ECCV2022 Paper - Dynamic Dual Trainable Bounds for Ultra-low Precision Super-Resolution Networks paper

Dependence

• Python 3.6
• PyTorch >= 1.7.0

Datasets

Please download DIV2K datasets.

Then, create a directory 'datasets' and re-organise the downloaded dataset directory as follows:

...
option.py
main_limitrange_incremental.py
datasets
  benchmark
  DIV2K

Usage

1: train full-precision models:

An example:

python main_ori.py --model edsr --scale 4 \
--save edsr_baseline_x4 \
--patch_size 192 \
--epochs 300 \
--decay 200 \
--gclip 0 \
--dir_data ./datasets

Please refer to 'baseline.sh' for more commands.

2: train quantized models:

An example:

python main_limitrange_incremental.py --scale 4 \
--k_bits 4 --model EDSR \
--pre_train ./pretrained/edsr_baseline_x4.pt --patch_size 192 \
--data_test Set14 \
--dynamic_ratio 0.3 \
--save "output/edsrx4/4bit" --dir_data ./datasets --print_every 10

Please refer to 'run.sh' for more commands.

3: test quantized models

An example:

python3 main_limitrange_incremental.py --scale 4 --model EDSR \
--k_bits 4 --save_results --test_only \
--data_test Set5+Set14+B100+Urban100  \
--save "../experiment/output/edsrx4/4bit" --dir_data ./datasets

Please refer to 'test.sh' for more commands.

calculate PSNR/SSIM

After saving the images, modify path inmetrics/calculate_PSNR_SSIM.m to generate results.

matlab -nodesktop -nosplash -r "calculate_PSNR_SSIM('$dataset',$scale,$bit);quit"

refer to metrics/run.sh for more details.

Trained FP models and quantized models: here

Download these model. Then use the commands above to obtain the reported results of the paper.

Model	Bit
EDSRx4	2
	3
	4
EDSRx2	2
	3
	4
RDNx4	2
	3
	4
RDNx2	2
	3
	4
SRResNetx4	2
	3
	4
SRResNetx2	2
	3
	4

Results of pre-trained models are shown below:

EDSR

Model	Dataset	Bit	DDTB(Ours)
EDSRx4	Set5	2	30.97/0.876
		3	31.52/0.883
		4	31.85/0.889
	Set14	2	27.87/0.764
		3	28.18/0.771
		4	28.39/0.777
	BSD100	2	27.09/0.719
		3	27.30/0.727
		4	27.44/0.732
	Urban100	2	24.82/0.742
		3	25.33/0.761
		4	25.69/0.774
EDSRx2	Set5	2	37.25/0.958
		3	37.51/0.958
		4	37.72/0.959
	Set14	2	32.87/0.911
		3	33.17/0.914
		4	33.35/0.916
	BSD100	2	31.67/0.893
		3	31.89/0.896
		4	32.01/0.898
	Urban100	2	30.34/0.910
		3	31.01/0.919
		4	31.39/0.922

RDN

Model	Dataset	Bit	DDTB(Ours)
RDNx4	Set5	2	30.57/0.867
		3	31.49/0.883
		4	31.97/0.891
	Set14	2	27.56/0.757
		3	28.17/0.772
		4	28.49/0.780
	BDS100	2	26.91/0.714
		3	27.30/0.728
		4	27.49/0.735
	Urban100	2	24.50/0.728
		3	25.35/0.764
		4	25.90/0.783
RDNx2	Set5	2	36.76/0.955
		3	37.61/0.959
		4	37.88/0.960
	Set14	2	32.54/0.908
		3	33.26/0.915
		4	33.51/0.917
	BSD100	2	31.44/0.890
		3	31.91/0.897
		4	32.12/0.899
	Urban100	2	29.77/0.903
		3	31.10/0.920
		4	31.76/0.926

SRResNet

Model	Dataset	Bit	DDTB(Ours)
SRResNetx4	Set5	2	31.51/0.887
		3	31.85/0.890
		4	31.97/0.892
	Set14	2	28.23/0.773
		3	28.39/0.776
		4	28.46/0.778
	BSD100	2	27.33/0.728
		3	27.44/0.731
		4	27.48/0.733
	Urban100	2	25.37/0.762
		3	25.64/0.770
		4	25.77/0.776
SRResNetx2	Set5	2	37.46/0.958
		3	37.67/0.959
		4	37.78/0.960
	Set14	2	33.02/0.913
		3	33.24/0.915
		4	33.32/0.916
	BSD100	2	31.78/0.895
		3	31.95/0.897
		4	32.03/0.898
	Urban100	2	30.57/0.913
		3	31.15/0.919
		4	31.40/0.921

Contact

For any question, be free to contact: viper.zhong@gmail.com. The github issue is also welcome.