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

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Co-authored-by: Snyk bot <snyk-bot@snyk.io>

docs/reference/nn/tasks.md

@@ -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

docs/reference/yolo/data/dataloaders/stream_loaders.md

@@ -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>

docs/reference/yolo/utils/loss.md

@@ -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>

docs/reference/yolo/v8/detect/train.md

@@ -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

docs/reference/yolo/v8/pose/train.md

@@ -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

docs/reference/yolo/v8/segment/train.md

@@ -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

ultralytics/__init__.py

@@ -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

ultralytics/nn/modules/__init__.py

@@ -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__ = [
+__all__ = ('Conv', 'Conv2', 'LightConv', 'RepConv', 'DWConv', 'DWConvTranspose2d', 'ConvTranspose', 'Focus',
-    'Conv', 'Conv2', 'LightConv', 'RepConv', 'DWConv', 'DWConvTranspose2d', 'ConvTranspose', 'Focus', 'GhostConv',
+           'GhostConv', 'ChannelAttention', 'SpatialAttention', 'CBAM', 'Concat', 'TransformerLayer',
-    'ChannelAttention', 'SpatialAttention', 'CBAM', 'Concat', 'TransformerLayer', 'TransformerBlock', 'MLPBlock',
+           'TransformerBlock', 'MLPBlock', 'LayerNorm2d', 'DFL', 'HGBlock', 'HGStem', 'SPP', 'SPPF', 'C1', 'C2', 'C3',
-    'LayerNorm2d', 'DFL', 'HGBlock', 'HGStem', 'SPP', 'SPPF', 'C1', 'C2', 'C3', 'C2f', 'C3x', 'C3TR', 'C3Ghost',
+           'C2f', 'C3x', 'C3TR', 'C3Ghost', 'GhostBottleneck', 'Bottleneck', 'BottleneckCSP', 'Proto', 'Detect',
-    'GhostBottleneck', 'Bottleneck', 'BottleneckCSP', 'Proto', 'Detect', 'Segment', 'Pose', 'Classify',
+           'Segment', 'Pose', 'Classify', 'TransformerEncoderLayer', 'RepC3', 'RTDETRDecoder', 'AIFI',
-    'TransformerEncoderLayer', 'RepC3', 'RTDETRDecoder', 'AIFI', 'DeformableTransformerDecoder',
+           'DeformableTransformerDecoder', 'DeformableTransformerDecoderLayer', 'MSDeformAttn', 'MLP')
-    'DeformableTransformerDecoderLayer', 'MSDeformAttn', 'MLP']

ultralytics/nn/modules/block.py

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

ultralytics/nn/modules/conv.py

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

ultralytics/nn/modules/head.py

@@ -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):

ultralytics/nn/modules/transformer.py

@@ -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__ = [
+__all__ = ('TransformerEncoderLayer', 'TransformerLayer', 'TransformerBlock', 'MLPBlock', 'LayerNorm2d', 'AIFI',
-    'TransformerEncoderLayer', 'TransformerLayer', 'TransformerBlock', 'MLPBlock', 'LayerNorm2d', 'AIFI',
+           'DeformableTransformerDecoder', 'DeformableTransformerDecoderLayer', 'MSDeformAttn', 'MLP')
-    'DeformableTransformerDecoder', 'DeformableTransformerDecoderLayer', 'MSDeformAttn', 'MLP']
 
 
 class TransformerEncoderLayer(nn.Module):

ultralytics/nn/modules/utils.py

@@ -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):

ultralytics/vit/rtdetr/val.py

@@ -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.