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demo.sh
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demo.sh
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# SRRFN for train
# cd Train/
## Using --ext sep_reset argument on your first running.
## You can skip the decoding part and use saved binaries with --ext sep argument in second time.
## If you have enough memory, using --ext bin.
# SRRFN x2 LR: 48 * 48 HR: 96 * 96
python main.py --template SRRFN --save SRRFN_X2 --scale 2 --reset --save_results --patch_size 96 --ext sep_reset
# SRRFN x3 LR: 48 * 48 HR: 144 * 144
python main.py --template SRRFN --save SRRFN_X3 --scale 3 --reset --save_results --patch_size 144 --ext sep_reset
# SRRFN x4 LR: 48 * 48 HR: 192 * 192
python main.py --template SRRFN --save SRRFN_X4 --scale 4 --reset --save_results --patch_size 192 --ext sep_reset
# SRRFN for test
# cd Test/code/
# BI model
#SRRFN x2
python main.py --data_test MyImage --scale 2 --model SRRFN --degradation BI --pre_train ../model/SRRFN_x2_BI.pt --test_only --save_results --chop --save "SRRFN" --testpath ../LR/LRBI --testset Set5
#SRRFN+ x2
python main.py --data_test MyImage --scale 2 --model SRRFN --degradation BI --pre_train ../model/SRRFN_x2_BI.pt --test_only --save_results --chop --self_ensemble --save "SRRFN_plus" --testpath ../LR/LRBI --testset Set5
#SRRFN x3
python main.py --data_test MyImage --scale 3 --model SRRFN --degradation BI --pre_train ../model/SRRFN_x3_BI.pt --test_only --save_results --chop --save "SRRFN" --testpath ../LR/LRBI --testset Set5
#SRRFN+ x3
python main.py --data_test MyImage --scale 3 --model SRRFN --degradation BI --pre_train ../model/SRRFN_x3_BI.pt --test_only --save_results --chop --self_ensemble --save "SRRFN_plus" --testpath ../LR/LRBI --testset Set5
#SRRFN x4
python main.py --data_test MyImage --scale 4 --model SRRFN --degradation BI --pre_train ../model/SRRFN_x4_BI.pt --test_only --save_results --chop --save "SRRFN" --testpath ../LR/LRBI --testset Set5
#SRRFN+ x4
python main.py --data_test MyImage --scale 4 --model SRRFN --degradation BI --pre_train ../model/SRRFN_x4_BI.pt --test_only --save_results --chop --self_ensemble --save "SRRFN_plus" --testpath ../LR/LRBI --testset Set5
# BD model
#SRRFN_BD x3
python main.py --data_test MyImage --scale 3 --model SRRFN --degradation BD --pre_train ../model/SRRFN_x3_BD.pt --test_only --save_results --chop --save "SRRF" --testpath ../LR/LRBD --testset Set5
#SRRFN+_BD x3
python main.py --data_test MyImage --scale 3 --model SRRFN --degradation BD --pre_train ../model/SRRFN_x3_BD.pt --test_only --save_results --chop --self_ensemble --save "SRRFN_plus" --testpath ../LR/LRBD --testset Set5
# DN model
#SRRFN_DN x3
python main.py --data_test MyImage --scale 3 --model SRRFN --degradation DN --pre_train ../model/SRRFN_x3D_DN.pt --test_only --save_results --chop --save "SRRF" --testpath ../LR/LRDN --testset Set5
#SRRFN+_DN x3
python main.py --data_test MyImage --scale 3 --model SRRFN --degradation DN --pre_train ../model/SRRFN_x3D_DN.pt --test_only --save_results --chop --self_ensemble --save "SRRFN_plus" --testpath ../LR/LRDN --testset Set5