v10出了就想看看它的loss设计有什么不同，看下来由于v8和v10的loss部分基本一致就放一起了。
v10的论文笔记，还没看的可以看看，初步尝试耗时确实有提升
好记性不如烂笔头，还是得记录一下，以免忘了，废话结束！！！
代码地址：GitHub - THU-MIG/yolov10: YOLOv10: Real-Time End-to-End Object Detection
论文地址：https://arxiv.org/pdf/2405.14458
YOLOv10/8从Anchor-Based(box anchor)换成了Anchor-Free(point anchor)，检测头也换成了Decoupled Head，这一结构具有提高收敛速度的好处，（在box anchor 方案中试过精度也有提升，但耗时增加了一些）但另一方面讲，也会遇到分类与回归不对齐的问题。在一些网络中，会通过将feature map中的cell（point anchor中心点所编码的box）与ground truth进行IOU计算以分配预测所用cell，但用来分类和回归的最佳cell通常不一致。为了解决这一问题，引入了TAL(Task Alignment Learning)来负责正负样本分配，使得分类和回归任务之间具有较高的对齐一致性。
yolov10/v8中的loss主要分为2部分3个loss：
一、回归分支的损失函数：
1、DFL(Distribution Focal Loss)，计算anchor point的中心点到左上角和右下角的偏移量
2、IoU Loss，定位损失，采用CIoU loss，只计算正样本的定位损失
二、分类损失：
1、分类损失，采用BCE loss，只计算正样本的分类损失。
v8DetectionLoss
v8和v10的loss最大的不同在于，v10有两个解耦头，一个计算one2one head，一个计算one2many head，但是两个head的loss函数一样，就是超参数有一些不同
class v10DetectLoss:
def __init__(self, model):
self.one2many = v8DetectionLoss(model, tal_topk=10)
self.one2one = v8DetectionLoss(model, tal_topk=1)
def __call__(self, preds, batch):
one2many = preds["one2many"]
loss_one2many = self.one2many(one2many, batch)
one2one = preds["one2one"]
loss_one2one = self.one2one(one2one, batch)
return loss_one2many[0] + loss_one2one[0], torch.cat((loss_one2many[1], loss_one2one[1]))one2many的topk为10，one2one的topk为1。（这部分代码和我写辅助监督的方式一样）
class v8DetectionLoss:
"""Criterion class for computing training losses."""
def __init__(self, model, tal_topk=10): # model must be de-paralleled
"""Initializes v8DetectionLoss with the model, defining model-related properties and BCE loss function."""
device = next(model.parameters()).device # get model device
h = model.args # hyperparameters
m = model.model[-1] # Detect() module
self.bce = nn.BCEWithLogitsLoss(reduction="none")
self.hyp = h
self.stride = m.stride # model strides
self.nc = m.nc # number of classes
self.no = m.no
self.reg_max = m.reg_max
self.device = device
self.use_dfl = m.reg_max > 1
self.assigner = TaskAlignedAssigner(topk=tal_topk, num_classes=self.nc, alpha=0.5, beta=6.0)
self.bbox_loss = BboxLoss(m.reg_max - 1, use_dfl=self.use_dfl).to(device)
self.proj = torch.arange(m.reg_max, dtype=torch.float, device=device)
def preprocess(self, targets, batch_size, scale_tensor):
"""Preprocesses the target counts and matches with the input batch size to output a tensor."""
if targets.shape[0] == 0:
out = torch.zeros(batch_size, 0, 5, device=self.device)
else:
i = targets[:, 0] # image index
_, counts = i.unique(return_counts=True)
counts = counts.to(dtype=torch.int32)
out = torch.zeros(batch_size, counts.max(), 5, device=self.device)
for j in range(batch_size):
matches = i == j
n = matches.sum()
if n:
out[j, :n] = targets[matches, 1:]
out[..., 1:5] = xywh2xyxy(out[..., 1:5].mul_(scale_tensor))
return out
def bbox_decode(self, anchor_points, pred_dist):
"""Decode predicted object bounding box coordinates from anchor points and distribution."""
