xyq7 / invcompress Goto Github PK

Thank you for your codes. However when I try to use the pre-trained model (exp_02_mse_q2), some errors occurred. Other pre-trained model including (q1, q3,q4,q5) can work normally.

Hi,

Great work ! I'm currently using it in my phd research project.

If I understand the code correctly, the context model (ctx_p) is significantly slowing down the entropy coding/decoding computation...

Is there any way to make this much faster ?

Thanks.

Hi! Thanks for your great work!

During measuring PSNR values of test images, is there any difference between them?
Since the quantization method of 'forward()' is slightly different from that of 'compress()',
I'm wondering how much the difference occurs.

Can you provide the results of opt-msssim as well?
Thanks

Thanks for your last reply! When I try to train the model under higher compression (nearing 0.06), I get a poor performance. So do you have the pretrained model under higher compression ?

And the size of different pictures at the same compression level is the same

Hi, I try to load the pre-trained model and fine-tune it in my own dataset. However, there exists a bug in loading the model:

In this line, there has an KeyError: 'state_dict'.

It seems that the model is not saved as train.py in line 501:

save_checkpoint(
{
"epoch": epoch + 1,
"state_dict": net.state_dict(),
"loss": loss,
"optimizer": optimizer.state_dict(),
"aux_optimizer": aux_optimizer.state_dict(),
"lr_scheduler": lr_scheduler.state_dict(),
},
is_best,
os.path.join('../experiments', args.experiment, 'checkpoints', "checkpoint_%03d.pth.tar" % (epoch + 1))
)

Hello,

Great work and awesome code. Thank you very much for sharing.
I see that total 20,745 images are present in Flickr dataset out of which 200 random images are taken away for validation, and then generated patches of size 256*256.
If I use the generate patch script, it results huge number of patches. How many patches are present in your training dataset?

Regards
Priyanka

Excellent work! When would you like to release the code?

Hi authors,
I found that the provided link only contains MSE models. How can I download the MS-SSIM models?
Thanks

How can you use the examples/codec.py file to evaluate your model?

Hi Yueqi, thanks for your great work!

I am wondering about a small bug of the network architecture.
According to the paper, for the SSIM models, the para N for the first two lambda values is 128, while that for the last three lambda values is 192.
However, according to the code, the para N for the first four lambda values is 128, while that for the last four lambda values is 192.

I want to make sure that if I want to train the all five SSIM models, my training code should be:

python examples/train.py ../ -exp exp_ssim_q1_01 -m invcompress -d ../data/flickr2w --epochs 600 -lr 1e-4 --batch-size 8 -n 8 --cuda --gpu_id 7 -q 1 --lambda 6 --metrics ms-ssim --save --seed 7

python examples/train.py ../ -exp exp_ssim_q2_01 -m invcompress -d ../data/flickr2w --epochs 600 -lr 1e-4 --batch-size 8 -n 8 --cuda --gpu_id 6 -q 1 --lambda 12 --metrics ms-ssim --save --seed 7

python examples/train.py ../ -exp exp_ssim_q3_01 -m invcompress -d ../data/flickr2w --epochs 600 -lr 1e-4 --batch-size 8 -n 8 --cuda --gpu_id 5 -q 5 --lambda 40 --metrics ms-ssim --save --seed 7

python examples/train.py ../ -exp exp_ssim_q4_01 -m invcompress -d ../data/flickr2w --epochs 600 -lr 1e-4 --batch-size 8 -n 8 --cuda --gpu_id 4 -q 5 --lambda 120 --metrics ms-ssim --save --seed 7

python examples/train.py ../ -exp exp_ssim_q5_01 -m invcompress -d ../data/flickr2w --epochs 600 -lr 1e-4 --batch-size 8 -n 8 --cuda --gpu_id 3 -q 5 --lambda 220 --metrics ms-ssim --save --seed 7

Is it correct? Thank you.