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sha256:72b5e6d25140ab634a89fdf43b02ea6a404617a0c75720bd194290dbbcd1e15b +size 471566223 diff --git a/checkpoints/ast_summer2winteryosemite/loss_log.txt b/checkpoints/ast_summer2winteryosemite/loss_log.txt new file mode 100644 index 0000000000000000000000000000000000000000..689023d976488dccee07d5122004350d2ac2cb78 --- /dev/null +++ b/checkpoints/ast_summer2winteryosemite/loss_log.txt @@ -0,0 +1,150 @@ +================ Training Loss (Tue Nov 12 18:13:45 2024) ================ +(epoch: 1, iters: 102, time: 104.360) GAN: -0.592 GAN_Feat: 0.843 VGG: 0.387 D_Fake: 0.000 D_real: 0.002 +(epoch: 1, iters: 201, time: 55.583) GAN: -0.008 GAN_Feat: 0.742 VGG: 0.514 D_Fake: 0.000 D_real: 0.000 +(epoch: 1, iters: 300, time: 24.383) GAN: -0.051 GAN_Feat: 0.831 VGG: 0.400 D_Fake: 0.000 D_real: 0.000 +(epoch: 1, iters: 402, time: 12.506) GAN: 0.870 GAN_Feat: 1.348 VGG: 0.342 D_Fake: 0.000 D_real: 0.000 +(epoch: 1, iters: 501, time: 12.043) GAN: -0.372 GAN_Feat: 0.698 VGG: 0.460 D_Fake: 0.000 D_real: 0.000 +(epoch: 1, iters: 600, time: 14.139) GAN: -1.419 GAN_Feat: 0.576 VGG: 0.437 D_Fake: 0.000 D_real: 0.000 +(epoch: 1, iters: 702, time: 13.188) GAN: -0.308 GAN_Feat: 0.873 VGG: 0.466 D_Fake: 0.000 D_real: 0.000 +================ Training Loss (Wed Nov 13 05:27:02 2024) ================ +(epoch: 1, iters: 36, time: 132.747) GAN: -2.133 GAN_Feat: 0.821 VGG: 0.349 D_Fake: 0.767 D_real: 0.280 +(epoch: 1, iters: 135, time: 83.010) GAN: -0.706 GAN_Feat: 0.745 VGG: 0.460 D_Fake: 0.000 D_real: 0.000 +(epoch: 1, iters: 237, time: 56.119) GAN: -0.356 GAN_Feat: 0.661 VGG: 0.474 D_Fake: 0.000 D_real: 0.000 +================ Training Loss (Wed Nov 13 12:59:18 2024) ================ +(epoch: 1, iters: 24, time: 59.741) GAN: -2.607 GAN_Feat: 1.078 VGG: 0.392 D_Fake: 0.094 D_real: 0.123 +(epoch: 1, iters: 124, time: 23.528) GAN: -0.161 GAN_Feat: 0.975 VGG: 0.353 D_Fake: 0.000 D_real: 0.000 +(epoch: 1, iters: 224, time: 11.029) GAN: 0.082 GAN_Feat: 1.054 VGG: 0.440 D_Fake: 0.000 D_real: 0.000 +(epoch: 1, iters: 324, time: 6.944) GAN: -2.299 GAN_Feat: 0.712 VGG: 0.523 D_Fake: 0.000 D_real: 0.002 +(epoch: 1, iters: 424, time: 7.939) GAN: 1.167 GAN_Feat: 1.390 VGG: 0.415 D_Fake: 0.000 D_real: 0.000 +(epoch: 1, iters: 524, time: 7.731) GAN: 0.411 GAN_Feat: 0.796 VGG: 0.388 D_Fake: 0.000 D_real: 0.000 +(epoch: 1, iters: 624, time: 7.942) GAN: 1.332 GAN_Feat: 1.184 VGG: 0.358 D_Fake: 0.000 D_real: 0.000 +(epoch: 1, iters: 724, time: 8.307) GAN: -1.703 GAN_Feat: 0.561 VGG: 0.551 D_Fake: 0.000 D_real: 0.000 +(epoch: 1, iters: 824, time: 9.101) GAN: 0.434 GAN_Feat: 1.299 VGG: 0.276 D_Fake: 0.000 D_real: 0.000 +(epoch: 1, iters: 924, time: 9.701) GAN: 0.715 GAN_Feat: 0.959 VGG: 0.551 D_Fake: 0.000 D_real: 0.000 +(epoch: 1, iters: 1024, time: 9.451) GAN: 0.304 GAN_Feat: 1.095 VGG: 0.440 D_Fake: 0.000 D_real: 0.000 +(epoch: 1, iters: 1124, time: 9.867) GAN: -0.163 GAN_Feat: 1.110 VGG: 0.422 D_Fake: 0.000 D_real: 0.000 +(epoch: 1, iters: 1224, time: 10.051) GAN: -0.312 GAN_Feat: 0.957 VGG: 0.585 D_Fake: 0.000 D_real: 0.000 +(epoch: 1, iters: 1324, time: 10.539) GAN: 2.037 GAN_Feat: 1.243 VGG: 0.352 D_Fake: 0.000 D_real: 0.000 +(epoch: 1, iters: 1424, time: 11.161) GAN: -1.317 GAN_Feat: 1.094 VGG: 0.383 D_Fake: 0.000 D_real: 0.000 +(epoch: 1, iters: 1524, time: 11.662) GAN: 0.836 GAN_Feat: 1.237 VGG: 0.491 D_Fake: 0.000 D_real: 0.000 +(epoch: 1, iters: 1624, time: 11.964) GAN: 1.126 GAN_Feat: 1.074 VGG: 0.465 D_Fake: 0.000 D_real: 0.000 +(epoch: 1, iters: 1724, time: 12.382) GAN: -0.513 GAN_Feat: 0.874 VGG: 0.583 D_Fake: 0.000 D_real: 0.000 +(epoch: 1, iters: 1824, time: 12.901) GAN: 0.468 GAN_Feat: 1.081 VGG: 0.479 D_Fake: 0.000 D_real: 0.000 +(epoch: 1, iters: 1924, time: 13.485) GAN: -1.474 GAN_Feat: 1.164 VGG: 0.289 D_Fake: 0.000 D_real: 0.000 +(epoch: 2, iters: 24, time: 132.615) GAN: -2.668 GAN_Feat: 1.319 VGG: 0.339 D_Fake: 0.359 D_real: 0.447 +(epoch: 2, iters: 124, time: 59.487) GAN: -0.423 GAN_Feat: 0.988 VGG: 0.444 D_Fake: 0.000 D_real: 0.000 +(epoch: 2, iters: 224, time: 27.672) GAN: 1.478 GAN_Feat: 1.258 VGG: 0.161 D_Fake: 0.000 D_real: 0.000 +(epoch: 2, iters: 324, time: 15.929) GAN: 1.116 GAN_Feat: 1.036 VGG: 0.357 D_Fake: 0.000 D_real: 0.000 +(epoch: 2, iters: 424, time: 14.193) GAN: -2.438 GAN_Feat: 1.289 VGG: 0.233 D_Fake: 0.000 D_real: 0.000 +(epoch: 2, iters: 524, time: 15.683) GAN: -0.748 GAN_Feat: 1.268 VGG: 0.336 D_Fake: 0.000 D_real: 0.000 +(epoch: 2, iters: 624, time: 15.232) GAN: -1.192 GAN_Feat: 1.071 VGG: 0.395 D_Fake: 0.000 D_real: 0.000 +(epoch: 2, iters: 724, time: 17.928) GAN: -1.744 GAN_Feat: 0.752 VGG: 0.525 D_Fake: 0.000 D_real: 0.000 +(epoch: 2, iters: 824, time: 18.835) GAN: 0.802 GAN_Feat: 1.365 VGG: 0.417 D_Fake: 0.000 D_real: 0.000 +(epoch: 2, iters: 924, time: 19.062) GAN: 0.884 GAN_Feat: 1.052 VGG: 0.501 D_Fake: 0.000 D_real: 0.000 +(epoch: 2, iters: 1024, time: 19.474) GAN: -2.183 GAN_Feat: 0.816 VGG: 0.460 D_Fake: 0.000 D_real: 0.000 +(epoch: 2, iters: 1124, time: 16.089) GAN: -0.860 GAN_Feat: 0.910 VGG: 0.568 D_Fake: 0.000 D_real: 0.000 +(epoch: 2, iters: 1224, time: 17.147) GAN: 0.579 GAN_Feat: 0.814 VGG: 0.529 D_Fake: 0.000 D_real: 0.000 +(epoch: 2, iters: 1324, time: 16.461) GAN: 2.396 GAN_Feat: 1.152 VGG: 0.434 D_Fake: 0.000 D_real: 0.000 +(epoch: 2, iters: 1424, time: 21.906) GAN: -1.268 GAN_Feat: 1.245 VGG: 0.275 D_Fake: 0.000 D_real: 0.000 +(epoch: 2, iters: 1524, time: 21.885) GAN: 0.134 GAN_Feat: 1.285 VGG: 0.330 D_Fake: 0.000 D_real: 0.000 +(epoch: 2, iters: 1624, time: 21.523) GAN: -1.539 GAN_Feat: 0.866 VGG: 0.372 D_Fake: 0.000 D_real: 0.000 +(epoch: 2, iters: 1724, time: 21.727) GAN: 2.624 GAN_Feat: 1.292 VGG: 0.434 D_Fake: 0.000 D_real: 0.000 +(epoch: 2, iters: 1824, time: 22.157) GAN: -0.119 GAN_Feat: 1.252 VGG: 0.294 D_Fake: 0.000 D_real: 0.000 +(epoch: 2, iters: 1924, time: 24.042) GAN: 0.697 GAN_Feat: 1.056 VGG: 0.500 D_Fake: 0.000 D_real: 0.000 +================ Training Loss (Sun Nov 24 08:10:13 2024) ================ +================ Training Loss (Sun Nov 24 08:13:51 2024) ================ +================ Training Loss (Sun Nov 24 08:15:32 2024) ================ +================ Training Loss (Sun Nov 24 08:17:09 2024) ================ +================ Training Loss (Sun Nov 24 08:28:19 2024) ================ +================ Training Loss (Sun Nov 24 08:35:31 2024) ================ +================ Training Loss (Sun Nov 24 08:40:54 2024) ================ +================ Training Loss (Sun Nov 24 08:45:21 2024) ================ +================ Training Loss (Sun Nov 24 08:46:56 2024) ================ +================ Training Loss (Mon Nov 25 14:47:28 2024) ================ +================ Training Loss (Mon Nov 25 14:51:14 2024) ================ +================ Training Loss (Mon Nov 25 14:56:17 2024) ================ +================ Training Loss (Mon Nov 25 15:05:58 2024) ================ +(epoch: 1, iters: 100, time: 83.223) GAN: -0.406 GAN_Feat: 1.235 VGG: 0.713 D_Fake: 0.000 D_real: 0.017 +(epoch: 1, iters: 200, time: 24.212) GAN: 0.432 GAN_Feat: 1.273 VGG: 0.314 D_Fake: 0.029 D_real: 0.001 +(epoch: 1, iters: 300, time: 13.464) GAN: -0.544 GAN_Feat: 0.661 VGG: 0.607 D_Fake: 0.068 D_real: 0.114 +(epoch: 1, iters: 400, time: 13.752) GAN: 0.784 GAN_Feat: 1.186 VGG: 0.436 D_Fake: 0.006 D_real: 0.000 +(epoch: 1, iters: 500, time: 14.254) GAN: 0.642 GAN_Feat: 1.126 VGG: 0.310 D_Fake: 0.000 D_real: 0.012 +(epoch: 1, iters: 600, time: 14.738) GAN: 1.245 GAN_Feat: 1.171 VGG: 0.179 D_Fake: 0.120 D_real: 0.024 +(epoch: 1, iters: 700, time: 15.410) GAN: 0.006 GAN_Feat: 1.241 VGG: 0.469 D_Fake: 0.065 D_real: 0.404 +(epoch: 1, iters: 800, time: 16.953) GAN: -0.168 GAN_Feat: 0.880 VGG: 0.717 D_Fake: 0.001 D_real: 0.000 +(epoch: 1, iters: 900, time: 18.410) GAN: 0.364 GAN_Feat: 1.253 VGG: 0.588 D_Fake: 0.012 D_real: 0.000 +(epoch: 1, iters: 1000, time: 19.801) GAN: 0.569 GAN_Feat: 1.092 VGG: 0.584 D_Fake: 0.004 D_real: 0.000 +(epoch: 1, iters: 1100, time: 18.661) GAN: 0.500 GAN_Feat: 1.011 VGG: 0.448 D_Fake: 0.835 D_real: 0.000 +(epoch: 1, iters: 1200, time: 19.281) GAN: 0.188 GAN_Feat: 0.896 VGG: 0.332 D_Fake: 0.273 D_real: 0.000 +(epoch: 1, iters: 1300, time: 19.937) GAN: -0.484 GAN_Feat: 0.844 VGG: 0.726 D_Fake: 0.206 D_real: 0.000 +(epoch: 1, iters: 1400, time: 21.018) GAN: -0.621 GAN_Feat: 1.129 VGG: 0.762 D_Fake: 0.000 D_real: 0.026 +(epoch: 1, iters: 1500, time: 21.784) GAN: 0.046 GAN_Feat: 1.210 VGG: 0.389 D_Fake: 0.000 D_real: 0.010 +(epoch: 1, iters: 1600, time: 22.941) GAN: -0.140 GAN_Feat: 0.964 VGG: 0.707 D_Fake: 0.097 D_real: 0.000 +(epoch: 1, iters: 1700, time: 23.887) GAN: -0.847 GAN_Feat: 1.016 VGG: 0.870 D_Fake: 2.200 D_real: 0.017 +(epoch: 1, iters: 1800, time: 24.975) GAN: 0.548 GAN_Feat: 0.950 VGG: 0.852 D_Fake: 0.192 D_real: 0.007 +(epoch: 1, iters: 1900, time: 26.070) GAN: 0.626 GAN_Feat: 1.158 VGG: 0.081 D_Fake: 0.000 D_real: 0.001 +(epoch: 1, iters: 2000, time: 27.734) GAN: 1.131 GAN_Feat: 1.216 VGG: 0.262 D_Fake: 0.000 D_real: 0.000 +(epoch: 1, iters: 2100, time: 28.834) GAN: 0.610 GAN_Feat: 1.066 VGG: 0.310 D_Fake: 0.034 D_real: 0.026 +(epoch: 1, iters: 2200, time: 29.702) GAN: -0.877 GAN_Feat: 0.976 VGG: 0.725 D_Fake: 0.000 D_real: 0.001 +(epoch: 1, iters: 2300, time: 30.823) GAN: -0.545 GAN_Feat: 0.721 VGG: 0.857 D_Fake: 2.177 D_real: 0.000 +(epoch: 1, iters: 2400, time: 31.934) GAN: -0.534 GAN_Feat: 0.858 VGG: 0.707 D_Fake: 0.012 D_real: 0.164 +(epoch: 1, iters: 2500, time: 32.999) GAN: 0.368 GAN_Feat: 0.982 VGG: 0.603 D_Fake: 0.002 D_real: 0.001 +(epoch: 1, iters: 2600, time: 33.972) GAN: 0.968 GAN_Feat: 1.019 VGG: 0.369 D_Fake: 0.043 D_real: 0.003 +(epoch: 1, iters: 2700, time: 35.315) GAN: 0.174 GAN_Feat: 1.009 VGG: 0.242 D_Fake: 0.000 D_real: 0.011 +(epoch: 1, iters: 2800, time: 36.950) GAN: 1.443 GAN_Feat: 1.228 VGG: 0.430 D_Fake: 0.021 D_real: 0.000 +(epoch: 1, iters: 2900, time: 37.990) GAN: 1.262 GAN_Feat: 1.105 VGG: 0.458 D_Fake: 0.000 D_real: 0.000 +(epoch: 1, iters: 3000, time: 39.058) GAN: 1.029 GAN_Feat: 1.049 VGG: 0.750 D_Fake: 0.030 D_real: 0.013 +(epoch: 1, iters: 3100, time: 34.384) GAN: 0.719 GAN_Feat: 0.908 VGG: 0.481 D_Fake: 0.909 D_real: 0.002 +(epoch: 1, iters: 3200, time: 38.973) GAN: 1.790 GAN_Feat: 0.929 VGG: 0.545 D_Fake: 0.000 D_real: 0.294 +(epoch: 1, iters: 3300, time: 39.311) GAN: 0.215 GAN_Feat: 1.155 VGG: 0.394 D_Fake: 0.000 D_real: 0.001 +(epoch: 1, iters: 3400, time: 39.274) GAN: 0.695 GAN_Feat: 0.936 VGG: 0.592 D_Fake: 0.000 D_real: 0.002 +(epoch: 1, iters: 3500, time: 39.566) GAN: -0.140 GAN_Feat: 0.818 VGG: 0.456 D_Fake: 1.383 D_real: 0.001 +(epoch: 1, iters: 3600, time: 41.063) GAN: 0.862 GAN_Feat: 0.921 VGG: 0.355 D_Fake: 0.308 D_real: 0.000 +(epoch: 1, iters: 3700, time: 40.733) GAN: 1.034 GAN_Feat: 0.962 VGG: 0.834 D_Fake: 0.055 D_real: 0.005 +(epoch: 1, iters: 3800, time: 40.840) GAN: 1.099 GAN_Feat: 0.816 VGG: 0.641 D_Fake: 0.012 D_real: 0.000 +(epoch: 1, iters: 3900, time: 42.253) GAN: -1.732 GAN_Feat: 0.882 VGG: 0.700 D_Fake: 0.509 D_real: 0.019 +(epoch: 1, iters: 4000, time: 43.014) GAN: 0.127 GAN_Feat: 0.925 VGG: 0.489 D_Fake: 0.015 D_real: 0.091 +(epoch: 1, iters: 4100, time: 43.386) GAN: 1.189 GAN_Feat: 0.904 VGG: 0.669 D_Fake: 0.001 D_real: 0.006 +(epoch: 1, iters: 4200, time: 43.634) GAN: 1.548 GAN_Feat: 0.899 VGG: 0.313 D_Fake: 0.000 D_real: 0.112 +(epoch: 1, iters: 4300, time: 43.748) GAN: 0.257 GAN_Feat: 0.731 VGG: 0.759 D_Fake: 0.907 D_real: 0.003 +(epoch: 1, iters: 4400, time: 46.206) GAN: 0.394 GAN_Feat: 0.958 VGG: 0.612 D_Fake: 0.032 D_real: 0.009 +(epoch: 1, iters: 4500, time: 46.080) GAN: 0.780 GAN_Feat: 0.868 VGG: 0.405 D_Fake: 0.388 D_real: 0.171 +(epoch: 1, iters: 4600, time: 47.704) GAN: 1.396 GAN_Feat: 0.850 VGG: 0.532 D_Fake: 0.002 D_real: 0.000 +(epoch: 1, iters: 4700, time: 53.360) GAN: -0.369 GAN_Feat: 0.855 VGG: 0.711 D_Fake: 0.042 D_real: 0.195 +(epoch: 1, iters: 4800, time: 59.216) GAN: 2.027 GAN_Feat: 0.908 VGG: 0.470 D_Fake: 0.298 D_real: 0.002 +(epoch: 1, iters: 4900, time: 59.609) GAN: -0.102 GAN_Feat: 1.012 VGG: 0.086 D_Fake: 0.000 D_real: 0.001 +(epoch: 1, iters: 5000, time: 61.366) GAN: 0.929 GAN_Feat: 0.816 VGG: 0.554 D_Fake: 0.035 D_real: 0.000 +(epoch: 1, iters: 5100, time: 62.976) GAN: 0.802 GAN_Feat: 1.000 VGG: 0.302 D_Fake: 0.000 D_real: 0.009 +(epoch: 1, iters: 5200, time: 63.006) GAN: 0.962 GAN_Feat: 0.869 VGG: 0.829 D_Fake: 0.036 D_real: 0.000 +(epoch: 1, iters: 5300, time: 64.442) GAN: 0.987 GAN_Feat: 0.954 VGG: 0.470 D_Fake: 0.000 D_real: 0.000 +(epoch: 1, iters: 5400, time: 65.159) GAN: 0.848 GAN_Feat: 0.795 VGG: 0.522 D_Fake: 0.000 D_real: 0.023 +(epoch: 1, iters: 5500, time: 65.969) GAN: 1.406 GAN_Feat: 0.989 VGG: 0.833 D_Fake: 1.414 D_real: 0.000 +(epoch: 1, iters: 5600, time: 62.957) GAN: 2.370 GAN_Feat: 1.044 VGG: 0.785 D_Fake: 0.000 D_real: 0.000 +(epoch: 1, iters: 5700, time: 65.466) GAN: -0.610 GAN_Feat: 0.866 VGG: 0.456 D_Fake: 0.413 D_real: 0.000 +(epoch: 1, iters: 5800, time: 63.111) GAN: 2.470 GAN_Feat: 1.033 VGG: 0.656 D_Fake: 0.002 D_real: 0.048 +(epoch: 1, iters: 5900, time: 57.251) GAN: 0.547 GAN_Feat: 0.716 VGG: 0.772 D_Fake: 0.111 D_real: 0.000 +(epoch: 1, iters: 6000, time: 57.912) GAN: -1.190 GAN_Feat: 0.820 VGG: 0.797 D_Fake: 0.000 D_real: 0.592 +(epoch: 1, iters: 6100, time: 58.271) GAN: 0.194 GAN_Feat: 1.155 VGG: 0.376 D_Fake: 0.000 D_real: 0.002 +(epoch: 1, iters: 6200, time: 58.099) GAN: 2.051 GAN_Feat: 0.901 VGG: 0.508 D_Fake: 0.000 D_real: 0.005 +(epoch: 1, iters: 6300, time: 59.516) GAN: 1.931 GAN_Feat: 0.928 VGG: 0.611 D_Fake: 0.001 D_real: 0.012 +(epoch: 1, iters: 6400, time: 59.794) GAN: 0.907 GAN_Feat: 0.894 VGG: 0.787 D_Fake: 0.003 D_real: 0.004 +(epoch: 1, iters: 6500, time: 61.569) GAN: 0.225 GAN_Feat: 0.709 VGG: 0.610 D_Fake: 0.000 D_real: 0.139 +(epoch: 1, iters: 6600, time: 62.035) GAN: 2.281 GAN_Feat: 0.944 VGG: 0.744 D_Fake: 1.329 D_real: 0.001 +(epoch: 1, iters: 6700, time: 62.331) GAN: 0.378 GAN_Feat: 0.727 VGG: 0.537 D_Fake: 1.732 D_real: 0.002 +(epoch: 1, iters: 6800, time: 63.397) GAN: 0.685 GAN_Feat: 0.961 VGG: 0.576 D_Fake: 0.000 D_real: 0.128 +(epoch: 1, iters: 6900, time: 63.809) GAN: -0.004 GAN_Feat: 0.913 VGG: 0.605 D_Fake: 0.094 D_real: 0.654 +(epoch: 1, iters: 7000, time: 64.830) GAN: 1.914 GAN_Feat: 0.972 VGG: 0.732 D_Fake: 0.000 D_real: 0.000 +(epoch: 1, iters: 7100, time: 65.630) GAN: 0.066 GAN_Feat: 0.790 VGG: 0.598 D_Fake: 0.000 D_real: 0.057 +(epoch: 1, iters: 7200, time: 66.481) GAN: -0.073 GAN_Feat: 1.005 VGG: 0.218 D_Fake: 0.000 D_real: 0.001 +(epoch: 1, iters: 7300, time: 67.309) GAN: -0.166 GAN_Feat: 0.826 VGG: 0.381 D_Fake: 0.000 D_real: 0.365 +(epoch: 1, iters: 7400, time: 68.055) GAN: 0.583 GAN_Feat: 0.923 VGG: 0.604 D_Fake: 0.000 D_real: 0.180 +(epoch: 1, iters: 7500, time: 69.229) GAN: 1.267 GAN_Feat: 1.017 VGG: 0.602 D_Fake: 0.249 D_real: 0.002 +(epoch: 1, iters: 7600, time: 69.595) GAN: 0.238 GAN_Feat: 0.817 VGG: 0.781 D_Fake: 0.001 D_real: 0.495 +(epoch: 1, iters: 7700, time: 70.678) GAN: -0.030 GAN_Feat: 0.572 VGG: 0.685 D_Fake: 0.814 D_real: 0.000 +(epoch: 1, iters: 7800, time: 71.189) GAN: 0.847 GAN_Feat: 0.912 VGG: 0.658 D_Fake: 0.001 D_real: 0.004 +(epoch: 1, iters: 7900, time: 72.171) GAN: -0.541 GAN_Feat: 1.038 VGG: 0.050 D_Fake: 0.000 D_real: 0.115 +(epoch: 1, iters: 8000, time: 73.012) GAN: 0.861 GAN_Feat: 1.001 VGG: 0.427 D_Fake: 0.000 D_real: 0.000 +(epoch: 1, iters: 8100, time: 74.091) GAN: -0.611 GAN_Feat: 0.956 VGG: 0.256 D_Fake: 0.000 D_real: 0.000 +(epoch: 1, iters: 8200, time: 74.954) GAN: 0.647 GAN_Feat: 0.871 VGG: 0.505 D_Fake: 0.000 D_real: 0.193 +(epoch: 1, iters: 8300, time: 75.692) GAN: 0.349 GAN_Feat: 0.862 VGG: 0.895 D_Fake: 0.000 D_real: 0.018 +(epoch: 1, iters: 8400, time: 76.395) GAN: 0.423 GAN_Feat: 0.793 VGG: 0.530 D_Fake: 0.059 D_real: 0.663 diff --git a/checkpoints/ast_summer2winteryosemite/opt.pkl b/checkpoints/ast_summer2winteryosemite/opt.pkl new file mode 100644 index 0000000000000000000000000000000000000000..7c1b3250868ff3d485cfde1255e2ebfc081f0576 --- /dev/null +++ b/checkpoints/ast_summer2winteryosemite/opt.pkl @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:512c34e5644577dd2c7e5d080bb61968253d19e725897bb47832e50248042f8c +size 1765 diff --git a/checkpoints/ast_summer2winteryosemite/opt.txt b/checkpoints/ast_summer2winteryosemite/opt.txt new file mode 100644 index 0000000000000000000000000000000000000000..cf83cff34b0d1cd96dda045f962d4e52912dbf54 --- /dev/null +++ b/checkpoints/ast_summer2winteryosemite/opt.txt @@ -0,0 +1,89 @@ + D_steps_per_G: 1 + alpha: 1.0 + aspect_ratio: 1.0 + batchSize: 2 [default: 1] + batch_size: 1 + beta1: 0.5 + beta2: 0.999 + cache_filelist_read: False + cache_filelist_write: False + checkpoints_dir: ./checkpoints + contain_dontcare_label: False + content_weight: 2.0 + continue_train: False + croot: ./datasets/summer2winter_yosemite [default: ./datasets/summer2winter_yosemite/] + crop_size: 256 + dataset_mode: summer2winteryosemite + debug: False + display_freq: 200 [default: 100] + display_winsize: 400 + gan_mode: hinge + gpu_ids: 0 + id1_weight: 50 + id2_weight: 1 + init_type: xavier + init_variance: 0.02 + isTrain: True [default: None] + label_nc: 3 + lambda_feat: 1.0 [default: 10.0] + lambda_kld: 0.05 + lambda_vgg: 1.0 [default: 10.0] + latent_dim: 256 + load_from_opt_file: False + load_size: 512 + loss_count_interval: 200 + lr: 0.0002 + lrG: 0.0012 + lrG_decay: 0.0001 + lr_decay: 0.0001 + lr_stytr2: 0.0005 + max_dataset_size: 9108 + max_iter: 700 + max_iterations_per_epoch: 1000 + mode: 1 + model: pix2pix + nThreads: 4 [default: 0] + n_layers_D: 4 + n_threads: 1 + name: ast_summer2winteryosemite + ndf: 64 + nef: 16 + netD: multiscale + netD_subarch: n_layer + netG: tsit + ngf: 64 + niter: 2 [default: 50] + niter_decay: 0 + no_TTUR: False + no_flip: False + no_ganFeat_loss: False + no_html: False + no_instance: True [default: False] + no_pairing_check: True [default: False] + no_ss: False + no_vgg_loss: False + norm_D: spectralinstance + norm_E: spectralinstance + norm_G: spectralfadesyncbatch3x3 + norm_S: 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07:16AM on November 30, 2024

+

epoch [1]

+ + + + + + +
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99111110116101110116

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1151211101161041011151051221011009510510997103101

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+ + +
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115116121108101

+

+
+ + \ No newline at end of file diff --git a/data/__init__.py b/data/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..d5ce7a2f6e69dc641b30525c5c25f6e1d2d8e3db --- /dev/null +++ b/data/__init__.py @@ -0,0 +1,71 @@ +import importlib +import torch.utils.data +from data.base_dataset import BaseDataset +from data.sampler import InfiniteSamplerWrapper + + +def find_dataset_using_name(dataset_name): + # Given the option --dataset [datasetname], + # the file "datasets/datasetname_dataset.py" + # will be imported. + dataset_filename = "data." + dataset_name + "_dataset" + datasetlib = importlib.import_module(dataset_filename) + + # In the file, the class called DatasetNameDataset() will + # be instantiated. It has to be a subclass of BaseDataset, + # and it is case-insensitive. + dataset = None + target_dataset_name = dataset_name.replace('_', '') + 'dataset' + for name, cls in datasetlib.__dict__.items(): + if name.lower() == target_dataset_name.lower() \ + and issubclass(cls, BaseDataset): + dataset = cls + + if dataset is None: + raise ValueError("In %s.py, there should be a subclass of BaseDataset " + "with class name that matches %s in lowercase." % + (dataset_filename, target_dataset_name)) + + return dataset + + +def get_option_setter(dataset_name): + dataset_class = find_dataset_using_name(dataset_name) + return dataset_class.modify_commandline_options + + +def create_dataloader(opt): + if opt.phase=='test': + dataset = find_dataset_using_name(opt.dataset_mode) + instance = dataset() + instance.initialize(opt) + print("dataset [%s] of size %d was created" % + (type(instance).__name__, len(instance))) + dataloader = torch.utils.data.DataLoader( + instance, + batch_size=opt.batchSize, + shuffle=not opt.serial_batches, + num_workers=int(opt.nThreads), + drop_last=opt.isTrain + ) + return dataloader + + else: + dataset = find_dataset_using_name(opt.dataset_mode) + instance = dataset() + instance.initialize(opt) + print("dataset [%s] of size %d was created" % + (type(instance).__name__, len(instance))) + + # Use InfiniteSamplerWrapper for an infinite sampler + sampler = InfiniteSamplerWrapper(instance) if opt.use_infinite_sampler else None + + dataloader = torch.utils.data.DataLoader( + instance, + batch_size=opt.batchSize, + sampler=sampler, + shuffle=not opt.serial_batches if sampler is None else False, + num_workers=int(opt.nThreads), + drop_last=opt.isTrain + ) + return dataloader diff --git a/data/__pycache__/__init__.cpython-310.pyc b/data/__pycache__/__init__.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..7d60297be10baf04cc2cc5860dff716e263edee1 Binary files /dev/null and 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is_train) + parser.set_defaults(preprocess_mode='resize_and_crop') + if is_train: + parser.set_defaults(load_size=286) + else: + parser.set_defaults(load_size=256) + parser.set_defaults(crop_size=256) + parser.set_defaults(display_winsize=256) + parser.set_defaults(label_nc=150) + parser.set_defaults(contain_dontcare_label=True) + parser.set_defaults(cache_filelist_read=False) + parser.set_defaults(cache_filelist_write=False) + parser.set_defaults(no_instance=True) + return parser + + def get_paths(self, opt): + root = opt.croot + phase = 'val' if opt.phase == 'test' else 'train' + + all_images = make_dataset(root, recursive=True, read_cache=False, write_cache=False) + image_paths = [] + label_paths = [] + for p in all_images: + if '_%s_' % phase not in p: + continue + if p.endswith('.jpg'): + image_paths.append(p) + elif p.endswith('.png'): + label_paths.append(p) + + instance_paths = [] # don't use instance map for ade20k + + return label_paths, image_paths, instance_paths + + # In ADE20k, 'unknown' label is of value 0. + # Change the 'unknown' label to the last label to match other datasets. + def postprocess(self, input_dict): + label = input_dict['label'] + label = label - 1 + label[label == -1] = self.opt.label_nc diff --git a/data/base_dataset.py b/data/base_dataset.py new file mode 100644 index 0000000000000000000000000000000000000000..7d674ac120713be98eb3893af0d7d00cf0a40698 --- /dev/null +++ b/data/base_dataset.py @@ -0,0 +1,249 @@ +# import torch.utils.data as data +# from PIL import Image +# import torchvision.transforms as transforms +# import numpy as np +# import random +# +# +# class BaseDataset(data.Dataset): +# def __init__(self): +# super(BaseDataset, self).__init__() +# +# @staticmethod +# def modify_commandline_options(parser, is_train): +# parser.add_argument('--random_crop', default=False, +# help='Randomize Crop Images') +# return parser +# +# def initialize(self, opt): +# pass +# +# +# def get_params(opt, size): +# w, h = size +# new_h = h +# new_w = w +# if opt.preprocess_mode == 'resize_and_crop': +# new_h = new_w = opt.load_size +# elif opt.preprocess_mode == 'scale_width_and_crop': +# new_w = opt.load_size +# new_h = opt.load_size * h // w +# elif opt.preprocess_mode == 'scale_shortside_and_crop': +# ss, ls = min(w, h), max(w, h) # shortside and longside +# width_is_shorter = w == ss +# ls = int(opt.load_size * ls / ss) +# new_w, new_h = (ss, ls) if width_is_shorter else (ls, ss) +# +# x = random.randint(0, np.maximum(0, new_w - opt.crop_size)) +# y = random.randint(0, np.maximum(0, new_h - opt.crop_size)) +# +# flip = random.random() > 0.5 +# return {'crop_pos': (x, y), 'flip': flip} +# +# +# def get_transform(opt, params, method=Image.BICUBIC, normalize=True, toTensor=True): +# transform_list = [] +# if 'resize' in opt.preprocess_mode: +# osize = [opt.load_size, opt.load_size] +# transform_list.append(transforms.Resize(osize, interpolation=method)) +# elif 'scale_width' in opt.preprocess_mode: +# transform_list.append(transforms.Lambda(lambda img: __scale_width(img, opt.load_size, method))) +# elif 'scale_shortside' in opt.preprocess_mode: +# transform_list.append(transforms.Lambda(lambda img: __scale_shortside(img, opt.load_size, method))) +# +# if 'crop' in opt.preprocess_mode: +# transform_list.append(transforms.RandomCrop(opt.crop_size)) +# +# if opt.preprocess_mode == 'none': +# base = 32 +# transform_list.append(transforms.Lambda(lambda img: __make_power_2(img, base, method))) +# +# if opt.preprocess_mode == 'fixed': +# w = opt.crop_size +# h = round(opt.crop_size / opt.aspect_ratio) +# transform_list.append(transforms.Lambda(lambda img: __resize(img, w, h, method))) +# +# if opt.isTrain and not opt.no_flip: +# transform_list.append(transforms.Lambda(lambda img: __flip(img, params['flip']))) +# +# if toTensor: +# transform_list += [transforms.ToTensor()] +# +# if normalize: +# transform_list += [transforms.Normalize((0.5, 0.5, 0.5), +# (0.5, 0.5, 0.5))] +# +# return transforms.Compose(transform_list) +# +# +# def normalize(): +# return transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)) +# +# +# def __resize(img, w, h, method=Image.BICUBIC): +# return img.resize((w, h), method) +# +# +# def __make_power_2(img, base, method=Image.BICUBIC): +# ow, oh = img.size +# h = int(round(oh / base) * base) +# w = int(round(ow / base) * base) +# if (h == oh) and (w == ow): +# return img +# return img.resize((w, h), method) +# +# +# def __scale_width(img, target_width, method=Image.BICUBIC): +# ow, oh = img.size +# if (ow == target_width): +# return img +# w = target_width +# h = int(target_width * oh / ow) +# return img.resize((w, h), method) +# +# +# def __scale_shortside(img, target_width, method=Image.BICUBIC): +# ow, oh = img.size +# ss, ls = min(ow, oh), max(ow, oh) # shortside and longside +# width_is_shorter = ow == ss +# if (ss == target_width): +# return img +# ls = int(target_width * ls / ss) +# nw, nh = (ss, ls) if width_is_shorter else (ls, ss) +# return img.resize((nw, nh), method) +# +# +# def __crop(img, pos, size): +# ow, oh = img.size +# x1, y1 = pos +# tw = th = size +# return img.crop((x1, y1, x1 + tw, y1 + th)) +# +# +# def __flip(img, flip): +# if flip: +# return img.transpose(Image.FLIP_LEFT_RIGHT) +# return img +import torch.utils.data as data +from PIL import Image +import torchvision.transforms as transforms +import numpy as np +import random + + +class BaseDataset(data.Dataset): + def __init__(self): + super(BaseDataset, self).__init__() + + @staticmethod + def modify_commandline_options(parser, is_train): + return parser + + def initialize(self, opt): + pass + + +def get_params(opt, size): + w, h = size + new_h = h + new_w = w + if opt.preprocess_mode == 'resize_and_crop': + new_h = new_w = opt.load_size + elif opt.preprocess_mode == 'scale_width_and_crop': + new_w = opt.load_size + new_h = opt.load_size * h // w + elif opt.preprocess_mode == 'scale_shortside_and_crop': + ss, ls = min(w, h), max(w, h) # shortside and longside + width_is_shorter = w == ss + ls = int(opt.load_size * ls / ss) + new_w, new_h = (ss, ls) if width_is_shorter else (ls, ss) + + x = random.randint(0, np.maximum(0, new_w - opt.crop_size)) + y = random.randint(0, np.maximum(0, new_h - opt.crop_size)) + + flip = random.random() > 0.5 + return {'crop_pos': (x, y), 'flip': flip} + + +def get_transform(opt, params, method=Image.BICUBIC, normalize=True, toTensor=True): + transform_list = [] + if 'resize' in opt.preprocess_mode: + osize = [opt.load_size, opt.load_size] + transform_list.append(transforms.Resize(osize, interpolation=method)) + elif 'scale_width' in opt.preprocess_mode: + transform_list.append(transforms.Lambda(lambda img: __scale_width(img, opt.load_size, method))) + elif 'scale_shortside' in opt.preprocess_mode: + transform_list.append(transforms.Lambda(lambda img: __scale_shortside(img, opt.load_size, method))) + + if 'crop' in opt.preprocess_mode: + transform_list.append(transforms.Lambda(lambda img: __crop(img, params['crop_pos'], opt.crop_size))) + + if opt.preprocess_mode == 'none': + base = 32 + transform_list.append(transforms.Lambda(lambda img: __make_power_2(img, base, method))) + + if opt.preprocess_mode == 'fixed': + w = opt.crop_size + h = round(opt.crop_size / opt.aspect_ratio) + transform_list.append(transforms.Lambda(lambda img: __resize(img, w, h, method))) + + if opt.isTrain and not opt.no_flip: + transform_list.append(transforms.Lambda(lambda img: __flip(img, params['flip']))) + + if toTensor: + transform_list += [transforms.ToTensor()] + + if normalize: + transform_list += [transforms.Normalize((0.5, 0.5, 0.5), + (0.5, 0.5, 0.5))] + return transforms.Compose(transform_list) + + +def normalize(): + return transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)) + + +def __resize(img, w, h, method=Image.BICUBIC): + return img.resize((w, h), method) + + +def __make_power_2(img, base, method=Image.BICUBIC): + ow, oh = img.size + h = int(round(oh / base) * base) + w = int(round(ow / base) * base) + if (h == oh) and (w == ow): + return img + return img.resize((w, h), method) + + +def __scale_width(img, target_width, method=Image.BICUBIC): + ow, oh = img.size + if (ow == target_width): + return img + w = target_width + h = int(target_width * oh / ow) + return img.resize((w, h), method) + + +def __scale_shortside(img, target_width, method=Image.BICUBIC): + ow, oh = img.size + ss, ls = min(ow, oh), max(ow, oh) # shortside and longside + width_is_shorter = ow == ss + if (ss == target_width): + return img + ls = int(target_width * ls / ss) + nw, nh = (ss, ls) if width_is_shorter else (ls, ss) + return img.resize((nw, nh), method) + + +def __crop(img, pos, size): + ow, oh = img.size + x1, y1 = pos + tw = th = size + return img.crop((x1, y1, x1 + tw, y1 + th)) + + +def __flip(img, flip): + if flip: + return img.transpose(Image.FLIP_LEFT_RIGHT) + return img \ No newline at end of file diff --git a/data/cityscapes_dataset.py b/data/cityscapes_dataset.py new file mode 100644 index 0000000000000000000000000000000000000000..b41e1c24c81776158d1061507c8c318785b704a4 --- /dev/null +++ b/data/cityscapes_dataset.py @@ -0,0 +1,46 @@ +import os +from data.pix2pix_dataset import Pix2pixDataset +from data.image_folder import make_dataset + + +class CityscapesDataset(Pix2pixDataset): + + @staticmethod + def modify_commandline_options(parser, is_train): + parser = Pix2pixDataset.modify_commandline_options(parser, is_train) + parser.set_defaults(preprocess_mode='fixed') + parser.set_defaults(load_size=512) + parser.set_defaults(crop_size=512) + parser.set_defaults(display_winsize=512) + parser.set_defaults(label_nc=35) + parser.set_defaults(aspect_ratio=2.0) + parser.set_defaults(batchSize=16) + opt, _ = parser.parse_known_args() + if hasattr(opt, 'num_upsampling_layers'): + parser.set_defaults(num_upsampling_layers='more') + return parser + + def get_paths(self, opt): + root = opt.croot + phase = 'val' if opt.phase == 'test' else 'train' + + label_dir = os.path.join(root, 'gtFine', phase) + label_paths_all = make_dataset(label_dir, recursive=True) + label_paths = [p for p in label_paths_all if p.endswith('_labelIds.png')] + + image_dir = os.path.join(root, 'leftImg8bit', phase) + image_paths = make_dataset(image_dir, recursive=True) + + if not opt.no_instance: + instance_paths = [p for p in label_paths_all if p.endswith('_instanceIds.png')] + else: + instance_paths = [] + + return label_paths, image_paths, instance_paths + + def paths_match(self, path1, path2): + name1 = os.path.basename(path1) + name2 = os.path.basename(path2) + # compare the first 3 components, [city]_[id1]_[id2] + return '_'.join(name1.split('_')[:3]) == \ + '_'.join(name2.split('_')[:3]) diff --git a/data/custom_dataset.py b/data/custom_dataset.py new file mode 100644 index 0000000000000000000000000000000000000000..5a83754832af15a8dd6f77107fcd0a7f511c7360 --- /dev/null +++ b/data/custom_dataset.py @@ -0,0 +1,45 @@ +from data.pix2pix_dataset import Pix2pixDataset +from data.image_folder import make_dataset + + +class CustomDataset(Pix2pixDataset): + """ Dataset that loads images from directories + Use option --label_dir, --image_dir, --instance_dir to specify the directories. + The images in the directories are sorted in alphabetical order and paired in order. + """ + + @staticmethod + def modify_commandline_options(parser, is_train): + parser = Pix2pixDataset.modify_commandline_options(parser, is_train) + parser.set_defaults(preprocess_mode='resize_and_crop') + load_size = 286 if is_train else 256 + parser.set_defaults(load_size=load_size) + parser.set_defaults(crop_size=256) + parser.set_defaults(display_winsize=256) + parser.set_defaults(label_nc=13) + parser.set_defaults(contain_dontcare_label=False) + + parser.add_argument('--label_dir', type=str, required=True, + help='path to the directory that contains label images') + parser.add_argument('--image_dir', type=str, required=True, + help='path to the directory that contains photo images') + parser.add_argument('--instance_dir', type=str, default='', + help='path to the directory that contains instance maps. Leave black if not exists') + return parser + + def get_paths(self, opt): + label_dir = opt.label_dir + label_paths = make_dataset(label_dir, recursive=False, read_cache=True) + + image_dir = opt.image_dir + image_paths = make_dataset(image_dir, recursive=False, read_cache=True) + + if len(opt.instance_dir) > 0: + instance_dir = opt.instance_dir + instance_paths = make_dataset(instance_dir, recursive=False, read_cache=True) + else: + instance_paths = [] + + assert len(label_paths) == len(image_paths), "The #images in %s and %s do not match. Is there something wrong?" + + return label_paths, image_paths, instance_paths diff --git a/data/day2night_dataset.py b/data/day2night_dataset.py new file mode 100644 index 0000000000000000000000000000000000000000..8c21291c8d361c94a3640ec2029212d5fd5f42c2 --- /dev/null +++ b/data/day2night_dataset.py @@ -0,0 +1,43 @@ +import os +from data.pix2pix_dataset import Pix2pixDataset + + +class Day2NightDataset(Pix2pixDataset): + + @staticmethod + def modify_commandline_options(parser, is_train): + parser = Pix2pixDataset.modify_commandline_options(parser, is_train) + parser.set_defaults(preprocess_mode='fixed') + parser.set_defaults(load_size=512) + parser.set_defaults(crop_size=512) + parser.set_defaults(display_winsize=512) + parser.set_defaults(aspect_ratio=2.0) + opt, _ = parser.parse_known_args() + if hasattr(opt, 'num_upsampling_layers'): + parser.set_defaults(num_upsampling_layers='more') + return parser + + def get_paths(self, opt): + croot = opt.croot + sroot = opt.sroot + + with open(os.path.join(croot, 'bdd100k_lists/day2night/day_%s.txt' % opt.phase)) as c_list: + c_image_paths_read = c_list.read().splitlines() + c_image_paths = [os.path.join(croot, p) for p in c_image_paths_read if p != ''] + + with open(os.path.join(sroot, 'bdd100k_lists/day2night/night_%s.txt' % opt.phase)) as s_list: + s_image_paths_read = s_list.read().splitlines() + s_image_paths = [os.path.join(sroot, p) for p in s_image_paths_read if p != ''] + + if opt.phase == 'train': + c_image_paths = c_image_paths + c_image_paths + + instance_paths = [] + + length = min(len(c_image_paths), len(s_image_paths)) + c_image_paths = c_image_paths[:length] + s_image_paths = s_image_paths[:length] + return c_image_paths, s_image_paths, instance_paths + + def paths_match(self, path1, path2): + return True \ No newline at end of file diff --git a/data/image_folder.py b/data/image_folder.py new file mode 100644 index 0000000000000000000000000000000000000000..7113f5fe07679bd87def3833baa4d1f0601be5c0 --- /dev/null +++ b/data/image_folder.py @@ -0,0 +1,93 @@ +############################################################################### +# Code from +# https://github.com/pytorch/vision/blob/master/torchvision/datasets/folder.py +# Modified the original code so that it also loads images from the current +# directory as well as the subdirectories +############################################################################### +import torch.utils.data as data +from PIL import Image +import os + +IMG_EXTENSIONS = [ + '.jpg', '.JPG', '.jpeg', '.JPEG', + '.png', '.PNG', '.ppm', '.PPM', '.bmp', '.BMP', '.tiff', '.webp' +] + + +def is_image_file(filename): + return any(filename.endswith(extension) for extension in IMG_EXTENSIONS) + + +def make_dataset_rec(dir, images): + assert os.path.isdir(dir), '%s is not a valid directory' % dir + + for root, dnames, fnames in sorted(os.walk(dir, followlinks=True)): + for fname in fnames: + if is_image_file(fname): + path = os.path.join(root, fname) + images.append(path) + + +def make_dataset(dir, recursive=False, read_cache=False, write_cache=False): + images = [] + + if read_cache: + possible_filelist = os.path.join(dir, 'files.list') + if os.path.isfile(possible_filelist): + with open(possible_filelist, 'r') as f: + images = f.read().splitlines() + return images + + if recursive: + make_dataset_rec(dir, images) + else: + assert os.path.isdir(dir) or os.path.islink(dir), '%s is not a valid directory' % dir + + for root, dnames, fnames in sorted(os.walk(dir)): + for