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dl2-g32
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jhtdb

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dgcnz commited on
Commit
79f266c
•
1 Parent(s): 6fad5c5

feat: add large_200

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Files changed (11) hide show
  1. .gitattributes +3 -0
  2. datasets/jhtdb/large_200/metadata_test.csv +21 -0
  3. datasets/jhtdb/large_200/metadata_train.csv +161 -0
  4. datasets/jhtdb/large_200/metadata_val.csv +21 -0
  5. datasets/jhtdb/large_200/test.zip +3 -0
  6. datasets/jhtdb/large_200/train.zip +3 -0
  7. datasets/jhtdb/large_200/val.zip +3 -0
  8. poetry.lock +16 -1
  9. pyproject.toml +1 -0
  10. scripts/generate.py +36 -12
  11. scripts/large_200.yaml +14 -0
.gitattributes CHANGED
@@ -56,3 +56,6 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
56
  datasets/jhtdb/small_50/train.zip filter=lfs diff=lfs merge=lfs -text
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  datasets/jhtdb/small_50/test.zip filter=lfs diff=lfs merge=lfs -text
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  datasets/jhtdb/small_50/val.zip filter=lfs diff=lfs merge=lfs -text
 
 
 
 
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  datasets/jhtdb/small_50/train.zip filter=lfs diff=lfs merge=lfs -text
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  datasets/jhtdb/small_50/test.zip filter=lfs diff=lfs merge=lfs -text
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  datasets/jhtdb/small_50/val.zip filter=lfs diff=lfs merge=lfs -text
59
+ datasets/jhtdb/large_200/val.zip filter=lfs diff=lfs merge=lfs -text
60
+ datasets/jhtdb/large_200/test.zip filter=lfs diff=lfs merge=lfs -text
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+ datasets/jhtdb/large_200/train.zip filter=lfs diff=lfs merge=lfs -text
datasets/jhtdb/large_200/metadata_test.csv ADDED
@@ -0,0 +1,21 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ time_step,window_size,sx,sy,sz,ex,ey,ez,lr_factor,hr_path,lr_paths
2
+ 512,3,494,78,9,557,141,72,4,datasets/jhtdb/large_200/test/512_494_78_9_557_141_72_1_1_1_1.npy,"[""datasets/jhtdb/large_200/test/511_494_78_9_557_141_72_4_4_4_4.npy"", ""datasets/jhtdb/large_200/test/512_494_78_9_557_141_72_4_4_4_4.npy"", ""datasets/jhtdb/large_200/test/513_494_78_9_557_141_72_4_4_4_4.npy""]"
3
+ 367,3,207,391,66,270,454,129,4,datasets/jhtdb/large_200/test/367_207_391_66_270_454_129_1_1_1_1.npy,"[""datasets/jhtdb/large_200/test/366_207_391_66_270_454_129_4_4_4_4.npy"", ""datasets/jhtdb/large_200/test/367_207_391_66_270_454_129_4_4_4_4.npy"", ""datasets/jhtdb/large_200/test/368_207_391_66_270_454_129_4_4_4_4.npy""]"
4
+ 384,3,223,199,553,286,262,616,4,datasets/jhtdb/large_200/test/384_223_199_553_286_262_616_1_1_1_1.npy,"[""datasets/jhtdb/large_200/test/383_223_199_553_286_262_616_4_4_4_4.npy"", ""datasets/jhtdb/large_200/test/384_223_199_553_286_262_616_4_4_4_4.npy"", ""datasets/jhtdb/large_200/test/385_223_199_553_286_262_616_4_4_4_4.npy""]"
5
+ 324,3,888,77,461,951,140,524,4,datasets/jhtdb/large_200/test/324_888_77_461_951_140_524_1_1_1_1.npy,"[""datasets/jhtdb/large_200/test/323_888_77_461_951_140_524_4_4_4_4.npy"", ""datasets/jhtdb/large_200/test/324_888_77_461_951_140_524_4_4_4_4.npy"", ""datasets/jhtdb/large_200/test/325_888_77_461_951_140_524_4_4_4_4.npy""]"
6
+ 990,3,666,491,636,729,554,699,4,datasets/jhtdb/large_200/test/990_666_491_636_729_554_699_1_1_1_1.npy,"[""datasets/jhtdb/large_200/test/989_666_491_636_729_554_699_4_4_4_4.npy"", ""datasets/jhtdb/large_200/test/990_666_491_636_729_554_699_4_4_4_4.npy"", ""datasets/jhtdb/large_200/test/991_666_491_636_729_554_699_4_4_4_4.npy""]"
