Comments (4)
@kaushik333 Your answer is very complete and correct. Thanks!
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Hi @Lukelluke
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I think they are just dummy labels being assigned to the dataset. A more generic framework of dataloader which gives you the (image, label) pair. If you look at Line 146, only the data is being used and not the label. And I dont see it being used in evaluate.py or the test() function too.
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To use 1 channel data, these are the changes I did.
a. Add a separate elif case for your data class in https://github.com/NVlabs/NVAE/blob/master/datasets.py
Add a "greyscale=True" parameter to the LMDBDataset class.
Change this toif not self.greyscale: img = img.convert('RGB') else: img = img.convert('L')
b. Change this to
Cin = 1 if self.dataset in {'mnist','yourDataName'} else 3
c. Change this to
C_out = 1 if self.dataset in {'mnist','yourDatasetName'} else 10 * self.num_mix_output
d. Since you're using grayscale images, change this to
if self.dataset in {'mnist', 'yourDatasetName'}:
or you can also NOT use bernoulli dist and use the mixture of dist instead.
@arash-vahdat please feel free to add anything else if you feel is important.
from nvae.
Thank you soooooooooooooo much! Dear @kaushik333 and Dr.@arash-vahdat !
Thank you for your quickly help!
I will follow your tutorial to practice right now.
Ps. Actually, I'm trying my dataset in .wav with mono, which channel==1 . And I get so much inspirations from your timely help, and as for dataset.py, there I did some changes in another way to fit it.
As for decoder_output , I need to take some more time to figure out how Bernoulli
and DiscMixLogistic
work.
All in all, thank you very much for your generous help ! Hope that I can become better in coding like you :) !
If it succeeds, I will give you good news as soon as possible and release related implement.
All the best,
Luke Huang
from nvae.
Hi, dear @kaushik333
I did as your help. Say thank you again sincerely ! And this help me understand NVAE better !
During this period, there still a big question hang over my head:
- As we know, in image field, usually we cut image to the shape of
[h, w]
, furthermore, we usually makeheight==wide
for convenience, just as NVAE do. - And I wonder, how to apply data which has the shape of
[H, W]
, whereH != W
to NVAE model ? - Just as: input(=[batch, channel, H, W]) ——> NVAE(input) ——> output(=[batch, channel, H, W])
H != W
Ps. This doubt is derived from audio field, where we usually turn audio spectrum to [batch, channel=1(mono), FRAME, Dimension of spectrum]. Where we usually make Dimension==80, however, frames(which denotes the length of one .wav field), is always != Dimension.
Hope to get any inspiration from IMG field, just as the 'Channel Problem' that you teach me above.
Please feel free to teach me anything, important or not important all is well !
Again to express my most sincere thanks to you !
All the best,
Luke Huang
from nvae.
Related Issues (20)
- FID score of CelebA-HQ 256x256 HOT 1
- NomalDecoder & num_bits
- TypeError: batch_norm_backward_elemt() missing 1 required positional arguments: "count" HOT 1
- How to run without using parallelization? HOT 1
- Can you provide pretrained models? HOT 1
- why is there self.prior_ftr0 in the decoder model?
- Why some of the generate images by the official checkpoint of CelebA64 are NaN-value? HOT 2
- Query: CelebA HQ 256
- Query: Dataset CelebA-HQ 256x256 issue
- Query: FFHQ Pre-Processing HOT 3
- FFHQ Training
- CelebA-HQ 256x256 Data Pre-processing HOT 1
- Possible typo in the log_p() function
- ImageNet Checkpoint
- Question regarding traversing the latent space
- Why output for 3-rd channel is unused in Logistic mixture? HOT 1
- how can i use the code on my own dataset. if it's necessary to modify the code carefully myself? HOT 1
- "arch_instance" argument
- Problem while converting tfrecord to lmdb data AttributeError: 'bytes' object has no attribute 'cpu' HOT 4
- Question about KL computation HOT 1
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