# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
import torch
import torch.nn as nn
import torch.nn.functional as F
from ultralytics.utils.metrics import OKS_SIGMA
from ultralytics.utils.ops import crop_mask, xywh2xyxy, xyxy2xywh
from ultralytics.utils.tal import RotatedTaskAlignedAssigner, TaskAlignedAssigner, dist2bbox, dist2rbox, make_anchors
from ultralytics.utils.torch_utils import autocast
from .metrics import bbox_iou, probiou
from .tal import bbox2dist
class VarifocalLoss(nn.Module):
"""
Varifocal loss by Zhang et al.
https://arxiv.org/abs/2008.13367.
"""
def __init__(self):
"""Initialize the VarifocalLoss class."""
super().__init__()
@staticmethod
def forward(pred_score, gt_score, label, alpha=0.75, gamma=2.0):
"""Computes varfocal loss."""
weight = alpha * pred_score.sigmoid().pow(gamma) * (1 - label) + gt_score * label
with autocast(enabled=False):
loss = (
(F.binary_cross_entropy_with_logits(pred_score.float(), gt_score.float(), reduction="none") * weight)
.mean(1)
.sum()
)
return loss
class FocalLoss(nn.Module):
"""Wraps focal loss around existing loss_fcn(), i.e. criteria = FocalLoss(nn.BCEWithLogitsLoss(), gamma=1.5)."""
def __init__(self):
"""Initializer for FocalLoss class with no parameters."""
super().__init__()
@staticmethod
def forward(pred, label, gamma=1.5, alpha=0.25):
"""Calculates and updates confusion matrix for object detection/classification tasks."""
loss = F.binary_cross_entropy_with_logits(pred, label, reduction="none")
# p_t = torch.exp(-loss)
# loss *= self.alpha * (1.000001 - p_t) ** self.gamma # non-zero power for gradient stability
# TF implementation https://github.com/tensorflow/addons/blob/v0.7.1/tensorflow_addons/losses/focal_loss.py
pred_prob = pred.sigmoid() # prob from logits
p_t = label * pred_prob + (1 - label) * (1 - pred_prob)
modulating_factor = (1.0 - p_t) ** gamma
loss *= modulating_factor
if alpha > 0:
alpha_factor = label * alpha + (1 - label) * (1 - alpha)
loss *= alpha_factor
return loss.mean(1).sum()
class DFLoss(nn.Module):
"""Criterion class for computing DFL losses during training."""
def __init__(self, reg_max=16) -> None:
"""Initialize the DFL module."""
super().__init__()
self.reg_max = reg_max
def __call__(self, 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
"""
target = target.clamp_(0, self.reg_max - 1 - 0.01)
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)
class BboxLoss(nn.Module):
"""Criterion class for computing training losses during training."""
def __init__(self, reg_max=16):
"""Initialize the BboxLoss module with regularization maximum and DFL settings."""
super().__init__()
self.dfl_loss = DFLoss(reg_max) if reg_max > 1 else None
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.dfl_loss:
target_ltrb = bbox2dist(anchor_points, target_bboxes, self.dfl_loss.reg_max - 1)
loss_dfl = self.dfl_loss(pred_dist[fg_mask].view(-1, self.dfl_loss.reg_max), 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
class RotatedBboxLoss(BboxLoss):
"""Criterion class for computing training losses during training."""
def __init__(self, reg_max):
"""Initialize the BboxLoss module with regularization maximum and DFL settings."""
super().__init__(reg_max)
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 = probiou(pred_bboxes[fg_mask], target_bboxes[fg_mask])
loss_iou = ((1.0 - iou) * weight).sum() / target_scores_sum
# DFL loss
if self.dfl_loss:
target_ltrb = bbox2dist(anchor_points, xywh2xyxy(target_bboxes[..., :4]), self.dfl_loss.reg_max - 1)
loss_dfl = self.dfl_loss(pred_dist[fg_mask].view(-1, self.dfl_loss.reg_max), 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
class KeypointLoss(nn.Module):
"""Criterion class for computing training losses."""
def __init__(self, sigmas) -> None:
"""Initialize the KeypointLoss class."""
