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cwerner
/
deadtrees
connected to https://github.com/cwerner/deadtrees.git

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  1. schema: '2.0'
    2. 

  2. stages:
    3. 

  3.   createtiles@2019:
    4. 

  4.     cmd: mkdir -p data/processed.images.2019; gdal_retile.py  -csv locations.csv  -v
    5. 

  5.       -ps 2048 2048 -co "TILED=YES" -co "COMPRESS=LZW" -co "PREDICTOR=2" -co "ALPHA=NO"
    6. 

  6.       -co "NUM_THREADS=ALL_CPUS" -targetDir data/processed.images.2019  data/raw/ortho_ms_2019_EPSG3044.tif
    7. 

  7.     deps:
    8. 

  8.     - path: data/raw/ortho_ms_2019_EPSG3044.tif
    9. 

  9.       md5: 28a27ea53b559c3cec937a3687407557
    10. 

  10.       size: 144920418531
    11. 

  11.     params:
    12. 

  12.       params.yaml:
    13. 

  13.         source_dim: 2048
    14. 

  14.     outs:
    15. 

  15.     - path: data/processed.images.2019
    16. 

  16.       md5: 35c31b781cb0bdb19650329132d83d05.dir
    17. 

  17.       size: 144926802415
    18. 

  18.       nfiles: 30489
    19. 

  19.   createmasks:
    20. 

  20.     cmd: python scripts/createmasks.py  data/processed.images.2019  data/processed.masks.2019  data/raw/shapefiles/deadtrees_2019/deadtrees_2019.shp
    21. 

  21.       --negativesample data/raw/shapefiles/deadtrees_notdead/deadtrees_notdead.shp
    22. 

  22.     deps:
    23. 

  23.     - path: data/processed.images.2019
    24. 

  24.       md5: 35c31b781cb0bdb19650329132d83d05.dir
    25. 

  25.       size: 144926802415
    26. 

  26.       nfiles: 30489
    27. 

  27.     - path: data/raw/shapefiles/deadtrees_2019
    28. 

  28.       md5: 206432f450611c44ceb32c49e50aa8c9.dir
    29. 

  29.       size: 3996517
    30. 

  30.       nfiles: 5
    31. 

  31.     - path: data/raw/shapefiles/deadtrees_notdead
    32. 

  32.       md5: 99b6d382aebfc46e79c9d044e95add4e.dir
    33. 

  33.       size: 31722
    34. 

  34.       nfiles: 5
    35. 

  35.     outs:
    36. 

  36.     - path: data/processed.masks.2019
    37. 

  37.       md5: 86dfe9fff1765527e6ab7bdbd50e873d.dir
    38. 

  38.       size: 4103103156
    39. 

  39.       nfiles: 978
    40. 

  40.     - path: data/processed.masks.2019.neg_sample
    41. 

  41.       md5: e39614f069603f65a333d0a8713072a9.dir
    42. 

  42.       size: 335615520
    43. 

  43.       nfiles: 80
    44. 

  44.   createdataset:
    45. 

  45.     cmd: python scripts/createdataset.py  data/processed.images.2019  data/processed.masks.2019  data/dataset  --source_dim
    46. 

  46.       2048 --tile_size 256 --format TIFF
    47. 

  47.     deps:
    48. 

  48.     - path: data/processed.images.2019
    49. 

  49.       md5: 35c31b781cb0bdb19650329132d83d05.dir
    50. 

  50.       size: 144926802415
    51. 

  51.       nfiles: 30489
    52. 

  52.     - path: data/processed.masks.2019
    53. 

  53.       md5: 86dfe9fff1765527e6ab7bdbd50e873d.dir
    54. 

  54.       size: 4103103156
    55. 

  55.       nfiles: 978
    56. 

  56.     - path: data/processed.masks.2019.neg_sample
    57. 

  57.       md5: e39614f069603f65a333d0a8713072a9.dir
    58. 

  58.       size: 335615520
    59. 

  59.       nfiles: 80
    60. 

  60.     params:
    61. 

  61.       params.yaml:
    62. 

  62.         createdataset.tile_size: 256
    63. 

  63.         file_type: TIFF
    64. 

  64.         source_dim: 2048
    65. 

  65.     outs:
    66. 

  66.     - path: data/dataset/stats.csv
    67. 

  67.       md5: 5d5e6ab2648bdb6b7846d4c8655a72a8
    68. 

  68.       size: 2539255
    69. 

  69.     - path: data/dataset/train
    70. 

  70.       md5: 09daf34f435e8a47ce8879a6f7a9899e.dir
    71. 