7
+ 100,3,498,136,782,561,199,845,4,datasets/jhtdb/large_200/test/100_498_136_782_561_199_845_1_1_1_1.npy,"[""datasets/jhtdb/large_200/test/99_498_136_782_561_199_845_4_4_4_4.npy"", ""datasets/jhtdb/large_200/test/100_498_136_782_561_199_845_4_4_4_4.npy"", ""datasets/jhtdb/large_200/test/101_498_136_782_561_199_845_4_4_4_4.npy""]"
8
+ 744,3,429,764,258,492,827,321,4,datasets/jhtdb/large_200/test/744_429_764_258_492_827_321_1_1_1_1.npy,"[""datasets/jhtdb/large_200/test/743_429_764_258_492_827_321_4_4_4_4.npy"", ""datasets/jhtdb/large_200/test/744_429_764_258_492_827_321_4_4_4_4.npy"", ""datasets/jhtdb/large_200/test/745_429_764_258_492_827_321_4_4_4_4.npy""]"
9
+ 19,3,760,682,207,823,745,270,4,datasets/jhtdb/large_200/test/19_760_682_207_823_745_270_1_1_1_1.npy,"[""datasets/jhtdb/large_200/test/18_760_682_207_823_745_270_4_4_4_4.npy"", ""datasets/jhtdb/large_200/test/19_760_682_207_823_745_270_4_4_4_4.npy"", ""datasets/jhtdb/large_200/test/20_760_682_207_823_745_270_4_4_4_4.npy""]"
10
+ 597,3,441,472,576,504,535,639,4,datasets/jhtdb/large_200/test/597_441_472_576_504_535_639_1_1_1_1.npy,"[""datasets/jhtdb/large_200/test/596_441_472_576_504_535_639_4_4_4_4.npy"", ""datasets/jhtdb/large_200/test/597_441_472_576_504_535_639_4_4_4_4.npy"", ""datasets/jhtdb/large_200/test/598_441_472_576_504_535_639_4_4_4_4.npy""]"
11
+ 108,3,482,516,18,545,579,81,4,datasets/jhtdb/large_200/test/108_482_516_18_545_579_81_1_1_1_1.npy,"[""datasets/jhtdb/large_200/test/107_482_516_18_545_579_81_4_4_4_4.npy"", ""datasets/jhtdb/large_200/test/108_482_516_18_545_579_81_4_4_4_4.npy"", ""datasets/jhtdb/large_200/test/109_482_516_18_545_579_81_4_4_4_4.npy""]"
12
+ 125,3,89,751,216,152,814,279,4,datasets/jhtdb/large_200/test/125_89_751_216_152_814_279_1_1_1_1.npy,"[""datasets/jhtdb/large_200/test/124_89_751_216_152_814_279_4_4_4_4.npy"", ""datasets/jhtdb/large_200/test/125_89_751_216_152_814_279_4_4_4_4.npy"", ""datasets/jhtdb/large_200/test/126_89_751_216_152_814_279_4_4_4_4.npy""]"
13
+ 571,3,198,610,563,261,673,626,4,datasets/jhtdb/large_200/test/571_198_610_563_261_673_626_1_1_1_1.npy,"[""datasets/jhtdb/large_200/test/570_198_610_563_261_673_626_4_4_4_4.npy"", ""datasets/jhtdb/large_200/test/571_198_610_563_261_673_626_4_4_4_4.npy"", ""datasets/jhtdb/large_200/test/572_198_610_563_261_673_626_4_4_4_4.npy""]"
14
+ 216,3,358,131,659,421,194,722,4,datasets/jhtdb/large_200/test/216_358_131_659_421_194_722_1_1_1_1.npy,"[""datasets/jhtdb/large_200/test/215_358_131_659_421_194_722_4_4_4_4.npy"", ""datasets/jhtdb/large_200/test/216_358_131_659_421_194_722_4_4_4_4.npy"", ""datasets/jhtdb/large_200/test/217_358_131_659_421_194_722_4_4_4_4.npy""]"
15
+ 739,3,815,558,954,878,621,1017,4,datasets/jhtdb/large_200/test/739_815_558_954_878_621_1017_1_1_1_1.npy,"[""datasets/jhtdb/large_200/test/738_815_558_954_878_621_1017_4_4_4_4.npy"", ""datasets/jhtdb/large_200/test/739_815_558_954_878_621_1017_4_4_4_4.npy"", ""datasets/jhtdb/large_200/test/740_815_558_954_878_621_1017_4_4_4_4.npy""]"
16
+ 98,3,932,531,476,995,594,539,4,datasets/jhtdb/large_200/test/98_932_531_476_995_594_539_1_1_1_1.npy,"[""datasets/jhtdb/large_200/test/97_932_531_476_995_594_539_4_4_4_4.npy"", ""datasets/jhtdb/large_200/test/98_932_531_476_995_594_539_4_4_4_4.npy"", ""datasets/jhtdb/large_200/test/99_932_531_476_995_594_539_4_4_4_4.npy""]"
17
+ 115,3,687,951,504,750,1014,567,4,datasets/jhtdb/large_200/test/115_687_951_504_750_1014_567_1_1_1_1.npy,"[""datasets/jhtdb/large_200/test/114_687_951_504_750_1014_567_4_4_4_4.npy"", ""datasets/jhtdb/large_200/test/115_687_951_504_750_1014_567_4_4_4_4.npy"", ""datasets/jhtdb/large_200/test/116_687_951_504_750_1014_567_4_4_4_4.npy""]"
