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@@ -384,3 +384,24 @@ class Classify(nn.Module):
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def forward(self, x):
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z = torch.cat([self.aap(y) for y in (x if isinstance(x, list) else [x])], 1) # cat if list
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return self.flat(self.conv(z)) # flatten to x(b,c2)
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+
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+
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+import warnings
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+
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+
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+class SPPF(nn.Module):
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+ # Spatial Pyramid Pooling - Fast (SPPF) layer for YOLOv5 by Glenn Jocher
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+ def __init__(self, c1, c2, k=5): # equivalent to SPP(k=(5, 9, 13))
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+ super().__init__()
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+ c_ = c1 // 2 # hidden channels
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+ self.cv1 = Conv(c1, c_, 1, 1)
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+ self.cv2 = Conv(c_ * 4, c2, 1, 1)
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+ self.m = nn.MaxPool2d(kernel_size=k, stride=1, padding=k // 2)
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+
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+ def forward(self, x):
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+ x = self.cv1(x)
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+ with warnings.catch_warnings():
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+ warnings.simplefilter('ignore') # suppress torch 1.9.0 max_pool2d() warning
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+ y1 = self.m(x)
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+ y2 = self.m(y1)
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+ return self.cv2(torch.cat([x, y1, y2, self.m(y2)], 1))
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