if self.use_dfl:
b, a, c = pred_dist.shape # batch, anchors, channels
pred_dist = pred_dist.view(b, a, 4, c // 4).softmax(3).matmul(self.proj.type(pred_dist.dtype))
# pred_dist = pred_dist.view(b, a, c // 4, 4).transpose(2,3).softmax(3).matmul(self.proj.type(pred_dist.dtype))
# pred_dist = (pred_dist.view(b, a, c // 4, 4).softmax(2) * self.proj.type(pred_dist.dtype).view(1, 1, -1, 1)).sum(2)
return dist2bbox(pred_dist, anchor_points, xywh=False)
def __call__(self, preds, batch):
"""Calculate the sum of the loss for box, cls and dfl multiplied by batch size."""
loss = torch.zeros(3, device=self.device) # box, cls, dfl
feats = preds[1] if isinstance(preds, tuple) else preds
pred_distri, pred_scores = torch.cat([xi.view(feats[0].shape[0], self.no, -1) for xi in feats], 2).split(
(self.reg_max * 4, self.nc), 1
)
pred_scores = pred_scores.permute(0, 2, 1).contiguous()
pred_distri = pred_distri.permute(0, 2, 1).contiguous()
dtype = pred_scores.dtype
batch_size = pred_scores.shape[0]
imgsz = torch.tensor(feats[0].shape[2:], device=self.device, dtype=dtype) * self.stride[0] # image size (h,w)
anchor_points, stride_tensor = make_anchors(feats, self.stride, 0.5)
# Targets
targets = torch.cat((batch["batch_idx"].view(-1, 1), batch["cls"].view(-1, 1), batch["bboxes"]), 1)
targets = self.preprocess(targets.to(self.device), batch_size, scale_tensor=imgsz[[1, 0, 1, 0]])
gt_labels, gt_bboxes = targets.split((1, 4), 2) # cls, xyxy
mask_gt = gt_bboxes.sum(2, keepdim=True).gt_(0)
# Pboxes
pred_bboxes = self.bbox_decode(anchor_points, pred_distri) # xyxy, (b, h*w, 4)
_, target_bboxes, target_scores, fg_mask, _ = self.assigner(
pred_scores.detach().sigmoid(),
(pred_bboxes.detach() * stride_tensor).type(gt_bboxes.dtype),
anchor_points * stride_tensor,
gt_labels,
gt_bboxes,
mask_gt,
)
target_scores_sum = max(target_scores.sum(), 1)
# Cls loss
# loss[1] = self.varifocal_loss(pred_scores, target_scores, target_labels) / target_scores_sum # VFL way
loss[1] = self.bce(pred_scores, target_scores.to(dtype)).sum() / target_scores_sum # BCE
# Bbox loss
if fg_mask.sum():
target_bboxes /= stride_tensor
loss[0], loss[2] = self.bbox_loss(
pred_distri, pred_bboxes, anchor_points, target_bboxes, target_scores, target_scores_sum, fg_mask
)
loss[0] *= self.hyp.box # box gain
loss[1] *= self.hyp.cls # cls gain
loss[2] *= self.hyp.dfl # dfl gain
return loss.sum() * batch_size, loss.detach() # loss(box, cls, dfl)v8DetectionLoss中preprocess
该函数主要是用来处理gt，将同一batch中不同长度的gt（cls + boxes）做对齐，短的gt用全0补齐。假设一个batch为2，其中image1的gt是[4,5]，image2的gt是[7,5]，那么取该batch中最长的7创建一个batch为2的张量[2,7,5]，batch1的前四维为gt信息，为全0。下面用一组实际数据为例：
对应的gt_labels，gt_bboxes，mask_gt（之后会提到）
v8DetectionLoss中bbox_decode
该函数主要是将每一个anchor point和预测的回归参数通过dist2bbox做解码，生成anchor box与gt计算iou
def dist2bbox(distance, anchor_points, xywh=True, dim=-1):
"""Transform distance(ltrb) to box(xywh or xyxy)."""
assert(distance.shape[dim] == 4)
lt, rb = distance.split([2, 2], dim)
x1y1 = anchor_points - lt
x2y2 = anchor_points + rb
if xywh:
c_xy = (x1y1 + x2y2) / 2
wh = x2y2 - x1y1
return torch.cat((c_xy, wh), dim) # xywh bbox
return torch.cat((x1y1, x2y2), dim) # xyxy bboxloss[1] bce loss对应类别损失
loss[1] = self.bce(pred_scores, target_scores.to(dtype)).sum() / target_scores_sum # BCE
loss[0] 对应iou loss
loss[2] 对应dfl loss
loss[0], loss[2] = self.bbox_loss(
pred_distri, pred_bboxes, anchor_points, target_bboxes, target_scores, target_scores_sum, fg_mask
)bbox loss的实现如下：
class BboxLoss(nn.Module):
"""Criterion class for computing training losses during training."""
