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306 lines
12 KiB
306 lines
12 KiB
# Ultralytics YOLO 🚀, AGPL-3.0 license
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"""
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Block modules
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"""
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import torch
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import torch.nn as nn
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import torch.nn.functional as F
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from .conv import Conv, DWConv, GhostConv, LightConv, RepConv
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from .transformer import TransformerBlock
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__all__ = [
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'DFL', 'HGBlock', 'HGStem', 'SPP', 'SPPF', 'C1', 'C2', 'C3', 'C2f', 'C3x', 'C3TR', 'C3Ghost', 'GhostBottleneck',
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'Bottleneck', 'BottleneckCSP', 'Proto', 'RepC3']
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class DFL(nn.Module):
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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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"""Initialize a convolutional layer with a given number of input channels."""
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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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x = torch.arange(c1, dtype=torch.float)
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self.conv.weight.data[:] = nn.Parameter(x.view(1, c1, 1, 1))
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self.c1 = c1
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def forward(self, x):
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"""Applies a transformer layer on input tensor 'x' and returns a tensor."""
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b, c, a = x.shape # batch, channels, anchors
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return self.conv(x.view(b, 4, self.c1, a).transpose(2, 1).softmax(1)).view(b, 4, a)
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# return self.conv(x.view(b, self.c1, 4, a).softmax(1)).view(b, 4, a)
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class Proto(nn.Module):
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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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self.upsample = nn.ConvTranspose2d(c_, c_, 2, 2, 0, bias=True) # nn.Upsample(scale_factor=2, mode='nearest')
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self.cv2 = Conv(c_, c_, k=3)
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self.cv3 = Conv(c_, c2)
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def forward(self, x):
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"""Performs a forward pass through layers using an upsampled input image."""
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return self.cv3(self.cv2(self.upsample(self.cv1(x))))
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class HGStem(nn.Module):
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"""StemBlock of PPHGNetV2 with 5 convolutions and one maxpool2d.
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https://github.com/PaddlePaddle/PaddleDetection/blob/develop/ppdet/modeling/backbones/hgnet_v2.py
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"""
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def __init__(self, c1, cm, c2):
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super().__init__()
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self.stem1 = Conv(c1, cm, 3, 2, act=nn.ReLU())
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self.stem2a = Conv(cm, cm // 2, 2, 1, 0, act=nn.ReLU())
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self.stem2b = Conv(cm // 2, cm, 2, 1, 0, act=nn.ReLU())
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self.stem3 = Conv(cm * 2, cm, 3, 2, act=nn.ReLU())
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self.stem4 = Conv(cm, c2, 1, 1, act=nn.ReLU())
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self.pool = nn.MaxPool2d(kernel_size=2, stride=1, padding=0, ceil_mode=True)
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def forward(self, x):
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"""Forward pass of a PPHGNetV2 backbone layer."""
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x = self.stem1(x)
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x = F.pad(x, [0, 1, 0, 1])
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x2 = self.stem2a(x)
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x2 = F.pad(x2, [0, 1, 0, 1])
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x2 = self.stem2b(x2)
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x1 = self.pool(x)
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x = torch.cat([x1, x2], dim=1)
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x = self.stem3(x)
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x = self.stem4(x)
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return x
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class HGBlock(nn.Module):
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"""HG_Block of PPHGNetV2 with 2 convolutions and LightConv.
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https://github.com/PaddlePaddle/PaddleDetection/blob/develop/ppdet/modeling/backbones/hgnet_v2.py
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"""
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def __init__(self, c1, cm, c2, k=3, n=6, lightconv=False, shortcut=False, act=nn.ReLU()):
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super().__init__()
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block = LightConv if lightconv else Conv
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self.m = nn.ModuleList(block(c1 if i == 0 else cm, cm, k=k, act=act) for i in range(n))
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self.sc = Conv(c1 + n * cm, c2 // 2, 1, 1, act=act) # squeeze conv
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self.ec = Conv(c2 // 2, c2, 1, 1, act=act) # excitation conv
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self.add = shortcut and c1 == c2
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def forward(self, x):
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"""Forward pass of a PPHGNetV2 backbone layer."""
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y = [x]
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y.extend(m(y[-1]) for m in self.m)
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y = self.ec(self.sc(torch.cat(y, 1)))
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return y + x if self.add else y
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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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def __init__(self, c1, c2, k=(5, 9, 13)):
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"""Initialize the SPP layer with input/output channels and pooling kernel sizes."""
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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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self.cv2 = Conv(c_ * (len(k) + 1), c2, 1, 1)
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self.m = nn.ModuleList([nn.MaxPool2d(kernel_size=x, stride=1, padding=x // 2) for x in k])
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def forward(self, x):
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"""Forward pass of the SPP layer, performing spatial pyramid pooling."""
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x = self.cv1(x)
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return self.cv2(torch.cat([x] + [m(x) for m in self.m], 1))
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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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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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self.cv2 = Conv(c_ * 4, c2, 1, 1)
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self.m = nn.MaxPool2d(kernel_size=k, stride=1, padding=k // 2)
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def forward(self, x):
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"""Forward pass through Ghost Convolution block."""
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x = self.cv1(x)
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y1 = self.m(x)
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y2 = self.m(y1)
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return self.cv2(torch.cat((x, y1, y2, self.m(y2)), 1))
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class C1(nn.Module):
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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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self.m = nn.Sequential(*(Conv(c2, c2, 3) for _ in range(n)))
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def forward(self, x):
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"""Applies cross-convolutions to input in the C3 module."""
