quaterion.utils.utils module

get_anchor_negative_mask(labels_a: Tensor, labels_b: Tensor) → BoolTensor

Creates a 2D mask of valid anchor-negative pairs.

Parameters:

• labels_a (Tensor) – Labels associated with embeddings in the batch A. Shape: (batch_size_a,)

• labels_b (Tensor) – Labels associated with embeddings in the batch B. Shape: (batch_size_b,)

Returns:

torch.Tensor – Anchor-negative mask. Shape: (batch_size_a, batch_size_b)

get_anchor_positive_mask(labels_a: Tensor, labels_b: Tensor) → BoolTensor

Creates a 2D mask of valid anchor-positive pairs.

Parameters:

• labels_a (Tensor) – Labels associated with embeddings in the batch A. Shape: (batch_size_a,)

• labels_b (Tensor) – Labels associated with embeddings in the batch B. Shape: (batch_size_b,)

Returns:

torch.Tensor – Anchor-positive mask. Shape: (batch_size_a, batch_size_b)

get_masked_maximum(dists: Tensor, mask: Tensor, dim: int = 1) → Tensor

Utility function for semi hard mining.

Parameters:

• dists – Tiled distance matrix.

• mask – Tiled mask.

• dim – Dimension to operate on.

Returns:

torch.Tensor - masked maximums.

get_masked_minimum(dists, mask, dim=1)

Utility function for semi hard mining.

Parameters:

• dists – Tiled distance matrix.

• mask – Tiled mask.

• dim – Dimension to operate on.

Returns:

torch.Tensor - masked maximums.

get_triplet_mask(labels: Tensor) → Tensor

Creates a 3D mask of valid triplets for the batch-all strategy.

Given a batch of labels with shape = (batch_size,) the number of possible triplets that can be formed is: batch_size^3, i.e. cube of batch_size, which can be represented as a tensor with shape = (batch_size, batch_size, batch_size). However, a triplet is valid if: labels[i] == labels[j] and labels[i] != labels[k] and i, j and k are distinct indices. This function calculates a mask indicating which ones of all the possible triplets are actually valid triplets based on the given criteria above.

Parameters:

labels (Tensor) – Labels associated with embeddings in the batch. Shape: (batch_size,)

Returns:

torch.Tensor – Triplet mask. Shape: (batch_size, batch_size, batch_size)

info_value_of_dtype(dtype: dtype) → Union[finfo, iinfo]

Returns the finfo or iinfo object of a given PyTorch data type.

Does not allow torch.bool.

Parameters:

dtype – dtype for which to return info value

Returns:

Union[torch.finfo, torch.iinfo] – info about given data type

Raises:

TypeError – if torch.bool is passed

iter_by_batch(sequence: Union[Sized, Iterable, Dataset], batch_size: int, log_progress: bool = True)

Iterate through index-able or iterable by batches

Try to iterate by indices, if fail - via iterable interface.

max_value_of_dtype(dtype: dtype) → float

Returns the maximum value of a given PyTorch data type.

Does not allow torch.bool.

Parameters:

dtype – dtype for which to return max value

Returns:

Union[int, float] – max value of dtype

min_value_of_dtype(dtype: dtype) → Union[int, float]

Returns the minimum value of a given PyTorch data type.

Does not allow torch.bool.

Parameters:

dtype – dtype for which to return min value

Returns:

Union[int, float] – max value of dtype