def __init__(self, reg_max, use_dfl=False):
"""Initialize the BboxLoss module with regularization maximum and DFL settings."""
super().__init__()
self.reg_max = reg_max
self.use_dfl = use_dfl
def forward(self, pred_dist, pred_bboxes, anchor_points, target_bboxes, target_scores, target_scores_sum, fg_mask):
"""IoU loss."""
weight = target_scores.sum(-1)[fg_mask].unsqueeze(-1)
iou = bbox_iou(pred_bboxes[fg_mask], target_bboxes[fg_mask], xywh=False, CIoU=True)
loss_iou = ((1.0 - iou) * weight).sum() / target_scores_sum
# DFL loss
if self.use_dfl:
target_ltrb = bbox2dist(anchor_points, target_bboxes, self.reg_max)
loss_dfl = self._df_loss(pred_dist[fg_mask].view(-1, self.reg_max + 1), target_ltrb[fg_mask]) * weight
loss_dfl = loss_dfl.sum() / target_scores_sum
else:
loss_dfl = torch.tensor(0.0).to(pred_dist.device)
return loss_iou, loss_dfl
@staticmethod
def _df_loss(pred_dist, target):
"""
Return sum of left and right DFL losses.
Distribution Focal Loss (DFL) proposed in Generalized Focal Loss
https://ieeexplore.ieee.org/document/9792391
"""
tl = target.long() # target left
tr = tl + 1 # target right
wl = tr - target # weight left
wr = 1 - wl # weight right
return (
F.cross_entropy(pred_dist, tl.view(-1), reduction="none").view(tl.shape) * wl
+ F.cross_entropy(pred_dist, tr.view(-1), reduction="none").view(tl.shape) * wr
).mean(-1, keepdim=True)TaskAlignedAssigner
这个我认为是整个loss设计中的重头戏
因为整个loss中不像anchor base算法中需要计算前背景的obj loss，所以在TaskAlignedAssigner中需要确定哪些anchor属于前景哪些anchor属于背景，所以TaskAlignedAssigner得到target_labels, target_bboxes, target_scores的同时还需要得到前景的mask--fg_mask.bool()
class TaskAlignedAssigner(nn.Module):
"""
A task-aligned assigner for object detection.
This class assigns ground-truth (gt) objects to anchors based on the task-aligned metric, which combines both
classification and localization information.
Attributes:
topk (int): The number of top candidates to consider.
num_classes (int): The number of object classes.
alpha (float): The alpha parameter for the classification component of the task-aligned metric.
beta (float): The beta parameter for the localization component of the task-aligned metric.
eps (float): A small value to prevent division by zero.
"""
def __init__(self, topk=13, num_classes=80, alpha=1.0, beta=6.0, eps=1e-9):
"""Initialize a TaskAlignedAssigner object with customizable hyperparameters."""
super().__init__()
self.topk = topk
self.num_classes = num_classes
self.bg_idx = num_classes
self.alpha = alpha
self.beta = beta
self.eps = eps
@torch.no_grad()
def forward(self, pd_scores, pd_bboxes, anc_points, gt_labels, gt_bboxes, mask_gt):
"""
Compute the task-aligned assignment. Reference code is available at
https://github.com/Nioolek/PPYOLOE_pytorch/blob/master/ppyoloe/assigner/tal_assigner.py.
