├── .gitattributes ├── .gitignore ├── README.md ├── Report.md ├── bmp ├── bicubic.bmp ├── bicubic120.bmp ├── bicubic127.bmp ├── bicubic65.bmp ├── bicubic86.bmp ├── butterfly.bmp ├── butterfly120.bmp ├── butterfly127.bmp ├── butterfly65.bmp ├── butterfly86.bmp ├── butterfly90.bmp ├── butterfly_crop235.bmp ├── butterfly_crop244.bmp └── butterfly_crop251.bmp ├── butterfly.py ├── dataset ├── PairRandomCrop.py ├── data.py ├── data_aug.m ├── dataset.py ├── generate_test.m ├── generate_train.m ├── img_cut.py ├── modcrop.m ├── store2hdf5.m ├── test │ ├── Set14 │ │ ├── baboon.bmp │ │ ├── barbara.bmp │ │ ├── bridge.bmp │ │ ├── coastguard.bmp │ │ ├── comic.bmp │ │ ├── face.bmp │ │ ├── flowers.bmp │ │ ├── foreman.bmp │ │ ├── lenna.bmp │ │ ├── man.bmp │ │ ├── monarch.bmp │ │ ├── pepper.bmp │ │ ├── ppt3.bmp │ │ └── zebra.bmp │ └── Set5 │ │ ├── baby_GT.bmp │ │ ├── bird_GT.bmp │ │ ├── butterfly_GT.bmp │ │ ├── head_GT.bmp │ │ └── woman_GT.bmp └── train │ ├── 91-images │ ├── t1.bmp │ ├── t10.bmp │ ├── t11.bmp │ ├── t12.bmp │ ├── t13.bmp │ ├── t14.bmp │ ├── t15.bmp │ ├── t16.bmp │ ├── t17.bmp │ ├── t18.bmp │ ├── t19.bmp │ ├── t2.bmp │ ├── t20.bmp │ ├── t21.bmp │ ├── t22.bmp │ ├── t23.bmp │ ├── t24.bmp │ ├── t25.bmp │ ├── t26.bmp │ ├── t27.bmp │ ├── t28.bmp │ ├── t29.bmp │ ├── t3.bmp │ ├── t30.bmp │ ├── t31.bmp │ ├── t32.bmp │ ├── t33.bmp │ ├── t34.bmp │ ├── t35.bmp │ ├── t36.bmp │ ├── t37.bmp │ ├── t38.bmp │ ├── t39.bmp │ ├── t4.bmp │ ├── t40.bmp │ ├── t42.bmp │ ├── t43.bmp │ ├── t44.bmp │ ├── t45.bmp │ ├── t46.bmp │ ├── t47.bmp │ ├── t48.bmp │ ├── t49.bmp │ ├── t5.bmp │ ├── t50.bmp │ ├── t51.bmp │ ├── t52.bmp │ ├── t53.bmp │ ├── t54.bmp │ ├── t55.bmp │ ├── t56.bmp │ ├── t57.bmp │ ├── t58.bmp │ ├── t59.bmp │ ├── t6.bmp │ ├── t60.bmp │ ├── t61.bmp │ ├── t62.bmp │ ├── t63.bmp │ ├── t64.bmp │ ├── t65.bmp │ ├── t66.bmp │ ├── t7.bmp │ ├── t8.bmp │ ├── t9.bmp │ ├── tt1.bmp │ ├── tt10.bmp │ ├── tt12.bmp │ ├── tt13.bmp │ ├── tt14.bmp │ ├── tt15.bmp │ ├── tt16.bmp │ ├── tt17.bmp │ ├── tt18.bmp │ ├── tt19.bmp │ ├── tt2.bmp │ ├── tt20.bmp │ ├── tt21.bmp │ ├── tt22.bmp │ ├── tt23.bmp │ ├── tt24.bmp │ ├── tt25.bmp │ ├── tt26.bmp │ ├── tt27.bmp │ ├── tt3.bmp │ ├── tt4.bmp │ ├── tt5.bmp │ ├── tt6.bmp │ ├── tt7.bmp │ ├── tt8.bmp │ └── tt9.bmp │ └── General-100 │ ├── im_1.bmp │ ├── im_10.bmp │ ├── im_100.bmp │ ├── im_11.bmp │ ├── im_12.bmp │ ├── im_13.bmp │ ├── im_14.bmp │ ├── im_15.bmp │ ├── im_16.bmp │ ├── im_17.bmp │ ├── im_18.bmp │ ├── im_19.bmp │ ├── im_2.bmp │ ├── im_20.bmp │ ├── im_21.bmp │ ├── im_22.bmp │ ├── im_23.bmp │ ├── im_24.bmp │ ├── im_25.bmp │ ├── im_26.bmp │ ├── im_27.bmp │ ├── im_28.bmp │ ├── im_29.bmp │ ├── im_3.bmp │ ├── im_30.bmp │ ├── im_31.bmp │ ├── im_32.bmp │ ├── im_33.bmp │ ├── im_34.bmp │ ├── im_35.bmp │ ├── im_36.bmp │ ├── im_37.bmp │ ├── im_38.bmp │ ├── im_39.bmp │ ├── im_4.bmp │ ├── im_40.bmp │ ├── im_41.bmp │ ├── im_42.bmp │ ├── im_43.bmp │ ├── im_44.bmp │ ├── im_45.bmp │ ├── im_46.bmp │ ├── im_47.bmp │ ├── im_48.bmp │ ├── im_49.bmp │ ├── im_5.bmp │ ├── im_50.bmp │ ├── im_51.bmp │ ├── im_52.bmp │ ├── im_53.bmp │ ├── im_54.bmp │ ├── im_55.bmp │ ├── im_56.bmp │ ├── im_57.bmp │ ├── im_58.bmp │ ├── im_59.bmp │ ├── im_6.bmp │ ├── im_60.bmp │ ├── im_61.bmp │ ├── im_62.bmp │ ├── im_63.bmp │ ├── im_64.bmp │ ├── im_65.bmp │ ├── im_66.bmp │ ├── im_67.bmp │ ├── im_68.bmp │ ├── im_69.bmp │ ├── im_7.bmp │ ├── im_70.bmp │ ├── im_71.bmp │ ├── im_72.bmp │ ├── im_73.bmp │ ├── im_74.bmp │ ├── im_75.bmp │ ├── im_76.bmp │ ├── im_77.bmp │ ├── im_78.bmp │ ├── im_79.bmp │ ├── im_8.bmp │ ├── im_80.bmp │ ├── im_81.bmp │ ├── im_82.bmp │ ├── im_83.bmp │ ├── im_84.bmp │ ├── im_85.bmp │ ├── im_86.bmp │ ├── im_87.bmp │ ├── im_88.bmp │ ├── im_89.bmp │ ├── im_9.bmp │ ├── im_90.bmp │ ├── im_91.bmp │ ├── im_92.bmp │ ├── im_93.bmp │ ├── im_94.bmp │ ├── im_95.bmp │ ├── im_96.bmp │ ├── im_97.bmp │ ├── im_98.bmp │ └── im_99.bmp ├── logger.py ├── logs ├── bicubic.bmp ├── no123 │ ├── network.png │ ├── sqrtd191-res │ │ ├── first_1.png │ │ ├── first_100.png │ │ ├── first_50.png │ │ ├── first_diff_1.png │ │ ├── first_diff_100.png │ │ ├── first_diff_50.png │ │ ├── first_layer_initial.png │ │ ├── individualImage.png │ │ ├── last_1.png │ │ ├── last_100.png │ │ ├── last_50.png │ │ ├── last_diff_1.png │ │ ├── last_diff_100.png │ │ ├── last_diff_50.png │ │ ├── last_layer_initial.png │ │ ├── loss.png │ │ ├── set14.png │ │ └── set5.png │ ├── sqrtd91-res │ │ ├── events.out.tfevents.1519821011.yip-PC │ │ ├── first_1.png │ │ ├── first_100.png │ │ ├── first_50.png │ │ ├── first_diff_1.png │ │ ├── first_diff_100.png │ │ ├── first_diff_50.png │ │ ├── first_layer_initial.png │ │ ├── individualImage.png │ │ ├── last_1.png │ │ ├── last_100.png │ │ ├── last_50.png │ │ ├── last_diff_1.png │ │ ├── last_diff_100.png │ │ ├── last_diff_50.png │ │ └── last_layer_initial.png │ ├── sqrtd91-xavier │ │ ├── Set14.png │ │ ├── Set5.png │ │ ├── events.out.tfevents.1519823209.yip-PC │ │ ├── first_1.png │ │ ├── first_100.png │ │ ├── first_50.png │ │ ├── first_diff_1.png │ │ ├── first_diff_100.png │ │ ├── first_diff_50.png │ │ ├── first_layer_initial.png │ │ ├── individualImage.png │ │ ├── last_1.png │ │ ├── last_100.png │ │ ├── last_50.png │ │ ├── last_diff_1.png │ │ ├── last_diff_100.png │ │ ├── last_diff_50.png │ │ ├── last_layer_initial.png │ │ └── loss.png │ └── sqrtd91 │ │ ├── events.out.tfevents.1519822203.yip-PC │ │ ├── first_1.png │ │ ├── first_100.png │ │ ├── first_50.png │ │ ├── first_diff_1.png │ │ ├── first_diff_100.png │ │ ├── first_diff_50.png │ │ ├── first_layer_initial.png │ │ ├── individualImage.png │ │ ├── last_1.png │ │ ├── last_100.png │ │ ├── last_50.png │ │ ├── last_diff_1.png │ │ ├── last_diff_100.png │ │ ├── last_diff_50.png │ │ ├── last_layer_initial.png │ │ ├── loss.png │ │ ├── set14.png │ │ └── set5.png ├── no4 │ ├── CLoss0.0001 │ │ ├── first_1.png │ │ ├── first_100.png │ │ ├── first_50.png │ │ ├── first_diff_1.png │ │ ├── first_diff_100.png │ │ ├── first_diff_50.png │ │ ├── first_layer_initial.png │ │ ├── individualImage.png │ │ ├── last_1.png │ │ ├── last_100.png │ │ ├── last_50.png │ │ ├── last_diff_1.png │ │ ├── last_diff_100.png │ │ ├── last_diff_50.png │ │ └── last_layer_initial.png │ ├── CLoss0.001 │ │ ├── first_1.png │ │ ├── first_100.png │ │ ├── first_50.png │ │ ├── first_diff_1.png │ │ ├── first_diff_100.png │ │ ├── first_diff_50.png │ │ ├── first_layer_initial.png │ │ ├── individualImage.png │ │ ├── last_1.png │ │ ├── last_100.png │ │ ├── last_50.png │ │ ├── last_diff_1.png │ │ ├── last_diff_100.png │ │ ├── last_diff_50.png │ │ └── last_layer_initial.png │ ├── CLoss0.01 │ │ ├── first_1.png │ │ ├── first_100.png │ │ ├── first_50.png │ │ ├── first_diff_1.png │ │ ├── first_diff_100.png │ │ ├── first_diff_50.png │ │ ├── first_layer_initial.png │ │ ├── individualImage.png │ │ ├── last_1.png │ │ ├── last_100.png │ │ ├── last_50.png │ │ ├── last_diff_1.png │ │ ├── last_diff_100.png │ │ ├── last_diff_50.png │ │ └── last_layer_initial.png │ ├── L2 │ │ ├── first_1.png │ │ ├── first_100.png │ │ ├── first_50.png │ │ ├── first_diff_1.png │ │ ├── first_diff_100.png │ │ ├── first_diff_50.png │ │ ├── first_layer_initial.png │ │ ├── individualImage.png │ │ ├── last_1.png │ │ ├── last_100.png │ │ ├── last_50.png │ │ ├── last_diff_1.png │ │ ├── last_diff_100.png │ │ ├── last_diff_50.png │ │ └── last_layer_initial.png │ ├── huber0.00001 │ │ ├── first_1.png │ │ ├── first_100.png │ │ ├── first_50.png │ │ ├── first_diff_1.png │ │ ├── first_diff_100.png │ │ ├── first_diff_50.png │ │ ├── first_layer_initial.png │ │ ├── individualImage.png │ │ ├── last_1.png │ │ ├── last_100.png │ │ ├── last_50.png │ │ ├── last_diff_1.png │ │ ├── last_diff_100.png │ │ ├── last_diff_50.png │ │ └── last_layer_initial.png │ ├── huber0.6 │ │ ├── first_1.png │ │ ├── first_100.png │ │ ├── first_50.png │ │ ├── first_diff_1.png │ │ ├── first_diff_100.png │ │ ├── first_diff_50.png │ │ ├── first_layer_initial.png │ │ ├── individualImage.png │ │ ├── last_1.png │ │ ├── last_100.png │ │ ├── last_50.png │ │ ├── last_diff_1.png │ │ ├── last_diff_100.png │ │ ├── last_diff_50.png │ │ └── last_layer_initial.png │ ├── huber0.9 │ │ ├── first_1.png │ │ ├── first_100.png │ │ ├── first_50.png │ │ ├── first_diff_1.png │ │ ├── first_diff_100.png │ │ ├── first_diff_50.png │ │ ├── first_layer_initial.png │ │ ├── individualImage.png │ │ ├── last_1.png │ │ ├── last_100.png │ │ ├── last_50.png │ │ ├── last_diff_1.png │ │ ├── last_diff_100.png │ │ ├── last_diff_50.png │ │ └── last_layer_initial.png │ ├── loss.png │ ├── network.png │ ├── set14.png │ └── set5.png ├── no56 │ ├── N2-10-4 │ │ ├── finetune-real-bicubic │ │ │ ├── first_1.png │ │ │ ├── first_100.png │ │ │ ├── first_50.png │ │ │ ├── first_diff_1.png │ │ │ ├── first_diff_100.png │ │ │ ├── first_diff_50.png │ │ │ ├── first_layer_initial.png │ │ │ ├── individualImage.png │ │ │ ├── last_1.png │ │ │ ├── last_100.png │ │ │ ├── last_50.png │ │ │ ├── last_diff_1.png │ │ │ ├── last_diff_100.png │ │ │ ├── last_diff_50.png │ │ │ ├── last_layer_initial.png │ │ │ ├── loss.png │ │ │ ├── set14.png │ │ │ └── set5.png │ │ ├── finetune-real-bilinear │ │ │ ├── first_1.png │ │ │ ├── first_100.png │ │ │ ├── first_50.png │ │ │ ├── first_diff_1.png │ │ │ ├── first_diff_100.png │ │ │ ├── first_diff_50.png │ │ │ ├── first_layer_initial.png │ │ │ ├── individualImage.png │ │ │ ├── last_1.png │ │ │ ├── last_100.png │ │ │ ├── last_50.png │ │ │ ├── last_diff_1.png │ │ │ ├── last_diff_100.png │ │ │ ├── last_diff_50.png │ │ │ └── last_layer_initial.png │ │ ├── finetune │ │ │ ├── first_1.png │ │ │ ├── first_100.png │ │ │ ├── first_50.png │ │ │ ├── first_diff_1.png │ │ │ ├── first_diff_100.png │ │ │ ├── first_diff_50.png │ │ │ ├── first_layer_initial.png │ │ │ ├── individualImage.png │ │ │ ├── last_1.png │ │ │ ├── last_100.png │ │ │ ├── last_50.png │ │ │ ├── last_diff_1.png │ │ │ ├── last_diff_100.png │ │ │ ├── last_diff_50.png │ │ │ └── last_layer_initial.png │ │ ├── first_1.png │ │ ├── first_100.png │ │ ├── first_50.png │ │ ├── first_diff_1.png │ │ ├── first_diff_100.png │ │ ├── first_diff_50.png │ │ ├── first_layer_initial.png │ │ ├── individualImage.png │ │ ├── last_1.png │ │ ├── last_100.png │ │ ├── last_50.png │ │ ├── last_diff_1.png │ │ ├── last_diff_100.png │ │ ├── last_diff_50.png │ │ └── last_layer_initial.png │ ├── N2-14-4 │ │ ├── finetune │ │ │ ├── first_1.png │ │ │ ├── first_100.png │ │ │ ├── first_50.png │ │ │ ├── first_diff_1.png │ │ │ ├── first_diff_100.png │ │ │ ├── first_diff_50.png │ │ │ ├── first_layer_initial.png │ │ │ ├── individualImage.png │ │ │ ├── last_1.png │ │ │ ├── last_100.png │ │ │ ├── last_50.png │ │ │ ├── last_diff_1.png │ │ │ ├── last_diff_100.png │ │ │ ├── last_diff_50.png │ │ │ └── last_layer_initial.png │ │ ├── first_1.png │ │ ├── first_100.png │ │ ├── first_50.png │ │ ├── first_diff_1.png │ │ ├── first_diff_100.png │ │ ├── first_diff_50.png │ │ ├── first_layer_initial.png │ │ ├── individualImage.png │ │ ├── last_1.png │ │ ├── last_100.png │ │ ├── last_50.png │ │ ├── last_diff_1.png │ │ ├── last_diff_100.png │ │ ├── last_diff_50.png │ │ └── last_layer_initial.png │ ├── N2-8-3 │ │ ├── finetune │ │ │ ├── first_1.png │ │ │ ├── first_100.png │ │ │ ├── first_50.png │ │ │ ├── first_diff_1.png │ │ │ ├── first_diff_100.png │ │ │ ├── first_diff_50.png │ │ │ ├── first_layer_initial.png │ │ │ ├── individualImage.png │ │ │ ├── last_1.png │ │ │ ├── last_100.png │ │ │ ├── last_50.png │ │ │ ├── last_diff_1.png │ │ │ ├── last_diff_100.png │ │ │ ├── last_diff_50.png │ │ │ └── last_layer_initial.png │ │ ├── first_1.png │ │ ├── first_100.png │ │ ├── first_50.png │ │ ├── first_diff_1.png │ │ ├── first_diff_100.png │ │ ├── first_diff_50.png │ │ ├── first_layer_initial.png │ │ ├── individualImage.png │ │ ├── last_1.png │ │ ├── last_100.png │ │ ├── last_50.png │ │ ├── last_diff_1.png │ │ ├── last_diff_100.png │ │ ├── last_diff_50.png │ │ └── last_layer_initial.png │ ├── loss.png │ ├── set14.png │ └── set5.png ├── no7 │ ├── loss.png │ ├── set14.png │ ├── set5.png │ ├── x2 │ │ ├── finetune │ │ │ ├── first_1.png │ │ │ ├── first_100.png │ │ │ ├── first_50.png │ │ │ ├── first_diff_1.png │ │ │ ├── first_diff_100.png │ │ │ ├── first_diff_50.png │ │ │ ├── first_layer_initial.png │ │ │ ├── individualImage.png │ │ │ ├── last_1.png │ │ │ ├── last_100.png │ │ │ ├── last_50.png │ │ │ ├── last_diff_1.png │ │ │ ├── last_diff_100.png │ │ │ ├── last_diff_50.png │ │ │ └── last_layer_initial.png │ │ ├── first_1.png │ │ ├── first_100.png │ │ ├── first_50.png │ │ ├── first_diff_1.png │ │ ├── first_diff_100.png │ │ ├── first_diff_50.png │ │ ├── first_layer_initial.png │ │ ├── individualImage.png │ │ ├── last_1.png │ │ ├── last_100.png │ │ ├── last_50.png │ │ ├── last_diff_1.png │ │ ├── last_diff_100.png │ │ ├── last_diff_50.png │ │ └── last_layer_initial.png │ └── x4 │ │ ├── finetune │ │ ├── first_1.png │ │ ├── first_100.png │ │ ├── first_50.png │ │ ├── first_diff_1.png │ │ ├── first_diff_100.png │ │ ├── first_diff_50.png │ │ ├── first_layer_initial.png │ │ ├── individualImage.png │ │ ├── last_1.png │ │ ├── last_100.png │ │ ├── last_50.png │ │ ├── last_diff_1.png │ │ ├── last_diff_100.png │ │ ├── last_diff_50.png │ │ └── last_layer_initial.png │ │ ├── first_1.png │ │ ├── first_100.png │ │ ├── first_50.png │ │ ├── first_diff_1.png │ │ ├── first_diff_100.png │ │ ├── first_diff_50.png │ │ ├── first_layer_initial.png │ │ ├── individualImage.png │ │ ├── last_1.png │ │ ├── last_100.png │ │ ├── last_50.png │ │ ├── last_diff_1.png │ │ ├── last_diff_100.png │ │ ├── last_diff_50.png │ │ └── last_layer_initial.png └── no8 │ ├── first_1.png │ ├── first_100.png │ ├── first_50.png │ ├── first_diff_1.png │ ├── first_diff_100.png │ ├── first_diff_50.png │ ├── first_layer_initial.png │ ├── individualImage.png │ ├── last_1.png │ ├── last_100.png │ ├── last_50.png │ ├── last_diff_1.png │ ├── last_diff_100.png │ ├── last_diff_50.png │ ├── last_layer_initial.png │ ├── loss.png │ ├── network.png │ ├── set14.png │ └── set5.png ├── loss.py ├── main.py ├── misc.py ├── model.py └── solver.py /.gitattributes: -------------------------------------------------------------------------------- 1 | # Auto detect text files and perform LF normalization 2 | * text=auto 3 | -------------------------------------------------------------------------------- /.gitignore: -------------------------------------------------------------------------------- 1 | # Byte-compiled / optimized / DLL files 2 | __pycache__/ 3 | *$py.class 4 | 5 | # C extensions 6 | *.so 7 | 8 | # Distribution / packaging 9 | .Python 10 | build/ 11 | develop-eggs/ 12 | dist/ 13 | downloads/ 14 | eggs/ 15 | .eggs/ 16 | lib/ 17 | lib64/ 18 | parts/ 19 | sdist/ 20 | var/ 21 | wheels/ 22 | *.egg-info/ 23 | .installed.cfg 24 | *.egg 25 | 26 | # PyInstaller 27 | # Usually these files are written by a python script from a template 28 | # before PyInstaller builds the exe, so as to inject date/other infos into it. 29 | *.manifest 30 | *.spec 31 | 32 | # Installer logs 33 | pip-log.txt 34 | pip-delete-this-directory.txt 35 | 36 | # Unit test / coverage reports 37 | htmlcov/ 38 | .tox/ 39 | .coverage 40 | .coverage.* 41 | .cache 42 | nosetests.xml 43 | coverage.xml 44 | *.cover 45 | .hypothesis/ 46 | 47 | # Translations 48 | *.mo 49 | *.pot 50 | 51 | # Django stuff: 52 | *.log 53 | local_settings.py 54 | 55 | # Flask stuff: 56 | instance/ 57 | .webassets-cache 58 | 59 | # Scrapy stuff: 60 | .scrapy 61 | 62 | # Sphinx documentation 63 | docs/_build/ 64 | 65 | # PyBuilder 66 | target/ 67 | 68 | # Jupyter Notebook 69 | .ipynb_checkpoints 70 | 71 | # pyenv 72 | .python-version 73 | 74 | # celery beat schedule file 75 | celerybeat-schedule 76 | 77 | # SageMath parsed files 78 | *.sage.py 79 | 80 | # Environments 81 | .env 82 | .venv 83 | env/ 84 | venv/ 85 | ENV/ 86 | 87 | # Spyder project settings 88 | .spyderproject 89 | .spyproject 90 | 91 | # Rope project settings 92 | .ropeproject 93 | 94 | # mkdocs documentation 95 | /site 96 | 97 | # mypy 98 | .mypy_cache/ 99 | *.xml 100 | *.iml 101 | *.pth 102 | 103 | *.yip-PC 104 | *.h5 105 | *.CR2 106 | *.dng 107 | *.xmp 108 | *.JPG 109 | dataset/real_raw/.DS_Store 110 | dataset/.DS_Store 111 | .DS_Store 112 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # FSRCNN 2 | Unofficial implementation of FSRCNN using PyTorch. 3 | 4 | ## Steps 5 | 1. Run `data_aug.m` to augment training set. 6 | 2. Run `generate_train.m` and `generate_test.m` to generate h5 files for data loader. 7 | (Note that you may need to change the path in the matlab code for different image source) 8 | 3. Run `main.py`. 9 | 10 | ## Requirement 11 | 1. python >= 3.4 12 | 2. pytorch >= 0.4 13 | 3. torchvision 14 | 4. tensorflow 15 | 16 | ## Reference 17 | Chao Dong, Chen Change Loy, Xiaoou Tang. Accelerating the Super-Resolution Convolutional Neural Network, in Proceedings of European Conference on Computer Vision (ECCV), 2016 18 | -------------------------------------------------------------------------------- /bmp/bicubic.bmp: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/yippp/FSRCNN/b82c173a998caf777ab57ea24ef8db1110458a39/bmp/bicubic.bmp -------------------------------------------------------------------------------- /bmp/bicubic120.bmp: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/yippp/FSRCNN/b82c173a998caf777ab57ea24ef8db1110458a39/bmp/bicubic120.bmp -------------------------------------------------------------------------------- /bmp/bicubic127.bmp: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/yippp/FSRCNN/b82c173a998caf777ab57ea24ef8db1110458a39/bmp/bicubic127.bmp -------------------------------------------------------------------------------- /bmp/bicubic65.bmp: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/yippp/FSRCNN/b82c173a998caf777ab57ea24ef8db1110458a39/bmp/bicubic65.bmp -------------------------------------------------------------------------------- /bmp/bicubic86.bmp: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/yippp/FSRCNN/b82c173a998caf777ab57ea24ef8db1110458a39/bmp/bicubic86.bmp -------------------------------------------------------------------------------- /bmp/butterfly.bmp: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/yippp/FSRCNN/b82c173a998caf777ab57ea24ef8db1110458a39/bmp/butterfly.bmp 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https://raw.githubusercontent.com/yippp/FSRCNN/b82c173a998caf777ab57ea24ef8db1110458a39/bmp/butterfly_crop244.bmp -------------------------------------------------------------------------------- /bmp/butterfly_crop251.bmp: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/yippp/FSRCNN/b82c173a998caf777ab57ea24ef8db1110458a39/bmp/butterfly_crop251.bmp -------------------------------------------------------------------------------- /butterfly.py: -------------------------------------------------------------------------------- 1 | """ 2 | Crop the butterfly image for prediction during training. 3 | """ 4 | 5 | from PIL import Image 6 | from dataset.dataset import load_img 7 | 8 | img = load_img('./dataset/test/Set5/butterfly_GT.bmp') 9 | # y90 = img.resize((90, 90), resample=Image.BICUBIC) 10 | # y90.save('butterfly90.bmp') 11 | img244 = img.crop((6, 6, 250, 250)) 12 | img244.save('butterfly_crop244.bmp') 13 | y86 = img.resize((120, 120), resample=Image.BICUBIC) 14 | y86.save('butterfly120.bmp') 15 | bicubic = y86.resize((256, 256), resample=Image.BICUBIC) 16 | bicubic.save('bicubic120.bmp') 17 | -------------------------------------------------------------------------------- /dataset/PairRandomCrop.py: -------------------------------------------------------------------------------- 1 | class PairRandomCrop: 2 | image_crop_position = {} 3 | 4 | def __init__(self, size): 5 | import random 6 | import os 7 | import numbers 8 | 9 | from PIL import ImageOps 10 | 11 | self.os = os 12 | self.random = random 13 | self.ImageOps = ImageOps 14 | 15 | if isinstance(size, numbers.Number): 16 | self.size = (int(size), int(size)) 17 | else: 18 | self.size = size 19 | 20 | def __call__(self, img): 21 | w, h = img.size 22 | th, tw = self.size 23 | if w == tw and h == th: 24 | return img 25 | 26 | pid = self.os.getpid() 27 | if pid in self.image_crop_position: 28 | x1, y1 = self.image_crop_position.pop(pid) 29 | else: 30 | x1 = self.random.randint(0, w - tw) 31 | y1 = self.random.randint(0, h - th) 32 | self.image_crop_position[pid] = (x1, y1) 33 | return img.crop((x1, y1, x1 + tw, y1 + th)) 34 | -------------------------------------------------------------------------------- /dataset/data.py: -------------------------------------------------------------------------------- 1 | from os.path import join 2 | from .dataset import LoadH5, LoadImg 3 | 4 | def get_h5_set(train_set): 5 | """ 6 | Load H5 dataset. 