├── LICENSE ├── README.md ├── db_files_uw3-500 ├── README.md ├── images.txt ├── imgs │ ├── 1001 │ │ └── 010061.bin.png │ ├── 1002 │ │ └── 010025.bin.png │ ├── 1003 │ │ └── 010049.bin.png │ ├── 1004 │ │ └── 010017.bin.png │ ├── 1011 │ │ └── 010053.bin.png │ ├── 1015 │ │ └── 010015.bin.png │ ├── 1021 │ │ └── 010037.bin.png │ ├── 1026 │ │ └── 010010.bin.png │ ├── 1031 │ │ └── 010014.bin.png │ ├── 1032 │ │ └── 010071.bin.png │ ├── 1036 │ │ └── 010021.bin.png │ ├── 1038 │ │ └── 010005.bin.png │ ├── 1039 │ │ └── 010051.bin.png │ ├── 1040 │ │ └── 010056.bin.png │ ├── 1051 │ │ └── 010039.bin.png │ ├── 1059 │ │ └── 010098.bin.png │ ├── 1061 │ │ └── 010055.bin.png │ ├── 1064 │ │ └── 010095.bin.png │ ├── 1067 │ │ └── 010058.bin.png │ ├── 1069 │ │ ├── 010008.bin.png │ │ └── 010107.bin.png │ ├── 1070 │ │ └── 010027.bin.png │ ├── 1071 │ │ ├── 010031.bin.png │ │ ├── 010061.bin.png │ │ └── 010112.bin.png │ ├── 1074 │ │ ├── 010040.bin.png │ │ └── 010051.bin.png │ ├── 1075 │ │ └── 010005.bin.png │ ├── 1077 │ │ └── 010059.bin.png │ ├── 1078 │ │ └── 010076.bin.png │ ├── 1080 │ │ └── 010090.bin.png │ ├── 1083 │ │ └── 010056.bin.png │ ├── 1090 │ │ └── 010009.bin.png │ ├── 1094 │ │ └── 010075.bin.png │ ├── 1097 │ │ └── 010025.bin.png │ ├── 1107 │ │ └── 010018.bin.png │ ├── 1108 │ │ └── 010026.bin.png │ ├── 1109 │ │ └── 010059.bin.png │ ├── 1113 │ │ └── 010043.bin.png │ ├── 1116 │ │ ├── 010030.bin.png │ │ └── 010089.bin.png │ ├── 1117 │ │ └── 010049.bin.png │ ├── 1123 │ │ └── 010102.bin.png │ ├── 1124 │ │ └── 010041.bin.png │ ├── 1125 │ │ └── 010013.bin.png │ ├── 1126 │ │ └── 010078.bin.png │ ├── 1127 │ │ └── 010102.bin.png │ ├── 1130 │ │ └── 010061.bin.png │ ├── 1132 │ │ ├── 010007.bin.png │ │ ├── 010040.bin.png │ │ └── 010098.bin.png │ ├── 1133 │ │ └── 010066.bin.png │ ├── 1137 │ │ ├── 010010.bin.png │ │ └── 010023.bin.png │ ├── 1138 │ │ └── 010077.bin.png │ ├── 1139 │ │ ├── 010029.bin.png │ │ ├── 010077.bin.png │ │ └── 010104.bin.png │ ├── 1140 │ │ └── 010018.bin.png │ ├── 1141 │ │ └── 010022.bin.png │ ├── 1142 │ │ └── 010118.bin.png │ ├── 1143 │ │ └── 010021.bin.png │ ├── 1147 │ │ └── 010030.bin.png │ ├── 1150 │ │ └── 010008.bin.png │ ├── 1152 │ │ ├── 010056.bin.png │ │ ├── 010058.bin.png │ │ └── 010060.bin.png │ ├── 1155 │ │ └── 010069.bin.png │ ├── 1158 │ │ ├── 010015.bin.png │ │ ├── 010035.bin.png │ │ ├── 010047.bin.png │ │ └── 010081.bin.png │ ├── 1160 │ │ └── 010026.bin.png │ ├── 1161 │ │ └── 010021.bin.png │ ├── 1162 │ │ └── 010004.bin.png │ ├── 1164 │ │ ├── 010063.bin.png │ │ └── 010084.bin.png │ ├── 1168 │ │ └── 010023.bin.png │ ├── 1169 │ │ ├── 010033.bin.png │ │ ├── 010057.bin.png │ │ └── 010100.bin.png │ ├── 1170 │ │ └── 010108.bin.png │ ├── 1171 │ │ └── 010046.bin.png │ ├── 1174 │ │ └── 010044.bin.png │ ├── 1175 │ │ └── 010006.bin.png │ ├── 1177 │ │ └── 010046.bin.png │ ├── 1180 │ │ └── 010028.bin.png │ ├── 1183 │ │ └── 010072.bin.png │ ├── 1184 │ │ └── 010045.bin.png │ ├── 1185 │ │ └── 010064.bin.png │ ├── 1193 │ │ └── 010046.bin.png │ ├── 1199 │ │ └── 010084.bin.png │ ├── 1206 │ │ └── 010018.bin.png │ ├── 1208 │ │ ├── 010017.bin.png │ │ └── 010032.bin.png │ ├── 1215 │ │ └── 010013.bin.png │ ├── 1216 │ │ └── 010035.bin.png │ ├── 1219 │ │ └── 010024.bin.png │ ├── 1223 │ │ ├── 010057.bin.png │ │ └── 010060.bin.png │ ├── 1229 │ │ ├── 010003.bin.png │ │ └── 010056.bin.png │ ├── 1237 │ │ └── 010059.bin.png │ ├── 1240 │ │ ├── 010038.bin.png │ │ └── 010045.bin.png │ ├── 1241 │ │ └── 010055.bin.png │ ├── 1243 │ │ └── 010056.bin.png │ ├── 1244 │ │ └── 010003.bin.png │ ├── 1252 │ │ ├── 010028.bin.png │ │ └── 010051.bin.png │ ├── 1253 │ │ └── 010027.bin.png │ ├── 1260 │ │ └── 010002.bin.png │ ├── 1262 │ │ └── 010065.bin.png │ ├── 1263 │ │ ├── 010018.bin.png │ │ └── 010022.bin.png │ ├── 1269 │ │ └── 010064.bin.png │ ├── 1271 │ │ └── 010003.bin.png │ ├── 1272 │ │ ├── 010076.bin.png │ │ └── 010077.bin.png │ ├── 1276 │ │ └── 010002.bin.png │ ├── 1282 │ │ └── 010021.bin.png │ ├── 1286 │ │ ├── 010007.bin.png │ │ └── 010051.bin.png │ ├── 1295 │ │ └── 010009.bin.png │ ├── 1305 │ │ └── 010024.bin.png │ ├── 1307 │ │ └── 010074.bin.png │ ├── 1314 │ │ └── 010062.bin.png │ ├── 1317 │ │ └── 010023.bin.png │ ├── 1318 │ │ └── 010031.bin.png │ ├── 1319 │ │ └── 010058.bin.png │ ├── 1320 │ │ └── 010046.bin.png │ ├── 1323 │ │ └── 010053.bin.png │ ├── 1325 │ │ └── 010036.bin.png │ ├── 1329 │ │ └── 010049.bin.png │ ├── 1335 │ │ └── 010027.bin.png │ ├── 1336 │ │ └── 010071.bin.png │ ├── 1337 │ │ └── 010044.bin.png │ ├── 1339 │ │ └── 010054.bin.png │ ├── 1346 │ │ └── 010012.bin.png │ ├── 1348 │ │ └── 010024.bin.png │ ├── 1352 │ │ └── 010022.bin.png │ ├── 1355 │ │ └── 010089.bin.png │ ├── 1357 │ │ └── 010001.bin.png │ ├── 1362 │ │ ├── 010055.bin.png │ │ └── 010116.bin.png │ ├── 1363 │ │ └── 010007.bin.png │ ├── 1367 │ │ └── 010084.bin.png │ ├── 1371 │ │ └── 010031.bin.png │ ├── 1375 │ │ └── 010007.bin.png │ ├── 1376 │ │ └── 010006.bin.png │ ├── 1377 │ │ └── 010039.bin.png │ ├── 1380 │ │ ├── 010003.bin.png │ │ └── 010086.bin.png │ ├── 1386 │ │ └── 010032.bin.png │ ├── 1387 │ │ ├── 010035.bin.png │ │ └── 010064.bin.png │ ├── 1389 │ │ └── 010003.bin.png │ ├── 1392 │ │ └── 010041.bin.png │ ├── 1394 │ │ ├── 010045.bin.png │ │ └── 010089.bin.png │ ├── 1395 │ │ └── 010044.bin.png │ ├── 1398 │ │ └── 010015.bin.png │ ├── 1402 │ │ └── 010020.bin.png │ ├── 1406 │ │ └── 010041.bin.png │ ├── 1409 │ │ └── 010004.bin.png │ ├── 1415 │ │ └── 010004.bin.png │ ├── 1417 │ │ └── 010035.bin.png │ ├── 1418 │ │ ├── 010025.bin.png │ │ ├── 010045.bin.png │ │ └── 010052.bin.png │ ├── 1420 │ │ ├── 010013.bin.png │ │ └── 010059.bin.png │ ├── 1422 │ │ └── 010016.bin.png │ ├── 1423 │ │ └── 010042.bin.png │ ├── 1425 │ │ └── 010035.bin.png │ ├── 1430 │ │ └── 010025.bin.png │ ├── 1432 │ │ └── 010005.bin.png │ ├── 1435 │ │ └── 010035.bin.png │ ├── 1437 │ │ └── 010049.bin.png │ ├── 1442 │ │ └── 010117.bin.png │ ├── 1443 │ │ ├── 010072.bin.png │ │ └── 010083.bin.png │ ├── 1447 │ │ └── 010091.bin.png │ ├── 1449 │ │ ├── 010014.bin.png │ │ ├── 010051.bin.png │ │ └── 010075.bin.png │ ├── 1452 │ │ └── 010022.bin.png │ ├── 1453 │ │ └── 010018.bin.png │ ├── 1458 │ │ └── 010008.bin.png │ ├── 1459 │ │ └── 010040.bin.png │ ├── 1462 │ │ └── 010070.bin.png │ ├── 1466 │ │ └── 010046.bin.png │ ├── 1469 │ │ ├── 010006.bin.png │ │ └── 010008.bin.png │ ├── 1471 │ │ └── 010085.bin.png │ ├── 1473 │ │ └── 010041.bin.png │ ├── 1477 │ │ ├── 010049.bin.png │ │ └── 010082.bin.png │ ├── 1478 │ │ └── 010047.bin.png │ ├── 1480 │ │ └── 010084.bin.png │ ├── 1482 │ │ └── 010042.bin.png │ ├── 1483 │ │ └── 010002.bin.png │ ├── 1488 │ │ └── 010042.bin.png │ ├── 1489 │ │ ├── 010089.bin.png │ │ └── 010091.bin.png │ ├── 1490 │ │ └── 010065.bin.png │ ├── 1492 │ │ └── 010014.bin.png │ ├── 1502 │ │ ├── 010044.bin.png │ │ └── 010057.bin.png │ ├── 1507 │ │ └── 010042.bin.png │ ├── 1508 │ │ └── 010033.bin.png │ ├── 1519 │ │ └── 010046.bin.png │ ├── 1520 │ │ └── 010021.bin.png │ ├── 1522 │ │ ├── 010058.bin.png │ │ └── 010065.bin.png │ ├── 1523 │ │ └── 010039.bin.png │ ├── 1524 │ │ └── 010099.bin.png │ ├── 1526 │ │ ├── 010034.bin.png │ │ └── 010049.bin.png │ ├── 1528 │ │ └── 010010.bin.png │ ├── 1531 │ │ └── 010022.bin.png │ ├── 1534 │ │ └── 010061.bin.png │ ├── 1539 │ │ └── 010002.bin.png │ ├── 1544 │ │ └── 010022.bin.png │ ├── 1546 │ │ └── 010031.bin.png │ ├── 1547 │ │ ├── 010006.bin.png │ │ └── 010052.bin.png │ ├── 1548 │ │ └── 010046.bin.png │ ├── 1549 │ │ └── 010041.bin.png │ ├── 1552 │ │ └── 010017.bin.png │ ├── 1554 │ │ └── 010025.bin.png │ ├── 1557 │ │ └── 010034.bin.png │ ├── 1568 │ │ └── 010075.bin.png │ ├── 1569 │ │ └── 010076.bin.png │ ├── 1583 │ │ └── 010065.bin.png │ ├── 1590 │ │ └── 010011.bin.png │ ├── 1591 │ │ └── 010070.bin.png │ ├── 1599 │ │ └── 010019.bin.png │ ├── 0005 │ │ └── 010001.bin.png │ ├── 0011 │ │ └── 010111.bin.png │ ├── 0012 │ │ ├── 010036.bin.png │ │ └── 010108.bin.png │ ├── 0015 │ │ └── 010023.bin.png │ ├── 0022 │ │ └── 010005.bin.png │ ├── 0026 │ │ └── 010010.bin.png │ ├── 0028 │ │ ├── 010004.bin.png │ │ └── 010012.bin.png │ ├── 0033 │ │ └── 010060.bin.png │ ├── 0034 │ │ └── 010034.bin.png │ ├── 0036 │ │ └── 010043.bin.png │ ├── 0038 │ │ └── 010048.bin.png │ ├── 0041 │ │ └── 010039.bin.png │ ├── 0043 │ │ └── 010006.bin.png │ ├── 0062 │ │ └── 010033.bin.png │ ├── 0066 │ │ └── 010016.bin.png │ ├── 0074 │ │ └── 010022.bin.png │ ├── 0095 │ │ └── 010007.bin.png │ ├── 0097 │ │ └── 010042.bin.png │ ├── 0104 │ │ └── 010009.bin.png │ ├── 0106 │ │ └── 010002.bin.png │ ├── 0121 │ │ └── 010017.bin.png │ ├── 0123 │ │ ├── 010003.bin.png │ │ └── 010034.bin.png │ ├── 0129 │ │ └── 010016.bin.png │ ├── 0134 │ │ └── 010016.bin.png │ ├── 0137 │ │ └── 010045.bin.png │ ├── 0140 │ │ └── 010095.bin.png │ ├── 0142 │ │ └── 010031.bin.png │ ├── 0157 │ │ └── 010037.bin.png │ ├── 0158 │ │ └── 010007.bin.png │ ├── 0159 │ │ └── 010007.bin.png │ ├── 0160 │ │ └── 010036.bin.png │ ├── 0172 │ │ ├── 010038.bin.png │ │ └── 010072.bin.png │ ├── 0173 │ │ ├── 