Basic layers.
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| | __init__ (self, in_channels, out_channels, kernel_size, bn_norm, stride=1, padding=0, groups=1, IN=False) |
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| | forward (self, x) |
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Basic layers.
Convolution layer (conv + bn + relu).
Definition at line 37 of file osnet.py.
◆ __init__()
| fastreid.modeling.backbones.osnet.ConvLayer.__init__ |
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| self, |
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| in_channels, |
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| out_channels, |
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| kernel_size, |
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| bn_norm, |
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| stride = 1, |
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| padding = 0, |
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| groups = 1, |
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| IN = False ) |
Definition at line 40 of file osnet.py.
50 ):
51 super(ConvLayer, self).__init__()
52 self.conv = nn.Conv2d(
53 in_channels,
54 out_channels,
55 kernel_size,
56 stride=stride,
57 padding=padding,
58 bias=False,
59 groups=groups
60 )
61 if IN:
62 self.bn = nn.InstanceNorm2d(out_channels, affine=True)
63 else:
64 self.bn = get_norm(bn_norm, out_channels)
65 self.relu = nn.ReLU(inplace=True)
66
◆ forward()
| fastreid.modeling.backbones.osnet.ConvLayer.forward |
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| self, |
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| x ) |
Definition at line 67 of file osnet.py.
67 def forward(self, x):
68 x = self.conv(x)
69 x = self.bn(x)
70 x = self.relu(x)
71 return x
72
73
◆ bn
| fastreid.modeling.backbones.osnet.ConvLayer.bn |
◆ conv
| fastreid.modeling.backbones.osnet.ConvLayer.conv |
◆ relu
| fastreid.modeling.backbones.osnet.ConvLayer.relu |
The documentation for this class was generated from the following file:
- smreid/fastreid/modeling/backbones/osnet.py