super().__init__()
self.sigmas = sigmas
def forward(self, pred_kpts, gt_kpts, kpt_mask, area):
"""Calculates keypoint loss factor and Euclidean distance loss for predicted and actual keypoints."""
d = (pred_kpts[..., 0] - gt_kpts[..., 0]).pow(2) + (pred_kpts[..., 1] - gt_kpts[..., 1]).pow(2)
kpt_loss_factor = kpt_mask.shape[1] / (torch.sum(kpt_mask != 0, dim=1) + 1e-9)
# e = d / (2 * (area * self.sigmas) ** 2 + 1e-9) # from formula
e = d / ((2 * self.sigmas).pow(2) * (area + 1e-9) * 2) # from cocoeval
return (kpt_loss_factor.view(-1, 1) * ((1 - torch.exp(-e)) * kpt_mask)).mean()
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.nc + m.reg_max * 4
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).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."""
nl, ne = targets.shape
if nl == 0:
out = torch.zeros(batch_size, 0, ne - 1, 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(), ne - 1, device=self.device)
for j in range(batch_size):
matches = i == j
if n := matches.sum():
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_(0.0)
# Pboxes
pred_bboxes = self.bbox_decode(anchor_points, pred_distri) # xyxy, (b, h*w, 4)
# dfl_conf = pred_distri.view(batch_size, -1, 4, self.reg_max).detach().softmax(-1)
# dfl_conf = (dfl_conf.amax(-1).mean(-1) + dfl_conf.amax(-1).amin(-1)) / 2
_, target_bboxes, target_scores, fg_mask, _ = self.assigner(
# pred_scores.detach().sigmoid() * 0.8 + dfl_conf.unsqueeze(-1) * 0.2,
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)
class v8SegmentationLoss(v8DetectionLoss):
"""Criterion class for computing training losses."""
def __init__(self, model): # model must be de-paralleled
"""Initializes the v8SegmentationLoss class, taking a de-paralleled model as argument."""
super().__init__(model)
self.overlap = model.args.overlap_mask
def __call__(self, preds, batch):
"""Calculate and return the loss for the YOLO model."""
loss = torch.zeros(4, device=self.device) # box, cls, dfl
feats, pred_masks, proto = preds if len(preds) == 3 else preds[1]
batch_size, _, mask_h, mask_w = proto.shape # batch size, number of masks, mask height, mask width
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
)
# B, grids, ..
pred_scores = pred_scores.permute(0, 2, 1).contiguous()
pred_distri = pred_distri.permute(0, 2, 1).contiguous()
pred_masks = pred_masks.permute(0, 2, 1).contiguous()
dtype = pred_scores.dtype
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
try:
batch_idx = batch["batch_idx"].view(-1, 1)
targets = torch.cat((batch_idx, 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_(0.0)
except RuntimeError as e:
raise TypeError(
"ERROR ❌ segment dataset incorrectly formatted or not a segment dataset.\n"
"This error can occur when incorrectly training a 'segment' model on a 'detect' dataset, "
"i.e. 'yolo train model=yolov8n-seg.pt data=coco8.yaml'.\nVerify your dataset is a "
"correctly formatted 'segment' dataset using 'data=coco8-seg.yaml' "
"as an example.\nSee https://docs.ultralytics.com/datasets/segment/ for help."
