Enable Class Balancing for Model Train Sampler

Summary:
This diff enables both category and datasets weight balancing at the same time by declaring "WeightedCategoryTrainingSampler" under "SAMPLER_TRAIN" in config file.

X-link: facebookresearch/detectron2#4995

Pull Request resolved: facebookresearch#570

Differential Revision: D46377371

fbshipit-source-id: 29f135e84f596e3e694a107bc676c2025e6e164f

d2go/data/build.py

@@ -66,7 +66,9 @@ def get_train_datasets_repeat_factors(cfg: CfgNode) -> Dict[str, float]:
  return name_to_weight
 
 
-def build_weighted_detection_train_loader(cfg: CfgNode, mapper=None):
+def build_weighted_detection_train_loader(
+ cfg: CfgNode, mapper=None, enable_category_balance=False
+):
  dataset_repeat_factors = get_train_datasets_repeat_factors(cfg)
  # OrderedDict to guarantee order of values() consistent with repeat factors
  dataset_name_to_dicts = OrderedDict(
@@ -98,12 +100,39 @@ def build_weighted_detection_train_loader(cfg: CfgNode, mapper=None):
  mapper = DatasetMapper(cfg, True)
  dataset = MapDataset(dataset, mapper)
 
- logger.info(
- "Using WeightedTrainingSampler with repeat_factors={}".format(
- cfg.DATASETS.TRAIN_REPEAT_FACTOR
+ repeat_factors = torch.tensor(repeat_factors)
+ if enable_category_balance:
+ """
+ 1. Calculate repeat factors using category frequency for each dataset and then merge them.
+ 2. Element wise dot producting the dataset frequency repeat factors with
+ the category frequency repeat factors gives the final repeat factors.
+ """
+ category_repeat_factors = [
+ RepeatFactorTrainingSampler.repeat_factors_from_category_frequency(
+ dataset_dict, cfg.DATALOADER.REPEAT_THRESHOLD
+ )
+ for dataset_dict in dataset_name_to_dicts.values()
+ ]
+ # flatten the category repeat factors from all datasets
+ category_repeat_factors = list(
+ itertools.chain.from_iterable(category_repeat_factors)
+ )
+ category_repeat_factors = torch.tensor(category_repeat_factors)
+ repeat_factors = torch.mul(category_repeat_factors, repeat_factors)
+ repeat_factors = repeat_factors / torch.min(repeat_factors)
+ logger.info(
+ "Using WeightedCategoryTrainingSampler with repeat_factors={}".format(
+ cfg.DATASETS.TRAIN_REPEAT_FACTOR
+ )
+ )
+ else:
+ logger.info(
+ "Using WeightedTrainingSampler with repeat_factors={}".format(
+ cfg.DATASETS.TRAIN_REPEAT_FACTOR
+ )
  )
- )
- sampler = RepeatFactorTrainingSampler(torch.tensor(repeat_factors))
+
+ sampler = RepeatFactorTrainingSampler(repeat_factors)
 
  return build_batch_data_loader(
  dataset,
@@ -149,7 +178,13 @@ def build_clip_grouping_data_loader(dataset, sampler, total_batch_size, num_work
 @fb_overwritable()
 def build_mapped_train_loader(cfg, mapper):
  if cfg.DATALOADER.SAMPLER_TRAIN == "WeightedTrainingSampler":
+ # balancing only datasets frequencies
  data_loader = build_weighted_detection_train_loader(cfg, mapper=mapper)
+ elif cfg.DATALOADER.SAMPLER_TRAIN == "WeightedCategoryTrainingSampler":
+ # balancing both datasets and its categories
+ data_loader = build_weighted_detection_train_loader(
+ cfg, mapper=mapper, enable_category_balance=True
+ )
  else:
  data_loader = build_detection_train_loader(cfg, mapper=mapper)
  return data_loader