This repository is for a new network pruning method (Aligned Structured Sparsity Learning, ASSL) for efficient single image super-resolution (SR), introduced in our NeurIPS 2021 Spotlight paper:
Aligned Structured Sparsity Learning for Efficient Image Super-Resolution [Camera Ready]
Yulun Zhang*, Huan Wang*, Can Qin, and Yun Fu (*Contribute Equally)
Northeastern University, Boston, MA, USA
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About Code release for Anomaly Transformer: Time Series Anomaly Detection with Association Discrepancy (ICLR 2022 Spotlight)
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@MingSun-Tse Thanks for your excellent work. I read the paper ,and I want to learn the details. Could you share the paper with me? Thank you very much!!
From the observation of training results, the hard mask's weights between the constrained layers are not exactly aligned. https://github.com/MingSun-Tse/ASSL/blob/a564556c8b578c2ee86d135044f088bfeaafc707/src/pruner/utils.py#L71
hello , I have some questiones about implementation details.
Data are obtained using the HR-LR data pairs obtained by the down-sampling code provided in BasicSR. The training data was DF2K (900 DIV2K + 2650 Flickr2K), and the test data was Set5.
I run this command to prune the EDSR_16_256 model to EDSR_16_48. Only the pruning ratio and storage path name are modified compared to the command provided by the official.
Prune from 256 to 48, pr=0.8125, x2, ASSL
python main.py --model LEDSR --scale 2 --patch_size 96 --ext sep --dir_data /home/notebook/data/group_cpfs/wurongyuan/data/data
--data_train DF2K --data_test DF2K --data_range 1-3550/3551-3555 --chop --save_results --n_resblocks 16 --n_feats 256
--method ASSL --wn --stage_pr [0-1000:0.8125] --skip_layers *mean*,*tail*
--same_pruned_wg_layers model.head.0,model.body.16,*body.2 --reg_upper_limit 0.5 --reg_granularity_prune 0.0001
--update_reg_interval 20 --stabilize_reg_interval 43150 --pre_train pretrained_models/LEDSR_F256R16BIX2_DF2K_M311.pt
--same_pruned_wg_criterion reg --save main/SR/LEDSR_F256R16BIX2_DF2K_ASSL_0.8125_RGP0.0001_RUL0.5_Pretrain_06011101 Results model_just_finished_prune ---> 33.739dB fine-tuning after one epoch ---> 37.781dB fine-tuning after 756 epoch ---> 37.940dB
The result (37.940dB) I obtained with the code provided by the official is still a certain gap from the result in the paper (38.12dB). I should have overlooked some details.
I also compared L1-norm method provided in the code. Prune from 256 to 48, pr=0.8125, x2, L1
python main.py --model LEDSR --scale 2 --patch_size 96 --ext sep --dir_data /home/notebook/data/group_cpfs/wurongyuan/data/data
--data_train DF2K --data_test DF2K --data_range 1-3550/3551-3555 --chop --save_results --n_resblocks 16 --n_feats 256
--method L1 --wn --stage_pr [0-1000:0.8125] --skip_layers *mean*,*tail*
--same_pruned_wg_layers model.head.0,model.body.16,*body.2 --reg_upper_limit 0.5 --reg_granularity_prune 0.0001
--update_reg_interval 20 --stabilize_reg_interval 43150 --pre_train pretrained_models/LEDSR_F256R16BIX2_DF2K_M311.pt
--same_pruned_wg_criterion reg --save main/SR/LEDSR_F256R16BIX2_DF2K_L1_0.8125_06011101
Results
model_just_finished_prune ---> 13.427dB fine-tuning after one epoch ---> 33.202dB fine-tuning after 756 epoch ---> 37.933dB
The difference between the results of L1-norm method and those of ASSL seems negligible at this pruning ratio (256->48)
Is there something I missed? Looking forward to your reply! >-<
Hello and thanks for your amazing work! When I try to reproduce the paper results, I met some trouble binarizing the DF2K data:
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data/DF2K/bin/DF2K_train_LR_bicubic/X4/3548x4.pt does not exist. Now making binary...
Direct pt file without name or image
data/DF2K/bin/DF2K_train_LR_bicubic/X4/3549x4.pt does not exist. Now making binary...
Direct pt file without name or image
data/DF2K/bin/DF2K_train_LR_bicubic/X4/3550x4.pt does not exist. Now making binary...
Direct pt file without name or image
data/DF2K/bin/DF2K_train_HR/3551.pt does not exist. Now making binary...
Traceback (most recent call last):
...
FileNotFoundError: No such file: '/home/nfs_data/shixiangsheng/projects/ModelCompression/Prune/ASSL/src/data/DF2K/DF2K_train_HR/3551.png'
I created dirs like this: ----data |__DF2K |__DF2K_train_HR |__DF2K_train_LR_bicubic
I put '0001.png' - '0900.png' from ./data/DIV2K/DIV2K_train_HR and '000001.png' - '002650.png' (renamed to '0901.png' - '3550.png') from .data/Flickr2K/Flickr2K_HR to ./DF2K/DF2K_train_HR. As for downsampled images, I created folders named in ['X2', 'X3', 'X4'] under ./DF2K/DF2K_train_LR_bicubic and copied related images from DIV2K_train_LR_bicubic and Flickr2K_LR_bicubic (with images renamed as '0001x_.png' to '3550x_.png'). At the first and second stages of binarization (binarizing HR images and X4 LR images), it seems OK, but then the above error emerged. It's kind of weird since the total training images are 900 + 2650 and I have no idea why it returned to binarize the HR images after binarizing X4 LR images. I'm new to SR and have tried to look up for data preparation of DF2K in other SR repos, but in vain. I wonder how you actually get DF2K images binarized. Thanks for your help in advance XD
Pretrained models, final models, and visual results on Set5, Set14, B100, Urban100, Manga109 (at scale x4).
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