) from e
# Pboxes
pred_bboxes = self.bbox_decode(anchor_points, pred_distri) # xyxy, (b, h*w, 4)
_, target_bboxes, target_scores, fg_mask, target_gt_idx = 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[2] = self.bce(pred_scores, target_scores.to(dtype)).sum() / target_scores_sum # BCE
if fg_mask.sum():
# Bbox loss
loss[0], loss[3] = self.bbox_loss(
pred_distri,
pred_bboxes,
anchor_points,
target_bboxes / stride_tensor,
target_scores,
target_scores_sum,
fg_mask,
)
# Masks loss
masks = batch["masks"].to(self.device).float()
if tuple(masks.shape[-2:]) != (mask_h, mask_w): # downsample
masks = F.interpolate(masks[None], (mask_h, mask_w), mode="nearest")[0]
loss[1] = self.calculate_segmentation_loss(
fg_mask, masks, target_gt_idx, target_bboxes, batch_idx, proto, pred_masks, imgsz, self.overlap
)
# WARNING: lines below prevent Multi-GPU DDP 'unused gradient' PyTorch errors, do not remove
else:
loss[1] += (proto * 0).sum() + (pred_masks * 0).sum() # inf sums may lead to nan loss
loss[0] *= self.hyp.box # box gain
loss[1] *= self.hyp.box # seg gain
loss[2] *= self.hyp.cls # cls gain
loss[3] *= self.hyp.dfl # dfl gain
return loss.sum() * batch_size, loss.detach() # loss(box, cls, dfl)
@staticmethod
def single_mask_loss(
gt_mask: torch.Tensor, pred: torch.Tensor, proto: torch.Tensor, xyxy: torch.Tensor, area: torch.Tensor
) -> torch.Tensor:
"""
Compute the instance segmentation loss for a single image.
Args:
gt_mask (torch.Tensor): Ground truth mask of shape (n, H, W), where n is the number of objects.
pred (torch.Tensor): Predicted mask coefficients of shape (n, 32).
proto (torch.Tensor): Prototype masks of shape (32, H, W).
xyxy (torch.Tensor): Ground truth bounding boxes in xyxy format, normalized to [0, 1], of shape (n, 4).
area (torch.Tensor): Area of each ground truth bounding box of shape (n,).
Returns:
(torch.Tensor): The calculated mask loss for a single image.
Notes:
The function uses the equation pred_mask = torch.einsum('in,nhw->ihw', pred, proto) to produce the
predicted masks from the prototype masks and predicted mask coefficients.
"""
pred_mask = torch.einsum("in,nhw->ihw", pred, proto) # (n, 32) @ (32, 80, 80) -> (n, 80, 80)
loss = F.binary_cross_entropy_with_logits(pred_mask, gt_mask, reduction="none")
return (crop_mask(loss, xyxy).mean(dim=(1, 2)) / area).sum()
def calculate_segmentation_loss(
self,
fg_mask: torch.Tensor,
masks: torch.Tensor,
target_gt_idx: torch.Tensor,
target_bboxes: torch.Tensor,
batch_idx: torch.Tensor,
proto: torch.Tensor,
pred_masks: torch.Tensor,
imgsz: torch.Tensor,
overlap: bool,
) -> torch.Tensor:
"""
Calculate the loss for instance segmentation.
Args:
fg_mask (torch.Tensor): A binary tensor of shape (BS, N_anchors) indicating which anchors are positive.
masks (torch.Tensor): Ground truth masks of shape (BS, H, W) if `overlap` is False, otherwise (BS, ?, H, W).
target_gt_idx (torch.Tensor): Indexes of ground truth objects for each anchor of shape (BS, N_anchors).
target_bboxes (torch.Tensor): Ground truth bounding boxes for each anchor of shape (BS, N_anchors, 4).
batch_idx (torch.Tensor): Batch indices of shape (N_labels_in_batch, 1).
proto (torch.Tensor): Prototype masks of shape (BS, 32, H, W).
pred_masks (torch.Tensor): Predicted masks for each anchor of shape (BS, N_anchors, 32).
imgsz (torch.Tensor): Size of the input image as a tensor of shape (2), i.e., (H, W).
overlap (bool): Whether the masks in `masks` tensor overlap.
Returns:
(torch.Tensor): The calculated loss for instance segmentation.
Notes:
The batch loss can be computed for improved speed at higher memory usage.