18
+ 640,3,74,575,708,137,638,771,4,datasets/jhtdb/large_200/test/640_74_575_708_137_638_771_1_1_1_1.npy,"[""datasets/jhtdb/large_200/test/639_74_575_708_137_638_771_4_4_4_4.npy"", ""datasets/jhtdb/large_200/test/640_74_575_708_137_638_771_4_4_4_4.npy"", ""datasets/jhtdb/large_200/test/641_74_575_708_137_638_771_4_4_4_4.npy""]"
19
+ 49,3,652,411,93,715,474,156,4,datasets/jhtdb/large_200/test/49_652_411_93_715_474_156_1_1_1_1.npy,"[""datasets/jhtdb/large_200/test/48_652_411_93_715_474_156_4_4_4_4.npy"", ""datasets/jhtdb/large_200/test/49_652_411_93_715_474_156_4_4_4_4.npy"", ""datasets/jhtdb/large_200/test/50_652_411_93_715_474_156_4_4_4_4.npy""]"
20
+ 75,3,460,907,900,523,970,963,4,datasets/jhtdb/large_200/test/75_460_907_900_523_970_963_1_1_1_1.npy,"[""datasets/jhtdb/large_200/test/74_460_907_900_523_970_963_4_4_4_4.npy"", ""datasets/jhtdb/large_200/test/75_460_907_900_523_970_963_4_4_4_4.npy"", ""datasets/jhtdb/large_200/test/76_460_907_900_523_970_963_4_4_4_4.npy""]"
21
+ 546,3,558,635,443,621,698,506,4,datasets/jhtdb/large_200/test/546_558_635_443_621_698_506_1_1_1_1.npy,"[""datasets/jhtdb/large_200/test/545_558_635_443_621_698_506_4_4_4_4.npy"", ""datasets/jhtdb/large_200/test/546_558_635_443_621_698_506_4_4_4_4.npy"", ""datasets/jhtdb/large_200/test/547_558_635_443_621_698_506_4_4_4_4.npy""]"
datasets/jhtdb/large_200/metadata_train.csv ADDED
@@ -0,0 +1,161 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ time_step,window_size,sx,sy,sz,ex,ey,ez,lr_factor,hr_path,lr_paths
2
+ 512,3,494,78,9,557,141,72,4,datasets/jhtdb/large_200/train/512_494_78_9_557_141_72_1_1_1_1.npy,"[""datasets/jhtdb/large_200/train/511_494_78_9_557_141_72_4_4_4_4.npy"", ""datasets/jhtdb/large_200/train/512_494_78_9_557_141_72_4_4_4_4.npy"", ""datasets/jhtdb/large_200/train/513_494_78_9_557_141_72_4_4_4_4.npy""]"
3
+ 367,3,207,391,66,270,454,129,4,datasets/jhtdb/large_200/train/367_207_391_66_270_454_129_1_1_1_1.npy,"[""datasets/jhtdb/large_200/train/366_207_391_66_270_454_129_4_4_4_4.npy"", ""datasets/jhtdb/large_200/train/367_207_391_66_270_454_129_4_4_4_4.npy"", ""datasets/jhtdb/large_200/train/368_207_391_66_270_454_129_4_4_4_4.npy""]"
4
+ 384,3,223,199,553,286,262,616,4,datasets/jhtdb/large_200/train/384_223_199_553_286_262_616_1_1_1_1.npy,"[""datasets/jhtdb/large_200/train/383_223_199_553_286_262_616_4_4_4_4.npy"", ""datasets/jhtdb/large_200/train/384_223_199_553_286_262_616_4_4_4_4.npy"", ""datasets/jhtdb/large_200/train/385_223_199_553_286_262_616_4_4_4_4.npy""]"
5
+ 324,3,888,77,461,951,140,524,4,datasets/jhtdb/large_200/train/324_888_77_461_951_140_524_1_1_1_1.npy,"[""datasets/jhtdb/large_200/train/323_888_77_461_951_140_524_4_4_4_4.npy"", ""datasets/jhtdb/large_200/train/324_888_77_461_951_140_524_4_4_4_4.npy"", ""datasets/jhtdb/large_200/train/325_888_77_461_951_140_524_4_4_4_4.npy""]"
6
+ 990,3,666,491,636,729,554,699,4,datasets/jhtdb/large_200/train/990_666_491_636_729_554_699_1_1_1_1.npy,"[""datasets/jhtdb/large_200/train/989_666_491_636_729_554_699_4_4_4_4.npy"", ""datasets/jhtdb/large_200/train/990_666_491_636_729_554_699_4_4_4_4.npy"", ""datasets/jhtdb/large_200/train/991_666_491_636_729_554_699_4_4_4_4.npy""]"
7
+ 100,3,498,136,782,561,199,845,4,datasets/jhtdb/large_200/train/100_498_136_782_561_199_845_1_1_1_1.npy,"[""datasets/jhtdb/large_200/train/99_498_136_782_561_199_845_4_4_4_4.npy"", ""datasets/jhtdb/large_200/train/100_498_136_782_561_199_845_4_4_4_4.npy"", ""datasets/jhtdb/large_200/train/101_498_136_782_561_199_845_4_4_4_4.npy""]"