  71.       size: 10832783360
    72. 

  72.       nfiles: 208
    73. 

  73.   createbalanced:
    74. 

  74.     cmd: python scripts/createbalanced.py  data/dataset/stats.csv data/dataset/train  data/dataset/train_balanced  data/dataset/train_balanced_short
    75. 

  75.       --format TIFF --tmp-dir ./tmp
    76. 

  76.     deps:
    77. 

  77.     - path: data/dataset/stats.csv
    78. 

  78.       md5: 8f24c9c9fcbcab4f9388a4b2b26ed58c
    79. 

  79.       size: 2415683
    80. 

  80.     - path: data/dataset/train
    81. 

  81.       md5: a5a9bd6897f3c8b0143e3a378928cc54.dir
    82. 

  82.       size: 20627302400
    83. 

  83.       nfiles: 121
    84. 

  84.     params:
    85. 

  85.       params.yaml:
    86. 

  86.         file_type: TIFF
    87. 

  87.     outs:
    88. 

  88.     - path: data/dataset/train_balanced
    89. 

  89.       md5: 00c0e3636b55b92b63add488a2e62a12.dir
    90. 

  90.       size: 20596592640
    91. 

  91.       nfiles: 241
    92. 

  92.     - path: data/dataset/train_balanced_short
    93. 

  93.       md5: 94e764aa3326c386524910bfbac0454d.dir
    94. 

  94.       size: 2072975360
    95. 

  95.       nfiles: 97
    96. 

  96.   createtiles@2017:
    97. 

  97.     cmd: mkdir -p data/processed.images.2017; gdal_retile.py  -csv locations.csv  -v
    98. 

  98.       -ps 2048 2048 -co "TILED=YES" -co "COMPRESS=LZW" -co "PREDICTOR=2" -co "ALPHA=NO"
    99. 

  99.       -co "NUM_THREADS=ALL_CPUS" -targetDir data/processed.images.2017  data/raw/ortho_ms_2017_EPSG3044.tif
    100. 

  100.     deps:
    101. 

  101.     - path: data/raw/ortho_ms_2017_EPSG3044.tif
    102. 

  102.       md5: 2389bdb4952d2c869c52e87abce30d4f
    103. 

  103.       size: 109896357004
    104. 

  104.     params:
    105. 

  105.       params.yaml:
    106. 

  106.         source_dim: 2048
    107. 

  107.     outs:
    108. 

  108.     - path: data/processed.images.2017
    109. 

  109.       md5: 66de96d201af5a47a09997691b992370.dir
    110. 

  110.       size: 109902740888
    111. 

  111.       nfiles: 30489
    112. 

  112.   inference@2017:
    113. 

  113.     cmd: mkdir -p data/predicted.2017; stdbuf -i0 -o0 -e0 python scripts/inference.py
    114. 

  114.       --all --nopreview -o data/predicted.2017 data/processed.images.2017; gdal_merge.py  -co
    115. 

  115.       "TILED=YES" -co "COMPRESS=LZW" -co "PREDICTOR=2" -co "NUM_THREADS=ALL_CPUS"
    116. 

  116.       -o data/predicted_mosaic_2017.tif  data/predicted.2017/ortho_ms_2017_EPSG3044_*
    117. 

  117.     deps:
    118. 

  118.     - path: checkpoints/bestmodel.ckpt
    119. 

  119.       md5: e543654648455b36411f4611eae36f22
    120. 

  120.       size: 378716076
    121. 

  121.     - path: data/processed.images.2017
    122. 

  122.       md5: 66de96d201af5a47a09997691b992370.dir
    123. 

  123.       size: 109902740888
    124. 

  124.       nfiles: 30489
    125. 

  125.     outs:
    126. 

  126.     - path: data/predicted.2017
    127. 

  127.       md5: 6a02ab7abf0eab217247b67ae69ae6f2.dir
    128. 

  128.       size: 607592250
    129. 

  129.       nfiles: 20827
    130. 

  130.     - path: data/predicted_mosaic_2017.tif
    131. 

  131.       md5: 79599364e67873cd97d420d8e5394988
    132. 

  132.       size: 845660272
    133. 

  133.   inference@2019:
    134. 

  134.     cmd: mkdir -p data/predicted.2019; stdbuf -i0 -o0 -e0 python scripts/inference.py
    135. 

  135.       --all --nopreview -o data/predicted.2019 data/processed.images.2019; gdal_merge.py  -co
    136. 

  136.       "TILED=YES" -co "COMPRESS=LZW" -co "PREDICTOR=2" -co "NUM_THREADS=ALL_CPUS"
    137. 