For example, pred_mask can be computed as follows:
pred_mask = torch.einsum('in,nhw->ihw', pred, proto) # (i, 32) @ (32, 160, 160) -> (i, 160, 160)
"""
_, _, mask_h, mask_w = proto.shape
loss = 0
# Normalize to 0-1
target_bboxes_normalized = target_bboxes / imgsz[[1, 0, 1, 0]]
# Areas of target bboxes
marea = xyxy2xywh(target_bboxes_normalized)[..., 2:].prod(2)
# Normalize to mask size
mxyxy = target_bboxes_normalized * torch.tensor([mask_w, mask_h, mask_w, mask_h], device=proto.device)
for i, single_i in enumerate(zip(fg_mask, target_gt_idx, pred_masks, proto, mxyxy, marea, masks)):
fg_mask_i, target_gt_idx_i, pred_masks_i, proto_i, mxyxy_i, marea_i, masks_i = single_i
if fg_mask_i.any():
mask_idx = target_gt_idx_i[fg_mask_i]
if overlap:
gt_mask = masks_i == (mask_idx + 1).view(-1, 1, 1)
gt_mask = gt_mask.float()
else:
gt_mask = masks[batch_idx.view(-1) == i][mask_idx]
loss += self.single_mask_loss(
gt_mask, pred_masks_i[fg_mask_i], proto_i, mxyxy_i[fg_mask_i], marea_i[fg_mask_i]
)
# WARNING: lines below prevents Multi-GPU DDP 'unused gradient' PyTorch errors, do not remove
else:
loss += (proto * 0).sum() + (pred_masks * 0).sum() # inf sums may lead to nan loss
return loss / fg_mask.sum()
class v8PoseLoss(v8DetectionLoss):
"""Criterion class for computing training losses."""
def __init__(self, model): # model must be de-paralleled
"""Initializes v8PoseLoss with model, sets keypoint variables and declares a keypoint loss instance."""
super().__init__(model)
self.kpt_shape = model.model[-1].kpt_shape
self.bce_pose = nn.BCEWithLogitsLoss()
is_pose = self.kpt_shape == [17, 3]
nkpt = self.kpt_shape[0] # number of keypoints
sigmas = torch.from_numpy(OKS_SIGMA).to(self.device) if is_pose else torch.ones(nkpt, device=self.device) / nkpt
self.keypoint_loss = KeypointLoss(sigmas=sigmas)
def __call__(self, preds, batch):
"""Calculate the total loss and detach it."""
loss = torch.zeros(5, device=self.device) # box, cls, dfl, kpt_location, kpt_visibility
feats, pred_kpts = preds if isinstance(preds[0], list) else preds[1]
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
)
# B, grids, ..
pred_scores = pred_scores.permute(0, 2, 1).contiguous()
pred_distri = pred_distri.permute(0, 2, 1).contiguous()
pred_kpts = pred_kpts.permute(0, 2, 1).contiguous()
dtype = pred_scores.dtype
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
batch_size = pred_scores.shape[0]
batch_idx = batch["batch_idx"].view(-1, 1)
targets = torch.cat((batch_idx, 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_(0.0)
# Pboxes
pred_bboxes = self.bbox_decode(anchor_points, pred_distri) # xyxy, (b, h*w, 4)
pred_kpts = self.kpts_decode(anchor_points, pred_kpts.view(batch_size, -1, *self.kpt_shape)) # (b, h*w, 17, 3)
_, target_bboxes, target_scores, fg_mask, target_gt_idx = 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[3] = 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[4] = self.bbox_loss(
pred_distri, pred_bboxes, anchor_points, target_bboxes, target_scores, target_scores_sum, fg_mask
)
keypoints = batch["keypoints"].to(self.device).float().clone()
keypoints[..., 0] *= imgsz[1]
keypoints[..., 1] *= imgsz[0]
loss[1], loss[2] = self.calculate_keypoints_loss(
fg_mask, target_gt_idx, keypoints, batch_idx, stride_tensor, target_bboxes, pred_kpts
)
loss[0] *= self.hyp.box # box gain
loss[1] *= self.hyp.pose # pose gain
loss[2] *= self.hyp.kobj # kobj gain
loss[3] *= self.hyp.cls # cls gain
loss[4] *= self.hyp.dfl # dfl gain
return loss.sum() * batch_size, loss.detach() # loss(box, cls, dfl)
@staticmethod
def kpts_decode(anchor_points, pred_kpts):
"""Decodes predicted keypoints to image coordinates."""
y = pred_kpts.clone()
y[..., :2] *= 2.0
y[..., 0] += anchor_points[:, [0]] - 0.5
y[..., 1] += anchor_points[:, [1]] - 0.5
return y
def calculate_keypoints_loss(
self, masks, target_gt_idx, keypoints, batch_idx, stride_tensor, target_bboxes, pred_kpts
):
"""
Calculate the keypoints loss for the model.
