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

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This commit is contained in:
Glenn Jocher
2023-05-30 20:23:30 +02:00
committed by GitHub
parent 305cde69d0
commit fd94d312da
14 changed files with 46 additions and 35 deletions
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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):