  137.       -o data/predicted_mosaic_2019.tif  data/predicted.2019/ortho_ms_2019_EPSG3044_*
    138. 

  138.     deps:
    139. 

  139.     - path: checkpoints/bestmodel.ckpt
    140. 

  140.       md5: e543654648455b36411f4611eae36f22
    141. 

  141.       size: 378716076
    142. 

  142.     - path: data/processed.images.2019
    143. 

  143.       md5: 35c31b781cb0bdb19650329132d83d05.dir
    144. 

  144.       size: 144926802415
    145. 

  145.       nfiles: 30489
    146. 

  146.     outs:
    147. 

  147.     - path: data/predicted.2019
    148. 

  148.       md5: 50287edaea44da4ebd415cededfc3323.dir
    149. 

  149.       size: 486417595
    150. 

  150.       nfiles: 16227
    151. 

  151.     - path: data/predicted_mosaic_2019.tif
    152. 

  152.       md5: 784f2a8d8d0ea1dfcfcfa5f0031cebf4
    153. 

  153.       size: 813668388
    154. 

  154.   computestats:
    155. 

  155.     cmd: 'python scripts/computestats.py --frac 0.1 data/processed.images.2017   data/processed.images.2018   data/processed.images.2019  data/processed.images.2020 '
    156. 

  156.     deps:
    157. 

  157.     - path: data/processed.images.2017
    158. 

  158.       md5: 66de96d201af5a47a09997691b992370.dir
    159. 

  159.       size: 109902740888
    160. 

  160.       nfiles: 30489
    161. 

  161.     - path: data/processed.images.2018
    162. 

  162.       md5: cfa0adee6401f838f162a0510085becf.dir
    163. 

  163.       size: 144861203951
    164. 

  164.       nfiles: 30489
    165. 

  165.     - path: data/processed.images.2019
    166. 

  166.       md5: 35c31b781cb0bdb19650329132d83d05.dir
    167. 

  167.       size: 144926802415
    168. 

  168.       nfiles: 30489
    169. 

  169.     - path: data/processed.images.2020
    170. 

  170.       md5: 76d4daf0d9e69904a9370a0c74006d17.dir
    171. 

  171.       size: 163333629599
    172. 

  172.       nfiles: 30489
    173. 

  173.     outs:
    174. 

  174.     - path: data/processed.images.stats.json
    175. 

  175.       md5: 1ed04cbf29c00ed439bfa15bc170c5cb
    176. 

  176.   computestatsinference:
    177. 

  177.     cmd: 'python scripts/computestats_inference.py data/predicted.2017   data/predicted.2018   data/predicted.2019  data/predicted.2020  '
    178. 

  178.     deps:
    179. 

  179.     - path: data/predicted.2017
    180. 

  180.       md5: 6a02ab7abf0eab217247b67ae69ae6f2.dir
    181. 

  181.       size: 607592250
    182. 

  182.       nfiles: 20827
    183. 

  183.     - path: data/predicted.2018
    184. 

  184.       md5: 438b72649cb14c89e2d44217df305ee2.dir
    185. 

  185.       size: 557696998
    186. 

  186.       nfiles: 19182
    187. 

  187.     - path: data/predicted.2019
    188. 

  188.       md5: 50287edaea44da4ebd415cededfc3323.dir
    189. 

  189.       size: 486417595
    190. 

  190.       nfiles: 16227
    191. 

  191.     - path: data/predicted.2020
    192. 

  192.       md5: 18a3b6acf6f32524f21936222d711f29.dir
    193. 

  193.       size: 497806592
    194. 

  194.       nfiles: 16125
    195. 

  195.     outs:
    196. 

  196.     - path: data/predicted.stats.csv
    197. 

  197.       md5: ad7e5fa98bb9737ea28e90928f6d49e5
    198. 

  198.       size: 1819683
    199. 

  199.   createtiles@2018:
    200. 

  200.     cmd: mkdir -p data/processed.images.2018; gdal_retile.py  -csv locations.csv  -v
    201. 

  201.       -ps 2048 2048 -co "TILED=YES" -co "COMPRESS=LZW" -co "PREDICTOR=2" -co "ALPHA=NO"
    202. 

  202.       -co "NUM_THREADS=ALL_CPUS" -targetDir data/processed.images.2018  data/raw/ortho_ms_2018_EPSG3044.tif
    203. 

  203.     deps:
    204. 

  204.     - path: data/raw/ortho_ms_2018_EPSG3044.tif
    205. 

  205.       md5: 59fc13ae47105e690ecc396202c2bc30
    206. 

  206.       size: 144854820067
    207. 