fname in fnames: + if is_image_file(fname): + path = os.path.join(root, fname) + images.append(path) + + if write_cache: + filelist_cache = os.path.join(dir, 'files.list') + with open(filelist_cache, 'w') as f: + for path in images: + f.write("%s\n" % path) + print('wrote filelist cache at %s' % filelist_cache) + + return images + + +def default_loader(path): + return Image.open(path).convert('RGB') + + +class ImageFolder(data.Dataset): + + def __init__(self, root, transform=None, return_paths=False, + loader=default_loader): + imgs = make_dataset(root) + if len(imgs) == 0: + raise(RuntimeError("Found 0 images in: " + root + "\n" + "Supported image extensions are: " + + ",".join(IMG_EXTENSIONS))) + + self.root = root + self.imgs = imgs + self.transform = transform + self.return_paths = return_paths + self.loader = loader + + def __getitem__(self, index): + path = self.imgs[index] + img = self.loader(path) + if self.transform is not None: + img = self.transform(img) + if self.return_paths: + return img, path + else: + return img + + def __len__(self): + return len(self.imgs) diff --git a/data/photo2art_dataset.py b/data/photo2art_dataset.py new file mode 100644 index 0000000000000000000000000000000000000000..dce3285dc977d923eb8c77ad5173ac1413c8d5f1 --- /dev/null +++ b/data/photo2art_dataset.py @@ -0,0 +1,43 @@ +import os +from data.pix2pix_dataset import Pix2pixDataset +from data.image_folder import make_dataset + + +class Photo2ArtDataset(Pix2pixDataset): + + @staticmethod + def modify_commandline_options(parser, is_train): + parser = Pix2pixDataset.modify_commandline_options(parser, is_train) + parser.set_defaults(preprocess_mode='fixed') + parser.set_defaults(load_size=256) + parser.set_defaults(crop_size=256) + parser.set_defaults(display_winsize=256) + parser.set_defaults(aspect_ratio=1.0) + opt, _ = parser.parse_known_args() + if hasattr(opt, 'num_upsampling_layers'): + parser.set_defaults(num_upsampling_layers='more') + return parser + + def get_paths(self, opt): + croot = opt.croot + sroot = opt.sroot + + c_image_dir = os.path.join(croot, '%sA' % opt.phase) + c_image_paths = sorted(make_dataset(c_image_dir, recursive=True)) + + s_image_dir = os.path.join(sroot, '%sB' % opt.phase) + s_image_paths = sorted(make_dataset(s_image_dir, recursive=True)) + + if opt.phase == 'train': + for i in range(2): + s_image_paths = s_image_paths + s_image_paths + + instance_paths = [] + + length = min(len(c_image_paths), len(s_image_paths)) + c_image_paths = c_image_paths[:length] + s_image_paths = s_image_paths[:length] + return c_image_paths, s_image_paths, instance_paths + + def paths_match(self, path1, path2): + return True \ No newline at end of file diff --git a/data/pix2pix_dataset.py b/data/pix2pix_dataset.py new file mode 100644 index 0000000000000000000000000000000000000000..032b371a36756b784e3268832f62a30312e2bb77 --- /dev/null +++ b/data/pix2pix_dataset.py @@ -0,0 +1,110 @@ +from data.base_dataset import BaseDataset, get_params, get_transform +from PIL import Image +import util.util as util +import os + + +class Pix2pixDataset(BaseDataset): + @staticmethod + def modify_commandline_options(parser, is_train): + parser.add_argument('--no_pairing_check', action='store_true', + help='If specified, skip sanity check of correct label-image file pairing') + return parser + + def initialize(self, opt): + self.opt = opt + + label_paths, image_paths, instance_paths = self.get_paths(opt) + + util.natural_sort(label_paths) + util.natural_sort(image_paths) + if not opt.no_instance: + util.natural_sort(instance_paths) + + label_paths = label_paths[:opt.max_dataset_size] + image_paths = image_paths[:opt.max_dataset_size] + instance_paths = instance_paths[:opt.max_dataset_size] + + if not opt.no_pairing_check: + for path1, path2 in zip(label_paths, image_paths): + assert self.paths_match(path1, path2), \ + "The label-image pair (%s, %s) do not look like the right pair because the filenames are quite different. Are you sure about the pairing? Please see data/pix2pix_dataset.py to see what is going on, and use --no_pairing_check to bypass this." % (path1, path2) + + self.label_paths = label_paths + self.image_paths = image_paths + self.instance_paths = instance_paths + + size = len(self.label_paths) + self.dataset_size = size + + def get_paths(self, opt): + label_paths = [] + image_paths = [] + instance_paths = [] + assert False, "A subclass of Pix2pixDataset must override self.get_paths(self, opt)" + return label_paths, image_paths, instance_paths + + def paths_match(self, path1, path2): + filename1_without_ext = os.path.splitext(os.path.basename(path1))[0] + filename2_without_ext = os.path.splitext(os.path.basename(path2))[0] + return filename1_without_ext == filename2_without_ext + + def __getitem__(self, index): + # Label (Content) Image + label_path = self.label_paths[index] + label = Image.open(label_path) + if self.opt.task != 'SIS': + label = label.convert('RGB') + params = get_params(self.opt, label.size) + + if self.opt.task != 'SIS': + transform_label = get_transform(self.opt, params) + label_tensor = transform_label(label) + else: + transform_label = get_transform(self.opt, params, method=Image.NEAREST, normalize=False) + label_tensor = transform_label(label) * 255.0 + label_tensor[label_tensor == 255] = self.opt.label_nc # 'unknown' is opt.label_nc + + # Real (Style) Image + image_path = self.image_paths[index] + print("Images_path",image_path) + assert self.paths_match(label_path, image_path), \ + "The label_path %s and image_path %s don't match." % \ + (label_path, image_path) + image = Image.open(image_path) + image = image.convert('RGB') + + transform_image = get_transform(self.opt, params) + image_tensor = transform_image(image) + + # if using instance maps + if self.opt.no_instance: + instance_tensor = 0 + else: + instance_path = self.instance_paths[index] + instance = Image.open(instance_path) + if instance.mode == 'L': + instance_tensor = transform_label(instance) * 255 + instance_tensor = instance_tensor.long() + else: + instance_tensor = transform_label(instance) + + input_dict = {'label': label_tensor, + 'instance': instance_tensor, + 'image': image_tensor, + 'path': image_path, + 'cpath': label_path + } + + # Give subclasses a chance to modify the final output + len_dict=len(input_dict['label']) + print("AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA",len_dict) + self.postprocess(input_dict) + + return input_dict + + def postprocess(self, input_dict): + return input_dict + + def __len__(self): + return self.dataset_size diff --git a/data/sampler.py b/data/sampler.py new file mode 100644 index 0000000000000000000000000000000000000000..caafb7de53ef62627ec3c1d83865c18f1ab00d09 --- /dev/null +++ b/data/sampler.py @@ -0,0 +1,25 @@ +import numpy as np +from torch.utils import data + +def InfiniteSampler(n): + # i = 0 + i = n - 1 + order = np.random.permutation(n) + while True: + yield order[i] + i += 1 + if i >= n: + np.random.seed() + order = np.random.permutation(n) + i = 0 + + +class InfiniteSamplerWrapper(data.sampler.Sampler): + def __init__(self, data_source): + self.num_samples = len(data_source) + + def __iter__(self): + return iter(InfiniteSampler(self.num_samples)) + + def __len__(self): + return 2 ** 31 \ No newline at end of file diff --git a/data/summer2winteryosemite_dataset.py b/data/summer2winteryosemite_dataset.py new file mode 100644 index 0000000000000000000000000000000000000000..50c3a6fe6ea0798c564f46e5d6cc08df2175913b --- /dev/null +++ b/data/summer2winteryosemite_dataset.py @@ -0,0 +1,53 @@ +import os +from data.pix2pix_dataset import Pix2pixDataset, BaseDataset +from data.image_folder import make_dataset +from torchvision import transforms +import os +from PIL import Image + +class Summer2WinterYosemiteDataset(BaseDataset): + @staticmethod + def modify_commandline_options(parser, is_train): + parser = Pix2pixDataset.modify_commandline_options(parser, is_train) + parser.set_defaults(preprocess_mode='resize_and_crop') + parser.set_defaults(load_size=512) + parser.set_defaults(crop_size=256) + return parser + + def initialize(self, opt): + self.opt = opt + label_paths, image_paths, instance_paths = self.get_paths(opt) + self.label_paths = label_paths[:opt.max_dataset_size] + self.image_paths = image_paths[:opt.max_dataset_size] + self.dataset_size = len(self.label_paths) + + print(f"Number of labels: {len(self.label_paths)}, Number of images: {len(self.image_paths)}") + if len(self.label_paths) != len(self.image_paths): + raise ValueError("The number of labels and images do not match.") + + + def get_paths(self, opt): + croot = opt.croot + sroot = opt.sroot + c_image_dir = os.path.join(croot, f'{opt.phase}A') + s_image_dir = os.path.join(sroot, f'{opt.phase}B') + c_image_paths = sorted(make_dataset(c_image_dir, recursive=True)) + s_image_paths = sorted(make_dataset(s_image_dir, recursive=True)) + return c_image_paths, s_image_paths, [] + + def __getitem__(self, index): + label_path = self.label_paths[index] + image_path = self.image_paths[index] + label = Image.open(label_path).convert('RGB') + image = Image.open(image_path).convert('RGB') + transform = transforms.Compose([ + transforms.Resize((self.opt.load_size, self.opt.load_size)), + transforms.RandomCrop(self.opt.crop_size), + transforms.ToTensor(), + transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)) + ]) + return {'image': transform(label), 'label': transform(image),"cpath":image_path} + + def __len__(self): + return self.dataset_size + diff --git a/data/summer2yosemite_test.py b/data/summer2yosemite_test.py new file mode 100644 index 0000000000000000000000000000000000000000..ef3fdab3c76687dfe7dbf917090e99130c31204c --- /dev/null +++ b/data/summer2yosemite_test.py @@ -0,0 +1,64 @@ +import os +from PIL import Image +import torch +from torch.utils.data import Dataset +from torchvision import transforms +import numpy as np + +def test_transform(size, crop): + transform_list = [] + + if size != 0: + transform_list.append(transforms.Resize(size)) + if crop: + transform_list.append(transforms.CenterCrop(size)) + transform_list.append(transforms.ToTensor()) + transform = transforms.Compose(transform_list) + return transform + + +def style_transform(h, w): + k = (h, w) + size = int(np.max(k)) + print(type(size)) + transform_list = [] + transform_list.append(transforms.CenterCrop((h, w))) + transform_list.append(transforms.ToTensor()) + transform = transforms.Compose(transform_list) + return transform + + +def content_transform(): + transform_list = [] + transform_list.append(transforms.Resize(256)) # Thay đổi kích thước trước + transform_list.append(transforms.ToTensor()) # Sau đó chuyển đổi thành tensor + transform = transforms.Compose(transform_list) + return transform # Trả về một đối tượng biến đổi + + + +class Summer2YosemiteDataset(Dataset): + def __init__(self, content_dir, style_dir, transform=None): + self.content_dir = content_dir + self.style_dir = style_dir + self.transform = transform + + self.content_images = sorted([os.path.join(content_dir, img) for img in os.listdir(content_dir)]) + self.style_images = sorted([os.path.join(style_dir, img) for img in os.listdir(style_dir)]) + + def __len__(self): + return min(len(self.content_images), len(self.style_images)) + + def __getitem__(self, index): + content_path = self.content_images[index] + style_path = self.style_images[index] + + # Load và áp dụng các biến đổi ảnh + content_image = Image.open(content_path).convert("RGB") + style_image = Image.open(style_path).convert("RGB") + + if self.transform: + content_image = self.transform(content_image) + style_image = self.transform(style_image) + + return {'label': content_image, 'image': style_image,'cpath': content_path} diff --git a/data/sunny2diffweathers_dataset.py b/data/sunny2diffweathers_dataset.py new file mode 100644 index 0000000000000000000000000000000000000000..ac8eb2c8f279b58e0f2b6b903ad744762a687101 --- /dev/null +++ b/data/sunny2diffweathers_dataset.py @@ -0,0 +1,54 @@ +import os +from data.pix2pix_dataset import Pix2pixDataset + + +class Sunny2DiffWeathersDataset(Pix2pixDataset): + + @staticmethod + def modify_commandline_options(parser, is_train): + parser = Pix2pixDataset.modify_commandline_options(parser, is_train) + parser.add_argument('--test_mode', type=str, default='all', + help='specify style mode to control multi-modal image synthesis (MMIS) during test phase:' + 'night | cloudy | rainy | snowy | all') + parser.set_defaults(preprocess_mode='fixed') + parser.set_defaults(load_size=512) + parser.set_defaults(crop_size=512) + parser.set_defaults(display_winsize=512) + parser.set_defaults(aspect_ratio=2.0) + opt, _ = parser.parse_known_args() + if hasattr(opt, 'num_upsampling_layers'): + parser.set_defaults(num_upsampling_layers='more') + return parser + + + def get_paths(self, opt): + croot = opt.croot + sroot = opt.sroot + + with open(os.path.join(croot, 'bdd100k_lists/sunny2diffweathers/sunny_%s.txt' % opt.phase)) as c_list: + c_image_paths_read = c_list.read().splitlines() + c_image_paths = [os.path.join(croot, p) for p in c_image_paths_read if p != ''] + + if opt.phase == 'train' or opt.test_mode == 'all': + mode_list = ['night', 'cloudy', 'rainy', 'snowy'] + else: + mode_list = [opt.test_mode] + s_image_paths = [] + for mode in mode_list: + with open(os.path.join(sroot, 'bdd100k_lists/sunny2diffweathers/%s_%s.txt' % (mode, opt.phase))) as s_list: + s_image_paths_read = s_list.read().splitlines() + s_image_paths_mode = [os.path.join(sroot, p) for p in s_image_paths_read if p != ''] + s_image_paths.extend(s_image_paths_mode) + + while len(s_image_paths) < len(c_image_paths): + s_image_paths = s_image_paths + s_image_paths + + instance_paths = [] + + length = min(len(c_image_paths), len(s_image_paths)) + c_image_paths = c_image_paths[:length] + s_image_paths = s_image_paths[:length] + return c_image_paths, s_image_paths, instance_paths + + def paths_match(self, path1, path2): + return True \ No newline at end of file diff --git a/experiments/test/0.jpg b/experiments/test/0.jpg new file mode 100644 index 0000000000000000000000000000000000000000..0ac8cf26fbcc8d0f55cbf17828396381bc91a5c2 --- /dev/null +++ b/experiments/test/0.jpg @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:a7e46b9d8e3176e5e889022fdf52bca6b1ea8b54a5528ce8fcaa8c8a8ee25069 +size 27032 diff --git a/experiments/test/100.jpg b/experiments/test/100.jpg new file mode 100644 index 0000000000000000000000000000000000000000..25fb01777a26b10ab1d10f5aa35c030e5f614c34 --- /dev/null +++ b/experiments/test/100.jpg @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:acebaf47da5608e312dc0416cff828a7670e38ba0bb984f490987e1c8cda3e97 +size 20626 diff --git a/experiments/test/200.jpg b/experiments/test/200.jpg new file mode 100644 index 0000000000000000000000000000000000000000..339e67a9c24192fb4dc6255cae2bd1f09d463161 --- /dev/null +++ b/experiments/test/200.jpg @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:e20426a62e1c3a66f5da26966c914d8dba2423ff7ad60dd7b29883f755e18285 +size 31086 diff --git a/experiments/test/300.jpg b/experiments/test/300.jpg new file mode 100644 index 0000000000000000000000000000000000000000..fb7d6f100b38bcdfec410966926ccd5f2bfaca5d --- /dev/null +++ b/experiments/test/300.jpg @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:64b2169ca28e64005cd71f58bdb8fa764bf745de9e923561dbcc3d0fa98b30da +size 21286 diff --git a/experiments/test/400.jpg b/experiments/test/400.jpg new file mode 100644 index 0000000000000000000000000000000000000000..a4f8bf4dcf6a17782dffecd76e81af981f544e57 --- /dev/null +++ b/experiments/test/400.jpg @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:b4d249afd8671302e7b87ec080c796a8e5ffb4c713a6d7ed5a66150bb785b497 +size 27710 diff --git a/experiments/test/500.jpg b/experiments/test/500.jpg new file mode 100644 index 0000000000000000000000000000000000000000..99eb2ee659569632c45703aa094d6a0a69060d9e --- /dev/null +++ b/experiments/test/500.jpg @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:d008e723aaef84ddb5795f3e5d1219cb97cdc173f2e334d52739c04abc046cb8 +size 28308 diff --git a/experiments/test/600.jpg b/experiments/test/600.jpg new file mode 100644 index 0000000000000000000000000000000000000000..2855809377f37925374d6d112d1b78d8996125d5 --- /dev/null +++ b/experiments/test/600.jpg @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:498a2fa040128b8ef2e800da2988e28504f8e9bd0bac61caa1052be6a025492f +size 28413 diff --git a/model_structures.txt b/model_structures.txt new file mode 100644 index 0000000000000000000000000000000000000000..a2f198bb54f2fd74150d02a809b6f7af40a7a4b0 --- /dev/null +++ b/model_structures.txt @@ -0,0 +1,144 @@ +Transformer Encoder Base- Content + +================================================================================================================================== +Layer (type (var_name)) Input Shape Output Shape Param # Trainable +================================================================================================================================== +TransformerEncoder (TransformerEncoder) [784, 2, 512] [784, 2, 512] -- True +├─ModuleList (layers) -- -- -- True +│ └─TransformerEncoderLayer (0) [784, 2, 512] [784, 2, 512] -- True +│ │ └─MultiheadAttention (self_attn) [784, 2, 512] [784, 2, 512] 1,050,624 True +│ │ └─Dropout (dropout1) [784, 2, 512] [784, 2, 512] -- -- +│ │ └─LayerNorm (norm1) [784, 2, 512] [784, 2, 512] 1,024 True +│ │ └─Linear (linear1) [784, 2, 512] [784, 2, 2048] 1,050,624 True +│ │ └─Dropout (dropout) [784, 2, 2048] [784, 2, 2048] -- -- +│ │ └─Linear (linear2) [784, 2, 2048] [784, 2, 512] 1,049,088 True +│ │ └─Dropout (dropout2) [784, 2, 512] [784, 2, 512] -- -- +│ │ └─LayerNorm (norm2) [784, 2, 512] [784, 2, 512] 1,024 True +│ └─TransformerEncoderLayer (1) [784, 2, 512] [784, 2, 512] -- True +│ │ └─MultiheadAttention (self_attn) [784, 2, 512] [784, 2, 512] 1,050,624 True +│ │ └─Dropout (dropout1) [784, 2, 512] [784, 2, 512] -- -- +│ │ └─LayerNorm (norm1) [784, 2, 512] [784, 2, 512] 1,024 True +│ │ └─Linear (linear1) [784, 2, 512] [784, 2, 2048] 1,050,624 True +│ │ └─Dropout (dropout) [784, 2, 2048] [784, 2, 2048] -- -- +│ │ └─Linear (linear2) [784, 2, 2048] [784, 2, 512] 1,049,088 True +│ │ └─Dropout (dropout2) [784, 2, 512] [784, 2, 512] -- -- +│ │ └─LayerNorm (norm2) [784, 2, 512] [784, 2, 512] 1,024 True +│ └─TransformerEncoderLayer (2) [784, 2, 512] [784, 2, 512] -- True +│ │ └─MultiheadAttention (self_attn) [784, 2, 512] [784, 2, 512] 1,050,624 True +│ │ └─Dropout (dropout1) [784, 2, 512] [784, 2, 512] -- -- +│ │ └─LayerNorm (norm1) [784, 2, 512] [784, 2, 512] 1,024 True +│ │ └─Linear (linear1) [784, 2, 512] [784, 2, 2048] 1,050,624 True +│ │ └─Dropout (dropout) [784, 2, 2048] [784, 2, 2048] -- -- +│ │ └─Linear (linear2) [784, 2, 2048] [784, 2, 512] 1,049,088 True +│ │ └─Dropout (dropout2) [784, 2, 512] [784, 2, 512] -- -- +│ │ └─LayerNorm (norm2) [784, 2, 512] [784, 2, 512] 1,024 True +================================================================================================================================== +Total params: 9,457,152 +Trainable params: 9,457,152 +Non-trainable params: 0 +Total mult-adds (G): 4.94 + +Transformer Encoder Base: Style +================================================================================================================================== +Layer (type (var_name)) Input Shape Output Shape Param # Trainable +================================================================================================================================== +TransformerEncoder (TransformerEncoder) [784, 2, 512] [784, 2, 512] -- True +├─ModuleList (layers) -- -- -- True +│ └─TransformerEncoderLayer (0) [784, 2, 512] [784, 2, 512] -- True +│ │ └─MultiheadAttention (self_attn) [784, 2, 512] [784, 2, 512] 1,050,624 True +│ │ └─Dropout (dropout1) [784, 2, 512] [784, 2, 512] -- -- +│ │ └─LayerNorm (norm1) [784, 2, 512] [784, 2, 512] 1,024 True +│ │ └─Linear (linear1) [784, 2, 512] [784, 2, 2048] 1,050,624 True +│ │ └─Dropout (dropout) [784, 2, 2048] [784, 2, 2048] -- -- +│ │ └─Linear (linear2) [784, 2, 2048] [784, 2, 512] 1,049,088 True +│ │ └─Dropout (dropout2) [784, 2, 512] [784, 2, 512] -- -- +│ │ └─LayerNorm (norm2) [784, 2, 512] [784, 2, 512] 1,024 True +│ └─TransformerEncoderLayer (1) [784, 2, 512] [784, 2, 512] -- True +│ │ └─MultiheadAttention (self_attn) [784, 2, 512] [784, 2, 512] 1,050,624 True +│ │ └─Dropout (dropout1) [784, 2, 512] [784, 2, 512] -- -- +│ │ └─LayerNorm (norm1) [784, 2, 512] [784, 2, 512] 1,024 True +│ │ └─Linear (linear1) [784, 2, 512] [784, 2, 2048] 1,050,624 True +│ │ └─Dropout (dropout) [784, 2, 2048] [784, 2, 2048] -- -- +│ │ └─Linear (linear2) [784, 2, 2048] [784, 2, 512] 1,049,088 True +│ │ └─Dropout (dropout2) [784, 2, 512] [784, 2, 512] -- -- +│ │ └─LayerNorm (norm2) [784, 2, 512] [784, 2, 512] 1,024 True +│ └─TransformerEncoderLayer (2) [784, 2, 512] [784, 2, 512] -- True +│ │ └─MultiheadAttention (self_attn) [784, 2, 512] [784, 2, 512] 1,050,624 True +│ │ └─Dropout (dropout1) [784, 2, 512] [784, 2, 512] -- -- +│ │ └─LayerNorm (norm1) [784, 2, 512] [784, 2, 512] 1,024 True +│ │ └─Linear (linear1) [784, 2, 512] [784, 2, 2048] 1,050,624 True +│ │ └─Dropout (dropout) [784, 2, 2048] [784, 2, 2048] -- -- +│ │ └─Linear (linear2) [784, 2, 2048] [784, 2, 512] 1,049,088 True +│ │ └─Dropout (dropout2) [784, 2, 512] [784, 2, 512] -- -- +│ │ └─LayerNorm (norm2) [784, 2, 512] [784, 2, 512] 1,024 True +================================================================================================================================== +Total params: 9,457,152 +Trainable params: 9,457,152 +Non-trainable params: 0 +Total mult-adds (G): 4.94 +================================================================================================================================== +Input size (MB): 3.21 +Forward/backward pass size (MB): 134.87 +Params size (MB): 25.22 +Estimated Total Size (MB): 163.31 +================================================================================================================================== + +Transfomer Decoder +======================================================================================================================================= +Layer (type (var_name)) Input Shape Output Shape Param # Trainable +======================================================================================================================================= +TransformerDecoder (TransformerDecoder) [784, 2, 512] [1, 784, 2, 512] -- True +├─ModuleList (layers) -- -- -- True +│ └─TransformerDecoderLayer (0) [784, 2, 512] [784, 2, 512] -- True +│ │ └─MultiheadAttention (self_attn) [784, 2, 512] [784, 2, 512] 1,050,624 True +│ │ └─Dropout (dropout1) [784, 2, 512] [784, 2, 512] -- -- +│ │ └─LayerNorm (norm1) [784, 2, 512] [784, 2, 512] 1,024 True +│ │ └─MultiheadAttention (multihead_attn) -- [784, 2, 512] 1,050,624 True +│ │ └─Dropout (dropout2) [784, 2, 512] [784, 2, 512] -- -- +│ │ └─LayerNorm (norm2) [784, 2, 512] [784, 2, 512] 1,024 True +│ │ └─Linear (linear1) [784, 2, 512] [784, 2, 2048] 1,050,624 True +│ │ └─Dropout (dropout) [784, 2, 2048] [784, 2, 2048] -- -- +│ │ └─Linear (linear2) [784, 2, 2048] [784, 2, 512] 1,049,088 True +│ │ └─Dropout (dropout3) [784, 2, 512] [784, 2, 512] -- -- +│ │ └─LayerNorm (norm3) [784, 2, 512] [784, 2, 512] 1,024 True +│ └─TransformerDecoderLayer (1) [784, 2, 512] [784, 2, 512] -- True +│ │ └─MultiheadAttention (self_attn) [784, 2, 512] [784, 2, 512] 1,050,624 True +│ │ └─Dropout (dropout1) [784, 2, 512] [784, 2, 512] -- -- +│ │ └─LayerNorm (norm1) [784, 2, 512] [784, 2, 512] 1,024 True +│ │ └─MultiheadAttention (multihead_attn) -- [784, 2, 512] 1,050,624 True +│ │ └─Dropout (dropout2) [784, 2, 512] [784, 2, 512] -- -- +│ │ └─LayerNorm (norm2) [784, 2, 512] [784, 2, 512] 1,024 True +│ │ └─Linear (linear1) [784, 2, 512] [784, 2, 2048] 1,050,624 True +│ │ └─Dropout (dropout) [784, 2, 2048] [784, 2, 2048] -- -- +│ │ └─Linear (linear2) [784, 2, 2048] [784, 2, 512] 1,049,088 True +│ │ └─Dropout (dropout3) [784, 2, 512] [784, 2, 512] -- -- +│ │ └─LayerNorm (norm3) [784, 2, 512] [784, 2, 512] 1,024 True +│ └─TransformerDecoderLayer (2) [784, 2, 512] [784, 2, 512] -- True +│ │ └─MultiheadAttention (self_attn) [784, 2, 512] [784, 2, 512] 1,050,624 True +│ │ └─Dropout (dropout1) [784, 2, 512] [784, 2, 512] -- -- +│ │ └─LayerNorm (norm1) [784, 2, 512] [784, 2, 512] 1,024 True +│ │ └─MultiheadAttention (multihead_attn) -- [784, 2, 512] 1,050,624 True +│ │ └─Dropout (dropout2) [784, 2, 512] [784, 2, 512] -- -- +│ │ └─LayerNorm (norm2) [784, 2, 512] [784, 2, 512] 1,024 True +│ │ └─Linear (linear1) [784, 2, 512] [784, 2, 2048] 1,050,624 True +│ │ └─Dropout (dropout) [784, 2, 2048] [784, 2, 2048] -- -- +│ │ └─Linear (linear2) [784, 2, 2048] [784, 2, 512] 1,049,088 True +│ │ └─Dropout (dropout3) [784, 2, 512] [784, 2, 512] -- -- +│ │ └─LayerNorm (norm3) [784, 2, 512] [784, 2, 512] 1,024 True +├─LayerNorm (norm) [784, 2, 512] [784, 2, 512] 1,024 True +======================================================================================================================================= +Total params: 12,613,120 +Trainable params: 12,613,120 +Non-trainable params: 0 +Total mult-adds (G): 4.95 +======================================================================================================================================= +Input size (MB): 6.42 +Forward/backward pass size (MB): 160.56 +Params size (MB): 25.24 +Estimated Total Size (MB): 192.22 + + + +All Layers + + diff --git a/models/__init__.py b/models/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e895b4bc509ade61d8e2a454e86817b50e85ac33 --- /dev/null +++ b/models/__init__.py @@ -0,0 +1,39 @@ +import importlib +import torch + + +def find_model_using_name(model_name): + # Given the option --model [modelname], + # the file "models/modelname_model.py" + # will be imported. + model_filename = "models." + model_name + "_model" + modellib = importlib.import_module(model_filename) + + # In the file, the class called ModelNameModel() will + # be instantiated. It has to be a subclass of torch.nn.Module, + # and it is case-insensitive. + model = None + target_model_name = model_name.replace('_', '') + 'model' + for name, cls in modellib.__dict__.items(): + if name.lower() == target_model_name.lower() \ + and issubclass(cls, torch.nn.Module): + model = cls + + if model is None: + print("In %s.py, there should be a subclass of torch.nn.Module with class name that matches %s in lowercase." % (model_filename, target_model_name)) + exit(0) + + return model + + +def get_option_setter(model_name): + model_class = find_model_using_name(model_name) + return model_class.modify_commandline_options + + +def create_model(opt): + model = find_model_using_name(opt.model) + instance = model(opt) + print("model [%s] was created" % (type(instance).__name__)) + + return instance diff --git a/models/__pycache__/__init__.cpython-310.pyc b/models/__pycache__/__init__.cpython-310.pyc new file mode 100644 index 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b/models/networks/AdaIN/__pycache__/function.cpython-37.pyc differ diff --git a/models/networks/AdaIN/function.py b/models/networks/AdaIN/function.py new file mode 100644 index 0000000000000000000000000000000000000000..9da189136db169d9f09f2b3535f5072c60837a7d --- /dev/null +++ b/models/networks/AdaIN/function.py @@ -0,0 +1,98 @@ +import torch +import torch.nn.functional as F + + +# The code was inspired from https://github.com/naoto0804/pytorch-AdaIN. +def calc_mean_std(feat, eps=1e-5, mask=None): + # eps is a small value added to the variance to avoid divide-by-zero. + size = feat.size() + assert (len(size) == 4) + if mask is not None: + msize = mask.size() + assert (msize[0] == size[0] and msize[2] == size[2] and msize[3] == size[3]) + N, C = size[:2] + if mask is not None: + cnt = mask.view(N, 1, -1).sum(2) + mf = mask * feat + mf = mf.view(N, C, -1).sum(2) + mf /= cnt + feat_mean = mf.view(N, C, 1, 1) + vf = ((feat - feat_mean) ** 2) * mask + vf = vf.view(N, C, -1).sum(2) + vf = vf / (cnt - 1) + vf += eps + feat_std = vf.sqrt().view(N, C, 1, 1) + else: + feat_var = feat.view(N, C, -1).var(dim=2) + eps + feat_std = feat_var.sqrt().view(N, C, 1, 1) + feat_mean = feat.view(N, C, -1).mean(dim=2).view(N, C, 1, 1) + return feat_mean, feat_std + + +def adaptive_instance_normalization(content_feat, style_feat, c_mask=None, s_mask=None): + assert (content_feat.size()[:2] == style_feat.size()[:2]) + + size = content_feat.size() + H, W = size[2], size[3] + msk = None + if c_mask is not None: + msk = F.interpolate(c_mask, (H, W)) + + s_size = style_feat.size() + s_H, s_W = s_size[2], s_size[3] + s_msk = None + if s_mask is not None: + s_msk = F.interpolate(s_mask, (s_H, s_W)) + + style_mean, style_std = calc_mean_std(style_feat, mask=s_msk) + content_mean, content_std = calc_mean_std(content_feat, mask=msk) + + normalized_feat = (content_feat - content_mean.expand(size)) / content_std.expand(size) + + if msk is not None: + return (normalized_feat * style_std.expand(size) + style_mean.expand(size)) * msk + content_feat * (1 - msk) + else: + return normalized_feat * style_std.expand(size) + style_mean.expand(size) + + +def _calc_feat_flatten_mean_std(feat): + # takes 3D feat (C, H, W), return mean and std of array within channels + assert (feat.size()[0] == 3) + assert (isinstance(feat, torch.FloatTensor)) + feat_flatten = feat.view(3, -1) + mean = feat_flatten.mean(dim=-1, keepdim=True) + std = feat_flatten.std(dim=-1, keepdim=True) + return feat_flatten, mean, std + + +def _mat_sqrt(x): + U, D, V = torch.svd(x) + return torch.mm(torch.mm(U, D.pow(0.5).diag()), V.t()) + + +def coral(source, target): + # assume both source and target are 3D array (C, H, W) + # Note: flatten -> f + source_f, source_f_mean, source_f_std = _calc_feat_flatten_mean_std(source) + source_f_norm = (source_f - source_f_mean.expand_as( + source_f)) / source_f_std.expand_as(source_f) + source_f_cov_eye = \ + torch.mm(source_f_norm, source_f_norm.t()) + torch.eye(3) + + target_f, target_f_mean, target_f_std = _calc_feat_flatten_mean_std(target) + target_f_norm = (target_f - target_f_mean.expand_as( + target_f)) / target_f_std.expand_as(target_f) + target_f_cov_eye = \ + torch.mm(target_f_norm, target_f_norm.t()) + torch.eye(3) + + source_f_norm_transfer = torch.mm( + _mat_sqrt(target_f_cov_eye), + torch.mm(torch.inverse(_mat_sqrt(source_f_cov_eye)), + source_f_norm) + ) + + source_f_transfer = source_f_norm_transfer * \ + target_f_std.expand_as(source_f_norm) + \ + target_f_mean.expand_as(source_f_norm) + + return source_f_transfer.view(source.size()) diff --git a/models/networks/__init__.py b/models/networks/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..88558b5322e23f6674d8eb0e2ac19f43e7c04caa --- /dev/null +++ b/models/networks/__init__.py @@ -0,0 +1,58 @@ +import torch +from models.networks.base_network import BaseNetwork +from models.networks.loss import * +from models.networks.discriminator import * +from models.networks.generator import * +from models.networks.encoder import * +import util.util as util + + +def find_network_using_name(target_network_name, filename): + target_class_name = target_network_name + filename + module_name = 'models.networks.' + filename + network = util.find_class_in_module(target_class_name, module_name) + + assert issubclass(network, BaseNetwork), \ + "Class %s should be a subclass of BaseNetwork" % network + + return network + + +def modify_commandline_options(parser, is_train): + opt, _ = parser.parse_known_args() + + netG_cls = find_network_using_name(opt.netG, 'generator') + parser = netG_cls.modify_commandline_options(parser, is_train) + if is_train: + netD_cls = find_network_using_name(opt.netD, 'discriminator') + parser = netD_cls.modify_commandline_options(parser, is_train) + netE_cls = find_network_using_name('conv', 'encoder') + parser = netE_cls.modify_commandline_options(parser, is_train) + + return parser + + +def create_network(cls, opt): + net = cls(opt) + net.print_network() + if len(opt.gpu_ids) > 0: + assert(torch.cuda.is_available()) + net.cuda() + net.init_weights(opt.init_type, opt.init_variance) + return net + + +def define_G(opt): + netG_cls = find_network_using_name(opt.netG, 'generator') + return create_network(netG_cls, opt) + + +def define_D(opt): + netD_cls = find_network_using_name(opt.netD, 'discriminator') + return create_network(netD_cls, opt) + + +def define_E(opt): + # there exists only one encoder type + netE_cls = find_network_using_name('conv', 'encoder') + return create_network(netE_cls, opt) diff --git a/models/networks/__pycache__/__init__.cpython-310.pyc b/models/networks/__pycache__/__init__.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..eaad4ceb13f9866037c88fde23b2e0d8f76bc441 Binary files /dev/null and b/models/networks/__pycache__/__init__.cpython-310.pyc differ diff --git a/models/networks/__pycache__/architecture.cpython-310.pyc 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0000000000000000000000000000000000000000..6f94ad549d8c479af62e49a76097d95f76b70aac Binary files /dev/null and b/models/networks/__pycache__/stream.cpython-37.pyc differ diff --git a/models/networks/architecture.py b/models/networks/architecture.py new file mode 100644 index 0000000000000000000000000000000000000000..404de17e46e194c4d36fa0899dd834d73712b23f --- /dev/null +++ b/models/networks/architecture.py @@ -0,0 +1,180 @@ +import torch +import torch.nn as nn +import torch.nn.functional as F +import torchvision +import torch.nn.utils.spectral_norm as spectral_norm +from models.networks.normalization import FADE +from models.networks.sync_batchnorm import SynchronizedBatchNorm2d + + +# ResNet block that uses FADE. +# It differs from the ResNet block of SPADE in that +# it takes in the feature map as input, learns the skip connection if necessary. +# This architecture seemed like a standard architecture for unconditional or +# class-conditional GAN architecture using residual block. +# The code was inspired from https://github.com/LMescheder/GAN_stability +# and https://github.com/NVlabs/SPADE. +class FADEResnetBlock(nn.Module): + def __init__(self, fin, fout, opt): + super().__init__() + # attributes + self.learned_shortcut = (fin != fout) + fmiddle = fin + + # create conv layers + self.conv_0 = nn.Conv2d(fin, fmiddle, kernel_size=3, padding=1) + self.conv_1 = nn.Conv2d(fmiddle, fout, kernel_size=3, padding=1) + if self.learned_shortcut: + self.conv_s = nn.Conv2d(fin, fout, kernel_size=1, bias=False) + + # apply spectral norm if specified + if 'spectral' in opt.norm_G: + self.conv_0 = spectral_norm(self.conv_0) + self.conv_1 = spectral_norm(self.conv_1) + if self.learned_shortcut: + self.conv_s = spectral_norm(self.conv_s) + + # define normalization layers + fade_config_str = opt.norm_G.replace('spectral', '') + self.norm_0 = FADE(fade_config_str, fin, fin) + self.norm_1 = FADE(fade_config_str, fmiddle, fmiddle) + if self.learned_shortcut: + self.norm_s = FADE(fade_config_str, fin, fin) + + # Note the resnet block with FADE also takes in |feat|, + # the feature map as input + def forward(self, x, feat): + x_s = self.shortcut(x, feat) + + dx = self.conv_0(self.actvn(self.norm_0(x, feat))) + dx = self.conv_1(self.actvn(self.norm_1(dx, feat))) + + out = x_s + dx + + return out + + def shortcut(self, x, feat): + if self.learned_shortcut: + x_s = self.conv_s(self.norm_s(x, feat)) + else: + x_s = x + return x_s + + def actvn(self, x): + return F.leaky_relu(x, 2e-1) + + +class StreamResnetBlock(nn.Module): + def __init__(self, fin, fout, opt): + super().__init__() + # attributes + self.learned_shortcut = (fin != fout) + fmiddle = fin + + # create conv layers + self.conv_0 = nn.Conv2d(fin, fmiddle, kernel_size=3, padding=1) + self.conv_1 = nn.Conv2d(fmiddle, fout, kernel_size=3, padding=1) + if self.learned_shortcut: + self.conv_s = nn.Conv2d(fin, fout, kernel_size=1, bias=False) + + # apply spectral norm if specified + if 'spectral' in opt.norm_S: + self.conv_0 = spectral_norm(self.conv_0) + self.conv_1 = spectral_norm(self.conv_1) + if self.learned_shortcut: + self.conv_s = spectral_norm(self.conv_s) + + # define normalization layers + subnorm_type = opt.norm_S.replace('spectral', '') + if subnorm_type == 'batch': + self.norm_layer_in = nn.BatchNorm2d(fin, affine=True) + self.norm_layer_out= nn.BatchNorm2d(fout, affine=True) + if self.learned_shortcut: + self.norm_layer_s = nn.BatchNorm2d(fout, affine=True) + elif subnorm_type == 'syncbatch': + self.norm_layer_in = SynchronizedBatchNorm2d(fin, affine=True) + self.norm_layer_out= SynchronizedBatchNorm2d(fout, affine=True) + if self.learned_shortcut: + self.norm_layer_s = SynchronizedBatchNorm2d(fout, affine=True) + elif subnorm_type == 'instance': + self.norm_layer_in = nn.InstanceNorm2d(fin, affine=False) + self.norm_layer_out= nn.InstanceNorm2d(fout, affine=False) + if self.learned_shortcut: + self.norm_layer_s = nn.InstanceNorm2d(fout, affine=False) + else: + raise ValueError('normalization layer %s is not recognized' % subnorm_type) + + def forward(self, x): + x_s = self.shortcut(x) + + dx = self.actvn(self.norm_layer_in(self.conv_0(x))) + dx = self.actvn(self.norm_layer_out(self.conv_1(dx))) + + out = x_s + dx + + return out + + def shortcut(self,x): + if self.learned_shortcut: + x_s = self.actvn(self.norm_layer_s(self.conv_s(x))) + else: + x_s = x + return x_s + + def actvn(self, x): + return F.leaky_relu(x, 2e-1) + + +# ResNet block used in pix2pixHD +# We keep the same architecture as pix2pixHD. +class ResnetBlock(nn.Module): + def __init__(self, dim, norm_layer, activation=nn.ReLU(False), kernel_size=3): + super().