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SPPF-imporve/SPPF-imporve.md

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+这是关于SPPF的系列改进包括SimSPPF，SPPCSPC,SimCSPSPPF
+
+SimSPPF为YOLOv6提出的，论文地址:[YOLOv6: A Single-Stage Object Detection Framework for Industrial Applications | PDF (arxiv.org)](https://arxiv.org/pdf/2209.02976v1.pdf)；
+
+SPPCSPC为YOLOv7提出的,论文地址:[YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors | PDF (arxiv.org)](https://arxiv.org/pdf/2207.02696v1.pdf)；
+
+SimCSPSPPF为YOLOv6 3.0提出的,论文地址：https://arxiv.org/pdf/2301.05586v1.pdf。
+
+具体改进详情见论文。
+
+使用方法:
+
+1.将SPPF_imporve.py复制到models文件夹下
+
+2.在models/yolo.py导入所需模块
+
+from SPPF_imporve import SimSPPF, SPPCSPC, SimCSPSPPF
+
+```
+elif m is [SimSPPF，SPPCSPC,SimCSPSPPF]:
+            c1, c2 = ch[f], args[0]
+            if c2 != no:  # if not output
+                c2 = make_divisible(c2 * gw, 8)
+            args = [c1, c2, *args[1:]]
+            if m in [SimSPPF，SPPCSPC,SimCSPSPPF]:
+                args.insert(2, n)  # number of repeats
+                n = 1
+```
+
+3.进行训练
+
+```python
+python train.py --cfg YOLOv5_sppf_imporve.yaml
+```
+