This function calculates the keypoints loss and keypoints object loss for a given batch. The keypoints loss is
based on the difference between the predicted keypoints and ground truth keypoints. The keypoints object loss is
a binary classification loss that classifies whether a keypoint is present or not.
Args:
masks (torch.Tensor): Binary mask tensor indicating object presence, shape (BS, N_anchors).
target_gt_idx (torch.Tensor): Index tensor mapping anchors to ground truth objects, shape (BS, N_anchors).
keypoints (torch.Tensor): Ground truth keypoints, shape (N_kpts_in_batch, N_kpts_per_object, kpts_dim).
batch_idx (torch.Tensor): Batch index tensor for keypoints, shape (N_kpts_in_batch, 1).
stride_tensor (torch.Tensor): Stride tensor for anchors, shape (N_anchors, 1).
target_bboxes (torch.Tensor): Ground truth boxes in (x1, y1, x2, y2) format, shape (BS, N_anchors, 4).
pred_kpts (torch.Tensor): Predicted keypoints, shape (BS, N_anchors, N_kpts_per_object, kpts_dim).
Returns:
kpts_loss (torch.Tensor): The keypoints loss.
kpts_obj_loss (torch.Tensor): The keypoints object loss.
"""
batch_idx = batch_idx.flatten()
batch_size = len(masks)
# Find the maximum number of keypoints in a single image
max_kpts = torch.unique(batch_idx, return_counts=True)[1].max()
# Create a tensor to hold batched keypoints
batched_keypoints = torch.zeros(
(batch_size, max_kpts, keypoints.shape[1], keypoints.shape[2]), device=keypoints.device
)
# TODO: any idea how to vectorize this?
# Fill batched_keypoints with keypoints based on batch_idx
for i in range(batch_size):
keypoints_i = keypoints[batch_idx == i]
batched_keypoints[i, : keypoints_i.shape[0]] = keypoints_i
# Expand dimensions of target_gt_idx to match the shape of batched_keypoints
target_gt_idx_expanded = target_gt_idx.unsqueeze(-1).unsqueeze(-1)
# Use target_gt_idx_expanded to select keypoints from batched_keypoints
selected_keypoints = batched_keypoints.gather(
1, target_gt_idx_expanded.expand(-1, -1, keypoints.shape[1], keypoints.shape[2])
)
# Divide coordinates by stride
selected_keypoints /= stride_tensor.view(1, -1, 1, 1)
kpts_loss = 0
kpts_obj_loss = 0
if masks.any():
gt_kpt = selected_keypoints[masks]
area = xyxy2xywh(target_bboxes[masks])[:, 2:].prod(1, keepdim=True)
pred_kpt = pred_kpts[masks]
kpt_mask = gt_kpt[..., 2] != 0 if gt_kpt.shape[-1] == 3 else torch.full_like(gt_kpt[..., 0], True)
kpts_loss = self.keypoint_loss(pred_kpt, gt_kpt, kpt_mask, area) # pose loss
if pred_kpt.shape[-1] == 3:
kpts_obj_loss = self.bce_pose(pred_kpt[..., 2], kpt_mask.float()) # keypoint obj loss
return kpts_loss, kpts_obj_loss
class v8ClassificationLoss:
"""Criterion class for computing training losses."""
def __call__(self, preds, batch):
"""Compute the classification loss between predictions and true labels."""
preds = preds[1] if isinstance(preds, (list, tuple)) else preds
loss = F.cross_entropy(preds, batch["cls"], reduction="mean")
loss_items = loss.detach()
return loss, loss_items
class v8OBBLoss(v8DetectionLoss):
"""Calculates losses for object detection, classification, and box distribution in rotated YOLO models."""
def __init__(self, model):
"""Initializes v8OBBLoss with model, assigner, and rotated bbox loss; note model must be de-paralleled."""
