From 527a97759b5ebee35be3a64a0a6b7b52356731db Mon Sep 17 00:00:00 2001
From: Ayush Chaurasia <ayush.chaurarsia@gmail.com>
Date: Sun, 28 May 2023 19:45:41 +0530
Subject: [PATCH] Revert loss head PR (#2873)
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com>
---
ultralytics/nn/tasks.py | 31 ---
ultralytics/yolo/engine/trainer.py | 9 +-
ultralytics/yolo/engine/validator.py | 3 +-
ultralytics/yolo/utils/loss.py | 293 --------------------------
ultralytics/yolo/v8/classify/train.py | 7 +
ultralytics/yolo/v8/detect/train.py | 107 ++++++++++
ultralytics/yolo/v8/pose/train.py | 104 +++++++++
ultralytics/yolo/v8/segment/train.py | 108 ++++++++++
8 files changed, 335 insertions(+), 327 deletions(-)
diff --git a/ultralytics/nn/tasks.py b/ultralytics/nn/tasks.py
index 28fa0336..05caf862 100644
--- a/ultralytics/nn/tasks.py
+++ b/ultralytics/nn/tasks.py
@@ -13,7 +13,6 @@ from ultralytics.nn.modules import (AIFI, C1, C2, C3, C3TR, SPP, SPPF, Bottlenec
RTDETRDecoder, Segment)
from ultralytics.yolo.utils import DEFAULT_CFG_DICT, DEFAULT_CFG_KEYS, LOGGER, colorstr, emojis, yaml_load
from ultralytics.yolo.utils.checks import check_requirements, check_suffix, check_yaml
-from ultralytics.yolo.utils.loss import v8ClassificationLoss, v8DetectionLoss, v8PoseLoss, v8SegmentationLoss
from ultralytics.yolo.utils.plotting import feature_visualization
from ultralytics.yolo.utils.torch_utils import (fuse_conv_and_bn, fuse_deconv_and_bn, initialize_weights,
intersect_dicts, make_divisible, model_info, scale_img, time_sync)
@@ -176,23 +175,6 @@ class BaseModel(nn.Module):
if verbose:
LOGGER.info(f'Transferred {len(csd)}/{len(self.model.state_dict())} items from pretrained weights')
- def loss(self, batch, preds=None):
- """
- Compute loss
-
- Args:
- batch (dict): Batch to compute loss on
- pred (torch.Tensor | List[torch.Tensor]): Predictions.
- """
- if not hasattr(self, 'criterion'):
- self.criterion = self.init_criterion()
-
- preds = self.forward(batch['img']) if preds is None else preds
- return self.criterion(preds, batch)
-
- def init_criterion(self):
- raise NotImplementedError('compute_loss() needs to be implemented by task heads')
-
class DetectionModel(BaseModel):
"""YOLOv8 detection model."""
@@ -269,9 +251,6 @@ class DetectionModel(BaseModel):
y[-1] = y[-1][..., i:] # small
return y
- def init_criterion(self):
- return v8DetectionLoss(self)
-
class SegmentationModel(DetectionModel):
"""YOLOv8 segmentation model."""
@@ -284,9 +263,6 @@ class SegmentationModel(DetectionModel):
"""Undocumented function."""
raise NotImplementedError(emojis('WARNING ⚠️ SegmentationModel has not supported augment inference yet!'))
- def init_criterion(self):
- return v8SegmentationLoss(self)
-
class PoseModel(DetectionModel):
"""YOLOv8 pose model."""
@@ -300,9 +276,6 @@ class PoseModel(DetectionModel):
cfg['kpt_shape'] = data_kpt_shape
super().__init__(cfg=cfg, ch=ch, nc=nc, verbose=verbose)
- def init_criterion(self):
- return v8PoseLoss(self)
-
class ClassificationModel(BaseModel):
"""YOLOv8 classification model."""
@@ -370,10 +343,6 @@ class ClassificationModel(BaseModel):
if m[i].out_channels != nc:
m[i] = nn.Conv2d(m[i].in_channels, nc, m[i].kernel_size, m[i].stride, bias=m[i].bias is not None)
- def init_criterion(self):
- """Compute the classification loss between predictions and true labels."""
