ultralytics 8.0.80 single-line docstring fixes (#2060)
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
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Glenn Jocher
GitHub
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48 changed files with 418 additions and 420 deletions
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@ -12,7 +12,7 @@ from ultralytics.yolo.utils.tal import dist2bbox, make_anchors
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def autopad(k, p=None, d=1): # kernel, padding, dilation
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# Pad to 'same' shape outputs
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"""Pad to 'same' shape outputs."""
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if d > 1:
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k = d * (k - 1) + 1 if isinstance(k, int) else [d * (x - 1) + 1 for x in k] # actual kernel-size
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if p is None:
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@ -21,7 +21,7 @@ def autopad(k, p=None, d=1): # kernel, padding, dilation
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class Conv(nn.Module):
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# Standard convolution with args(ch_in, ch_out, kernel, stride, padding, groups, dilation, activation)
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"""Standard convolution with args(ch_in, ch_out, kernel, stride, padding, groups, dilation, activation)."""
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default_act = nn.SiLU() # default activation
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def __init__(self, c1, c2, k=1, s=1, p=None, g=1, d=1, act=True):
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@ -38,19 +38,21 @@ class Conv(nn.Module):
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class DWConv(Conv):
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# Depth-wise convolution
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"""Depth-wise convolution."""
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def __init__(self, c1, c2, k=1, s=1, d=1, act=True): # ch_in, ch_out, kernel, stride, dilation, activation
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super().__init__(c1, c2, k, s, g=math.gcd(c1, c2), d=d, act=act)
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class DWConvTranspose2d(nn.ConvTranspose2d):
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# Depth-wise transpose convolution
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"""Depth-wise transpose convolution."""
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def __init__(self, c1, c2, k=1, s=1, p1=0, p2=0): # ch_in, ch_out, kernel, stride, padding, padding_out
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super().__init__(c1, c2, k, s, p1, p2, groups=math.gcd(c1, c2))
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class ConvTranspose(nn.Module):
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# Convolution transpose 2d layer
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"""Convolution transpose 2d layer."""
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default_act = nn.SiLU() # default activation
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def __init__(self, c1, c2, k=2, s=2, p=0, bn=True, act=True):
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@ -67,8 +69,11 @@ class ConvTranspose(nn.Module):
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class DFL(nn.Module):
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# Integral module of Distribution Focal Loss (DFL)
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# Proposed in Generalized Focal Loss https://ieeexplore.ieee.org/document/9792391
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"""
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Integral module of Distribution Focal Loss (DFL).
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Proposed in Generalized Focal Loss https://ieeexplore.ieee.org/document/9792391
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"""
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def __init__(self, c1=16):
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super().__init__()
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self.conv = nn.Conv2d(c1, 1, 1, bias=False).requires_grad_(False)
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@ -83,7 +88,8 @@ class DFL(nn.Module):
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class TransformerLayer(nn.Module):
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# Transformer layer https://arxiv.org/abs/2010.11929 (LayerNorm layers removed for better performance)
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"""Transformer layer https://arxiv.org/abs/2010.11929 (LayerNorm layers removed for better performance)."""
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def __init__(self, c, num_heads):
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super().__init__()
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self.q = nn.Linear(c, c, bias=False)
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@ -100,7 +106,8 @@ class TransformerLayer(nn.Module):
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class TransformerBlock(nn.Module):
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# Vision Transformer https://arxiv.org/abs/2010.11929
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"""Vision Transformer https://arxiv.org/abs/2010.11929."""
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def __init__(self, c1, c2, num_heads, num_layers):
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super().__init__()
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self.conv = None
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@ -119,7 +126,8 @@ class TransformerBlock(nn.Module):
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class Bottleneck(nn.Module):
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# Standard bottleneck
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"""Standard bottleneck."""
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def __init__(self, c1, c2, shortcut=True, g=1, k=(3, 3), e=0.5): # ch_in, ch_out, shortcut, groups, kernels, expand
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super().__init__()
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c_ = int(c2 * e) # hidden channels
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@ -132,7 +140,8 @@ class Bottleneck(nn.Module):
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class BottleneckCSP(nn.Module):
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# CSP Bottleneck https://github.com/WongKinYiu/CrossStagePartialNetworks
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"""CSP Bottleneck https://github.com/WongKinYiu/CrossStagePartialNetworks."""
