.builders.append(builder_instance)
self.datasets.append(dataset_instance)
self.per_dataset_lengths.append(len(dataset_instance))
self.total_length += len(dataset_instance)
else:
print(
"Dataset %s is not a valid dataset for task %s. Skipping"
% (dataset, self.task_name)
)
self.num_datasets = len(self.datasets)
self.dataset_probablities = [1 for _ in range(self.num_datasets)]
sampling = self.opts.get("dataset_size_proportional_sampling", None)
if sampling is True:
self.dataset_probablities = self.per_dataset_lengths[:]
self.dataset_probablities = [
prob / self.total_length for prob in self.dataset_probablities
]
self.change_dataset()
def _load(self, dataset_type, config, *args, **kwargs):
if dataset_type == 'train':
self.dataset = ImageNetDataset(dataset_type, config)
else:
# use coco dataset to inference
# therefore, in configuration file,
# we should embedd configuration of coco
# into it
attributes = config['coco']
coco_builder = registry.get_builder_class('coco')
coco_builder_instance = coco_builder()
coco_builder_instance.build(dataset_type, attributes)
self.dataset = coco_builder_instance.load(dataset_type, attributes)
return self.dataset
def load(self, **opts):
self.opts = opts
self._process_datasets()
self._datasets = []
self._builders = []
self._loaders = []
self._samplers = []
self._iterators = []
self._total_length = 0
self._per_dataset_lengths = []
self._num_datasets = 0
self._finished_iterators = {}
self._used_once = {}
for dataset in self._given_datasets:
builder_class = registry.get_builder_class(dataset)
if builder_class is None:
print("No builder class found for %s." % dataset)
continue
builder_instance = builder_class()
if dataset in self.opts["dataset_attributes"]:
attributes = self.opts["dataset_attributes"][dataset]
else:
self.writer.write(
"Dataset %s is missing from "
"dataset_attributes in config." % dataset,
"error",
)
sys.exit(1)
builder_instance.build(self._dataset_type, attributes)
dataset_instance = builder_instance.load(self._dataset_type,
attributes)
if dataset_instance is None:
continue
loader_instance, sampler_instance = self.build_dataloader(
dataset_instance, self.opts)
self._builders.append(builder_instance)
self._datasets.append(dataset_instance)
self._loaders.append(loader_instance)
self._samplers.append(sampler_instance)
self._per_dataset_lengths.append(len(dataset_instance))
self._total_length += len(dataset_instance)
self._num_datasets = len(self._datasets)
self._dataset_probablities = [
1 / self._num_datasets for _ in range(self._num_datasets)
]
training_parameters = self._global_config.training_parameters
self._proportional_sampling = training_parameters.dataset_size_proportional_sampling
if self._dataset_type != "train":
# If it is val or test, it needs to be all datasets need to be fully iterated
# as metrics will be calculated in eval mode over complete datasets
self._proportional_sampling = True
if self._proportional_sampling is True:
self._dataset_probablities = self._per_dataset_lengths[:]
self._dataset_probablities = [
prob / self._total_length
for prob in self._dataset_probablities
]
self._loader_index = 0
self._chosen_dataset = self._datasets[self._loader_index]
self._chosen_loader = self._loaders[self._loader_index]