Args:
pd_scores (Tensor): shape(bs, num_total_anchors, num_classes)
pd_bboxes (Tensor): shape(bs, num_total_anchors, 4)
anc_points (Tensor): shape(num_total_anchors, 2)
gt_labels (Tensor): shape(bs, n_max_boxes, 1)
gt_bboxes (Tensor): shape(bs, n_max_boxes, 4)
mask_gt (Tensor): shape(bs, n_max_boxes, 1)
Returns:
target_labels (Tensor): shape(bs, num_total_anchors)
target_bboxes (Tensor): shape(bs, num_total_anchors, 4)
target_scores (Tensor): shape(bs, num_total_anchors, num_classes)
fg_mask (Tensor): shape(bs, num_total_anchors)
target_gt_idx (Tensor): shape(bs, num_total_anchors)
"""
self.bs = pd_scores.shape[0]
self.n_max_boxes = gt_bboxes.shape[1]
if self.n_max_boxes == 0:
device = gt_bboxes.device
return (
torch.full_like(pd_scores[..., 0], self.bg_idx).to(device),
torch.zeros_like(pd_bboxes).to(device),
torch.zeros_like(pd_scores).to(device),
torch.zeros_like(pd_scores[..., 0]).to(device),
torch.zeros_like(pd_scores[..., 0]).to(device),
)
mask_pos, align_metric, overlaps = self.get_pos_mask(
pd_scores, pd_bboxes, gt_labels, gt_bboxes, anc_points, mask_gt
)
target_gt_idx, fg_mask, mask_pos = self.select_highest_overlaps(mask_pos, overlaps, self.n_max_boxes)
# Assigned target
target_labels, target_bboxes, target_scores = self.get_targets(gt_labels, gt_bboxes, target_gt_idx, fg_mask)
# Normalize
align_metric *= mask_pos
pos_align_metrics = align_metric.amax(dim=-1, keepdim=True) # b, max_num_obj
pos_overlaps = (overlaps * mask_pos).amax(dim=-1, keepdim=True) # b, max_num_obj
norm_align_metric = (align_metric * pos_overlaps / (pos_align_metrics + self.eps)).amax(-2).unsqueeze(-1)
target_scores = target_scores * norm_align_metric
return target_labels, target_bboxes, target_scores, fg_mask.bool(), target_gt_idx
get_pos_mask
def get_pos_mask(self, pd_scores, pd_bboxes, gt_labels, gt_bboxes, anc_points, mask_gt):
"""Get in_gts mask, (b, max_num_obj, h*w)."""
mask_in_gts = self.select_candidates_in_gts(anc_points, gt_bboxes) # 表示anchor中心是否位于对应的ground truth bounding box内
# Get anchor_align metric, (b, max_num_obj, h*w)
align_metric, overlaps = self.get_box_metrics(pd_scores, pd_bboxes, gt_labels, gt_bboxes, mask_in_gts * mask_gt)
# Get topk_metric mask, (b, max_num_obj, h*w)
mask_topk = self.select_topk_candidates(align_metric, topk_mask=mask_gt.expand(-1, -1, self.topk).bool())
# Merge all mask to a final mask, (b, max_num_obj, h*w)
mask_pos = mask_topk * mask_in_gts * mask_gt # 一个anchor point 负责一个gt object的预测
return mask_pos, align_metric, overlaps其中包含select_candidates_in_gts，get_box_metrics，select_topk_candidates，由这三个函数共同选择正样本anchor point的位置
def select_candidates_in_gts(xy_centers, gt_bboxes, eps=1e-9):
"""
Select the positive anchor center in gt.
Args:
xy_centers (Tensor): shape(h*w, 2)
gt_bboxes (Tensor): shape(b, n_boxes, 4)
Returns:
(Tensor): shape(b, n_boxes, h*w)
"""
n_anchors = xy_centers.shape[0] # 表示anchor中心的数量
bs, n_boxes, _ = gt_bboxes.shape
lt, rb = gt_bboxes.view(-1, 1, 4).chunk(2, 2) # left-top, right-bottom
# 通过计算每个anchor中心与每个gt_bboxes的左上角和右下角之间的差值，以及右下角和左上角之间的差值，并将结果拼接为形状为 (bs, n_boxes, n_anchors, -1) 的张量。
bbox_deltas = torch.cat((xy_centers[None] - lt, rb - xy_centers[None]), dim=2).view(bs, n_boxes, n_anchors, -1)
# return (bbox_deltas.min(3)[0] > eps).to(gt_bboxes.dtype)
# 计算 bbox_deltas 张量沿着第3个维度的最小值，形状为 (b, n_boxes, h*w) 的布尔型张量，表示anchor中心是否位于对应的ground truth bounding box内(最小值都为正数)
return bbox_deltas.amin(3).gt_(eps) 实现思想很简单就是，将anchor point的坐标与gt box的左上角坐标相减，得到一个差值，同时gt box右下角的坐标与anchor point的坐标相减，同样得到一个差值，如果anchor point位于gt box内，那么这两组差值的数值都应该是大于0的数。
select_candidates_in_gts用于初步筛选位于gt box中的anchor points
如上图，假设绿色的为gt box，红色的anchor points就是通过 select_candidates_in_gts筛选出来用于预测该gt box表示的object的可能的anchor point，最后返回的是关于这些anchor point的位置mask
get_box_metrics
它具有如下参数：
pd_scores：就是分类head输出的结果，shape一般为[bs, 8400, 80](以coco数据集，输入640*640为例)
pd_bboxes：回归head输出的结果，shape一般为[bs, 8400, 4]
gt_labels，gt_bboxes，mask_gt为gt所包含的信息，由于gt有做过数据用0补齐，mask_gt表示实际上非零的数据
mask_in_gts * mask_gt：表示实际上有gt标签位置上的候选anchor的位置的mask
def get_box_metrics(self, pd_scores, pd_bboxes, gt_labels, gt_bboxes, mask_gt):
"""Compute alignment metric given predicted and ground truth bounding boxes."""
