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+version https://git-lfs.github.com/spec/v1
+oid 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
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+ 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
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+ dataset_mode: summer2winteryosemite
+ debug: False
+ display_freq: 200 [default: 100]
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+ gan_mode: hinge
+ gpu_ids: 0
+ id1_weight: 50
+ id2_weight: 1
+ init_type: xavier
+ init_variance: 0.02
+ isTrain: True [default: None]
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+ loss_count_interval: 200
+ lr: 0.0002
+ lrG: 0.0012
+ lrG_decay: 0.0001
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+ 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]
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+ no_TTUR: False
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+ no_html: False
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+ norm_S: spectralinstance
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+ num_upsampling_layers: more
+ optimizer: adam
+ output_nc: 3
+ phase: train
+ preprocess_mode: resize_and_crop
+ print_freq: 100
+ save_dir: /home/share/VAL_ImgTranslations/experiments
+ save_epoch_freq: 2 [default: 1]
+ save_latest_freq: 5
+ save_model_interval: 300
+ semantic_nc: 3 [default: None]
+ serial_batches: False
+ sroot: ./datasets/summer2winter_yosemite [default: ./datasets/summer2winter_yosemite/]
+ style_weight: 3.0
+ task: AST
+ tf_log: False
+ use_infinite_sampler: False
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+
+
+
+ Experiment name = ast_summer2winteryosemite
+
+
+
+ 07:16AM on November 30, 2024
+ epoch [1]
+
+
+
+
+
+
+
+ 99111110116101110116
+
+ |
+
+
+
+
+
+ 1151211101161041011151051221011009510510997103101
+
+ |
+
+
+
+
+
+ 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
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diff --git a/data/__pycache__/summer2yosemite_test.cpython-310.pyc b/data/__pycache__/summer2yosemite_test.cpython-310.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..d67a4c6eda06c0da957c45e4df34e4129b6fd053
Binary files /dev/null and b/data/__pycache__/summer2yosemite_test.cpython-310.pyc differ
diff --git a/data/ade20k_dataset.py b/data/ade20k_dataset.py
new file mode 100644
index 0000000000000000000000000000000000000000..62489e3f695e4e298b291e53823121668c6a1ec6
--- /dev/null
+++ b/data/ade20k_dataset.py
@@ -0,0 +1,48 @@
+from data.pix2pix_dataset import Pix2pixDataset
+from data.image_folder import make_dataset
+
+
+class ADE20KDataset(Pix2pixDataset):
+
+ @staticmethod
+ def modify_commandline_options(parser, is_train):
+ parser = Pix2pixDataset.modify_commandline_options(parser, 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
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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)
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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
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diff --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 https://git-lfs.github.com/spec/v1
+oid sha256:ade4ece39a3ea86879e3b6070564b1cdc00e61408ddb0a40446d517cf0d19c56
+size 40
diff --git a/models/networks/logs/events.out.tfevents.1720811290.df626c6db989 b/models/networks/logs/events.out.tfevents.1720811290.df626c6db989
new file mode 100644
index 0000000000000000000000000000000000000000..20dbc46fdf92078d1a45c4d67a3897e3f4a82f0f
--- /dev/null
+++ b/models/networks/logs/events.out.tfevents.1720811290.df626c6db989
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:3c86463db7821e18ce5d1c8b4f445c5139fe96449a9b6945b21af33c1d94d60e
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diff --git a/models/networks/logs/events.out.tfevents.1720811326.df626c6db989 b/models/networks/logs/events.out.tfevents.1720811326.df626c6db989
new file mode 100644
index 0000000000000000000000000000000000000000..2749e87a61934f06abc8c3bb71c7b11a7391e576
--- /dev/null
+++ b/models/networks/logs/events.out.tfevents.1720811326.df626c6db989
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:9ec60b1bf00395ff8d764d148930c776c1133e994d072f5321e0bc17991ab688
+size 40