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def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5): # ch_in, ch_out, number, shortcut, groups, expansion
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super().__init__()
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c_ = int(c2 * e) # hidden channels
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@ -151,7 +160,8 @@ class BottleneckCSP(nn.Module):
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class C3(nn.Module):
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# CSP Bottleneck with 3 convolutions
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"""CSP Bottleneck with 3 convolutions."""
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def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5): # ch_in, ch_out, number, shortcut, groups, expansion
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super().__init__()
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c_ = int(c2 * e) # hidden channels
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@ -165,7 +175,8 @@ class C3(nn.Module):
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class C2(nn.Module):
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# CSP Bottleneck with 2 convolutions
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"""CSP Bottleneck with 2 convolutions."""
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def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5): # ch_in, ch_out, number, shortcut, groups, expansion
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super().__init__()
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self.c = int(c2 * e) # hidden channels
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@ -180,7 +191,8 @@ class C2(nn.Module):
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class C2f(nn.Module):
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# CSP Bottleneck with 2 convolutions
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"""CSP Bottleneck with 2 convolutions."""
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def __init__(self, c1, c2, n=1, shortcut=False, g=1, e=0.5): # ch_in, ch_out, number, shortcut, groups, expansion
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super().__init__()
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self.c = int(c2 * e) # hidden channels
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@ -200,7 +212,8 @@ class C2f(nn.Module):
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class ChannelAttention(nn.Module):
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# Channel-attention module https://github.com/open-mmlab/mmdetection/tree/v3.0.0rc1/configs/rtmdet
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"""Channel-attention module https://github.com/open-mmlab/mmdetection/tree/v3.0.0rc1/configs/rtmdet."""
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def __init__(self, channels: int) -> None:
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super().__init__()
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self.pool = nn.AdaptiveAvgPool2d(1)
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@ -212,7 +225,8 @@ class ChannelAttention(nn.Module):
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class SpatialAttention(nn.Module):
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# Spatial-attention module
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"""Spatial-attention module."""
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def __init__(self, kernel_size=7):
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super().__init__()
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assert kernel_size in (3, 7), 'kernel size must be 3 or 7'
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@ -225,7 +239,8 @@ class SpatialAttention(nn.Module):
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class CBAM(nn.Module):
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# Convolutional Block Attention Module
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"""Convolutional Block Attention Module."""
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def __init__(self, c1, kernel_size=7): # ch_in, kernels
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super().__init__()
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self.channel_attention = ChannelAttention(c1)
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@ -236,7 +251,8 @@ class CBAM(nn.Module):
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class C1(nn.Module):
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# CSP Bottleneck with 1 convolution
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"""CSP Bottleneck with 1 convolution."""
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def __init__(self, c1, c2, n=1): # ch_in, ch_out, number
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super().__init__()
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self.cv1 = Conv(c1, c2, 1, 1)
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@ -248,7 +264,8 @@ class C1(nn.Module):
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class C3x(C3):
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# C3 module with cross-convolutions
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"""C3 module with cross-convolutions."""
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def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5):
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super().__init__(c1, c2, n, shortcut, g, e)
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self.c_ = int(c2 * e)
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@ -256,7 +273,8 @@ class C3x(C3):
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class C3TR(C3):
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# C3 module with TransformerBlock()
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"""C3 module with TransformerBlock()."""
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def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5):
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super().__init__(c1, c2, n, shortcut, g, e)
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c_ = int(c2 * e)
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@ -264,7 +282,8 @@ class C3TR(C3):
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class C3Ghost(C3):
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# C3 module with GhostBottleneck()
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"""C3 module with GhostBottleneck()."""
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def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5):
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super().__init__(c1, c2, n, shortcut, g, e)
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c_ = int(c2 * e) # hidden channels
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@ -272,7 +291,8 @@ class C3Ghost(C3):
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class SPP(nn.Module):
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# Spatial Pyramid Pooling (SPP) layer https://arxiv.org/abs/1406.4729
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"""Spatial Pyramid Pooling (SPP) layer https://arxiv.org/abs/1406.4729."""
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def __init__(self, c1, c2, k=(5, 9, 13)):
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super().__init__()
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c_ = c1 // 2 # hidden channels
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@ -286,7 +306,8 @@ class SPP(nn.Module):
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class SPPF(nn.Module):
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# Spatial Pyramid Pooling - Fast (SPPF) layer for YOLOv5 by Glenn Jocher
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"""Spatial Pyramid Pooling - Fast (SPPF) layer for YOLOv5 by Glenn Jocher."""