010009.bin.png │ │ └── 010015.bin.png │ ├── 0174 │ │ └── 010060.bin.png │ ├── 0175 │ │ └── 010047.bin.png │ ├── 0176 │ │ └── 010077.bin.png │ ├── 0183 │ │ └── 010006.bin.png │ ├── 0184 │ │ └── 010002.bin.png │ ├── 0185 │ │ └── 010089.bin.png │ ├── 0186 │ │ └── 010028.bin.png │ ├── 0187 │ │ └── 010046.bin.png │ ├── 0188 │ │ └── 010059.bin.png │ ├── 0189 │ │ └── 010096.bin.png │ ├── 0191 │ │ └── 010040.bin.png │ ├── 0192 │ │ └── 010012.bin.png │ ├── 0194 │ │ └── 010024.bin.png │ ├── 0195 │ │ └── 010019.bin.png │ ├── 0200 │ │ └── 010007.bin.png │ ├── 0207 │ │ └── 010017.bin.png │ ├── 0211 │ │ └── 010012.bin.png │ ├── 0214 │ │ └── 010002.bin.png │ ├── 0222 │ │ ├── 010007.bin.png │ │ └── 010017.bin.png │ ├── 0225 │ │ └── 010074.bin.png │ ├── 0240 │ │ ├── 010002.bin.png │ │ └── 010027.bin.png │ ├── 0241 │ │ └── 010015.bin.png │ ├── 0255 │ │ └── 010031.bin.png │ ├── 0257 │ │ └── 010001.bin.png │ ├── 0272 │ │ ├── 010008.bin.png │ │ └── 010025.bin.png │ ├── 0280 │ │ └── 010022.bin.png │ ├── 0291 │ │ └── 010070.bin.png │ ├── 0293 │ │ └── 010123.bin.png │ ├── 0306 │ │ └── 010003.bin.png │ ├── 0313 │ │ └── 010021.bin.png │ ├── 0315 │ │ ├── 010029.bin.png │ │ └── 010034.bin.png │ ├── 0316 │ │ └── 010063.bin.png │ ├── 0317 │ │ ├── 010042.bin.png │ │ ├── 010093.bin.png │ │ └── 010133.bin.png │ ├── 0318 │ │ └── 010085.bin.png │ ├── 0326 │ │ └── 010061.bin.png │ ├── 0328 │ │ └── 010050.bin.png │ ├── 0331 │ │ └── 010014.bin.png │ ├── 0349 │ │ └── 010036.bin.png │ ├── 0350 │ │ └── 010054.bin.png │ ├── 0355 │ │ └── 010001.bin.png │ ├── 0360 │ │ └── 010027.bin.png │ ├── 0370 │ │ └── 010099.bin.png │ ├── 0373 │ │ ├── 010001.bin.png │ │ ├── 010036.bin.png │ │ └── 010046.bin.png │ ├── 0376 │ │ ├── 010048.bin.png │ │ └── 010085.bin.png │ ├── 0377 │ │ ├── 010039.bin.png │ │ ├── 010044.bin.png │ │ └── 010047.bin.png │ ├── 0380 │ │ └── 010074.bin.png │ ├── 0383 │ │ └── 010001.bin.png │ ├── 0385 │ │ └── 010009.bin.png │ ├── 0394 │ │ └── 010060.bin.png │ ├── 0395 │ │ ├── 010001.bin.png │ │ ├── 010063.bin.png │ │ ├── 010064.bin.png │ │ └── 010088.bin.png │ ├── 0407 │ │ └── 010046.bin.png │ ├── 0408 │ │ ├── 010003.bin.png │ │ ├── 010047.bin.png │ │ └── 010049.bin.png │ ├── 0410 │ │ └── 010043.bin.png │ ├── 0412 │ │ └── 010052.bin.png │ ├── 0413 │ │ ├── 010009.bin.png │ │ └── 010076.bin.png │ ├── 0414 │ │ └── 010068.bin.png │ ├── 0415 │ │ ├── 010033.bin.png │ │ ├── 010035.bin.png │ │ └── 010057.bin.png │ ├── 0416 │ │ └── 010055.bin.png │ ├── 0417 │ │ ├── 010033.bin.png │ │ └── 010056.bin.png │ ├── 0422 │ │ └── 010017.bin.png │ ├── 0456 │ │ └── 010058.bin.png │ ├── 0460 │ │ └── 010004.bin.png │ ├── 0461 │ │ └── 010074.bin.png │ ├── 0468 │ │ └── 010060.bin.png │ ├── 0476 │ │ └── 010023.bin.png │ ├── 0479 │ │ └── 010101.bin.png │ ├── 0488 │ │ └── 010006.bin.png │ ├── 0489 │ │ └── 010028.bin.png │ ├── 0496 │ │ └── 010012.bin.png │ ├── 0498 │ │ └── 010019.bin.png │ ├── 0508 │ │ └── 010056.bin.png │ ├── 0513 │ │ └── 010064.bin.png │ ├── 0517 │ │ └── 010039.bin.png │ ├── 0518 │ │ └── 010044.bin.png │ ├── 0523 │ │ └── 010068.bin.png │ ├── 0525 │ │ └── 010030.bin.png │ ├── 0526 │ │ └── 010045.bin.png │ ├── 0528 │ │ └── 010015.bin.png │ ├── 0539 │ │ ├── 010061.bin.png │ │ └── 010091.bin.png │ ├── 0540 │ │ └── 010011.bin.png │ ├── 0565 │ │ └── 010001.bin.png │ ├── 0570 │ │ └── 010008.bin.png │ ├── 0575 │ │ └── 010019.bin.png │ ├── 0587 │ │ ├── 010001.bin.png │ │ └── 010023.bin.png │ ├── 0590 │ │ └── 010033.bin.png │ ├── 0591 │ │ └── 010099.bin.png │ ├── 0593 │ │ └── 010078.bin.png │ ├── 0596 │ │ └── 010002.bin.png │ ├── 0599 │ │ └── 010020.bin.png │ ├── 0601 │ │ └── 010068.bin.png │ ├── 0603 │ │ ├── 010018.bin.png │ │ ├── 010031.bin.png │ │ └── 010051.bin.png │ ├── 0604 │ │ └── 010010.bin.png │ ├── 0605 │ │ └── 010026.bin.png │ ├── 0607 │ │ └── 010008.bin.png │ ├── 0623 │ │ └── 010038.bin.png │ ├── 0625 │ │ └── 010007.bin.png │ ├── 0626 │ │ └── 010062.bin.png │ ├── 0628 │ │ └── 010013.bin.png │ ├── 0629 │ │ └── 010020.bin.png │ ├── 0630 │ │ └── 010019.bin.png │ ├── 0633 │ │ └── 010014.bin.png │ ├── 0634 │ │ └── 010085.bin.png │ ├── 0635 │ │ └── 010021.bin.png │ ├── 0638 │ │ └── 010025.bin.png │ ├── 0639 │ │ └── 010060.bin.png │ ├── 0640 │ │ └── 010018.bin.png │ ├── 0643 │ │ └── 010024.bin.png │ ├── 0648 │ │ └── 010028.bin.png │ ├── 0651 │ │ ├── 010054.bin.png │ │ └── 010072.bin.png │ ├── 0653 │ │ └── 010086.bin.png │ ├── 0654 │ │ └── 010043.bin.png │ ├── 0658 │ │ └── 010002.bin.png │ ├── 0659 │ │ └── 010017.bin.png │ ├── 0661 │ │ └── 010061.bin.png │ ├── 0664 │ │ ├── 010033.bin.png │ │ └── 010049.bin.png │ ├── 0677 │ │ └── 010015.bin.png │ ├── 0691 │ │ └── 010073.bin.png │ ├── 0700 │ │ └── 010021.bin.png │ ├── 0701 │ │ └── 010023.bin.png │ ├── 0705 │ │ ├── 010045.bin.png │ │ └── 010051.bin.png │ ├── 0706 │ │ └── 010004.bin.png │ ├── 0709 │ │ └── 010002.bin.png │ ├── 0712 │ │ └── 010013.bin.png │ ├── 0735 │ │ └── 010002.bin.png │ ├── 0749 │ │ └── 010030.bin.png │ ├── 0755 │ │ └── 010030.bin.png │ ├── 0768 │ │ └── 010009.bin.png │ ├── 0785 │ │ └── 010022.bin.png │ ├── 0787 │ │ └── 010001.bin.png │ ├── 0795 │ │ └── 010011.bin.png │ ├── 0796 │ │ ├── 010041.bin.png │ │ └── 010043.bin.png │ ├── 0798 │ │ └── 010045.bin.png │ ├── 0808 │ │ └── 010031.bin.png │ ├── 0809 │ │ └── 010039.bin.png │ ├── 0812 │ │ └── 010008.bin.png │ ├── 0814 │ │ └── 010004.bin.png │ ├── 0823 │ │ └── 010034.bin.png │ ├── 0830 │ │ └── 010130.bin.png │ ├── 0833 │ │ └── 010089.bin.png │ ├── 0834 │ │ ├── 010023.bin.png │ │ └── 010025.bin.png │ ├── 0835 │ │ └── 010039.bin.png │ ├── 0852 │ │ └── 010006.bin.png │ ├── 0853 │ │ └── 010092.bin.png │ ├── 0854 │ │ └── 010034.bin.png │ ├── 0855 │ │ └── 010082.bin.png │ ├── 0857 │ │ ├── 010001.bin.png │ │ └── 010109.bin.png │ ├── 0859 │ │ └── 010037.bin.png │ ├── 0864 │ │ └── 010038.bin.png │ ├── 0865 │ │ ├── 010022.bin.png │ │ ├── 010042.bin.png │ │ └── 010091.bin.png │ ├── 0868 │ │ ├── 010028.bin.png │ │ └── 010037.bin.png │ ├── 0873 │ │ └── 010074.bin.png │ ├── 0877 │ │ └── 010004.bin.png │ ├── 0878 │ │ └── 010086.bin.png │ ├── 0885 │ │ └── 010006.bin.png │ ├── 0892 │ │ └── 010031.bin.png │ ├── 0893 │ │ └── 010116.bin.png │ ├── 0895 │ │ └── 010030.bin.png │ ├── 0896 │ │ └── 010059.bin.png │ ├── 0911 │ │ └── 010008.bin.png │ ├── 0913 │ │ ├── 010019.bin.png │ │ └── 010034.bin.png │ ├── 0917 │ │ └── 010054.bin.png │ ├── 0924 │ │ └── 010019.bin.png │ ├── 0931 │ │ ├── 010007.bin.png │ │ └── 010023.bin.png │ ├── 0934 │ │ ├── 010081.bin.png │ │ └── 010084.bin.png │ ├── 0937 │ │ └── 010001.bin.png │ ├── 0939 │ │ └── 010086.bin.png │ ├── 0941 │ │ └── 010039.bin.png │ ├── 0945 │ │ └── 010008.bin.png │ ├── 0948 │ │ └── 010014.bin.png │ ├── 0953 │ │ └── 010015.bin.png │ ├── 0955 │ │ └── 010055.bin.png │ ├── 0956 │ │ └── 010023.bin.png │ ├── 0961 │ │ └── 010054.bin.png │ ├── 0963 │ │ └── 010026.bin.png │ ├── 0969 │ │ ├── 010034.bin.png │ │ └── 010098.bin.png │ ├── 0971 │ │ └── 010005.bin.png │ ├── 0972 │ │ ├── 010026.bin.png │ │ └── 010043.bin.png │ ├── 0974 │ │ └── 010042.bin.png │ ├── 0981 │ │ └── 010043.bin.png │ ├── 0989 │ │ └── 010035.bin.png │ ├── 0995 │ │ └── 010028.bin.png │ └── 0999 │ │ ├── 010003.bin.png │ │ ├── 010022.bin.png │ │ ├── 010034.bin.png │ │ ├── 010047.bin.png │ │ └── 010050.bin.png ├── sortedTrain.txt └── valid.txt ├── default_trainer_params.json ├── main.py ├── ocr ├── __init__.py ├── __init__.pyc ├── batch_generator.py ├── batch_generator.pyc ├── data.py ├── data.pyc ├── decoder.py ├── decoder.pyc ├── gt_preprocessor.py ├── gt_preprocessor.pyc ├── image_preprocessor.py ├── image_preprocessor.pyc ├── logger.py ├── loss.py ├── loss.pyc ├── model.py ├── model.pyc ├── preprocessing_runner.py ├── preprocessing_runner.pyc ├── trainer.py ├── trainer.pyc ├── utils.py └── utils.pyc └── quantized_params └── QLSTM128_W2B8A4I32_FC_W32B32.json /LICENSE: -------------------------------------------------------------------------------- 1 | Components: nervanasystems-deepspeech 2 | 3 | 4 | # ---------------------------------------------------------------------------- 5 | # Copyright 2015-2016 Nervana Systems Inc. 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 | # ---------------------------------------------------------------------------- 18 | # Modified to support pytorch Tensors 19 | # ---------------------------------------------------------------------------- 20 | # Taken and adapted from: 21 | # https://github.com/SeanNaren/deepspeech.pytorch/blob/master/decoder.py 22 | 23 | # Copyright (c) 2018, Xilinx, Inc. 24 | # All rights reserved. 25 | # 26 | # Redistribution and use in source and binary forms, with or without 27 | # modification, are permitted provided that the following conditions are met: 28 | # 29 | # 1. Redistributions of source code must retain the above copyright notice, 30 | # this list of conditions and the following disclaimer. 31 | # 32 | # 2. Redistributions in binary form must reproduce the above copyright 33 | # notice, this list of conditions and the following disclaimer in the 34 | # documentation and/or other materials provided with the distribution. 35 | # 36 | # 3. Neither the name of the copyright holder nor the names of its 37 | # contributors may be used to endorse or promote products derived from 38 | # this software without specific prior written permission. 39 | # 40 | # THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" 41 | # AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, 42 | # THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR 43 | # PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR 44 | # CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, 45 | # EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, 46 | # PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; 47 | # OR BUSINESS INTERRUPTION). HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, 48 | # WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR 49 | # OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF 50 | # ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. 51 | 52 | 53 | 54 | Copyright 2015-2016 Nervana Systems Inc. Licensed under the Apache License Version 55 | Copyright 2018 Xilinx 56 | Copyright 2018 Xilinx Inc 57 | 58 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # Quantized LSTMs for OCR 2 | 3 | This Pytorch-based repository allows to train a full-precision or quantized bidirectional LSTM to perform OCR on the included dataset. A quantized trained model can be accelerated on the LSTM-PYNQ overlay found here: [https://github.com/Xilinx/LSTM-PYNQ](https://github.com/Xilinx/LSTM-PYNQ) 4 | 5 | ## Requirements 6 | 7 | An Nvidia GPU with a CUDA+CuDNN installation is suggested but not required, training with quantization is supported on CPUs as well. 8 | 9 | * Python environment, including Numpy and Pillow (tested with Python 2.7, Pillow 4.2.1, Numpy 1.13.3). 10 | * [Pytorch](https://pytorch.org) (tested with version 0.3.1) 11 | * [pytorch-quantization](https://github.com/xilinx/pytorch-quantization) (tested with master branch) 12 | * [Warp-CTC with Pytorch bindings](https://github.com/SeanNaren/warp-ctc) (tested with commit aba791f) 13 | * [python-levenshtein](https://pypi.python.org/pypi/python-Levenshtein/0.12.0) (tested with version 0.12.0) 14 | * [TensorboardX](https://pypi.python.org/pypi/tensorboardX) (tested with version 1.1) 15 | * [Tensorboard](https://github.com/tensorflow/tensorboard) (optional) 16 | 17 | ## Suggested Setup 18 | 19 | Assuming your OS is a recent linux based distribution, such as Ubuntu 16.04, you can follow the following steps. 20 | 21 | ### CUDA 22 | 23 | * Current repos have been tested against CUDA 9.0. Compatibility with earlier or later CUDA releases is not guaranteed. 24 | * Follow the instructions [here](https://developer.nvidia.com/cuda-90-download-archive?target_os=Linux&target_arch=x86_64&target_distro=Ubuntu&target_version=1604&target_type=deblocal) on how to install CUDA and get the meta-package `cuda-libraries-dev-9-0`. 25 | 26 | ### Python 27 | 28 | * Get a complete Python distribution installer from Anaconda [here](https://repo.anaconda.com/archive/Anaconda2-5.1.0-Linux-x86_64.sh). 29 | 30 | * Run the installer in a bash shell with: 31 | 32 | ```bash 33 | bash Anaconda2-5.1.0-Linux-x86_64.sh 34 | ``` 35 | 36 | and set everything to default. More information about the installation process can be found [here](https://conda.io/docs/user-guide/install/linux.html). 37 | 38 | ### Pytorch 39 | 40 | * assuming a CUDA 9.0 environment (replace `cuda90` otherwise), install Pytorch 0.3.1 through *conda* (the Anaconda package manager) with: 41 | 42 | ```bash 43 | conda install pytorch=0.3.1 torchvision cuda90 -c pytorch 44 | ``` 45 | 46 | More installer combinations are available [here](https://pytorch.org/). 47 | 48 | ### Pytorch Quantization 49 | 50 | * Install a recent CMake version with: 51 | 52 | ```bash 53 | conda install cmake 54 | ``` 55 | 56 | * Clone the repo: 57 | ``` 58 | git clone https://github.com/xilinx/pytorch-quantization 59 | ``` 60 | 61 | * Build the library by running `python build.py` . 62 | * Add the library to your python's path with `export PYTHONPATH=PYTHONPATH:/path/to/pytorch-quantization`, with */path/to/pytorch-quantization* the full path to where you cloned the repo (with no trailing slashes, as shown here). 