HorNet+YOLOv5改进方案-创新互联
HorNet理论请参考论文：https://arxiv.org/pdf/2207.14284.pdf
1.在 common.py 文件中添加如下代码
部分代码如下
class Gnconv(nn.Module):
def __init__(self, dim, order=5, gflayer=None, h=14, w=8, s=1.0):
super().__init__()
self.order = order
self.dims = [dim // 2 ** i for i in range(order)]
self.dims.reverse()
self.proj_in = nn.Conv2d(dim, 2*dim, 1)
if gflayer is None:
self.dwconv = get_dwconv(sum(self.dims), 7, True)
else:
self.dwconv = gflayer(sum(self.dims), h=h, w=w)
self.proj_out = nn.Conv2d(dim, dim, 1)
self.pws = nn.ModuleList(
[nn.Conv2d(self.dims[i], self.dims[i+1], 1) for i in range(order-1)]
)
self.scale = s
print('[gnconv]', order, 'order with dims=', self.dims, 'scale=%.4f'%self.scale)
class GnBlock(nn.Module):
def __init__(self, dim, shortcut=False, layer_scale_init_value=1e-6):
super().__init__()
self.shortcut = shortcut
self.norm1 = LayerNorm(dim, eps=1e-6, data_format='channels_first')
self.gnconv = GnConv(dim, dim) # depthwise conv
self.norm2 = LayerNorm(dim, eps=1e-6)
self.pwconv1 = nn.Linear(dim, 2 * dim) # pointwise/1x1 convs, implemented with linear layers
self.act = nn.GELU()
self.pwconv2 = nn.Linear(2 * dim, dim)
self.gamma1 = nn.Parameter(layer_scale_init_value * torch.ones(dim),
requires_grad=True) if layer_scale_init_value >0 else None
self.gamma2 = nn.Parameter(layer_scale_init_value * torch.ones((dim)),
requires_grad=True) if layer_scale_init_value >0 else None
def forward(self, x):
B, C, H, W = x.shape
if self.gamma1 is not None:
gamma1 = self.gamma1.view(C, 1, 1)
else:
gamma1 = 1
x = (x + gamma1 * self.gnconv(self.norm1(x))) if self.shortcut else gamma1 * self.Gnconv(self.norm1(x))
input = x
x = x.permute(0, 2, 3, 1) # (N, C, H, W) ->(N, H, W, C)
x = self.norm2(x)
x = self.pwconv1(x)
x = self.act(x)
x = self.pwconv2(x)
if self.gamma2 is not None:
x = self.gamma2 * x
x = x.permute(0, 3, 1, 2) # (N, H, W, C) ->(N, C, H, W)
x = (input + x) if self.shortcut else x
return x
class GNCSP(nn.Module):
# CSP GnBlock with 3 GnConv
def __init__(self, c1, c2, n=1, shortcut=True, e=0.5): # ch_in, ch_out, number, shortcut, groups, expansion
super().__init__()
c_ = int(c2 * e) # hidden channels
self.cv1 = GnConv(c1, c_, 3)
self.cv2 = GnConv(c1, c_, 3)
self.cv3 = GnConv(2 * c_, c2, 3) # act=FReLU(c2)
self.m = nn.Sequential(*[GnBlock(c_, shortcut) for _ in range(n)])
def forward(self, x):
return self.cv3(torch.cat((self.m(self.cv1(x)), self.cv2(x)), dim=1))
2.创建yolov5GNCSP.yaml配置文件
# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
# Slim-neck by GSConv: A better design paradigm of detector architectures for autonomous vehicle
# Parameters
nc: 80 # number of classes
depth_multiple: 0.33 # model depth multiple
width_multiple: 0.50 # layer channel multiple
anchors:
- [10,13, 16,30, 33,23] # P3/8
- [30,61, 62,45, 59,119] # P4/16
- [116,90, 156,198, 373,326] # P5/32
# YOLOv5 v6.0 backbone
backbone:
# [from, number, module, args]
[[-1, 1, Conv, [64, 6, 2, 2]], # 0-P1/2
[-1, 1, Conv, [128, 3, 2]], # 1-P2/4
[-1, 3, C3, [128]],
[-1, 1, Conv, [256, 3, 2]], # 3-P3/8
[-1, 6, C3, [256]],
[-1, 1, Conv, [512, 3, 2]], # 5-P4/16
[-1, 9, C3, [512]],
[-1, 1, Conv, [1024, 3, 2]], # 7-P5/32
[-1, 3, C3, [1024]],
[-1, 1, SPPF, [1024, 5]], # 9
]
# YOLOv5 v6.0 head
head:
[[-1, 1, GnConv, [512, 1, 1]],
[-1, 1, nn.Upsample, [None, 2, 'nearest']],
[[-1, 6], 1, Concat, [1]], # cat backbone P4
[-1, 3, GNCSP, [512, False]], # 13
[-1, 1, GnConv, [256, 1, 1]],
[-1, 1, nn.Upsample, [None, 2, 'nearest']],
[[-1, 4], 1, Concat, [1]], # cat backbone P3
[-1, 3, GNCSP, [256, False]], # 17 (P3/8-small)
[-1, 1, GnConv, [256, 3, 2]],
[[-1, 14], 1, Concat, [1]], # cat head P4
[-1, 3, GNCSP, [512, False]], # 20 (P4/16-medium)
[-1, 1, GnConv, [512, 3, 2]],
[[-1, 10], 1, Concat, [1]], # cat head P5
[-1, 3, GNCSP, [1024, False]], # 23 (P5/32-large)
[[17, 20, 23], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5)
]3.在 models/yolo.py文件夹下找到parse_model函数
在459行 for i, (f, n, m, args) in enumerate(d['backbone'] + d['head']):下添加以下代码
elif m is GNCSP:
c1, c2 = ch[f], args[0]
if c2 != no:
c2 = make_divisible(c2 * gw, 8)
args = [c1, c2, *args[1:]]
if m is GNCSP:
args.insert(2, n)
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