diff --git a/models/networks/logs/events.out.tfevents.1720811362.df626c6db989 b/models/networks/logs/events.out.tfevents.1720811362.df626c6db989
new file mode 100644
index 0000000000000000000000000000000000000000..66a3ad6c825c3eebe4dd52614378b466c17484f8
--- /dev/null
+++ b/models/networks/logs/events.out.tfevents.1720811362.df626c6db989
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
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diff --git a/models/networks/logs/events.out.tfevents.1720811453.df626c6db989 b/models/networks/logs/events.out.tfevents.1720811453.df626c6db989
new file mode 100644
index 0000000000000000000000000000000000000000..877b5e9c9ea60906c65b2d9cfb16c7b94eb760e3
--- /dev/null
+++ b/models/networks/logs/events.out.tfevents.1720811453.df626c6db989
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
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diff --git a/models/networks/logs/events.out.tfevents.1720859879.df626c6db989 b/models/networks/logs/events.out.tfevents.1720859879.df626c6db989
new file mode 100644
index 0000000000000000000000000000000000000000..041629684c621e19fd87cdc3f4a201af98a9657d
--- /dev/null
+++ b/models/networks/logs/events.out.tfevents.1720859879.df626c6db989
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:2868f816ea27e387a4d7975c2a40c1e15d90a78620913a00fdd269133f06416e
+size 540
diff --git a/models/networks/logs/events.out.tfevents.1720860073.df626c6db989 b/models/networks/logs/events.out.tfevents.1720860073.df626c6db989
new file mode 100644
index 0000000000000000000000000000000000000000..345b5886c35956bea16575f6c5744624a8f3b668
--- /dev/null
+++ b/models/networks/logs/events.out.tfevents.1720860073.df626c6db989
@@ -0,0 +1,3 @@
+version 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 DataParallelWithCallback, patch_replication_callback
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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
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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
+pandas==2.2.1
+matplotlib==3.7.5
+beautifulsoup4==4.12.2
+charset-normalizer==3.3.2
+dominate==2.7.0
+torchinfo==1.8.0
+graphviz==0.20.3
+torchviz==0.0.2
+torchview==0.2.6
+dill==0.3.7
+Pillow==10.0.0
+tqdm==4.65.2
+rich
\ No newline at end of file
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+size 133463
diff --git a/results/ast_summer2winteryosemite/test_latest/images/1.0/1047_1.000.png b/results/ast_summer2winteryosemite/test_latest/images/1.0/1047_1.000.png
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@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
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+size 138490
diff --git a/results/ast_summer2winteryosemite/test_latest/images/1.0/berlin_000000_000019_leftImg8bit_1.000.png b/results/ast_summer2winteryosemite/test_latest/images/1.0/berlin_000000_000019_leftImg8bit_1.000.png
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@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
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+size 167658
diff --git a/results/ast_summer2winteryosemite/test_latest/images/1.0/berlin_000001_000019_leftImg8bit_1.000.png b/results/ast_summer2winteryosemite/test_latest/images/1.0/berlin_000001_000019_leftImg8bit_1.000.png
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@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
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diff --git a/results/ast_summer2winteryosemite/test_latest/images/1.0/berlin_000002_000019_leftImg8bit_1.000.png b/results/ast_summer2winteryosemite/test_latest/images/1.0/berlin_000002_000019_leftImg8bit_1.000.png
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+++ 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))
+ self.old_lr = new_lr
diff --git a/util/__init__.py b/util/__init__.py
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diff --git a/util/coco.py b/util/coco.py
new file mode 100644
index 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
+nvidia-cusolver-cu12==11.4.5.107
+mdurl==0.1.2
+six==1.16.0
+dominate==2.7.0
+typing_extensions==4.12.2
+docker-pycreds==0.4.0
+wheel==0.41.2
+setproctitle==1.3.3
+pip==23.2.1
+cycler==0.12.1
+torchviz==0.0.2
+threadpoolctl==3.5.0
+lazy_loader==0.4
