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# Copyright (c) OpenMMLab. All rights reserved.
# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
# Modified from https://github.com/ShoufaChen/DiffusionDet/blob/main/diffusiondet/loss.py # noqa
# This work is licensed under the CC-BY-NC 4.0 License.
# Users should be careful about adopting these features in any commercial matters. # noqa
# For more details, please refer to https://github.com/ShoufaChen/DiffusionDet/blob/main/LICENSE # noqa
from typing import List, Tuple, Union
import torch
import torch.nn as nn
from mmengine.config import ConfigDict
from mmengine.structures import InstanceData
from torch import Tensor
from mmdet.registry import MODELS, TASK_UTILS
from mmdet.structures.bbox import bbox_cxcywh_to_xyxy, bbox_xyxy_to_cxcywh
from mmdet.utils import ConfigType
@TASK_UTILS.register_module()
class DiffusionDetCriterion(nn.Module):
def __init__(
self,
num_classes,
assigner: Union[ConfigDict, nn.Module],
deep_supervision=True,
loss_cls=dict(
type='FocalLoss',
use_sigmoid=True,
alpha=0.25,
gamma=2.0,
reduction='sum',
loss_weight=2.0),
loss_bbox=dict(type='L1Loss', reduction='sum', loss_weight=5.0),
loss_giou=dict(type='GIoULoss', reduction='sum', loss_weight=2.0),
):
super().__init__()
self.num_classes = num_classes
if isinstance(assigner, nn.Module):
self.assigner = assigner
else:
self.assigner = TASK_UTILS.build(assigner)
self.deep_supervision = deep_supervision
self.loss_cls = MODELS.build(loss_cls)
self.loss_bbox = MODELS.build(loss_bbox)
self.loss_giou = MODELS.build(loss_giou)
def forward(self, outputs, batch_gt_instances, batch_img_metas):
batch_indices = self.assigner(outputs, batch_gt_instances,
batch_img_metas)
# Compute all the requested losses
loss_cls = self.loss_classification(outputs, batch_gt_instances,
batch_indices)
loss_bbox, loss_giou = self.loss_boxes(outputs, batch_gt_instances,
batch_indices)
losses = dict(
loss_cls=loss_cls, loss_bbox=loss_bbox, loss_giou=loss_giou)
if self.deep_supervision:
assert 'aux_outputs' in outputs
for i, aux_outputs in enumerate(outputs['aux_outputs']):
batch_indices = self.assigner(aux_outputs, batch_gt_instances,
batch_img_metas)
loss_cls = self.loss_classification(aux_outputs,
batch_gt_instances,
batch_indices)
loss_bbox, loss_giou = self.loss_boxes(aux_outputs,
batch_gt_instances,
batch_indices)
tmp_losses = dict(
loss_cls=loss_cls,
loss_bbox=loss_bbox,
loss_giou=loss_giou)
for name, value in tmp_losses.items():
losses[f's.{i}.{name}'] = value
return losses
def loss_classification(self, outputs, batch_gt_instances, indices):
assert 'pred_logits' in outputs
src_logits = outputs['pred_logits']
target_classes_list = [
gt.labels[J] for gt, (_, J) in zip(batch_gt_instances, indices)
]
target_classes = torch.full(
src_logits.shape[:2],
self.num_classes,
dtype=torch.int64,
device=src_logits.device)
for idx in range(len(batch_gt_instances)):
target_classes[idx, indices[idx][0]] = target_classes_list[idx]
src_logits = src_logits.flatten(0, 1)
target_classes = target_classes.flatten(0, 1)
# comp focal loss.
num_instances = max(torch.cat(target_classes_list).shape[0], 1)
loss_cls = self.loss_cls(
src_logits,
target_classes,
) / num_instances
return loss_cls
def loss_boxes(self, outputs, batch_gt_instances, indices):
assert 'pred_boxes' in outputs
pred_boxes = outputs['pred_boxes']
target_bboxes_norm_list = [
gt.norm_bboxes_cxcywh[J]
for gt, (_, J) in zip(batch_gt_instances, indices)
]
target_bboxes_list = [
gt.bboxes[J] for gt, (_, J) in zip(batch_gt_instances, indices)
]
pred_bboxes_list = []
pred_bboxes_norm_list = []
for idx in range(len(batch_gt_instances)):
pred_bboxes_list.append(pred_boxes[idx, indices[idx][0]])
image_size = batch_gt_instances[idx].image_size
pred_bboxes_norm_list.append(pred_boxes[idx, indices[idx][0]] /
image_size)
pred_boxes_cat = torch.cat(pred_bboxes_list)
pred_boxes_norm_cat = torch.cat(pred_bboxes_norm_list)
target_bboxes_cat = torch.cat(target_bboxes_list)
target_bboxes_norm_cat = torch.cat(target_bboxes_norm_list)
if len(pred_boxes_cat) > 0:
num_instances = pred_boxes_cat.shape[0]
loss_bbox = self.loss_bbox(
pred_boxes_norm_cat,
bbox_cxcywh_to_xyxy(target_bboxes_norm_cat)) / num_instances
loss_giou = self.loss_giou(pred_boxes_cat,
target_bboxes_cat) / num_instances
else:
loss_bbox = pred_boxes.sum() * 0
loss_giou = pred_boxes.sum() * 0
return loss_bbox, loss_giou
@TASK_UTILS.register_module()
class DiffusionDetMatcher(nn.Module):
"""This class computes an assignment between the targets and the
predictions of the network For efficiency reasons, the targets don't
include the no_object.
