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# Ultralytics YOLO 🚀, AGPL-3.0 license
"""
Block modules
"""
import torch
import torch.nn as nn
import torch.nn.functional as F
from timm.models.layers import DropPath
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')
class DFL(nn.Module):
"""
Integral module of Distribution Focal Loss (DFL).
Proposed in Generalized Focal Loss https://ieeexplore.ieee.org/document/9792391
"""
def __init__(self, c1=16):
"""Initialize a convolutional layer with a given number of input channels."""
super().__init__()
self.conv = nn.Conv2d(c1, 1, 1, bias=False).requires_grad_(False)
x = torch.arange(c1, dtype=torch.float)
self.conv.weight.data[:] = nn.Parameter(x.view(1, c1, 1, 1))
self.c1 = c1
def forward(self, x):
"""Applies a transformer layer on input tensor 'x' and returns a tensor."""
b, c, a = x.shape # batch, channels, anchors
return self.conv(x.view(b, 4, self.c1, a).transpose(2, 1).softmax(1)).view(b, 4, a)
# return self.conv(x.view(b, self.c1, 4, a).softmax(1)).view(b, 4, a)
class Proto(nn.Module):
"""YOLOv8 mask Proto module for segmentation models."""
def __init__(self, c1, c_=256, c2=32): # ch_in, number of protos, number of masks
super().__init__()
self.cv1 = Conv(c1, c_, k=3)
self.upsample = nn.ConvTranspose2d(c_, c_, 2, 2, 0, bias=True) # nn.Upsample(scale_factor=2, mode='nearest')
self.cv2 = Conv(c_, c_, k=3)
self.cv3 = Conv(c_, c2)
def forward(self, x):
"""Performs a forward pass through layers using an upsampled input image."""
return self.cv3(self.cv2(self.upsample(self.cv1(x))))
class HGStem(nn.Module):
"""StemBlock of PPHGNetV2 with 5 convolutions and one maxpool2d.
https://github.com/PaddlePaddle/PaddleDetection/blob/develop/ppdet/modeling/backbones/hgnet_v2.py
"""
def __init__(self, c1, cm, c2):
super().__init__()
self.stem1 = Conv(c1, cm, 3, 2, act=nn.ReLU())
self.stem2a = Conv(cm, cm // 2, 2, 1, 0, act=nn.ReLU())
self.stem2b = Conv(cm // 2, cm, 2, 1, 0, act=nn.ReLU())
self.stem3 = Conv(cm * 2, cm, 3, 2, act=nn.ReLU())
self.stem4 = Conv(cm, c2, 1, 1, act=nn.ReLU())
self.pool = nn.MaxPool2d(kernel_size=2, stride=1, padding=0, ceil_mode=True)
def forward(self, x):
"""Forward pass of a PPHGNetV2 backbone layer."""
x = self.stem1(x)
x = F.pad(x, [0, 1, 0, 1])
x2 = self.stem2a(x)
x2 = F.pad(x2, [0, 1, 0, 1])
x2 = self.stem2b(x2)
x1 = self.pool(x)
x = torch.cat([x1, x2], dim=1)
x = self.stem3(x)
x = self.stem4(x)
return x
class HGBlock(nn.Module):
"""HG_Block of PPHGNetV2 with 2 convolutions and LightConv.
https://github.com/PaddlePaddle/PaddleDetection/blob/develop/ppdet/modeling/backbones/hgnet_v2.py
"""
def __init__(self, c1, cm, c2, k=3, n=6, lightconv=False, shortcut=False, act=nn.ReLU()):
super().__init__()
block = LightConv if lightconv else Conv
self.m = nn.ModuleList(block(c1 if i == 0 else cm, cm, k=k, act=act) for i in range(n))
self.sc = Conv(c1 + n * cm, c2 // 2, 1, 1, act=act) # squeeze conv
self.ec = Conv(c2 // 2, c2, 1, 1, act=act) # excitation conv
self.add = shortcut and c1 == c2
def forward(self, x):
"""Forward pass of a PPHGNetV2 backbone layer."""
y = [x]
y.extend(m(y[-1]) for m in self.m)
y = self.ec(self.sc(torch.cat(y, 1)))
return y + x if self.add else y
class SPP(nn.Module):
"""Spatial Pyramid Pooling (SPP) layer https://arxiv.org/abs/1406.4729."""
def __init__(self, c1, c2, k=(5, 9, 13)):
"""Initialize the SPP layer with input/output channels and pooling kernel sizes."""
