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结构化剪枝后的模型(剪枝率约为45%)与原模型的速度在GPU上测的结果没有提升,但是在CPU上测的结果有大幅提升(剪枝后的模型速度约为未剪枝的两倍),请问这是为什么?剪枝后模型需要额外的GPU设置才能加速吗 剪枝前的部分参数shape如下: backbone.conv1.conv1.conv.weight:[64, 3, 7, 7] backbone.conv1.conv1.norm.weight:[64] backbone.conv1.conv1.norm.bias:[64] backbone.conv1.conv1.norm._mean:[64] backbone.conv1.conv1.norm._variance:[64] backbone.res2.res2a.branch2a.conv.weight:[64, 64, 1, 1] backbone.res2.res2a.branch2a.norm.weight:[64] backbone.res2.res2a.branch2a.norm.bias:[64] backbone.res2.res2a.branch2a.norm._mean:[64] backbone.res2.res2a.branch2a.norm._variance:[64] 与之对应的剪枝后的部分参数shape如下: backbone.conv1.conv1.conv.weight:[56, 3, 7, 7] backbone.conv1.conv1.norm.weight:[56] backbone.conv1.conv1.norm.bias:[56] backbone.conv1.conv1.norm._mean:[56] backbone.conv1.conv1.norm._variance:[56] backbone.res2.res2a.branch2a.conv.weight:[40, 56, 1, 1] backbone.res2.res2a.branch2a.norm.weight:[40] backbone.res2.res2a.branch2a.norm.bias:[40] backbone.res2.res2a.branch2a.norm._mean:[40] backbone.res2.res2a.branch2a.norm._variance:[40]
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@wanghaoshuang 能否解答一下?
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结构化剪枝后的模型(剪枝率约为45%)与原模型的速度在GPU上测的结果没有提升,但是在CPU上测的结果有大幅提升(剪枝后的模型速度约为未剪枝的两倍),请问这是为什么?剪枝后模型需要额外的GPU设置才能加速吗
剪枝前的部分参数shape如下:
backbone.conv1.conv1.conv.weight:[64, 3, 7, 7]
backbone.conv1.conv1.norm.weight:[64]
backbone.conv1.conv1.norm.bias:[64]
backbone.conv1.conv1.norm._mean:[64]
backbone.conv1.conv1.norm._variance:[64]
backbone.res2.res2a.branch2a.conv.weight:[64, 64, 1, 1]
backbone.res2.res2a.branch2a.norm.weight:[64]
backbone.res2.res2a.branch2a.norm.bias:[64]
backbone.res2.res2a.branch2a.norm._mean:[64]
backbone.res2.res2a.branch2a.norm._variance:[64]
与之对应的剪枝后的部分参数shape如下:
backbone.conv1.conv1.conv.weight:[56, 3, 7, 7]
backbone.conv1.conv1.norm.weight:[56]
backbone.conv1.conv1.norm.bias:[56]
backbone.conv1.conv1.norm._mean:[56]
backbone.conv1.conv1.norm._variance:[56]
backbone.res2.res2a.branch2a.conv.weight:[40, 56, 1, 1]
backbone.res2.res2a.branch2a.norm.weight:[40]
backbone.res2.res2a.branch2a.norm.bias:[40]
backbone.res2.res2a.branch2a.norm._mean:[40]
backbone.res2.res2a.branch2a.norm._variance:[40]
The text was updated successfully, but these errors were encountered: