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Message type "caffe.BatchNormParameter" has no field named "slope_filler". #168
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@h-bo Having the same issue. Please update here if you get it working.. |
@bhadresh74 I met this issue when using tsn-caffe. In fact, I found the reason was that the caffe version tsn used was different from the latest, and was modified by WangLiMin(author of tsn). |
@h-bo Yeah, I am using the original PSPNet caffe .prototext which has the BN Parameter "slove_filler" which is unrecognized by this code. Not sure why. Any help would be appreciated. Thanks (Y) |
@h-bo Could you please tell me how you got this working or if you have the tsn models in tensorflow please? |
@Pedro-Abreu I upload my code in my github. But its result is worse than expected. The author of tsn recommend me to use their pyTorch version. Hope to help you. |
@h-bo I'm just asking because I wanted to port their weights to keras and use the Inceptionv3 that exists in keras, which might have a slightly more optimized BN than tf. However conversion of tensorflow checkpoints to keras is much easier than from their custom caffe to keras. If you had .ckpt from tf it would be cool but I assume your .npy or .pkl files have the weights? |
I have converted Caffe weights to TensorFlow with approximate difference of
10e-4.
The way I did it,
1. Convert .caffemodel to .npy
2. Implement caffe model architecture in TensorFlow
3. Load .npy file in the TensorFlow model
4. Compare layer by layer result with Caffe.
SMU if you are stuck at, I might be able to help you.
Bhadresh
…On Sun, Jun 17, 2018 at 10:48 AM Pedro Abreu ***@***.***> wrote:
@h-bo <https://github.com/h-bo> I'm just asking because I wanted to port
their weights to keras and use the Inceptionv3 that exists in keras, which
might have a slightly optimized BN than tf. However conversion of
tensorflow checkpoints to keras is much easier than from their custom caffe
to keras. If you had .ckpt from tf it would be cool but I assume your .npy
or .pkl files have the weights?
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