SPPF-imporve/SPPF_imporve.py

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+import time
+import torch
+import torch.nn as nn
+
+def autopad(k, p=None):  # kernel, padding
+    # Pad to 'same'
+    if p is None:
+        p = k // 2 if isinstance(k, int) else [x // 2 for x in k]  # auto-pad
+    return p
+
+class Conv(nn.Module):
+    # Standard convolution
+    def __init__(self, c1, c2, k=1, s=1, p=None, g=1, act=True):  # ch_in, ch_out, kernel, stride, padding, groups
+        super(Conv, self).__init__()
+        self.conv = nn.Conv2d(c1, c2, k, s, autopad(k, p), groups=g, bias=False)
+        self.bn = nn.BatchNorm2d(c2)
+        self.act = nn.SiLU() if act is True else (act if isinstance(act, nn.Module) else nn.Identity())
+
+    def forward(self, x):
+        return self.act(self.bn(self.conv(x)))
+
+    def forward_fuse(self, x):
+        return self.act(self.conv(x))
+
+class SimConv(nn.Module):
+    '''Normal Conv with ReLU activation'''
+
+    def __init__(self, in_channels, out_channels, kernel_size, stride, groups=1, bias=False):
+        super().__init__()
+        padding = kernel_size // 2
+        self.conv = nn.Conv2d(
+            in_channels,
+            out_channels,
+            kernel_size=kernel_size,
+            stride=stride,
+            padding=padding,
+            groups=groups,
+            bias=bias,
+        )
+        self.bn = nn.BatchNorm2d(out_channels)
+        self.act = nn.ReLU()
+
+    def forward(self, x):
+        return self.act(self.bn(self.conv(x)))
+
+    def forward_fuse(self, x):
+        return self.act(self.conv(x))
+
+class SimSPPF(nn.Module):
+    '''Simplified SPPF with ReLU activation'''
+
+    def __init__(self, in_channels, out_channels, kernel_size=5):
+        super().__init__()
+        c_ = in_channels // 2  # hidden channels
+        self.cv1 = SimConv(in_channels, c_, 1, 1)
+        self.cv2 = SimConv(c_ * 4, out_channels, 1, 1)
+        self.m = nn.MaxPool2d(kernel_size=kernel_size, stride=1, padding=kernel_size // 2)
+
+    def forward(self, x):
+        x = self.cv1(x)
+        with warnings.catch_warnings():
+            warnings.simplefilter('ignore')
+            y1 = self.m(x)
+            y2 = self.m(y1)
+            return self.cv2(torch.cat([x, y1, y2, self.m(y2)], 1))
+
+class SPPCSPC(nn.Module):
+    # CSP https://github.com/WongKinYiu/CrossStagePartialNetworks
+    def __init__(self, c1, c2, n=1, shortcut=False, g=1, e=0.5, k=(5, 9, 13)):
+        super(SPPCSPC, self).__init__()
+        c_ = int(2 * c2 * e)  # hidden channels
+        self.cv1 = Conv(c1, c_, 1, 1)
+        self.cv2 = Conv(c1, c_, 1, 1)
+        self.cv3 = Conv(c_, c_, 3, 1)
+        self.cv4 = Conv(c_, c_, 1, 1)
+        self.m = nn.ModuleList([nn.MaxPool2d(kernel_size=x, stride=1, padding=x // 2) for x in k])
+        self.cv5 = Conv(4 * c_, c_, 1, 1)
+        self.cv6 = Conv(c_, c_, 3, 1)
+        self.cv7 = Conv(2 * c_, c2, 1, 1)
+
+    def forward(self, x):
+        x1 = self.cv4(self.cv3(self.cv1(x)))
+        y1 = self.cv6(self.cv5(torch.cat([x1] + [m(x1) for m in self.m], 1)))
+        y2 = self.cv2(x)
+        return self.cv7(torch.cat((y1, y2), dim=1))
+
+
+class SimCSPSPPF(nn.Module):
+    # CSP https://github.com/WongKinYiu/CrossStagePartialNetworks
+    def __init__(self, in_channels, out_channels, kernel_size=5, e=0.5):
+        super(SimCSPSPPF, self).__init__()
+        c_ = int(out_channels * e)  # hidden channels
+        self.cv1 = SimConv(in_channels, c_, 1, 1)
+        self.cv2 = SimConv(in_channels, c_, 1, 1)
+        self.cv3 = SimConv(c_, c_, 3, 1)
+        self.cv4 = SimConv(c_, c_, 1, 1)
+
+        self.m = nn.MaxPool2d(kernel_size=kernel_size, stride=1, padding=kernel_size // 2)
+        self.cv5 = SimConv(4 * c_, c_, 1, 1)
+        self.cv6 = SimConv(c_, c_, 3, 1)
+        self.cv7 = SimConv(2 * c_, out_channels, 1, 1)
+
+    def forward(self, x):
+        x1 = self.cv4(self.cv3(self.cv1(x)))
+        y0 = self.cv2(x)
+        with warnings.catch_warnings():
+            warnings.simplefilter('ignore')
+            y1 = self.m(x1)
+            y2 = self.m(y1)
+            y3 = self.cv6(self.cv5(torch.cat([x1, y1, y2, self.m(y2)], 1)))
+        return self.cv7(torch.cat((y0, y3), dim=1))

SPPF-imporve/YOLOv5_sppf_imporve.yaml.yaml

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+# YOLOv5 🚀 by Ultralytics, AGPL-3.0 license
+
+# 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
+   # [-1, 1, SimSPPF, [1024, 5]],
+   # [-1, 1, SPPCSPC, [1024]],
+   # [-1, 1, SimCSPSPPF, [1024, 5]], 
+  ]
+
+
+# YOLOv5 v6.0 head
+head:
+  [[-1, 1, Conv, [512, 1, 1]],
+   [-1, 1, nn.Upsample, [None, 2, 'nearest']],
+   [[-1, 6], 1, Concat, [1]],  # cat backbone P4
+   [-1, 3, C3, [512, False]],  # 13
+
+   [-1, 1, Conv, [256, 1, 1]],
+   [-1, 1, nn.Upsample, [None, 2, 'nearest']],
+   [[-1, 4], 1, Concat, [1]],  # cat backbone P3
+   [-1, 3, C3, [256, False]],  # 17 (P3/8-small)
+
+   [-1, 1, Conv, [256, 3, 2]],
+   [[-1, 14], 1, Concat, [1]],  # cat head P4
+   [-1, 3, C3, [512, False]],  # 20 (P4/16-medium)
+
+   [-1, 1, Conv, [512, 3, 2]],
+   [[-1, 10], 1, Concat, [1]],  # cat head P5
+   [-1, 3, C3, [1024, False]],  # 23 (P5/32-large)
+
+   [[17, 20, 23], 1, Detect, [nc, anchors]],  # Detect(P3, P4, P5)
+  ]