Comments (1)
目前,目前我们的mgeconvert是基于dump出的细粒度cpp图的,由于python层linear的实现是matmul + add,导致dump出的mge的op也是matmul + add,从而导致转到onnx也是matmul + add。
针对这个问题,我们在caffe转换器中加了pattern match以解决conv+bias, linear + bias的问题,见代码,后续我们重构一下,在解析mge层实现这种策略以支持onnx等其他框架。
另外,我们正在开发更高层表达的traced module以彻底解决这些问题
from mgeconvert.
Related Issues (15)
- onnx 模型转换问题 HOT 3
- 找不到cuda_fp16.h HOT 2
- OP Split is not supported HOT 4
- onnx->mge 需要支持算子
- 是否有计划提供其他框架到MegEngine的转换器 HOT 1
- 是否支持多输入多输出的mge到onnx转化 HOT 6
- 希望有大佬能贡献一个转换到 NCNN 模型的例子 HOT 4
- tm convert 到 caffe 参数问题 HOT 4
- ConvolutionBackwardDataOpr 初始化错误 HOT 1
- 无法转换一个简单的 UNet HOT 1
- torch->onnx->mge过程中的后半段(onnx->mge)的问题 HOT 12
- .pkl转.onnx有问题 HOT 3
- 为什么命名是以 mge 开头? HOT 1
- 执行convert mge_to_onnx ... 报错 HOT 8
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from mgeconvert.