Replace inline comments with docstrings (#12764)
* Add docstrings * Add docstrings * Add docstrings * Add docstrings * Add docstrings * Add docstrings * Add docstrings * Add docstrings * Add docstrings * Add docstrings * Auto-format by https://ultralytics.com/actions * Add docstrings * Add docstrings * Add docstrings * Add docstrings * Auto-format by https://ultralytics.com/actions * Update plots.py Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com> --------- Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com> Co-authored-by: UltralyticsAssistant <web@ultralytics.com>
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@@ -57,8 +57,12 @@ from utils.general import (
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from utils.torch_utils import copy_attr, smart_inference_mode
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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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def autopad(k, p=None, d=1):
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"""
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Pads kernel to 'same' output shape, adjusting for optional dilation; returns padding size.
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`k`: kernel, `p`: padding, `d`: dilation.
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"""
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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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@@ -88,13 +92,19 @@ class Conv(nn.Module):
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class DWConv(Conv):
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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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def __init__(self, c1, c2, k=1, s=1, d=1, act=True):
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"""Initializes a depth-wise convolution layer with optional activation; args: input channels (c1), output
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channels (c2), kernel size (k), stride (s), dilation (d), and activation flag (act).
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"""
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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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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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def __init__(self, c1, c2, k=1, s=1, p1=0, p2=0):
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"""Initializes a depth-wise transpose convolutional layer for YOLOv5; args: input channels (c1), output channels
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(c2), kernel size (k), stride (s), input padding (p1), output padding (p2).
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"""
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super().__init__(c1, c2, k, s, p1, p2, groups=math.gcd(c1, c2))
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@@ -148,7 +158,10 @@ class TransformerBlock(nn.Module):
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class Bottleneck(nn.Module):
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# Standard bottleneck
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def __init__(self, c1, c2, shortcut=True, g=1, e=0.5): # ch_in, ch_out, shortcut, groups, expansion
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def __init__(self, c1, c2, shortcut=True, g=1, e=0.5):
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"""Initializes a standard bottleneck layer with optional shortcut and group convolution, supporting channel
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expansion.
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"""
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super().__init__()
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c_ = int(c2 * e) # hidden channels
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self.cv1 = Conv(c1, c_, 1, 1)
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@@ -164,7 +177,10 @@ 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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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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def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5):
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"""Initializes CSP bottleneck with optional shortcuts; args: ch_in, ch_out, number of repeats, shortcut bool,
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groups, expansion.
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"""
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super().__init__()
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c_ = int(c2 * e) # hidden channels
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self.cv1 = Conv(c1, c_, 1, 1)
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@@ -206,7 +222,10 @@ class CrossConv(nn.Module):
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class C3(nn.Module):
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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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def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5):
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"""Initializes C3 module with options for channel count, bottleneck repetition, shortcut usage, group
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convolutions, and expansion.
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"""
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super().__init__()
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c_ = int(c2 * e) # hidden channels
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self.cv1 = Conv(c1, c_, 1, 1)
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@@ -283,7 +302,13 @@ 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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def __init__(self, c1, c2, k=5): # equivalent to SPP(k=(5, 9, 13))
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def __init__(self, c1, c2, k=5):
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"""
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Initializes YOLOv5 SPPF layer with given channels and kernel size for YOLOv5 model, combining convolution and
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max pooling.
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Equivalent to SPP(k=(5, 9, 13)).
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"""
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super().__init__()
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c_ = c1 // 2 # hidden channels
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self.cv1 = Conv(c1, c_, 1, 1)
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@@ -302,19 +327,26 @@ 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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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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def __init__(self, c1, c2, k=1, s=1, p=None, g=1, act=True):
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"""Initializes Focus module to concentrate width-height info into channel space with configurable convolution
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parameters.
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"""
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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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# self.contract = Contract(gain=2)
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def forward(self, x): # x(b,c,w,h) -> y(b,4c,w/2,h/2)
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def forward(self, x):
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"""Processes input through Focus mechanism, reshaping (b,c,w,h) to (b,4c,w/2,h/2) then applies convolution."""
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return self.conv(torch.cat((x[..., ::2, ::2], x[..., 1::2, ::2], x[..., ::2, 1::2], x[..., 1::2, 1::2]), 1))
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# return self.conv(self.contract(x))
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class GhostConv(nn.Module):
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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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def __init__(self, c1, c2, k=1, s=1, g=1, act=True):
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"""Initializes GhostConv with in/out channels, kernel size, stride, groups, and activation; halves out channels
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for efficiency.
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"""
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super().__init__()
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c_ = c2 // 2 # hidden channels
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self.cv1 = Conv(c1, c_, k, s, None, g, act=act)
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@@ -328,7 +360,8 @@ 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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def __init__(self, c1, c2, k=3, s=1): # ch_in, ch_out, kernel, stride
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def __init__(self, c1, c2, k=3, s=1):
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"""Initializes GhostBottleneck with ch_in `c1`, ch_out `c2`, kernel size `k`, stride `s`; see https://github.com/huawei-noah/ghostnet."""
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super().__init__()
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c_ = c2 // 2
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self.conv = nn.Sequential(
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@@ -982,10 +1015,14 @@ class Detections:
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"""Logs the string representation of the current object's state via the LOGGER."""
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LOGGER.info(self.__str__())
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def __len__(self): # override len(results)
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def __len__(self):
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"""Returns the number of results stored, overrides the default len(results)."""
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return self.n
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def __str__(self): # override print(results)
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def __str__(self):
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"""Returns a string representation of the model's results, suitable for printing, overrides default
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print(results).
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"""
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return self._run(pprint=True) # print results
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def __repr__(self):
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@@ -995,7 +1032,8 @@ class Detections:
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class Proto(nn.Module):
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# YOLOv5 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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def __init__(self, c1, c_=256, c2=32):
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"""Initializes YOLOv5 Proto module for segmentation with input, proto, and mask channels configuration."""
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super().__init__()
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self.cv1 = Conv(c1, c_, k=3)
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self.upsample = nn.Upsample(scale_factor=2, mode="nearest")
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