__init__() + + pw = (kernel_size - 1) // 2 + self.conv_block = nn.Sequential( + nn.ReflectionPad2d(pw), + norm_layer(nn.Conv2d(dim, dim, kernel_size=kernel_size)), + activation, + nn.ReflectionPad2d(pw), + norm_layer(nn.Conv2d(dim, dim, kernel_size=kernel_size)) + ) + + def forward(self, x): + y = self.conv_block(x) + out = x + y + return out + + +# VGG architecture, used for the perceptual loss using a pretrained VGG network +class VGG19(torch.nn.Module): + def __init__(self, requires_grad=False): + super().__init__() + vgg_pretrained_features = torchvision.models.vgg19(pretrained=True).features + self.slice1 = torch.nn.Sequential() + self.slice2 = torch.nn.Sequential() + self.slice3 = torch.nn.Sequential() + self.slice4 = torch.nn.Sequential() + self.slice5 = torch.nn.Sequential() + for x in range(2): + self.slice1.add_module(str(x), vgg_pretrained_features[x]) + for x in range(2, 7): + self.slice2.add_module(str(x), vgg_pretrained_features[x]) + for x in range(7, 12): + self.slice3.add_module(str(x), vgg_pretrained_features[x]) + for x in range(12, 21): + self.slice4.add_module(str(x), vgg_pretrained_features[x]) + for x in range(21, 30): + self.slice5.add_module(str(x), vgg_pretrained_features[x]) + if not requires_grad: + for param in self.parameters(): + param.requires_grad = False + + def forward(self, X): + h_relu1 = self.slice1(X) + h_relu2 = self.slice2(h_relu1) + h_relu3 = self.slice3(h_relu2) + h_relu4 = self.slice4(h_relu3) + h_relu5 = self.slice5(h_relu4) + out = [h_relu1, h_relu2, h_relu3, h_relu4, h_relu5] + return out diff --git a/models/networks/base_network.py b/models/networks/base_network.py new file mode 100644 index 0000000000000000000000000000000000000000..70a90654ff799aae894c42f360990e26ff89a5d5 --- /dev/null +++ b/models/networks/base_network.py @@ -0,0 +1,55 @@ +import torch.nn as nn +from torch.nn import init +from torchinfo import summary + +class BaseNetwork(nn.Module): + def __init__(self): + super(BaseNetwork, self).__init__() + + @staticmethod + def modify_commandline_options(parser, is_train): + return parser + + def print_network(self): + if isinstance(self, list): + self = self[0] + num_params = 0 + for param in self.parameters(): + num_params += param.numel() + print('Network [%s] was created. Total number of parameters: %.1f million. ' + 'To see the architecture, do print(network).' + % (type(self).__name__, num_params / 1000000)) + print(self) + + def init_weights(self, init_type='normal', gain=0.02): + def init_func(m): + classname = m.__class__.__name__ + if classname.find('BatchNorm2d') != -1: + if hasattr(m, 'weight') and m.weight is not None: + init.normal_(m.weight.data, 1.0, gain) + if hasattr(m, 'bias') and m.bias is not None: + init.constant_(m.bias.data, 0.0) + elif hasattr(m, 'weight') and (classname.find('Conv') != -1 or classname.find('Linear') != -1): + if init_type == 'normal': + init.normal_(m.weight.data, 0.0, gain) + elif init_type == 'xavier': + init.xavier_normal_(m.weight.data, gain=gain) + elif init_type == 'xavier_uniform': + init.xavier_uniform_(m.weight.data, gain=1.0) + elif init_type == 'kaiming': + init.kaiming_normal_(m.weight.data, a=0, mode='fan_in') + elif init_type == 'orthogonal': + init.orthogonal_(m.weight.data, gain=gain) + elif init_type == 'none': # uses pytorch's default init method + m.reset_parameters() + else: + raise NotImplementedError('initialization method [%s] is not implemented' % init_type) + if hasattr(m, 'bias') and m.bias is not None: + init.constant_(m.bias.data, 0.0) + + self.apply(init_func) + + # propagate to children + for m in self.children(): + if hasattr(m, 'init_weights'): + m.init_weights(init_type, gain) diff --git a/models/networks/discriminator.py b/models/networks/discriminator.py new file mode 100644 index 0000000000000000000000000000000000000000..1a808c2af931d7c469e403ed2921cccb06020dcc --- /dev/null +++ b/models/networks/discriminator.py @@ -0,0 +1,119 @@ +import torch.nn as nn +import numpy as np +import torch.nn.functional as F +from models.networks.base_network import BaseNetwork +from models.networks.normalization import get_norm_layer +import util.util as util + + +class MultiscaleDiscriminator(BaseNetwork): + @staticmethod + def modify_commandline_options(parser, is_train): + parser.add_argument('--netD_subarch', type=str, default='n_layer', + help='architecture of each discriminator') + parser.add_argument('--num_D', type=int, default=2, + help='number of discriminators to be used in multiscale') + opt, _ = parser.parse_known_args() + + # define properties of each discriminator of the multiscale discriminator + subnetD = util.find_class_in_module(opt.netD_subarch + 'discriminator', + 'models.networks.discriminator') + subnetD.modify_commandline_options(parser, is_train) + + return parser + + def __init__(self, opt): + super().__init__() + self.opt = opt + + for i in range(opt.num_D): + subnetD = self.create_single_discriminator(opt) + self.add_module('discriminator_%d' % i, subnetD) + + def create_single_discriminator(self, opt): + subarch = opt.netD_subarch + if subarch == 'n_layer': + netD = NLayerDiscriminator(opt) + else: + raise ValueError('unrecognized discriminator subarchitecture %s' % subarch) + return netD + + def downsample(self, input): + return F.avg_pool2d(input, kernel_size=3, + stride=2, padding=[1, 1], + count_include_pad=False) + + # Returns list of lists of discriminator outputs. + # The final result is of size opt.num_D x opt.n_layers_D + def forward(self, input): + result = [] + get_intermediate_features = not self.opt.no_ganFeat_loss + for name, D in self.named_children(): + out = D(input) + if not get_intermediate_features: + out = [out] + result.append(out) + input = self.downsample(input) + + return result + + +# Defines the PatchGAN discriminator with the specified arguments. +class NLayerDiscriminator(BaseNetwork): + @staticmethod + def modify_commandline_options(parser, is_train): + parser.add_argument('--n_layers_D', type=int, default=4, + help='# layers in each discriminator') + return parser + + def __init__(self, opt): + super().__init__() + if opt.task != 'SIS': + opt.n_layers_D = 3 + self.opt = opt + + kw = 4 + padw = int(np.ceil((kw - 1.0) / 2)) + nf = opt.ndf + input_nc = self.compute_D_input_nc(opt) + + norm_layer = get_norm_layer(opt, opt.norm_D) + sequence = [[nn.Conv2d(input_nc, nf, kernel_size=kw, stride=2, padding=padw), + nn.LeakyReLU(0.2, False)]] + + for n in range(1, opt.n_layers_D): + nf_prev = nf + nf = min(nf * 2, 512) + stride = 1 if n == 3 else 2 + sequence += [[norm_layer(nn.Conv2d(nf_prev, nf, kernel_size=kw, + stride=stride, padding=padw)), + nn.LeakyReLU(0.2, False) + ]] + + sequence += [[nn.Conv2d(nf, 1, kernel_size=kw, stride=1, padding=padw)]] + + # We divide the layers into groups to extract intermediate layer outputs + for n in range(len(sequence)): + self.add_module('model' + str(n), nn.Sequential(*sequence[n])) + + def compute_D_input_nc(self, opt): + input_nc = opt.output_nc + if opt.task == 'SIS': + input_nc += opt.label_nc + if opt.contain_dontcare_label: + input_nc += 1 + if not opt.no_instance: + input_nc += 1 + return input_nc + + def forward(self, input): + results = [input] + for submodel in self.children(): + intermediate_output = submodel(results[-1]) + results.append(intermediate_output) + + get_intermediate_features = not self.opt.no_ganFeat_loss + if get_intermediate_features: + return results[1:] + else: + return results[-1] diff --git a/models/networks/encoder.py b/models/networks/encoder.py new file mode 100644 index 0000000000000000000000000000000000000000..ade9c060f14df5baf4573978b67401d0843da1eb --- /dev/null +++ b/models/networks/encoder.py @@ -0,0 +1,49 @@ +import torch.nn as nn +import numpy as np +import torch.nn.functional as F +from models.networks.base_network import BaseNetwork +from models.networks.normalization import get_norm_layer + + +class ConvEncoder(BaseNetwork): + """ Same architecture as the image discriminator """ + + def __init__(self, opt): + super().__init__() + self.opt = opt + kw = 3 + pw = int(np.ceil((kw - 1.0) / 2)) + ndf = opt.ngf + norm_layer = get_norm_layer(opt, opt.norm_E) + self.layer1 = norm_layer(nn.Conv2d(3, ndf, kw, stride=2, padding=pw)) + self.layer2 = norm_layer(nn.Conv2d(ndf * 1, ndf * 2, kw, stride=2, padding=pw)) + self.layer3 = norm_layer(nn.Conv2d(ndf * 2, ndf * 4, kw, stride=2, padding=pw)) + self.layer4 = norm_layer(nn.Conv2d(ndf * 4, ndf * 8, kw, stride=2, padding=pw)) + self.layer5 = norm_layer(nn.Conv2d(ndf * 8, ndf * 8, kw, stride=2, padding=pw)) + if opt.crop_size >= 256: + self.layer6 = norm_layer(nn.Conv2d(ndf * 8, ndf * 8, kw, stride=2, padding=pw)) + + self.so = s0 = 4 + self.fc_mu = nn.Linear(ndf * 8 * s0 * s0, 256) + self.fc_var = nn.Linear(ndf * 8 * s0 * s0, 256) + + self.actvn = nn.LeakyReLU(0.2, False) + + def forward(self, x): + if x.size(2) != 256 or x.size(3) != 256: + x = F.interpolate(x, size=(256, 256), mode='bilinear') + + x = self.layer1(x) + x = self.layer2(self.actvn(x)) + x = self.layer3(self.actvn(x)) + x = self.layer4(self.actvn(x)) + x = self.layer5(self.actvn(x)) + if self.opt.crop_size >= 256: + x = self.layer6(self.actvn(x)) + x = self.actvn(x) + + x = x.view(x.size(0), -1) + mu = self.fc_mu(x) + logvar = self.fc_var(x) + + return mu, logvar diff --git a/models/networks/experiments/vgg_normalised.pth b/models/networks/experiments/vgg_normalised.pth new file mode 100644 index 0000000000000000000000000000000000000000..702e1f7bd706fef379525ed2402bdfcdaa0416b9 --- /dev/null +++ b/models/networks/experiments/vgg_normalised.pth @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:804ca2835ecf7539f0cd2a7ac3c18ce81e6f8468969ae7117ac0c148d286bb4a +size 80102481 diff --git a/models/networks/generator.py b/models/networks/generator.py new file mode 100644 index 0000000000000000000000000000000000000000..264e10dd962dab29bd2e605b07de7f2250c66310 --- /dev/null +++ b/models/networks/generator.py @@ -0,0 +1,59 @@ +import torch.nn.functional as F +from models.networks.base_network import BaseNetwork +import torch +import torch.nn as nn +from .generator_module.StyTR import StyTr +from torchvision.utils import save_image + +from .generator_module.transformerEncoder import TransformerEncoder +from .generator_module.HEtransformerEncoder import HeTransformerEncoder +from .generator_module.schedule import CosineAnnealingWarmUpLR +from .generator_module.DecoderCNN import Decoder_MV, vgg_structures,decoder_stem # DecoderCNN +from .generator_module.transformer_decoder import TransformerDecoder + +class TSITGenerator(BaseNetwork): + @staticmethod + def modify_commandline_options(parser, is_train): + parser.set_defaults(norm_G='spectralfadesyncbatch3x3') + parser.add_argument('--num_upsampling_layers', + choices=('normal', 'more', 'most'), default='more', + help="If 'more', adds upsampling layer between the two middle resnet blocks." + "If 'most', also add one more upsampling + resnet layer at the end of the generator." + "We only use 'more' as the default setting.") + parser.add_argument('--lr_decay', type=float, default=1e-4, help='learning rate decay') + parser.add_argument('--lr_stytr2', type=float, default=5e-4, help='initial learning rate for adam') + return parser + + def __init__(self, opt): + super().__init__() + self.vgg_path = r'models/networks/experiments/vgg_normalised.pth' + self.CNNdecoder = Decoder_MV(d_model=768,seq_input=True) + self.HeTransEncoder=HeTransformerEncoder(img_size=224,patch_size=2,in_chans=3,embed_dim=192,depths=[2, 2, 2],nhead=[3, 6, 12],strip_width=[2, 4, 7],drop_path_rate=0.,patch_norm=True) + self.TransEncoder= TransformerEncoder(img_size=224,patch_size=2,in_chans=3,embed_dim=192,depths=[2, 2, 2],nhead=[3, 6, 12],strip_width=[2, 4, 7],drop_path_rate=0.,patch_norm=True) + self.TransDecoder = TransformerDecoder(nlayer=3,d_model=768,nhead=8,mlp_ratio=4,qkv_bias=False,attn_drop=0.,drop=0.,drop_path=0.,act_layer=nn.GELU,norm_layer=nn.LayerNorm,norm_first=True) + self.vgg=vgg_structures + + self.content_weight= opt.content_weight + self.style_weight = opt.style_weight + self.lr_decay = opt.lr_decay + self.lr_stytr2 = opt.lr_stytr2 + self.device="cuda" if torch.cuda.is_available() else "cpu" + self.iters_count=0 + self.save_dir= opt.save_dir + self.opt=opt + self.mode=opt.mode + + def SetUp_model(self, TransEncoder,HeTransEncoder ,CNN_decoder,TransDecoder,vgg,alpha,mode): + vgg.load_state_dict(torch.load(self.vgg_path)) + # vgg = nn.Sequential(*list(vgg.children())[:44]) + + + network=StyTr(TransEncoder,HeTransEncoder,CNN_decoder,TransDecoder,vgg,alpha,mode) + network.train() + network.to(self.device) + return network + + def forward(self): + network = self.SetUp_model(self.TransEncoder,self.HeTransEncoder,self.CNNdecoder, self.TransDecoder, self.vgg,self.opt.alpha,self.mode) + return network + diff --git a/models/networks/generator_module/DecoderCNN.py b/models/networks/generator_module/DecoderCNN.py new file mode 100644 index 0000000000000000000000000000000000000000..ab23358d1145673d7c07b2ad9813c2bea491df18 --- /dev/null +++ b/models/networks/generator_module/DecoderCNN.py @@ -0,0 +1,143 @@ +import torch.nn as nn +class Decoder_MV(nn.Module): + def __init__(self, d_model=768, seq_input=False): + super(Decoder_MV, self).__init__() + self.d_model = d_model + self.seq_input = seq_input + self.decoder = nn.Sequential( + # Proccess Layer 1 + + # Upsample Layer 2 + nn.ReflectionPad2d(1), + nn.Conv2d(int(self.d_model), 256, 3, 1, 0), + nn.ReLU(), + nn.Upsample(scale_factor=2, mode='nearest'), + nn.ReflectionPad2d(1), + nn.Conv2d(256, 256, 3, 1, 0), + nn.ReLU(), + nn.ReflectionPad2d(1), + nn.Conv2d(256, 256, 3, 1, 0), + nn.ReLU(), + nn.ReflectionPad2d(1), + nn.Conv2d(256, 256, 3, 1, 0), + nn.ReLU(), + + # Upsample Layer 3 + nn.ReflectionPad2d(1), + nn.Conv2d(256, 128, 3, 1, 0), + nn.ReLU(), + nn.Upsample(scale_factor=2, mode='nearest'), + nn.ReflectionPad2d(1), + nn.Conv2d(128, 128, 3, 1, 0), + nn.ReLU(), + + # Upsample Layer 4 + nn.ReflectionPad2d(1), + nn.Conv2d(128, 64, 3, 1, 0), + nn.ReLU(), + nn.Upsample(scale_factor=2, mode='nearest'), + nn.ReflectionPad2d(1), + nn.Conv2d(64, 64, 3, 1, 0), + nn.ReLU(), + + # Channel to 3 + nn.ReflectionPad2d(1), + nn.Conv2d(64, 3, 3, 1, 0), + ) + + def forward(self, x, input_resolution): + if self.seq_input == True: + B, N, C = x.size() + # H, W = math.ceil(self.img_H//self.patch_size), math.ceil(self.img_W//self.patch_size) + (H, W) = input_resolution + x = x.permute(0, 2, 1).reshape(B, C, H, W) + x = self.decoder(x) + return x + +vgg_structures = nn.Sequential( + nn.Conv2d(3, 3, (1, 1)), + nn.ReflectionPad2d((1, 1, 1, 1)), + nn.Conv2d(3, 64, (3, 3)), + nn.ReLU(), # relu1-1 + nn.ReflectionPad2d((1, 1, 1, 1)), + nn.Conv2d(64, 64, (3, 3)), + nn.ReLU(), # relu1-2 + nn.MaxPool2d((2, 2), (2, 2), (0, 0), ceil_mode=True), + nn.ReflectionPad2d((1, 1, 1, 1)), + nn.Conv2d(64, 128, (3, 3)), + nn.ReLU(), # relu2-1 + nn.ReflectionPad2d((1, 1, 1, 1)), + nn.Conv2d(128, 128, (3, 3)), + nn.ReLU(), # relu2-2 + nn.MaxPool2d((2, 2), (2, 2), (0, 0), ceil_mode=True), + nn.ReflectionPad2d((1, 1, 1, 1)), + nn.Conv2d(128, 256, (3, 3)), + nn.ReLU(), # relu3-1 + nn.ReflectionPad2d((1, 1, 1, 1)), + nn.Conv2d(256, 256, (3, 3)), + nn.ReLU(), # relu3-2 + nn.ReflectionPad2d((1, 1, 1, 1)), + nn.Conv2d(256, 256, (3, 3)), + nn.ReLU(), # relu3-3 + nn.ReflectionPad2d((1, 1, 1, 1)), + nn.Conv2d(256, 256, (3, 3)), + nn.ReLU(), # relu3-4 + nn.MaxPool2d((2, 2), (2, 2), (0, 0), ceil_mode=True), + nn.ReflectionPad2d((1, 1, 1, 1)), + nn.Conv2d(256, 512, (3, 3)), + nn.ReLU(), # relu4-1, this is the last layer used + nn.ReflectionPad2d((1, 1, 1, 1)), + nn.Conv2d(512, 512, (3, 3)), + nn.ReLU(), # relu4-2 + nn.ReflectionPad2d((1, 1, 1, 1)), + nn.Conv2d(512, 512, (3, 3)), + nn.ReLU(), # relu4-3 + nn.ReflectionPad2d((1, 1, 1, 1)), + nn.Conv2d(512, 512, (3, 3)), + nn.ReLU(), # relu4-4 + nn.MaxPool2d((2, 2), (2, 2), (0, 0), ceil_mode=True), + nn.ReflectionPad2d((1, 1, 1, 1)), + nn.Conv2d(512, 512, (3, 3)), + nn.ReLU(), # relu5-1 + nn.ReflectionPad2d((1, 1, 1, 1)), + nn.Conv2d(512, 512, (3, 3)), + nn.ReLU(), # relu5-2 + nn.ReflectionPad2d((1, 1, 1, 1)), + nn.Conv2d(512, 512, (3, 3)), + nn.ReLU(), # relu5-3 + nn.ReflectionPad2d((1, 1, 1, 1)), + nn.Conv2d(512, 512, (3, 3)), + nn.ReLU() # relu5-4 +) + +decoder_stem = nn.Sequential( + nn.ReflectionPad2d((1, 1, 1, 1)), + nn.Conv2d(512, 256, (3, 3)), + nn.ReLU(), + nn.Upsample(scale_factor=2, mode='nearest'), + nn.ReflectionPad2d((1, 1, 1, 1)), + nn.Conv2d(256, 256, (3, 3)), + nn.ReLU(), + nn.ReflectionPad2d((1, 1, 1, 1)), + nn.Conv2d(256, 256, (3, 3)), + nn.ReLU(), + nn.ReflectionPad2d((1, 1, 1, 1)), + nn.Conv2d(256, 256, (3, 3)), + nn.ReLU(), + nn.ReflectionPad2d((1, 1, 1, 1)), + nn.Conv2d(256, 128, (3, 3)), + nn.ReLU(), + nn.Upsample(scale_factor=2, mode='nearest'), + nn.ReflectionPad2d((1, 1, 1, 1)), + nn.Conv2d(128, 128, (3, 3)), + nn.ReLU(), + nn.ReflectionPad2d((1, 1, 1, 1)), + nn.Conv2d(128, 64, (3, 3)), + nn.ReLU(), + nn.Upsample(scale_factor=2, mode='nearest'), + nn.ReflectionPad2d((1, 1, 1, 1)), + nn.Conv2d(64, 64, (3, 3)), + nn.ReLU(), + nn.ReflectionPad2d((1, 1, 1, 1)), + nn.Conv2d(64, 3, (3, 3)), +) \ No newline at end of file diff --git a/models/networks/generator_module/HEtransformerEncoder.py b/models/networks/generator_module/HEtransformerEncoder.py new file mode 100644 index 0000000000000000000000000000000000000000..79b53055dcbded6f1a81f49d718b360c07f69fb4 --- /dev/null +++ b/models/networks/generator_module/HEtransformerEncoder.py @@ -0,0 +1,795 @@ +import torch +import torch.nn as nn +import torch.nn.functional as F +import torch.utils.checkpoint as checkpoint + +from .ViT_helper import to_2tuple, to_ntuple,DropPath + +class Mlp(nn.Module): + """MLP as implemented in timm + """ + + def __init__(self, in_features, hidden_features=None, out_features=None, act_layer=nn.GELU, drop=0.): + super().__init__() + out_features = out_features or in_features + hidden_features = hidden_features or in_features + drops = to_2tuple(drop) + + self.fc1 = nn.Linear(in_features, hidden_features) + self.act = act_layer() + self.drop1 = nn.Dropout(drops[0]) + self.fc2 = nn.Linear(hidden_features, out_features) + self.drop2 = nn.Dropout(drops[1]) + + def forward(self, x): + x = self.fc1(x) + x = self.act(x) + x = self.drop1(x) + x = self.fc2(x) + x = self.drop2(x) + return x + + +class Attention(nn.Module): + """Self Attention as implemented in timm + """ + + def __init__(self, d_model, nhead=8, qkv_bias=False, attn_drop=0., proj_drop=0.): + super().__init__() + assert d_model % nhead == 0, 'd_model needs to be divisible by nhead' + self.nhead = nhead + self.scale = (d_model // nhead) ** -0.5 + + self.to_qkv = nn.Linear(d_model, d_model * 3, bias=qkv_bias) + self.attn_drop = nn.Dropout(attn_drop) + self.proj = nn.Linear(d_model, d_model) + self.proj_drop = nn.Dropout(proj_drop) + + def forward(self, x): + B, N, C = x.size() + qkv = self.to_qkv(x).reshape(B, N, 3, self.nhead, C // self.nhead).permute(2, 0, 3, 1, 4) + q, k, v = qkv.unbind(0) + + attn = (q @ k.transpose(-1, -2)) * self.scale + attn = attn.softmax(dim=-1) + attn = self.attn_drop(attn) + + x = (attn @ v).transpose(1, 2).reshape(B, N, C) + x = self.proj(x) + x = self.proj_drop(x) + + return x + + +class Attention_Cross(nn.Module): + """Attention for decoder layer.Some palce may be called "inter attention" + """ + + def __init__(self, d_model, nhead=8, qkv_bias=False, attn_drop=0., proj_drop=0.): + super().__init__() + assert d_model % nhead == 0, 'd_model needs to be divisible by nhead' + self.nhead = nhead + self.scale = (d_model // nhead) ** -0.5 + + self.to_q = nn.Linear(d_model, d_model, bias=qkv_bias) + self.to_kv = nn.Linear(d_model, d_model * 2, bias=qkv_bias) + self.attn_drop = nn.Dropout(attn_drop) + self.proj = nn.Linear(d_model, d_model) + self.proj_drop = nn.Dropout(proj_drop) + + def forward(self, x, y): + """ + Args: + x: output of the former layer + y: memery of the encoder layer + """ + B, Nx, C = x.size() + _, Ny, _ = y.size() + q = self.to_q(x).reshape(B, Nx, self.nhead, C // self.nhead).permute(0, 2, 1, 3) + kv = self.to_kv(y).reshape(B, Ny, 2, self.nhead, C // self.nhead).permute(2, 0, 3, 1, 4) + k, v = kv.unbind(0) + + attn = (q @ k.transpose(-1, -2)) * self.scale + attn = attn.softmax(dim=-1) + attn = self.attn_drop(attn) + + x = (attn @ v).transpose(1, 2).reshape(B, Nx, C) + x = self.proj(x) + x = self.proj_drop(x) + + return x + +def split_int(num): + """Split an integer into 2 integers evenly + Args: + num (int): The input integer + Returns: + num_1 (int) + num_2 (int) + """ + if num % 2 == 0: + num_1 = num_2 = num // 2 + else: + num_1 = num // 2 + num_2 = num_1 + 1 + return num_1, num_2 + + +def unpad2D(input, pad): + """Crop the input tensor according to pad.(Inverse operation for padding) + Args: + input (Tensor): (B, C, H, W) + pad (Tuple of int): (left, right, top, bottom) + Returns: + output (Tensor): (B, C, new_H, new_W) + """ + pad_W_left, pad_W_right, pad_H_top, pad_H_bottom = pad + if pad_H_top == 0 and pad_H_bottom == 0 and not (pad_W_left == 0 and pad_W_right == 0): + output = input[:, :, :, pad_W_left:-pad_W_right] + elif pad_W_left == 0 and pad_W_right == 0 and not (pad_H_top == 0 and pad_H_bottom == 0): + output = input[:, :, pad_H_top:-pad_H_bottom, :] + elif pad_H_top == 0 and pad_H_bottom == 0 and pad_W_left == 0 and pad_W_right == 0: + output = input + else: + output = input[:, :, pad_H_top:-pad_H_bottom, pad_W_left:-pad_W_right] + return output + + +def seq_padding(x, dividable_size, input_resolution, pad_mode='constant'): + """Padding for sequential data + Args: + x (Tensor): (B, L, C) + dividable_size (Tuple | int): dividable size + input_resolution (Tuple): resolution of x + Returns: + x (Tensor): (B, new_L, C) + output_resolution (Tuple): new resolution of x + pad (Tuple of int): (left, right, top, bottom) + """ + H, W = input_resolution + B, L, C = x.shape + assert L == H * W, 'Input of wrong size.' + dividable_size = to_2tuple(dividable_size) + x = x.permute(0, 2, 1).reshape(B, C, H, W) + + rema_H, rema_W = H % dividable_size[0], W % dividable_size[1] + pad_H, pad_W = dividable_size[0] - rema_H, dividable_size[1] - rema_W + + pad_H_top, pad_H_bottom = split_int(pad_H) if rema_H != 0 else (0, 0) + pad_W_left, pad_W_right = split_int(pad_W) if rema_W != 0 else (0, 0) + + x = F.pad(x, (pad_W_left, pad_W_right, pad_H_top, pad_H_bottom), pad_mode, 0) + + padded_H, padded_W = x.shape[-2:] + x = x.reshape(B, C, -1).permute(0, 2, 1) + return x, (padded_H, padded_W), (pad_W_left, pad_W_right, pad_H_top, pad_H_bottom) + + +# Example +# x = torch.randn(1, 14, 768) +# y = seq_padding(x, dividable_size=7, input_resolution=(2, 7)) +# print(y[0].shape, y[1], y[2]) + + +def seq_unpad(x, input_resolution, pad): + """Unpadding for sequential data + Args: + x (Tensor): (B, L, C) + input_resolution (Tuple): resolution of x + pad (Tuple of int): (left, right, top, bottom) + Returns: + x (Tensor): (B, new_L, C) + output_resolution (Tuple): new resolution of x + """ + padded_H, padded_W = input_resolution + B, L, C = x.shape + assert L == padded_H * padded_W, 'Input of wrong size.' + x = x.permute(0, 2, 1).reshape(B, C, padded_H, padded_W) + + x = unpad2D(x, pad=pad) + + H, W = x.shape[-2:] + x = x.reshape(B, C, -1).permute(0, 2, 1) + return x, (H, W) +def window_partition(x, window_size): + """Slightly modified for arbitrary window_size & resolution combination + Args: + x: (B,H,W,C) + window_size (tuple[int] | int): window size + Returns: + windows: (num_windows*B, window_size, window_size, C) + """ + window_size = to_2tuple(window_size) + B, H, W, C = x.shape + n_win_H = H // window_size[0] + n_win_W = W // window_size[1] + if not (H % window_size[0] == 0 and W % window_size[1] == 0): + x = x[:, :n_win_H * window_size[0], :n_win_W * window_size[1], :] + x = x.view(B, n_win_H, window_size[0], n_win_W, window_size[1], C) + windows = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(-1, window_size[0], window_size[1], C) + return windows + + +def window_reverse(windows, window_size, H, W): + """Slightly modified for arbitrary window_size & resolution combination + Args: + windows: (num_windows*B, window_size, window_size, C) + window_size (tuple[int] | int): Window size + H (int): Height of image + W (int): Width of image + Returns: + x: (B, H, W, C) + """ + window_size = to_2tuple(window_size) + n_win_H = H // window_size[0] + n_win_W = W // window_size[1] + B = windows.shape[0] // (n_win_H * n_win_W) + x = windows.view(B, n_win_H, n_win_W, window_size[0], window_size[1], -1) + x = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(B, n_win_H * window_size[0], n_win_W * window_size[1], -1) + return x + + +# Example +# H, W = 6, 6 +# window_size = 2 +# x = torch.randn(1, H, W, 3) +# win = window_partition(x, window_size=window_size) +# y = window_reverse(win, window_size=window_size, H=H, W=W) +# print(x.shape) +# print(win.shape) +# print(y.shape) +# print(x[0].permute(2,0,1)[0]) +# print(y[0].permute(2,0,1)[0]) + + +def seq_crop(x, dividable_size, input_resolution): + """ + Arg: + x (Tensor): (B, L, C) + dividable_size (Tuple | int): dividable size + input_resolution (Tuple): resolution of x + Returns: + x (Tensor): (B, new_L, C) + output_resolution (Tuple): new resolution of x + """ + H, W = input_resolution + B, L, C = x.shape + assert L == H * W, 'Input of wrong size.' + dividable_size = to_2tuple(dividable_size) + x = x.reshape(B, H, W, C) + + rema_H, rema_W = H % dividable_size[0], W % dividable_size[1] + new_H, new_W = H - rema_H, W - rema_W + if rema_H != 0 or rema_W != 0: + x = x[:, :new_H, :new_W, :] + + x = x.reshape(B, -1, C) + return x, (new_H, new_W) + + +# Example +# H, W = 22, 34 +# x = torch.randn(2, H*W, 96) +# x, new_size = seq_crop(x, dividable_size=7, input_resolution=(H, W)) +# print(x.shape, new_size) + + +class PatchEmbed_Kai(nn.Module): + """ 2D Image to Patch Embedding + """ + + def __init__(self, patch_size=4, in_chans=3, embed_dim=96, norm_layer=None, flatten=True): + super().__init__() + patch_size = to_2tuple(patch_size) + self.in_chans = in_chans + self.flatten = flatten + + self.proj = nn.Conv2d(self.in_chans, embed_dim, kernel_size=patch_size, stride=patch_size) + self.norm = norm_layer(embed_dim) if norm_layer else nn.Identity() + + def forward(self, x): + B, C, H, W = x.shape + assert C == self.in_chans, 'Input image need to have same numbers of channels with the initialed.' + x = self.proj(x) + H, W = x.shape[2], x.shape[3] + if self.flatten: + x = x.flatten(2).transpose(1, 2) # BCHW -> BNC + x = self.norm(x) + return x, (H, W) + + +# Example +# model = PatchEmbed_Kai(patch_size=4, in_chans=3, embed_dim=96) +# x = torch.randn(1, 3, 224, 224) +# y = model(x) +# torch.save(model.state_dict(), "./PatchEmbed.pkl") +# print(y[0].shape, y[1]) + + +class PatchMerging_Kai(nn.Module): + """ Patch Merging Layer. + Args: + input_resolution (tuple[int] | int): Resolution of input feature. + d_model (int): Number of input channels. + norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm + """ + + def __init__(self, input_resolution, d_model, norm_layer=nn.LayerNorm): + super().__init__() + self.input_resolution = to_2tuple(input_resolution) + self.d_model = d_model + self.reduction = nn.Linear(4 * d_model, 2 * d_model, bias=False) + self.norm = norm_layer(4 * d_model) + + def forward(self, x): + """ + Args: + x (Tuple): (Tensor, arbitrary_input, (H,W)), arbitrary_input (bool) + if arbitrary_input=False, (H,W) will not be required + B, H*W, C -> B, H/2*W/2, 4*C + """ + arbitrary_input = x[1] + if arbitrary_input: + H, W = x[2] + else: + H, W = self.input_resolution + + x = x[0] + B, L, C = x.shape + assert L == H * W, "input feature has wrong size" + + x = x.view(B, H, W, C) + if H % 2 != 0: + x = x[:, 0:-1, :, :] + if W % 2 != 0: + x = x[:, :, 0:-1, :] + + x0 = x[:, 0::2, 0::2, :] # B H/2 W/2 C + x1 = x[:, 1::2, 0::2, :] # B H/2 W/2 C + x2 = x[:, 0::2, 1::2, :] # B H/2 W/2 C + x3 = x[:, 1::2, 1::2, :] # B H/2 W/2 C + x = torch.cat([x0, x1, x2, x3], -1) # B H/2 W/2 4*C + H, W = x.shape[1], x.shape[2] + x = x.view(B, -1, 4 * C) # B H/2*W/2 4*C + + x = self.norm(x) + x = self.reduction(x) + + return x, arbitrary_input, (H, W) + + +# Example +# model = PatchMerging_Kai(input_resolution=(5,4), d_model=3) +# arbitrary_input = True +# # x = torch.randn(1, 20, 3) +# # y = model((x, arbitrary_input)) +# x = torch.randn(1, 45, 3) +# y = model((x, arbitrary_input, (5,9))) +# print(y[0].shape, y[2]) + + +class WindowAttention_Kai(nn.Module): + def __init__(self, d_model, window_size, nhead, qkv_bias=True, attn_drop=0., proj_drop=0.): + super().__init__() + assert d_model % nhead == 0, 'd_model needs to be divisible by nhead' + self.window_size = to_2tuple(window_size) + self.nhead = nhead + self.scale = (d_model // nhead) ** -0.5 + + # Relative Position Bias's parameter Table + self.relative_position_bias_table = nn.Parameter( + torch.zeros((2 * self.window_size[0] - 1) * (2 * self.window_size[1] - 1), nhead) + ) + + # Compute indice of relative_position_bias_table for attention matrix + coords_h = torch.arange(self.window_size[0]) + coords_w = torch.arange(self.window_size[1]) + coords = torch.stack(torch.meshgrid([coords_h, coords_w])) + coords_flatten = torch.flatten(coords, 1) + relative_coords = coords_flatten[:, :, None] - coords_flatten[:, None, :] + relative_coords = relative_coords.permute(1, 2, 0).contiguous() + relative_coords[:, :, 0] += self.window_size[0] - 1 + relative_coords[:, :, 1] += self.window_size[1] - 1 + relative_coords[:, :, 0] *= 2 * self.window_size[1] - 1 + relative_position_index = relative_coords.sum(-1) + self.register_buffer("relative_position_index", relative_position_index) + + self.qkv = nn.Linear(d_model, d_model * 3, bias=qkv_bias) + self.attn_drop = nn.Dropout(attn_drop) + self.proj = nn.Linear(d_model, d_model) + self.proj_drop = nn.Dropout(proj_drop) + + nn.init.trunc_normal_(self.relative_position_bias_table, std=.02) + + def forward(self, x, shape): + H, W = shape + Bi, Ni, Ci = x.size() + assert Ni == H * W, "Inputs with wrong size." + x = x.reshape(Bi, H, W, Ci) + + # print(self.window_size) + x = window_partition(x, self.window_size) + x = x.reshape(-1, self.window_size[0] * self.window_size[1], Ci) + + B_, N, C = x.shape + qkv = self.qkv(x).reshape(B_, N, 3, self.nhead, C // self.nhead).permute(2, 0, 3, 1, 4) + q, k, v = qkv.unbind(0) + + attn = (q @ k.transpose(-2, -1)) * self.scale + + relative_position_bias = self.relative_position_bias_table[self.relative_position_index.view(-1)].view( + self.window_size[0] * self.window_size[1], self.window_size[0] * self.window_size[1], self.nhead) + relative_position_bias = relative_position_bias.permute(2, 0, 1).contiguous() + attn = attn + relative_position_bias.unsqueeze(0) + + attn = attn.softmax(dim=-1) + attn = self.attn_drop(attn) + + x = (attn @ v).transpose(1, 2).reshape(B_, N, C) + x = self.proj(x) + x = self.proj_drop(x) + + x = window_reverse(x, self.window_size, H, W) + x = x.reshape(Bi, Ni, Ci) + return x + + # Example + + +# model = WindowAttention_Kai( +# d_model=768, +# window_size=7, +# nhead=8 +# ) +# x = torch.randn(1, 784, 768) +# y = model(x, shape=(28, 28)) +# print(y.shape) + + +class StripAttention(nn.Module): + def __init__(self, d_model, nhead=8, strip_width=7, is_vertical=False, qkv_bias=False, attn_drop=0., proj_drop=0.): + super().__init__() + self.d_model = d_model + self.strip_width = strip_width + self.is_vertical = is_vertical + + self.attn = Attention( + d_model=d_model, + nhead=nhead, + qkv_bias=qkv_bias, + attn_drop=attn_drop, + proj_drop=proj_drop, + ) + + def forward(self, x, shape): + H, W = shape + B, N, C = x.size() + assert N == H * W, "Inputs with wrong size." + x = x.reshape(B, H, W, C) + + # print(self.strip_width) + if self.is_vertical: + x = window_partition(x, (H, self.strip_width)) + x = x.reshape(-1, H * self.strip_width, C) + else: + x = window_partition(x, (self.strip_width, W)) + x = x.reshape(-1, W * self.strip_width, C) + + wins = self.attn(x) + + if self.is_vertical: + x = window_reverse(wins, (H, self.strip_width), H, W) + else: + x = window_reverse(wins, (self.strip_width, W), H, W) + + x = x.reshape(B, N, C) + return x + + +# Example +# model = StripAttention( +# d_model=768, +# nhead=8, +# strip_width=7, +# is_vertical=False +# ) +# x = torch.randn(1, 784, 768) +# y = model(x, (28, 28)) +# print(y.shape) + + +class StripAttentionBlock(nn.Module): + def __init__(self, d_model, input_resolution, nhead=8, strip_width=7, + mlp_ratio=4, qkv_bias=False, drop=0., attn_drop=0., drop_path=0., + act_layer=nn.GELU, norm_layer=nn.LayerNorm): + super().__init__() + self.d_model = d_model + self.input_resolution = to_2tuple(input_resolution) + self.strip_width = strip_width + + self.norm1 = norm_layer(d_model) + + self.attn1 = StripAttention( + d_model=d_model, + nhead=nhead, + strip_width=strip_width, + is_vertical=False, + qkv_bias=qkv_bias, + attn_drop=attn_drop, + proj_drop=drop + ) + self.attn2 = StripAttention( + d_model=d_model, + nhead=nhead, + strip_width=strip_width, + is_vertical=True, + qkv_bias=qkv_bias, + attn_drop=attn_drop, + proj_drop=drop + ) + self.attn3 = WindowAttention_Kai( + d_model=d_model, + window_size=(strip_width * 2, strip_width * 2), + nhead=nhead, + qkv_bias=qkv_bias, + attn_drop=attn_drop, + proj_drop=drop + ) + + self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity() + self.norm2 = norm_layer(d_model) + mlp_hidden_dim = int(d_model * mlp_ratio) + self.mlp = Mlp(d_model, hidden_features=mlp_hidden_dim, out_features=d_model, act_layer=act_layer, drop=drop) + + def forward(self, x): + arbitrary_input = x[1] + if arbitrary_input: + H, W = x[2] + # x, (H, W) = seq_crop(x[0], dividable_size=self.strip_width*2, input_resolution=(H, W)) + x, (H, W), pad = seq_padding(x[0], dividable_size=self.strip_width * 2, input_resolution=(H, W), + pad_mode='constant') + else: + H, W = self.input_resolution + x = x[0] + + B, L, C = x.shape + assert L == H * W, "input feature has wrong size" + + shortcut = x + x = self.norm1(x) + + x1 = self.attn1(x, shape=(H, W)) + x2 = self.attn2(x, shape=(H, W)) + x3 = self.attn3(x, shape=(H, W)) + + # Method 1 + # x = x1 + x2 + x3 + # Method 2 + q_x = x.unsqueeze(dim=2) + k_x = torch.stack([x, x1, x2, x3], dim=2) + attn_x = (q_x @ k_x.transpose(-1, -2)).softmax(dim=-1) + x = attn_x @ k_x + x = x.squeeze(dim=2) + x = shortcut + self.drop_path(x) + + # FFN + x = x + self.drop_path(self.mlp(self.norm2(x))) + + if arbitrary_input: + x, (H, W) = seq_unpad(x, (H, W), pad) + + return (x, arbitrary_input, (H, W)) + + +# Example +# model = StripAttentionBlock( +# d_model=96, +# input_resolution=28, +# nhead=8, +# strip_width=7 +# ) +# arbitrary_input = True +# # x = (torch.randn(1, 784, 96), arbitrary_input, (28,28)) +# # y = model(x) +# x = (torch.randn(1, 840, 96), arbitrary_input, (28, 30)) +# y = model(x) +# print(y[0].shape, y[2]) + + +class BasicLayer_SA(nn.Module): + def __init__(self, d_model, input_resolution, depth, nhead, strip_width, + mlp_ratio=4., qkv_bias=True, drop=0., attn_drop=0., + drop_path=0., norm_layer=nn.LayerNorm, downsample=None, use_checkpoint=False): + super().__init__() + self.d_model = d_model + self.input_resolution = to_2tuple(input_resolution) + self.depth = depth + self.strip_width = list(to_ntuple(self.depth)(strip_width)) + self.use_checkpoint = use_checkpoint + + # build blocks + self.blocks = nn.ModuleList([ + StripAttentionBlock( + d_model=d_model, + input_resolution=self.input_resolution, + nhead=nhead, + strip_width=self.strip_width[i], + mlp_ratio=mlp_ratio, + qkv_bias=qkv_bias, + drop=drop, + attn_drop=attn_drop, + drop_path=drop_path[i] if isinstance(drop_path, list) else drop_path, + norm_layer=norm_layer + ) + for i in range(self.depth) + ]) + + # patch merging layer + if downsample is not None: + self.downsample = downsample(self.input_resolution, d_model=d_model, norm_layer=norm_layer) + else: + self.downsample = None + + def forward(self, x): + for blk in self.blocks: + if self.use_checkpoint: + x = checkpoint.checkpoint(blk, x) + else: + x = blk(x) + # print(x[0].shape, x[1], x[2]) + if self.downsample is not None: + x = self.downsample(x) + return x + + +# Example +# model = BasicLayer_SA( +# d_model=768, +# input_resolution=112, +# depth=3, +# nhead=8, +# strip_width=[7, 2, 7], +# drop_path=0., +# downsample=PatchMerging_Kai, +# use_checkpoint=False +# ) +# arbitrary_input = True +# # x = (torch.randn(1, 12544, 768), arbitrary_input, (112,112)) +# # y = model(x) +# x = (torch.randn(1, 810, 768), arbitrary_input, (27,30)) +# y = model(x) +# print(y[0].shape, y[2]) + +########################################## S2WAT ########################################## + +class HeTransformerEncoder(nn.Module): + def __init__(self, img_size=224, patch_size=4, in_chans=3, + embed_dim=96, depths=[2, 2, 6, 2], nhead=[3, 6, 12, 24], + strip_width=7, mlp_ratio=4., qkv_bias=True, + drop_rate=0., attn_drop_rate=0., drop_path_rate=0.1, + norm_layer=nn.LayerNorm, ape=False, patch_norm=True, + use_checkpoint=False): + super().