Update variables (#4273)
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306fc0119a
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9c6732f61c
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@ -30,7 +30,7 @@ def autopad(k, p=None): # kernel, padding
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def DWConv(c1, c2, k=1, s=1, act=True):
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# Depthwise convolution
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# Depth-wise convolution
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return Conv(c1, c2, k, s, g=math.gcd(c1, c2), act=act)
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@ -183,11 +183,11 @@ class Contract(nn.Module):
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self.gain = gain
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def forward(self, x):
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N, C, H, W = x.size() # assert (H / s == 0) and (W / s == 0), 'Indivisible gain'
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b, c, h, w = x.size() # assert (h / s == 0) and (W / s == 0), 'Indivisible gain'
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s = self.gain
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x = x.view(N, C, H // s, s, W // s, s) # x(1,64,40,2,40,2)
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x = x.view(b, c, h // s, s, w // s, s) # x(1,64,40,2,40,2)
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x = x.permute(0, 3, 5, 1, 2, 4).contiguous() # x(1,2,2,64,40,40)
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return x.view(N, C * s * s, H // s, W // s) # x(1,256,40,40)
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return x.view(b, c * s * s, h // s, w // s) # x(1,256,40,40)
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class Expand(nn.Module):
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@ -197,11 +197,11 @@ class Expand(nn.Module):
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self.gain = gain
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def forward(self, x):
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N, C, H, W = x.size() # assert C / s ** 2 == 0, 'Indivisible gain'
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b, c, h, w = x.size() # assert C / s ** 2 == 0, 'Indivisible gain'
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s = self.gain
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x = x.view(N, s, s, C // s ** 2, H, W) # x(1,2,2,16,80,80)
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x = x.view(b, s, s, c // s ** 2, h, w) # x(1,2,2,16,80,80)
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x = x.permute(0, 3, 4, 1, 5, 2).contiguous() # x(1,16,80,2,80,2)
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return x.view(N, C // s ** 2, H * s, W * s) # x(1,16,160,160)
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return x.view(b, c // s ** 2, h * s, w * s) # x(1,16,160,160)
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class Concat(nn.Module):
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