小编Dhr*_*aik的帖子

我在 pytorch 中用 float 数据类型训练了一个模型。我想通过将此模型转换为量化模型来缩短推理时间。我使用torch.quantization.convert api 将模型的权重转换为 uint8 数据类型。然而，当我使用这个模型进行推理时，我没有得到任何性能改进。我在这里做错了什么吗？

Unet模型代码：

def gen_initialization(m):
    if type(m) == nn.Conv2d:
        sh = m.weight.shape
        nn.init.normal_(m.weight, std=math.sqrt(2.0 / (sh[0]*sh[2]*sh[3])))
        nn.init.constant_(m.bias, 0)
    elif type(m) == nn.BatchNorm2d:
        nn.init.constant_(m.weight, 1)
        nn.init.constant_(m.bias, 0)

class TripleConv(nn.Module):
    def __init__(self, in_ch, out_ch):
        super(TripleConv, self).__init__()
        mid_ch = (in_ch + out_ch) // 2
        self.conv = nn.Sequential(
            nn.Conv2d(in_ch, mid_ch, kernel_size=3, stride=1, padding=1, bias=True),
            nn.BatchNorm2d(num_features=mid_ch),
            nn.LeakyReLU(negative_slope=0.1),
            nn.Conv2d(mid_ch, mid_ch, kernel_size=3, stride=1, padding=1, bias=True),
            nn.BatchNorm2d(num_features=mid_ch),
            nn.LeakyReLU(negative_slope=0.1),
            nn.Conv2d(mid_ch, out_ch, kernel_size=3, stride=1, padding=1, bias=True),
            nn.BatchNorm2d(num_features=out_ch),
            nn.LeakyReLU(negative_slope=0.1)
        )
        self.conv.apply(gen_initialization)

    def forward(self, …