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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; mkdir -p data/predicted.2017_preview; stdbuf
    114. 

  114.       -i0 -o0 -e0 python scripts/inference.py --all -o data/predicted.2017 data/processed.images.2017;
    115. 

  115.       gdal_merge.py  -co "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: 1e90e80f26474efe37a8131cc2bf3a55
    120. 

  120.       size: 156969896
    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: d90f790fd888a18f5143915f50c1ca1e.dir
    128. 

  128.       size: 593340873
    129. 

  129.       nfiles: 20827
    130. 

  130.     - path: data/predicted.2017_preview
    131. 

  131.       md5: ad638b7452fcfdb7895311029d331bb7.dir
    132. 

  132.       size: 87265506654
    133. 

  133.       nfiles: 20827
    134. 

  134.     - path: data/predicted_mosaic_2017.tif
    135. 

  135.       md5: f2718984e6d468e41ae743811f234d8c
    136. 

  136.       size: 829735326
    137. 

  137.   inference@2019:
    138. 

  138.     cmd: mkdir -p data/predicted.2019; mkdir -p data/predicted.2019_preview; stdbuf
    139. 

  139.       -i0 -o0 -e0 python deadtrees/deployment/tiler.py --all -o data/predicted.2019
    140. 

  140.       data/processed.images.2019; gdal_merge.py  -co "TILED=YES" -co "COMPRESS=LZW"
    141. 

  141.       -co "PREDICTOR=2" -co "NUM_THREADS=ALL_CPUS" -o data/predicted_mosaic_2019.tif  data/predicted.2019/ortho_ms_2019_EPSG3044_*
    142. 

  142.     deps:
    143. 

  143.     - path: checkpoints/bestmodel.ckpt
    144. 

  144.       md5: 1e90e80f26474efe37a8131cc2bf3a55
    145. 

  145.       size: 156969896
    146. 

  146.     - path: data/processed.images.2019
    147. 

  147.       md5: 35c31b781cb0bdb19650329132d83d05.dir
    148. 

  148.       size: 144926802415
    149. 

  149.       nfiles: 30489
    150. 

  150.     outs:
    151. 

  151.     - path: data/predicted.2019
    152. 

  152.       md5: 4dcd85d9bc3dac83be89d8453ba2a084.dir
    153. 

  153.       size: 475391400
    154. 

  154.       nfiles: 16227
    155. 

  155.     - path: data/predicted.2019_preview
    156. 

  156.       md5: 15c8ec77694103dc45e93170e8843ba3.dir
    157. 

  157.       size: 68062950702
    158. 

  158.       nfiles: 16227
    159. 

  159.     - path: data/predicted_mosaic_2019.tif
    160. 

  160.       md5: d30d9f7c49c1a4e9b421e60bd78b9392
    161. 

  161.       size: 801132490
    162. 

  162.   computestats:
    163. 

  163.     cmd: 'python scripts/computestats.py --frac 0.1 data/processed.images.2017   data/processed.images.2018   data/processed.images.2019 '
    164. 

  164.     deps:
    165. 

  165.     - path: data/processed.images.2017
    166. 

  166.       md5: 66de96d201af5a47a09997691b992370.dir
    167. 

  167.       size: 109902740888
    168. 

  168.       nfiles: 30489
    169. 

  169.     - path: data/processed.images.2018
    170. 

  170.       md5: cfa0adee6401f838f162a0510085becf.dir
    171. 

  171.       size: 144861203951
    172. 

  172.       nfiles: 30489
    173. 

  173.     - path: data/processed.images.2019
    174. 

  174.       md5: 35c31b781cb0bdb19650329132d83d05.dir
    175. 

  175.       size: 144926802415
    176. 

  176.       nfiles: 30489
    177. 

  177.     outs:
    178. 

  178.     - path: data/processed.images.stats.json
    179. 

  179.       md5: 5891b858619ea1ca39629f445a38253f
    180. 

  180.       size: 621
    181. 

  181.   computestatsinference:
    182. 

  182.     cmd: 'python scripts/computestats_inference.py data/predicted.2017   data/predicted.2019 '
    183. 

  183.     deps:
    184. 

  184.     - path: data/predicted.2017
    185. 

  185.       md5: d90f790fd888a18f5143915f50c1ca1e.dir
    186. 

  186.       size: 593340873
    187. 

  187.       nfiles: 20827
    188. 

  188.     - path: data/predicted.2019
    189. 

  189.       md5: 4dcd85d9bc3dac83be89d8453ba2a084.dir
    190. 

  190.       size: 475391400
    191. 

  191.       nfiles: 16227
    192. 

  192.     outs:
    193. 

  193.     - path: data/predicted.stats.csv
    194. 

  194.       md5: ba132c0b453a9b1dfb0f6fd56a8353b4
    195. 

  195.       size: 1309714
    196. 

  196.   createtiles@2018:
    197. 

  197.     cmd: mkdir -p data/processed.images.2018; gdal_retile.py  -csv locations.csv  -v
    198. 

  198.       -ps 2048 2048 -co "TILED=YES" -co "COMPRESS=LZW" -co "PREDICTOR=2" -co "ALPHA=NO"
    199. 

  199.       -co "NUM_THREADS=ALL_CPUS" -targetDir data/processed.images.2018  data/raw/ortho_ms_2018_EPSG3044.tif
    200. 

  200.     deps:
    201. 

  201.     - path: data/raw/ortho_ms_2018_EPSG3044.tif
    202. 

  202.       md5: 59fc13ae47105e690ecc396202c2bc30
    203. 

  203.       size: 144854820067
    204. 

  204.     params:
    205. 

  205.       params.yaml:
    206. 

  206.         source_dim: 2048
    207. 

  207.     outs:
    208. 

  208.     - path: data/processed.images.2018
    209. 

  209.       md5: cfa0adee6401f838f162a0510085becf.dir
    210. 

  210.       size: 144861203951
    211. 

  211.       nfiles: 30489
    212.
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