__init__() + + self.img_size = to_2tuple(img_size) + self.patch_size = to_2tuple(patch_size) + self.num_layers = len(depths) + self.strip_width = list(to_ntuple(self.num_layers)(strip_width)) + self.embed_dim = embed_dim + self.ape = ape + self.printed_modes = set() + self.patch_norm = patch_norm + self.device="cuda" if torch.cuda.is_available() else "cpu" + + # split image into non-overlapping patches + self.patch_embed = PatchEmbed_Kai( + patch_size=patch_size, + in_chans=in_chans, + embed_dim=embed_dim, + norm_layer=norm_layer if self.patch_norm else None + ) + self.patches_resolution = (self.img_size[0] // self.patch_size[0], self.img_size[1] // self.patch_size[1]) + self.num_patches = self.patches_resolution[0] * self.patches_resolution[1] + + # absolute position embedding + if self.ape: + self.absolute_pos_embed = nn.Parameter(torch.zeros(1, self.num_patches, embed_dim)) + nn.init.trunc_normal_(self.absolute_pos_embed, std=.02) + self.pos_drop = nn.Dropout(drop_rate) + + # stochastic depth + dpr = [x.item() for x in torch.linspace(0, drop_path_rate, sum(depths))] # stochastic depth decay rule + + # build layers + self.layers = nn.ModuleList() + for i in range(self.num_layers): + layer = BasicLayer_SA( + d_model=int(self.embed_dim * 2 ** i), + input_resolution=(self.patches_resolution[0] // (2 ** i), + self.patches_resolution[1] // (2 ** i)), + depth=depths[i], + nhead=nhead[i], + strip_width=self.strip_width[i], + mlp_ratio=mlp_ratio, + qkv_bias=qkv_bias, + drop=drop_rate, + attn_drop=attn_drop_rate, + drop_path=dpr[sum(depths[:i]):sum(depths[:i + 1])], + norm_layer=norm_layer, + downsample=PatchMerging_Kai if (i < self.num_layers - 1) else None, + use_checkpoint=use_checkpoint + ) + self.layers.append(layer) + + self.apply(self._init_weights) + + def _init_weights(self, m): + if isinstance(m, nn.Linear): + nn.init.trunc_normal_(m.weight, std=.02) + if m.bias is not None: + nn.init.constant_(m.bias, 0) + elif isinstance(m, nn.LayerNorm): + nn.init.constant_(m.bias, 0) + nn.init.constant_(m.weight, 1.0) + + @torch.jit.ignore + def no_weight_decay(self): + return {'absolute_pos_embed'} + + @torch.jit.ignore + def no_weight_decay_keywords(self): + return {'relative_position_bias_table'} + + + def add_z(self, x, alpha, mode): + if mode not in self.printed_modes: + if mode == 1: + print("Using Hadamard Product => Element-wise multiplication") + elif mode == 2: + print("Using Addition") + elif mode == 3: + print("Joint Embedding (concatenation along a new dimension)") + else: + raise ValueError("Invalid mode. Please choose 1, 2, or 3") + + self.printed_modes.add(mode) + + size = x[0].size() + alpha_exp = alpha.expand(size) + + if mode == 1: + x_concat_alpha = x[0].to(self.device) * alpha_exp.to(self.device) + elif mode == 2: + x_concat_alpha = x[0].to(self.device) + alpha_exp.to(self.device) + elif mode == 3: + x_concat_alpha = torch.cat((x[0].to(self.device), alpha_exp.to(self.device))) + else: + raise ValueError("Invalid mode. Please choose 1, 2, or 3") + + return x_concat_alpha.to(self.device) + + + def forward_features(self, x,alpha,mode): + x, arbitrary_input = x[0], x[1] + x, (H, W) = self.patch_embed(x) + if self.ape: + x = x + self.absolute_pos_embed + x = self.pos_drop(x) + + x = (x, arbitrary_input, (H, W)) + for layer in self.layers: + x = layer(x) + x = (self.add_z(x, alpha, mode), x[1], x[2]) + + return x + + def forward(self, x,arbitrary_input=False,alpha=None,mode=2): + if arbitrary_input: + H, W = x.shape[2], x.shape[3] + x = (x, arbitrary_input, (H, W)) + else: + x = (x, arbitrary_input) + + x = self.forward_features(x,alpha,mode) + + return x diff --git a/models/networks/generator_module/StyTR.py b/models/networks/generator_module/StyTR.py new file mode 100644 index 0000000000000000000000000000000000000000..01f207850dfcd7f5410f8904c579dda110c99e05 --- /dev/null +++ b/models/networks/generator_module/StyTR.py @@ -0,0 +1,107 @@ +import torch.nn as nn +from .DecoderCNN import Decoder_MV, vgg_structures,decoder_stem # DecoderCNN +from .transformer_decoder import TransformerDecoder # TransformerDecoder + +# compute channel-wise means and variances of features +def calc_mean_std(feat, eps=1e-5): + size = feat.size() + assert len(size) == 4, 'The shape of feature needs to be a tuple with length 4.' + B, C = size[:2] + feat_mean = feat.reshape(B, C, -1).mean(dim=2).reshape(B, C, 1, 1) + feat_std = (feat.reshape(B, C, -1).var(dim=2) + eps).sqrt().reshape(B, C, 1, 1) + return feat_mean, feat_std + + +# normalize features +def mean_variance_norm(feat): + size = feat.size() + mean, std = calc_mean_std(feat) + normalized_feat = (feat - mean.expand(size)) / std.expand(size) + return normalized_feat + + +########################################## Net ########################################## + +class StyTr(nn.Module): + def __init__(self, SEencoder,HEencoder, decoder, transModule, lossNet,alpha,mode): + super(StyTr, self).__init__() + self.mse_loss = nn.MSELoss() + self.SEencoder = SEencoder + self.HEencoder=HEencoder + self.decoder = decoder + self.transModule = transModule + self.alpha=alpha + self.mode=mode + + # features of intermediate layers + lossNet_layers = list(lossNet.children()) + self.feat_1 = nn.Sequential(*lossNet_layers[:4]) # input -> relu1_1 + self.feat_2 = nn.Sequential(*lossNet_layers[4:11]) # relu1_1 -> relu2_1 + self.feat_3 = nn.Sequential(*lossNet_layers[11:18]) # relu2_1 -> relu3_1 + self.feat_4 = nn.Sequential(*lossNet_layers[18:31]) # relu3_1 -> relu4_1 + self.feat_5 = nn.Sequential(*lossNet_layers[31:44]) # relu3_1 -> relu4_1 + + # fix parameters + for name in ['feat_1', 'feat_2', 'feat_3', 'feat_4', 'feat_5']: + for param in getattr(self, name).parameters(): + param.requires_grad = False + + # get intermediate features + def get_interal_feature(self, input): + result = [] + for i in range(5): + input = getattr(self, 'feat_{:d}'.format(i + 1))(input) + result.append(input) + return result + + def calc_content_loss(self, input, target, norm=False): + assert input.size() == target.size(), 'To calculate loss needs the same shape between input and taget.' + assert target.requires_grad == False, 'To calculate loss target shoud not require grad.' + if norm == False: + return self.mse_loss(input, target) + else: + return self.mse_loss(mean_variance_norm(input), mean_variance_norm(target)) + + def calc_style_loss(self, input, target): + assert input.size() == target.size(), 'To calculate loss needs the same shape between input and taget.' + assert target.requires_grad == False, 'To calculate loss target shoud not require grad.' + input_mean, input_std = calc_mean_std(input) + target_mean, target_std = calc_mean_std(target) + return self.mse_loss(input_mean, target_mean) + \ + self.mse_loss(input_std, target_std) + + # calculate losses + def forward(self, i_c, i_s): + f_c = self.SEencoder(i_c) + + f_s = self.HEencoder(i_s,alpha=self.alpha,mode=self.mode) ### ADD Haze input noise adjust in this output + + + f_c, f_c_reso = f_c[0], f_c[2] + f_s, f_s_reso = f_s[0], f_s[2] + + f_cs = self.transModule(f_c, f_s) + f_cc = self.transModule(f_c, f_c) + f_ss = self.transModule(f_s, f_s) + + i_cs = self.decoder(f_cs, f_c_reso) + i_cc = self.decoder(f_cc, f_c_reso) + i_ss = self.decoder(f_ss, f_c_reso) + + f_c_loss = self.get_interal_feature(i_c) + f_s_loss = self.get_interal_feature(i_s) + f_i_cs_loss = self.get_interal_feature(i_cs) + f_i_cc_loss = self.get_interal_feature(i_cc) + f_i_ss_loss = self.get_interal_feature(i_ss) + + loss_id_1 = self.mse_loss(i_cc, i_c) + self.mse_loss(i_ss, i_s) + + loss_c, loss_s, loss_id_2 = 0, 0, 0 + + loss_c = self.calc_content_loss(f_i_cs_loss[-2], f_c_loss[-2], norm=True) + \ + self.calc_content_loss(f_i_cs_loss[-1], f_c_loss[-1], norm=True) + for i in range(1, 5): + loss_s += self.calc_style_loss(f_i_cs_loss[i], f_s_loss[i]) + loss_id_2 += self.mse_loss(f_i_cc_loss[i], f_c_loss[i]) + self.mse_loss(f_i_ss_loss[i], f_s_loss[i]) + + return loss_c, loss_s, loss_id_1, loss_id_2, i_cs diff --git a/models/networks/generator_module/ViT_helper.py b/models/networks/generator_module/ViT_helper.py new file mode 100644 index 0000000000000000000000000000000000000000..1cfdfccca50c885412d0b92c5b5876083a658444 --- /dev/null +++ b/models/networks/generator_module/ViT_helper.py @@ -0,0 +1,44 @@ +import torch.nn as nn +from itertools import repeat +from typing import Iterable + + +def _ntuple(n): + """Copy item to be a tuple with n length (Implemented as timm) + """ + + def parse(x): + if isinstance(x, Iterable): + return x + else: + return tuple(repeat(x, n)) + + return parse + + +to_2tuple = _ntuple(2) +to_ntuple = _ntuple + + +class DropPath(nn.Module): + """Stochasticly zero channels of data.(Implemented as timm) + """ + + def __init__(self, drop=0.5, scale=True): + super().__init__() + self.drop = drop + self.scale = scale + + def forward(self, x): + return self.drop_path(x, self.drop, self.training, self.scale) + + def drop_path(self, x, drop=0.5, training=True, scale=True): + if drop == 0. or not training: + return x + drop_p = 1 - drop + shape = (x.shape[0],) + (1,) * (x.ndim - 1) + random_tensor = x.new_empty(shape).bernoulli_(drop_p) + if drop_p > 0. and scale: + random_tensor.div_(drop_p) + + return x * random_tensor \ No newline at end of file diff --git a/models/networks/generator_module/__init__.py b/models/networks/generator_module/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..ca145683f587f507868a8f1f9dbbbda6f1513e43 --- /dev/null +++ b/models/networks/generator_module/__init__.py @@ -0,0 +1,6 @@ +from . import transformerEncoder +from . import StyTR +from . import transformer_decoder +from . import ViT_helper +from . import HEtransformerEncoder +# from . import networks diff --git a/models/networks/generator_module/__pycache__/DecoderCNN.cpython-310.pyc b/models/networks/generator_module/__pycache__/DecoderCNN.cpython-310.pyc new file mode 100644 index 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--git a/models/networks/generator_module/schedule.py b/models/networks/generator_module/schedule.py new file mode 100644 index 0000000000000000000000000000000000000000..f34c8e6db8fd658e66512a6817a7b46f29d8b9ad --- /dev/null +++ b/models/networks/generator_module/schedule.py @@ -0,0 +1,61 @@ +import math +import numpy as np +from torch.optim import Optimizer, lr_scheduler + + +class WarmUpLR(lr_scheduler._LRScheduler): + """Warm Up learning rate strategy + + Before warmup_step, lr increases from 0 to initial learning rate gradually + After warmup_step, lr decreases from initial learning rate according to the negative square root of "step" + If warmup_step=0, skip the phase of warm up and step into decay phase directly + + """ + + def __init__(self, optimizer, warmup_step, last_epoch=-1): + self.warmup_step = warmup_step + super(WarmUpLR, self).__init__(optimizer, last_epoch=last_epoch) + + def get_lr(self): + if self.last_epoch < self.warmup_step: + return [base_lr * ((self.last_epoch + 1) / self.warmup_step) \ + for base_lr in self.base_lrs] + else: + return [base_lr * math.pow(self.last_epoch + 1, -0.5) \ + for base_lr in self.base_lrs] + + +class CosineAnnealingWarmUpLR(lr_scheduler._LRScheduler): + """Warm Up learning rate strategy + + Before warmup_step, lr increases from 0 to initial learning rate of "optimizer" gradually + After warmup_step, lr decreases from initial learning rate according to cosine annealing + If warmup_step=0, skip the phase of warm up and step into decay phase directly + + """ + + def __init__(self, optimizer, warmup_step, max_step, min_lr=0, last_epoch=-1): + self.warmup_step = warmup_step + self.max_step = max_step + self.min_lr = min_lr + super(CosineAnnealingWarmUpLR, self).__init__(optimizer, last_epoch=last_epoch) + + def get_lr(self): + if self.last_epoch < self.warmup_step: + return [base_lr * ((self.last_epoch + 1) / self.warmup_step) \ + for base_lr in self.base_lrs] + else: + return [self.min_lr + 0.5 * (base_lr - self.min_lr) * \ + (1 + np.cos(np.pi * (self.last_epoch - self.warmup_step) / (self.max_step - self.warmup_step))) \ + for base_lr in self.base_lrs] + + +def adjust_learning_rate(optimizer, iteration_count, lr, lr_decay): + """Imitating the original implementation""" + lr = lr / (1.0 + lr_decay * iteration_count) + for param_group in optimizer.param_groups: + param_group['lr'] = lr + +import torch.nn as nn +class EarlyStopping(nn.Module): + pass \ No newline at end of file diff --git a/models/networks/generator_module/transformerEncoder.py b/models/networks/generator_module/transformerEncoder.py new file mode 100644 index 0000000000000000000000000000000000000000..4f26e8cbaf6ec01916a767cdcaa22ef28fad43b0 --- /dev/null +++ b/models/networks/generator_module/transformerEncoder.py @@ -0,0 +1,776 @@ +import torch +import torch.nn as nn +import torch.nn.functional as F +import torch.utils.checkpoint as checkpoint + +from .ViT_helper import to_2tuple, to_ntuple,DropPath + +class Mlp(nn.Module): + """MLP as implemented in timm + """ + + def __init__(self, in_features, hidden_features=None, out_features=None, act_layer=nn.GELU, drop=0.): + super().__init__() + out_features = out_features or in_features + hidden_features = hidden_features or in_features + drops = to_2tuple(drop) + + self.fc1 = nn.Linear(in_features, hidden_features) + self.act = act_layer() + self.drop1 = nn.Dropout(drops[0]) + self.fc2 = nn.Linear(hidden_features, out_features) + self.drop2 = nn.Dropout(drops[1]) + + def forward(self, x): + x = self.fc1(x) + x = self.act(x) + x = self.drop1(x) + x = self.fc2(x) + x = self.drop2(x) + return x + + +class Attention(nn.Module): + """Self Attention as implemented in timm + """ + + def __init__(self, d_model, nhead=8, qkv_bias=False, attn_drop=0., proj_drop=0.): + super().__init__() + assert d_model % nhead == 0, 'd_model needs to be divisible by nhead' + self.nhead = nhead + self.scale = (d_model // nhead) ** -0.5 + + self.to_qkv = nn.Linear(d_model, d_model * 3, bias=qkv_bias) + self.attn_drop = nn.Dropout(attn_drop) + self.proj = nn.Linear(d_model, d_model) + self.proj_drop = nn.Dropout(proj_drop) + + def forward(self, x): + B, N, C = x.size() + qkv = self.to_qkv(x).reshape(B, N, 3, self.nhead, C // self.nhead).permute(2, 0, 3, 1, 4) + q, k, v = qkv.unbind(0) + + attn = (q @ k.transpose(-1, -2)) * self.scale + attn = attn.softmax(dim=-1) + attn = self.attn_drop(attn) + + x = (attn @ v).transpose(1, 2).reshape(B, N, C) + x = self.proj(x) + x = self.proj_drop(x) + + return x + + +class Attention_Cross(nn.Module): + """Attention for decoder layer.Some palce may be called "inter attention" + """ + + def __init__(self, d_model, nhead=8, qkv_bias=False, attn_drop=0., proj_drop=0.): + super().__init__() + assert d_model % nhead == 0, 'd_model needs to be divisible by nhead' + self.nhead = nhead + self.scale = (d_model // nhead) ** -0.5 + + self.to_q = nn.Linear(d_model, d_model, bias=qkv_bias) + self.to_kv = nn.Linear(d_model, d_model * 2, bias=qkv_bias) + self.attn_drop = nn.Dropout(attn_drop) + self.proj = nn.Linear(d_model, d_model) + self.proj_drop = nn.Dropout(proj_drop) + + def forward(self, x, y): + """ + Args: + x: output of the former layer + y: memery of the encoder layer + """ + B, Nx, C = x.size() + _, Ny, _ = y.size() + q = self.to_q(x).reshape(B, Nx, self.nhead, C // self.nhead).permute(0, 2, 1, 3) + kv = self.to_kv(y).reshape(B, Ny, 2, self.nhead, C // self.nhead).permute(2, 0, 3, 1, 4) + k, v = kv.unbind(0) + + attn = (q @ k.transpose(-1, -2)) * self.scale + attn = attn.softmax(dim=-1) + attn = self.attn_drop(attn) + + x = (attn @ v).transpose(1, 2).reshape(B, Nx, C) + x = self.proj(x) + x = self.proj_drop(x) + + return x + +def split_int(num): + """Split an integer into 2 integers evenly + Args: + num (int): The input integer + Returns: + num_1 (int) + num_2 (int) + """ + if num % 2 == 0: + num_1 = num_2 = num // 2 + else: + num_1 = num // 2 + num_2 = num_1 + 1 + return num_1, num_2 + + +def unpad2D(input, pad): + """Crop the input tensor according to pad.(Inverse operation for padding) + Args: + input (Tensor): (B, C, H, W) + pad (Tuple of int): (left, right, top, bottom) + Returns: + output (Tensor): (B, C, new_H, new_W) + """ + pad_W_left, pad_W_right, pad_H_top, pad_H_bottom = pad + if pad_H_top == 0 and pad_H_bottom == 0 and not (pad_W_left == 0 and pad_W_right == 0): + output = input[:, :, :, pad_W_left:-pad_W_right] + elif pad_W_left == 0 and pad_W_right == 0 and not (pad_H_top == 0 and pad_H_bottom == 0): + output = input[:, :, pad_H_top:-pad_H_bottom, :] + elif pad_H_top == 0 and pad_H_bottom == 0 and pad_W_left == 0 and pad_W_right == 0: + output = input + else: + output = input[:, :, pad_H_top:-pad_H_bottom, pad_W_left:-pad_W_right] + return output + + +def seq_padding(x, dividable_size, input_resolution, pad_mode='constant'): + """Padding for sequential data + Args: + x (Tensor): (B, L, C) + dividable_size (Tuple | int): dividable size + input_resolution (Tuple): resolution of x + Returns: + x (Tensor): (B, new_L, C) + output_resolution (Tuple): new resolution of x + pad (Tuple of int): (left, right, top, bottom) + """ + H, W = input_resolution + B, L, C = x.shape + assert L == H * W, 'Input of wrong size.' + dividable_size = to_2tuple(dividable_size) + x = x.permute(0, 2, 1).reshape(B, C, H, W) + + rema_H, rema_W = H % dividable_size[0], W % dividable_size[1] + pad_H, pad_W = dividable_size[0] - rema_H, dividable_size[1] - rema_W + + pad_H_top, pad_H_bottom = split_int(pad_H) if rema_H != 0 else (0, 0) + pad_W_left, pad_W_right = split_int(pad_W) if rema_W != 0 else (0, 0) + + x = F.pad(x, (pad_W_left, pad_W_right, pad_H_top, pad_H_bottom), pad_mode, 0) + + padded_H, padded_W = x.shape[-2:] + x = x.reshape(B, C, -1).permute(0, 2, 1) + return x, (padded_H, padded_W), (pad_W_left, pad_W_right, pad_H_top, pad_H_bottom) + + +# Example +# x = torch.randn(1, 14, 768) +# y = seq_padding(x, dividable_size=7, input_resolution=(2, 7)) +# print(y[0].shape, y[1], y[2]) + + +def seq_unpad(x, input_resolution, pad): + """Unpadding for sequential data + Args: + x (Tensor): (B, L, C) + input_resolution (Tuple): resolution of x + pad (Tuple of int): (left, right, top, bottom) + Returns: + x (Tensor): (B, new_L, C) + output_resolution (Tuple): new resolution of x + """ + padded_H, padded_W = input_resolution + B, L, C = x.shape + assert L == padded_H * padded_W, 'Input of wrong size.' + x = x.permute(0, 2, 1).reshape(B, C, padded_H, padded_W) + + x = unpad2D(x, pad=pad) + + H, W = x.shape[-2:] + x = x.reshape(B, C, -1).permute(0, 2, 1) + return x, (H, W) +def window_partition(x, window_size): + """Slightly modified for arbitrary window_size & resolution combination + Args: + x: (B,H,W,C) + window_size (tuple[int] | int): window size + Returns: + windows: (num_windows*B, window_size, window_size, C) + """ + window_size = to_2tuple(window_size) + B, H, W, C = x.shape + n_win_H = H // window_size[0] + n_win_W = W // window_size[1] + if not (H % window_size[0] == 0 and W % window_size[1] == 0): + x = x[:, :n_win_H * window_size[0], :n_win_W * window_size[1], :] + x = x.view(B, n_win_H, window_size[0], n_win_W, window_size[1], C) + windows = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(-1, window_size[0], window_size[1], C) + return windows + + +def window_reverse(windows, window_size, H, W): + """Slightly modified for arbitrary window_size & resolution combination + Args: + windows: (num_windows*B, window_size, window_size, C) + window_size (tuple[int] | int): Window size + H (int): Height of image + W (int): Width of image + Returns: + x: (B, H, W, C) + """ + window_size = to_2tuple(window_size) + n_win_H = H // window_size[0] + n_win_W = W // window_size[1] + B = windows.shape[0] // (n_win_H * n_win_W) + x = windows.view(B, n_win_H, n_win_W, window_size[0], window_size[1], -1) + x = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(B, n_win_H * window_size[0], n_win_W * window_size[1], -1) + return x + + +# Example +# H, W = 6, 6 +# window_size = 2 +# x = torch.randn(1, H, W, 3) +# win = window_partition(x, window_size=window_size) +# y = window_reverse(win, window_size=window_size, H=H, W=W) +# print(x.shape) +# print(win.shape) +# print(y.shape) +# print(x[0].permute(2,0,1)[0]) +# print(y[0].permute(2,0,1)[0]) + + +def seq_crop(x, dividable_size, input_resolution): + """ + Arg: + x (Tensor): (B, L, C) + dividable_size (Tuple | int): dividable size + input_resolution (Tuple): resolution of x + Returns: + x (Tensor): (B, new_L, C) + output_resolution (Tuple): new resolution of x + """ + H, W = input_resolution + B, L, C = x.shape + assert L == H * W, 'Input of wrong size.' + dividable_size = to_2tuple(dividable_size) + x = x.reshape(B, H, W, C) + + rema_H, rema_W = H % dividable_size[0], W % dividable_size[1] + new_H, new_W = H - rema_H, W - rema_W + if rema_H != 0 or rema_W != 0: + x = x[:, :new_H, :new_W, :] + + x = x.reshape(B, -1, C) + return x, (new_H, new_W) + + +# Example +# H, W = 22, 34 +# x = torch.randn(2, H*W, 96) +# x, new_size = seq_crop(x, dividable_size=7, input_resolution=(H, W)) +# print(x.shape, new_size) + + +class PatchEmbed_Kai(nn.Module): + """ 2D Image to Patch Embedding + """ + + def __init__(self, patch_size=4, in_chans=3, embed_dim=96, norm_layer=None, flatten=True): + super().__init__() + patch_size = to_2tuple(patch_size) + self.in_chans = in_chans + self.flatten = flatten + + self.proj = nn.Conv2d(self.in_chans, embed_dim, kernel_size=patch_size, stride=patch_size) + self.norm = norm_layer(embed_dim) if norm_layer else nn.Identity() + + def forward(self, x): + B, C, H, W = x.shape + assert C == self.in_chans, 'Input image need to have same numbers of channels with the initialed.' + x = self.proj(x) + H, W = x.shape[2], x.shape[3] + if self.flatten: + x = x.flatten(2).transpose(1, 2) # BCHW -> BNC + x = self.norm(x) + return x, (H, W) + + +# Example +# model = PatchEmbed_Kai(patch_size=4, in_chans=3, embed_dim=96) +# x = torch.randn(1, 3, 224, 224) +# y = model(x) +# torch.save(model.state_dict(), "./PatchEmbed.pkl") +# print(y[0].shape, y[1]) + + +class PatchMerging_Kai(nn.Module): + """ Patch Merging Layer. + Args: + input_resolution (tuple[int] | int): Resolution of input feature. + d_model (int): Number of input channels. + norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm + """ + + def __init__(self, input_resolution, d_model, norm_layer=nn.LayerNorm): + super().__init__() + self.input_resolution = to_2tuple(input_resolution) + self.d_model = d_model + self.reduction = nn.Linear(4 * d_model, 2 * d_model, bias=False) + self.norm = norm_layer(4 * d_model) + + def forward(self, x): + """ + Args: + x (Tuple): (Tensor, arbitrary_input, (H,W)), arbitrary_input (bool) + if arbitrary_input=False, (H,W) will not be required + B, H*W, C -> B, H/2*W/2, 4*C + """ + arbitrary_input = x[1] + if arbitrary_input: + H, W = x[2] + else: + H, W = self.input_resolution + + x = x[0] + B, L, C = x.shape + assert L == H * W, "input feature has wrong size" + + x = x.view(B, H, W, C) + if H % 2 != 0: + x = x[:, 0:-1, :, :] + if W % 2 != 0: + x = x[:, :, 0:-1, :] + + x0 = x[:, 0::2, 0::2, :] # B H/2 W/2 C + x1 = x[:, 1::2, 0::2, :] # B H/2 W/2 C + x2 = x[:, 0::2, 1::2, :] # B H/2 W/2 C + x3 = x[:, 1::2, 1::2, :] # B H/2 W/2 C + x = torch.cat([x0, x1, x2, x3], -1) # B H/2 W/2 4*C + H, W = x.shape[1], x.shape[2] + x = x.view(B, -1, 4 * C) # B H/2*W/2 4*C + + x = self.norm(x) + x = self.reduction(x) + + return x, arbitrary_input, (H, W) + + +# Example +# model = PatchMerging_Kai(input_resolution=(5,4), d_model=3) +# arbitrary_input = True +# # x = torch.randn(1, 20, 3) +# # y = model((x, arbitrary_input)) +# x = torch.randn(1, 45, 3) +# y = model((x, arbitrary_input, (5,9))) +# print(y[0].shape, y[2]) + + +class WindowAttention_Kai(nn.Module): + def __init__(self, d_model, window_size, nhead, qkv_bias=True, attn_drop=0., proj_drop=0.): + super().__init__() + assert d_model % nhead == 0, 'd_model needs to be divisible by nhead' + self.window_size = to_2tuple(window_size) + self.nhead = nhead + self.scale = (d_model // nhead) ** -0.5 + + # Relative Position Bias's parameter Table + self.relative_position_bias_table = nn.Parameter( + torch.zeros((2 * self.window_size[0] - 1) * (2 * self.window_size[1] - 1), nhead) + ) + + # Compute indice of relative_position_bias_table for attention matrix + coords_h = torch.arange(self.window_size[0]) + coords_w = torch.arange(self.window_size[1]) + coords = torch.stack(torch.meshgrid([coords_h, coords_w])) + coords_flatten = torch.flatten(coords, 1) + relative_coords = coords_flatten[:, :, None] - coords_flatten[:, None, :] + relative_coords = relative_coords.permute(1, 2, 0).contiguous() + relative_coords[:, :, 0] += self.window_size[0] - 1 + relative_coords[:, :, 1] += self.window_size[1] - 1 + relative_coords[:, :, 0] *= 2 * self.window_size[1] - 1 + relative_position_index = relative_coords.sum(-1) + self.register_buffer("relative_position_index", relative_position_index) + + self.qkv = nn.Linear(d_model, d_model * 3, bias=qkv_bias) + self.attn_drop = nn.Dropout(attn_drop) + self.proj = nn.Linear(d_model, d_model) + self.proj_drop = nn.Dropout(proj_drop) + + nn.init.trunc_normal_(self.relative_position_bias_table, std=.02) + + def forward(self, x, shape): + H, W = shape + Bi, Ni, Ci = x.size() + assert Ni == H * W, "Inputs with wrong size." + x = x.reshape(Bi, H, W, Ci) + + # print(self.window_size) + x = window_partition(x, self.window_size) + x = x.reshape(-1, self.window_size[0] * self.window_size[1], Ci) + + B_, N, C = x.shape + qkv = self.qkv(x).reshape(B_, N, 3, self.nhead, C // self.nhead).permute(2, 0, 3, 1, 4) + q, k, v = qkv.unbind(0) + + attn = (q @ k.transpose(-2, -1)) * self.scale + + relative_position_bias = self.relative_position_bias_table[self.relative_position_index.view(-1)].view( + self.window_size[0] * self.window_size[1], self.window_size[0] * self.window_size[1], self.nhead) + relative_position_bias = relative_position_bias.permute(2, 0, 1).contiguous() + attn = attn + relative_position_bias.unsqueeze(0) + + attn = attn.softmax(dim=-1) + attn = self.attn_drop(attn) + + x = (attn @ v).transpose(1, 2).reshape(B_, N, C) + x = self.proj(x) + x = self.proj_drop(x) + + x = window_reverse(x, self.window_size, H, W) + x = x.reshape(Bi, Ni, Ci) + return x + + # Example + + +# model = WindowAttention_Kai( +# d_model=768, +# window_size=7, +# nhead=8 +# ) +# x = torch.randn(1, 784, 768) +# y = model(x, shape=(28, 28)) +# print(y.shape) + + +class StripAttention(nn.Module): + def __init__(self, d_model, nhead=8, strip_width=7, is_vertical=False, qkv_bias=False, attn_drop=0., proj_drop=0.): + super().__init__() + self.d_model = d_model + self.strip_width = strip_width + self.is_vertical = is_vertical + + self.attn = Attention( + d_model=d_model, + nhead=nhead, + qkv_bias=qkv_bias, + attn_drop=attn_drop, + proj_drop=proj_drop, + ) + + def forward(self, x, shape): + H, W = shape + B, N, C = x.size() + assert N == H * W, "Inputs with wrong size." + x = x.reshape(B, H, W, C) + + # print(self.strip_width) + if self.is_vertical: + x = window_partition(x, (H, self.strip_width)) + x = x.reshape(-1, H * self.strip_width, C) + else: + x = window_partition(x, (self.strip_width, W)) + x = x.reshape(-1, W * self.strip_width, C) + + wins = self.attn(x) + + if self.is_vertical: + x = window_reverse(wins, (H, self.strip_width), H, W) + else: + x = window_reverse(wins, (self.strip_width, W), H, W) + + x = x.reshape(B, N, C) + return x + + +# Example +# model = StripAttention( +# d_model=768, +# nhead=8, +# strip_width=7, +# is_vertical=False +# ) +# x = torch.randn(1, 784, 768) +# y = model(x, (28, 28)) +# print(y.shape) + + +class StripAttentionBlock(nn.Module): + def __init__(self, d_model, input_resolution, nhead=8, strip_width=7, + mlp_ratio=4, qkv_bias=False, drop=0., attn_drop=0., drop_path=0., + act_layer=nn.GELU, norm_layer=nn.LayerNorm): + super().__init__() + self.d_model = d_model + self.input_resolution = to_2tuple(input_resolution) + self.strip_width = strip_width + + self.norm1 = norm_layer(d_model) + + self.attn1 = StripAttention( + d_model=d_model, + nhead=nhead, + strip_width=strip_width, + is_vertical=False, + qkv_bias=qkv_bias, + attn_drop=attn_drop, + proj_drop=drop + ) + self.attn2 = StripAttention( + d_model=d_model, + nhead=nhead, + strip_width=strip_width, + is_vertical=True, + qkv_bias=qkv_bias, + attn_drop=attn_drop, + proj_drop=drop + ) + self.attn3 = WindowAttention_Kai( + d_model=d_model, + window_size=(strip_width * 2, strip_width * 2), + nhead=nhead, + qkv_bias=qkv_bias, + attn_drop=attn_drop, + proj_drop=drop + ) + + self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity() + self.norm2 = norm_layer(d_model) + mlp_hidden_dim = int(d_model * mlp_ratio) + self.mlp = Mlp(d_model, hidden_features=mlp_hidden_dim, out_features=d_model, act_layer=act_layer, drop=drop) + + def forward(self, x): + arbitrary_input = x[1] + if arbitrary_input: + H, W = x[2] + # x, (H, W) = seq_crop(x[0], dividable_size=self.strip_width*2, input_resolution=(H, W)) + x, (H, W), pad = seq_padding(x[0], dividable_size=self.strip_width * 2, input_resolution=(H, W), + pad_mode='constant') + else: + H, W = self.input_resolution + x = x[0] + + B, L, C = x.shape + assert L == H * W, "input feature has wrong size" + + shortcut = x + x = self.norm1(x) + + x1 = self.attn1(x, shape=(H, W)) + x2 = self.attn2(x, shape=(H, W)) + x3 = self.attn3(x, shape=(H, W)) + + # Method 1 + # x = x1 + x2 + x3 + # Method 2 + q_x = x.unsqueeze(dim=2) + k_x = torch.stack([x, x1, x2, x3], dim=2) + attn_x = (q_x @ k_x.transpose(-1, -2)).softmax(dim=-1) + x = attn_x @ k_x + x = x.squeeze(dim=2) + x = shortcut + self.drop_path(x) + + # FFN + x = x + self.drop_path(self.mlp(self.norm2(x))) + + if arbitrary_input: + x, (H, W) = seq_unpad(x, (H, W), pad) + + return (x, arbitrary_input, (H, W)) + + +# Example +# model = StripAttentionBlock( +# d_model=96, +# input_resolution=28, +# nhead=8, +# strip_width=7 +# ) +# arbitrary_input = True +# # x = (torch.randn(1, 784, 96), arbitrary_input, (28,28)) +# # y = model(x) +# x = (torch.randn(1, 840, 96), arbitrary_input, (28, 30)) +# y = model(x) +# print(y[0].shape, y[2]) + + +class BasicLayer_SA(nn.Module): + def __init__(self, d_model, input_resolution, depth, nhead, strip_width, + mlp_ratio=4., qkv_bias=True, drop=0., attn_drop=0., + drop_path=0., norm_layer=nn.LayerNorm, downsample=None, use_checkpoint=False): + super().__init__() + self.d_model = d_model + self.input_resolution = to_2tuple(input_resolution) + self.depth = depth + self.strip_width = list(to_ntuple(self.depth)(strip_width)) + self.use_checkpoint = use_checkpoint + + # build blocks + self.blocks = nn.ModuleList([ + StripAttentionBlock( + d_model=d_model, + input_resolution=self.input_resolution, + nhead=nhead, + strip_width=self.strip_width[i], + mlp_ratio=mlp_ratio, + qkv_bias=qkv_bias, + drop=drop, + attn_drop=attn_drop, + drop_path=drop_path[i] if isinstance(drop_path, list) else drop_path, + norm_layer=norm_layer + ) + for i in range(self.depth) + ]) + + # patch merging layer + if downsample is not None: + self.downsample = downsample(self.input_resolution, d_model=d_model, norm_layer=norm_layer) + else: + self.downsample = None + + def forward(self, x): + for blk in self.blocks: + if self.use_checkpoint: + x = checkpoint.checkpoint(blk, x) + else: + x = blk(x) + # print(x[0].shape, x[1], x[2]) + if self.downsample is not None: + x = self.downsample(x) + return x + + +# Example +# model = BasicLayer_SA( +# d_model=768, +# input_resolution=112, +# depth=3, +# nhead=8, +# strip_width=[7, 2, 7], +# drop_path=0., +# downsample=PatchMerging_Kai, +# use_checkpoint=False +# ) +# arbitrary_input = True +# # x = (torch.randn(1, 12544, 768), arbitrary_input, (112,112)) +# # y = model(x) +# x = (torch.randn(1, 810, 768), arbitrary_input, (27,30)) +# y = model(x) +# print(y[0].shape, y[2]) + + +########################################## S2WAT ########################################## + +class TransformerEncoder(nn.Module): + @staticmethod + def modify_commandline_options(parser, is_train): + parser.set_defaults(norm_G='spectralfadesyncbatch3x3') + parser.add_argument('--num_upsampling_layers', + choices=('normal', 'more', 'most'), default='more', + help="If 'more', adds upsampling layer between the two middle resnet blocks." + "If 'most', also add one more upsampling + resnet layer at the end of the generator." + "We only use 'more' as the default setting.") + parser.add_argument('--lr_decay', type=float, default=1e-4, help='learning rate decay') + parser.add_argument('--lr_stytr2', type=float, default=5e-4, help='initial learning rate for adam') + return parser + + def __init__(self, img_size=224, patch_size=4, in_chans=3, + embed_dim=96, depths=[2, 2, 6, 2], nhead=[3, 6, 12, 24], + strip_width=7, mlp_ratio=4., qkv_bias=True, + drop_rate=0., attn_drop_rate=0., drop_path_rate=0.1, + norm_layer=nn.LayerNorm, ape=False, patch_norm=True, + use_checkpoint=False): + super().__init__() + + self.img_size = to_2tuple(img_size) + self.patch_size = to_2tuple(patch_size) + self.num_layers = len(depths) + self.strip_width = list(to_ntuple(self.num_layers)(strip_width)) + self.embed_dim = embed_dim + self.ape = ape + self.patch_norm = patch_norm + + # split image into non-overlapping patches + self.patch_embed = PatchEmbed_Kai( + patch_size=patch_size, + in_chans=in_chans, + embed_dim=embed_dim, + norm_layer=norm_layer if self.patch_norm else None + ) + self.patches_resolution = (self.img_size[0] // self.patch_size[0], self.img_size[1] // self.patch_size[1]) + self.num_patches = self.patches_resolution[0] * self.patches_resolution[1] + + # absolute position embedding + if self.ape: + self.absolute_pos_embed = nn.Parameter(torch.zeros(1, self.num_patches, embed_dim)) + nn.init.trunc_normal_(self.absolute_pos_embed, std=.02) + self.pos_drop = nn.Dropout(drop_rate) + + # stochastic depth + dpr = [x.item() for x in torch.linspace(0, drop_path_rate, sum(depths))] # stochastic depth decay rule + + # build layers + self.layers = nn.ModuleList() + for i in range(self.num_layers): + layer = BasicLayer_SA( + d_model=int(self.embed_dim * 2 ** i), + input_resolution=(self.patches_resolution[0] // (2 ** i), + self.patches_resolution[1] // (2 ** i)), + depth=depths[i], + nhead=nhead[i], + strip_width=self.strip_width[i], + mlp_ratio=mlp_ratio, + qkv_bias=qkv_bias, + drop=drop_rate, + attn_drop=attn_drop_rate, + drop_path=dpr[sum(depths[:i]):sum(depths[:i + 1])], + norm_layer=norm_layer, + downsample=PatchMerging_Kai if (i < self.num_layers - 1) else None, + use_checkpoint=use_checkpoint + ) + self.layers.append(layer) + + self.apply(self._init_weights) + + def _init_weights(self, m): + if isinstance(m, nn.Linear): + nn.init.trunc_normal_(m.weight, std=.02) + if m.bias is not None: + nn.init.constant_(m.bias, 0) + elif isinstance(m, nn.LayerNorm): + nn.init.constant_(m.bias, 0) + nn.init.constant_(m.weight, 1.0) + + @torch.jit.ignore + def no_weight_decay(self): + return {'absolute_pos_embed'} + + @torch.jit.ignore + def no_weight_decay_keywords(self): + return {'relative_position_bias_table'} + + def forward_features(self, x): + x, arbitrary_input = x[0], x[1] + x, (H, W) = self.patch_embed(x) + if self.ape: + x = x + self.absolute_pos_embed + x = self.pos_drop(x) + + x = (x, arbitrary_input, (H, W)) + for layer in self.layers: + x = layer(x) + # print(x[0].shape) + + return x + + def forward(self, x, arbitrary_input=False): + if arbitrary_input: + H, W = x.shape[2], x.shape[3] + x = (x, arbitrary_input, (H, W)) + else: + x = (x, arbitrary_input) + + x = self.forward_features(x) + return x diff --git a/models/networks/generator_module/transformer_decoder.py b/models/networks/generator_module/transformer_decoder.py new file mode 100644 index 0000000000000000000000000000000000000000..efabd3699d388245cb80291f9a642ae87956f97d --- /dev/null +++ b/models/networks/generator_module/transformer_decoder.py @@ -0,0 +1,62 @@ +import copy +from typing import Optional, List +import torch +import torch.nn.functional as F +from torch import nn, Tensor +import numpy as np +import os +from torch.distributions import Beta +from .transformerEncoder import Attention, Mlp, DropPath, Attention_Cross +import torch +from torchinfo import summary + + +class TransformerDecoder(nn.Module): + def __init__(self,nlayer=3,d_model=512,nhead=8,mlp_ratio=4,qkv_bias=False,attn_drop=0.,drop=0.,drop_path=0.,act_layer=nn.GELU,norm_layer=nn.LayerNorm,norm_first=False): + super(TransformerDecoder, self).__init__() + self.layers = nn.ModuleList([ + TransformerDecoderLayer(d_model=d_model,nhead=nhead,mlp_ratio=mlp_ratio,qkv_bias=qkv_bias,attn_drop=attn_drop,drop=drop,drop_path=drop_path,act_layer=act_layer,norm_layer=norm_layer,norm_first=norm_first + ) \ + for i in range(nlayer) + ]) + + def forward(self, content_feature, style_feature): + for layer in self.layers: + content_feature = layer(content_feature, style_feature) + + return content_feature + +class TransformerDecoderLayer(nn.Module): + """Transformer Decoder Layer + """ + + def __init__(self, d_model, nhead=8, mlp_ratio=4, qkv_bias=False, attn_drop=0., + drop=0., drop_path=0., act_layer=nn.GELU, norm_layer=nn.LayerNorm, norm_first=False): + super().