- return v8ClassificationLoss()
-
class Ensemble(nn.ModuleList):
"""Ensemble of models."""
diff --git a/ultralytics/yolo/engine/trainer.py b/ultralytics/yolo/engine/trainer.py
index c7754109..f220ad7f 100644
--- a/ultralytics/yolo/engine/trainer.py
+++ b/ultralytics/yolo/engine/trainer.py
@@ -325,7 +325,8 @@ class BaseTrainer:
# Forward
with torch.cuda.amp.autocast(self.amp):
batch = self.preprocess_batch(batch)
- self.loss, self.loss_items = de_parallel(self.model).loss(batch)
+ preds = self.model(batch['img'])
+ self.loss, self.loss_items = self.criterion(preds, batch)
if RANK != -1:
self.loss *= world_size
self.tloss = (self.tloss * i + self.loss_items) / (i + 1) if self.tloss is not None \
@@ -495,6 +496,12 @@ class BaseTrainer:
"""Build dataset"""
raise NotImplementedError('build_dataset function not implemented in trainer')
+ def criterion(self, preds, batch):
+ """
+ Returns loss and individual loss items as Tensor.
+ """
+ raise NotImplementedError('criterion function not implemented in trainer')
+
def label_loss_items(self, loss_items=None, prefix='train'):
"""
Returns a loss dict with labelled training loss items tensor
diff --git a/ultralytics/yolo/engine/validator.py b/ultralytics/yolo/engine/validator.py
index 09f297bd..ad107a2c 100644
--- a/ultralytics/yolo/engine/validator.py
+++ b/ultralytics/yolo/engine/validator.py
@@ -162,8 +162,7 @@ class BaseValidator:
# Loss
with dt[2]:
if self.training:
- loss_items = model.loss(batch, preds)
- self.loss += loss_items[1]
+ self.loss += trainer.criterion(preds, batch)[1]
# Postprocess
with dt[3]:
diff --git a/ultralytics/yolo/utils/loss.py b/ultralytics/yolo/utils/loss.py
index 5266982b..73aba68f 100644
--- a/ultralytics/yolo/utils/loss.py
+++ b/ultralytics/yolo/utils/loss.py
@@ -4,10 +4,6 @@ import torch
import torch.nn as nn
import torch.nn.functional as F
-from ultralytics.yolo.utils.metrics import OKS_SIGMA
-from ultralytics.yolo.utils.ops import crop_mask, xywh2xyxy, xyxy2xywh
-from ultralytics.yolo.utils.tal import TaskAlignedAssigner, dist2bbox, make_anchors
-
from .metrics import bbox_iou
from .tal import bbox2dist
@@ -77,292 +73,3 @@ class KeypointLoss(nn.Module):
# e = d / (2 * (area * self.sigmas) ** 2 + 1e-9) # from formula
e = d / (2 * self.sigmas) ** 2 / (area + 1e-9) / 2 # from cocoeval
return kpt_loss_factor * ((1 - torch.exp(-e)) * kpt_mask).mean()
-
-
-# Criterion class for computing Detection training losses
-class v8DetectionLoss:
-
- def __init__(self, model): # model must be de-paralleled
-
- 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=10, 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)
-
-
-# Criterion class for computing training losses
-class v8SegmentationLoss(v8DetectionLoss):
-
- def __init__(self, model, overlap=True): # model must be de-paralleled
- super().__init__(model)
- self.nm = model.model[-1].nm # number of masks
- self.overlap = overlap
-
- 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)
- 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=coco128.yaml'.\nVerify your dataset is a "
- "correctly formatted 'segment' dataset using 'data=coco128-seg.yaml' "
- 'as an example.\nSee https://docs.ultralytics.com/tasks/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]
-
- for i in range(batch_size):
- if fg_mask[i].sum():
- mask_idx = target_gt_idx[i][fg_mask[i]]
- if self.overlap:
- gt_mask = torch.where(masks[[i]] == (mask_idx + 1).view(-1, 1, 1), 1.0, 0.0)
- else:
- gt_mask = masks[batch_idx.view(-1) == i][mask_idx]
- xyxyn = target_bboxes[i][fg_mask[i]] / imgsz[[1, 0, 1, 0]]
- marea = xyxy2xywh(xyxyn)[:, 2:].prod(1)
- mxyxy = xyxyn * torch.tensor([mask_w, mask_h, mask_w, mask_h], device=self.device)
- loss[1] += self.single_mask_loss(gt_mask, pred_masks[i][fg_mask[i]], proto[i], mxyxy, marea) # seg
-
- # WARNING: lines below prevents 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
-
- # 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 / batch_size # 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)
-
- def single_mask_loss(self, gt_mask, pred, proto, xyxy, area):
- """Mask loss for one image."""