na = pd_bboxes.shape[-2]
mask_gt = mask_gt.bool() # b, max_num_obj, h*w
overlaps = torch.zeros([self.bs, self.n_max_boxes, na], dtype=pd_bboxes.dtype, device=pd_bboxes.device) # 存储iou
bbox_scores = torch.zeros([self.bs, self.n_max_boxes, na], dtype=pd_scores.dtype, device=pd_scores.device) # 存储边界框的分数
ind = torch.zeros([2, self.bs, self.n_max_boxes], dtype=torch.long) # torch.Size([2, 2, 7]) * 0 # 2, b, max_num_obj
ind[0] = torch.arange(end=self.bs).view(-1, 1).expand(-1, self.n_max_boxes) # b, max_num_obj # 批次信息 为从0到 self.bs-1 的序列，将其展开为形状为 (self.bs, self.n_max_boxes)
ind[1] = gt_labels.squeeze(-1) # b, max_num_obj # 类别信息 为 gt_labels 的挤压操作（squeeze(-1)），将其形状变为 (self.bs, self.n_max_boxes)
# Get the scores of each grid for each gt cls
bbox_scores[mask_gt] = pd_scores[ind[0], :, ind[1]][mask_gt] # b, max_num_obj, h*w 根据实际边界框的掩码来获取每个网格单元的预测分数，并存储在 bbox_scores 中
# (b, max_num_obj, 1, 4), (b, 1, h*w, 4)
pd_boxes = pd_bboxes.unsqueeze(1).expand(-1, self.n_max_boxes, -1, -1)[mask_gt]
gt_boxes = gt_bboxes.unsqueeze(2).expand(-1, -1, na, -1)[mask_gt]
overlaps[mask_gt] = self.iou_calculation(gt_boxes, pd_boxes)
# 对于满足实际边界框掩码的每个位置，从 pd_bboxes 中获取预测边界框（pd_boxes）和实际边界框（gt_boxes）计算iou，并将结果存储在 overlaps 中
align_metric = bbox_scores.pow(self.alpha) * overlaps.pow(self.beta) # align_metric = bbox_scores^alpha * overlaps^beta 计算对齐度量，其中 alpha 和 beta 是超参数
return align_metric, overlaps通过iou计算预测框（解码后的）与gt box之间的iou得到overlap；由于每个anchor point都有80个类别的预测得分，通过该处gt box对应的类别标签得到预测得分，得到bbox_scores，通过align_metric = bbox_scores^alpha * overlaps^beta 计算对齐度量。该度量同时考虑得分和框的重叠度。
select_topk_candidates
就是通过get_box_metrics中得到的align_metric来确定所有与gt有重叠的anchor中align_metric最高的前十（或前一）
def select_topk_candidates(self, metrics, largest=True, topk_mask=None):
"""
Select the top-k candidates based on the given metrics.
Args:
metrics (Tensor): A tensor of shape (b, max_num_obj, h*w), where b is the batch size,
max_num_obj is the maximum number of objects, and h*w represents the
total number of anchor points.
largest (bool): If True, select the largest values; otherwise, select the smallest values.
topk_mask (Tensor): An optional boolean tensor of shape (b, max_num_obj, topk), where
topk is the number of top candidates to consider. If not provided,
the top-k values are automatically computed based on the given metrics.
Returns:
(Tensor): A tensor of shape (b, max_num_obj, h*w) containing the selected top-k candidates.
