yechengxi / deconvolution Goto Github PK

great work!
network deconvolution has many good features. However, in your paper, the inference time with DC is not mentioned. Is it almostly the same with BN method? I guess.

Really nice work!

We are interested in your experimental results on semantic segmentation tasks (Cityscapes).

According to Figure-6, it seems that the proposed DeConv outperforms the BN by a really large margin of around 7~8%. However, it seems that you only report the results trained with 30 epochs and we are wondering about the performance gap after more training, e.g., 100 epochs.

Besides, in the current stage, most of the state-of-the-art segmentation methods use the Sync-BN to improve the results, thus I am also wondering whether have you compared your approach with the Sync-BN.

Last, we hope you could share with us the ImageNet pre-trained checkpoints of ResNet-101/50 based on Deconvolution and we might help to verify the effectiveness of your approach based on the current state-of-the-art segmentation systems.

It would be great if you could share with us your suggestions.

Thanks,

Hi,
I bet this would also work for speech recognition and signal processing. maybe even nlp and algo trading.
I would love to try this out for speech recognition tasks in particular.
Can you please generate a 1d devonvolution class. Seems like it shouldn't be too difficult - especially if you really understand every line of code you wrote...

Thanks
Dan

How to replace deconv layer with the combination of depthwise convolution and batchnorm?

First, let me congratulate you on your paper and also thank you for open-sourcing the code. I was porting the deconv operations/layers to Tensorflow and was wondering about something.

1. Is the deconv covariance buffer the vast majority of non-trainable parameters in your models? Practically speaking, without groups (which Tensorflow doesn't support easily), the cost for that matrix in terms of parameters = [K1 * K2 * num_blocks] ^ 2. For a 3x3 kernel with 64 blocks, that's roughly 330K parameters right there. Are grouped convolutions the only remedy to this parameter explosion? It might become a network bandwidth issue in multi-node distributed training setups.

2. Under what circumstances would one prefer the Delinear implementation over the FastDeconv implementation?

Edit: It seems the link to the paper in the readme is broken.

When setting bias = False in the constructor to FastDeconv, then the forward pass fails at:

b = self.bias - (w @ (X_mean.unsqueeze(1))).view(self.weight.shape[0], -1).sum(1)

This is because self.bias will be None and this line breaks.

I guess this would be possible:

if self.bias is None:
    b = - (w @ (X_mean.unsqueeze(1))).view(self.weight.shape[0], -1).sum(1)
else:
    b = self.bias - (w @ (X_mean.unsqueeze(1))).view(self.weight.shape[0], -1).sum(1)

When using Conv2d with BatchNorm, usually no bias is used in the Conv2d. However, when replacing both, then I guess a bias is needed again. So I'm not sure if there are useful cases for not using a bias when using the FastDeconv?

Thanks for the paper and the implementation!

Hi,

Thanks for your code, the results are very interesting.
Wondering if you could please update the repository with the LICENSE file?

the call to .view() in the function accuracy for the correct tensor (net_util.py line 342), fail when the tensor is not contiguous.
the solution it to transform the tensor to contiguous before apply the view.
a PR is on the way.

Thanks for this paper, I really enjoyed reading it.

I replaced all the batch norm layers in a ResNeXt-50 model with ChannelDeconv(block=64) layers, but I found that training takes much longer doing so, running about 30% slower. Did you notice this too with your experiments? Do you have any suggestions for speeding up the deconvolution layers?