7 | :param train_set: the filename of the dataset 8 | :return: the loaded data 9 | """ 10 | train_dir = join("./dataset", train_set) 11 | 12 | return LoadH5(train_dir) 13 | 14 | 15 | def get_img_set(test_set): 16 | """ 17 | Load images file data. 18 | :param train_set: the folder name of the images in 19 | :return: the loaded data 20 | """ 21 | test_dir = join("./dataset", test_set) 22 | 23 | return LoadImg(test_dir) 24 | -------------------------------------------------------------------------------- /dataset/data_aug.m: -------------------------------------------------------------------------------- 1 | clear; 2 | %% To do data augmentation 3 | folder = 'train/91-images'; 4 | savepath = 'train/91-images-aug/'; 5 | 6 | filepaths = dir(fullfile(folder,'*.bmp')); 7 | 8 | for i = 1 : length(filepaths) 9 | filename = filepaths(i).name; 10 | [add, im_name, type] = fileparts(filepaths(i).name); 11 | image = imread(fullfile(folder, filename)); 12 | 13 | for angle = 0: 90 :270 14 | im_rot = rot90(image, angle); 15 | imwrite(im_rot, [savepath im_name, '_rot' num2str(angle) '.bmp']); 16 | 17 | for scale = 0.6 : 0.1 :0.9 18 | im_down = imresize(im_rot, scale, 'bicubic'); 19 | imwrite(im_down, [savepath im_name, '_rot' num2str(angle) '_s' num2str(scale*10) '.bmp']); 20 | end 21 | 22 | end 23 | end 24 | -------------------------------------------------------------------------------- /dataset/dataset.py: -------------------------------------------------------------------------------- 1 | import torch.utils.data as data 2 | from PIL import Image 3 | from torchvision.transforms import ToTensor, CenterCrop, Resize 4 | import h5py 5 | import numpy as np 6 | from torch import from_numpy 7 | from math import floor 8 | from os import listdir 9 | from os.path import join 10 | 11 | def is_image_file(filename): 12 | """ 13 | Check wheather the file is a image file. 14 | :param filename: name of the file 15 | :return: bool value shows that whether it is a image 16 | """ 17 | return any(filename.endswith(extension) for extension in [".png", ".jpg", ".jpeg", ".bmp"]) 18 | 19 | 20 | def load_img(filepath): 21 | """ 22 | Load the image and get the luminance data. 23 | :param filepath: path of the image. 24 | :return: luminance data 25 | """ 26 | img = Image.open(filepath).convert('YCbCr') 27 | y, _, _ = img.split() 28 | return y 29 | 30 | 31 | class LoadH5(data.Dataset): 32 | def __init__(self, image_h5): 33 | super(LoadH5, self).__init__() 34 | 35 | self.to_tensor = ToTensor() 36 | 37 | self.input_patch = [] 38 | self.target_patch = [] 39 | 40 | with h5py.File(image_h5, 'r') as hf: 41 | self.input_patch = np.array(hf.get('data')) 42 | self.target_patch = np.array(hf.get('label')) 43 | 44 | def __getitem__(self, index): 45 | input_image = self.input_patch[index] 46 | target_image = self.target_patch[index] 47 | return from_numpy(input_image), from_numpy(target_image) 48 | 49 | def __len__(self): 50 | return len(self.input_patch) 51 | 52 | class LoadImg(data.Dataset): 53 | def __init__(self, image_dir): 54 | super(LoadImg, self).__init__() 55 | self.image_filenames = [join(image_dir, x) for x in listdir(image_dir) if is_image_file(x)] 56 | self.to_tensor = ToTensor() 57 | 58 | 59 | def __getitem__(self, index): 60 | input_image = load_img(self.image_filenames[index]) 61 | x_re = floor((input_image.size[0] - 1) / 3 + 5) 62 | x = (x_re - 5) * 3 + 1 63 | if x != input_image.size[0]: 64 | x = floor(x) 65 | y_re = floor((input_image.size[1] - 1) / 3 + 5) 66 | y = (y_re - 5) * 3 + 1 67 | if y != input_image.size[1]: 68 | y = floor(y) 69 | 70 | self.crop = CenterCrop((x,y)) 71 | input_image = self.crop(input_image) 72 | target = input_image.copy() 73 | target = self.to_tensor(target) 74 | 75 | self.resize = Resize((x_re, y_re)) 76 | input_image = self.resize(input_image) 77 | input_image = self.to_tensor(input_image) 78 | 79 | return input_image, target 80 | 81 | def __len__(self): 82 | return len(self.image_filenames) -------------------------------------------------------------------------------- /dataset/generate_test.m: -------------------------------------------------------------------------------- 1 | clear; close all; 2 | %% settings 3 | folder = 'test/Set5'; %% Set14 4 | savepath = 'test/Set5.h5'; 5 | size_input = 11; 6 | size_label = 19; 7 | scale = 3; 8 | stride = size_input; 9 | 10 | %% initialization 11 | data = zeros(size_input, size_input, 1, 1); 12 | label = zeros(size_label, size_label, 1, 1); 13 | padding = abs(size_input - size_label)/2; 14 | count = 0; 15 | 16 | %% generate data 17 | filepaths = dir(fullfile(folder,'*.bmp')); 18 | 19 | for i = 1 : length(filepaths) 20 | 21 | image = imread(fullfile(folder, filepaths(i).name)); 22 | img_size = size(size(image)); 23 | if img_size(2) == 2 24 | image = cat(3, image, image, image); 25 | end 26 | image = rgb2ycbcr(image); 27 | image = im2double(image(:, :, 1)); 28 | 29 | im_label = modcrop(image, scale); 30 | im_input = imresize(im_label, 1/scale, 'bicubic'); 31 | [hei,wid] = size(im_input); 32 | 33 | for x = 1 : stride : hei - size_input + 1 34 | for y = 1 : stride : wid - size_input + 1 35 | 36 | locx = scale * (x + floor((size_input - 1)/2)) - floor((size_label + scale)/2 - 1); 37 | locy = scale * (y + floor((size_input - 1)/2)) - floor((size_label + scale)/2 - 1); 38 | 39 | subim_input = im_input(x : size_input + x - 1, y : size_input + y - 1); 40 | subim_label = im_label(locx : size_label + locx - 1, locy : size_label + locy - 1); 41 | 42 | count = count + 1; 43 | data(:, :, 1, count) = subim_input; 44 | label(:, :, 1, count) = subim_label; 45 | end 46 | end 47 | end 48 | order = randperm(count); 49 | data = data(:, :, 1, order); 50 | label = label(:, :, 1, order); 51 | 52 | %% writing to HDF5 53 | chunksz = 2; 54 | created_flag = false; 55 | totalct = 0; 56 | 57 | for batchno = 1:floor(count/chunksz) 58 | last_read = (batchno-1)*chunksz; 59 | batchdata = data(:,:,1,last_read+1:last_read+chunksz); 60 | batchlabs = label(:,:,1,last_read+1:last_read+chunksz); 61 | 62 | startloc = struct('dat',[1,1,1,totalct+1], 'lab', [1,1,1,totalct+1]); 63 | curr_dat_sz = store2hdf5(savepath, batchdata, batchlabs, ~created_flag, startloc, chunksz); 64 | created_flag = true; 65 | totalct = curr_dat_sz(end); 66 | end 67 | h5disp(savepath); -------------------------------------------------------------------------------- /dataset/generate_train.m: -------------------------------------------------------------------------------- 1 | clear; close all; 2 | %% settings 3 | folder = 'train/91-images-aug';% 4 | savepath = 'train/91.h5';% 5 | size_input = 11;% There are 4 pixels padding. Paper presents 7 6 | size_label = 