Source code for catalyst.metrics.functional._cmc_score
import torch
[docs]def cmc_score_count(
distances: torch.Tensor, conformity_matrix: torch.Tensor, topk: int = 1,
) -> float:
"""
Function to count CMC from distance matrix and conformity matrix.
Args:
distances: distance matrix shape of (n_embeddings_x, n_embeddings_y)
conformity_matrix: binary matrix with 1 on same label pos and 0 otherwise
topk: number of top examples for cumulative score counting
Returns:
cmc score
"""
perm_matrix = torch.argsort(distances)
position_matrix = torch.argsort(perm_matrix)
conformity_matrix = conformity_matrix.type(torch.bool)
position_matrix[~conformity_matrix] = topk + 1 # value large enough not to be counted
closest = position_matrix.min(dim=1)[0]
k_mask = (closest < topk).type(torch.float)
return k_mask.mean().item()
[docs]def cmc_score(
query_embeddings: torch.Tensor,
gallery_embeddings: torch.Tensor,
conformity_matrix: torch.Tensor,
topk: int = 1,
) -> float:
"""
Function to count CMC score from query and gallery embeddings.
Args:
query_embeddings: tensor shape of (n_embeddings, embedding_dim)
embeddings of the objects in query
gallery_embeddings: tensor shape of (n_embeddings, embedding_dim)
embeddings of the objects in gallery
conformity_matrix: binary matrix with 1 on same label pos and 0 otherwise
topk: number of top examples for cumulative score counting
Returns:
cmc score
"""
distances = torch.cdist(query_embeddings, gallery_embeddings)
return cmc_score_count(distances, conformity_matrix, topk)
__all__ = ["cmc_score_count", "cmc_score"]