"""
# (b, max_num_obj, topk)
# 使用 torch.topk 函数在给定的度量指标张量 metrics 的最后一个维度上选择前 k 个最大。
# 这将返回两个张量：topk_metrics (形状为 (b, max_num_obj, topk)) 包含了选定的度量指标，以及 topk_idxs (形状为 (b, max_num_obj, topk)) 包含了相应的索引
topk_metrics, topk_idxs = torch.topk(metrics, self.topk, dim=-1, largest=largest)
if topk_mask is None:
topk_mask = (topk_metrics.max(-1, keepdim=True)[0] > self.eps).expand_as(topk_idxs)
# (b, max_num_obj, topk)
topk_idxs.masked_fill_(~topk_mask, 0) # 使用 topk_mask 将 topk_idxs 张量中未选中的索引位置（~topk_mask）用零进行填充
# (b, max_num_obj, topk, h*w) -> (b, max_num_obj, h*w)
count_tensor = torch.zeros(metrics.shape, dtype=torch.int8, device=topk_idxs.device)
ones = torch.ones_like(topk_idxs[:, :, :1], dtype=torch.int8, device=topk_idxs.device)
for k in range(self.topk):
# Expand topk_idxs for each value of k and add 1 at the specified positions
count_tensor.scatter_add_(-1, topk_idxs[:, :, k : k + 1], ones) # 使用 scatter_add_ 函数根据索引 topk_idxs[:, :, k : k + 1]，将 ones 张量的值相加到 count_tensor 张量的相应位置上
# count_tensor.scatter_add_(-1, topk_idxs, torch.ones_like(topk_idxs, dtype=torch.int8, device=topk_idxs.device))
# Filter invalid bboxes
count_tensor.masked_fill_(count_tensor > 1, 0) # 将 count_tensor 中大于 1 的值用零进行填充，以过滤掉超过一个的边界框
return count_tensor.to(metrics.dtype)
比如上图，由于这里只是作为示例，只表示其中一个特征图上gt样例，其他层的gt位置可能有更多的anchor point满足 align_metric的条件被保留下来（不必太纠结这里是不是有10个），因为PAN输出了三层特征图，anchor对应每层特征图的中心，而实践中将每层的anchor展平之后合并在一起得到8400的长度，而最终是在这8400中取前十的anchor，所以每层特征图上保留的anchor可能数量不等。
此时被保留下来的anchor point的位置用1表示，其余位置为0，仅保留了指标前十的样本作为正样本
select_highest_overlaps
def select_highest_overlaps(mask_pos, overlaps, n_max_boxes):
"""
If an anchor box is assigned to multiple gts, the one with the highest IoU will be selected.
Args:
mask_pos (Tensor): shape(b, n_max_boxes, h*w)
overlaps (Tensor): shape(b, n_max_boxes, h*w)
Returns:
target_gt_idx (Tensor): shape(b, h*w)
fg_mask (Tensor): shape(b, h*w)
mask_pos (Tensor): shape(b, n_max_boxes, h*w)
"""
# (b, n_max_boxes, h*w) -> (b, h*w)
fg_mask = mask_pos.sum(-2) # 对 mask_pos 沿着倒数第二个维度求和，得到形状为 (b, h*w) 的张量 fg_mask，表示每个网格单元上非背景anchor box的数量
if fg_mask.max() > 1: # one anchor is assigned to multiple gt_bboxes
# 创建一个布尔型张量 mask_multi_gts，形状为 (b, n_max_boxes, h*w)，用于指示哪些网格单元拥有多个ground truth bounding boxes
mask_multi_gts = (fg_mask.unsqueeze(1) > 1).expand(-1, n_max_boxes, -1) # (b, n_max_boxes, h*w)
# 获取每个网格单元上具有最高IoU的ground truth bounding box的索引，并创建一个张量 is_max_overlaps，形状与 mask_pos 相同，
# 其中最高IoU的ground truth bounding box对应的位置上为1，其余位置为0。
max_overlaps_idx = overlaps.argmax(1) # (b, h*w)
is_max_overlaps = torch.zeros(mask_pos.shape, dtype=mask_pos.dtype, device=mask_pos.device)
is_max_overlaps.scatter_(1, max_overlaps_idx.unsqueeze(1), 1) # max_overlaps_idx表示具有最大iou的索引，将具有最大iou的位置设置为1
# 根据 mask_multi_gts 来更新 mask_pos。对于存在多个ground truth bounding box的网格单元，将 is_max_overlaps 中
# 对应位置的值赋给 mask_pos，以保留具有最高IoU的ground truth bounding box的匹配情况
mask_pos = torch.where(mask_multi_gts, is_max_overlaps, mask_pos).float() # (b, n_max_boxes, h*w)
fg_mask = mask_pos.sum(-2)