- pred_mask = (pred @ proto.view(self.nm, -1)).view(-1, *proto.shape[1:]) # (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).mean()
-
-
-# Criterion class for computing training losses
-class v8PoseLoss(v8DetectionLoss):
-
- def __init__(self, model): # model must be de-paralleled
- 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)
-
- # 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]
- for i in range(batch_size):
- if fg_mask[i].sum():
- idx = target_gt_idx[i][fg_mask[i]]
- gt_kpt = keypoints[batch_idx.view(-1) == i][idx] # (n, 51)
- gt_kpt[..., 0] /= stride_tensor[fg_mask[i]]
- gt_kpt[..., 1] /= stride_tensor[fg_mask[i]]
- area = xyxy2xywh(target_bboxes[i][fg_mask[i]])[:, 2:].prod(1, keepdim=True)
- pred_kpt = pred_kpts[i][fg_mask[i]]
- kpt_mask = gt_kpt[..., 2] != 0
- loss[1] += self.keypoint_loss(pred_kpt, gt_kpt, kpt_mask, area) # pose loss
- # kpt_score loss
- if pred_kpt.shape[-1] == 3:
- loss[2] += self.bce_pose(pred_kpt[..., 2], kpt_mask.float()) # keypoint obj loss
-
- loss[0] *= self.hyp.box # box gain
- loss[1] *= self.hyp.pose / batch_size # pose gain
- loss[2] *= self.hyp.kobj / batch_size # 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)
-
- def kpts_decode(self, 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
-
-
-class v8ClassificationLoss:
-
- def __call__(self, preds, batch):
- """Compute the classification loss between predictions and true labels."""
- loss = torch.nn.functional.cross_entropy(preds, batch['cls'], reduction='sum') / 64 # TODO: remove hardcoding
- loss_items = loss.detach()
- return loss, loss_items
diff --git a/ultralytics/yolo/v8/classify/train.py b/ultralytics/yolo/v8/classify/train.py
index 29496446..6c8b6574 100644
--- a/ultralytics/yolo/v8/classify/train.py
+++ b/ultralytics/yolo/v8/classify/train.py
@@ -41,6 +41,7 @@ class ClassificationTrainer(BaseTrainer):
m.p = self.args.dropout # set dropout
for p in model.parameters():
p.requires_grad = True # for training
+
return model
def setup_model(self):
@@ -102,6 +103,12 @@ class ClassificationTrainer(BaseTrainer):
self.loss_names = ['loss']
return v8.classify.ClassificationValidator(self.test_loader, self.save_dir)
+ def criterion(self, preds, batch):
+ """Compute the classification loss between predictions and true labels."""
+ loss = torch.nn.functional.cross_entropy(preds, batch['cls'], reduction='sum') / self.args.nbs
+ loss_items = loss.detach()
+ return loss, loss_items
+
def label_loss_items(self, loss_items=None, prefix='train'):
"""
Returns a loss dict with labelled training loss items tensor
diff --git a/ultralytics/yolo/v8/detect/train.py b/ultralytics/yolo/v8/detect/train.py
index 1b475ed0..7f0fc1df 100644
--- a/ultralytics/yolo/v8/detect/train.py
+++ b/ultralytics/yolo/v8/detect/train.py
@@ -2,6 +2,8 @@
from copy import copy
import numpy as np
+import torch
+import torch.nn as nn
from ultralytics.nn.tasks import DetectionModel
from ultralytics.yolo import v8
@@ -9,7 +11,10 @@ from ultralytics.yolo.data import build_dataloader, build_yolo_dataset
from ultralytics.yolo.data.dataloaders.v5loader import create_dataloader
from ultralytics.yolo.engine.trainer import BaseTrainer
from ultralytics.yolo.utils import DEFAULT_CFG, LOGGER, RANK, colorstr
+from ultralytics.yolo.utils.loss import BboxLoss
+from ultralytics.yolo.utils.ops import xywh2xyxy
from ultralytics.yolo.utils.plotting import plot_images, plot_labels, plot_results
+from ultralytics.yolo.utils.tal import TaskAlignedAssigner, dist2bbox, make_anchors
from ultralytics.yolo.utils.torch_utils import de_parallel, torch_distributed_zero_first
@@ -86,6 +91,12 @@ class DetectionTrainer(BaseTrainer):
self.loss_names = 'box_loss', 'cls_loss', 'dfl_loss'
return v8.detect.DetectionValidator(self.test_loader, save_dir=self.save_dir, args=copy(self.args))
+ def criterion(self, preds, batch):
+ """Compute loss for YOLO prediction and ground-truth."""
