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
Examples:
.. code-block:: python
import torch
from catalyst import metrics
metrics.cmc_score_count(
distances=torch.tensor([[1, 2], [2, 1]]),
conformity_matrix=torch.tensor([[0, 1], [1, 0]]),
topk=1,
)
# 0.0
.. code-block:: python
import torch
from catalyst import metrics
metrics.cmc_score_count(
distances=torch.tensor([[1, 0.5, 0.2], [2, 3, 4], [0.4, 3, 4]]),
conformity_matrix=torch.tensor([[1, 0, 0], [0, 1, 0], [0, 0, 1]]),
topk=2,
)
# 0.33
"""
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
Example:
.. code-block:: python
import torch
from catalyst import metrics
metrics.cmc_score(
query_embeddings=torch.tensor([
[1, 1, 0, 0], [1, 0, 0, 0], [0, 1, 1, 1], [0, 0, 1, 1],
]).float(),
gallery_embeddings=torch.tensor([
[1, 1, 1, 0], [1, 1, 1, 1], [0, 1, 1, 0],
]).float(),
conformity_matrix=torch.tensor([
[True, False, False],
[True, False, False],
[False, True, True],
[False, True, True],
]),
topk=1,
)
# 1.0
"""
distances = torch.cdist(query_embeddings, gallery_embeddings)
return cmc_score_count(distances, conformity_matrix, topk)
[docs]def masked_cmc_score(
query_embeddings: torch.Tensor,
gallery_embeddings: torch.Tensor,
conformity_matrix: torch.Tensor,
available_samples: torch.Tensor,
topk: int = 1,
) -> float:
"""
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
available_samples: tensor of shape (query_size, gallery_size),
available_samples[i][j] == 1 means that j-th element of gallery
should be used while scoring i-th query one
topk: number of top examples for cumulative score counting
Returns:
cmc score with mask
Raises:
ValueError: if there are items that have different labels and are
unavailable for each other according to availability matrix
Example:
.. code-block:: python
import torch
from catalyst import metrics
metrics.masked_cmc_score(
query_embeddings=torch.tensor([
[1, 1, 0, 0], [1, 0, 0, 0], [0, 1, 1, 1], [0, 0, 1, 1],
]).float(),
gallery_embeddings=torch.tensor([
[1, 1, 1, 0], [1, 1, 1, 1], [0, 1, 1, 0],
]).float(),
conformity_matrix=torch.tensor([
[True, False, False],
[True, False, False],
[False, True, True],
[False, True, True],
]),
available_samples=torch.tensor([
[False, True, True],
[True, True, True],
[True, False, True],
[True, True, True],
]),
topk=1,
)
# 0.75
"""
if not available_samples[conformity_matrix == 0].all():
raise ValueError(
"There is something wrong with available_samples matrix.\n"
"If we calculate masked_cmc_score for person pid_i, we should "
"take into account all the photos of people other than pid_i; "
"it means that all of them should be available according to "
"available_samples matrix.\n"
"It seems that it's not so for your one."
)
distances = torch.cdist(query_embeddings, gallery_embeddings)
distances[~available_samples] = float("inf")
return cmc_score_count(distances, conformity_matrix, topk)
__all__ = ["cmc_score_count", "cmc_score", "masked_cmc_score"]