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# Copyright (c) OpenMMLab. All rights reserved.
from abc import abstractmethod
from typing import Union
import torch
import torch.nn.functional as F
from mmengine.structures import InstanceData
from torch import Tensor
from mmseg.registry import TASK_UTILS
class BaseMatchCost:
"""Base match cost class.
Args:
weight (Union[float, int]): Cost weight. Defaults to 1.
"""
def __init__(self, weight: Union[float, int] = 1.) -> None:
self.weight = weight
@abstractmethod
def __call__(self, pred_instances: InstanceData,
gt_instances: InstanceData, **kwargs) -> Tensor:
"""Compute match cost.
Args:
pred_instances (InstanceData): Instances of model predictions.
It often includes "labels" and "scores".
gt_instances (InstanceData): Ground truth of instance
annotations. It usually includes "labels".
Returns:
Tensor: Match Cost matrix of shape (num_preds, num_gts).
"""
pass
@TASK_UTILS.register_module()
class ClassificationCost(BaseMatchCost):
"""ClsSoftmaxCost.
Args:
weight (Union[float, int]): Cost weight. Defaults to 1.
Examples:
>>> from mmseg.models.assigners import ClassificationCost
>>> import torch
>>> self = ClassificationCost()
>>> cls_pred = torch.rand(4, 3)
>>> gt_labels = torch.tensor([0, 1, 2])
>>> factor = torch.tensor([10, 8, 10, 8])
>>> self(cls_pred, gt_labels)
tensor([[-0.3430, -0.3525, -0.3045],
[-0.3077, -0.2931, -0.3992],
[-0.3664, -0.3455, -0.2881],
[-0.3343, -0.2701, -0.3956]])
"""
def __init__(self, weight: Union[float, int] = 1) -> None:
super().__init__(weight=weight)
def __call__(self, pred_instances: InstanceData,
gt_instances: InstanceData, **kwargs) -> Tensor:
"""Compute match cost.
Args:
pred_instances (InstanceData): "scores" inside is
predicted classification logits, of shape
(num_queries, num_class).
gt_instances (InstanceData): "labels" inside should have
shape (num_gt, ).
Returns:
Tensor: Match Cost matrix of shape (num_preds, num_gts).
"""
assert hasattr(pred_instances, 'scores'), \
"pred_instances must contain 'scores'"
assert hasattr(gt_instances, 'labels'), \
"gt_instances must contain 'labels'"
pred_scores = pred_instances.scores
gt_labels = gt_instances.labels
pred_scores = pred_scores.softmax(-1)
cls_cost = -pred_scores[:, gt_labels]
return cls_cost * self.weight
@TASK_UTILS.register_module()
class DiceCost(BaseMatchCost):
"""Cost of mask assignments based on dice losses.
Args:
pred_act (bool): Whether to apply sigmoid to mask_pred.
Defaults to False.
eps (float): Defaults to 1e-3.
naive_dice (bool): If True, use the naive dice loss
in which the power of the number in the denominator is
the first power. If False, use the second power that
is adopted by K-Net and SOLO. Defaults to True.
weight (Union[float, int]): Cost weight. Defaults to 1.
"""
def __init__(self,
pred_act: bool = False,
eps: float = 1e-3,
naive_dice: bool = True,
weight: Union[float, int] = 1.) -> None:
super().__init__(weight=weight)
self.pred_act = pred_act
self.eps = eps
self.naive_dice = naive_dice
def _binary_mask_dice_loss(self, mask_preds: Tensor,
gt_masks: Tensor) -> Tensor:
"""
Args:
mask_preds (Tensor): Mask prediction in shape (num_queries, *).
gt_masks (Tensor): Ground truth in shape (num_gt, *)
store 0 or 1, 0 for negative class and 1 for
positive class.
Returns:
Tensor: Dice cost matrix in shape (num_queries, num_gt).
"""
mask_preds = mask_preds.flatten(1)
gt_masks = gt_masks.flatten(1).float()
numerator = 2 * torch.einsum('nc,mc->nm', mask_preds, gt_masks)
if self.naive_dice:
denominator = mask_preds.sum(-1)[:, None] + \
gt_masks.sum(-1)[None, :]
else:
denominator = mask_preds.pow(2).sum(1)[:, None] + \
gt_masks.pow(2).sum(1)[None, :]
loss = 1 - (numerator + self.eps) / (denominator + self.eps)
return loss
def __call__(self, pred_instances: InstanceData,
gt_instances: InstanceData, **kwargs) -> Tensor:
"""Compute match cost.
Args:
pred_instances (InstanceData): Predicted instances which
must contain "masks".
gt_instances (InstanceData): Ground truth which must contain
"mask".
Returns:
Tensor: Match Cost matrix of shape (num_preds, num_gts).
"""
assert hasattr(pred_instances, 'masks'), \
"pred_instances must contain 'masks'"
assert hasattr(gt_instances, 'masks'), \
"gt_instances must contain 'masks'"
pred_masks = pred_instances.masks
gt_masks = gt_instances.masks
if self.pred_act:
pred_masks = pred_masks.sigmoid()
dice_cost = self._binary_mask_dice_loss(pred_masks, gt_masks)
return dice_cost * self.weight
@TASK_UTILS.register_module()
class CrossEntropyLossCost(BaseMatchCost):
"""CrossEntropyLossCost.
Args:
use_sigmoid (bool): Whether the prediction uses sigmoid
of softmax. Defaults to True.
weight (Union[float, int]): Cost weight. Defaults to 1.
"""
def __init__(self,
use_sigmoid: bool = True,
weight: Union[float, int] = 1.) -> None:
super().__init__(weight=weight)
self.use_sigmoid = use_sigmoid
def _binary_cross_entropy(self, cls_pred: Tensor,
gt_labels: Tensor) -> Tensor:
"""
Args:
cls_pred (Tensor): The prediction with shape (num_queries, 1, *) or
(num_queries, *).
gt_labels (Tensor): The learning label of prediction with
shape (num_gt, *).
Returns:
Tensor: Cross entropy cost matrix in shape (num_queries, num_gt).
"""
cls_pred = cls_pred.flatten(1).float()
gt_labels = gt_labels.flatten(1).float()
n = cls_pred.shape[1]
pos = F.binary_cross_entropy_with_logits(
cls_pred, torch.ones_like(cls_pred), reduction='none')
neg = F.binary_cross_entropy_with_logits(
cls_pred, torch.zeros_like(cls_pred), reduction='none')
cls_cost = torch.einsum('nc,mc->nm', pos, gt_labels) + \
torch.einsum('nc,mc->nm', neg, 1 - gt_labels)
cls_cost = cls_cost / n
return cls_cost
def __call__(self, pred_instances: InstanceData,
gt_instances: InstanceData, **kwargs) -> Tensor:
"""Compute match cost.
Args:
pred_instances (:obj:`InstanceData`): Predicted instances which
must contain ``masks``.
gt_instances (:obj:`InstanceData`): Ground truth which must contain
``masks``.
Returns:
Tensor: Match Cost matrix of shape (num_preds, num_gts).
"""
assert hasattr(pred_instances, 'masks'), \
"pred_instances must contain 'masks'"
assert hasattr(gt_instances, 'masks'), \
"gt_instances must contain 'masks'"
pred_masks = pred_instances.masks
gt_masks = gt_instances.masks
if self.use_sigmoid:
cls_cost = self._binary_cross_entropy(pred_masks, gt_masks)
else:
raise NotImplementedError
return cls_cost * self.weight
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