+ if not hasattr(self, 'compute_loss'):
+ self.compute_loss = Loss(de_parallel(self.model))
+ return self.compute_loss(preds, batch)
+
def label_loss_items(self, loss_items=None, prefix='train'):
"""
Returns a loss dict with labelled training loss items tensor
@@ -124,6 +135,102 @@ class DetectionTrainer(BaseTrainer):
plot_labels(boxes, cls.squeeze(), names=self.data['names'], save_dir=self.save_dir, on_plot=self.on_plot)
+# Criterion class for computing training losses
+class Loss:
+
+ def __init__(self, model): # model must be de-paralleled
+
+ 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=10, 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)
+
+
def train(cfg=DEFAULT_CFG, use_python=False):
"""Train and optimize YOLO model given training data and device."""
model = cfg.model or 'yolov8n.pt'
diff --git a/ultralytics/yolo/v8/pose/train.py b/ultralytics/yolo/v8/pose/train.py
index af3043c1..b7890f89 100644
--- a/ultralytics/yolo/v8/pose/train.py
+++ b/ultralytics/yolo/v8/pose/train.py
@@ -2,10 +2,19 @@
from copy import copy
+import torch
+import torch.nn as nn
+
from ultralytics.nn.tasks import PoseModel
from ultralytics.yolo import v8
from ultralytics.yolo.utils import DEFAULT_CFG
+from ultralytics.yolo.utils.loss import KeypointLoss
+from ultralytics.yolo.utils.metrics import OKS_SIGMA
+from ultralytics.yolo.utils.ops import xyxy2xywh
from ultralytics.yolo.utils.plotting import plot_images, plot_results
+from ultralytics.yolo.utils.tal import make_anchors
+from ultralytics.yolo.utils.torch_utils import de_parallel
+from ultralytics.yolo.v8.detect.train import Loss
# BaseTrainer python usage
@@ -36,6 +45,12 @@ class PoseTrainer(v8.detect.DetectionTrainer):
self.loss_names = 'box_loss', 'pose_loss', 'kobj_loss', 'cls_loss', 'dfl_loss'
return v8.pose.PoseValidator(self.test_loader, save_dir=self.save_dir, args=copy(self.args))
+ def criterion(self, preds, batch):
+ """Computes pose loss for the YOLO model."""
+ if not hasattr(self, 'compute_loss'):
+ self.compute_loss = PoseLoss(de_parallel(self.model))
+ return self.compute_loss(preds, batch)
+
def plot_training_samples(self, batch, ni):
"""Plot a batch of training samples with annotated class labels, bounding boxes, and keypoints."""
images = batch['img']
@@ -58,6 +73,95 @@ class PoseTrainer(v8.detect.DetectionTrainer):
plot_results(file=self.csv, pose=True, on_plot=self.on_plot) # save results.png
+# Criterion class for computing training losses
+class PoseLoss(Loss):
+
+ def __init__(self, model): # model must be de-paralleled
+ 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)
+
+ # 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]
+ for i in range(batch_size):
+ if fg_mask[i].sum():
+ idx = target_gt_idx[i][fg_mask[i]]
+ gt_kpt = keypoints[batch_idx.view(-1) == i][idx] # (n, 51)
+ gt_kpt[..., 0] /= stride_tensor[fg_mask[i]]
+ gt_kpt[..., 1] /= stride_tensor[fg_mask[i]]
+ area = xyxy2xywh(target_bboxes[i][fg_mask[i]])[:, 2:].prod(1, keepdim=True)
+ pred_kpt = pred_kpts[i][fg_mask[i]]
+ kpt_mask = gt_kpt[..., 2] != 0
+ loss[1] += self.keypoint_loss(pred_kpt, gt_kpt, kpt_mask, area) # pose loss
+ # kpt_score loss
+ if pred_kpt.shape[-1] == 3:
+ loss[2] += self.bce_pose(pred_kpt[..., 2], kpt_mask.float()) # keypoint obj loss
+
+ loss[0] *= self.hyp.box # box gain
+ loss[1] *= self.hyp.pose / batch_size # pose gain
+ loss[2] *= self.hyp.kobj / batch_size # 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)
+
+ def kpts_decode(self, 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 train(cfg=DEFAULT_CFG, use_python=False):
"""Train the YOLO model on the given data and device."""