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y = self.cv1(x)
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return self.m(y) + y
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class C2(nn.Module):
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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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self.cv1 = Conv(c1, 2 * self.c, 1, 1)
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self.cv2 = Conv(2 * self.c, c2, 1) # optional act=FReLU(c2)
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# self.attention = ChannelAttention(2 * self.c) # or SpatialAttention()
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self.m = nn.Sequential(*(Bottleneck(self.c, self.c, shortcut, g, k=((3, 3), (3, 3)), e=1.0) for _ in range(n)))
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def forward(self, x):
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"""Forward pass through the CSP bottleneck with 2 convolutions."""
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a, b = self.cv1(x).chunk(2, 1)
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return self.cv2(torch.cat((self.m(a), b), 1))
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class C2f(nn.Module):
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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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self.cv1 = Conv(c1, 2 * self.c, 1, 1)
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self.cv2 = Conv((2 + n) * self.c, c2, 1) # optional act=FReLU(c2)
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self.m = nn.ModuleList(Bottleneck(self.c, self.c, shortcut, g, k=((3, 3), (3, 3)), e=1.0) for _ in range(n))
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def forward(self, x):
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"""Forward pass of a YOLOv5 CSPDarknet backbone layer."""
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y = list(self.cv1(x).chunk(2, 1))
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y.extend(m(y[-1]) for m in self.m)
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return self.cv2(torch.cat(y, 1))
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def forward_split(self, x):
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"""Applies spatial attention to module's input."""
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y = list(self.cv1(x).split((self.c, self.c), 1))
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y.extend(m(y[-1]) for m in self.m)
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return self.cv2(torch.cat(y, 1))
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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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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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self.cv2 = Conv(c1, c_, 1, 1)
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self.cv3 = Conv(2 * c_, c2, 1) # optional act=FReLU(c2)
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self.m = nn.Sequential(*(Bottleneck(c_, c_, shortcut, g, k=((1, 1), (3, 3)), e=1.0) for _ in range(n)))
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def forward(self, x):
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"""Forward pass through the CSP bottleneck with 2 convolutions."""
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return self.cv3(torch.cat((self.m(self.cv1(x)), self.cv2(x)), 1))
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class C3x(C3):
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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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"""Initialize C3TR instance and set default parameters."""
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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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self.m = nn.Sequential(*(Bottleneck(self.c_, self.c_, shortcut, g, k=((1, 3), (3, 1)), e=1) for _ in range(n)))
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class RepC3(nn.Module):
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"""Rep C3."""
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def __init__(self, c1, c2, n=3, e=1.0):
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super().__init__()
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c_ = int(c2 * e) # hidden channels
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self.cv1 = Conv(c1, c2, 1, 1)
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self.cv2 = Conv(c1, c2, 1, 1)
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self.m = nn.Sequential(*[RepConv(c_, c_) for _ in range(n)])
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self.cv3 = Conv(c_, c2, 1, 1) if c_ != c2 else nn.Identity()
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def forward(self, x):
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"""Forward pass of RT-DETR neck layer."""
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return self.cv3(self.m(self.cv1(x)) + self.cv2(x))
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class C3TR(C3):
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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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"""Initialize C3Ghost module with GhostBottleneck()."""
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super().__init__(c1, c2, n, shortcut, g, e)
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c_ = int(c2 * e)
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self.m = TransformerBlock(c_, c_, 4, n)
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class C3Ghost(C3):
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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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"""Initialize 'SPP' module with various pooling sizes for spatial pyramid pooling."""
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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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self.m = nn.Sequential(*(GhostBottleneck(c_, c_) for _ in range(n)))
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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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super().__init__()
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c_ = c2 // 2
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self.conv = nn.Sequential(
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GhostConv(c1, c_, 1, 1), # pw
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DWConv(c_, c_, k, s, act=False) if s == 2 else nn.Identity(), # dw
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GhostConv(c_, c2, 1, 1, act=False)) # pw-linear
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self.shortcut = nn.Sequential(DWConv(c1, c1, k, s, act=False), Conv(c1, c2, 1, 1,
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act=False)) if s == 2 else nn.Identity()
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def forward(self, x):
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"""Applies skip connection and concatenation to input tensor."""
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return self.conv(x) + self.shortcut(x)
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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, 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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self.cv1 = Conv(c1, c_, k[0], 1)
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self.cv2 = Conv(c_, c2, k[1], 1, g=g)
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self.add = shortcut and c1 == c2
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def forward(self, x):
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"""'forward()' applies the YOLOv5 FPN to input data."""
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return x + self.cv2(self.cv1(x)) if self.add else self.cv2(self.cv1(x))
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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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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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self.cv2 = nn.Conv2d(c1, c_, 1, 1, bias=False)
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self.cv3 = nn.Conv2d(c_, c_, 1, 1, bias=False)
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self.cv4 = Conv(2 * c_, c2, 1, 1)
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self.bn = nn.BatchNorm2d(2 * c_) # applied to cat(cv2, cv3)
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self.act = nn.SiLU()
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self.m = nn.Sequential(*(Bottleneck(c_, c_, shortcut, g, e=1.0) for _ in range(n)))
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def forward(self, x):
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"""Applies a CSP bottleneck with 3 convolutions."""
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y1 = self.cv3(self.m(self.cv1(x)))
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y2 = self.cv2(x)
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return self.cv4(self.act(self.bn(torch.cat((y1, y2), 1))))
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