+filelock==3.16.1
+wandb==0.18.6
+einops==0.8.0
+nvidia-ml-py==12.535.161
+platformdirs==4.3.6
+sympy==1.13.3
+nvidia-cufft-cu12==11.0.2.54
+nvidia-nvtx-cu12==12.1.105
+tqdm==4.65.2
+nvidia-cuda-nvrtc-cu12==12.1.105
+kiwisolver==1.4.7
+certifi==2024.8.30
+torch==2.1.2
+nvidia-nvjitlink-cu12==12.6.77
+nvidia-cudnn-cu12==8.9.2.26
+dill==0.3.7
+matplotlib==3.7.5
+soupsieve==2.6
+requests==2.32.3
+psutil==6.1.0
+fonttools==4.54.1
+nvidia-cusparse-cu12==12.1.0.106
+Jinja2==3.1.4
+urllib3==2.2.3
+fsspec==2024.10.0
+networkx==3.4.2
+click==8.1.7
+nvidia-cublas-cu12==12.1.3.1
+Pygments==2.18.0
+torchvision==0.16.2
+GitPython==3.1.43
+timm==1.0.3
+PyYAML==6.0.2
+Pillow==10.0.0
+termcolor==2.5.0
+scikit-learn==1.2.2
+scipy==1.11.4
+joblib==1.4.2
+torchinfo==1.8.0
+packaging==24.2
+pyparsing==3.2.0
+tensorboardX==2.6.2.2
+scikit-image==0.22.0
+tifffile==2024.9.20
+pandas==2.2.1
+charset-normalizer==3.3.2
+safetensors==0.4.5
+imageio==2.36.0
+nvidia-cuda-runtime-cu12==12.1.105
+gitdb==4.0.11
+huggingface-hub==0.26.2
+tzdata==2024.2
+idna==3.10
+smmap==5.0.1
+rich==13.9.4
+numpy==1.26.4
+nvitop==1.3.2
+cachetools==5.5.0
+torchview==0.2.6
+beautifulsoup4==4.12.2
+contourpy==1.3.1
+nvidia-cuda-cupti-cu12==12.1.105
+protobuf==3.20.3
+pytz==2024.2
+MarkupSafe==3.0.2
diff --git a/wandb/offline-run-20241124_081359-82pwze46/files/wandb-metadata.json b/wandb/offline-run-20241124_081359-82pwze46/files/wandb-metadata.json
new file mode 100644
index 0000000000000000000000000000000000000000..92a1438da5111210cad9f0987d8a7b4964fd9a7b
--- /dev/null
+++ b/wandb/offline-run-20241124_081359-82pwze46/files/wandb-metadata.json
@@ -0,0 +1,79 @@
+{
+ "os": "Linux-6.8.0-48-generic-x86_64-with-glibc2.31",
+ "python": "3.10.15",
+ "startedAt": "2024-11-24T08:13:59.788131Z",
+ "args": [
+ "--name",
+ "ast_summer2winteryosemite",
+ "--task",
+ "AST",
+ "--gpu_ids",
+ "0",
+ "--checkpoints_dir",
+ "./checkpoints",
+ "--batchSize",
+ "2",
+ "--dataset_mode",
+ "summer2winteryosemite",
+ "--croot",
+ "./datasets/summer2winter_yosemite",
+ "--sroot",
+ "./datasets/summer2winter_yosemite",
+ "--nThreads",
+ "4",
+ "--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"
+ ],
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+ "disk": {
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+ "host": "9139923abb9b",
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+ "gpu": "NVIDIA GeForce RTX 4060 Ti",
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+ "disk": {
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+}
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+2024-11-25 14:47:32,055 INFO MainThread:2733 [wandb_run.py:_redirect():2364] Wrapping output streams.
+2024-11-25 14:47:32,056 INFO MainThread:2733 [wandb_run.py:_redirect():2389] Redirects installed.
+2024-11-25 14:47:32,059 INFO MainThread:2733 [wandb_init.py:init():911] run started, returning control to user process
+2024-11-25 14:47:37,365 WARNING MsgRouterThr:2733 [router.py:message_loop():75] message_loop has been closed
diff --git a/wandb/offline-run-20241125_144730-pfzsdr81/run-pfzsdr81.wandb b/wandb/offline-run-20241125_144730-pfzsdr81/run-pfzsdr81.wandb
new file mode 100644
index 0000000000000000000000000000000000000000..bb0fac0550ab39e0578ec7f72a85f93fa2bbd400
Binary files /dev/null and b/wandb/offline-run-20241125_144730-pfzsdr81/run-pfzsdr81.wandb differ
diff --git a/wandb_disable.py b/wandb_disable.py
new file mode 100644
index 0000000000000000000000000000000000000000..08e0b2c69c2a0a59782b9600c473b2aa5fb496d3
--- /dev/null
+++ b/wandb_disable.py
@@ -0,0 +1,2 @@
+import os
+os.environ['WANDB_DISABLED'] = 'true'
\ No newline at end of file
diff --git a/wget-log b/wget-log
new file mode 100644
index 0000000000000000000000000000000000000000..a6696f07528ba1acebe33363e6a75e7dba70c242
--- /dev/null
+++ b/wget-log
@@ -0,0 +1,6 @@
+--2024-07-11 18:08:40-- https://storage.googleapis.com/kaggle-data-sets/925857/1567181/bundle/archive.zip?X-Goog-Algorithm=GOOG4-RSA-SHA256
+Resolving storage.googleapis.com (storage.googleapis.com)... 142.250.71.251, 142.251.220.27, 172.217.24.251, ...
+Connecting to storage.googleapis.com (storage.googleapis.com)|142.250.71.251|:443... connected.
+HTTP request sent, awaiting response... 400 Bad Request
+2024-07-11 18:08:40 ERROR 400: Bad Request.
+