Because of this, in general, there are more predictions than targets. In
this case, we do a 1-to-k (dynamic) matching of the best predictions, while
the others are un-matched (and thus treated as non-objects).
"""
def __init__(self,
match_costs: Union[List[Union[dict, ConfigDict]], dict,
ConfigDict],
center_radius: float = 2.5,
candidate_topk: int = 5,
iou_calculator: ConfigType = dict(type='BboxOverlaps2D'),
**kwargs):
super().__init__()
self.center_radius = center_radius
self.candidate_topk = candidate_topk
if isinstance(match_costs, dict):
match_costs = [match_costs]
elif isinstance(match_costs, list):
assert len(match_costs) > 0, \
'match_costs must not be a empty list.'
self.use_focal_loss = False
self.use_fed_loss = False
for _match_cost in match_costs:
if _match_cost.get('type') == 'FocalLossCost':
self.use_focal_loss = True
if _match_cost.get('type') == 'FedLoss':
self.use_fed_loss = True
raise NotImplementedError
self.match_costs = [
TASK_UTILS.build(match_cost) for match_cost in match_costs
]
self.iou_calculator = TASK_UTILS.build(iou_calculator)
def forward(self, outputs, batch_gt_instances, batch_img_metas):
assert 'pred_logits' in outputs and 'pred_boxes' in outputs
pred_logits = outputs['pred_logits']
pred_bboxes = outputs['pred_boxes']
batch_size = len(batch_gt_instances)
assert batch_size == pred_logits.shape[0] == pred_bboxes.shape[0]
batch_indices = []
for i in range(batch_size):
pred_instances = InstanceData()
pred_instances.bboxes = pred_bboxes[i, ...]
pred_instances.scores = pred_logits[i, ...]
gt_instances = batch_gt_instances[i]
img_meta = batch_img_metas[i]
indices = self.single_assigner(pred_instances, gt_instances,
img_meta)
batch_indices.append(indices)
return batch_indices
def single_assigner(self, pred_instances, gt_instances, img_meta):
with torch.no_grad():
gt_bboxes = gt_instances.bboxes
pred_bboxes = pred_instances.bboxes
num_gt = gt_bboxes.size(0)
if num_gt == 0: # empty object in key frame
valid_mask = pred_bboxes.new_zeros((pred_bboxes.shape[0], ),
dtype=torch.bool)
matched_gt_inds = pred_bboxes.new_zeros((gt_bboxes.shape[0], ),
dtype=torch.long)
return valid_mask, matched_gt_inds
valid_mask, is_in_boxes_and_center = \
self.get_in_gt_and_in_center_info(
bbox_xyxy_to_cxcywh(pred_bboxes),
bbox_xyxy_to_cxcywh(gt_bboxes)
)
cost_list = []
for match_cost in self.match_costs:
cost = match_cost(
pred_instances=pred_instances,
gt_instances=gt_instances,
img_meta=img_meta)
cost_list.append(cost)
pairwise_ious = self.iou_calculator(pred_bboxes, gt_bboxes)
cost_list.append((~is_in_boxes_and_center) * 100.0)
cost_matrix = torch.stack(cost_list).sum(0)
cost_matrix[~valid_mask] = cost_matrix[~valid_mask] + 10000.0
fg_mask_inboxes, matched_gt_inds = \
self.dynamic_k_matching(
cost_matrix, pairwise_ious, num_gt)
return fg_mask_inboxes, matched_gt_inds
def get_in_gt_and_in_center_info(
self, pred_bboxes: Tensor,
gt_bboxes: Tensor) -> Tuple[Tensor, Tensor]:
"""Get the information of which prior is in gt bboxes and gt center
priors."""