__init__() + mlp_hidden_dim = int(d_model * mlp_ratio) + + self.attn1 = Attention(d_model, nhead=nhead, qkv_bias=qkv_bias, attn_drop=attn_drop, proj_drop=drop) + self.attn2 = Attention_Cross(d_model, nhead=nhead, qkv_bias=qkv_bias, attn_drop=attn_drop, proj_drop=drop) + self.mlp = Mlp(d_model, hidden_features=mlp_hidden_dim, out_features=d_model, act_layer=act_layer, drop=drop) + + self.norm_first = norm_first + self.norm1 = norm_layer(d_model) + self.norm2 = norm_layer(d_model) + self.norm3 = norm_layer(d_model) + self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity() + + def forward(self, x, y): + """ + Args: + x: output of the former layer + y: memery of the encoder layer + """ + if self.norm_first == True: + x = x + self.drop_path(self.attn1(self.norm1(x))) + x = x + self.drop_path(self.attn2(self.norm2(x), y)) + x = x + self.drop_path(self.mlp(self.norm3(x))) + else: + x = self.norm1(x + self.drop_path(self.attn1(x))) + x = self.norm2(x + self.drop_path(self.attn2(x, y))) + x = self.norm3(x + self.drop_path(self.mlp(x))) + return x diff --git a/models/networks/logs/events.out.tfevents.1720811165.df626c6db989 b/models/networks/logs/events.out.tfevents.1720811165.df626c6db989 new file mode 100644 index 0000000000000000000000000000000000000000..bca14b68106981ce3ce54f86c1b8dfb2738c8353 --- /dev/null +++ b/models/networks/logs/events.out.tfevents.1720811165.df626c6db989 @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:a191ac9b99c00eddabb080e470e1d4173d53e0305809a0027c270c8456023f9e +size 40 diff --git a/models/networks/logs/events.out.tfevents.1720811216.df626c6db989 b/models/networks/logs/events.out.tfevents.1720811216.df626c6db989 new file mode 100644 index 0000000000000000000000000000000000000000..b6e88406632fcd9df9488e383c788a10e2c71b42 --- /dev/null +++ b/models/networks/logs/events.out.tfevents.1720811216.df626c6db989 @@ -0,0 +1,3 @@ +version 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https://git-lfs.github.com/spec/v1 +oid sha256:0c18aa027a02515074d50dcf6ec5405d1411e5144465ad3e2512885e9e862b50 +size 4290 diff --git a/models/networks/loss.py b/models/networks/loss.py new file mode 100644 index 0000000000000000000000000000000000000000..b55148f2ebe143a73693463483938794fc854257 --- /dev/null +++ b/models/networks/loss.py @@ -0,0 +1,115 @@ +import torch +import torch.nn as nn +import torch.nn.functional as F +from models.networks.architecture import VGG19 + + +# Defines the GAN loss which uses either LSGAN or the regular GAN. +# When LSGAN is used, it is basically same as MSELoss, +# but it abstracts away the need to create the target label tensor +# that has the same size as the input +class GANLoss(nn.Module): + def __init__(self, gan_mode, target_real_label=1.0, target_fake_label=0.0, + tensor=torch.FloatTensor, opt=None): + super(GANLoss, self).__init__() + self.real_label = target_real_label + self.fake_label = target_fake_label + self.real_label_tensor = None + self.fake_label_tensor = None + self.zero_tensor = None + self.Tensor = tensor + self.gan_mode = gan_mode + self.opt = opt + if gan_mode == 'ls': + pass + elif gan_mode == 'original': + pass + elif gan_mode == 'w': + pass + elif gan_mode == 'hinge': + pass + else: + raise ValueError('Unexpected gan_mode {}'.format(gan_mode)) + + def get_target_tensor(self, input, target_is_real): + if target_is_real: + if self.real_label_tensor is None: + self.real_label_tensor = self.Tensor(1).fill_(self.real_label) + self.real_label_tensor.requires_grad_(False) + return self.real_label_tensor.expand_as(input) + else: + if self.fake_label_tensor is None: + self.fake_label_tensor = self.Tensor(1).fill_(self.fake_label) + self.fake_label_tensor.requires_grad_(False) + return self.fake_label_tensor.expand_as(input) + + def get_zero_tensor(self, input): + if self.zero_tensor is None: + self.zero_tensor = self.Tensor(1).fill_(0) + self.zero_tensor.requires_grad_(False) + return self.zero_tensor.expand_as(input) + + def loss(self, input, target_is_real, for_discriminator=True): + if self.gan_mode == 'original': # cross entropy loss + target_tensor = self.get_target_tensor(input, target_is_real) + loss = F.binary_cross_entropy_with_logits(input, target_tensor) + return loss + elif self.gan_mode == 'ls': + target_tensor = self.get_target_tensor(input, target_is_real) + return F.mse_loss(input, target_tensor) + elif self.gan_mode == 'hinge': + if for_discriminator: + if target_is_real: + minval = torch.min(input - 1, self.get_zero_tensor(input)) + loss = -torch.mean(minval) + else: + minval = torch.min(-input - 1, self.get_zero_tensor(input)) + loss = -torch.mean(minval) + else: + assert target_is_real, "The generator's hinge loss must be aiming for real" + loss = -torch.mean(input) + return loss + else: + # wgan + if target_is_real: + return -input.mean() + else: + return input.mean() + + def __call__(self, input, target_is_real, for_discriminator=True): + # computing loss is a bit complicated because |input| may not be + # a tensor, but list of tensors in case of multiscale discriminator + if isinstance(input, list): + loss = 0 + for pred_i in input: + if isinstance(pred_i, list): + pred_i = pred_i[-1] + loss_tensor = self.loss(pred_i, target_is_real, for_discriminator) + bs = 1 if len(loss_tensor.size()) == 0 else loss_tensor.size(0) + new_loss = torch.mean(loss_tensor.view(bs, -1), dim=1) + loss += new_loss + return loss / len(input) + else: + return self.loss(input, target_is_real, for_discriminator) + + +# Perceptual loss that uses a pretrained VGG network +class VGGLoss(nn.Module): + def __init__(self, gpu_ids): + super(VGGLoss, self).__init__() + self.vgg = VGG19().cuda() + self.criterion = nn.L1Loss() + self.weights = [1.0 / 32, 1.0 / 16, 1.0 / 8, 1.0 / 4, 1.0] + + def forward(self, x, y): + x_vgg, y_vgg = self.vgg(x), self.vgg(y) + loss = 0 + for i in range(len(x_vgg)): + loss += self.weights[i] * self.criterion(x_vgg[i], y_vgg[i].detach()) + return loss + + +# KL Divergence loss used in VAE with an image encoder +class KLDLoss(nn.Module): + def forward(self, mu, logvar): + return -0.5 * torch.sum(1 + logvar - mu.pow(2) - logvar.exp()) diff --git a/models/networks/normalization.py b/models/networks/normalization.py new file mode 100644 index 0000000000000000000000000000000000000000..ad565e3ac00b6d8f57f90def94a545c2f1b2faa9 --- /dev/null +++ b/models/networks/normalization.py @@ -0,0 +1,80 @@ +import re +import torch.nn as nn +from models.networks.sync_batchnorm import SynchronizedBatchNorm2d +import torch.nn.utils.spectral_norm as spectral_norm + + +# Returns a function that creates a standard normalization function +def get_norm_layer(opt, norm_type='instance'): + # helper function to get # output channels of the previous layer + def get_out_channel(layer): + if hasattr(layer, 'out_channels'): + return getattr(layer, 'out_channels') + return layer.weight.size(0) + + # this function will be returned + def add_norm_layer(layer): + nonlocal norm_type + if norm_type.startswith('spectral'): + layer = spectral_norm(layer) + subnorm_type = norm_type[len('spectral'):] + + if subnorm_type == 'none' or len(subnorm_type) == 0: + return layer + + # remove bias in the previous layer, which is meaningless + # since it has no effect after normalization + if getattr(layer, 'bias', None) is not None: + delattr(layer, 'bias') + layer.register_parameter('bias', None) + + if subnorm_type == 'batch': + norm_layer = nn.BatchNorm2d(get_out_channel(layer), affine=True) + elif subnorm_type == 'syncbatch': + norm_layer = SynchronizedBatchNorm2d(get_out_channel(layer), affine=True) + elif subnorm_type == 'instance': + norm_layer = nn.InstanceNorm2d(get_out_channel(layer), affine=False) + else: + raise ValueError('normalization layer %s is not recognized' % subnorm_type) + + return nn.Sequential(layer, norm_layer) + + return add_norm_layer + + +# Creates FADE normalization layer based on the given configuration +class FADE(nn.Module): + def __init__(self, config_text, norm_nc, label_nc): + super().__init__() + + assert config_text.startswith('fade') + parsed = re.search('fade(\D+)(\d)x\d', config_text) + param_free_norm_type = str(parsed.group(1)) + ks = int(parsed.group(2)) + + if param_free_norm_type == 'instance': + self.param_free_norm = nn.InstanceNorm2d(norm_nc, affine=False) + elif param_free_norm_type == 'syncbatch': + self.param_free_norm = SynchronizedBatchNorm2d(norm_nc, affine=False) + elif param_free_norm_type == 'batch': + self.param_free_norm = nn.BatchNorm2d(norm_nc, affine=False) + else: + raise ValueError('%s is not a recognized param-free norm type in FADE' + % param_free_norm_type) + + pw = ks // 2 + self.mlp_gamma = nn.Conv2d(label_nc, norm_nc, kernel_size=ks, padding=pw) + self.mlp_beta = nn.Conv2d(label_nc, norm_nc, kernel_size=ks, padding=pw) + + def forward(self, x, feat): + # Step 1. generate parameter-free normalized activations + normalized = self.param_free_norm(x) + + # Step 2. produce scale and bias conditioned on feature map + gamma = self.mlp_gamma(feat) + beta = self.mlp_beta(feat) + + # Step 3. apply scale and bias + out = normalized * (1 + gamma) + beta + + return out diff --git a/models/networks/stream.py b/models/networks/stream.py new file mode 100644 index 0000000000000000000000000000000000000000..632b69809a1031038cbc094296a06cd95b6580a1 --- /dev/null +++ b/models/networks/stream.py @@ -0,0 +1,52 @@ +import torch.nn.functional as F +from models.networks.base_network import BaseNetwork +from models.networks.architecture import StreamResnetBlock as StreamResnetBlock + + +# Content/style stream. +# The two streams are symmetrical with the same network structure, +# aiming at extracting corresponding feature representations in different levels. +class Stream(BaseNetwork): + def __init__(self, opt): + super().__init__() + self.opt = opt + nf = opt.ngf + self.res_0 = StreamResnetBlock(opt.semantic_nc, 1 * nf, opt) # 64-ch feature + self.res_1 = StreamResnetBlock(1 * nf, 2 * nf, opt) # 128-ch feature + self.res_2 = StreamResnetBlock(2 * nf, 4 * nf, opt) # 256-ch feature + self.res_3 = StreamResnetBlock(4 * nf, 8 * nf, opt) # 512-ch feature + self.res_4 = StreamResnetBlock(8 * nf, 16 * nf, opt) # 1024-ch feature + self.res_5 = StreamResnetBlock(16 * nf, 16 * nf, opt) # 1024-ch feature + self.res_6 = StreamResnetBlock(16 * nf, 16 * nf, opt) # 1024-ch feature + self.res_7 = StreamResnetBlock(16 * nf, 16 * nf, opt) # 1024-ch feature + + def down(self, input): + return F.interpolate(input, scale_factor=0.5) + + def forward(self,input): + # assume that input shape is (n,c,256,512) + + x0 = self.res_0(input) # (n,64,256,512) + + x1 = self.down(x0) + x1 = self.res_1(x1) # (n,128,128,256) + + x2 = self.down(x1) + x2 = self.res_2(x2) # (n,256,64,128) + + x3 = self.down(x2) + x3 = self.res_3(x3) # (n,512,32,64) + + x4 = self.down(x3) + x4 = self.res_4(x4) # (n,1024,16,32) + + x5 = self.down(x4) + x5 = self.res_5(x5) # (n,1024,8,16) + + x6 = self.down(x5) + x6 = self.res_6(x6) # (n,1024,4,8) + + x7 = self.down(x6) + x7 = self.res_7(x7) # (n,1024,2,4) + + return [x0, x1, x2, x3, x4, x5, x6, x7] diff --git a/models/networks/sync_batchnorm/__init__.py b/models/networks/sync_batchnorm/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..6d9b36c74b1808b56ded68cf080a689db7e0ee4e --- /dev/null +++ b/models/networks/sync_batchnorm/__init__.py @@ -0,0 +1,14 @@ +# -*- coding: utf-8 -*- +# File : __init__.py +# Author : Jiayuan Mao +# Email : maojiayuan@gmail.com +# Date : 27/01/2018 +# +# This file is part of Synchronized-BatchNorm-PyTorch. +# https://github.com/vacancy/Synchronized-BatchNorm-PyTorch +# Distributed under MIT License. + +from .batchnorm import set_sbn_eps_mode +from .batchnorm import SynchronizedBatchNorm1d, SynchronizedBatchNorm2d, SynchronizedBatchNorm3d +from .batchnorm import patch_sync_batchnorm, convert_model +from .replicate import 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differ diff --git a/models/networks/sync_batchnorm/batchnorm.py b/models/networks/sync_batchnorm/batchnorm.py new file mode 100644 index 0000000000000000000000000000000000000000..bf8d7a7325b474771a11a137053971fd40426079 --- /dev/null +++ b/models/networks/sync_batchnorm/batchnorm.py @@ -0,0 +1,412 @@ +# -*- coding: utf-8 -*- +# File : batchnorm.py +# Author : Jiayuan Mao +# Email : maojiayuan@gmail.com +# Date : 27/01/2018 +# +# This file is part of Synchronized-BatchNorm-PyTorch. +# https://github.com/vacancy/Synchronized-BatchNorm-PyTorch +# Distributed under MIT License. + +import collections +import contextlib + +import torch +import torch.nn.functional as F + +from torch.nn.modules.batchnorm import _BatchNorm + +try: + from torch.nn.parallel._functions import ReduceAddCoalesced, Broadcast +except ImportError: + ReduceAddCoalesced = Broadcast = None + +try: + from jactorch.parallel.comm import SyncMaster + from jactorch.parallel.data_parallel import JacDataParallel as DataParallelWithCallback +except ImportError: + from .comm import SyncMaster + from .replicate import DataParallelWithCallback + +__all__ = [ + 'set_sbn_eps_mode', + 'SynchronizedBatchNorm1d', 'SynchronizedBatchNorm2d', 'SynchronizedBatchNorm3d', + 'patch_sync_batchnorm', 'convert_model' +] + + +SBN_EPS_MODE = 'clamp' + + +def set_sbn_eps_mode(mode): + global SBN_EPS_MODE + assert mode in ('clamp', 'plus') + SBN_EPS_MODE = mode + + +def _sum_ft(tensor): + """sum over the first and last dimention""" + return tensor.sum(dim=0).sum(dim=-1) + + +def _unsqueeze_ft(tensor): + """add new dimensions at the front and the tail""" + return tensor.unsqueeze(0).unsqueeze(-1) + + +_ChildMessage = collections.namedtuple('_ChildMessage', ['sum', 'ssum', 'sum_size']) +_MasterMessage = collections.namedtuple('_MasterMessage', ['sum', 'inv_std']) + + +class _SynchronizedBatchNorm(_BatchNorm): + def __init__(self, num_features, eps=1e-5, momentum=0.1, affine=True, track_running_stats=True): + assert ReduceAddCoalesced is not None, 'Can not use Synchronized Batch Normalization without CUDA support.' + + super(_SynchronizedBatchNorm, self).__init__(num_features, eps=eps, momentum=momentum, affine=affine, + track_running_stats=track_running_stats) + + if not self.track_running_stats: + import warnings + warnings.warn('track_running_stats=False is not supported by the SynchronizedBatchNorm.') + + self._sync_master = SyncMaster(self._data_parallel_master) + + self._is_parallel = False + self._parallel_id = None + self._slave_pipe = None + + def forward(self, input): + # If it is not parallel computation or is in evaluation mode, use PyTorch's implementation. + if not (self._is_parallel and self.training): + return F.batch_norm( + input, self.running_mean, self.running_var, self.weight, self.bias, + self.training, self.momentum, self.eps) + + # Resize the input to (B, C, -1). + input_shape = input.size() + assert input.size(1) == self.num_features, 'Channel size mismatch: got {}, expect {}.'.format(input.size(1), self.num_features) + input = input.view(input.size(0), self.num_features, -1) + + # Compute the sum and square-sum. + sum_size = input.size(0) * input.size(2) + input_sum = _sum_ft(input) + input_ssum = _sum_ft(input ** 2) + + # Reduce-and-broadcast the statistics. + if self._parallel_id == 0: + mean, inv_std = self._sync_master.run_master(_ChildMessage(input_sum, input_ssum, sum_size)) + else: + mean, inv_std = self._slave_pipe.run_slave(_ChildMessage(input_sum, input_ssum, sum_size)) + + # Compute the output. + if self.affine: + # MJY:: Fuse the multiplication for speed. + output = (input - _unsqueeze_ft(mean)) * _unsqueeze_ft(inv_std * self.weight) + _unsqueeze_ft(self.bias) + else: + output = (input - _unsqueeze_ft(mean)) * _unsqueeze_ft(inv_std) + + # Reshape it. + return output.view(input_shape) + + def __data_parallel_replicate__(self, ctx, copy_id): + self._is_parallel = True + self._parallel_id = copy_id + + # parallel_id == 0 means master device. + if self._parallel_id == 0: + ctx.sync_master = self._sync_master + else: + self._slave_pipe = ctx.sync_master.register_slave(copy_id) + + def _data_parallel_master(self, intermediates): + """Reduce the sum and square-sum, compute the statistics, and broadcast it.""" + + # Always using same "device order" makes the ReduceAdd operation faster. + # Thanks to:: Tete Xiao (http://tetexiao.com/) + intermediates = sorted(intermediates, key=lambda i: i[1].sum.get_device()) + + to_reduce = [i[1][:2] for i in intermediates] + to_reduce = [j for i in to_reduce for j in i] # flatten + target_gpus = [i[1].sum.get_device() for i in intermediates] + + sum_size = sum([i[1].sum_size for i in intermediates]) + sum_, ssum = ReduceAddCoalesced.apply(target_gpus[0], 2, *to_reduce) + mean, inv_std = self._compute_mean_std(sum_, ssum, sum_size) + + broadcasted = Broadcast.apply(target_gpus, mean, inv_std) + + outputs = [] + for i, rec in enumerate(intermediates): + outputs.append((rec[0], _MasterMessage(*broadcasted[i*2:i*2+2]))) + + return outputs + + def _compute_mean_std(self, sum_, ssum, size): + """Compute the mean and standard-deviation with sum and square-sum. This method + also maintains the moving average on the master device.""" + assert size > 1, 'BatchNorm computes unbiased standard-deviation, which requires size > 1.' + mean = sum_ / size + sumvar = ssum - sum_ * mean + unbias_var = sumvar / (size - 1) + bias_var = sumvar / size + + if hasattr(torch, 'no_grad'): + with torch.no_grad(): + self.running_mean = (1 - self.momentum) * self.running_mean + self.momentum * mean.data + self.running_var = (1 - self.momentum) * self.running_var + self.momentum * unbias_var.data + else: + self.running_mean = (1 - self.momentum) * self.running_mean + self.momentum * mean.data + self.running_var = (1 - self.momentum) * self.running_var + self.momentum * unbias_var.data + + if SBN_EPS_MODE == 'clamp': + return mean, bias_var.clamp(self.eps) ** -0.5 + elif SBN_EPS_MODE == 'plus': + return mean, (bias_var + self.eps) ** -0.5 + else: + raise ValueError('Unknown EPS mode: {}.'.format(SBN_EPS_MODE)) + + +class SynchronizedBatchNorm1d(_SynchronizedBatchNorm): + r"""Applies Synchronized Batch Normalization over a 2d or 3d input that is seen as a + mini-batch. + + .. math:: + + y = \frac{x - mean[x]}{ \sqrt{Var[x] + \epsilon}} * gamma + beta + + This module differs from the built-in PyTorch BatchNorm1d as the mean and + standard-deviation are reduced across all devices during training. + + For example, when one uses `nn.DataParallel` to wrap the network during + training, PyTorch's implementation normalize the tensor on each device using + the statistics only on that device, which accelerated the computation and + is also easy to implement, but the statistics might be inaccurate. + Instead, in this synchronized version, the statistics will be computed + over all training samples distributed on multiple devices. + + Note that, for one-GPU or CPU-only case, this module behaves exactly same + as the built-in PyTorch implementation. + + The mean and standard-deviation are calculated per-dimension over + the mini-batches and gamma and beta are learnable parameter vectors + of size C (where C is the input size). + + During training, this layer keeps a running estimate of its computed mean + and variance. The running sum is kept with a default momentum of 0.1. + + During evaluation, this running mean/variance is used for normalization. + + Because the BatchNorm is done over the `C` dimension, computing statistics + on `(N, L)` slices, it's common terminology to call this Temporal BatchNorm + + Args: + num_features: num_features from an expected input of size + `batch_size x num_features [x width]` + eps: a value added to the denominator for numerical stability. + Default: 1e-5 + momentum: the value used for the running_mean and running_var + computation. Default: 0.1 + affine: a boolean value that when set to ``True``, gives the layer learnable + affine parameters. Default: ``True`` + + Shape:: + - Input: :math:`(N, C)` or :math:`(N, C, L)` + - Output: :math:`(N, C)` or :math:`(N, C, L)` (same shape as input) + + Examples: + >>> # With Learnable Parameters + >>> m = SynchronizedBatchNorm1d(100) + >>> # Without Learnable Parameters + >>> m = SynchronizedBatchNorm1d(100, affine=False) + >>> input = torch.autograd.Variable(torch.randn(20, 100)) + >>> output = m(input) + """ + + def _check_input_dim(self, input): + if input.dim() != 2 and input.dim() != 3: + raise ValueError('expected 2D or 3D input (got {}D input)' + .format(input.dim())) + + +class SynchronizedBatchNorm2d(_SynchronizedBatchNorm): + r"""Applies Batch Normalization over a 4d input that is seen as a mini-batch + of 3d inputs + + .. math:: + + y = \frac{x - mean[x]}{ \sqrt{Var[x] + \epsilon}} * gamma + beta + + This module differs from the built-in PyTorch BatchNorm2d as the mean and + standard-deviation are reduced across all devices during training. + + For example, when one uses `nn.DataParallel` to wrap the network during + training, PyTorch's implementation normalize the tensor on each device using + the statistics only on that device, which accelerated the computation and + is also easy to implement, but the statistics might be inaccurate. + Instead, in this synchronized version, the statistics will be computed + over all training samples distributed on multiple devices. + + Note that, for one-GPU or CPU-only case, this module behaves exactly same + as the built-in PyTorch implementation. + + The mean and standard-deviation are calculated per-dimension over + the mini-batches and gamma and beta are learnable parameter vectors + of size C (where C is the input size). + + During training, this layer keeps a running estimate of its computed mean + and variance. The running sum is kept with a default momentum of 0.1. + + During evaluation, this running mean/variance is used for normalization. + + Because the BatchNorm is done over the `C` dimension, computing statistics + on `(N, H, W)` slices, it's common terminology to call this Spatial BatchNorm + + Args: + num_features: num_features from an expected input of + size batch_size x num_features x height x width + eps: a value added to the denominator for numerical stability. + Default: 1e-5 + momentum: the value used for the running_mean and running_var + computation. Default: 0.1 + affine: a boolean value that when set to ``True``, gives the layer learnable + affine parameters. Default: ``True`` + + Shape:: + - Input: :math:`(N, C, H, W)` + - Output: :math:`(N, C, H, W)` (same shape as input) + + Examples: + >>> # With Learnable Parameters + >>> m = SynchronizedBatchNorm2d(100) + >>> # Without Learnable Parameters + >>> m = SynchronizedBatchNorm2d(100, affine=False) + >>> input = torch.autograd.Variable(torch.randn(20, 100, 35, 45)) + >>> output = m(input) + """ + + def _check_input_dim(self, input): + if input.dim() != 4: + raise ValueError('expected 4D input (got {}D input)' + .format(input.dim())) + + +class SynchronizedBatchNorm3d(_SynchronizedBatchNorm): + r"""Applies Batch Normalization over a 5d input that is seen as a mini-batch + of 4d inputs + + .. math:: + + y = \frac{x - mean[x]}{ \sqrt{Var[x] + \epsilon}} * gamma + beta + + This module differs from the built-in PyTorch BatchNorm3d as the mean and + standard-deviation are reduced across all devices during training. + + For example, when one uses `nn.DataParallel` to wrap the network during + training, PyTorch's implementation normalize the tensor on each device using + the statistics only on that device, which accelerated the computation and + is also easy to implement, but the statistics might be inaccurate. + Instead, in this synchronized version, the statistics will be computed + over all training samples distributed on multiple devices. + + Note that, for one-GPU or CPU-only case, this module behaves exactly same + as the built-in PyTorch implementation. + + The mean and standard-deviation are calculated per-dimension over + the mini-batches and gamma and beta are learnable parameter vectors + of size C (where C is the input size). + + During training, this layer keeps a running estimate of its computed mean + and variance. The running sum is kept with a default momentum of 0.1. + + During evaluation, this running mean/variance is used for normalization. + + Because the BatchNorm is done over the `C` dimension, computing statistics + on `(N, D, H, W)` slices, it's common terminology to call this Volumetric BatchNorm + or Spatio-temporal BatchNorm + + Args: + num_features: num_features from an expected input of + size batch_size x num_features x depth x height x width + eps: a value added to the denominator for numerical stability. + Default: 1e-5 + momentum: the value used for the running_mean and running_var + computation. Default: 0.1 + affine: a boolean value that when set to ``True``, gives the layer learnable + affine parameters. Default: ``True`` + + Shape:: + - Input: :math:`(N, C, D, H, W)` + - Output: :math:`(N, C, D, H, W)` (same shape as input) + + Examples: + >>> # With Learnable Parameters + >>> m = SynchronizedBatchNorm3d(100) + >>> # Without Learnable Parameters + >>> m = SynchronizedBatchNorm3d(100, affine=False) + >>> input = torch.autograd.Variable(torch.randn(20, 100, 35, 45, 10)) + >>> output = m(input) + """ + + def _check_input_dim(self, input): + if input.dim() != 5: + raise ValueError('expected 5D input (got {}D input)' + .format(input.dim())) + + +@contextlib.contextmanager +def patch_sync_batchnorm(): + import torch.nn as nn + + backup = nn.BatchNorm1d, nn.BatchNorm2d, nn.BatchNorm3d + + nn.BatchNorm1d = SynchronizedBatchNorm1d + nn.BatchNorm2d = SynchronizedBatchNorm2d + nn.BatchNorm3d = SynchronizedBatchNorm3d + + yield + + nn.BatchNorm1d, nn.BatchNorm2d, nn.BatchNorm3d = backup + + +def convert_model(module): + """Traverse the input module and its child recursively + and replace all instance of torch.nn.modules.batchnorm.BatchNorm*N*d + to SynchronizedBatchNorm*N*d + + Args: + module: the input module needs to be convert to SyncBN model + + Examples: + >>> import torch.nn as nn + >>> import torchvision + >>> # m is a standard pytorch model + >>> m = torchvision.models.resnet18(True) + >>> m = nn.DataParallel(m) + >>> # after convert, m is using SyncBN + >>> m = convert_model(m) + """ + if isinstance(module, torch.nn.DataParallel): + mod = module.module + mod = convert_model(mod) + mod = DataParallelWithCallback(mod, device_ids=module.device_ids) + return mod + + mod = module + for pth_module, sync_module in zip([torch.nn.modules.batchnorm.BatchNorm1d, + torch.nn.modules.batchnorm.BatchNorm2d, + torch.nn.modules.batchnorm.BatchNorm3d], + [SynchronizedBatchNorm1d, + SynchronizedBatchNorm2d, + SynchronizedBatchNorm3d]): + if isinstance(module, pth_module): + mod = sync_module(module.num_features, module.eps, module.momentum, module.affine) + mod.running_mean = module.running_mean + mod.running_var = module.running_var + if module.affine: + mod.weight.data = module.weight.data.clone().detach() + mod.bias.data = module.bias.data.clone().detach() + + for name, child in module.named_children(): + mod.add_module(name, convert_model(child)) + + return mod diff --git a/models/networks/sync_batchnorm/batchnorm_reimpl.py b/models/networks/sync_batchnorm/batchnorm_reimpl.py new file mode 100644 index 0000000000000000000000000000000000000000..18145c3353e13d482c492ae46df91a537669fca0 --- /dev/null +++ b/models/networks/sync_batchnorm/batchnorm_reimpl.py @@ -0,0 +1,74 @@ +#! /usr/bin/env python3 +# -*- coding: utf-8 -*- +# File : batchnorm_reimpl.py +# Author : acgtyrant +# Date : 11/01/2018 +# +# This file is part of Synchronized-BatchNorm-PyTorch. +# https://github.com/vacancy/Synchronized-BatchNorm-PyTorch +# Distributed under MIT License. + +import torch +import torch.nn as nn +import torch.nn.init as init + +__all__ = ['BatchNorm2dReimpl'] + + +class BatchNorm2dReimpl(nn.Module): + """ + A re-implementation of batch normalization, used for testing the numerical + stability. + + Author: acgtyrant + See also: + https://github.com/vacancy/Synchronized-BatchNorm-PyTorch/issues/14 + """ + def __init__(self, num_features, eps=1e-5, momentum=0.1): + super().__init__() + + self.num_features = num_features + self.eps = eps + self.momentum = momentum + self.weight = nn.Parameter(torch.empty(num_features)) + self.bias = nn.Parameter(torch.empty(num_features)) + self.register_buffer('running_mean', torch.zeros(num_features)) + self.register_buffer('running_var', torch.ones(num_features)) + self.reset_parameters() + + def reset_running_stats(self): + self.running_mean.zero_() + self.running_var.fill_(1) + + def reset_parameters(self): + self.reset_running_stats() + init.uniform_(self.weight) + init.zeros_(self.bias) + + def forward(self, input_): + batchsize, channels, height, width = input_.size() + numel = batchsize * height * width + input_ = input_.permute(1, 0, 2, 3).contiguous().view(channels, numel) + sum_ = input_.sum(1) + sum_of_square = input_.pow(2).sum(1) + mean = sum_ / numel + sumvar = sum_of_square - sum_ * mean + + self.running_mean = ( + (1 - self.momentum) * self.running_mean + + self.momentum * mean.detach() + ) + unbias_var = sumvar / (numel - 1) + self.running_var = ( + (1 - self.momentum) * self.running_var + + self.momentum * unbias_var.detach() + ) + + bias_var = sumvar / numel + inv_std = 1 / (bias_var + self.eps).pow(0.5) + output = ( + (input_ - mean.unsqueeze(1)) * inv_std.unsqueeze(1) * + self.weight.unsqueeze(1) + self.bias.unsqueeze(1)) + + return output.view(channels, batchsize, height, width).permute(1, 0, 2, 3).contiguous() + diff --git a/models/networks/sync_batchnorm/comm.py b/models/networks/sync_batchnorm/comm.py new file mode 100644 index 0000000000000000000000000000000000000000..922f8c4a3adaa9b32fdcaef09583be03b0d7eb2b --- /dev/null +++ b/models/networks/sync_batchnorm/comm.py @@ -0,0 +1,137 @@ +# -*- coding: utf-8 -*- +# File : comm.py +# Author : Jiayuan Mao +# Email : maojiayuan@gmail.com +# Date : 27/01/2018 +# +# This file is part of Synchronized-BatchNorm-PyTorch. +# https://github.com/vacancy/Synchronized-BatchNorm-PyTorch +# Distributed under MIT License. + +import queue +import collections +import threading + +__all__ = ['FutureResult', 'SlavePipe', 'SyncMaster'] + + +class FutureResult(object): + """A thread-safe future implementation. Used only as one-to-one pipe.""" + + def __init__(self): + self._result = None + self._lock = threading.Lock() + self._cond = threading.Condition(self._lock) + + def put(self, result): + with self._lock: + assert self._result is None, 'Previous result has\'t been fetched.' + self._result = result + self._cond.notify() + + def get(self): + with self._lock: + if self._result is None: + self._cond.wait() + + res = self._result + self._result = None + return res + + +_MasterRegistry = collections.namedtuple('MasterRegistry', ['result']) +_SlavePipeBase = collections.namedtuple('_SlavePipeBase', ['identifier', 'queue', 'result']) + + +class SlavePipe(_SlavePipeBase): + """Pipe for master-slave communication.""" + + def run_slave(self, msg): + self.queue.put((self.identifier, msg)) + ret = self.result.get() + self.queue.put(True) + return ret + + +class SyncMaster(object): + """An abstract `SyncMaster` object. + + - During the replication, as the data parallel will trigger an callback of each module, all slave devices should + call `register(id)` and obtain an `SlavePipe` to communicate with the master. + - During the forward pass, master device invokes `run_master`, all messages from slave devices will be collected, + and passed to a registered callback. + - After receiving the messages, the master device should gather the information and determine to message passed + back to each slave devices. + """ + + def __init__(self, master_callback): + """ + + Args: + master_callback: a callback to be invoked after having collected messages from slave devices. + """ + self._master_callback = master_callback + self._queue = queue.Queue() + self._registry = collections.OrderedDict() + self._activated = False + + def __getstate__(self): + return {'master_callback': self._master_callback} + + def __setstate__(self, state): + self.__init__(state['master_callback']) + + def register_slave(self, identifier): + """ + Register an slave device. + + Args: + identifier: an identifier, usually is the device id. + + Returns: a `SlavePipe` object which can be used to communicate with the master device. + + """ + if self._activated: + assert self._queue.empty(), 'Queue is not clean before next initialization.' + self._activated = False + self._registry.clear() + future = FutureResult() + self._registry[identifier] = _MasterRegistry(future) + return SlavePipe(identifier, self._queue, future) + + def run_master(self, master_msg): + """ + Main entry for the master device in each forward pass. + The messages were first collected from each devices (including the master device), and then + an callback will be invoked to compute the message to be sent back to each devices + (including the master device). + + Args: + master_msg: the message that the master want to send to itself. This will be placed as the first + message when calling `master_callback`. For detailed usage, see `_SynchronizedBatchNorm` for an example. + + Returns: the message to be sent back to the master device. + + """ + self._activated = True + + intermediates = [(0, master_msg)] + for i in range(self.nr_slaves): + intermediates.append(self._queue.get()) + + results = self._master_callback(intermediates) + assert results[0][0] == 0, 'The first result should belongs to the master.' + + for i, res in results: + if i == 0: + continue + self._registry[i].result.put(res) + + for i in range(self.nr_slaves): + assert self._queue.get() is True + + return results[0][1] + + @property + def nr_slaves(self): + return len(self._registry) diff --git a/models/networks/sync_batchnorm/replicate.py b/models/networks/sync_batchnorm/replicate.py new file mode 100644 index 0000000000000000000000000000000000000000..b71c7b8ed51a1d6c55b1f753bdd8d90bad79bd06 --- /dev/null +++ b/models/networks/sync_batchnorm/replicate.py @@ -0,0 +1,94 @@ +# -*- coding: utf-8 -*- +# File : replicate.py +# Author : Jiayuan Mao +# Email : maojiayuan@gmail.com +# Date : 27/01/2018 +# +# This file is part of Synchronized-BatchNorm-PyTorch. +# https://github.com/vacancy/Synchronized-BatchNorm-PyTorch +# Distributed under MIT License. + +import functools + +from torch.nn.parallel.data_parallel import DataParallel + +__all__ = [ + 'CallbackContext', + 'execute_replication_callbacks', + 'DataParallelWithCallback', + 'patch_replication_callback' +] + + +class CallbackContext(object): + pass + + +def execute_replication_callbacks(modules): + """ + Execute an replication callback `__data_parallel_replicate__` on each module created by original replication. + + The callback will be invoked with arguments `__data_parallel_replicate__(ctx, copy_id)` + + Note that, as all modules are isomorphism, we assign each sub-module with a context + (shared among multiple copies of this module on different devices). + Through this context, different copies can share some information. + + We guarantee that the callback on the master copy (the first copy) will be called ahead of calling the callback + of any slave copies. + """ + master_copy = modules[0] + nr_modules = len(list(master_copy.modules())) + ctxs = [CallbackContext() for _ in range(nr_modules)] + + for i, module in enumerate(modules): + for j, m in enumerate(module.modules()): + if hasattr(m, '__data_parallel_replicate__'): + m.__data_parallel_replicate__(ctxs[j], i) + + +class DataParallelWithCallback(DataParallel): + """ + Data Parallel with a replication callback. + + An replication callback `__data_parallel_replicate__` of each module will be invoked after being created by + original `replicate` function. + The callback will be invoked with arguments `__data_parallel_replicate__(ctx, copy_id)` + + Examples: + > sync_bn = SynchronizedBatchNorm1d(10, eps=1e-5, affine=False) + > sync_bn = DataParallelWithCallback(sync_bn, device_ids=[0, 1]) + # sync_bn.