19;% (11-4) *3 - (3-1) 7 | scale = 3; 8 | stride = 4; 9 | 10 | %% initialization 11 | data = zeros(size_input, size_input, 1, 1); 12 | label = zeros(size_label, size_label, 1, 1); 13 | padding = abs(size_input - size_label)/2; 14 | count = 0; 15 | 16 | %% generate data 17 | filepaths = dir(fullfile(folder,'*.bmp')); 18 | 19 | for i = 1 : length(filepaths) 20 | 21 | image = imread(fullfile(folder, filepaths(i).name)); 22 | image = rgb2ycbcr(image); 23 | image = im2double(image(:, :, 1)); 24 | 25 | im_label = modcrop(image, scale); 26 | im_input = imresize(im_label, 1/scale, 'bicubic'); 27 | [hei,wid] = size(im_input); 28 | 29 | for x = 1 : stride : hei - size_input + 1 30 | for y = 1 : stride : wid - size_input + 1 31 | 32 | locx = scale * (x + floor((size_input - 1)/2)) - floor((size_label + scale)/2 - 1); 33 | locy = scale * (y + floor((size_input - 1)/2)) - floor((size_label + scale)/2 - 1); 34 | 35 | subim_input = im_input(x : size_input + x - 1, y : size_input + y - 1); 36 | subim_label = im_label(locx : size_label + locx - 1, locy : size_label + locy - 1); 37 | 38 | count = count + 1; 39 | data(:, :, 1, count) = subim_input; 40 | label(:, :, 1, count) = subim_label; 41 | end 42 | end 43 | end 44 | order = randperm(count); 45 | data = data(:, :, 1, order); 46 | label = label(:, :, 1, order); 47 | 48 | %% writing to HDF5 49 | chunksz = 128; 50 | created_flag = false; 51 | totalct = 0; 52 | 53 | for batchno = 1:floor(count/chunksz) 54 | last_read = (batchno-1)*chunksz; 55 | batchdata = data(:,:,1,last_read+1:last_read+chunksz); 56 | batchlabs = label(:,:,1,last_read+1:last_read+chunksz); 57 | 58 | startloc = struct('dat',[1,1,1,totalct+1], 'lab', [1,1,1,totalct+1]); 59 | curr_dat_sz = store2hdf5(savepath, batchdata, batchlabs, ~created_flag, startloc, chunksz); 60 | created_flag = true; 61 | totalct = curr_dat_sz(end); 62 | end 63 | h5disp(savepath); 64 | 65 | -------------------------------------------------------------------------------- /dataset/img_cut.py: -------------------------------------------------------------------------------- 1 | from PIL import Image 2 | import os 3 | import numpy as np 4 | from math import floor 5 | 6 | x_cut = 19 7 | y_cut = 19 8 | 9 | imgTypes = [".bmp"] 10 | 11 | for root, dirs, files in os.walk("./test/Set5"): 12 | for afile in files: 13 | ffile = root + "/" + afile 14 | print('cutting', afile) 15 | 16 | if ffile[ffile.rindex("."):].lower() in imgTypes: 17 | im = Image.open(ffile) 18 | 19 | nx = im.size[0] // x_cut 20 | if nx * x_cut != im.size[0]: 21 | nx += 1 22 | ny = im.size[1] // y_cut 23 | if ny * y_cut != im.size[1]: 24 | ny += 1 25 | 26 | n = 1 27 | 28 | for x in range(nx): 29 | for y in range(ny): 30 | name = "./test/5cut/" + afile.replace('.bmp', '_' + str(n) + '.bmp') 31 | im2 = im.crop((floor(x / nx * im.size[0]), floor(y / ny * im.size[1]), 32 | floor(x / nx * im.size[0]) + x_cut, floor(y / ny * im.size[1]) + y_cut)) 33 | check = np.asarray(im2) 34 | if not check.max() == 0: 35 | im2.save(name) 36 | n += 1 37 | -------------------------------------------------------------------------------- /dataset/modcrop.m: -------------------------------------------------------------------------------- 1 | function imgs = modcrop(imgs, modulo) 2 | if size(imgs,3)==1 3 | sz = size(imgs); 4 | sz = sz - mod(sz, modulo); 5 | imgs = imgs(1:sz(1), 1:sz(2)); 6 | else 7 | tmpsz = size(imgs); 8 | sz = tmpsz(1:2); 9 | sz = sz - mod(sz, modulo); 10 | imgs = imgs(1:sz(1), 1:sz(2),:); 11 | end 12 | 13 | -------------------------------------------------------------------------------- /dataset/store2hdf5.m: -------------------------------------------------------------------------------- 1 | function [curr_dat_sz, curr_lab_sz] = store2hdf5(filename, data, labels, create, startloc, chunksz) 2 | % *data* is W*H*C*N matrix of images should be normalized (e.g. to lie between 0 and 1) beforehand 3 | % *label* is D*N matrix of labels (D labels per sample) 4 | % *create* [0/1] specifies whether to create file newly or to append to previously created file, useful to store information in batches when a dataset is too big to be held in memory (default: 1) 5 | % *startloc* (point at which to start writing data). By default, 6 | % if create=1 (create mode), startloc.data=[1 1 1 1], and startloc.lab=[1 1]; 7 | % if create=0 (append mode), startloc.data=[1 1 1 K+1], and startloc.lab = [1 K+1]; where K is the current number of samples stored in the HDF 8 | % chunksz (used only in create mode), specifies number of samples to be stored per chunk (see HDF5 documentation on chunking) for creating HDF5 files with unbounded maximum size - TLDR; higher chunk sizes allow faster read-write operations 9 | 10 | % verify that format is right 11 | dat_dims=size(data); 12 | lab_dims=size(labels); 13 | num_samples=dat_dims(end); 14 | 15 | assert(lab_dims(end)==num_samples, 'Number of samples should be matched between data and labels'); 16 | 17 | if ~exist('create','var') 18 | create=true; 19 | end 20 | 21 | 22 | if create 23 | %fprintf('Creating dataset with %d samples\n', num_samples); 24 | if ~exist('chunksz', 'var') 25 | chunksz=1000; 26 | end 27 | if exist(filename, 'file') 28 | fprintf('Warning: replacing existing file %s \n', filename); 29 | delete(filename); 30 | end 31 | h5create(filename, '/data', [dat_dims(1:end-1) Inf], 'Datatype', 'single', 'ChunkSize', [dat_dims(1:end-1) chunksz]); % width, height, channels, number 32 | h5create(filename, '/label', [lab_dims(1:end-1) Inf], 'Datatype', 'single', 'ChunkSize', [lab_dims(1:end-1) chunksz]); % width, height, channels, number 33 | if ~exist('startloc','var') 34 | startloc.dat=[ones(1,length(dat_dims)-1), 1]; 35 | startloc.lab=[ones(1,length(lab_dims)-1), 1]; 36 | end 37 | else % append mode 38 | if ~exist('startloc','var') 39 | info=h5info(filename); 40 | prev_dat_sz=info.Datasets(1).Dataspace.Size; 41 | prev_lab_sz=info.Datasets(2).Dataspace.Size; 42 | assert(prev_dat_sz(1:end-1)==dat_dims(1:end-1), 'Data dimensions must match existing dimensions in dataset'); 43 | assert(prev_lab_sz(1:end-1)==lab_dims(1:end-1), 'Label dimensions must match existing dimensions in dataset'); 44 | startloc.dat=[ones(1,length(dat_dims)-1), prev_dat_sz(end)+1]; 45 | startloc.lab=[ones(1,length(lab_dims)-1), prev_lab_sz(end)+1]; 46 | end 47 | end 48 | 49 | if ~isempty(data) 50 | h5write(filename, '/data', single(data), startloc.dat, size(data)); 51 | h5write(filename, '/label', single(labels), startloc.lab, size(labels)); 52 | end 53 | 54 | if nargout 55 | info=h5info(filename); 56 | curr_dat_sz=info.Datasets(1).Dataspace.Size; 57 | curr_lab_sz=info.Datasets(2).Dataspace.Size; 58 | end 59 | end 60 | -------------------------------------------------------------------------------- /dataset/test/Set14/baboon.bmp: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/yippp/FSRCNN/b82c173a998caf777ab57ea24ef8db1110458a39/dataset/test/Set14/baboon.bmp -------------------------------------------------------------------------------- /dataset/test/Set14/barbara.bmp: -------------------------------------------------------------------------------- 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a summary writer logging to log_dir.""" 16 | self.writer = tf.summary.FileWriter(log_dir) 17 | self.writer.flush() 18 | 19 | def scalar_summary(self, tag, value, step): 20 | """Log a scalar variable.""" 