# Find each grid serve which gt(index)
target_gt_idx = mask_pos.argmax(-2) # (b, h*w) # 得到每个网格单元上具有最高IoU的ground truth bounding box的索引 target_gt_idx
return target_gt_idx, fg_mask, mask_pos
对被分配了多个gt的anchor去重，得到前景的mask以及anchor point上具有最高IoU的ground truth bounding box的索引。假设上图中红色的anchor被分配给了两个gt，通select_highest_overlaps后会保留gt与该anchor的iou最大的那个，并用该anchor来预测该gt，另一个gt则可能会被周围的其他anchor所负责。此时也要更新mask_pos，毕竟重新对anchor做了处理。
因为每个anchor负责一个类别的检测，mask_pos表示最终确定的anchor的mask，如下图所示为其中一个batch中数据形式
该batch中8240，8241，8242为最终确定的anchor，其在红色箭头所示维度上对应的索引为2，target_gt_idx在该batch上的最终表示为：
get_targets
有了以上的信息之后就获取gt了
def get_targets(self, gt_labels, gt_bboxes, target_gt_idx, fg_mask):
"""
Compute target labels, target bounding boxes, and target scores for the positive anchor points.
Args:
gt_labels (Tensor): Ground truth labels of shape (b, max_num_obj, 1), where b is the
batch size and max_num_obj is the maximum number of objects.
gt_bboxes (Tensor): Ground truth bounding boxes of shape (b, max_num_obj, 4).
target_gt_idx (Tensor): Indices of the assigned ground truth objects for positive
anchor points, with shape (b, h*w), where h*w is the total
number of anchor points.
fg_mask (Tensor): A boolean tensor of shape (b, h*w) indicating the positive
(foreground) anchor points.
Returns:
(Tuple[Tensor, Tensor, Tensor]): A tuple containing the following tensors:
- target_labels (Tensor): Shape (b, h*w), containing the target labels for
positive anchor points.
- target_bboxes (Tensor): Shape (b, h*w, 4), containing the target bounding boxes
for positive anchor points.
- target_scores (Tensor): Shape (b, h*w, num_classes), containing the target scores
for positive anchor points, where num_classes is the number
of object classes.
"""
# Assigned target labels, (b, 1)
batch_ind = torch.arange(end=self.bs, dtype=torch.int64, device=gt_labels.device)[..., None]
# 使用 target_gt_idx 加上偏移量，得到形状为 (b, h*w) 的 target_gt_idx 张量，表示正样本anchor point的真实类别索引
target_gt_idx = target_gt_idx + batch_ind * self.n_max_boxes # (b, h*w)
# 使用 flatten 函数将 gt_labels 张量展平为形状为 (b * max_num_obj) 的张量，然后使用 target_gt_idx 进行索引，
# 得到形状为 (b, h*w) 的 target_labels 张量，表示正样本anchor point的目标标签
target_labels = gt_labels.long().flatten()[target_gt_idx] # (b, h*w)
# Assigned target boxes, (b, max_num_obj, 4) -> (b, h*w, 4)
target_bboxes = gt_bboxes.view(-1, gt_bboxes.shape[-1])[target_gt_idx] # 表示正样本anchor point的目标边界框
# Assigned target scores
target_labels.clamp_(0)
# 10x faster than F.one_hot()
target_scores = torch.zeros(
(target_labels.shape[0], target_labels.shape[1], self.num_classes),
dtype=torch.int64,
device=target_labels.device,
) # (b, h*w, 80)
target_scores.scatter_(2, target_labels.unsqueeze(-1), 1) # 使用 scatter_ 函数将 target_labels 的值进行 one-hot 编码，将张量中每个位置上的目标类别置为 1
fg_scores_mask = fg_mask[:, :, None].repeat(1, 1, self.num_classes) # (b, h*w, 80)
target_scores = torch.where(fg_scores_mask > 0, target_scores, 0) # 根据 fg_scores_mask 的值，将 target_scores 张量中的非正样本位置（值小于等于 0）即背景类置为零
return target_labels, target_bboxes, target_scores该函数的要点基本都在代码里注释了
得到target后还要对target_scores做一些归一化操作