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ultralytics 8.0.111 refactored model.loss() method (#2911)

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5
docs/reference/nn/tasks.md
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@ -27,6 +27,11 @@ description: Learn how to work with Ultralytics YOLO Detection, Segmentation & C
:::ultralytics.nn.tasks.ClassificationModel
<br><br>
# RTDETRDetectionModel
---
:::ultralytics.nn.tasks.RTDETRDetectionModel
<br><br>
# Ensemble
---
:::ultralytics.nn.tasks.Ensemble

5
docs/reference/yolo/data/dataloaders/stream_loaders.md
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@ -36,3 +36,8 @@ description: 'Ultralytics YOLO Docs: Learn about stream loaders for image and te
---
:::ultralytics.yolo.data.dataloaders.stream_loaders.autocast_list
<br><br>
# get_best_youtube_url
---
:::ultralytics.yolo.data.dataloaders.stream_loaders.get_best_youtube_url
<br><br>

20
docs/reference/yolo/utils/loss.md
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@ -16,3 +16,23 @@ description: Learn about Varifocal Loss and Keypoint Loss in Ultralytics YOLO fo
---
:::ultralytics.yolo.utils.loss.KeypointLoss
<br><br>
# v8DetectionLoss
---
:::ultralytics.yolo.utils.loss.v8DetectionLoss
<br><br>
# v8SegmentationLoss
---
:::ultralytics.yolo.utils.loss.v8SegmentationLoss
<br><br>
# v8PoseLoss
---
:::ultralytics.yolo.utils.loss.v8PoseLoss
<br><br>
# v8ClassificationLoss
---
:::ultralytics.yolo.utils.loss.v8ClassificationLoss
<br><br>

5
docs/reference/yolo/v8/detect/train.md
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@ -7,11 +7,6 @@ description: Train and optimize custom object detection models with Ultralytics
:::ultralytics.yolo.v8.detect.train.DetectionTrainer
<br><br>
# Loss
---
:::ultralytics.yolo.v8.detect.train.Loss
<br><br>
# train
---
:::ultralytics.yolo.v8.detect.train.train

5
docs/reference/yolo/v8/pose/train.md
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@ -7,11 +7,6 @@ description: Boost posture detection using PoseTrainer and train models using tr
:::ultralytics.yolo.v8.pose.train.PoseTrainer
<br><br>
# PoseLoss
---
:::ultralytics.yolo.v8.pose.train.PoseLoss
<br><br>
# train
---
:::ultralytics.yolo.v8.pose.train.train

5
docs/reference/yolo/v8/segment/train.md
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@ -7,11 +7,6 @@ description: Learn about SegmentationTrainer and Train in Ultralytics YOLO v8 fo
:::ultralytics.yolo.v8.segment.train.SegmentationTrainer
<br><br>
# SegLoss
---
:::ultralytics.yolo.v8.segment.train.SegLoss
<br><br>
# train
---
:::ultralytics.yolo.v8.segment.train.train

2
ultralytics/__init__.py
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@ -1,6 +1,6 @@
# Ultralytics YOLO 🚀, AGPL-3.0 license
__version__ = '8.0.110'
__version__ = '8.0.111'
from ultralytics.hub import start
from ultralytics.vit.rtdetr import RTDETR

13
ultralytics/nn/modules/__init__.py
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@ -21,10 +21,9 @@ from .head import Classify, Detect, Pose, RTDETRDecoder, Segment
from .transformer import (AIFI, MLP, DeformableTransformerDecoder, DeformableTransformerDecoderLayer, LayerNorm2d,
MLPBlock, MSDeformAttn, TransformerBlock, TransformerEncoderLayer, TransformerLayer)
__all__ = [
'Conv', 'Conv2', 'LightConv', 'RepConv', 'DWConv', 'DWConvTranspose2d', 'ConvTranspose', 'Focus', 'GhostConv',
'ChannelAttention', 'SpatialAttention', 'CBAM', 'Concat', 'TransformerLayer', 'TransformerBlock', 'MLPBlock',
'LayerNorm2d', 'DFL', 'HGBlock', 'HGStem', 'SPP', 'SPPF', 'C1', 'C2', 'C3', 'C2f', 'C3x', 'C3TR', 'C3Ghost',
'GhostBottleneck', 'Bottleneck', 'BottleneckCSP', 'Proto', 'Detect', 'Segment', 'Pose', 'Classify',
'TransformerEncoderLayer', 'RepC3', 'RTDETRDecoder', 'AIFI', 'DeformableTransformerDecoder',
'DeformableTransformerDecoderLayer', 'MSDeformAttn', 'MLP']
__all__ = ('Conv', 'Conv2', 'LightConv', 'RepConv', 'DWConv', 'DWConvTranspose2d', 'ConvTranspose', 'Focus',
'GhostConv', 'ChannelAttention', 'SpatialAttention', 'CBAM', 'Concat', 'TransformerLayer',
'TransformerBlock', 'MLPBlock', 'LayerNorm2d', 'DFL', 'HGBlock', 'HGStem', 'SPP', 'SPPF', 'C1', 'C2', 'C3',
'C2f', 'C3x', 'C3TR', 'C3Ghost', 'GhostBottleneck', 'Bottleneck', 'BottleneckCSP', 'Proto', 'Detect',
'Segment', 'Pose', 'Classify', 'TransformerEncoderLayer', 'RepC3', 'RTDETRDecoder', 'AIFI',
'DeformableTransformerDecoder', 'DeformableTransformerDecoderLayer', 'MSDeformAttn', 'MLP')