__data_parallel_replicate__ will be invoked. + """ + + def replicate(self, module, device_ids): + modules = super(DataParallelWithCallback, self).replicate(module, device_ids) + execute_replication_callbacks(modules) + return modules + + +def patch_replication_callback(data_parallel): + """ + Monkey-patch an existing `DataParallel` object. Add the replication callback. + Useful when you have customized `DataParallel` implementation. + + Examples: + > sync_bn = SynchronizedBatchNorm1d(10, eps=1e-5, affine=False) + > sync_bn = DataParallel(sync_bn, device_ids=[0, 1]) + > patch_replication_callback(sync_bn) + # this is equivalent to + > sync_bn = SynchronizedBatchNorm1d(10, eps=1e-5, affine=False) + > sync_bn = DataParallelWithCallback(sync_bn, device_ids=[0, 1]) + """ + + assert isinstance(data_parallel, DataParallel) + + old_replicate = data_parallel.replicate + + @functools.wraps(old_replicate) + def new_replicate(module, device_ids): + modules = old_replicate(module, device_ids) + execute_replication_callbacks(modules) + return modules + + data_parallel.replicate = new_replicate diff --git a/models/networks/sync_batchnorm/unittest.py b/models/networks/sync_batchnorm/unittest.py new file mode 100644 index 0000000000000000000000000000000000000000..998223a0e0242dc4a5b2fcd74af79dc7232794da --- /dev/null +++ b/models/networks/sync_batchnorm/unittest.py @@ -0,0 +1,29 @@ +# -*- coding: utf-8 -*- +# File : unittest.py +# Author : Jiayuan Mao +# Email : maojiayuan@gmail.com +# Date : 27/01/2018 +# +# This file is part of Synchronized-BatchNorm-PyTorch. +# https://github.com/vacancy/Synchronized-BatchNorm-PyTorch +# Distributed under MIT License. + +import unittest +import torch + + +class TorchTestCase(unittest.TestCase): + def assertTensorClose(self, x, y): + adiff = float((x - y).abs().max()) + if (y == 0).all(): + rdiff = 'NaN' + else: + rdiff = float((adiff / y).abs().max()) + + message = ( + 'Tensor close check failed\n' + 'adiff={}\n' + 'rdiff={}\n' + ).format(adiff, rdiff) + self.assertTrue(torch.allclose(x, y, atol=1e-5, rtol=1e-3), message) + diff --git a/models/networks/train.py b/models/networks/train.py new file mode 100644 index 0000000000000000000000000000000000000000..24596a259404d01a266437de0315b8bf1ff5286d --- /dev/null +++ b/models/networks/train.py @@ -0,0 +1,210 @@ +import argparse +import os +import torch +import torch.nn as nn +import torch.utils.data as data +from PIL import Image +from PIL import ImageFile +from tensorboardX import SummaryWriter +from torchvision import transforms +from tqdm import tqdm +from pathlib import Path +import StyTr2.models.transformer_decoder as transformer +from StyTr2.models.StyTR import StyTr +from StyTr2.sampler import InfiniteSamplerWrapper +from torchvision.utils import save_image + + +from StyTr2.models.transformerEncoder import TransformerEncoder +from StyTr2.models.schedule import CosineAnnealingWarmUpLR +from StyTr2.models.DecoderCNN import Decoder_MV, vgg_structures,decoder_stem # DecoderCNN +from StyTr2.models.transformer_decoder import TransformerDecoder # TransformerDecoder + +def train_transform(): + transform_list = [ + transforms.Resize(size=(512, 512)), + transforms.RandomCrop(size=(224, 224)), + transforms.ToTensor() + ] + return transforms.Compose(transform_list) + + +class FlatFolderDataset(data.Dataset): + def __init__(self, root, transform): + super(FlatFolderDataset, self).__init__() + self.root = root + print(self.root) + self.path = os.listdir(self.root) + if os.path.isdir(os.path.join(self.root, self.path[0])): + self.paths = [] + for file_name in os.listdir(self.root): + for file_name1 in os.listdir(os.path.join(self.root, file_name)): + self.paths.append(self.root + "/" + file_name + "/" + file_name1) + else: + self.paths = list(Path(self.root).glob('*')) + self.transform = transform + + def __getitem__(self, index): + path = self.paths[index] + img = Image.open(str(path)).convert('RGB') + img = self.transform(img) + return img + + def __len__(self): + return len(self.paths) + + def name(self): + return 'FlatFolderDataset' + +def save_checkpoint(encoder, transModule, decoder, optimizer, scheduler, epoch, + log_c, log_s, log_id1, log_id2, log_all, loss_count_interval, save_path): + checkpoint = { + 'encoder': encoder.state_dict() if not encoder is None else None, + 'transModule': transModule.state_dict() if not transModule is None else None, + 'decoder': decoder.state_dict() if not decoder is None else None, + 'optimizer': optimizer.state_dict() if not optimizer is None else None, + 'scheduler': scheduler.state_dict() if not scheduler is None else None, + 'epoch': epoch if not epoch is None else None, + 'log_c': log_c if not log_c is None else None, + 'log_s': log_s if not log_s is None else None, + 'log_id1': log_id1 if not log_id1 is None else None, + 'log_id2': log_id2 if not log_id2 is None else None, + 'log_all': log_all if not log_all is None else None, + 'loss_count_interval': loss_count_interval if not loss_count_interval is None else None + } + + torch.save(checkpoint, save_path) + + +parser = argparse.ArgumentParser() +# Basic options +parser.add_argument('--content_dir', default=r'E:\NLP\VAL_Transformers\models\StyTr2\images', type=str, + help='Directory path to a batch of content images') +parser.add_argument('--style_dir', default=r'E:\NLP\VAL_Transformers\models\StyTr2\style', type=str, + # wikiart dataset crawled from https://www.wikiart.org/ + help='Directory path to a batch of style images') +parser.add_argument('--vgg', type=str, + default=r'/home/share/VAL_ImageTranslation/models/networks/StyTr2/experiments/vgg_normalised.pth') # run the train.py, please download the pretrained vgg checkpoint + +# training options +parser.add_argument('--save_dir', default='./experiments', + help='Directory to save the model') +parser.add_argument('--log_dir', default='./logs', + help='Directory to save the log') +parser.add_argument('--lr', type=float, default=5e-4) +parser.add_argument('--lr_decay', type=float, default=1e-4) +parser.add_argument('--max_iter', type=int, default=3000) +parser.add_argument('--batch_size', type=int, default=8) +parser.add_argument('--style_weight', type=float, default=10.0) +parser.add_argument('--content_weight', type=float, default=7.0) +parser.add_argument('--n_threads', type=int, default=1) +parser.add_argument('--id1_weight', type=float, default=50) +parser.add_argument('--id2_weight', type=float, default=1) +parser.add_argument('--save_model_interval', type=int, default=3000) +parser.add_argument('--loss_count_interval', type=int, default=400) + +args = parser.parse_args() +loss_count_interval = args.loss_count_interval + +USE_CUDA = torch.cuda.is_available() +device = torch.device("cuda" if USE_CUDA else "cpu") +print(device) + +if not os.path.exists(args.save_dir): + os.makedirs(args.save_dir) + +if not os.path.exists(args.log_dir): + os.mkdir(args.log_dir) + +vgg = vgg_structures +vgg.load_state_dict(torch.load(args.vgg)) +vgg = nn.Sequential(*list(vgg.children())[:44]) + +encoder=TransformerEncoder(img_size=224,patch_size=2,in_chans=3,embed_dim=192,depths=[2, 2, 2],nhead=[3, 6, 12],strip_width=[2, 4, 7],drop_path_rate=0.,patch_norm=True) +decoder=Decoder_MV(d_model=768,seq_input=True) +transformer_decoder=TransformerDecoder(nlayer=3,d_model=768,nhead=8,mlp_ratio=4,qkv_bias=False,attn_drop=0.,drop=0.,drop_path=0.,act_layer=nn.GELU,norm_layer=nn.LayerNorm,norm_first=True) + +network=StyTr(encoder,decoder,transformer_decoder,vgg) + +optimizer = torch.optim.Adam([ + {'params': network.encoder.parameters()}, + {'params': network.decoder.parameters()}, + {'params': network.transModule.parameters()}, +], lr=args.lr_decay) +scheduler = CosineAnnealingWarmUpLR(optimizer, warmup_step=args.max_iter//4, max_step=args.max_iter, min_lr=0) + + +log_c, log_s, log_id1, log_id2, log_all = [],[],[],[],[] +log_c_temp, log_s_temp, log_id1_temp, log_id2_temp, log_all_temp = [],[],[],[],[] +network.train() +network.to(device) + +content_tf = train_transform() +style_tf = train_transform() + +content_dataset = FlatFolderDataset(args.content_dir, content_tf) +style_dataset = FlatFolderDataset(args.style_dir, style_tf) + +content_iter = iter(data.DataLoader( + content_dataset, batch_size=args.batch_size, + sampler=InfiniteSamplerWrapper(content_dataset), + num_workers=args.n_threads)) +style_iter = iter(data.DataLoader( + style_dataset, batch_size=args.batch_size, + sampler=InfiniteSamplerWrapper(style_dataset), + num_workers=args.n_threads)) + +if not os.path.exists(args.save_dir + "/test"): + os.makedirs(args.save_dir + "/test") + +for i in tqdm(range(args.max_iter)): + + content_images = next(content_iter).to(device) + style_images = next(style_iter).to(device) + + loss_c, loss_s, loss_id_1, loss_id_2, out = network(content_images, style_images) + loss_all = args.content_weight * loss_c + args.style_weight * loss_s + args.id1_weight * loss_id_1 + args.id2_weight * loss_id_2 + print("loss_all",loss_all.sum().cpu().detach().numpy(),"==>loss_c",loss_c.sum().cpu().detach().numpy(),"==>loss_s",loss_s.sum().cpu().detach().numpy(),"==>loss_id_1",loss_id_1.sum().cpu().detach().numpy(),"==>loss_id_2",loss_id_2.sum().cpu().detach().numpy()) + + log_c_temp.append(loss_c.item()) + log_s_temp.append(loss_s.item()) + log_id1_temp.append(loss_id_1.item()) + log_id2_temp.append(loss_id_2.item()) + log_all_temp.append(loss_all.item()) + + # update parameters + optimizer.zero_grad() + loss_all.backward() + optimizer.step() + scheduler.step() + + + + if i % 100 == 0: + output_name = '{:s}/test/{:s}{:s}'.format( + args.save_dir, str(i), ".jpg" + ) + out = torch.cat((content_images, out), 0) + out = torch.cat((style_images, out), 0) + save_image(out, output_name) + + + + if i % args.save_model_interval == 0: + save_checkpoint( + encoder=network.encoder, + transModule=network.transModule, + decoder=network.decoder, + optimizer=optimizer, + scheduler=scheduler, + epoch=i, + log_c=log_c, + log_s=log_s, + log_id1=log_id1, + log_id2=log_id2, + log_all=log_all, + loss_count_interval=loss_count_interval, + save_path=os.path.join(args.save_dir, 'checkpoint_{}_epoch.pkl'.format(i)) + ) + + diff --git a/models/pix2pix_model.py b/models/pix2pix_model.py new file mode 100644 index 0000000000000000000000000000000000000000..c78daa824ef81e5c1341d8af22d5ec389502dfee --- /dev/null +++ b/models/pix2pix_model.py @@ -0,0 +1,469 @@ +import torch + +import models.networks as networks +import util.util as util +import util.logging_wandb as wblogging +import os +import numpy as np +import torch.nn.functional as F +from tqdm import tqdm +from torchvision.utils import save_image +import torch.nn as nn +import torchvision.transforms as transforms +import torch.utils.data as data_torch +from torch.utils.data import Dataset +from .sampler import InfiniteSamplerWrapper,save_checkpoint +from .networks.generator_module.schedule import CosineAnnealingWarmUpLR +import torch.utils.data as data +from rich.progress import Progress, TimeRemainingColumn, BarColumn, TextColumn +import datetime + +class Pix2PixModel(torch.nn.Module): + @staticmethod + def modify_commandline_options(parser, is_train): + networks.modify_commandline_options(parser, is_train) + parser.add_argument('--lrG_decay', type=float, default=1e-4, help='learning rate decay') + parser.add_argument('--lrG', type=float, default=12e-4, help='initial learning rate for adam') + parser.add_argument('--save_dir', default=r'/home/share/VAL_ImgTranslations/experiments', + help='Directory to save the model') + parser.add_argument('--content_weight', type=float, default=2.0, help='weight for content reconstruction loss') + parser.add_argument('--save_model_interval', type=int, default=300) + parser.add_argument('--style_weight', type=float, default=3.0, help='weight for style reconstruction loss') + parser.add_argument('--max_iter', type=int, default=700) + parser.add_argument('--n_threads', type=int, default=1) + parser.add_argument('--batch_size', type=int, default=1) + parser.add_argument('--id1_weight', type=float, default=50) + parser.add_argument('--id2_weight', type=float, default=1) + parser.add_argument('--loss_count_interval', type=int, default=200) + return parser + + def __init__(self, opt): + super().__init__() + self.opt = opt + self.loss=0 + self.out=None + self.content_weight=opt.content_weight + self.style_weight=opt.style_weight + self.lrG_decay=opt.lrG_decay + self.lrG=opt.lrG + self.loss_count_interval=opt.loss_count_interval + self.save_model_interval=opt.save_model_interval + self.n_threads=opt.n_threads + self.save_dir=opt.save_dir + self.id1_weight=opt.id1_weight + self.id2_weight=opt.id2_weight + self.max_iter=opt.max_iter + self.epoch=0 + self.batch_size=opt.batch_size + self.FloatTensor = torch.cuda.FloatTensor if self.use_gpu() \ + else torch.FloatTensor + self.ByteTensor = torch.cuda.ByteTensor if self.use_gpu() \ + else torch.ByteTensor + + self.netG, self.netD,self.netD2, self.netE = self.initialize_networks(opt) + self.device="cuda" if torch.cuda.is_available() else "cpu" + + # set loss functions + if opt.isTrain: + self.criterionGAN = networks.GANLoss( + opt.gan_mode, tensor=self.FloatTensor, opt=self.opt) + self.criterionFeat = torch.nn.L1Loss() + if not opt.no_vgg_loss: + self.criterionVGG = networks.VGGLoss(self.opt.gpu_ids) + if opt.use_vae: + self.KLDLoss = networks.KLDLoss() + + # Entry point for all calls involving forward pass + # of deep networks. We used this approach since DataParallel module + # can't parallelize custom functions, we branch to different + # routines based on |mode|. + def forward(self, data, mode,iters,progress,epochs,images_iters): + input_semantics, real_image = self.preprocess_input(data) + # print("input_semantics: ", input_semantics) + # print("real_image: ", real_image.shape) + if mode == 'generator': + g_loss, generated = self.compute_generator_loss(input_semantics, real_image,iters=iters,progress=progress,epoch_model=epochs,images_iter=images_iters) + return g_loss, generated + elif mode == 'discriminator': + d_loss, d2_loss = self.compute_discriminator_loss(input_semantics, real_image,iters=iters) + return d_loss, d2_loss + elif mode == 'encode_only': + z, mu, logvar = self.encode_z(real_image) + return mu, logvar + elif mode == 'inference': + contents = F.interpolate(real_image, size=(224, 224), mode='bilinear', align_corners=False) + styles = F.interpolate(input_semantics, size=(224, 224), mode='bilinear', align_corners=False) + model = self.generate_fake(contents, styles, iters, mode="gen",progress=None) + with torch.no_grad(): + self.opt.alpha = self.opt.alpha.to(self.device) + _, _, _, _, output = model(contents, styles) + print("OUTPUT",output.shape) + upsample_layer = nn.Sequential(nn.Upsample(scale_factor=8 / 7, mode='bilinear', align_corners=False)) + fake_image = upsample_layer(output) + return fake_image + else: + raise ValueError("|mode| is invalid") + + def create_optimizers(self, opt): + G_params = list(self.netG.parameters()) + if opt.use_vae: + G_params += list(self.netE.parameters()) + if opt.isTrain: + D_params = list(self.netD.parameters()) + D2_params = list(self.netD2.parameters()) + + if opt.no_TTUR: + beta1, beta2 = opt.beta1, opt.beta2 + G_lr, D_lr = opt.lr, opt.lr + else: + beta1, beta2 = 0, 0.9 + G_lr, D_lr = opt.lr / 2, opt.lr * 2 + + optimizer_G = torch.optim.Adam(G_params, lr=G_lr, betas=(beta1, beta2)) + optimizer_D = torch.optim.Adam(D_params, lr=D_lr, betas=(beta1, beta2)) + optimizer_D2 = torch.optim.Adam(D2_params, lr=D_lr, betas=(beta1, beta2)) + + return optimizer_G, optimizer_D, optimizer_D2 + + def save(self, epoch): + util.save_network(self.netG, 'G', epoch, self.opt) + util.save_network(self.netD, 'D', epoch, self.opt) + util.save_network(self.netD2, 'D2', epoch, self.opt) + if self.opt.use_vae: + util.save_network(self.netE, 'E', epoch, self.opt) + + ############################################################################ + # Private helper methods + ############################################################################ + + def initialize_networks(self, opt): + netG = networks.define_G(opt) + netD = networks.define_D(opt) if opt.isTrain else None + netD2 = networks.define_D(opt) if opt.isTrain else None + netE = networks.define_E(opt) if opt.use_vae else None + + if not opt.isTrain or opt.continue_train: + netG = util.load_network(netG, 'G', opt.which_epoch, opt) + if opt.isTrain: + netD = util.load_network(netD, 'D', opt.which_epoch, opt) + netD2 = util.load_network(netD2, 'D2', opt.which_epoch, opt) + if opt.use_vae: + netE = util.load_network(netE, 'E', opt.which_epoch, opt) + + return netG, netD, netD2, netE + + # preprocess the input, such as moving the tensors to GPUs + # and transforming the label map to one-hot encoding (for SIS) + # |data|: dictionary of the input data + def preprocess_input(self, data): + # move to GPU and change data types + if self.opt.task == 'SIS': + data['label'] = data['label'].long() + if self.use_gpu(): + data['label'] = data['label'].cuda() + if 'instance' in data: + try: + data['instance'] = data['instance'].cuda() + except: + data['instance'] = data['instance'] + else: + data['instance'] = None # or handle as appropriate for your model + + data['image'] = data['image'].cuda() + + # create one-hot label map for SIS + if self.opt.task == 'SIS': + label_map = data['label'] + bs, _, h, w = label_map.size() + nc = self.opt.label_nc + 1 if self.opt.contain_dontcare_label \ + else self.opt.label_nc + input_label = self.FloatTensor(bs, nc, h, w).zero_() + input_semantics = input_label.scatter_(1, label_map, 1.0) + + # concatenate instance map if it exists + if not self.opt.no_instance: + inst_map = data['instance'] + instance_edge_map = self.get_edges(inst_map) + input_semantics = torch.cat((input_semantics, instance_edge_map), dim=1) + else: + input_semantics = data['label'] + + return input_semantics, data['image'] + + def compute_generator_loss(self, content, style,iters,progress,epoch_model,images_iter): + G_losses = {} + + fake_image, loss_StyTr2 = self.generate_fake(content, style,iters=iters,progress=progress,epochs=epoch_model,image_iters=images_iter) + pred_fake, pred_real_c = self.discriminate(fake_image, content) + + # # computer loss using Discriminator 1 + G_losses['GAN'] = self.criterionGAN(pred_fake, True, for_discriminator=False) * (1 - self.opt.alpha) + + # computer loss using Discriminator 2 + pred_fake, pred_real = self.discriminate(fake_image, style, type='D2') + G_losses['GAN'] = self.criterionGAN(pred_fake, True, for_discriminator=False) * self.opt.alpha + + if self.opt.task == 'SIS': + pred_fake, pred_real = self.discriminate(fake_image, style, content) + else: + pred_fake, pred_real = self.discriminate(fake_image, style) + + G_losses['GAN'] = self.criterionGAN(pred_fake, True,for_discriminator=False) + gan_loss_value = G_losses['GAN'].item() + new_GAN_loss = gan_loss_value + loss_StyTr2 + G_losses['GAN'] = torch.tensor([new_GAN_loss], device=self.device) + + if not self.opt.no_ganFeat_loss: + num_D = len(pred_fake) + GAN_Feat_loss = torch.tensor(0.0, dtype=torch.float32, device=self.device) + for i in range(num_D): # for each discriminator + # last output is the final prediction, so we exclude it + num_intermediate_outputs = len(pred_fake[i]) - 1 + for j in range(num_intermediate_outputs): # for each layer output + unweighted_loss = self.criterionFeat( + pred_fake[i][j], pred_real[i][j].detach()) + GAN_Feat_loss += unweighted_loss * self.opt.lambda_feat / num_D + G_losses['GAN_Feat'] = GAN_Feat_loss + + if not self.opt.no_vgg_loss: + target = style if self.opt.task == 'SIS' else content + G_losses['VGG'] = self.criterionVGG(fake_image, target) * self.opt.lambda_vgg + + return G_losses, fake_image + + def compute_discriminator_loss(self, content, style,iters): + D_losses = {} + D2_losses = {} + contents = F.interpolate(content, size=(224, 224), mode='bilinear', align_corners=False) + styles = F.interpolate(style, size=(224, 224), mode='bilinear', align_corners=False) + model=self.generate_fake(contents,styles,iters,mode="gen",progress=None) + with torch.no_grad(): + _, _, _, _, output=model(contents,styles) + upsample_layer = nn.Sequential(nn.Upsample(scale_factor=8 / 7, mode='bilinear', align_corners=False)) + fake_image = upsample_layer(output) + + # For Discriminator 1 between fake and content + pred_fake, pred_real = self.discriminate(fake_image, content) + D_losses['D_Fake'] = self.criterionGAN(pred_fake, False, for_discriminator=True) * (1 - self.opt.alpha) + D_losses['D_real'] = self.criterionGAN(pred_real, True, for_discriminator=True) + + pred_fake, pred_real = self.discriminate(fake_image, style, type='D2') + D2_losses['D_Fake'] = self.criterionGAN(pred_fake, False, for_discriminator=True) * self.opt.alpha + D2_losses['D_real'] = self.criterionGAN(pred_real, True, for_discriminator=True) + + return D_losses, D2_losses + + def encode_z(self, real_image): + mu, logvar = self.netE(real_image) + z = self.reparameterize(mu, logvar) + return z, mu, logvar + + def custom_epoch(self, curr_index_image): + if curr_index_image <= 5: + return self.max_iter + elif 5 < curr_index_image < 15: + return int(self.max_iter * 0.6) + elif 15 <= curr_index_image < 25: + return int(self.max_iter * 0.55) + elif 25 <= curr_index_image < 40: + return int(self.max_iter * 0.5) + elif 40 <= curr_index_image < 60: + return int(self.max_iter * 0.375) + elif 60 <= curr_index_image < 80: + return int(self.max_iter * 0.25) + elif 80 <= curr_index_image < 120: + return int(self.max_iter * 0.1) + else: + return int(self.max_iter * 0.05) + + def save_generator_weights_by_iters(self,net, label, epoch,iters): + util.save_generator_by_iter(net,label,epoch,iters,self.opt) + def generate_fake(self, input_semantics, real_image, iters,mode="train",progress=None,epochs=None,image_iters=None): + ### FOR GENERATE-FAKE IMAGES + if mode == "train": + models_Generator = self.netG() + optimizer = torch.optim.Adam([ + {'params': models_Generator.SEencoder.parameters()}, + {'params': models_Generator.decoder.parameters()}, + {'params': models_Generator.transModule.parameters()}, + ], lr=self.lrG_decay) + scheduler = CosineAnnealingWarmUpLR(optimizer, warmup_step=self.max_iter // 4, max_step=self.max_iter, + min_lr=0) + + contents = real_image.to(self.device) + style = input_semantics.to(self.device) + + content_images = F.interpolate(contents, size=(224, 224), mode='bilinear', align_corners=False) + style_images = F.interpolate(style, size=(224, 224), mode='bilinear', align_corners=False) + total_loss = 0 + + + # Mở file ghi log + log_file = open("train_log.txt", "a") + + with progress: + task = progress.add_task( + f"[green]Iters on one Images [{iters}|{self.custom_epoch(iters)}]- Total_Loss: {total_loss}", + total=self.custom_epoch(iters) + ) + total_i = 0 + step_wb=0 + + for i in range(self.custom_epoch(curr_index_image=iters)): + # Cộng dồn giá trị i vào tổng + if (i-self.custom_epoch(curr_index_image=iters)): + total_i=i + step_wb+=1 + if (step_wb>=1): + total_i=total_i+1 + + + loss_c, loss_s, loss_id_1, loss_id_2, out = models_Generator(content_images, style_images) + self.out = out + loss_all = (self.content_weight * loss_c + self.style_weight * loss_s + + self.id1_weight * loss_id_1 + self.id2_weight * loss_id_2) + + # Tính tổng loss + total_loss = round(float(loss_all.sum().cpu().detach().numpy()), 3) + print("Loss_model", loss_all.sum().cpu().detach().numpy(), "==>Content_Loss", + loss_c.sum().cpu().detach().numpy(), + "==>Style_Loss", loss_s.sum().cpu().detach().numpy(), "==>ID_1_Loss", + loss_id_1.sum().cpu().detach().numpy(), + "==>ID_2_Loss", loss_id_2.sum().cpu().detach().numpy()) + + #### WANDB ONE IMAGES + # wblogging.upload_all_loss_on_one_images(loss_value=total_loss, iters_all_images=image_iters, + # iters_one_imgs=total_i, epoch=epochs) + # wblogging.upload_l1_loss_on_one_images( + # loss_value=round(float(loss_id_1.sum().cpu().detach().numpy()), 3), + # iters_all_images=image_iters, iters_one_imgs=total_i, epoch=epochs) + # wblogging.upload_l2_loss_on_one_images( + # loss_value=round(float(loss_id_2.sum().cpu().detach().numpy()), 3), + # iters_all_images=image_iters, iters_one_imgs=total_i, epoch=epochs) + # wblogging.upload_content_loss_on_one_images( + # loss_value=round(float(loss_c.sum().cpu().detach().numpy()), 3), + # iters_all_images=image_iters, iters_one_imgs=total_i, epoch=epochs) + # wblogging.upload_style_loss_on_one_images( + # loss_value=round(float(loss_s.sum().cpu().detach().numpy()), 3), + # iters_all_images=image_iters, iters_one_imgs=total_i, epoch=epochs) + + current_lr = optimizer.param_groups[0]['lr'] + wblogging.upload_lr(current_lr, epoch=epochs) + + ### END LOGGING ON ONE IMAGES + + # Lấy thời gian hiện tại với format [ss/mm/hh/dd/mm/yyyy] + current_time = datetime.datetime.now().strftime("[%S-%M-%H-%d-%m-%Y]") + + # Ghi log vào file + log_file.write( + f"{current_time} Iter: {i}, Total Loss: {total_loss}, Content Loss: {loss_c.sum().cpu().detach().numpy()}, " + f"Style Loss: {loss_s.sum().cpu().detach().numpy()}, ID1 Loss: {loss_id_1.sum().cpu().detach().numpy()}, " + f"ID2 Loss: {loss_id_2.sum().cpu().detach().numpy()}\n") + + + progress.update(task, advance=1, + description=f"[green]Iters on one Images [{i + 1}|{self.custom_epoch(iters)}]- Total_Loss: {total_loss}") + + # Update parameters + optimizer.zero_grad() + loss_all.backward() + optimizer.step() + scheduler.step() + + # SAVE Weight by Iters + if ((i + 1) - (self.custom_epoch(curr_index_image=iters))) == 0: + self.save_generator_weights_by_iters(self.netG, 'G', epoch=self.epoch, iters=i + 1) + + if i % 100 == 0: + output_name = f'{self.save_dir}/test/{str(i)}.jpg' + out = torch.cat((content_images, out), 0) + out = torch.cat((style_images, out), 0) + save_image(out, output_name) + + # Đóng file log sau khi kết thúc + log_file.close() + + # #### WANDB ALL IMAGES + # wblogging.upload_loss_all(loss_all=total_loss, iters=image_iters, epoch=epochs) + # wblogging.upload_l1_loss_all(loss_values=round(float(loss_id_1.sum().cpu().detach().numpy()), 3), + # iters_images=image_iters, epoch=epochs) + # wblogging.upload_l2_loss(loss_values=round(float(loss_id_2.sum().cpu().detach().numpy()), 3), + # iters=image_iters, epoch=epochs) + # wblogging.upload_content_loss(loss_c=round(float(loss_c.sum().cpu().detach().numpy()), 3), + # iters=image_iters, epoch=epochs) + # wblogging.upload_style_loss(loss_s=round(float(loss_id_2.sum().cpu().detach().numpy()), 3), + # iters=image_iters, epoch=epochs) + ### END WANDB ON ALL IMAGES + + upsample_layer = nn.Sequential(nn.Upsample(scale_factor=8 / 7, mode='bilinear', align_corners=False)) + out = upsample_layer(self.out) + + return out, self.loss + + # FOR GENERATOR IMAGES - VAL + if(mode=="gen"): + models_Generator = self.netG() + models_Generator.eval() + models_Generator.to(self.device) + return models_Generator + + + # Given fake and real image, return the prediction of discriminator + # for each fake and real image. The condition is used in SIS. + def discriminate(self, fake_image, real_image, condition=None,type='D1'): + if self.opt.task == 'SIS': + assert condition is not None + fake_concat = torch.cat([condition, fake_image], dim=1) + real_concat = torch.cat([condition, real_image], dim=1) + else: + assert condition is None + fake_concat = fake_image + real_concat = real_image + + # In Batch Normalization, the fake and real images are + # recommended to be in the same batch to avoid disparate + # statistics in fake and real images. + # So both fake and real images are fed to D all at once. + fake_and_real = torch.cat([fake_concat, real_concat], dim=0) + + if type == 'D1': + discriminator_out = self.netD(fake_and_real) + else: + discriminator_out = self.netD2(fake_and_real) + + pred_fake, pred_real = self.divide_pred(discriminator_out) + + return pred_fake, pred_real + + # Take the prediction of fake and real images from the combined batch + def divide_pred(self, pred): + # the prediction contains the intermediate outputs of multi-scale GAN, + # so it's usually a list + if type(pred) == list: + fake = [] + real = [] + for p in pred: + fake.append([tensor[:tensor.size(0) // 2] for tensor in p]) + real.append([tensor[tensor.size(0) // 2:] for tensor in p]) + else: + fake = pred[:pred.size(0) // 2] + real = pred[pred.size(0) // 2:] + + return fake, real + + def get_edges(self, t): + edge = self.ByteTensor(t.size()).zero_() + edge[:, :, :, 1:] = edge[:, :, :, 1:] | (t[:, :, :, 1:] != t[:, :, :, :-1]) + edge[:, :, :, :-1] = edge[:, :, :, :-1] | (t[:, :, :, 1:] != t[:, :, :, :-1]) + edge[:, :, 1:, :] = edge[:, :, 1:, :] | (t[:, :, 1:, :] != t[:, :, :-1, :]) + edge[:, :, :-1, :] = edge[:, :, :-1, :] | (t[:, :, 1:, :] != t[:, :, :-1, :]) + return edge.float() + + def reparameterize(self, mu, logvar): + std = torch.exp(0.5 * logvar) + eps = torch.randn_like(std) + return eps.mul(std) + mu + + def use_gpu(self): + return len(self.opt.gpu_ids) > 0 diff --git a/models/sampler.py b/models/sampler.py new file mode 100644 index 0000000000000000000000000000000000000000..8d55a01a8232e776db32d73d59d2619a570784c7 --- /dev/null +++ b/models/sampler.py @@ -0,0 +1,48 @@ +import numpy as np +import torch +from torch.utils import data + + +def InfiniteSampler(n): + # i = 0 + i = n - 1 + order = np.random.permutation(n) + while True: + yield order[i] + i += 1 + if i >= n: + np.random.seed() + order = np.random.permutation(n) + i = 0 + + +class InfiniteSamplerWrapper(data.sampler.Sampler): + def __init__(self, data_source): + self.num_samples = len(data_source) + + def __iter__(self): + return iter(InfiniteSampler(self.num_samples)) + + def __len__(self): + return 2 ** 31 + + +def save_checkpoint(encoder1,encoder2, transModule, decoder, optimizer, scheduler, epoch, + log_c, log_s, log_id1, log_id2, log_all, loss_count_interval, save_path): + checkpoint = { + 'encoder1': encoder1.state_dict() if not encoder1 is None else None, + 'encoder2': encoder2.state_dict() if not encoder2 is None else None, + 'transModule': transModule.state_dict() if not transModule is None else None, + 'decoder': decoder.state_dict() if not decoder is None else None, + 'optimizer': optimizer.state_dict() if not optimizer is None else None, + 'scheduler': scheduler.state_dict() if not scheduler is None else None, + 'epoch': epoch if not epoch is None else None, + 'log_c': log_c if not log_c is None else None, + 'log_s': log_s if not log_s is None else None, + 'log_id1': log_id1 if not log_id1 is None else None, + 'log_id2': log_id2 if not log_id2 is None else None, + 'log_all': log_all if not log_all is None else None, + 'loss_count_interval': loss_count_interval if not loss_count_interval is None else None + } + + torch.save(checkpoint, save_path) \ No newline at end of file diff --git a/options/__init__.py b/options/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/options/__pycache__/__init__.cpython-310.pyc b/options/__pycache__/__init__.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..9c424ac808491573cae31303d3360f48759840e6 Binary files /dev/null and b/options/__pycache__/__init__.cpython-310.pyc differ diff --git a/options/__pycache__/__init__.cpython-37.pyc b/options/__pycache__/__init__.cpython-37.pyc new file mode 100644 index 0000000000000000000000000000000000000000..58b7241472a00aabbf4a52cc244bc49dd525cb03 Binary files /dev/null and b/options/__pycache__/__init__.cpython-37.pyc differ diff --git a/options/__pycache__/base_options.cpython-310.pyc b/options/__pycache__/base_options.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..a07080beeb0c0007d9aae325ab81ba1c7aed3de6 Binary files /dev/null and b/options/__pycache__/base_options.cpython-310.pyc differ diff --git a/options/__pycache__/base_options.cpython-37.pyc b/options/__pycache__/base_options.cpython-37.pyc new file mode 100644 index 0000000000000000000000000000000000000000..ff0694ca195176a035247e53d33cb0a22dbd1e28 Binary files /dev/null and b/options/__pycache__/base_options.cpython-37.pyc differ diff --git a/options/__pycache__/test_options.cpython-310.pyc b/options/__pycache__/test_options.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..7453bb3e4ba90d20db34f2e23635391ed771fdcd Binary files /dev/null and b/options/__pycache__/test_options.cpython-310.pyc differ diff --git a/options/__pycache__/test_options.cpython-37.pyc b/options/__pycache__/test_options.cpython-37.pyc new file mode 100644 index 0000000000000000000000000000000000000000..e2dc2752e86d159a5d8538dccd9041cf5987af1a Binary files /dev/null and b/options/__pycache__/test_options.cpython-37.pyc differ diff --git a/options/__pycache__/train_options.cpython-310.pyc b/options/__pycache__/train_options.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..dc272321414b5a8abfd67c6cddf206c1a8a508f5 Binary files /dev/null and b/options/__pycache__/train_options.cpython-310.pyc differ diff --git a/options/__pycache__/train_options.cpython-37.pyc b/options/__pycache__/train_options.cpython-37.pyc new file mode 100644 index 0000000000000000000000000000000000000000..d9385d67b99abde97040f2d82303f815a1850fd3 Binary files /dev/null and b/options/__pycache__/train_options.cpython-37.pyc differ diff --git a/options/base_options.py b/options/base_options.py new file mode 100644 index 0000000000000000000000000000000000000000..ce58df6c4268eb89b786f42f3e47e45f6140b7dc --- /dev/null +++ b/options/base_options.py @@ -0,0 +1,188 @@ +import sys +import argparse +import os +from util import util +import torch +import models +import data +import pickle + + +class BaseOptions(): + def __init__(self): + self.initialized = False + + def initialize(self, parser): + # experiment specifics + parser.add_argument('--name', type=str, default='ast_summer2winteryosemite', help='name of the experiment. It decides where to store samples and models') + parser.add_argument('--task', type=str, default='AST', help='task type: AST | SIS | MMIS') + + parser.add_argument('--gpu_ids', type=str, default='0', help='gpu ids: e.g. 0 0,1,2, 0,2. use -1 for CPU') + parser.add_argument('--checkpoints_dir', type=str, default='./checkpoints', help='models are saved here') + parser.add_argument('--model', type=str, default='pix2pix', help='which model to use') + parser.add_argument('--norm_G', type=str, default='spectralinstance', help='instance normalization or batch normalization') + parser.add_argument('--norm_D', type=str, default='spectralinstance', help='instance normalization or batch normalization') + parser.add_argument('--norm_S', type=str, default='spectralinstance', help='instance normalization or batch normalization') + parser.add_argument('--norm_E', type=str, default='spectralinstance', help='instance normalization or batch normalization') + parser.add_argument('--phase', type=str, default='train', help='train, val, test, etc') + + # input/output sizes + parser.add_argument('--batchSize', type=int, default=1, help='input batch size') + parser.add_argument('--preprocess_mode', type=str, default='scale_width_and_crop', help='scaling and cropping of images at load time.', choices=("resize_and_crop", "crop", "scale_width", "scale_width_and_crop", "scale_shortside", "scale_shortside_and_crop", "fixed", "none")) + parser.add_argument('--load_size', type=int, default=224, help='Scale images to this size. The final image will be cropped to --crop_size.') + parser.add_argument('--crop_size', type=int, default=224, help='Crop to the width of crop_size (after initially scaling the images to load_size.)') + parser.add_argument('--aspect_ratio', type=float, default=1.0, help='The ratio width/height. The final height of the load image will be crop_size/aspect_ratio') + parser.add_argument('--label_nc', type=int, default=3, help='# of input label classes without unknown class. If you have unknown class as class label, specify --contain_dopntcare_label.') + parser.add_argument('--contain_dontcare_label', action='store_true', help='if the label map contains dontcare label (dontcare=255)') + parser.add_argument('--output_nc', type=int, default=3, help='# of output image channels') + parser.add_argument('--latent_dim', type=int, default=256, help='dimension of latent style code') + + # for setting inputs + parser.add_argument('--dataset_mode', type=str, default='summer2winteryosemite') + parser.add_argument('--croot', type=str, default='./datasets/summer2winter_yosemite/', help='content dataroot') + parser.add_argument('--sroot', type=str, default='./datasets/summer2winter_yosemite/', help='style dataroot') + parser.add_argument('--serial_batches', action='store_true', help='if true, takes images in order to make batches, otherwise takes them randomly') + parser.add_argument('--no_flip', action='store_true', help='if specified, do not flip the images for data argumentation') + parser.add_argument('--nThreads', default=0, type=int, help='# threads for loading data') + parser.add_argument('--max_dataset_size', type=int, default=9108, help='Maximum number of samples allowed per dataset. If the dataset directory contains more than max_dataset_size, only a subset is loaded.') + parser.add_argument('--load_from_opt_file', action='store_true', help='load the options from checkpoints and use that as default') + parser.add_argument('--cache_filelist_write', action='store_true', help='saves the current filelist into a text file, so that it loads faster') + parser.add_argument('--cache_filelist_read', action='store_true', help='reads from the file list cache') + + # for displays + parser.add_argument('--display_winsize', type=int, default=400, help='display window size') + + # for generator + parser.add_argument('--netG', type=str, default='tsit', help='selects model to use for netG (tsit | pix2pixhd)') + parser.add_argument('--ngf', type=int, default=64, help='# of gen filters in first conv layer') + parser.add_argument('--init_type', type=str, default='xavier', help='network initialization [normal|xavier|kaiming|orthogonal]') + parser.add_argument('--init_variance', type=float, default=0.02, help='variance of the initialization distribution') + parser.add_argument('--z_dim', type=int, default=256, help="dimension of the latent z vector") + parser.add_argument('--alpha', type=float, default=1.0, help='The parameter that controls the degree of stylization (between 0 and 1)') + parser.add_argument('--no_ss', action='store_true', help='discard