  207.     params:
    208. 

  208.       params.yaml:
    209. 

  209.         source_dim: 2048
    210. 

  210.     outs:
    211. 

  211.     - path: data/processed.images.2018
    212. 

  212.       md5: cfa0adee6401f838f162a0510085becf.dir
    213. 

  213.       size: 144861203951
    214. 

  214.       nfiles: 30489
    215. 

  215.   createtiles@2020:
    216. 

  216.     cmd: mkdir -p data/processed.images.2020; gdal_retile.py  -csv locations.csv  -v
    217. 

  217.       -ps 2048 2048 -co "TILED=YES" -co "COMPRESS=LZW" -co "PREDICTOR=2" -co "ALPHA=NO"
    218. 

  218.       -co "NUM_THREADS=ALL_CPUS" -targetDir data/processed.images.2020  data/raw/ortho_ms_2020_EPSG3044.tif
    219. 

  219.     deps:
    220. 

  220.     - path: data/raw/ortho_ms_2020_EPSG3044.tif
    221. 

  221.       md5: 0ecdb70decb68b2d37446b21977d09f9
    222. 

  222.       size: 163327245715
    223. 

  223.     params:
    224. 

  224.       params.yaml:
    225. 

  225.         source_dim: 2048
    226. 

  226.     outs:
    227. 

  227.     - path: data/processed.images.2020
    228. 

  228.       md5: 76d4daf0d9e69904a9370a0c74006d17.dir
    229. 

  229.       size: 163333629599
    230. 

  230.       nfiles: 30489
    231. 

  231.   inference@2018:
    232. 

  232.     cmd: mkdir -p data/predicted.2018; stdbuf -i0 -o0 -e0 python scripts/inference.py
    233. 

  233.       --all --nopreview -o data/predicted.2018 data/processed.images.2018; gdal_merge.py  -co
    234. 

  234.       "TILED=YES" -co "COMPRESS=LZW" -co "PREDICTOR=2" -co "NUM_THREADS=ALL_CPUS"
    235. 

  235.       -o data/predicted_mosaic_2018.tif  data/predicted.2018/ortho_ms_2018_EPSG3044_*
    236. 

  236.     deps:
    237. 

  237.     - path: checkpoints/bestmodel.ckpt
    238. 

  238.       md5: e543654648455b36411f4611eae36f22
    239. 

  239.       size: 378716076
    240. 

  240.     - path: data/processed.images.2018
    241. 

  241.       md5: cfa0adee6401f838f162a0510085becf.dir
    242. 

  242.       size: 144861203951
    243. 

  243.       nfiles: 30489
    244. 

  244.     outs:
    245. 

  245.     - path: data/predicted.2018
    246. 

  246.       md5: 438b72649cb14c89e2d44217df305ee2.dir
    247. 

  247.       size: 557696998
    248. 

  248.       nfiles: 19182
    249. 

  249.     - path: data/predicted_mosaic_2018.tif
    250. 

  250.       md5: d01f8ece18c0d81e1b4fd06e7f08f22e
    251. 

  251.       size: 838475732
    252. 

  252.   inference@2020:
    253. 

  253.     cmd: mkdir -p data/predicted.2020; stdbuf -i0 -o0 -e0 python scripts/inference.py
    254. 

  254.       --all --nopreview -o data/predicted.2020 data/processed.images.2020; gdal_merge.py  -co
    255. 

  255.       "TILED=YES" -co "COMPRESS=LZW" -co "PREDICTOR=2" -co "NUM_THREADS=ALL_CPUS"
    256. 

  256.       -o data/predicted_mosaic_2020.tif  data/predicted.2020/ortho_ms_2020_EPSG3044_*
    257. 

  257.     deps:
    258. 

  258.     - path: checkpoints/bestmodel.ckpt
    259. 

  259.       md5: e543654648455b36411f4611eae36f22
    260. 

  260.       size: 378716076
    261. 

  261.     - path: data/processed.images.2020
    262. 

  262.       md5: 76d4daf0d9e69904a9370a0c74006d17.dir
    263. 

  263.       size: 163333629599
    264. 

  264.       nfiles: 30489
    265. 

  265.     outs:
    266. 

  266.     - path: data/predicted.2020
    267. 

  267.       md5: 18a3b6acf6f32524f21936222d711f29.dir
    268. 

  268.       size: 497806592
    269. 

  269.       nfiles: 16125
    270. 

  270.     - path: data/predicted_mosaic_2020.tif
    271. 

  271.       md5: 9997d2e303d794311b55c66f6d625406
    272. 

  272.       size: 822729460
    273.
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