8
+ 744,3,429,764,258,492,827,321,4,datasets/jhtdb/large_200/train/744_429_764_258_492_827_321_1_1_1_1.npy,"[""datasets/jhtdb/large_200/train/743_429_764_258_492_827_321_4_4_4_4.npy"", ""datasets/jhtdb/large_200/train/744_429_764_258_492_827_321_4_4_4_4.npy"", ""datasets/jhtdb/large_200/train/745_429_764_258_492_827_321_4_4_4_4.npy""]"
9
+ 19,3,760,682,207,823,745,270,4,datasets/jhtdb/large_200/train/19_760_682_207_823_745_270_1_1_1_1.npy,"[""datasets/jhtdb/large_200/train/18_760_682_207_823_745_270_4_4_4_4.npy"", ""datasets/jhtdb/large_200/train/19_760_682_207_823_745_270_4_4_4_4.npy"", ""datasets/jhtdb/large_200/train/20_760_682_207_823_745_270_4_4_4_4.npy""]"
10
+ 597,3,441,472,576,504,535,639,4,datasets/jhtdb/large_200/train/597_441_472_576_504_535_639_1_1_1_1.npy,"[""datasets/jhtdb/large_200/train/596_441_472_576_504_535_639_4_4_4_4.npy"", ""datasets/jhtdb/large_200/train/597_441_472_576_504_535_639_4_4_4_4.npy"", ""datasets/jhtdb/large_200/train/598_441_472_576_504_535_639_4_4_4_4.npy""]"
11
+ 108,3,482,516,18,545,579,81,4,datasets/jhtdb/large_200/train/108_482_516_18_545_579_81_1_1_1_1.npy,"[""datasets/jhtdb/large_200/train/107_482_516_18_545_579_81_4_4_4_4.npy"", ""datasets/jhtdb/large_200/train/108_482_516_18_545_579_81_4_4_4_4.npy"", ""datasets/jhtdb/large_200/train/109_482_516_18_545_579_81_4_4_4_4.npy""]"
12
+ 125,3,89,751,216,152,814,279,4,datasets/jhtdb/large_200/train/125_89_751_216_152_814_279_1_1_1_1.npy,"[""datasets/jhtdb/large_200/train/124_89_751_216_152_814_279_4_4_4_4.npy"", ""datasets/jhtdb/large_200/train/125_89_751_216_152_814_279_4_4_4_4.npy"", ""datasets/jhtdb/large_200/train/126_89_751_216_152_814_279_4_4_4_4.npy""]"
13
+ 571,3,198,610,563,261,673,626,4,datasets/jhtdb/large_200/train/571_198_610_563_261_673_626_1_1_1_1.npy,"[""datasets/jhtdb/large_200/train/570_198_610_563_261_673_626_4_4_4_4.npy"", ""datasets/jhtdb/large_200/train/571_198_610_563_261_673_626_4_4_4_4.npy"", ""datasets/jhtdb/large_200/train/572_198_610_563_261_673_626_4_4_4_4.npy""]"
14
+ 216,3,358,131,659,421,194,722,4,datasets/jhtdb/large_200/train/216_358_131_659_421_194_722_1_1_1_1.npy,"[""datasets/jhtdb/large_200/train/215_358_131_659_421_194_722_4_4_4_4.npy"", ""datasets/jhtdb/large_200/train/216_358_131_659_421_194_722_4_4_4_4.npy"", ""datasets/jhtdb/large_200/train/217_358_131_659_421_194_722_4_4_4_4.npy""]"
15
+ 739,3,815,558,954,878,621,1017,4,datasets/jhtdb/large_200/train/739_815_558_954_878_621_1017_1_1_1_1.npy,"[""datasets/jhtdb/large_200/train/738_815_558_954_878_621_1017_4_4_4_4.npy"", ""datasets/jhtdb/large_200/train/739_815_558_954_878_621_1017_4_4_4_4.npy"", ""datasets/jhtdb/large_200/train/740_815_558_954_878_621_1017_4_4_4_4.npy""]"
16
+ 98,3,932,531,476,995,594,539,4,datasets/jhtdb/large_200/train/98_932_531_476_995_594_539_1_1_1_1.npy,"[""datasets/jhtdb/large_200/train/97_932_531_476_995_594_539_4_4_4_4.npy"", ""datasets/jhtdb/large_200/train/98_932_531_476_995_594_539_4_4_4_4.npy"", ""datasets/jhtdb/large_200/train/99_932_531_476_995_594_539_4_4_4_4.npy""]"
17
+ 115,3,687,951,504,750,1014,567,4,datasets/jhtdb/large_200/train/115_687_951_504_750_1014_567_1_1_1_1.npy,"[""datasets/jhtdb/large_200/train/114_687_951_504_750_1014_567_4_4_4_4.npy"", ""datasets/jhtdb/large_200/train/115_687_951_504_750_1014_567_4_4_4_4.npy"", ""datasets/jhtdb/large_200/train/116_687_951_504_750_1014_567_4_4_4_4.npy""]"
18
+ 640,3,74,575,708,137,638,771,4,datasets/jhtdb/large_200/train/640_74_575_708_137_638_771_1_1_1_1.npy,"[""datasets/jhtdb/large_200/train/639_74_575_708_137_638_771_4_4_4_4.npy"", ""datasets/jhtdb/large_200/train/640_74_575_708_137_638_771_4_4_4_4.npy"", ""datasets/jhtdb/large_200/train/641_74_575_708_137_638_771_4_4_4_4.npy""]"
19
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20
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21
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22
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23
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24
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25
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26
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27
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28
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29
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30
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31
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32
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33
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34
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35
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36
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37
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38
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39
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40
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41
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42
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43
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44
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45
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46
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48
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49
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50
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51
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52
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53
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54
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55
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56
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57
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58
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59
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60
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61
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62
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104
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111
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112
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113
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115
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120
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122
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123
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139
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140
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141
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142
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143
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144
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145
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+ content-hash = "38736cab0ad26c46ff14bbde6e388e35899cd0bb7435248dd54729b6a55f7944"
pyproject.toml CHANGED
@@ -19,6 +19,7 @@ black = "^24.4.2"
19
  flake8 = "^7.0.0"
20
  jsonargparse = "^4.28.0"
21
  pandas = "^2.2.2"
 
