You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
392 lines
14 KiB
392 lines
14 KiB
# Ultralytics YOLO 🚀, AGPL-3.0 license |
|
"""Block modules.""" |
|
|
|
import torch |
|
import torch.nn as nn |
|
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", |
|
"ResNetLayer", |
|
) |
|
|
|
|
|
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): |
|
""" |
|
Initializes the YOLOv8 mask Proto module with specified number of protos and masks. |
|
|
|
Input arguments are 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): |
|
"""Initialize the SPP layer with input/output channels and specified kernel sizes for max pooling.""" |
|
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()): |
|
"""Initializes a CSP Bottleneck with 1 convolution using specified input and output channels.""" |
|
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): |
|
""" |
|
Initializes the SPPF layer with given input/output channels and kernel size. |
|
|
|
This module is 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): |
|
"""Initializes the CSP Bottleneck with configurations for 1 convolution with arguments 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): |
|
"""Initializes the CSP Bottleneck with 2 convolutions module with arguments 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): |
|
"""Initialize CSP bottleneck layer with two convolutions with arguments 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 + 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) for _ 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): |
|
"""Initialize the CSP Bottleneck with given channels, number, shortcut, groups, and expansion values.""" |
|
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): |
|
"""Initialize CSP Bottleneck with a single convolution using input channels, output channels, and number.""" |
|
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): |
|
"""Initializes GhostBottleneck module with arguments 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): |
|
"""Initializes a bottleneck module with given input/output channels, shortcut option, group, kernels, and |
|
expansion. |
|
""" |
|
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 |
|
|
|
def forward(self, x): |
|
"""'forward()' applies the YOLO FPN to input data.""" |
|
return x + 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): |
|
"""Initializes the CSP Bottleneck given arguments for 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)))) |
|
|
|
|
|
class ResNetBlock(nn.Module): |
|
"""ResNet block with standard convolution layers.""" |
|
|
|
def __init__(self, c1, c2, s=1, e=4): |
|
"""Initialize convolution with given parameters.""" |
|
super().__init__() |
|
c3 = e * c2 |
|
self.cv1 = Conv(c1, c2, k=1, s=1, act=True) |
|
self.cv2 = Conv(c2, c2, k=3, s=s, p=1, act=True) |
|
self.cv3 = Conv(c2, c3, k=1, act=False) |
|
self.shortcut = nn.Sequential(Conv(c1, c3, k=1, s=s, act=False)) if s != 1 or c1 != c3 else nn.Identity() |
|
|
|
def forward(self, x): |
|
"""Forward pass through the ResNet block.""" |
|
return F.relu(self.cv3(self.cv2(self.cv1(x))) + self.shortcut(x)) |
|
|
|
|
|
class ResNetLayer(nn.Module): |
|
"""ResNet layer with multiple ResNet blocks.""" |
|
|
|
def __init__(self, c1, c2, s=1, is_first=False, n=1, e=4): |
|
"""Initializes the ResNetLayer given arguments.""" |
|
super().__init__() |
|
self.is_first = is_first |
|
|
|
if self.is_first: |
|
self.layer = nn.Sequential( |
|
Conv(c1, c2, k=7, s=2, p=3, act=True), nn.MaxPool2d(kernel_size=3, stride=2, padding=1) |
|
) |
|
else: |
|
blocks = [ResNetBlock(c1, c2, s, e=e)] |
|
blocks.extend([ResNetBlock(e * c2, c2, 1, e=e) for _ in range(n - 1)]) |
|
self.layer = nn.Sequential(*blocks) |
|
|
|
def forward(self, x): |
|
"""Forward pass through the ResNet layer.""" |
|
return self.layer(x)
|
|
|