# Source code for catalyst.utils.numpy

import numpy as np
[docs]def get_one_hot(
label: int, num_classes: int, smoothing: float = None
) -> np.ndarray:
"""
Applies OneHot vectorization to a giving scalar, optional with
label smoothing as described in `Bag of Tricks for Image Classification
with Convolutional Neural Networks`_.
Args:
label: scalar value to be vectorized
num_classes: total number of classes
smoothing (float, optional): if specified applies label smoothing
from ``Bag of Tricks for Image Classification
with Convolutional Neural Networks`` paper
Returns:
np.ndarray: a one-hot vector with shape ``(num_classes,)``
.. _Bag of Tricks for Image Classification with
Convolutional Neural Networks: https://arxiv.org/abs/1812.01187
"""
assert (
num_classes is not None and num_classes > 0
), f"Expect num_classes to be > 0, got {num_classes}"
assert (
label is not None and 0 <= label < num_classes
), f"Expect label to be in [0; {num_classes}), got {label}"
if smoothing is not None:
assert (
0.0 < smoothing < 1.0
), f"If smoothing is specified it must be in (0; 1), got {smoothing}"
smoothed = smoothing / float(num_classes - 1)
result = np.full((num_classes,), smoothed, dtype=np.float32)
result[label] = 1.0 - smoothing
return result
result = np.zeros(num_classes, dtype=np.float32)
result[label] = 1.0
return result
__all__ = ["get_one_hot"]