the style stream (better results in certain cases).') + parser.add_argument('--mode',type=int,default=1,help='Mode concat alpha with style') + + # for instance-wise features + parser.add_argument('--no_instance', action='store_true', help='if specified, do *not* add instance map as input') + parser.add_argument('--nef', type=int, default=16, help='# of encoder filters in the first conv layer') + parser.add_argument('--use_vae', action='store_true', help='enable training with an image encoder.') + + + self.initialized = True + + return parser + + def gather_options(self): + # initialize parser with basic options + if not self.initialized: + parser = argparse.ArgumentParser( + formatter_class=argparse.ArgumentDefaultsHelpFormatter) + parser = self.initialize(parser) + + # get the basic options + opt, unknown = parser.parse_known_args() + + # modify model-related parser options + model_name = opt.model + model_option_setter = models.get_option_setter(model_name) + parser = model_option_setter(parser, self.isTrain) + + # modify dataset-related parser options + dataset_mode = opt.dataset_mode + dataset_option_setter = data.get_option_setter(dataset_mode) + parser = dataset_option_setter(parser, self.isTrain) + + opt, unknown = parser.parse_known_args() + + # if there is opt_file, load it. + # The previous default options will be overwritten + if opt.load_from_opt_file: + parser = self.update_options_from_file(parser, opt) + + opt = parser.parse_args() + self.parser = parser + return opt + + def print_options(self, opt): + message = '' + message += '----------------- Options ---------------\n' + for k, v in sorted(vars(opt).items()): + comment = '' + default = self.parser.get_default(k) + if v != default: + comment = '\t[default: %s]' % str(default) + message += '{:>25}: {:<30}{}\n'.format(str(k), str(v), comment) + message += '----------------- End -------------------' + print(message) + + def option_file_path(self, opt, makedir=False): + expr_dir = os.path.join(opt.checkpoints_dir, opt.name) + if makedir: + util.mkdirs(expr_dir) + file_name = os.path.join(expr_dir, 'opt') + return file_name + + def save_options(self, opt): + file_name = self.option_file_path(opt, makedir=True) + with open(file_name + '.txt', 'wt') as opt_file: + for k, v in sorted(vars(opt).items()): + comment = '' + default = self.parser.get_default(k) + if v != default: + comment = '\t[default: %s]' % str(default) + opt_file.write('{:>25}: {:<30}{}\n'.format(str(k), str(v), comment)) + + with open(file_name + '.pkl', 'wb') as opt_file: + pickle.dump(opt, opt_file) + + def update_options_from_file(self, parser, opt): + new_opt = self.load_options(opt) + for k, v in sorted(vars(opt).items()): + if hasattr(new_opt, k) and v != getattr(new_opt, k): + new_val = getattr(new_opt, k) + parser.set_defaults(**{k: new_val}) + return parser + + def load_options(self, opt): + file_name = self.option_file_path(opt, makedir=False) + new_opt = pickle.load(open(file_name + '.pkl', 'rb')) + return new_opt + + def parse(self, save=False): + + opt = self.gather_options() + opt.num_upsampling_layers = 'more' + opt.isTrain = self.isTrain # train or test + assert opt.task == 'AST' or opt.task == 'SIS' or opt.task == 'MMIS', \ + f'Task type should be: AST | SIS | MMIS, but got {opt.task}.' + + # Set semantic_nc based on the option. + # This will be convenient in many places + if opt.task == 'SIS': + opt.semantic_nc = opt.label_nc + \ + (1 if opt.contain_dontcare_label else 0) + \ + (0 if opt.no_instance else 1) + opt.no_ss = True + else: + opt.semantic_nc = 3 + + self.print_options(opt) + if opt.isTrain: + self.save_options(opt) + + # set gpu ids + str_ids = opt.gpu_ids.split(',') + opt.gpu_ids = [] + for str_id in str_ids: + id = int(str_id) + if id >= 0: + opt.gpu_ids.append(id) + if len(opt.gpu_ids) > 0: + torch.cuda.set_device(opt.gpu_ids[0]) + + assert len(opt.gpu_ids) == 0 or opt.batchSize % len(opt.gpu_ids) == 0, \ + "Batch size %d is wrong. It must be a multiple of # GPUs %d." \ + % (opt.batchSize, len(opt.gpu_ids)) + + self.opt = opt + return self.opt diff --git a/options/test_options.py b/options/test_options.py new file mode 100644 index 0000000000000000000000000000000000000000..5e5e2e186a175776f46c995a190c74767556204a --- /dev/null +++ b/options/test_options.py @@ -0,0 +1,20 @@ +from .base_options import BaseOptions + + +class TestOptions(BaseOptions): + def initialize(self, parser): + BaseOptions.initialize(self, parser) + parser.add_argument('--results_dir', type=str, default='./results/', help='saves results here.') + parser.add_argument('--which_epoch', type=str, default='latest', help='which epoch to load? set to latest to use latest cached model') + parser.add_argument('--how_many', type=int, default=float("inf"), help='how many test images to run') + parser.add_argument('--show_input', action='store_true', help='show input images with the synthesized image') + parser.add_argument('--use_infinite_sampler', action='store_true', + help="Use infinite sampler for the dataloader") + + parser.set_defaults(preprocess_mode='scale_width_and_crop', crop_size=256, load_size=256, display_winsize=256) + parser.set_defaults(serial_batches=True) + parser.set_defaults(no_flip=True) + parser.set_defaults(phase='test') + + self.isTrain = False + return parser diff --git a/options/train_options.py b/options/train_options.py new file mode 100644 index 0000000000000000000000000000000000000000..0bb9b74a27fcb2856a0767910495a74850b1e14b --- /dev/null +++ b/options/train_options.py @@ -0,0 +1,41 @@ +from .base_options import BaseOptions + + +class TrainOptions(BaseOptions): + def initialize(self, parser): + BaseOptions.initialize(self, parser) + # for displays + parser.add_argument('--display_freq', type=int, default=100, help='frequency of showing training results on screen') + parser.add_argument('--print_freq', type=int, default=100, help='frequency of showing training results on console') + parser.add_argument('--save_latest_freq', type=int, default=5, help='frequency of saving the latest results') + parser.add_argument('--save_epoch_freq', type=int, default=1, help='frequency of saving checkpoints at the end of epochs') + parser.add_argument('--no_html', action='store_true', help='do not save intermediate training results to [opt.checkpoints_dir]/[opt.name]/web/') + parser.add_argument('--debug', action='store_true', help='only do one epoch and displays at each iteration') + parser.add_argument('--tf_log', action='store_true', help='if specified, use tensorboard logging. Requires tensorflow installed') + parser.add_argument('--use_infinite_sampler', action='store_true', + help="Use infinite sampler for the dataloader") + parser.add_argument('--max_iterations_per_epoch', type=int, default=1000, help='Maximum iterations per epoch') + + # for training + parser.add_argument('--continue_train', action='store_true', help='continue training: load the latest model') + parser.add_argument('--which_epoch', type=str, default='latest', help='which epoch to load? set to latest to use latest cached model') + parser.add_argument('--niter', type=int, default=50, help='# of iter at starting learning rate. This is NOT the total #epochs. Totla #epochs is niter + niter_decay') + parser.add_argument('--niter_decay', type=int, default=0, help='# of iter to linearly decay learning rate to zero') + parser.add_argument('--optimizer', type=str, default='adam') + parser.add_argument('--beta1', type=float, default=0.5, help='momentum term of adam') + parser.add_argument('--beta2', type=float, default=0.999, help='momentum term of adam') + parser.add_argument('--lr', type=float, default=0.0002, help='initial learning rate for adam') + parser.add_argument('--D_steps_per_G', type=int, default=1, help='number of discriminator iterations per generator iterations.') + + # for discriminators + parser.add_argument('--ndf', type=int, default=64, help='# of discrim filters in first conv layer') + parser.add_argument('--lambda_feat', type=float, default=10.0, help='weight for feature matching loss') + parser.add_argument('--lambda_vgg', type=float, default=10.0, help='weight for vgg loss') + parser.add_argument('--no_ganFeat_loss', action='store_true', help='if specified, do *not* use discriminator feature matching loss') + parser.add_argument('--no_vgg_loss', action='store_true', help='if specified, do *not* use VGG feature matching loss') + parser.add_argument('--gan_mode', type=str, default='hinge', help='(ls|original|hinge)') + parser.add_argument('--netD', type=str, default='multiscale', help='(n_layers|multiscale|image)') + parser.add_argument('--no_TTUR', action='store_true', help='Use TTUR training scheme') + parser.add_argument('--lambda_kld', type=float, default=0.05) + self.isTrain = True + return parser diff --git a/requirements.txt b/requirements.txt new file mode 100644 index 0000000000000000000000000000000000000000..bf47d2320d84d36091cc550e51a8a64701bd2dc3 --- /dev/null +++ b/requirements.txt @@ -0,0 +1,24 @@ +#python3.10 +torch==2.1.2 +torchvision==0.16.2 +timm==1.0.3 +einops==0.8.0 +tensorboardX==2.6.2.2 +protobuf==3.20.3 +scipy==1.11.4 +scikit-learn==1.2.2 +scikit-image==0.22.0 +numpy==1.26.4 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b/results/ast_summer2winteryosemite/test_latest/images/1.0/berlin_000002_000019_leftImg8bit_1.000.png new file mode 100644 index 0000000000000000000000000000000000000000..f618c6b3e57c85662f5c98b1a2fdb831afcce170 --- /dev/null +++ b/results/ast_summer2winteryosemite/test_latest/images/1.0/berlin_000002_000019_leftImg8bit_1.000.png @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:2d786f4103d04b486bd94567b37bee45c299fa41bc9271d4508e5a49a5f7987c +size 202235 diff --git a/results/ast_summer2winteryosemite/test_latest/index.html b/results/ast_summer2winteryosemite/test_latest/index.html new file mode 100644 index 0000000000000000000000000000000000000000..f87908a7e13798bbdf3d9e1810d388869d46b0bd --- /dev/null +++ b/results/ast_summer2winteryosemite/test_latest/index.html @@ -0,0 +1,9 @@ + + + + Experiment = ast_summer2winteryosemite, Phase = test, Epoch = latest + + +

03:17AM on November 15, 2024

+ + \ No newline at end of file diff --git a/sampler.py b/sampler.py new file mode 100644 index 0000000000000000000000000000000000000000..8d55a01a8232e776db32d73d59d2619a570784c7 --- /dev/null +++ b/sampler.py @@ -0,0 +1,48 @@ +import numpy as np +import torch +from torch.utils import data + + +def InfiniteSampler(n): + # i = 0 + i = n - 1 + order = np.random.permutation(n) + while True: + yield order[i] + i += 1 + if i >= n: + np.random.seed() + order = np.random.permutation(n) + i = 0 + + +class InfiniteSamplerWrapper(data.sampler.Sampler): + def __init__(self, data_source): + self.num_samples = len(data_source) + + def __iter__(self): + return iter(InfiniteSampler(self.num_samples)) + + def __len__(self): + return 2 ** 31 + + +def save_checkpoint(encoder1,encoder2, transModule, decoder, optimizer, scheduler, epoch, + log_c, log_s, log_id1, log_id2, log_all, loss_count_interval, save_path): + checkpoint = { + 'encoder1': encoder1.state_dict() if not encoder1 is None else None, + 'encoder2': encoder2.state_dict() if not encoder2 is None else None, + 'transModule': transModule.state_dict() if not transModule is None else None, + 'decoder': decoder.state_dict() if not decoder is None else None, + 'optimizer': optimizer.state_dict() if not optimizer is None else None, + 'scheduler': scheduler.state_dict() if not scheduler is None else None, + 'epoch': epoch if not epoch is None else None, + 'log_c': log_c if not log_c is None else None, + 'log_s': log_s if not log_s is None else None, + 'log_id1': log_id1 if not log_id1 is None else None, + 'log_id2': log_id2 if not log_id2 is None else None, + 'log_all': log_all if not log_all is None else None, + 'loss_count_interval': loss_count_interval if not loss_count_interval is None else None + } + + torch.save(checkpoint, save_path) \ No newline at end of file diff --git a/test.ipynb b/test.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..22e9adf50e5df1d65a4c917f982e74098bcd9598 --- /dev/null +++ b/test.ipynb @@ -0,0 +1,53 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "id": "initial_id", + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "" + ] + }, + { + "metadata": {}, + "cell_type": "code", + "outputs": [], + "execution_count": null, + "source": "", + "id": "13d3dcab9a8dab3a" + }, + { + "metadata": {}, + "cell_type": "code", + "outputs": [], + "execution_count": null, + "source": "", + "id": "9eb1c54d9533054" + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.6" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/test.py b/test.py new file mode 100644 index 0000000000000000000000000000000000000000..705a1fa5ffbe047bcf1bb8668611c591ee9b3eef --- /dev/null +++ b/test.py @@ -0,0 +1,86 @@ +from concurrent.futures import process +import os +from collections import OrderedDict +# from tkinter import Image +import torch +import torchvision +import ntpath + +import data +from options.test_options import TestOptions +from models.pix2pix_model import Pix2PixModel +from util.util import mkdir +from util.visualizer import Visualizer +from util import html +from tqdm import tqdm +import numpy as np + +opt = TestOptions().parse() + + +dataloader = data.create_dataloader(opt) + +model = Pix2PixModel(opt) +if opt.task != 'MMIS' and opt.dataset_mode != 'photo2art': + model.eval() + +visualizer = Visualizer(opt) + +web_dir = os.path.join(opt.results_dir, opt.name, + '%s_%s' % (opt.phase, opt.which_epoch)) +webpage = html.HTML(web_dir, + 'Experiment = %s, Phase = %s, Epoch = %s' % + (opt.name, opt.phase, opt.which_epoch)) + + + +# test +print('Number of images: ', len(dataloader)) +alpha_list = torch.linspace(0, 1.0, 20) #generate 20 images for each input image +stack = False +samples = len(dataloader) +processed = -1 +for i, data_i in enumerate(tqdm(dataloader)): + processed += 1 + if processed > samples: + break + alpha_imgs = [] + for j, alpha in enumerate(alpha_list): + opt.alpha = alpha + generated = model(data_i, mode='inference',iters=0,progress=None,epochs=None,images_iters=None) + + #self, data, mode,iters,progress,epochs,images_iters + img_path = data_i['cpath'] + + if j == 0: + alpha_imgs.append(data_i['image']) + alpha_imgs.append(data_i['label']) + alpha_imgs.append(generated) + if stack: + image_dir = webpage.get_image_dir() + short_path = ntpath.basename(img_path[0]) + name = os.path.splitext(short_path)[0] + image_name = '%s.png' % name + os.makedirs(image_dir, exist_ok=True) + save_path = os.path.join(image_dir, image_name) + alpha_stack = torch.cat(alpha_imgs, dim=0) + im_grid = torchvision.utils.make_grid(alpha_stack, nrow=len(alpha_imgs) + 2, padding=0, normalize=True) + torchvision.utils.save_image(im_grid, save_path) + + else: + for b in range(generated.shape[0]): + # print(i, 'process image... %s' % img_path[b]) + if opt.show_input: + if opt.task == 'SIS': + visuals = OrderedDict([('input_label', data_i['label'][b]), + ('real_image', data_i['image'][b]), + ('synthesized_image', generated[b])]) + else: + visuals = OrderedDict([('content', data_i['label'][b]), + ('style', data_i['image'][b]), + ('synthesized_image', generated[b])]) + else: + visuals = OrderedDict([('synthesized_image', generated[b])]) + visualizer.save_images(webpage, visuals, img_path[b:b + 1], alpha=opt.alpha) + +webpage.save() diff --git a/test3.py b/test3.py new file mode 100644 index 0000000000000000000000000000000000000000..9ac9a1d58a306372d131d1e38e0eaf27d3a9cf2d --- /dev/null +++ b/test3.py @@ -0,0 +1,206 @@ +import argparse +import torch +import torch.nn as nn +from pathlib import Path +import os +from tqdm import tqdm +import torchvision.transforms as transforms +from torchvision.utils import save_image + +import os +import zipfile +from PIL import Image + +# +# class Sample_Test_Net(nn.Module): +# def __init__(self, encoder, decoder, transModule, patch_size=8): +# super(Sample_Test_Net, self).__init__() +# self.encoder = encoder +# self.decoder = decoder +# self.transModule = transModule +# self.patch_size = patch_size +# +# def forward(self, i_c, i_s, arbitrary_input=False): +# _, _, H, W = i_c.size() +# self.decoder.img_H = H +# self.decoder.img_W = W +# f_c = self.encoder(i_c, arbitrary_input) +# f_s = self.encoder(i_s, arbitrary_input) +# f_c, f_c_reso = f_c[0], f_c[2] +# f_s, f_s_reso = f_s[0], f_s[2] +# f_cs = self.transModule(f_c, f_s) +# i_cs = self.decoder(f_cs, f_c_reso) +# return i_cs +# +# +# def content_style_transTo_pt(i_c_path, i_s_path, i_c_size=None): +# """Resize the pics of arbitrary size to the shape of content image +# """ +# i_c_pil = Image.open(i_c_path) +# i_s_pil = Image.open(i_s_path) +# +# if not i_c_size is None: +# i_c_tf = transforms.Compose([ +# transforms.Resize(i_c_size), +# transforms.ToTensor() +# ]) +# else: +# i_c_tf = transforms.Compose([ +# transforms.ToTensor() +# ]) +# +# i_s_size = min(i_c_pil.size[1], i_c_pil.size[0]) +# i_s_tf = transforms.Compose([ +# transforms.Resize(i_s_size), +# transforms.ToTensor() +# ]) +# +# i_c_pt = i_c_tf(i_c_pil).unsqueeze(dim=0) +# i_s_pt = i_s_tf(i_s_pil).unsqueeze(dim=0) +# +# return i_c_pt, i_s_pt +# +# @torch.no_grad() +# def save_transferred_imgs(network, samples_path, img_saved_path, device=torch.device('cpu')): +# print('Image generation starts:') +# +# i_c_names = os.listdir(os.path.join(samples_path, 'Content')) +# i_s_names = os.listdir(os.path.join(samples_path, 'Style')) +# for i_c_name in tqdm(i_c_names): +# for i_s_name in tqdm(i_s_names): +# i_c_path = os.path.join(samples_path, 'Content', i_c_name) +# i_s_path = os.path.join(samples_path, 'Style', i_s_name) +# i_c, i_s = content_style_transTo_pt(i_c_path, i_s_path) +# i_cs = network(i_c.to(device), i_s.to(device), arbitrary_input=True) +# +# stem_c, suffix_c = os.path.splitext(i_c_name) +# stem_s, suffix_s = os.path.splitext(i_s_name) +# output_name = os.path.join(img_saved_path, f'{stem_c}_+_{stem_s}.{suffix_c}') +# save_image(i_cs, output_name) +# +# +# parser = argparse.ArgumentParser() +# # Basic options +# parser.add_argument('--input_dir', type=str, default='./input/Test', +# help='Directory path to a batch of content and style images ' + \ +# 'which are loaded in "Content"/"Style" subfolders respectively.') +# parser.add_argument('--output_dir', type=str, default='./output', +# help='Directory to save the output image(s)') +# parser.add_argument('--checkpoint_import_path', type=str, +# default='./pre_trained_models/checkpoint/checkpoint_40000_epoch.pkl', +# help='Directory path to the importing checkpoint') +# +# args = parser.parse_args() + + +from models.pix2pix_model import Pix2PixModel +from options.test_options import TestOptions +import numpy as np +opt = TestOptions().parse() + + + + +def test_transform(size, crop): + transform_list = [] + + if size != 0: + transform_list.append(transforms.Resize(size)) + if crop: + transform_list.append(transforms.CenterCrop(size)) + transform_list.append(transforms.ToTensor()) + transform = transforms.Compose(transform_list) + return transform + +from os.path import basename +from os.path import splitext + +def style_transform(h, w): + k = (h, w) + size = int(np.max(k)) + print(type(size)) + transform_list = [] + transform_list.append(transforms.CenterCrop((h, w))) + transform_list.append(transforms.ToTensor()) + transform = transforms.Compose(transform_list) + return transform + + +def content_transform(): + transform_list = [] + transform_list.append(transforms.ToTensor()) + transform = transforms.Compose(transform_list) + return transform + + +# Advanced options +content_size = 512 +style_size = 512 +crop = 'store_true' +save_ext = '.jpg' +output_path = opt.output_dir +preserve_color = 'store_true' +alpha = opt.a + +device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") + +# Either --content or --content_dir should be given. +if opt.content: + content_paths = [Path(opt.content)] +else: + content_dir = Path(opt.content_dir) + content_paths = [f for f in content_dir.glob('*')] + +# Either --style or --style_dir should be given. +if opt.style: + style_paths = [Path(opt.style)] +else: + style_dir = Path(opt.style_dir) + style_paths = [f for f in style_dir.glob('*')] + +if not os.path.exists(output_path): + os.mkdir(output_path) + +network=torch.load(opt.network_path) +network = Pix2PixModel(opt) +print(network) +network.eval() +network.to(device) + +content_tf = test_transform(content_size, crop) +style_tf = test_transform(style_size, crop) +import torch.nn.functional as F + + + +for content_path in content_paths: + for style_path in style_paths: + print(content_path) + + content_tf1 = content_transform() + content = content_tf(Image.open(content_path).convert("RGB")) + + h, w, c = np.shape(content) + style_tf1 = style_transform(h, w) + style = style_tf(Image.open(style_path).convert("RGB")) + + style = style.to(device).unsqueeze(0) + content = content.to(device).unsqueeze(0) + + contents = F.interpolate(content, size=(224, 224), mode='bilinear', align_corners=False) + styles = F.interpolate(style, size=(224, 224), mode='bilinear', align_corners=False) + + model_out = network(data=None, mode="inference",iters=0,progress=None,epochs=None,images_iters=None) + with torch.no_grad(): + + _, _, _, _, output = model_out(contents, styles) + print("OUTPUT",output.shape) + upsample_layer = nn.Sequential(nn.Upsample(scale_factor=8 / 7, mode='bilinear', align_corners=False)) + fake_image = upsample_layer(output) + + output_name = '{:s}/{:s}_stylized_{:s}{:s}'.format( + output_path, splitext(basename(content_path))[0], + splitext(basename(style_path))[0], save_ext + ) + + save_image(fake_image, output_name) \ No newline at end of file diff --git a/test_2.py b/test_2.py new file mode 100644 index 0000000000000000000000000000000000000000..705a1fa5ffbe047bcf1bb8668611c591ee9b3eef --- /dev/null +++ b/test_2.py @@ -0,0 +1,86 @@ +from concurrent.futures import process +import os +from collections import OrderedDict +# from tkinter import Image +import torch +import torchvision +import ntpath + +import data +from options.test_options import TestOptions +from models.pix2pix_model import Pix2PixModel +from util.util import mkdir +from util.visualizer import Visualizer +from util import html +from tqdm import tqdm +import numpy as np + +opt = TestOptions().parse() + + +dataloader = data.create_dataloader(opt) + +model = Pix2PixModel(opt) +if opt.task != 'MMIS' and opt.dataset_mode != 'photo2art': + model.eval() + +visualizer = Visualizer(opt) + +web_dir = os.path.join(opt.results_dir, opt.name, + '%s_%s' % (opt.phase, opt.which_epoch)) +webpage = html.HTML(web_dir, + 'Experiment = %s, Phase = %s, Epoch = %s' % + (opt.name, opt.phase, opt.which_epoch)) + + + +# test +print('Number of images: ', len(dataloader)) +alpha_list = torch.linspace(0, 1.0, 20) #generate 20 images for each input image +stack = False +samples = len(dataloader) +processed = -1 +for i, data_i in enumerate(tqdm(dataloader)): + processed += 1 + if processed > samples: + break + alpha_imgs = [] + for j, alpha in enumerate(alpha_list): + opt.alpha = alpha + generated = model(data_i, mode='inference',iters=0,progress=None,epochs=None,images_iters=None) + + #self, data, mode,iters,progress,epochs,images_iters + img_path = data_i['cpath'] + + if j == 0: + alpha_imgs.append(data_i['image']) + alpha_imgs.append(data_i['label']) + alpha_imgs.append(generated) + if stack: + image_dir = webpage.get_image_dir() + short_path = ntpath.basename(img_path[0]) + name = os.path.splitext(short_path)[0] + image_name = '%s.png' % name + os.makedirs(image_dir, exist_ok=True) + save_path = os.path.join(image_dir, image_name) + alpha_stack = torch.cat(alpha_imgs, dim=0) + im_grid = torchvision.utils.make_grid(alpha_stack, nrow=len(alpha_imgs) + 2, padding=0, normalize=True) + torchvision.utils.save_image(im_grid, save_path) + + else: + for b in range(generated.shape[0]): + # print(i, 'process image... %s' % img_path[b]) + if opt.show_input: + if opt.task == 'SIS': + visuals = OrderedDict([('input_label', data_i['label'][b]), + ('real_image', data_i['image'][b]), + ('synthesized_image', generated[b])]) + else: + visuals = OrderedDict([('content', data_i['label'][b]), + ('style', data_i['image'][b]), + ('synthesized_image', generated[b])]) + else: + visuals = OrderedDict([('synthesized_image', generated[b])]) + visualizer.save_images(webpage, visuals, img_path[b:b + 1], alpha=opt.alpha) + +webpage.save() diff --git a/test_scripts/ast_day2night.sh b/test_scripts/ast_day2night.sh new file mode 100644 index 0000000000000000000000000000000000000000..d9a53aad240a9b125bc2b2fcbf17aa0b5d711f81 --- /dev/null +++ b/test_scripts/ast_day2night.sh @@ -0,0 +1,31 @@ +#!/usr/bin/env bash + +set -x + +NAME='ast_day2night' +TASK='AST' +DATA='day2night' +CROOT='./datasets/bdd100k' +SROOT='./datasets/bdd100k' +CKPTROOT='./checkpoints' +WORKER=4 +RESROOT='./results' +EPOCH='latest' + +python test.py \ + --name $NAME \ + --task $TASK \ + --gpu_ids 0 \ + --checkpoints_dir $CKPTROOT \ + --batchSize 1 \ + --dataset_mode $DATA \ + --croot $CROOT \ + --sroot $SROOT \ + --nThreads $WORKER \ + --no_pairing_check \ + --no_instance \ + --num_upsampling_layers more \ + --alpha 1.0 \ + --results_dir $RESROOT \ + --which_epoch $EPOCH \ + --show_input diff --git a/test_scripts/ast_photo2art.sh b/test_scripts/ast_photo2art.sh new file mode 100644 index 0000000000000000000000000000000000000000..19ac4222dce3339e1d5a29fc46f98069ac3254b8 --- /dev/null +++ b/test_scripts/ast_photo2art.sh @@ -0,0 +1,31 @@ +#!/usr/bin/env bash + +set -x + +NAME='ast_photo2art' +TASK='AST' +DATA='photo2art' +CROOT='./datasets/photo2art' +SROOT='./datasets/photo2art' +CKPTROOT='./checkpoints' +WORKER=4 +RESROOT='./results' +EPOCH='latest' + +python test.py \ + --name $NAME \ + --task $TASK \ + --gpu_ids 0 \ + --checkpoints_dir $CKPTROOT \ + --batchSize 1 \ + --dataset_mode $DATA \ + --croot $CROOT \ + --sroot $SROOT \ + --nThreads $WORKER \ + --no_pairing_check \ + --no_instance \ + --num_upsampling_layers more \ + --alpha 1.0 \ + --results_dir $RESROOT \ + --which_epoch $EPOCH \ + --show_input diff --git a/test_scripts/ast_summer2winteryosemite.sh b/test_scripts/ast_summer2winteryosemite.sh new file mode 100644 index 0000000000000000000000000000000000000000..87709103b00a095e138329dd4e20c2ef1b197ec6 --- /dev/null +++ b/test_scripts/ast_summer2winteryosemite.sh @@ -0,0 +1,31 @@ +#!/usr/bin/env bash + +set -x + +NAME='ast_summer2winteryosemite' +TASK='AST' +DATA='summer2winteryosemite' +CROOT='/home/share/VAL_ImgTranslations/datasets/summer2winter_yosemite' +SROOT='/home/share/VAL_ImgTranslations/datasets/summer2winter_yosemite' +CKPTROOT='./checkpoints' +WORKER=4 +RESROOT='./results' +EPOCH='latest' + +python test.py \ + --name $NAME \ + --task $TASK \ + --gpu_ids 0 \ + --checkpoints_dir $CKPTROOT \ + --batchSize 4 \ + --dataset_mode $DATA \ + --croot $CROOT \ + --sroot $SROOT \ + --nThreads $WORKER \ + --no_pairing_check \ + --no_instance \ + --num_upsampling_layers more \ + --alpha 1.0 \ + --results_dir $RESROOT \ + --which_epoch $EPOCH \ + --show_input diff --git a/test_scripts/mmis_sunny2diffweathers.sh b/test_scripts/mmis_sunny2diffweathers.sh new file mode 100644 index 0000000000000000000000000000000000000000..d7058f4fc3627c19380ba71e20603dc0dfdfc18f --- /dev/null +++ b/test_scripts/mmis_sunny2diffweathers.sh @@ -0,0 +1,33 @@ +#!/usr/bin/env bash + +set -x + +NAME='mmis_sunny2diffweathers' +TASK='MMIS' +DATA='sunny2diffweathers' +CROOT='./datasets/bdd100k' +SROOT='./datasets/bdd100k' +CKPTROOT='./checkpoints' +WORKER=4 +RESROOT='./results' +EPOCH='latest' +MODE=${1:-'all'} + +python test.py \ + --name $NAME \ + --task $TASK \ + --gpu_ids 0 \ + --checkpoints_dir $CKPTROOT \ + --batchSize 1 \ + --dataset_mode $DATA \ + --croot $CROOT \ + --sroot $SROOT \ + --nThreads $WORKER \ + --no_pairing_check \ + --no_instance \ + --num_upsampling_layers more \ + --alpha 1.0 \ + --results_dir $RESROOT \ + --which_epoch $EPOCH \ + --show_input \ + --test_mode $MODE diff --git a/test_scripts/sis_ade20k.sh b/test_scripts/sis_ade20k.sh new file mode 100644 index 0000000000000000000000000000000000000000..5932236cefe63b48cc2d9eebbdc20a1b392d5c22 --- /dev/null +++ b/test_scripts/sis_ade20k.sh @@ -0,0 +1,30 @@ +#!/usr/bin/env bash + +set -x + +NAME='sis_ade20k' +TASK='SIS' +DATA='ade20k' +CROOT='./datasets/ADEChallengeData2016' +SROOT='./datasets/ADEChallengeData2016' +CKPTROOT='./checkpoints' +WORKER=4 +RESROOT='./results' +EPOCH='latest' + +python test.py \ + --name $NAME \ + --task $TASK \ + --gpu_ids 0 \ + --checkpoints_dir $CKPTROOT \ + --batchSize 1 \ + --dataset_mode $DATA \ + --croot $CROOT \ + --sroot $SROOT \ + --nThreads $WORKER \ + --num_upsampling_layers more \ + --use_vae \ + --alpha 1.0 \ + --results_dir $RESROOT \ + --which_epoch $EPOCH \ + --show_input diff --git a/test_scripts/sis_cityscapes.sh b/test_scripts/sis_cityscapes.sh new file mode 100644 index 0000000000000000000000000000000000000000..ba5ec6472d01237343a47a09437f6235002dd05f --- /dev/null +++ b/test_scripts/sis_cityscapes.sh @@ -0,0 +1,30 @@ +#!/usr/bin/env bash + +set -x + +NAME='sis_cityscapes' +TASK='SIS' +DATA='cityscapes' +CROOT='./datasets/Cityscapes' +SROOT='./datasets/Cityscapes' +CKPTROOT='./checkpoints' +WORKER=4 +RESROOT='./results' +EPOCH='latest' + +python test.py \ + --name $NAME \ + --task $TASK \ + --gpu_ids 0 \ + --checkpoints_dir $CKPTROOT \ + --batchSize 1 \ + --dataset_mode $DATA \ + --croot $CROOT \ + --sroot $SROOT \ + --nThreads $WORKER \ + --num_upsampling_layers more \ + --use_vae \ + --alpha 1.0 \ + --results_dir $RESROOT \ + --which_epoch $EPOCH \ + --show_input diff --git a/train.py b/train.py new file mode 100644 index 0000000000000000000000000000000000000000..d25b309176e64e4d76500db82f28ad54ebccf4c1 --- /dev/null +++ b/train.py @@ -0,0 +1,121 @@ +import sys +from collections import OrderedDict +from rich.progress import Progress, TextColumn, BarColumn, TaskProgressColumn, TimeRemainingColumn + +import data +from options.train_options import TrainOptions +from util.iter_counter import IterationCounter +from util.visualizer import Visualizer +from trainers.pix2pix_trainer import Pix2PixTrainer +from util.logging_wandb import init_project,stop +import torch +from torch.distributions import Beta + +# parse options +opt = TrainOptions().parse() +# print options to help debugging +print(' '.join(sys.argv)) + +# load the dataset +dataloader = data.create_dataloader(opt) + +# create trainer for our model +trainer = Pix2PixTrainer(opt) +print(trainer) + +# create tool for counting iterations +iter_counter = IterationCounter(opt, len(dataloader)) + +# create tool for visualization +visualizer = Visualizer(opt) +T = len(dataloader) +b = torch.tensor([1], dtype=torch.float32) + +# Setup rich progress bar +progress = Progress( + TextColumn("[bold blue]{task.description}"), + BarColumn(), + TaskProgressColumn(), + TimeRemainingColumn(), +) + +## INIT WANDB +init_project("results") + +epoch = 0 +with progress: + epoch_task = progress.add_task(f"[deep_pink4]Total Epochs[{epoch + 1}|{max(iter_counter.training_epochs())}]", + total=len(iter_counter.training_epochs())) + for epoch in iter_counter.training_epochs(): + iter_counter.record_epoch_start(epoch) + progress.update(epoch_task, advance=1) + img_index = 0 + iter_task = progress.add_task(f"[cyan]Images Index [{img_index}]", total=len(dataloader)) + + iteration = 0 + while iteration < len(dataloader): + for i,data_i in enumerate(dataloader,start=iter_counter.epoch_iter): # This will loop indefinitely if using InfiniteSamplerWrapper + iter_counter.record_one_iteration() + progress.update(iter_task, advance=1) + + t = iteration + 1 + a = torch.tensor(((t - (0.5 * T)) / (0.25 * T)), dtype=torch.float32).exp() + m = Beta(a, b) + opt.alpha = m.sample().cuda() + + # Training + # train generator + if iteration % opt.D_steps_per_G == 0: + trainer.run_generator_one_step(data_i, iteration, progress=progress, epoch=epoch, + images_iter=img_index) + img_index += 1 + progress.update(iter_task, + description=f"[cyan]Images Index [{img_index}|{len(dataloader)}]") + + # train discriminator + trainer.run_discriminator_one_step(data_i, iteration) + + # Visualizations + if iter_counter.needs_printing(): + losses = trainer.get_latest_losses() + visualizer.print_current_errors(epoch, iter_counter.epoch_iter, + losses, iter_counter.time_per_iter) + visualizer.plot_current_errors(losses, iter_counter.total_steps_so_far) + + if iter_counter.needs_displaying(): + if opt.task == 'SIS': + visuals = OrderedDict([('input_label', data_i['label'][0]), + ('synthesized_image', trainer.get_latest_generated()[0]), + ('real_image', data_i['image'][0])]) + else: + visuals = OrderedDict([('content', data_i['label'][0]), + ('synthesized_image', trainer.get_latest_generated()[0]), + ('style', data_i['image'][0])]) + visualizer.display_current_results(visuals, epoch, iter_counter.total_steps_so_far) + + if iter_counter.needs_saving(): + print('saving the latest model (epoch %d, total_steps %d)' % + (epoch, iter_counter.total_steps_so_far)) + trainer.save('latest') + iter_counter.record_current_iter() + + # Visualizations and model saving as in the original script + + iteration += 1 + if iteration >= opt.max_iterations_per_epoch: + break + + trainer.update_learning_rate(epoch) + iter_counter.record_epoch_end() + progress.update(epoch_task, + description=f"[deep_pink4]Total Epochs[{epoch + 1}|{max(iter_counter.training_epochs())}]") + + if epoch % opt.save_epoch_freq == 0 or epoch == iter_counter.total_epochs: + print('saving the model at the end of epoch %d, iters %d' % + (epoch, iter_counter.total_steps_so_far)) + trainer.save('latest') + trainer.save(epoch) + stop() + +print('Training was successfully finished.') + diff --git a/train_log.txt b/train_log.txt new file mode 100644 index 0000000000000000000000000000000000000000..134f693196c74aaffc4e267d241144411422644c --- /dev/null +++ b/train_log.txt @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:2982244b79f429d094d92038671c7162274a98c0c9e3280d270e33e96961fa31 +size 61937492 diff --git a/train_scripts/ast_day2night.sh b/train_scripts/ast_day2night.sh new file mode 100644 index 0000000000000000000000000000000000000000..3a7c9f22a4e73597e19c266089aa86e0b41a0ad5 --- /dev/null +++ b/train_scripts/ast_day2night.sh @@ -0,0 +1,32 @@ +#!/usr/bin/env bash + +set -x + +NAME='ast_day2night' +TASK='AST' +DATA='day2night' +CROOT='./datasets/bdd100k' +SROOT='./datasets/bdd100k' +CKPTROOT='./checkpoints' +WORKER=4 + +python train.py \ + --name $NAME \ + --task $TASK \ + --gpu_ids 0 \ + --checkpoints_dir $CKPTROOT \ + --batchSize 1 \ + --dataset_mode $DATA \ + --croot $CROOT \ + --sroot $SROOT \ + --nThreads $WORKER \ + --no_pairing_check \ + --no_instance \ + --gan_mode hinge \ + --num_upsampling_layers more \ + --alpha 1.0 \ + --display_freq 200 \ + --save_epoch_freq 2 \ + --niter 10 \ + --lambda_vgg 2 \ + --lambda_feat 1 diff --git a/train_scripts/ast_photo2art.sh b/train_scripts/ast_photo2art.sh new file mode 100644 index 0000000000000000000000000000000000000000..24902b6bdb61819c78f29e3f3222b24e4a8e4ed2 --- /dev/null +++ b/train_scripts/ast_photo2art.sh @@ -0,0 +1,32 @@ +#!/usr/bin/env bash + +set -x + +NAME='ast_photo2art' +TASK='AST' +DATA='photo2art' +CROOT='./datasets/photo2art' +SROOT='./datasets/photo2art' +CKPTROOT='./checkpoints' +WORKER=4 + +python train.py \ + --name $NAME \ + --task $TASK \ + --gpu_ids 0 \ + --checkpoints_dir $CKPTROOT \ + --batchSize 1 \ + --dataset_mode $DATA \ + --croot $CROOT \ + --sroot $SROOT \ + --nThreads $WORKER \ + --no_pairing_check \ + --no_instance \ + --gan_mode hinge \ + --num_upsampling_layers more \ + --alpha 1.0 \ + --display_freq 200 \ + --save_epoch_freq 10 \ + --niter 40 \ + --lambda_vgg 1 \ + --lambda_feat 2 diff --git a/train_scripts/ast_summer2winteryosemite.sh b/train_scripts/ast_summer2winteryosemite.sh new file mode 100644 index 0000000000000000000000000000000000000000..b644955fe789df481c1356ed79c6438c9bff809c --- /dev/null +++ b/train_scripts/ast_summer2winteryosemite.sh @@ -0,0 +1,36 @@ +#!/usr/bin/env bash +set -x + +NAME='ast_summer2winteryosemite' +TASK='AST' +DATA='summer2winteryosemite' +CROOT='./datasets/summer2winter_yosemite' +SROOT='./datasets/summer2winter_yosemite' +CKPTROOT='./checkpoints' +WORKER=4 + +python train.py \ + --name $NAME \ + --task $TASK \ + --gpu_ids 0 \ + --checkpoints_dir $CKPTROOT \ + --batchSize 2 \ + --dataset_mode $DATA \ + --croot $CROOT \ + --sroot $SROOT \ + --nThreads $WORKER \ + --no_pairing_check \ + --no_instance \ + --gan_mode hinge \ + --num_upsampling_layers more \ + --alpha 1.0 \ + --display_freq 200 \ + --save_epoch_freq 2 \ + --niter 2 \ + --lambda_vgg 1 \ + --lambda_feat 1 +# --continue_train \ +# --which_epoch 'latest' +# --load_size 896 \ +# --crop_size 224 \ +# --aspect_ratio 1.0 diff --git a/train_scripts/mmis_sunny2diffweathers.sh b/train_scripts/mmis_sunny2diffweathers.sh new file mode 100644 index 0000000000000000000000000000000000000000..54906e3b205d39d16f901d6f40562fae627c0aee --- /dev/null +++ b/train_scripts/mmis_sunny2diffweathers.sh @@ -0,0 +1,32 @@ +#!