21 | summary = tf.Summary(value=[tf.Summary.Value(tag=tag, simple_value=value)]) 22 | self.writer.add_summary(summary, step) 23 | self.writer.flush() 24 | 25 | def image_summary(self, tag, image, step): 26 | """Log a list of images.""" 27 | 28 | img_summaries = [] 29 | # Write the image to a string 30 | try: 31 | s = StringIO() 32 | except: 33 | s = BytesIO() 34 | scipy.misc.toimage(image).save(s, format="png") 35 | 36 | # Create an Image object 37 | img_sum = tf.Summary.Image(encoded_image_string=s.getvalue(), 38 | height=image.shape[0], 39 | width=image.shape[1]) 40 | # Create a Summary value 41 | img_summaries.append(tf.Summary.Value(tag='%s' % tag, image=img_sum)) 42 | 43 | # Create and write Summary 44 | summary = tf.Summary(value=img_summaries) 45 | self.writer.add_summary(summary, step) 46 | self.writer.flush() 47 | 48 | def histo_summary(self, tag, values, step, bins=1000): 49 | """Log a histogram of the tensor of values.""" 50 | 51 | # Create a histogram using numpy 52 | counts, bin_edges = np.histogram(values, bins=bins) 53 | 54 | # Fill the fields of the histogram proto 55 | hist = tf.HistogramProto() 56 | hist.min = float(np.min(values)) 57 | hist.max = float(np.max(values)) 58 | hist.num = int(np.prod(values.shape)) 59 | hist.sum = float(np.sum(values)) 60 | hist.sum_squares = float(np.sum(values ** 2)) 61 | 62 | # Drop the start of the first bin 63 | bin_edges = bin_edges[1:] 64 | 65 | # Add bin edges and counts 66 | for edge in bin_edges: 67 | hist.bucket_limit.append(edge) 68 | for c in counts: 69 | hist.bucket.append(c) 70 | 71 | # Create and write Summary 72 | summary = tf.Summary(value=[tf.Summary.Value(tag=tag, histo=hist)]) 73 | self.writer.add_summary(summary, step) 74 | self.writer.flush() 75 | 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-------------------------------------------------------------------------------- /loss.py: -------------------------------------------------------------------------------- 1 | import torch.nn as nn 2 | from torch import mean, sqrt, pow 3 | 4 | 5 | class HuberLoss(nn.Module): 6 | def __init__(self, delta=1): 7 | super(HuberLoss, self).__init__() 8 | self.delta = delta 9 | self.SmoothL1Loss = nn.SmoothL1Loss() 10 | return 11 | 12 | def forward(self, input, target): 13 | loss = self.SmoothL1Loss(input / self.delta, target / self.delta) 14 | return loss * self.delta * self.delta 15 | 16 | 17 | class CharbonnierLoss(nn.Module): 18 | def __init__(self, delta=1e-3): 19 | super(CharbonnierLoss, self).__init__() 20 | self.delta = delta 21 | return 22 | 23 | def forward(self, input, target): 24 | return mean(sqrt(pow((input - target), 2) + self.delta * self.delta)) 25 | -------------------------------------------------------------------------------- /main.py: -------------------------------------------------------------------------------- 1 | from dataset.data import get_h5_set, get_img_set 2 | from torch.utils.data import DataLoader 3 | import argparse 4 | from solver import solver 5 | 6 | parser = argparse.ArgumentParser(description='FSRCNN') 7 | # hyper-parameters 8 | parser.add_argument('--batch_size', type=int, default=128, help='trainingbatch size. Default=128') 9 | parser.add_argument('--n_epochs', type=int, default=100, help='number of epochs to train for. Default=100') 10 | parser.add_argument('--lr', type=float, default=1e-3, help='Learning Rate. Default=0.001') 11 | parser.add_argument('--mom', type=float, default=0.9, help='Momentum. Default=0.9') 12 | parser.add_argument('--seed', type=int, default=1, help='random seed to use. Default=1') 13 | parser.add_argument('--train_set', type=str, default='train/91.h5', help='name of train set h5 file.') 14 | parser.add_argument('--logs', type=str, default='./logs/', 15 | help='folder to save the log file. Default=./logs/') 16 | 17 | args = parser.parse_args() 18 | 19 | 20 | def main(): 21 | print('===> Loading datasets') 22 | train_set = get_h5_set(args.train_set) 23 | set5_h5 = get_h5_set('test/Set5.h5') 24 | set14_h5 = get_h5_set('test/Set14.h5') 25 | set5_img = get_img_set('test/Set5') 26 | set14_img = get_img_set('test/Set14') 27 | training_data_loader = DataLoader(dataset=train_set, batch_size=args.batch_size, shuffle=True) 28 | Set5_h5_loader = DataLoader(dataset=set5_h5, batch_size=1, shuffle=False) 29 | Set14_h5_loader = DataLoader(dataset=set14_h5, batch_size=1, shuffle=False) 30 | Set5_img_loader = DataLoader(dataset=set5_img, batch_size=1, shuffle=False) 31 | Set14_img_loader = DataLoader(dataset=set14_img, batch_size=1, shuffle=False) 32 | 33 | model = solver(args, training_data_loader, Set5_h5_loader, Set14_h5_loader, Set5_img_loader, Set14_img_loader) 34 | model.validate() 35 | 36 | 37 | if __name__ == '__main__': 38 | main() 39 | -------------------------------------------------------------------------------- /misc.py: -------------------------------------------------------------------------------- 1 | import sys 2 | import time 3 | 4 | TOTAL_BAR_LENGTH = 50 5 | LAST_T = time.time() 6 | BEGIN_T = LAST_T 7 | 8 | 9 | def progress_bar(current, total, msg=None): 10 | global LAST_T, BEGIN_T 11 | if current == 0: 12 | BEGIN_T = time.time() # Reset for new bar. 13 | 14 | current_time = time.time() 15 | LAST_T = current_time 16 | total_time = current_time - BEGIN_T 17 | 18 | if current == total - 1: 19 | time_used = ' ' 20 | time_used += 'Time: {:.3f}s'.format(total_time) 21 | if msg: 22 | time_used += ' | ' + msg 23 | 24 | msg = time_used 25 | sys.stdout.write(' %d batches' % total) 26 | sys.stdout.write(msg) 27 | sys.stdout.write('\n') 28 | sys.stdout.flush() 29 | -------------------------------------------------------------------------------- /model.py: -------------------------------------------------------------------------------- 1 | import torch 2 | import torch.nn as nn 3 | from math import sqrt 4 | 5 | 6 | class Net(torch.nn.Module): 7 | def __init__(self, n_channels=1, d=56, s=12, m=4): 8 | # too big network may leads to over-fitting 9 | super(Net, self).