add-yolov7-slimneck

readme.md

@@ -52,5 +52,5 @@
 
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+# Alipay(这几天付款人数有点多,目前ZFB给限制收款了,需要购买直接加我QQ:1069614715(添加不上请在B站上私信发我qq号))
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yolo-improve/readme.md

@@ -228,4 +228,8 @@
     reference: [链接](https://github.com/QY1994-0919/CFPNet)  
 - **yolov7-head**  
     P2,P6检测层在YOLOV7中的添加.  
-    视频教学地址：[哔哩哔哩](https://www.bilibili.com/video/BV12u4y1f7np/).  
+    视频教学地址：[哔哩哔哩](https://www.bilibili.com/video/BV1LX4y1a72m/).  
+- **yolov7-slimneck**  
+    使用VOVGSCSP轻量化yolov7的Neck.  
+    视频教学地址：[哔哩哔哩](https://www.bilibili.com/video/BV12u4y1f7np/).  
+    reference: [链接](https://github.com/AlanLi1997/slim-neck-by-gsconv)  

yolo-improve/yolov7-slimneck.py

@@ -0,0 +1,133 @@
+class GSConv(nn.Module):
+    # GSConv https://github.com/AlanLi1997/slim-neck-by-gsconv
+    def __init__(self, c1, c2, k=1, s=1, g=1, act=True):
+        super().__init__()
+        c_ = c2 // 2
+        self.cv1 = Conv(c1, c_, k, s, None, g, act)
+        self.cv2 = Conv(c_, c_, 5, 1, None, c_, act)
+
+    def forward(self, x):
+        x1 = self.cv1(x)
+        x2 = torch.cat((x1, self.cv2(x1)), 1)
+        # shuffle
+        # y = x2.reshape(x2.shape[0], 2, x2.shape[1] // 2, x2.shape[2], x2.shape[3])
+        # y = y.permute(0, 2, 1, 3, 4)
+        # return y.reshape(y.shape[0], -1, y.shape[3], y.shape[4])
+
+        b, n, h, w = x2.data.size()
+        b_n = b * n // 2
+        y = x2.reshape(b_n, 2, h * w)
+        y = y.permute(1, 0, 2)
+        y = y.reshape(2, -1, n // 2, h, w)
+
+        return torch.cat((y[0], y[1]), 1)
+
+class GSBottleneck(nn.Module):
+    # GS Bottleneck https://github.com/AlanLi1997/slim-neck-by-gsconv
+    def __init__(self, c1, c2, k=3, s=1, e=0.5):
+        super().__init__()
+        c_ = int(c2*e)
+        # for lighting
+        self.conv_lighting = nn.Sequential(
+            GSConv(c1, c_, 1, 1),
+            GSConv(c_, c2, 3, 1, act=False))
+        self.shortcut = Conv(c1, c2, 1, 1, act=False)
+
+    def forward(self, x):
+        return self.conv_lighting(x) + self.shortcut(x)
+
+class GSBottleneckC(GSBottleneck):
+    # cheap GS Bottleneck https://github.com/AlanLi1997/slim-neck-by-gsconv
+    def __init__(self, c1, c2, k=3, s=1):
+        super().__init__(c1, c2, k, s)
+        self.shortcut = DWConv(c1, c2, k, s, act=False)
+
+class VoVGSCSP(nn.Module):
+    # VoVGSCSP module with GSBottleneck
+    def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5):
+        super().__init__()
+        c_ = int(c2 * e)  # hidden channels
+        self.cv1 = Conv(c1, c_, 1, 1)
+        self.cv2 = Conv(c1, c_, 1, 1)
+        self.gsb = nn.Sequential(*(GSBottleneck(c_, c_, e=1.0) for _ in range(n)))
+        self.res = Conv(c_, c_, 3, 1, act=False)
+        self.cv3 = Conv(2 * c_, c2, 1)  #
+
+
+    def forward(self, x):
+        x1 = self.gsb(self.cv1(x))
+        y = self.cv2(x)
+        return self.cv3(torch.cat((y, x1), dim=1))
+
+class VoVGSCSPC(VoVGSCSP):
+    # cheap VoVGSCSP module with GSBottleneck
+    def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5):
+        super().__init__(c1, c2)
+        c_ = int(c2 * 0.5)  # hidden channels
+        self.gsb = GSBottleneckC(c_, c_, 1, 1)
+
+
+# parameters
+nc: 80  # number of classes
+depth_multiple: 1.0  # model depth multiple
+width_multiple: 1.0  # layer channel multiple
+
+# anchors
+anchors:
+  - [12,16, 19,36, 40,28]  # P3/8
+  - [36,75, 76,55, 72,146]  # P4/16
+  - [142,110, 192,243, 459,401]  # P5/32
+
+# yolov7 backbone
+backbone:
+  # [from, number, module, args]
+  [[-1, 1, Conv, [32, 3, 1]],  # 0
+
+   [-1, 1, Conv, [64, 3, 2]],  # 1-P1/2      
+   [-1, 1, Conv, [64, 3, 1]],
+
+   [-1, 1, Conv, [128, 3, 2]],  # 3-P2/4  
+   [-1, 1, Yolov7_E_ELAN, [256, 64]], # 4
+
+   [-1, 1, V7DownSampling, [128]],  # 5-P3/8  
+   [-1, 1, Yolov7_E_ELAN, [512, 128]], # 6
+
+   [-1, 1, V7DownSampling, [256]],  # 7-P4/16  
+   [-1, 1, Yolov7_E_ELAN, [1024, 256]], # 8
+
+   [-1, 1, V7DownSampling, [512]],  # 9-P5/32  
+   [-1, 1, Yolov7_E_ELAN, [1024, 256]],  # 10
+  ]
+
+# yolov7 head
+head:
+  [[-1, 1, SPPCSPC, [512]], # 11
+
+   [-1, 1, GSConv, [256, 1, 1]],
+   [-1, 1, nn.Upsample, [None, 2, 'nearest']],
+   [8, 1, GSConv, [256, 1, 1]], # 14 route backbone P4
+   [[-1, -2], 1, Concat, [1]], # 15
+
+   [-1, 1, VoVGSCSP, [256]], # 16
+
+   [-1, 1, GSConv, [128, 1, 1]],
+   [-1, 1, nn.Upsample, [None, 2, 'nearest']],
+   [6, 1, GSConv, [128, 1, 1]], # 19 route backbone P3
+   [[-1, -2], 1, Concat, [1]], # 20
+
+   [-1, 1, VoVGSCSP, [128]], # 21
+
+   [[-1, 16], 1, V7DownSampling_Neck, [128]], # 22
+
+   [-1, 1, VoVGSCSP, [256]], # 23
+
+   [[-1, 11], 1, V7DownSampling_Neck, [256]], # 24
+
+   [-1, 1, VoVGSCSP, [512]], # 25
+
+   [21, 1, RepConv, [256, 3, 1]], # 26-P3
+   [23, 1, RepConv, [512, 3, 1]], # 27-P4
+   [25, 1, RepConv, [1024, 3, 1]], # 28-P5
+
+   [[26, 27, 28], 1, IDetect, [nc, anchors]],   # Detect(P3, P4, P5)
+  ]