22
 
23
  [build-system]
24
  requires = ["poetry-core"]
 
19
  flake8 = "^7.0.0"
20
  jsonargparse = "^4.28.0"
21
  pandas = "^2.2.2"
22
+ tenacity = "^8.3.0"
23
 
24
  [build-system]
25
  requires = ["poetry-core"]
scripts/generate.py CHANGED
@@ -1,3 +1,19 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
  import numpy as np
2
  import json
3
  from jsonargparse import CLI
@@ -9,6 +25,10 @@ from tqdm import tqdm
9
  from pathlib import Path
10
  from itertools import chain
11
  import zipfile
 
 
 
 
12
 
13
 
14
  def get_filename(
@@ -34,6 +54,7 @@ def get_filename(
34
  )
35
 
36
 
 
37
  def download_jhtdb(
38
  loader: pyJHTDB.libJHTDB,
39
  time_step: int,
@@ -55,6 +76,9 @@ def download_jhtdb(
55
  :param path: path to save the data
56
  :param dataset: dataset to download from. Default is "isotropic1024coarse"
57
  :param field: velocity ("u") or pressure ("p") field
 
 
 
58
  """
59
  if not path.exists():
60
  results: np.ndarray = loader.getCutout(
@@ -67,22 +91,20 @@ def download_jhtdb(
67
  filter_width=filter_width,
68
  )
69
  if results is None:
 
 
 
 
 
 
 
70
  raise Exception("Could not download data from JHTDB")
71
- results = np.rollaxis(
72
- results, -1, 0
73
- ) # Move the [x, y, z] dimensions to the front
74
  np.save(path, results)
75
  return np.load(path)
76
 
77
 
78
- def download_all(params: dict, loader: pyJHTDB.libJHTDB):
79
- """Download all the data from the JHTDB database.
80
- TODO: parallelize this function
81
- """
82
- for p in tqdm(params):
83
- download_jhtdb(loader=loader, **p)
84
-
85
-
86
  def get_params(
87
  total_samples: int,
88
  domain_size: int,
@@ -155,7 +177,9 @@ def download_generic(
155
  # all_params_hr.shape = [total_samples]
156
  # flatten nested params
157
  all_params = list(chain.from_iterable(all_params_lr)) + all_params_hr
158
- download_all(all_params, lJHTDB)
 