super().__init__()
c_ = c1 // 2 # hidden channels
self.cv1 = Conv(c1, c_, 1, 1)
self.cv2 = Conv(c_ * (len(k) + 1), c2, 1, 1)
self.m = nn.ModuleList([nn.MaxPool2d(kernel_size=x, stride=1, padding=x // 2) for x in k])
def forward(self, x):
"""Forward pass of the SPP layer, performing spatial pyramid pooling."""
x = self.cv1(x)
return self.cv2(torch.cat([x] + [m(x) for m in self.m], 1))
class SPPF(nn.Module):
"""Spatial Pyramid Pooling - Fast (SPPF) layer for YOLOv5 by Glenn Jocher."""
def __init__(self, c1, c2, k=5): # equivalent to SPP(k=(5, 9, 13))
super().__init__()
c_ = c1 // 2 # hidden channels
self.cv1 = Conv(c1, c_, 1, 1)
self.cv2 = Conv(c_ * 4, c2, 1, 1)
self.m = nn.MaxPool2d(kernel_size=k, stride=1, padding=k // 2)
def forward(self, x):
"""Forward pass through Ghost Convolution block."""
x = self.cv1(x)
y1 = self.m(x)
y2 = self.m(y1)
return self.cv2(torch.cat((x, y1, y2, self.m(y2)), 1))
class C1(nn.Module):
"""CSP Bottleneck with 1 convolution."""
def __init__(self, c1, c2, n=1): # ch_in, ch_out, number
super().__init__()
self.cv1 = Conv(c1, c2, 1, 1)
self.m = nn.Sequential(*(Conv(c2, c2, 3) for _ in range(n)))
def forward(self, x):
"""Applies cross-convolutions to input in the C3 module."""
y = self.cv1(x)
return self.m(y) + y
class C2(nn.Module):
"""CSP Bottleneck with 2 convolutions."""
def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5): # ch_in, ch_out, number, shortcut, groups, expansion
super().__init__()
self.c = int(c2 * e) # hidden channels
self.cv1 = Conv(c1, 2 * self.c, 1, 1)
self.cv2 = Conv(2 * self.c, c2, 1) # optional act=FReLU(c2)
# self.attention = ChannelAttention(2 * self.c) # or SpatialAttention()
self.m = nn.Sequential(*(Bottleneck(self.c, self.c, shortcut, g, k=((3, 3), (3, 3)), e=1.0) for _ in range(n)))
def forward(self, x):
"""Forward pass through the CSP bottleneck with 2 convolutions."""
a, b = self.cv1(x).chunk(2, 1)
return self.cv2(torch.cat((self.m(a), b), 1))
class C2f(nn.Module):
"""Faster Implementation of CSP Bottleneck with 2 convolutions."""
def __init__(self, c1, c2, n=1, shortcut=False, g=1, e=0.5, drop_path=None): # ch_in, ch_out, number, shortcut, groups, expansion
super().__init__()
if drop_path is None:
drop_path = [0.0] * n
self.c = int(c2 * e) # hidden channels
self.cv1 = Conv(c1, 2 * self.c, 1, 1)
self.cv2 = Conv((2 + n) * self.c, c2, 1) # optional act=FReLU(c2)
self.m = nn.ModuleList(Bottleneck(self.c, self.c, shortcut, g, k=((3, 3), (3, 3)), e=1.0, drop_path=drop_path[i]) for i in range(n))
def forward(self, x):
"""Forward pass through C2f layer."""
y = list(self.cv1(x).chunk(2, 1))
y.extend(m(y[-1]) for m in self.m)
return self.cv2(torch.cat(y, 1))
def forward_split(self, x):
"""Forward pass using split() instead of chunk()."""
y = list(self.cv1(x).split((self.c, self.c), 1))
y.extend(m(y[-1]) for m in self.m)
return self.cv2(torch.cat(y, 1))
class C3(nn.Module):
"""CSP Bottleneck with 3 convolutions."""
def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5): # ch_in, ch_out, number, shortcut, groups, expansion
super().__init__()
c_ = int(c2 * e) # hidden channels
self.cv1 = Conv(c1, c_, 1, 1)
self.cv2 = Conv(c1, c_, 1, 1)
self.cv3 = Conv(2 * c_, c2, 1) # optional act=FReLU(c2)
self.m = nn.Sequential(*(Bottleneck(c_, c_, shortcut, g, k=((1, 1), (3, 3)), e=1.0) for _ in range(n)))
def forward(self, x):
"""Forward pass through the CSP bottleneck with 2 convolutions."""