xy_target_gts = bbox_cxcywh_to_xyxy(gt_bboxes) # (x1, y1, x2, y2)
pred_bboxes_center_x = pred_bboxes[:, 0].unsqueeze(1)
pred_bboxes_center_y = pred_bboxes[:, 1].unsqueeze(1)
# whether the center of each anchor is inside a gt box
b_l = pred_bboxes_center_x > xy_target_gts[:, 0].unsqueeze(0)
b_r = pred_bboxes_center_x < xy_target_gts[:, 2].unsqueeze(0)
b_t = pred_bboxes_center_y > xy_target_gts[:, 1].unsqueeze(0)
b_b = pred_bboxes_center_y < xy_target_gts[:, 3].unsqueeze(0)
# (b_l.long()+b_r.long()+b_t.long()+b_b.long())==4 [300,num_gt] ,
is_in_boxes = ((b_l.long() + b_r.long() + b_t.long() +
b_b.long()) == 4)
is_in_boxes_all = is_in_boxes.sum(1) > 0 # [num_query]
# in fixed center
center_radius = 2.5
# Modified to self-adapted sampling --- the center size depends
# on the size of the gt boxes
# https://github.com/dulucas/UVO_Challenge/blob/main/Track1/detection/mmdet/core/bbox/assigners/rpn_sim_ota_assigner.py#L212 # noqa
b_l = pred_bboxes_center_x > (
gt_bboxes[:, 0] -
(center_radius *
(xy_target_gts[:, 2] - xy_target_gts[:, 0]))).unsqueeze(0)
b_r = pred_bboxes_center_x < (
gt_bboxes[:, 0] +
(center_radius *
(xy_target_gts[:, 2] - xy_target_gts[:, 0]))).unsqueeze(0)
b_t = pred_bboxes_center_y > (
gt_bboxes[:, 1] -
(center_radius *
(xy_target_gts[:, 3] - xy_target_gts[:, 1]))).unsqueeze(0)
b_b = pred_bboxes_center_y < (
gt_bboxes[:, 1] +
(center_radius *
(xy_target_gts[:, 3] - xy_target_gts[:, 1]))).unsqueeze(0)
is_in_centers = ((b_l.long() + b_r.long() + b_t.long() +
b_b.long()) == 4)
is_in_centers_all = is_in_centers.sum(1) > 0
is_in_boxes_anchor = is_in_boxes_all | is_in_centers_all
is_in_boxes_and_center = (is_in_boxes & is_in_centers)
return is_in_boxes_anchor, is_in_boxes_and_center
def dynamic_k_matching(self, cost: Tensor, pairwise_ious: Tensor,
num_gt: int) -> Tuple[Tensor, Tensor]:
"""Use IoU and matching cost to calculate the dynamic top-k positive
targets."""
matching_matrix = torch.zeros_like(cost)
# select candidate topk ious for dynamic-k calculation
candidate_topk = min(self.candidate_topk, pairwise_ious.size(0))
topk_ious, _ = torch.topk(pairwise_ious, candidate_topk, dim=0)
# calculate dynamic k for each gt
dynamic_ks = torch.clamp(topk_ious.sum(0).int(), min=1)
for gt_idx in range(num_gt):
_, pos_idx = torch.topk(
cost[:, gt_idx], k=dynamic_ks[gt_idx], largest=False)
matching_matrix[:, gt_idx][pos_idx] = 1
del topk_ious, dynamic_ks, pos_idx
prior_match_gt_mask = matching_matrix.sum(1) > 1
if prior_match_gt_mask.sum() > 0:
_, cost_argmin = torch.min(cost[prior_match_gt_mask, :], dim=1)
matching_matrix[prior_match_gt_mask, :] *= 0
matching_matrix[prior_match_gt_mask, cost_argmin] = 1
while (matching_matrix.sum(0) == 0).any():
matched_query_id = matching_matrix.sum(1) > 0
cost[matched_query_id] += 100000.0
unmatch_id = torch.nonzero(
matching_matrix.sum(0) == 0, as_tuple=False).squeeze(1)
for gt_idx in unmatch_id:
pos_idx = torch.argmin(cost[:, gt_idx])
matching_matrix[:, gt_idx][pos_idx] = 1.0
if (matching_matrix.sum(1) > 1).sum() > 0:
_, cost_argmin = torch.min(cost[prior_match_gt_mask], dim=1)
matching_matrix[prior_match_gt_mask] *= 0
matching_matrix[prior_match_gt_mask, cost_argmin, ] = 1
assert not (matching_matrix.sum(0) == 0).any()
# get foreground mask inside box and center prior
fg_mask_inboxes = matching_matrix.sum(1) > 0
matched_gt_inds = matching_matrix[fg_mask_inboxes, :].argmax(1)
return fg_mask_inboxes, matched_gt_inds
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