5
ultralytics/nn/modules/block.py
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@ -10,9 +10,8 @@ import torch.nn.functional as F
from .conv import Conv, DWConv, GhostConv, LightConv, RepConv
from .transformer import TransformerBlock
__all__ = [
'DFL', 'HGBlock', 'HGStem', 'SPP', 'SPPF', 'C1', 'C2', 'C3', 'C2f', 'C3x', 'C3TR', 'C3Ghost', 'GhostBottleneck',
'Bottleneck', 'BottleneckCSP', 'Proto', 'RepC3']
__all__ = ('DFL', 'HGBlock', 'HGStem', 'SPP', 'SPPF', 'C1', 'C2', 'C3', 'C2f', 'C3x', 'C3TR', 'C3Ghost',
'GhostBottleneck', 'Bottleneck', 'BottleneckCSP', 'Proto', 'RepC3')
class DFL(nn.Module):

5
ultralytics/nn/modules/conv.py
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@ -9,9 +9,8 @@ import numpy as np
import torch
import torch.nn as nn
__all__ = [
'Conv', 'LightConv', 'DWConv', 'DWConvTranspose2d', 'ConvTranspose', 'Focus', 'GhostConv', 'ChannelAttention',
'SpatialAttention', 'CBAM', 'Concat', 'RepConv']
__all__ = ('Conv', 'LightConv', 'DWConv', 'DWConvTranspose2d', 'ConvTranspose', 'Focus', 'GhostConv',
'ChannelAttention', 'SpatialAttention', 'CBAM', 'Concat', 'RepConv')
def autopad(k, p=None, d=1): # kernel, padding, dilation

2
ultralytics/nn/modules/head.py
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@ -16,7 +16,7 @@ from .conv import Conv
from .transformer import MLP, DeformableTransformerDecoder, DeformableTransformerDecoderLayer
from .utils import bias_init_with_prob, linear_init_
__all__ = ['Detect', 'Segment', 'Pose', 'Classify', 'RTDETRDecoder']
__all__ = 'Detect', 'Segment', 'Pose', 'Classify', 'RTDETRDecoder'
class Detect(nn.Module):

5
ultralytics/nn/modules/transformer.py
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@ -13,9 +13,8 @@ from torch.nn.init import constant_, xavier_uniform_
from .conv import Conv
from .utils import _get_clones, inverse_sigmoid, multi_scale_deformable_attn_pytorch
__all__ = [
'TransformerEncoderLayer', 'TransformerLayer', 'TransformerBlock', 'MLPBlock', 'LayerNorm2d', 'AIFI',
'DeformableTransformerDecoder', 'DeformableTransformerDecoderLayer', 'MSDeformAttn', 'MLP']
__all__ = ('TransformerEncoderLayer', 'TransformerLayer', 'TransformerBlock', 'MLPBlock', 'LayerNorm2d', 'AIFI',
'DeformableTransformerDecoder', 'DeformableTransformerDecoderLayer', 'MSDeformAttn', 'MLP')
class TransformerEncoderLayer(nn.Module):

2
ultralytics/nn/modules/utils.py
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@ -12,7 +12,7 @@ import torch.nn as nn
import torch.nn.functional as F
from torch.nn.init import uniform_
__all__ = ['multi_scale_deformable_attn_pytorch', 'inverse_sigmoid']
__all__ = 'multi_scale_deformable_attn_pytorch', 'inverse_sigmoid'
def _get_clones(module, n):

2
ultralytics/vit/rtdetr/val.py
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@ -9,7 +9,7 @@ from ultralytics.yolo.data.augment import Compose, Format, LetterBox
from ultralytics.yolo.utils import colorstr, ops
from ultralytics.yolo.v8.detect import DetectionValidator
__all__ = ['RTDETRValidator']
__all__ = 'RTDETRValidator', # tuple or list
# TODO: Temporarily, RT-DETR does not need padding.