return self.cv3(torch.cat((self.m(self.cv1(x)), self.cv2(x)), 1))
class C3x(C3):
"""C3 module with cross-convolutions."""
def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5):
"""Initialize C3TR instance and set default parameters."""
super().__init__(c1, c2, n, shortcut, g, e)
self.c_ = int(c2 * e)
self.m = nn.Sequential(*(Bottleneck(self.c_, self.c_, shortcut, g, k=((1, 3), (3, 1)), e=1) for _ in range(n)))
class RepC3(nn.Module):
"""Rep C3."""
def __init__(self, c1, c2, n=3, e=1.0):
super().__init__()
c_ = int(c2 * e) # hidden channels
self.cv1 = Conv(c1, c2, 1, 1)
self.cv2 = Conv(c1, c2, 1, 1)
self.m = nn.Sequential(*[RepConv(c_, c_) for _ in range(n)])
self.cv3 = Conv(c_, c2, 1, 1) if c_ != c2 else nn.Identity()
def forward(self, x):
"""Forward pass of RT-DETR neck layer."""
return self.cv3(self.m(self.cv1(x)) + self.cv2(x))
class C3TR(C3):
"""C3 module with TransformerBlock()."""
def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5):
"""Initialize C3Ghost module with GhostBottleneck()."""
super().__init__(c1, c2, n, shortcut, g, e)
c_ = int(c2 * e)
self.m = TransformerBlock(c_, c_, 4, n)
class C3Ghost(C3):
"""C3 module with GhostBottleneck()."""
def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5):
"""Initialize 'SPP' module with various pooling sizes for spatial pyramid pooling."""
super().__init__(c1, c2, n, shortcut, g, e)
c_ = int(c2 * e) # hidden channels
self.m = nn.Sequential(*(GhostBottleneck(c_, c_) for _ in range(n)))
class GhostBottleneck(nn.Module):
"""Ghost Bottleneck https://github.com/huawei-noah/ghostnet."""
def __init__(self, c1, c2, k=3, s=1): # ch_in, ch_out, kernel, stride
super().__init__()
c_ = c2 // 2
self.conv = nn.Sequential(
GhostConv(c1, c_, 1, 1), # pw
DWConv(c_, c_, k, s, act=False) if s == 2 else nn.Identity(), # dw
GhostConv(c_, c2, 1, 1, act=False)) # pw-linear
self.shortcut = nn.Sequential(DWConv(c1, c1, k, s, act=False), Conv(c1, c2, 1, 1,
act=False)) if s == 2 else nn.Identity()
def forward(self, x):
"""Applies skip connection and concatenation to input tensor."""
return self.conv(x) + self.shortcut(x)
class Bottleneck(nn.Module):
"""Standard bottleneck."""
def __init__(self, c1, c2, shortcut=True, g=1, k=(3, 3), e=0.5, drop_path=0.0): # ch_in, ch_out, shortcut, groups, kernels, expand
super().__init__()
c_ = int(c2 * e) # hidden channels
self.cv1 = Conv(c1, c_, k[0], 1)
self.cv2 = Conv(c_, c2, k[1], 1, g=g)
self.add = shortcut and c1 == c2
self.drop_path1 = DropPath(drop_path) if drop_path > 0. else nn.Identity()
def forward(self, x):
"""'forward()' applies the YOLOv5 FPN to input data."""
return x + self.drop_path1(self.cv2(self.cv1(x))) if self.add else self.cv2(self.cv1(x))
class BottleneckCSP(nn.Module):
"""CSP Bottleneck https://github.com/WongKinYiu/CrossStagePartialNetworks."""
def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5): # ch_in, ch_out, number, shortcut, groups, expansion
super().__init__()
c_ = int(c2 * e) # hidden channels
self.cv1 = Conv(c1, c_, 1, 1)
self.cv2 = nn.Conv2d(c1, c_, 1, 1, bias=False)
self.cv3 = nn.Conv2d(c_, c_, 1, 1, bias=False)
self.cv4 = Conv(2 * c_, c2, 1, 1)
self.bn = nn.BatchNorm2d(2 * c_) # applied to cat(cv2, cv3)
self.act = nn.SiLU()
self.m = nn.Sequential(*(Bottleneck(c_, c_, shortcut, g, e=1.0) for _ in range(n)))
def forward(self, x):
"""Applies a CSP bottleneck with 3 convolutions."""
y1 = self.cv3(self.m(self.cv1(x)))
y2 = self.cv2(x)
return self.cv4(self.act(self.bn(torch.cat((y1, y2), 1)))) |