 
159
  return all_params_lr, all_params_hr, all_params
160
 
161
 
 
1
+ # Sources:
2
+ # - https://github.com/NVIDIA/modulus-sym/blob/main/examples/super_resolution/jhtdb_utils.py
3
+ # - https://docs.nvidia.com/deeplearning/modulus/modulus-v2209/user_guide/intermediate/turbulence_super_resolution.html
4
+ # - https://arxiv.org/abs/2310.02299
5
+ #
6
+ # Licensed under the Apache License, Version 2.0 (the "License");
7
+ # you may not use this file except in compliance with the License.
8
+ # You may obtain a copy of the License at
9
+ #
10
+ # http://www.apache.org/licenses/LICENSE-2.0
11
+ #
12
+ # Unless required by applicable law or agreed to in writing, software
13
+ # distributed under the License is distributed on an "AS IS" BASIS,
14
+ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
15
+ # See the License for the specific language governing permissions and
16
+ # limitations under the License.
17
  import numpy as np
18
  import json
19
  from jsonargparse import CLI
 
25
  from pathlib import Path
26
  from itertools import chain
27
  import zipfile
28
+ import logging
29
+ from tenacity import retry, stop_after_attempt, wait_fixed
30
+
31
+ logging.basicConfig(level=logging.INFO)
32
 
33
 
34
  def get_filename(
 
54
  )
55
 
56
 
57
+ @retry(wait=wait_fixed(2), stop=stop_after_attempt(3))
58
  def download_jhtdb(
59
  loader: pyJHTDB.libJHTDB,
60
  time_step: int,
 
76
  :param path: path to save the data
77
  :param dataset: dataset to download from. Default is "isotropic1024coarse"
78
  :param field: velocity ("u") or pressure ("p") field
79
+
80
+ Note that retrying doesn't affect the random seed,
81
+ as all the params have been fixed beforehand.
82
  """
83
  if not path.exists():
84
  results: np.ndarray = loader.getCutout(
 
91
  filter_width=filter_width,
92
  )
93
  if results is None:
94
+ logging.error(
95
+ f"time_step: {time_step}, "
96
+ f"start: {start.tolist()}, "
97
+ f"end: {end.tolist()}, "
98
+ f"step: {step.tolist()}, "
99
+ f"filter_width: {filter_width}"
100
+ )
101
  raise Exception("Could not download data from JHTDB")
102
+ # Move the [x, y, z] dimensions to the front
103
+ results = np.rollaxis(results, -1, 0)
 
104
  np.save(path, results)
105
  return np.load(path)
106
 
107
 
 
 
 
 
 
 
 
 
108
  def get_params(
109
  total_samples: int,
110
  domain_size: int,
 
177
  # all_params_hr.shape = [total_samples]
178
  # flatten nested params
179
  all_params = list(chain.from_iterable(all_params_lr)) + all_params_hr
180
+ # This could be parallelizable, but it is non-trivial to get around the rate limiting.
181
+ for p in tqdm(all_params):
182
+ download_jhtdb(loader=lJHTDB, **p)
183
  return all_params_lr, all_params_hr, all_params
184
 
185
 
scripts/large_200.yaml ADDED
@@ -0,0 +1,14 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ name: "large_200"
2
+ total_samples: 200
3
+ train_split: 0.8
4
+ val_split: 0.1
5
+ test_split: 0.1
6
+ domain_size: 64
7
+ lr_factor: 4
8
+ root: "datasets/jhtdb"
9
+ time_range:
10
+ - 2
11
+ - 1023
12
+ window_size: 3
13
+ seed: 123
14
+ token: "edu.jhu.pha.turbulence.testing-201311"