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Initial commit: DCNv4 custom op mirror setup

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- Add enhanced README with project structure and quick start guide
- Initialize repository with DCNv4 CUDA extension (PyTorch module)
- Include classification, detection, and segmentation subdirectories
- Reference upstream OpenGVLab DCNv4 implementation

Co-Authored-By: Claude Haiku 4.5 <noreply@anthropic.com>
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Pikaliov
2026-06-11 10:30:44 +03:00
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segmentation/mmseg_custom/models/losses/match_costs.py Normal file
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# Copyright (c) OpenMMLab. All rights reserved.
import torch
import torch.nn as nn
import torch.nn.functional as F
from ..builder import MATCH_COST
@MATCH_COST.register_module()
class FocalLossCost:
"""FocalLossCost.
Args:
weight (int | float, optional): loss_weight
alpha (int | float, optional): focal_loss alpha
gamma (int | float, optional): focal_loss gamma
eps (float, optional): default 1e-12
Examples:
>>> from mmdet.core.bbox.match_costs.match_cost import FocalLossCost
>>> import torch
>>> self = FocalLossCost()
>>> 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([[-0.3236, -0.3364, -0.2699],
[-0.3439, -0.3209, -0.4807],
[-0.4099, -0.3795, -0.2929],
[-0.1950, -0.1207, -0.2626]])
"""
def __init__(self, weight=1., alpha=0.25, gamma=2, eps=1e-12):
self.weight = weight
self.alpha = alpha
self.gamma = gamma
self.eps = eps
def __call__(self, cls_pred, gt_labels):
"""
Args:
cls_pred (Tensor): Predicted classification logits, shape
[num_query, num_class].
gt_labels (Tensor): Label of `gt_bboxes`, shape (num_gt,).
Returns:
torch.Tensor: cls_cost value with weight
"""
cls_pred = cls_pred.sigmoid()
neg_cost = -(1 - cls_pred + self.eps).log() * (
1 - self.alpha) * cls_pred.pow(self.gamma)
pos_cost = -(cls_pred + self.eps).log() * self.alpha * (
1 - cls_pred).pow(self.gamma)
cls_cost = pos_cost[:, gt_labels] - neg_cost[:, gt_labels]
return cls_cost * self.weight
@MATCH_COST.register_module()
class MaskFocalLossCost(FocalLossCost):
"""Cost of mask assignments based on focal losses.
Args:
weight (int | float, optional): loss_weight.
alpha (int | float, optional): focal_loss alpha.
gamma (int | float, optional): focal_loss gamma.
eps (float, optional): default 1e-12.
"""
def __call__(self, cls_pred, gt_labels):
"""
Args:
cls_pred (Tensor): Predicted classfication logits
in shape (N1, H, W), dtype=torch.float32.
gt_labels (Tensor): Ground truth in shape (N2, H, W),
dtype=torch.long.
Returns:
Tensor: classification cost matrix in shape (N1, N2).
"""
cls_pred = cls_pred.reshape((cls_pred.shape[0], -1))
gt_labels = gt_labels.reshape((gt_labels.shape[0], -1)).float()
hw = cls_pred.shape[1]
cls_pred = cls_pred.sigmoid()
neg_cost = -(1 - cls_pred + self.eps).log() * (
1 - self.alpha) * cls_pred.pow(self.gamma)
pos_cost = -(cls_pred + self.eps).log() * self.alpha * (
1 - cls_pred).pow(self.gamma)
cls_cost = torch.einsum('nc,mc->nm', pos_cost, gt_labels) + \
torch.einsum('nc,mc->nm', neg_cost, (1 - gt_labels))
return cls_cost / hw * self.weight
@MATCH_COST.register_module()
class ClassificationCost:
"""ClsSoftmaxCost.Borrow from
mmdet.core.bbox.match_costs.match_cost.ClassificationCost.
Args:
weight (int | float, optional): loss_weight
Examples:
>>> 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=1.):
self.weight = weight
def __call__(self, cls_pred, gt_labels):
"""
Args:
cls_pred (Tensor): Predicted classification logits, shape
[num_query, num_class].
gt_labels (Tensor): Label of `gt_bboxes`, shape (num_gt,).
Returns:
torch.Tensor: cls_cost value with weight
"""
# Following the official DETR repo, contrary to the loss that
# NLL is used, we approximate it in 1 - cls_score[gt_label].
# The 1 is a constant that doesn't change the matching,
# so it can be omitted.
cls_score = cls_pred.softmax(-1)
cls_cost = -cls_score[:, gt_labels]
return cls_cost * self.weight
@MATCH_COST.register_module()
class DiceCost:
"""Cost of mask assignments based on dice losses.
Args:
weight (int | float, optional): loss_weight. Defaults to 1.
pred_act (bool, optional): Whether to apply sigmoid to mask_pred.
Defaults to False.
eps (float, optional): default 1e-12.
"""
def __init__(self, weight=1., pred_act=False, eps=1e-3):
self.weight = weight
self.pred_act = pred_act
self.eps = eps
def binary_mask_dice_loss(self, mask_preds, gt_masks):
"""
Args:
mask_preds (Tensor): Mask prediction in shape (N1, H, W).
gt_masks (Tensor): Ground truth in shape (N2, H, W)
store 0 or 1, 0 for negative class and 1 for
positive class.
Returns:
Tensor: Dice cost matrix in shape (N1, N2).
"""
mask_preds = mask_preds.reshape((mask_preds.shape[0], -1))
gt_masks = gt_masks.reshape((gt_masks.shape[0], -1)).float()
numerator = 2 * torch.einsum('nc,mc->nm', mask_preds, gt_masks)
denominator = mask_preds.sum(-1)[:, None] + gt_masks.sum(-1)[None, :]
loss = 1 - (numerator + self.eps) / (denominator + self.eps)
return loss
def __call__(self, mask_preds, gt_masks):
"""
Args:
mask_preds (Tensor): Mask prediction logits in shape (N1, H, W).
gt_masks (Tensor): Ground truth in shape (N2, H, W).
Returns:
Tensor: Dice cost matrix in shape (N1, N2).
"""
if self.pred_act:
mask_preds = mask_preds.sigmoid()
dice_cost = self.binary_mask_dice_loss(mask_preds, gt_masks)
return dice_cost * self.weight
@MATCH_COST.register_module()
class CrossEntropyLossCost:
"""CrossEntropyLossCost.
Args:
weight (int | float, optional): loss weight. Defaults to 1.
use_sigmoid (bool, optional): Whether the prediction uses sigmoid
of softmax. Defaults to True.
"""
def __init__(self, weight=1., use_sigmoid=True):
assert use_sigmoid, 'use_sigmoid = False is not supported yet.'
self.weight = weight
self.use_sigmoid = use_sigmoid
def _binary_cross_entropy(self, cls_pred, gt_labels):
"""
Args:
cls_pred (Tensor): The prediction with shape (num_query, 1, *) or
(num_query, *).
gt_labels (Tensor): The learning label of prediction with
shape (num_gt, *).
Returns:
Tensor: Cross entropy cost matrix in shape (num_query, 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, cls_pred, gt_labels):
"""
Args:
cls_pred (Tensor): Predicted classification logits.
gt_labels (Tensor): Labels.
Returns:
Tensor: Cross entropy cost matrix with weight in
shape (num_query, num_gt).
"""
if self.use_sigmoid:
cls_cost = self._binary_cross_entropy(cls_pred, gt_labels)
else:
raise NotImplementedError
return cls_cost * self.weight