63 | 64 | ### Pytorch OCR 65 | 66 | * Install the required conda dependencies with: 67 | 68 | ```bash 69 | conda install -c anaconda pillow 70 | ``` 71 | 72 | * Install the required pip dependencies with: 73 | 74 | ```bash 75 | pip install scikit-learn python-levensthein tensorboardX 76 | ``` 77 | 78 | * Compile Warp-CTC with: 79 | 80 | ```bash 81 | git clone https://github.com/SeanNaren/warp-ctc 82 | cd warp-ctc && git checkout aba791f 83 | mkdir build && cd build && cmake .. && make 84 | ``` 85 | 86 | * Then install Warp-CTC Pytorch bindings with: 87 | 88 | ```bash 89 | export CUDA_HOME=/usr/local/cuda 90 | cd warp-ctc/pytorch_binding 91 | python setup.py install 92 | ``` 93 | 94 | * Finally, clone the actual repo: 95 | * 96 | ``` 97 | git clone https://github.com/xilinx/pytorch-ocr 98 | ``` 99 | 100 | * And create a folder for experiments inside: 101 | 102 | ```bash 103 | cd pytorch-ocr && mkdir experiments 104 | ``` 105 | 106 | 107 | ### Tensorboard (optional) 108 | 109 | Besides logging to stdout, the training scripts generates (through TensorboardX) a visual trace of loss and accuracy that can been visualized with Tensorboard. 110 | 111 | To visualize it, first install tensorboard with: 112 | 113 | ```bash 114 | conda install -c anaconda tensorboard 115 | ``` 116 | 117 | And then run it on the experiments folder, such as: 118 | 119 | ```bash 120 | tensorboard --logdir=pytorch-ocr/experiments 121 | ``` 122 | 123 | The UI will default to port 6006, which must be open to incoming TCP connections. 124 | 125 | ## Training 126 | 127 | Training support a set of *hyperparameters* specified in *.json* file, plus a set of *arguments* specified on the command line for things such as I/O locations, picking between training or evaluation, exporting weights etc. Both are specified in their respective sections below. You will also find a few examples for different types of runs. 128 | 129 | ### Architecture 130 | 131 | The supported architecture is composed of a single recurrent layer, a single fully connected layer, a batch normalization step (between the recurrent and the fully connected layers), and respectively a CTC decoder+loss layer for training and a greedy decoder for evaulation. 132 | 133 | The naming for experiments has the following convention. For an architecture such as *QLSTM128_W2B8A4I32_FC_W32B32* we have: 134 | 135 | * *Q* stands for quantized neuron. 136 | * *LSTM* is the type of recurrent neuron. 137 | * *128* is the number of neurons. 138 | * *W2* is the number of bits for weights. 139 | * *B8* is the number of bits for the recurrent bias. 140 | * *A4* is the number of bits for the recurrent activation (= hidden state). 141 | * *I32* is the number of bits for the internal activations (= sigmoid and tanh). 142 | * *FC_W32A32* are the number of bits allocated to the weights and activations of the fully connected layer. 143 | 144 | 145 | ### Arguments 146 | 147 | Hyperparameters, arguments, output logs and a tensorboard trace are persisted to disk as a reference during training, unless `--dry_run` is specified. 148 | 149 | Training supports *resuming* or *retraining* from a checkpoint with the argument `-i` (to specify the input checkpoint path) and the argument `--pretrained_policy` (to specify `RESUME` or `RETRAIN`). 150 | Resuming ignores the `default_trainer_params.json` file and reads the training hyperparameters from within the checkpoint, unless a different `.json` file is specified with the `-p` argument. 151 | Retraining ignores the hyperparamters found within the checkpoint, as well as the optimizer state, and reads the hyperparameters from the `.json` file, either the default one or one specified with the `-p` argument. 152 | 153 | Export an appropriate pretrained model to an HLS-friendly header with argument `--export`, and optional arguments `--simd_factor` to specify a scaling factor for the unrolling *within* a neuron (`1` is full unrolling, `2` is half unrolling etc.) and `--pe` to specify the number of processing elements allocated to compute a neuron. 154 | 155 | More arguments and their usage can be found by invoking the helper. 