这样定义的conv是可以量化的,不算自定义
您好,请问像这样的层能被量化吗?算是自定义的层吗? class Conv2dStaticSamePadding(nn.Module): """ created by Zylo117 The real keras/tensorflow conv2d with same padding """ def __init__(self, in_channels, out_channels, kernel_size, stride=1, bias=True, groups=1, dilation=1, **kwargs): super().__init__() self.conv = nn.Conv2d(in_channels, out_channels, kernel_size, stride=stride, bias=bias, groups=groups) self.stride = self.conv.stride self.kernel_size = self.conv.kernel_size self.dilation = self.conv.dilation 类Conv2dStaticSamePadding的self.conv属性值是nn.Conv2d,而Conv2d是继承的_ConvNd,代码如下: class Conv2d(_ConvNd):展开
卷积/全连接 层一定可以被量化,而且必须量化。其他层都不能量化
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