__init__() 10 | 11 | # Feature extraction 12 | self.first_part = nn.Sequential( 13 | nn.Conv2d(in_channels=n_channels, out_channels=d, kernel_size=5, stride=1, padding=0), 14 | nn.PReLU()) 15 | # H_out = floor((H_in+2*padding-(kernal_size-1)-1)/stride+1) 16 | # = floor(H_in-4) 17 | # for x2 floor(H_in-2) 18 | self.layers = [] 19 | # Shrinking 20 | self.layers.append(nn.Sequential(nn.Conv2d(in_channels=d, out_channels=s, kernel_size=1, stride=1, padding=0), 21 | nn.PReLU())) 22 | 23 | # Non-linear Mapping 24 | for _ in range(m): 25 | self.layers.append( 26 | nn.Sequential(nn.Conv2d(in_channels=s, out_channels=s, kernel_size=3, stride=1, padding=1), 27 | nn.PReLU())) 28 | 29 | # # Expanding 30 | self.layers.append(nn.Sequential(nn.Conv2d(in_channels=s, out_channels=d, kernel_size=1, stride=1, padding=0), 31 | nn.PReLU())) 32 | 33 | self.mid_part = torch.nn.Sequential(*self.layers) 34 | 35 | # Deconvolution 36 | self.last_part = nn.ConvTranspose2d(in_channels=d, out_channels=n_channels, kernel_size=9, stride=3, padding=4, 37 | output_padding=0) 38 | # self.last_part = nn.Sequential( 39 | # nn.Conv2d(in_channels=d, out_channels=n_channels * 2 * 2, kernel_size=3, stride=1, padding=1), 40 | # nn.PixelShuffle(2)) 41 | # H_out = (H_in-1)*stride-2*padding+kernal_size+out_padding 42 | # = (H_in-1)*3+1 43 | # test input should be (y-5)*3+1 44 | # for x2 2x-3 45 | # for x4 4x-25 46 | 47 | def forward(self, x): 48 | out = self.first_part(x) 49 | out = self.mid_part(out) 50 | out = self.last_part(out) 51 | return out 52 | 53 | def weight_init(self): 54 | """ 55 | Initial the weights. 56 | """ 57 | for m in self.modules(): 58 | if isinstance(m, nn.Conv2d) or isinstance(m, nn.ConvTranspose2d): 59 | # m.weight.data.normal_(0.0, 0.2) 60 | m.weight.data.normal_(0.0, sqrt(2 / m.out_channels / m.kernel_size[0] / m.kernel_size[0])) # MSRA 61 | # nn.init.xavier_normal(m.weight) # Xavier 62 | if m.bias is not None: 63 | m.bias.data.zero_() 64 | -------------------------------------------------------------------------------- /solver.py: -------------------------------------------------------------------------------- 1 | from math import log10, ceil 2 | import os 3 | import torch 4 | import torch.nn as nn 5 | import torch.optim as optim 6 | import torch.backends.cudnn as cudnn 7 | from model import Net 8 | from misc import progress_bar 9 | from matplotlib import pyplot as plt 10 | from logger import Logger 11 | from matplotlib.colors import Normalize 12 | from dataset.dataset import load_img 13 | from torchvision.transforms import ToTensor 14 | from scipy.misc import imsave 15 | # from torchviz import make_dot 16 | from loss import HuberLoss, CharbonnierLoss 17 | 18 | 19 | class solver(object): 20 | def __init__(self, config, train_loader, set5_h5_loader, set14_h5_loader, set5_img_loader, set14_img_loader): 21 | self.model = None 22 | self.lr = config.lr 23 | self.batch_size = config.batch_size 24 | self.mom = config.mom 25 | self.logs = config.logs 26 | self.n_epochs = config.n_epochs 27 | self.criterion = None 28 | self.optimizer = None 29 | self.scheduler = None 30 | self.GPU = torch.cuda.is_available() 31 | self.seed = config.seed 32 | self.train_loader = train_loader 33 | self.set5_h5_loader = set5_h5_loader 34 | self.set14_h5_loader = set14_h5_loader 35 | self.set5_img_loader = set5_img_loader 36 | self.set14_img_loader = set14_img_loader 37 | self.logger = Logger(self.logs + '/') 38 | self.info = {'loss': 0, 'PSNR for Set5': 0, 'PSNR for Set14': 0} 39 | self.final_para = [] 40 | self.initial_para = [] 41 | self.graph = True 42 | self.to_tensor = ToTensor() 43 | 44 | if not os.path.isdir(self.logs): 45 | os.makedirs(self.logs) 46 | 47 | def build_model(self): 48 | """ 49 | Build the model. 50 | """ 51 | self.model = Net() 52 | self.model.weight_init() 53 | # self.model = torch.load('./logs/no7/x2/FSRCNN_model100.pth') 54 | 55 | self.criterion = nn.MSELoss() 56 | # self.criterion = HuberLoss(delta=0.9) # Huber loss 57 | # self.criterion = CharbonnierLoss(delta=0.0001) # Charbonnier Loss 58 | torch.manual_seed(self.seed) 59 | 60 | if self.GPU: 61 | torch.cuda.manual_seed(self.seed) 62 | self.model.cuda() 63 | cudnn.benchmark = True 64 | self.criterion.cuda() 65 | 66 | # folloe the setting in the official caffe prototext 67 | self.optimizer = optim.SGD([{'params': self.model.first_part[0].weight}, # feature extraction layer 68 | {'params': self.model.first_part[0].bias, 'lr': 0.1 * self.lr}, 69 | {'params': self.model.mid_part[0][0].weight}, # shrinking layer 70 | {'params': self.model.mid_part[0][0].bias, 'lr': 0.1 * self.lr}, 71 | {'params': self.model.mid_part[1][0].weight}, # mapping layers 72 | {'params': self.model.mid_part[1][0].bias, 'lr': 0.1 * self.lr}, 73 | {'params': self.model.mid_part[2][0].weight}, 74 | {'params': self.model.mid_part[2][0].bias, 'lr': 0.1 * self.lr}, 75 | {'params': self.model.mid_part[3][0].weight}, 76 | {'params': self.model.mid_part[3][0].bias, 'lr': 0.1 * self.lr}, 77 | {'params': self.model.mid_part[4][0].weight}, 78 | {'params': self.model.mid_part[4][0].bias, 'lr': 0.1 * self.lr}, 79 | {'params': self.model.mid_part[5][0].weight}, # expanding layer 80 | {'params': self.model.mid_part[5][0].bias, 'lr': 0.1 * self.lr}, 81 | {'params': self.model.last_part.weight, 'lr': 0.1 * self.lr}, # deconvolution layer 82 | {'params': self.model.last_part.bias, 'lr': 0.1 * self.lr}], 83 | lr=self.lr, momentum=self.mom) 84 | # self.optimizer = optim.SGD(self.model.parameters(), lr=self.lr, momentum=self.mom) 85 | # self.scheduler = optim.lr_scheduler.MultiStepLR(self.optimizer, milestones=[50, 75, 100], gamma=0.5) 86 | # lr decay 87 | print(self.model) 88 | 89 | def save(self, epoch): 90 | """ 91 | Save model. 92 | :param epoch: number of current epoch 93 | """ 94 | model_out_path = self.logs + '/FSRCNN_model' + str(epoch) + '.pth' 95 | torch.save(self.model, model_out_path) 96 | print("Checkpoint saved to {}".format(model_out_path)) 97 | 98 | def train(self): 99 | """ 100 | The main traning function. 101 | """ 102 | self.model.train() 103 | train_loss = 0 104 | for batch_num, (data, target) in enumerate(self.train_loader): 105 | if self.GPU: 106 | data, target = data.cuda(), target.cuda() 107 | 108 | # if self.graph: # plot the network 109 | # graph = make_dot(self.model(data)) 110 | # graph.view() 111 | # self.graph = False 112 | 113 | self.optimizer.zero_grad() 114 | model_out = self.model(data) 115 | loss = self.criterion(model_out, target) 116 | train_loss += loss.item() 117 | loss.backward() 118 | self.optimizer.step() 119 | progress_bar(batch_num, len(self.train_loader), 'Loss: %.5f' % (train_loss / (batch_num + 1))) 120 | 121 | self.info['loss'] = train_loss / len(self.train_loader) 122 | 123 | def test_set5_patch(self): 124 | self.model.eval() 125 | avg_psnr = 0 126 | for batch_num, (data, target) in enumerate(self.set5_h5_loader): 127 | if self.GPU: 128 | data, target = data.cuda(), target.cuda() 129 | else: 130 | data, target = data, target 131 | 132 | prediction = self.model(data) 133 | mse = self.criterion(prediction, target) 134 | psnr = 10 * log10(1 / mse.item()) 135 | avg_psnr += psnr 136 | progress_bar(batch_num, len(self.set5_h5_loader), 'PSNR: %.4fdB' % (avg_psnr / (batch_num + 1))) 137 | 138 | self.info['PSNR for Set5 patch'] = avg_psnr / len(self.set5_h5_loader) 139 | 140 | def test_set14_patch(self): 141 | self.model.eval() 142 | avg_psnr = 0 143 | for batch_num, (data, target) in enumerate(self.set14_h5_loader): 144 | if self.GPU: 145 | data, target = data.cuda(), target.cuda() 146 | 147 | prediction = self.model(data) 148 | mse = self.criterion(prediction, target) 149 | psnr = 10 * log10(1 / mse.item()) 150 | avg_psnr += psnr 151 | progress_bar(batch_num, len(self.set14_h5_loader), 'PSNR: %.4fdB' % (avg_psnr / (batch_num + 1))) 152 | 153 | self.info['PSNR for Set14 patch'] = avg_psnr / len(self.set14_h5_loader) 154 | 155 | def test_set5_img(self): 156 | """ 157 | Get PSNR value for test set Set 5 images, and write to Tensorboards logs. 158 | """ 159 | self.model.eval() 160 | avg_psnr = 0 161 | for batch_num, (data, target) in enumerate(self.set5_img_loader): 162 | target = target.numpy() 163 | target = target[:, :, 6:target.shape[2] - 6, 6:target.shape[3] - 6] 164 | # target = torch.from_numpy(target) 165 | if self.GPU: 166 | data, target = data.cuda(), torch.from_numpy(target).cuda() 167 | else: 168 | data, target = data, torch.from_numpy(target) 169 | 170 | prediction = self.model(data) 171 | prediction = prediction.data.cpu().numpy() 172 | prediction = prediction[:, :, 6:prediction.shape[2] - 6, 6:prediction.shape[3] - 6] 173 | if self.GPU: 174 | prediction = torch.from_numpy(prediction).cuda() 175 | else: 176 | prediction = torch.from_numpy(prediction) 177 | mse = self.criterion(prediction, target) 178 | psnr = 10 * log10(1 / mse.item()) 179 | avg_psnr += psnr 180 | progress_bar(batch_num, len(self.set5_img_loader), 'PSNR: %.4fdB' % (avg_psnr / (batch_num + 1))) 181 | 182 | self.info['PSNR for Set5'] = avg_psnr / len(self.set5_img_loader) 183 | 184 | def test_set14_img(self): 185 | """ 186 | Get PSNR value for test set Set 14 images, and write to Tensorboards logs. 187 | """ 188 | self.model.eval() 189 | avg_psnr = 0 190 | for batch_num, (data, target) in enumerate(self.set14_img_loader): 191 | target = target.numpy() 192 | target = target[:, :, 6:target.shape[2] - 6, 6:target.shape[3] - 6] 193 | # target = torch.from_numpy(target) 194 | if self.GPU: 195 | data, target = data.cuda(), torch.from_numpy(target).cuda() 196 | else: 197 | data, target = data, torch.from_numpy(target) 198 | 199 | prediction = self.model(data) 200 | prediction = prediction.data.cpu().numpy() 201 | prediction = prediction[:, :, 6:prediction.shape[2] - 6, 6:prediction.shape[3] - 6] 202 | if self.GPU: 203 | prediction = torch.from_numpy(prediction).cuda() 204 | else: 205 | prediction = torch.from_numpy(prediction) 206 | mse = self.criterion(prediction, target) 207 | psnr = 10 * log10(1 / mse.item()) 208 | avg_psnr += psnr 209 | progress_bar(batch_num, len(self.set14_img_loader), 'PSNR: %.4fdB' % (avg_psnr / (batch_num + 1))) 210 | 211 | self.info['PSNR for Set14'] = avg_psnr / len(self.set14_img_loader) 212 | 213 | def predict(self, epoch): 214 | """ 215 | Get the prediction result and write to Tensorboard logs. 216 | :param epoch: the current epoch number 217 | """ 218 | self.model.eval() 219 | butterfly = load_img('./bmp/butterfly86.bmp') 220 | butterfly = torch.unsqueeze(self.to_tensor(butterfly), 0) 221 | if self.GPU: 222 | data = butterfly.cuda() 223 | else: 224 | data = butterfly 225 | prediction = self.model(data).data.cpu().numpy()[0][0] 226 | self.logger.image_summary('prediction', prediction, epoch) 227 | # imsave(self.logs + '/prediction_' + str(epoch) + '.bmp', prediction) 228 | 229 | def plot_fig(self, tensor, filename, num_cols=8): 230 | """ 231 | Plot the parameters to images. 232 | :param tensor: the tensor need to plot 233 | :param filename: the filename of the saved images 234 | :param num_cols: number of columns of filters in the images 235 | """ 236 | num_kernels = tensor.shape[0] 237 | num_rows = ceil(num_kernels / num_cols) 238 | fig = plt.figure(figsize=(num_cols, num_rows)) 239 | for i in range(tensor.shape[0]): 240 | ax1 = fig.add_subplot(num_rows, num_cols, i + 1) 241 | ax1.imshow(tensor[i][0], norm=Normalize()) 242 | ax1.axis('off') 243 | ax1.set_xticklabels([]) 244 | ax1.set_yticklabels([]) 245 | 246 | plt.subplots_adjust(wspace=0.1, hspace=0.1) 247 | plt.savefig(self.logs + '/' + filename + '.png') 248 | # plt.show() 249 | 250 | def get_para(self): 251 | """ 252 | Return the pamameters in the model. 253 | """ 254 | para = [] 255 | for parameter in self.model.parameters(): 256 | para.append(parameter.data.cpu().numpy()) 257 | return para 258 | 259 | def validate(self): 260 | """ 261 | Main function to run solver. 262 | """ 263 | self.build_model() 264 | 265 | self.initial_para = self.get_para() 266 | for epoch in range(1, self.n_epochs + 1): 267 | if epoch == 1: # log initial para 268 | self.logger.histo_summary('initial fisrt layer para', self.initial_para[0], epoch) 269 | self.logger.histo_summary('initial last layer para', self.initial_para[-2], epoch) 270 | self.plot_fig(self.initial_para[0], 'first_layer_initial') 271 | self.plot_fig(self.initial_para[-2], 'last_layer_initial') 272 | elif (epoch % 5 == 0) or (epoch == self.n_epochs): # log para 273 | self.logger.histo_summary('fisrt layer para', self.final_para[0] - self.initial_para[0], epoch) 274 | self.logger.histo_summary('last layer para', self.final_para[-2] - self.initial_para[-2], epoch) 275 | print("\n===> Epoch {} starts:".format(epoch)) 276 | 277 | self.train() 278 | 279 | if (epoch % 2 == 0) and (self.train_loader.batch_size < self.batch_size): 280 | self.train_loader.batch_size *= 2 281 | self.train_loader.batch_sampler.batch_size *= 2 282 | 283 | # print('Testing Set5 patch:') 284 | # self.test_set5_patch() 285 | # print('Testing Set14 patch:') 286 | # self.test_set14_patch() 287 | print('Testing Set5:') 288 | with torch.no_grad(): 289 | self.test_set5_img() 290 | print('Testing Set14:') 291 | with torch.no_grad(): 292 | self.test_set14_img() 293 | # self.scheduler.step(epoch) 294 | self.final_para = self.get_para() 295 | 296 | for tag, value in self.info.items(): 297 | self.logger.scalar_summary(tag, value, epoch) 298 | 299 | self.predict(epoch) 300 | if (epoch % 50 == 0) or (epoch == self.n_epochs) or (epoch == 1): 301 | if epoch != 1: 302 | self.save(epoch) 303 | # plot the para 304 | self.plot_fig(self.final_para[0] - self.initial_para[0], '/first_diff_' + str(epoch)) 305 | self.plot_fig(self.final_para[-2] - self.initial_para[-2], '/last_diff_' + str(epoch)) 306 | self.plot_fig(self.final_para[0], '/first_' + str(epoch)) 307 | self.plot_fig(self.final_para[-2], '/last_' + str(epoch)) 308 | --------------------------------------------------------------------------------