156 | 157 | ### Hyperparameters 158 | 159 | The support hyperparameters with their default values are (taken from *default_trainer_params.json* with added comments): 160 | 161 | "random_seed": 123456, # Seed value to init all the randomness, to enforce reproducibility 162 | "batch_size" : 32, # Training batch size 163 | "num_workers" : 0, # CPU workers to prepare training batch, has to be 0 on Python 2.7 164 | "layer_size" : 128, # Number of neurons in a direction of the recurrent neuron 165 | "neuron_type" : "QLSTM", # Type of recurrent neurons, tested with LSTM (for Pytorch default backend), or QLSTM (for pytorch-quantization backend) 166 | "target_height" : 32, # Height to which the dataset images are resized, translates to input size of the recurrent neuron 167 | "epochs" : 4000, # Number of training epochs 168 | "lr" : 1e-4, # Starting learning rate 169 | "lr_schedule" : "FIXED", # Learning rate policy, allowed values are STEP/FIXED 170 | "lr_step" : 40, # Step size in number of epochs for STEP lr policy 171 | "lr_gamma" : 0.5, # Gamma value for STEP lr policy 172 | "max_norm" : 400, # Max value for gradient clipping 173 | "seq_to_random_threshold": 20, # Number epochs after which the training batches switch from being taken in increasing order of sequence length (where sequence length means image width for OCR) to being sampled randomly 174 | "bidirectional" : true, # Enable bidirectional recurrent layer 175 | "reduce_bidirectional": "CONCAT", # How to reduce the two output sequences coming out of a bidirectional (if enabled) recurrent layer, allowed values are SUM/CONCAT 176 | "recurrent_bias_enabled": true, # Enable bias in reccurent layer 177 | "checkpoint_interval": 10, # Internal in number of epochs after which a checkpoint of the model is saved 178 | "recurrent_weight_bit_width": 32, # Number of bits to which the recurrent layer's weights are quantized 179 | "recurrent_weight_quantization": "FP", # Quantization strategy for the recurrent layer's weights 180 | "recurrent_bias_bit_width": 32, # Number of bits to which the recurrent layer's bias is quantized 181 | "recurrent_bias_quantization": "FP", # Quantization strategy for the recurrent layer's bias 182 | "recurrent_activation_bit_width": 32, # Number of bits to which the recurrent layer's activations (along both the recurrency and the output path) are quantized 183 | "recurrent_activation_quantization": "FP", # Quantization strategy for the recurrent layer's activation 184 | "internal_activation_bit_width": 32, # Number of bits to which the recurrent layer internal non-linearities (sigmoid and tanh) are quantized 185 | "fc_weight_bit_width": 32, # Number of bits to which the fully connected layer's weights are quantized 186 | "fc_weight_quantization": "FP", # Quantization strategy for the fully connected layer's weights 187 | "fc_bias_bit_width": 32, # Number of bits to which the fully connected layer's bias is quantized 188 | "fc_bias_quantization": "FP", # Quantization strategy for the fully connected layer's bias 189 | "quantize_input": true, # Quantize the input according to the recurrent_activation bit width and quantization 190 | "mask_padded": true, # Mask output values coming from padding of the input sequence 191 | "prefused_bn_fc": false # Signal that batch norm and the fully connected layer have to be considered as fused already, so that the batch norm step is skipped. 192 | 193 | Currently dropout is not implemented, which is why the best set of weights (w.r.t. validation accuracy) is tracked and saved to disk at every improvement (with a single epoch granularity). 194 | 195 | ### Strategy 196 | 197 | Training a model that can be reproduced accurately in hardware requires at least one retraining step. The reason is that the batch norm coefficients have to be fused into the quantized fully connected layer, since batch norm is not implemented in hardware. 198 | The suggested strategy goes as follow: 199 | 200 | * Train with quantized input, quantized recurrent weights/activations/bias, full-precision internal activations and full-precision fully connected layer, either from scratch or from a pretrained full precision model. 201 | * Perform batch norm-fully connected fusion and retrain with quantized internal activations and quantized fully connected layer. 202 | 203 | ### Example: Full Precision BiLSTM 204 | 205 | To start training a full-precision model with default hyperparameters, simply run: 206 | 207 | ```bash 208 | python main.py 209 | ``` 210 | 211 | ### Example: Quantized W2A4 BiLSTM 212 | 213 | ```bash 214 | python main.py -p quantized_params/QLSTM128_W2B8A4I32_FC_W32B32.json 215 | ``` 216 | -------------------------------------------------------------------------------- /db_files_uw3-500/README.md: -------------------------------------------------------------------------------- 1 | Plain-text dataset shared by [Insiders Technologies GmbH](https://www.insiders-technologies.de/home.html). -------------------------------------------------------------------------------- /db_files_uw3-500/images.txt: -------------------------------------------------------------------------------- 1 | 1418/010045.bin,In Proceedings of the Human Factors Society 26th 2 | 0468/010060.bin,tawa, Ontario, Canada K1A 0Z4. 