model = cfg.model or 'yolov8n-pose.yaml'
diff --git a/ultralytics/yolo/v8/segment/train.py b/ultralytics/yolo/v8/segment/train.py
index ab66cf06..32ce510e 100644
--- a/ultralytics/yolo/v8/segment/train.py
+++ b/ultralytics/yolo/v8/segment/train.py
@@ -1,10 +1,17 @@
# Ultralytics YOLO 🚀, AGPL-3.0 license
from copy import copy
+import torch
+import torch.nn.functional as F
+
from ultralytics.nn.tasks import SegmentationModel
from ultralytics.yolo import v8
from ultralytics.yolo.utils import DEFAULT_CFG, RANK
+from ultralytics.yolo.utils.ops import crop_mask, xyxy2xywh
from ultralytics.yolo.utils.plotting import plot_images, plot_results
+from ultralytics.yolo.utils.tal import make_anchors
+from ultralytics.yolo.utils.torch_utils import de_parallel
+from ultralytics.yolo.v8.detect.train import Loss
# BaseTrainer python usage
@@ -30,6 +37,12 @@ class SegmentationTrainer(v8.detect.DetectionTrainer):
self.loss_names = 'box_loss', 'seg_loss', 'cls_loss', 'dfl_loss'
return v8.segment.SegmentationValidator(self.test_loader, save_dir=self.save_dir, args=copy(self.args))
+ def criterion(self, preds, batch):
+ """Returns the computed loss using the SegLoss class on the given predictions and batch."""
+ if not hasattr(self, 'compute_loss'):
+ self.compute_loss = SegLoss(de_parallel(self.model), overlap=self.args.overlap_mask)
+ return self.compute_loss(preds, batch)
+
def plot_training_samples(self, batch, ni):
"""Creates a plot of training sample images with labels and box coordinates."""
plot_images(batch['img'],
@@ -46,6 +59,101 @@ class SegmentationTrainer(v8.detect.DetectionTrainer):
plot_results(file=self.csv, segment=True, on_plot=self.on_plot) # save results.png
+# Criterion class for computing training losses
+class SegLoss(Loss):
+
+ def __init__(self, model, overlap=True): # model must be de-paralleled
+ super().__init__(model)
+ self.nm = model.model[-1].nm # number of masks
+ self.overlap = overlap
+
+ 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)
+ 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=coco128.yaml'.\nVerify your dataset is a "
+ "correctly formatted 'segment' dataset using 'data=coco128-seg.yaml' "
+ 'as an example.\nSee https://docs.ultralytics.com/tasks/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]
+
+ for i in range(batch_size):
+ if fg_mask[i].sum():
+ mask_idx = target_gt_idx[i][fg_mask[i]]
+ if self.overlap:
+ gt_mask = torch.where(masks[[i]] == (mask_idx + 1).view(-1, 1, 1), 1.0, 0.0)
+ else:
+ gt_mask = masks[batch_idx.view(-1) == i][mask_idx]
+ xyxyn = target_bboxes[i][fg_mask[i]] / imgsz[[1, 0, 1, 0]]
+ marea = xyxy2xywh(xyxyn)[:, 2:].prod(1)
+ mxyxy = xyxyn * torch.tensor([mask_w, mask_h, mask_w, mask_h], device=self.device)
+ loss[1] += self.single_mask_loss(gt_mask, pred_masks[i][fg_mask[i]], proto[i], mxyxy, marea) # seg
+
+ # WARNING: lines below prevents 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
+
+ # 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 / batch_size # 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)
+
+ def single_mask_loss(self, gt_mask, pred, proto, xyxy, area):
+ """Mask loss for one image."""
+ pred_mask = (pred @ proto.view(self.nm, -1)).view(-1, *proto.shape[1:]) # (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).mean()
+
+
def train(cfg=DEFAULT_CFG, use_python=False):
"""Train a YOLO segmentation model based on passed arguments."""
model = cfg.model or 'yolov8n-seg.pt'