Enable Class Balancing for Model Train Sampler

Summary:
X-link: facebookresearch/detectron2#4995

Pull Request resolved: facebookresearch#570

Differential Revision: D46377371

fbshipit-source-id: 8e5e8b859a77eb291bfa30e54ce45126e0d2cd60

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(
@@ -103,7 +105,22 @@ def build_weighted_detection_train_loader(cfg: CfgNode, mapper=None):
  cfg.DATASETS.TRAIN_REPEAT_FACTOR
  )
  )
- sampler = RepeatFactorTrainingSampler(torch.tensor(repeat_factors))
+ repeat_factors = torch.tensor(repeat_factors)
+ if enable_category_balance:
+ """
+ Current repeat factor is already balanced by dataset frequency
+ Use annotations information in dataset_dicts to compute repeat factors to balance category frequencies.
+ Element wise dot producting these two list of repeat factors gives the final balanced repeat factors
+ """
+ class_balance_repeat_factors = (
+ RepeatFactorTrainingSampler.repeat_factors_from_category_frequency(
+ dataset_dicts, cfg.DATALOADER.REPEAT_THRESHOLD
+ )
+ )
+ repeat_factors = torch.mul(class_balance_repeat_factors, repeat_factors)
+ repeat_factors = repeat_factors / torch.min(repeat_factors)
+
+ sampler = RepeatFactorTrainingSampler(repeat_factors)
 
  return build_batch_data_loader(
  dataset,
@@ -149,7 +166,13 @@ 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_class_balance=True
+ )
  else:
  data_loader = build_detection_train_loader(cfg, mapper=mapper)
  return data_loader