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[doc] further detail for the doc for datasets/dataset_wrappers/ClassBalancedDataset #901

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Nov 2, 2022
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[doc] further detail for the doc for datasets/dataset_wrappers/ClassBalancedDataset #901

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2cafaee
futher detail for the doc for datasets/dataset_wrappers/ClassBalanced…
xiaoyuan0203 Jul 7, 2022
c9425f7
fix
xiaoyuan0203 Jul 7, 2022
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19 changes: 11 additions & 8 deletions mmcls/datasets/dataset_wrappers.py
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Original file line number Diff line number Diff line change
Expand Up @@ -175,17 +175,20 @@ class ClassBalancedDataset(object):

1. For each category c, compute the fraction :math:`f(c)` of images that
contain it.
2. For each category c, compute the category-level repeat factor
2. For each category c, compute the category-level repeat factor.

.. math::
r(c) = \max(1, \sqrt{\frac{t}{f(c)}})

3. For each image I and its labels :math:`L(I)`, compute the image-level

where :math:`t` is `oversample_thr`.
3. For each image I and its labels :math:`L(I)`, compute the image-level.
repeat factor

.. math::
r(I) = \max_{c \in L(I)} r(c)

Each image repeats :math:`\lceil r(I) \rceil` times.

Args:
dataset (:obj:`BaseDataset`): The dataset to be repeated.
oversample_thr (float): frequency threshold below which data is
Expand Down Expand Up @@ -214,8 +217,8 @@ def __init__(self, dataset, oversample_thr):
self.flag = np.asarray(flags, dtype=np.uint8)

def _get_repeat_factors(self, dataset, repeat_thr):
# 1. For each category c, compute the fraction # of images
# that contain it: f(c)
# 1. For each category c, compute the fraction of images
# that contain it: f(c)
category_freq = defaultdict(int)
num_images = len(dataset)
for idx in range(num_images):
Expand All @@ -227,15 +230,15 @@ def _get_repeat_factors(self, dataset, repeat_thr):
category_freq[k] = v / num_images

# 2. For each category c, compute the category-level repeat factor:
# r(c) = max(1, sqrt(t/f(c)))
# r(c) = max(1, sqrt(t/f(c)))
category_repeat = {
cat_id: max(1.0, math.sqrt(repeat_thr / cat_freq))
for cat_id, cat_freq in category_freq.items()
}

# 3. For each image I and its labels L(I), compute the image-level
# repeat factor:
# r(I) = max_{c in L(I)} r(c)
# repeat factor:
# r(I) = max_{c in L(I)} r(c)
repeat_factors = []
for idx in range(num_images):
cat_ids = set(self.dataset.get_cat_ids(idx))
Expand Down