super().__init__(model)
self.assigner = RotatedTaskAlignedAssigner(topk=10, num_classes=self.nc, alpha=0.5, beta=6.0)
self.bbox_loss = RotatedBboxLoss(self.reg_max).to(self.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, 6, 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(), 6, device=self.device)
for j in range(batch_size):
matches = i == j
if n := matches.sum():
bboxes = targets[matches, 2:]
bboxes[..., :4].mul_(scale_tensor)
out[j, :n] = torch.cat([targets[matches, 1:2], bboxes], dim=-1)
return out
def __call__(self, preds, batch):
"""Calculate and return the loss for the YOLO model."""
loss = torch.zeros(3, device=self.device) # box, cls, dfl
feats, pred_angle = preds if isinstance(preds[0], list) else preds[1]
batch_size = pred_angle.shape[0] # batch size, number of masks, mask height, mask width
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
)
# b, grids, ..
pred_scores = pred_scores.permute(0, 2, 1).contiguous()
pred_distri = pred_distri.permute(0, 2, 1).contiguous()
pred_angle = pred_angle.permute(0, 2, 1).contiguous()
dtype = pred_scores.dtype
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
try:
batch_idx = batch["batch_idx"].view(-1, 1)
targets = torch.cat((batch_idx, batch["cls"].view(-1, 1), batch["bboxes"].view(-1, 5)), 1)
rw, rh = targets[:, 4] * imgsz[0].item(), targets[:, 5] * imgsz[1].item()
targets = targets[(rw >= 2) & (rh >= 2)] # filter rboxes of tiny size to stabilize training
targets = self.preprocess(targets.to(self.device), batch_size, scale_tensor=imgsz[[1, 0, 1, 0]])
gt_labels, gt_bboxes = targets.split((1, 5), 2) # cls, xywhr
mask_gt = gt_bboxes.sum(2, keepdim=True).gt_(0.0)
except RuntimeError as e:
raise TypeError(
"ERROR ❌ OBB dataset incorrectly formatted or not a OBB dataset.\n"
"This error can occur when incorrectly training a 'OBB' model on a 'detect' dataset, "
"i.e. 'yolo train model=yolov8n-obb.pt data=dota8.yaml'.\nVerify your dataset is a "
"correctly formatted 'OBB' dataset using 'data=dota8.yaml' "
"as an example.\nSee https://docs.ultralytics.com/datasets/obb/ for help."
) from e
# Pboxes
pred_bboxes = self.bbox_decode(anchor_points, pred_distri, pred_angle) # xyxy, (b, h*w, 4)
bboxes_for_assigner = pred_bboxes.clone().detach()
# Only the first four elements need to be scaled
bboxes_for_assigner[..., :4] *= stride_tensor
_, target_bboxes, target_scores, fg_mask, _ = self.assigner(
pred_scores.detach().sigmoid(),
bboxes_for_assigner.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[..., :4] /= stride_tensor
loss[0], loss[2] = self.bbox_loss(
pred_distri, pred_bboxes, anchor_points, target_bboxes, target_scores, target_scores_sum, fg_mask
)
else:
loss[0] += (pred_angle * 0).sum()
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)
def bbox_decode(self, anchor_points, pred_dist, pred_angle):
"""
Decode predicted object bounding box coordinates from anchor points and distribution.
Args:
anchor_points (torch.Tensor): Anchor points, (h*w, 2).
pred_dist (torch.Tensor): Predicted rotated distance, (bs, h*w, 4).
pred_angle (torch.Tensor): Predicted angle, (bs, h*w, 1).
Returns:
(torch.Tensor): Predicted rotated bounding boxes with angles, (bs, h*w, 5).
"""
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))
return torch.cat((dist2rbox(pred_dist, pred_angle, anchor_points), pred_angle), dim=-1)
class E2EDetectLoss:
"""Criterion class for computing training losses."""
def __init__(self, model):
"""Initialize E2EDetectLoss with one-to-many and one-to-one detection losses using the provided model."""
self.one2many = v8DetectionLoss(model, tal_topk=10)
self.one2one = v8DetectionLoss(model, tal_topk=1)
def __call__(self, preds, batch):
"""Calculate the sum of the loss for box, cls and dfl multiplied by batch size."""
preds = preds[1] if isinstance(preds, tuple) else preds
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], loss_one2many[1] + loss_one2one[1]