/usr/bin/env bash + +set -x + +NAME='mmis_sunny2diffweathers' +TASK='MMIS' +DATA='sunny2diffweathers' +CROOT='./datasets/bdd100k' +SROOT='./datasets/bdd100k' +CKPTROOT='./checkpoints' +WORKER=4 + +python train.py \ + --name $NAME \ + --task $TASK \ + --gpu_ids 0 \ + --checkpoints_dir $CKPTROOT \ + --batchSize 1 \ + --dataset_mode $DATA \ + --croot $CROOT \ + --sroot $SROOT \ + --nThreads $WORKER \ + --no_pairing_check \ + --no_instance \ + --gan_mode hinge \ + --num_upsampling_layers more \ + --alpha 1.0 \ + --display_freq 200 \ + --save_epoch_freq 5 \ + --niter 20 \ + --lambda_vgg 1 \ + --lambda_feat 1 diff --git a/train_scripts/sis_ade20k.sh b/train_scripts/sis_ade20k.sh new file mode 100644 index 0000000000000000000000000000000000000000..a0657ba1d55d2ff3c58c049eddf22780173279b6 --- /dev/null +++ b/train_scripts/sis_ade20k.sh @@ -0,0 +1,32 @@ +#!/usr/bin/env bash + +set -x + +NAME='sis_ade20k' +TASK='SIS' +DATA='ade20k' +CROOT='./datasets/ADEChallengeData2016' +SROOT='./datasets/ADEChallengeData2016' +CKPTROOT='./checkpoints' +WORKER=4 + +python train.py \ + --name $NAME \ + --task $TASK \ + --gpu_ids 0,1 \ + --checkpoints_dir $CKPTROOT \ + --batchSize 16 \ + --dataset_mode $DATA \ + --croot $CROOT \ + --sroot $SROOT \ + --nThreads $WORKER \ + --gan_mode hinge \ + --num_upsampling_layers more \ + --use_vae \ + --alpha 1.0 \ + --display_freq 5000 \ + --save_epoch_freq 20 \ + --niter 100 \ + --niter_decay 100 \ + --lambda_vgg 20 \ + --lambda_feat 10 diff --git a/train_scripts/sis_cityscapes.sh b/train_scripts/sis_cityscapes.sh new file mode 100644 index 0000000000000000000000000000000000000000..66e2dea9517e3607c4f3614cb408e10bad04b08a --- /dev/null +++ b/train_scripts/sis_cityscapes.sh @@ -0,0 +1,32 @@ +#!/usr/bin/env bash + +set -x + +NAME='sis_cityscapes' +TASK='SIS' +DATA='cityscapes' +CROOT='./datasets/Cityscapes' +SROOT='./datasets/Cityscapes' +CKPTROOT='./checkpoints' +WORKER=4 + +python train.py \ + --name $NAME \ + --task $TASK \ + --gpu_ids 0,1 \ + --checkpoints_dir $CKPTROOT \ + --batchSize 16 \ + --dataset_mode $DATA \ + --croot $CROOT \ + --sroot $SROOT \ + --nThreads $WORKER \ + --gan_mode hinge \ + --num_upsampling_layers more \ + --use_vae \ + --alpha 1.0 \ + --display_freq 1000 \ + --save_epoch_freq 20 \ + --niter 100 \ + --niter_decay 100 \ + --lambda_vgg 20 \ + --lambda_feat 10 diff --git a/trainers/__init__.py b/trainers/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/trainers/__pycache__/__init__.cpython-310.pyc b/trainers/__pycache__/__init__.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..de12ef2a314e5b014b126f309e8dc264d11d83fa Binary files /dev/null and b/trainers/__pycache__/__init__.cpython-310.pyc differ diff --git a/trainers/__pycache__/__init__.cpython-37.pyc b/trainers/__pycache__/__init__.cpython-37.pyc new file mode 100644 index 0000000000000000000000000000000000000000..11e1a6d9321f581a303db436a5a764678c441c5a Binary files /dev/null and b/trainers/__pycache__/__init__.cpython-37.pyc differ diff --git a/trainers/__pycache__/pix2pix_trainer.cpython-310.pyc b/trainers/__pycache__/pix2pix_trainer.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..d1a1ceb4d887983c45a91dc173d29b4852370ee7 Binary files /dev/null and b/trainers/__pycache__/pix2pix_trainer.cpython-310.pyc differ diff --git a/trainers/__pycache__/pix2pix_trainer.cpython-37.pyc b/trainers/__pycache__/pix2pix_trainer.cpython-37.pyc new file mode 100644 index 0000000000000000000000000000000000000000..f898c3e4162abdbeb6dbbc8c2297a0c7eab87383 Binary files /dev/null and b/trainers/__pycache__/pix2pix_trainer.cpython-37.pyc differ diff --git a/trainers/pix2pix_trainer.py b/trainers/pix2pix_trainer.py new file mode 100644 index 0000000000000000000000000000000000000000..a2b9ef10746ddf4a784bfb24b17dce1f9b6dec07 --- /dev/null +++ b/trainers/pix2pix_trainer.py @@ -0,0 +1,104 @@ +from models.networks.sync_batchnorm import DataParallelWithCallback +from models.pix2pix_model import Pix2PixModel +from tqdm import tqdm + + +class Pix2PixTrainer(): + """ + Trainer creates the model and optimizers, and uses them to + updates the weights of the network while reporting losses + and the latest visuals to visualize the progress in training. + """ + + def __init__(self, opt): + self.opt = opt + self.pix2pix_model = Pix2PixModel(opt) + if len(opt.gpu_ids) > 0: + self.pix2pix_model = DataParallelWithCallback(self.pix2pix_model, + device_ids=opt.gpu_ids) + self.pix2pix_model_on_one_gpu = self.pix2pix_model.module + else: + self.pix2pix_model_on_one_gpu = self.pix2pix_model + + self.generated = None + if opt.isTrain: + self.optimizer_G, self.optimizer_D,self.optimizer_D2 = \ + self.pix2pix_model_on_one_gpu.create_optimizers(opt) + self.old_lr = opt.lr + + # def run_generator_one_step(self, data,iters): + # print(type(data)) + # for i in tqdm(range(self.max_iters)): + # self.optimizer_G.zero_grad() + # g_losses, generated = self.pix2pix_model(data, mode='generator',iters=i) + # g_loss = sum(g_losses.values()).mean() + # g_loss.backward() + # self.optimizer_G.step() + # self.g_losses = g_losses + # self.generated = generated + def run_generator_one_step(self, data,iters,progress,epoch,images_iter): + g_losses, generated = self.pix2pix_model(data, mode='generator',iters=iters,progress=progress,epochs=epoch,images_iters=images_iter) + g_loss = sum(g_losses.values()).mean() + + self.g_losses = g_losses + self.generated = generated + + def run_discriminator_one_step(self,data,iters): + self.optimizer_D.zero_grad() + self.optimizer_D2.zero_grad() + d_losses, d2_losses = self.pix2pix_model(data,mode='discriminator',iters=iters,progress=None,epochs=None,images_iters=None) + + # for discriminator 1 + d_loss = sum(d_losses.values()).mean() + d_loss.backward() + self.optimizer_D.step() + self.d_losses = d_losses + + # for discriminator 2 + d2_loss = sum(d2_losses.values()).mean() + d2_loss.backward() + self.optimizer_D2.step() + self.d2_losses = d2_losses + + def get_latest_losses(self): + + return {**self.g_losses, **self.d_losses,**self.d2_losses} + + def get_latest_generated(self): + return self.generated + + def update_learning_rate(self, epoch): + self.update_learning_rate(epoch) + + def save(self, epoch): + self.pix2pix_model_on_one_gpu.save(epoch) + + ################################################################## + # Helper functions + ################################################################## + + def update_learning_rate(self, epoch): + if epoch > self.opt.niter: + lrd = self.opt.lr / self.opt.niter_decay + new_lr = self.old_lr - lrd + else: + new_lr = self.old_lr + + if new_lr != self.old_lr: + if self.opt.no_TTUR: + new_lr_G = new_lr + new_lr_D = new_lr + new_lr_D2 = new_lr + else: + new_lr_G = new_lr / 2 + new_lr_D = new_lr * 2 + new_lr_D2 = new_lr * 2 + + for param_group in self.optimizer_D.param_groups: + param_group['lr'] = new_lr_D + for param_group in self.optimizer_D2.param_groups: + param_group['lr'] = new_lr_D2 + for param_group in self.optimizer_G.param_groups: + param_group['lr'] = new_lr_G + print('update learning rate: %f -> %f' % (self.old_lr, new_lr)) + 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0000000000000000000000000000000000000000..804339e7458e2ca03a155ab590df992015967a86 --- /dev/null +++ b/util/coco.py @@ -0,0 +1,193 @@ +def id2label(id): + if id == 182: + id = 0 + else: + id = id + 1 + labelmap = \ + {0: 'unlabeled', + 1: 'person', + 2: 'bicycle', + 3: 'car', + 4: 'motorcycle', + 5: 'airplane', + 6: 'bus', + 7: 'train', + 8: 'truck', + 9: 'boat', + 10: 'traffic light', + 11: 'fire hydrant', + 12: 'street sign', + 13: 'stop sign', + 14: 'parking meter', + 15: 'bench', + 16: 'bird', + 17: 'cat', + 18: 'dog', + 19: 'horse', + 20: 'sheep', + 21: 'cow', + 22: 'elephant', + 23: 'bear', + 24: 'zebra', + 25: 'giraffe', + 26: 'hat', + 27: 'backpack', + 28: 'umbrella', + 29: 'shoe', + 30: 'eye glasses', + 31: 'handbag', + 32: 'tie', + 33: 'suitcase', + 34: 'frisbee', + 35: 'skis', + 36: 'snowboard', + 37: 'sports ball', + 38: 'kite', + 39: 'baseball bat', + 40: 'baseball glove', + 41: 'skateboard', + 42: 'surfboard', + 43: 'tennis racket', + 44: 'bottle', + 45: 'plate', + 46: 'wine glass', + 47: 'cup', + 48: 'fork', + 49: 'knife', + 50: 'spoon', + 51: 'bowl', + 52: 'banana', + 53: 'apple', + 54: 'sandwich', + 55: 'orange', + 56: 'broccoli', + 57: 'carrot', + 58: 'hot dog', + 59: 'pizza', + 60: 'donut', + 61: 'cake', + 62: 'chair', + 63: 'couch', + 64: 'potted plant', + 65: 'bed', + 66: 'mirror', + 67: 'dining table', + 68: 'window', + 69: 'desk', + 70: 'toilet', + 71: 'door', + 72: 'tv', + 73: 'laptop', + 74: 'mouse', + 75: 'remote', + 76: 'keyboard', + 77: 'cell phone', + 78: 'microwave', + 79: 'oven', + 80: 'toaster', + 81: 'sink', + 82: 'refrigerator', + 83: 'blender', + 84: 'book', + 85: 'clock', + 86: 'vase', + 87: 'scissors', + 88: 'teddy bear', + 89: 'hair drier', + 90: 'toothbrush', + 91: 'hair brush', # Last class of Thing + 92: 'banner', # Beginning of Stuff + 93: 'blanket', + 94: 'branch', + 95: 'bridge', + 96: 'building-other', + 97: 'bush', + 98: 'cabinet', + 99: 'cage', + 100: 'cardboard', + 101: 'carpet', + 102: 'ceiling-other', + 103: 'ceiling-tile', + 104: 'cloth', + 105: 'clothes', + 106: 'clouds', + 107: 'counter', + 108: 'cupboard', + 109: 'curtain', + 110: 'desk-stuff', + 111: 'dirt', + 112: 'door-stuff', + 113: 'fence', + 114: 'floor-marble', + 115: 'floor-other', + 116: 'floor-stone', + 117: 'floor-tile', + 118: 'floor-wood', + 119: 'flower', + 120: 'fog', + 121: 'food-other', + 122: 'fruit', + 123: 'furniture-other', + 124: 'grass', + 125: 'gravel', + 126: 'ground-other', + 127: 'hill', + 128: 'house', + 129: 'leaves', + 130: 'light', + 131: 'mat', + 132: 'metal', + 133: 'mirror-stuff', + 134: 'moss', + 135: 'mountain', + 136: 'mud', + 137: 'napkin', + 138: 'net', + 139: 'paper', + 140: 'pavement', + 141: 'pillow', + 142: 'plant-other', + 143: 'plastic', + 144: 'platform', + 145: 'playingfield', + 146: 'railing', + 147: 'railroad', + 148: 'river', + 149: 'road', + 150: 'rock', + 151: 'roof', + 152: 'rug', + 153: 'salad', + 154: 'sand', + 155: 'sea', + 156: 'shelf', + 157: 'sky-other', + 158: 'skyscraper', + 159: 'snow', + 160: 'solid-other', + 161: 'stairs', + 162: 'stone', + 163: 'straw', + 164: 'structural-other', + 165: 'table', + 166: 'tent', + 167: 'textile-other', + 168: 'towel', + 169: 'tree', + 170: 'vegetable', + 171: 'wall-brick', + 172: 'wall-concrete', + 173: 'wall-other', + 174: 'wall-panel', + 175: 'wall-stone', + 176: 'wall-tile', + 177: 'wall-wood', + 178: 'water-other', + 179: 'waterdrops', + 180: 'window-blind', + 181: 'window-other', + 182: 'wood'} + if id in labelmap: + return labelmap[id] + else: + return 'unknown' diff --git a/util/html.py b/util/html.py new file mode 100644 index 0000000000000000000000000000000000000000..50f67c74420e871aab4783cdd03ad13cb1ff33de --- /dev/null +++ b/util/html.py @@ -0,0 +1,71 @@ +import datetime +import dominate +from dominate.tags import * +import os + + +class HTML: + def __init__(self, web_dir, title, refresh=0): + if web_dir.endswith('.html'): + web_dir, html_name = os.path.split(web_dir) + else: + web_dir, html_name = web_dir, 'index.html' + self.title = title + self.web_dir = web_dir + self.html_name = html_name + self.img_dir = os.path.join(self.web_dir, 'images') + if len(self.web_dir) > 0 and not os.path.exists(self.web_dir): + os.makedirs(self.web_dir) + if len(self.web_dir) > 0 and not os.path.exists(self.img_dir): + os.makedirs(self.img_dir) + + self.doc = dominate.document(title=title) + with self.doc: + h1(datetime.datetime.now().strftime("%I:%M%p on %B %d, %Y")) + if refresh > 0: + with self.doc.head: + meta(http_equiv="refresh", content=str(refresh)) + + def get_image_dir(self): + return self.img_dir + + def add_header(self, str): + with self.doc: + h3(str) + + def add_table(self, border=1): + self.t = table(border=border, style="table-layout: fixed;") + self.doc.add(self.t) + + def add_images(self, ims, txts, links, width=512): + self.add_table() + with self.t: + with tr(): + for im, txt, link in zip(ims, txts, links): + with td(style="word-wrap: break-word;", halign="center", valign="top"): + with p(): + with a(href=os.path.join('images', link)): + img(style="width:%dpx" % (width), src=os.path.join('images', im)) + br() + p(txt.encode('utf-8')) + + def save(self): + html_file = os.path.join(self.web_dir, self.html_name) + f = open(html_file, 'wt') + f.write(self.doc.render()) + f.close() + + +if __name__ == '__main__': + html = HTML('web/', 'test_html') + html.add_header('hello world') + + ims = [] + txts = [] + links = [] + for n in range(4): + ims.append('image_%d.jpg' % n) + txts.append('text_%d' % n) + links.append('image_%d.jpg' % n) + html.add_images(ims, txts, links) + html.save() diff --git a/util/iter_counter.py b/util/iter_counter.py new file mode 100644 index 0000000000000000000000000000000000000000..1d79d9793a4d3a79343b72fec776a67abd9e6528 --- /dev/null +++ b/util/iter_counter.py @@ -0,0 +1,69 @@ +import os +import time +import numpy as np + + +# Helper class that keeps track of training iterations +class IterationCounter(): + def __init__(self, opt, dataset_size): + self.opt = opt + self.dataset_size = dataset_size + + self.first_epoch = 1 + self.total_epochs = opt.niter + opt.niter_decay + self.epoch_iter = 0 # iter number within each epoch + self.iter_record_path = os.path.join(self.opt.checkpoints_dir, self.opt.name, 'iter.txt') + if opt.isTrain and opt.continue_train: + try: + self.first_epoch, self.epoch_iter = np.loadtxt( + self.iter_record_path, delimiter=',', dtype=int) + print('Resuming from epoch %d at iteration %d' % (self.first_epoch, self.epoch_iter)) + except: + print('Could not load iteration record at %s. Starting from beginning.' % + self.iter_record_path) + + self.total_steps_so_far = (self.first_epoch - 1) * dataset_size + self.epoch_iter + + # return the iterator of epochs for the training + def training_epochs(self): + return range(self.first_epoch, self.total_epochs + 1) + + def record_epoch_start(self, epoch): + self.epoch_start_time = time.time() + self.epoch_iter = 0 + self.last_iter_time = time.time() + self.current_epoch = epoch + + def record_one_iteration(self): + current_time = time.time() + + # the last remaining batch is dropped (see data/__init__.py), + # so we can assume batch size is always opt.batchSize + self.time_per_iter = (current_time - self.last_iter_time) / self.opt.batchSize + self.last_iter_time = current_time + self.total_steps_so_far += self.opt.batchSize + self.epoch_iter += self.opt.batchSize + + def record_epoch_end(self): + current_time = time.time() + self.time_per_epoch = current_time - self.epoch_start_time + print('End of epoch %d / %d \t Time Taken: %d sec' % + (self.current_epoch, self.total_epochs, self.time_per_epoch)) + if self.current_epoch % self.opt.save_epoch_freq == 0: + np.savetxt(self.iter_record_path, (self.current_epoch + 1, 0), + delimiter=',', fmt='%d') + print('Saved current iteration count at %s.' % self.iter_record_path) + + def record_current_iter(self): + np.savetxt(self.iter_record_path, (self.current_epoch, self.epoch_iter), + delimiter=',', fmt='%d') + print('Saved current iteration count at %s.' % self.iter_record_path) + + def needs_saving(self): + return (self.total_steps_so_far % self.opt.save_latest_freq) < self.opt.batchSize + + def needs_printing(self): + return (self.total_steps_so_far % self.opt.print_freq) < self.opt.batchSize + + def needs_displaying(self): + return (self.total_steps_so_far % self.opt.display_freq) < self.opt.batchSize diff --git a/util/logging_wandb.py b/util/logging_wandb.py new file mode 100644 index 0000000000000000000000000000000000000000..9f2e6fce6da3f8c6f0ca8b2bb995c5aef0711974 --- /dev/null +++ b/util/logging_wandb.py @@ -0,0 +1,191 @@ +import wandb +import os +import matplotlib.pyplot as plt +import random + +is_wandb_enabled = os.getenv("WANDB_DISABLED", "false").lower() != "true" + +def get_next_directory(base_dir="results", sub_dir="train"): + result_dir = os.path.join(base_dir, sub_dir) + if not os.path.exists(result_dir): + os.makedirs(result_dir) + + run_id = 0 + while True: + run_dir = os.path.join(result_dir, f"VAL_Images_StyleTransfer_{run_id}") + if not os.path.exists(run_dir): + os.makedirs(run_dir) + return run_dir, run_id + run_id += 1 + + +def init_project(base_dir="results", sub_dir="train"): + project_dir, run_id = get_next_directory(base_dir, sub_dir) + + if is_wandb_enabled: + # `name` là cố định, còn `project` sẽ là VAL_Images_StyleTransfer_{run_id} + wandb.init(project=f"VAL_Images_StyleTransfer", name=f"VAL_Images_StyleTransfer_{run_id}r") + + return project_dir + + +def upload_images(images, iters, img_index, epoch): + dir_name = f"epoch_{epoch}_iters_{iters}" + if not os.path.exists(dir_name): + os.makedirs(dir_name) + + for i, img in enumerate(images): + img_name = f"img_{img_index}_{iters}_{epoch}.png" + img_path = os.path.join(dir_name, img_name) + plt.figure() + plt.imshow(img) + plt.title(f"Image {img_index} | Iter {iters} | Epoch {epoch}") + plt.axis('off') + + plt.savefig(img_path) + plt.close() + + if is_wandb_enabled: + wandb.log({f"epoch_{epoch}/img_{img_index}_{iters}_{epoch}": wandb.Image(img_path)}) + + +#### ON ONE IMAGES +previous_iters_all_images = -1 +last_iters=0 + + +def reset_iters_if_needed(iters_all_images): + global previous_iters_all_images + + if iters_all_images > previous_iters_all_images: + previous_iters_all_images = iters_all_images + return True # Cần reset cho section mới + return False + + +def get_section_name(epoch, iters_all_images): + return f"Iters on all Images_epoch{epoch}_iters{iters_all_images}" + + +def define_wandb_metrics(epoch, iters_all_images): + section_name = get_section_name(epoch, iters_all_images) + if is_wandb_enabled: + # Định nghĩa metric cho từng section (epoch + iters_all_images) + wandb.define_metric(f"{section_name}/All_loss_on_one_images", step_metric=f"{section_name}_step") + wandb.define_metric(f"{section_name}/L1_loss_on_one_images", step_metric=f"{section_name}_step") + wandb.define_metric(f"{section_name}/L2_loss_on_one_images", step_metric=f"{section_name}_step") + wandb.define_metric(f"{section_name}/Content_loss_on_one_images", step_metric=f"{section_name}_step") + wandb.define_metric(f"{section_name}/Style_loss_on_one_images", step_metric=f"{section_name}_step") + + +def upload_all_loss_on_one_images(loss_value, iters_one_imgs, iters_all_images, epoch): + if reset_iters_if_needed(iters_all_images): + iters_one_imgs = last_iters+iters_one_imgs + define_wandb_metrics(epoch, iters_all_images) # Định nghĩa các metric cho section mới + + section_name = get_section_name(epoch, iters_all_images) + + if is_wandb_enabled: + wandb.log({f"{section_name}/All_loss_on_one_images": loss_value, f"{section_name}_step": iters_one_imgs}) + + +def upload_l1_loss_on_one_images(loss_value, iters_one_imgs, iters_all_images, epoch): + section_name = get_section_name(epoch, iters_all_images) + + if is_wandb_enabled: + wandb.log({f"{section_name}/L1_loss_on_one_images": loss_value, f"{section_name}_step": iters_one_imgs}) + + +def upload_l2_loss_on_one_images(loss_value, iters_one_imgs, iters_all_images, epoch): + section_name = get_section_name(epoch, iters_all_images) + + if is_wandb_enabled: + wandb.log({f"{section_name}/L2_loss_on_one_images": loss_value, f"{section_name}_step": iters_one_imgs}) + + +def upload_content_loss_on_one_images(loss_value, iters_one_imgs, iters_all_images, epoch): + section_name = get_section_name(epoch, iters_all_images) + + if is_wandb_enabled: + wandb.log({f"{section_name}/Content_loss_on_one_images": loss_value, f"{section_name}_step": iters_one_imgs}) + + +def upload_style_loss_on_one_images(loss_value, iters_one_imgs, iters_all_images, epoch): + section_name = get_section_name(epoch, iters_all_images) + + if is_wandb_enabled: + wandb.log({f"{section_name}/Style_loss_on_one_images": loss_value, f"{section_name}_step": iters_one_imgs}) + + + + +### ON ALL IMAGES + +def upload_l1_loss_all(loss_values, iters_images, epoch): + if is_wandb_enabled: + wandb.log({f"epoch_{epoch}/L1_loss": loss_values}, step=iters_images) + + + +def upload_l2_loss(loss_values, iters, epoch): + if is_wandb_enabled: + wandb.log({f"epoch_{epoch}/L2_loss": loss_values}, step=iters) + + +def upload_content_loss(loss_c, iters, epoch): + if is_wandb_enabled: + wandb.log({f"epoch_{epoch}/Content_loss": loss_c}, step=iters) + + +def upload_style_loss(loss_s, iters, epoch): + if is_wandb_enabled: + wandb.log({f"epoch_{epoch}/Style_loss": loss_s}, step=iters) + + +def upload_epoch(epoch,iter_images): + if is_wandb_enabled: + wandb.log({"epoch": epoch}) + + +def upload_lr(lr,epoch): + if is_wandb_enabled: + wandb.log({"learning_rate": lr}) + + +def upload_loss_all(loss_all, iters, epoch): + if is_wandb_enabled: + wandb.log({f"epoch_{epoch}/Total_loss": loss_all}, step=iters) + + +def logs_model(model_params): + """Ghi log các tham số của mô hình lên Wandb.""" + if is_wandb_enabled: + wandb.log({"model_parameters": model_params}) + + +def logs_params(params): + """Ghi log các tham số khác lên Wandb.""" + if is_wandb_enabled: + wandb.log({"params": params}) + + +# def save_model_weights(model, epoch, project_dir): +# """Lưu weights của model theo từng epoch.""" +# weights_dir = os.path.join(project_dir, f"epoch_{epoch}") +# if not os.path.exists(weights_dir): +# os.makedirs(weights_dir) +# +# weights_path = os.path.join(weights_dir, f"weights_epoch_{epoch}.pth") +# torch.save(model.state_dict(), weights_path) # Lưu weights vào file .pth +# +# if is_wandb_enabled: +# wandb.save(weights_path) + + +def logs_running(terminal_output): + if is_wandb_enabled: + wandb.log({"terminal_output": wandb.Html(terminal_output)}) + +def stop(): + if is_wandb_enabled: + wandb.finish() diff --git a/util/util.py b/util/util.py new file mode 100644 index 0000000000000000000000000000000000000000..09bc4c7da0678f76dbf00cbfbce03d49bbc5e080 --- /dev/null +++ b/util/util.py @@ -0,0 +1,294 @@ +import re +import importlib +import torch +from argparse import Namespace +import numpy as np +from PIL import Image +import os +import argparse +import dill as pickle +import util.coco + + +def save_obj(obj, name): + with open(name, 'wb') as f: + pickle.dump(obj, f, pickle.HIGHEST_PROTOCOL) + + +def load_obj(name): + with open(name, 'rb') as f: + return pickle.load(f) + + +# returns a configuration for creating a generator +# |default_opt| should be the opt of the current experiment +# |**kwargs|: if any configuration should be overriden, it can be specified here +def copyconf(default_opt, **kwargs): + conf = argparse.Namespace(**vars(default_opt)) + for key in kwargs: + print(key, kwargs[key]) + setattr(conf, key, kwargs[key]) + return conf + + +def tile_images(imgs, picturesPerRow=4): + """ Code borrowed from + https://stackoverflow.com/questions/26521365/cleanly-tile-numpy-array-of-images-stored-in-a-flattened-1d-format/26521997 + """ + + # Padding + if imgs.shape[0] % picturesPerRow == 0: + rowPadding = 0 + else: + rowPadding = picturesPerRow - imgs.shape[0] % picturesPerRow + if rowPadding > 0: + imgs = np.concatenate([imgs, np.zeros((rowPadding, *imgs.shape[1:]), dtype=imgs.dtype)], axis=0) + + # Tiling Loop (The conditionals are not necessary anymore) + tiled = [] + for i in range(0, imgs.shape[0], picturesPerRow): + tiled.append(np.concatenate([imgs[j] for j in range(i, i + picturesPerRow)], axis=1)) + + tiled = np.concatenate(tiled, axis=0) + return tiled + + +# Converts a Tensor into a Numpy array +# |imtype|: the desired type of the converted numpy array +def tensor2im(image_tensor, imtype=np.uint8, normalize=True, tile=False): + if isinstance(image_tensor, list): + image_numpy = [] + for i in range(len(image_tensor)): + image_numpy.append(tensor2im(image_tensor[i], imtype, normalize)) + return image_numpy + + if image_tensor.dim() == 4: + # transform each image in the batch + images_np = [] + for b in range(image_tensor.size(0)): + one_image = image_tensor[b] + one_image_np = tensor2im(one_image) + images_np.append(one_image_np.reshape(1, *one_image_np.shape)) + images_np = np.concatenate(images_np, axis=0) + if tile: + images_tiled = tile_images(images_np) + return images_tiled + else: + return images_np + + if image_tensor.dim() == 2: + image_tensor = image_tensor.unsqueeze(0) + image_numpy = image_tensor.detach().cpu().float().numpy() + if normalize: + image_numpy = (np.transpose(image_numpy, (1, 2, 0)) + 1) / 2.0 * 255.0 + else: + image_numpy = np.transpose(image_numpy, (1, 2, 0)) * 255.0 + image_numpy = np.clip(image_numpy, 0, 255) + if image_numpy.shape[2] == 1: + image_numpy = image_numpy[:, :, 0] + return image_numpy.astype(imtype) + + +# Converts a one-hot tensor into a colorful label map +def tensor2label(label_tensor, n_label, imtype=np.uint8, tile=False): + if label_tensor.dim() == 4: + # transform each image in the batch + images_np = [] + for b in range(label_tensor.size(0)): + one_image = label_tensor[b] + one_image_np = tensor2label(one_image, n_label, imtype) + images_np.append(one_image_np.reshape(1, *one_image_np.shape)) + images_np = np.concatenate(images_np, axis=0) + if tile: + images_tiled = tile_images(images_np) + return images_tiled + else: + images_np = images_np[0] + return images_np + + if label_tensor.dim() == 1: + return np.zeros((64, 64, 3), dtype=np.uint8) + if n_label == 0: + return tensor2im(label_tensor, imtype) + label_tensor = label_tensor.cpu().float() + if label_tensor.size()[0] > 1: + label_tensor = label_tensor.max(0, keepdim=True)[1] + label_tensor = Colorize(n_label)(label_tensor) + label_numpy = np.transpose(label_tensor.numpy(), (1, 2, 0)) + result = label_numpy.astype(imtype) + return result + + +def save_image(image_numpy, image_path, create_dir=False): + if create_dir: + os.makedirs(os.path.dirname(image_path), exist_ok=True) + if len(image_numpy.shape) == 2: + image_numpy = np.expand_dims(image_numpy, axis=2) + if image_numpy.shape[2] == 1: + image_numpy = np.repeat(image_numpy, 3, 2) + image_pil = Image.fromarray(image_numpy) + + # save to png + image_pil.save(image_path.replace('.jpg', '.png')) + + +def mkdirs(paths): + if isinstance(paths, list) and not isinstance(paths, str): + for path in paths: + mkdir(path) + else: + mkdir(paths) + + +def mkdir(path): + if not os.path.exists(path): + os.makedirs(path) + + +def atoi(text): + return int(text) if text.isdigit() else text + + +def natural_keys(text): + ''' + alist.sort(key=natural_keys) sorts in human order + http://nedbatchelder.com/blog/200712/human_sorting.html + (See Toothy's implementation in the comments) + ''' + return [atoi(c) for c in re.split('(\d+)', text)] + + +def natural_sort(items): + items.sort(key=natural_keys) + + +def str2bool(v): + if v.lower() in ('yes', 'true', 't', 'y', '1'): + return True + elif v.lower() in ('no', 'false', 'f', 'n', '0'): + return False + else: + raise argparse.ArgumentTypeError('Boolean value expected.') + + +def find_class_in_module(target_cls_name, module): + target_cls_name = target_cls_name.replace('_', '').lower() + clslib = importlib.import_module(module) + cls = None + for name, clsobj in clslib.__dict__.items(): + if name.lower() == target_cls_name: + cls = clsobj + + if cls is None: + print("In %s, there should be a class whose name matches %s in lowercase without underscore(_)" % (module, target_cls_name)) + exit(0) + + return cls + + +def save_network(net, label, epoch, opt): + save_filename = '%s_net_%s.pth' % (epoch, label) + save_path = os.path.join(opt.checkpoints_dir, opt.name, save_filename) + torch.save(net.cpu().state_dict(), save_path) + if len(opt.gpu_ids) and torch.cuda.is_available(): + net.cuda() + +def save_generator_by_iter(net, label, epoch,iters, opt): + gen_path = os.path.join(opt.checkpoints_dir, opt.name, "generators_by_iters") + os.makedirs(gen_path,exist_ok=True) + save_filename = '%s_iters_%s_net_%s.pth' % (epoch, iters, label) + save_path = os.path.join(gen_path, save_filename) + torch.save(net.cpu().state_dict(), save_path) + if len(opt.gpu_ids) and torch.cuda.is_available(): + net.cuda() + +def load_network(net, label, epoch, opt): + save_filename = '%s_net_%s.pth' % (epoch, label) + save_dir = os.path.join(opt.checkpoints_dir, opt.name) + save_path = os.path.join(save_dir, save_filename) + weights = torch.load(save_path) + net.load_state_dict(weights) + return net + +def load_genrator_network(model,checkpoint_path): + print("======> Loading Checkpoint ====================>") + device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') + if torch.cuda.is_available(): + torch.cuda.empty_cache() + checkpoint = torch.load(checkpoint_path, map_location=device) + + model.TransEncoder.load_state_dict(checkpoint['encoder1']) + model.HeTransEncoder.load_state_dict(checkpoint['encoder2']) + model.CNNdecoder.load_state_dict(checkpoint['decoder']) + model.transModule.load_state_dict(checkpoint['transModule']) + loss_count_interval = checkpoint['loss_count_interval'] + print('======> loading finished') + return model + + +############################################################################### +# Code from +# https://github.com/ycszen/pytorch-seg/blob/master/transform.py +# Modified so it complies with the Citscape label map colors +############################################################################### +def uint82bin(n, count=8): + """returns the binary of integer n, count refers to amount of bits""" + return ''.join([str((n >> y) & 1) for y in range(count - 1, -1, -1)]) + + +def labelcolormap(N): + if N == 35: # cityscape + cmap = np.array([(0, 0, 0), (0, 0, 0), (0, 0, 0), (0, 0, 0), (0, 0, 0), (111, 74, 0), (81, 0, 81), + (128, 64, 128), (244, 35, 232), (250, 170, 160), (230, 150, 140), (70, 70, 70), (102, 102, 156), (190, 153, 153), + (180, 165, 180), (150, 100, 100), (150, 120, 90), (153, 153, 153), (153, 153, 153), (250, 170, 30), (220, 220, 0), + (107, 142, 35), (152, 251, 152), (70, 130, 180), (220, 20, 60), (255, 0, 0), (0, 0, 142), (0, 0, 70), + (0, 60, 100), (0, 0, 90), (0, 0, 110), (0, 80, 100), (0, 0, 230), (119, 11, 32), (0, 0, 142)], + dtype=np.uint8) + else: + cmap = np.zeros((N, 3), dtype=np.uint8) + for i in range(N): + r, g, b = 0, 0, 0 + id = i + 1 # let's give 0 a color + for j in range(7): + str_id = uint82bin(id) + r = r ^ (np.uint8(str_id[-1]) << (7 - j)) + g = g ^ (np.uint8(str_id[-2]) << (7 - j)) + b = b ^ (np.uint8(str_id[-3]) << (7 - j)) + id = id >> 3 + cmap[i, 0] = r + cmap[i, 1] = g + cmap[i, 2] = b + + if N == 182: # COCO + important_colors = { + 'sea': (54, 62, 167), + 'sky-other': (95, 219, 255), + 'tree': (140, 104, 47), + 'clouds': (170, 170, 170), + 'grass': (29, 195, 49) + } + for i in range(N): + name = util.coco.id2label(i) + if name in important_colors: + color = important_colors[name] + cmap[i] = np.array(list(color)) + + return cmap + + +class Colorize(object): + def __init__(self, n=35): + self.cmap = labelcolormap(n) + self.cmap = torch.from_numpy(self.cmap[:n]) + + def __call__(self, gray_image): + size = gray_image.size() + color_image = torch.ByteTensor(3, size[1], size[2]).fill_(0) + + for label in range(0, len(self.cmap)): + mask = (label == gray_image[0]).cpu() + color_image[0][mask] = self.cmap[label][0] + color_image[1][mask] = self.cmap[label][1] + color_image[2][mask] = self.cmap[label][2] + + return color_image diff --git a/util/visualizer.py b/util/visualizer.py new file mode 100644 index 0000000000000000000000000000000000000000..21bad314f2b4db47dc7c6d2196f661c858f234dd --- /dev/null +++ b/util/visualizer.py @@ -0,0 +1,151 @@ +import os +import ntpath +import time +from . import util +from . import html +import numpy as np +import scipy.misc +try: + from StringIO import StringIO # Python 2.7 +except ImportError: + from io import BytesIO # Python 3.x + + +class Visualizer(): + def __init__(self, opt): + self.opt = opt + self.tf_log = opt.isTrain and opt.tf_log + self.use_html = opt.isTrain and not opt.no_html + self.win_size = opt.display_winsize + self.name = opt.name + if self.tf_log: + import tensorflow as tf + self.tf = tf + self.log_dir = os.path.join(opt.checkpoints_dir, opt.name, 'logs') + self.writer = tf.summary.FileWriter(self.log_dir) + + if self.use_html: + self.web_dir = os.path.join(opt.checkpoints_dir, opt.name, 'web') + self.img_dir = os.path.join(self.web_dir, 'images') + print('create web directory %s...' % self.web_dir) + util.mkdirs([self.web_dir, self.img_dir]) + if opt.isTrain: + self.log_name = os.path.join(opt.checkpoints_dir, opt.name, 'loss_log.txt') + with open(self.log_name, "a") as log_file: + now = time.strftime("%c") + log_file.write('================ Training Loss (%s) ================\n' % now) + + # |visuals|: dictionary of images to display or save + def display_current_results(self, visuals, epoch, step): + + ## convert tensors to numpy arrays + visuals = self.convert_visuals_to_numpy(visuals) + + if self.tf_log: # show images in tensorboard output + img_summaries = [] + for label, image_numpy in visuals.items(): + # Write the image to a string + try: + s = StringIO() + except: + s = BytesIO() + if len(image_numpy.shape) >= 4: + image_numpy = image_numpy[0] + scipy.misc.toimage(image_numpy).save(s, format="jpeg") + # Create an Image object + img_sum = self.tf.Summary.Image(encoded_image_string=s.getvalue(), height=image_numpy.shape[0], width=image_numpy.shape[1]) + # Create a Summary value + img_summaries.append(self.tf.Summary.Value(tag=label, image=img_sum)) + + # Create and write Summary + summary = self.tf.Summary(value=img_summaries) + self.writer.add_summary(summary, step) + + if self.use_html: # save images to a html file + img_path = os.path.join(self.img_dir, 'epoch%.3d_iter%.7d.png' % (epoch, step)) + visuals_lst = [] + for label, image_numpy in visuals.items(): + if len(image_numpy.shape) >= 4: + image_numpy = image_numpy[0] + visuals_lst.append(image_numpy) + image_cath = np.concatenate(visuals_lst, axis=0) + util.save_image(image_cath, img_path) + + # update website + webpage = html.HTML(self.web_dir, 'Experiment name = %s' % self.name, refresh=5) + for n in range(epoch, 0, -1): + webpage.add_header('epoch [%d]' % n) + ims = [] + txts = [] + links = [] + + for label, image_numpy in visuals.items(): + if isinstance(image_numpy, list): + for i in range(len(image_numpy)): + img_path = 'epoch%.3d_iter%.3d_%s_%d.png' % (n, step, label, i) + ims.append(img_path) + txts.append(label+str(i)) + links.append(img_path) + else: + img_path = 'epoch%.3d_iter%.3d_%s.png' % (n, step, label) + ims.append(img_path) + txts.append(label) + links.append(img_path) + if len(ims) < 10: + webpage.add_images(ims, txts, links, width=self.win_size) + else: + num = int(round(len(ims)/2.0)) + webpage.add_images(ims[:num], txts[:num], links[:num], width=self.win_size) + webpage.add_images(ims[num:], txts[num:], links[num:], width=self.win_size) + webpage.save() + + # errors: dictionary of error labels and values + def plot_current_errors(self, errors, step): + if self.tf_log: + for tag, value in errors.items(): + value = value.mean().float() + summary = self.tf.Summary(value=[self.tf.Summary.Value(tag=tag, simple_value=value)]) + self.writer.add_summary(summary, step) + + # errors: same format as |errors| of plotCurrentErrors + def print_current_errors(self, epoch, i, errors, t): + message = '(epoch: %d, iters: %d, time: %.3f) ' % (epoch, i, t) + for k, v in errors.items(): + #print(v) + #if v != 0: + v = v.mean().float() + message += '%s: %.3f ' % (k, v) + + print(message) + with open(self.log_name, "a") as log_file: + log_file.write('%s\n' % message) + + def convert_visuals_to_numpy(self, visuals): + for key, t in visuals.items(): + tile = self.opt.batchSize > 8 + if 'input_label' == key: + t = util.tensor2label(t, self.opt.label_nc, tile=tile) + else: + t = util.tensor2im(t, tile=tile) + visuals[key] = t + return visuals + + # save image to the disk + def save_images(self, webpage, visuals, image_path, alpha=1.0): + visuals = self.convert_visuals_to_numpy(visuals) + + image_dir = webpage.get_image_dir() + short_path = ntpath.basename(image_path[0]) + name = os.path.splitext(short_path)[0] + + visuals_lst = [] + # image_name = '%s.png' % name + # save image name with alpha value (upto 3 digits) + image_name = '%s_%s.png' % (name, "{0:.3f}".format(alpha)) + alpha = alpha.item() + save_path = os.path.join(image_dir, str(alpha), image_name) + for label, image_numpy in visuals.items(): + visuals_lst.append(image_numpy) + + image_cath = np.concatenate(visuals_lst, axis=1) + util.save_image(image_cath, save_path, create_dir=True) diff --git a/wandb/offline-run-20241124_081359-82pwze46/files/output.log b/wandb/offline-run-20241124_081359-82pwze46/files/output.log new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/wandb/offline-run-20241124_081359-82pwze46/files/requirements.txt b/wandb/offline-run-20241124_081359-82pwze46/files/requirements.txt new file mode 100644 index 0000000000000000000000000000000000000000..7cada84441d78ef110b36343a08ee97dd4374ef1 --- /dev/null +++ b/wandb/offline-run-20241124_081359-82pwze46/files/requirements.txt @@ -0,0 +1,87 @@ +sentry-sdk==2.18.0 +nvidia-nccl-cu12==2.18.1 +mpmath==1.3.0 +triton==2.1.0 +graphviz==0.20.3 +setuptools==68.2.0 +markdown-it-py==3.0.0 +python-dateutil==2.9.0.post0 +nvidia-curand-cu12==10.3.2.106 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