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def __init__(self, c1, c2, k=5): # equivalent to SPP(k=(5, 9, 13))
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super().__init__()
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c_ = c1 // 2 # hidden channels
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@ -302,7 +323,8 @@ class SPPF(nn.Module):
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class Focus(nn.Module):
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# Focus wh information into c-space
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"""Focus wh information into c-space."""
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def __init__(self, c1, c2, k=1, s=1, p=None, g=1, act=True): # ch_in, ch_out, kernel, stride, padding, groups
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super().__init__()
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self.conv = Conv(c1 * 4, c2, k, s, p, g, act=act)
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@ -314,7 +336,8 @@ class Focus(nn.Module):
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class GhostConv(nn.Module):
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# Ghost Convolution https://github.com/huawei-noah/ghostnet
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"""Ghost Convolution https://github.com/huawei-noah/ghostnet."""
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def __init__(self, c1, c2, k=1, s=1, g=1, act=True): # ch_in, ch_out, kernel, stride, groups
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super().__init__()
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c_ = c2 // 2 # hidden channels
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@ -327,7 +350,8 @@ class GhostConv(nn.Module):
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class GhostBottleneck(nn.Module):
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# Ghost Bottleneck https://github.com/huawei-noah/ghostnet
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"""Ghost Bottleneck https://github.com/huawei-noah/ghostnet."""
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def __init__(self, c1, c2, k=3, s=1): # ch_in, ch_out, kernel, stride
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super().__init__()
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c_ = c2 // 2
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@ -343,7 +367,8 @@ class GhostBottleneck(nn.Module):
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class Concat(nn.Module):
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# Concatenate a list of tensors along dimension
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"""Concatenate a list of tensors along dimension."""
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def __init__(self, dimension=1):
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super().__init__()
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self.d = dimension
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@ -353,7 +378,8 @@ class Concat(nn.Module):
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class Proto(nn.Module):
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# YOLOv8 mask Proto module for segmentation models
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"""YOLOv8 mask Proto module for segmentation models."""
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def __init__(self, c1, c_=256, c2=32): # ch_in, number of protos, number of masks
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super().__init__()
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self.cv1 = Conv(c1, c_, k=3)
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@ -366,7 +392,8 @@ class Proto(nn.Module):
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class Ensemble(nn.ModuleList):
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# Ensemble of models
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"""Ensemble of models."""
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def __init__(self):
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super().__init__()
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@ -382,7 +409,7 @@ class Ensemble(nn.ModuleList):
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class Detect(nn.Module):
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# YOLOv8 Detect head for detection models
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"""YOLOv8 Detect head for detection models."""
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dynamic = False # force grid reconstruction
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export = False # export mode
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shape = None
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@ -423,7 +450,7 @@ class Detect(nn.Module):
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return y if self.export else (y, x)
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def bias_init(self):
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# Initialize Detect() biases, WARNING: requires stride availability
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"""Initialize Detect() biases, WARNING: requires stride availability."""
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m = self # self.model[-1] # Detect() module
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# cf = torch.bincount(torch.tensor(np.concatenate(dataset.labels, 0)[:, 0]).long(), minlength=nc) + 1
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# ncf = math.log(0.6 / (m.nc - 0.999999)) if cf is None else torch.log(cf / cf.sum()) # nominal class frequency
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@ -433,7 +460,8 @@ class Detect(nn.Module):
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class Segment(Detect):
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# YOLOv8 Segment head for segmentation models
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"""YOLOv8 Segment head for segmentation models."""
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def __init__(self, nc=80, nm=32, npr=256, ch=()):
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super().__init__(nc, ch)
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self.nm = nm # number of masks
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@ -456,7 +484,8 @@ class Segment(Detect):
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class Pose(Detect):
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# YOLOv8 Pose head for keypoints models
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"""YOLOv8 Pose head for keypoints models."""
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def __init__(self, nc=80, kpt_shape=(17, 3), ch=()):
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super().__init__(nc, ch)
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self.kpt_shape = kpt_shape # number of keypoints, number of dims (2 for x,y or 3 for x,y,visible)
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@ -486,7 +515,8 @@ class Pose(Detect):
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class Classify(nn.Module):
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# YOLOv8 classification head, i.e. x(b,c1,20,20) to x(b,c2)
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"""YOLOv8 classification head, i.e. x(b,c1,20,20) to x(b,c2)."""
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def __init__(self, c1, c2, k=1, s=1, p=None, g=1): # ch_in, ch_out, kernel, stride, padding, groups
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super().__init__()
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c_ = 1280 # efficientnet_b0 size
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