3 | 0376/010085.bin,sphere. 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-------------------------------------------------------------------------------- 1 | 1418/010045.bin,In Proceedings of the Human Factors Society 26th 2 | 1272/010077.bin,M. Sabourin, A. Mitiche, D. Thomas, and G. Nagy 3 | 0468/010060.bin,tawa, Ontario, Canada K1A 0Z4. 4 | 0376/010085.bin,sphere. There are at least two possible explana- 5 | 0593/010078.bin,768 6 | 0956/010023.bin,such a structure. 7 | 1443/010072.bin,Second, we need to model the probability of an inter- 8 | 1031/010014.bin,vertices of the upper levels' graphs may become its children. 9 | 0878/010086.bin,The sponsors established the Joint 10 | 1138/010077.bin,the image plane to determine rough distance from the 11 | 0172/010072.bin,The quantitative analysis made on the products 12 | 0189/010096.bin,shows the energy levels that the dihy- 13 | 0074/010022.bin,Minerals 8, 59-69. 14 | 1161/010021.bin,Figure 18: User-assisted 3 point verification. 15 | 0924/010019.bin,rately by category to get around this problem. 16 | 0795/010011.bin,2. The major additional assumption at this well site is that there are both 17 | 1420/010059.bin,the vanishing line of an image of a coplanar structure 18 | 1432/010005.bin,pose estimates) in a fixed-length first-in last-out 19 | 1137/010010.bin,used for a least-squares solution). Then, for any ad- 20 | 1375/010007.bin,ency lists. 21 | 1389/010003.bin,2.2 Approach 22 | 0192/010012.bin,Having specified our prior, we need now to consider the other probability dis- 23 | 1323/010053.bin,segmented into spatially connected surface regions. For each 24 | 1260/010002.bin,document image. The document is organized 25 | 1069/010107.bin,the adjacency list for region k. It has been con- 26 | 1183/010072.bin,[22] J. Dominguez, ``Effortless Internal Camera Calibration 27 | 0948/010014.bin,indication that a horizontal segment either began or 28 | 1480/010084.bin,resolution panchromatic imagery has been 29 | 0394/010060.bin,ities may carry out instructions regard- 30 | 1272/010076.bin,terested in maintaining a high recognition 31 | 0539/010091.bin,the Constructive Trend in Mathematics. VI. In 32 | 1415/010004.bin,by image analysis systems using this model as the 33 | 1337/010044.bin,(1) as well. 34 | 0601/010068.bin,sons of internal nodes. (The usual heap is 35 | 1184/010045.bin,and Solid State Sensors, Santa Clara, CA, pp. 152-161, 36 | 1263/010022.bin,Engineering (1987) p. 416-422. 37 | 0036/010043.bin,dry seasons. 38 | 1001/010061.bin,40 39 | 1524/010099.bin,cate their resources to match a given problem, 40 | 1469/010008.bin,the region is less than the lowest of its neighbors, 41 | 0028/010004.bin,the distributed piezoelectric sensor layer is much thinner than that of the shell structure. The piezoelectric 42 | 1077/010059.bin,another paper in these proceedings [2]. 43 | 0859/010037.bin,Chilopoda, and Araneae. Several pinned salamanders are also present. 44 | 0355/010001.bin,66 45 | 1599/010019.bin,of Arabic and Nepali (written using the De- 46 | 0413/010009.bin,and Azriel Rosenfeld, 47 | 0272/010008.bin,work. Each network contains one and only 48 | 1459/010040.bin,This is done by using shifting windows, as illustrated 49 | 1325/010036.bin,Unstable This category is not tested by the analysis of the 50 | 0476/010023.bin,ference approximation used in calculating the pressure gra- 51 | 0709/010002.bin,as soon as they are in reach. This, though, generally induces 52 | 1534/010061.bin,ceedings 1991 IEEE International Conference 53 | 0408/010003.bin,processing, but these are expensive, and are non- 54 | 1442/010117.bin,However, using view-based representations only solves 55 | 0812/010008.bin,the present-day stream channels, rather than by subsurface flow 56 | 1262/010065.bin,determine dependence and thus perform CEO 57 | 0194/010024.bin,largest and longest ever recorded in the 55 year history of 58 | 1170/010108.bin,Mapping and Spatial Modelling for 59 | 1158/010035.bin,accurate delineation. One way to visualize the 60 | 0377/010044.bin,11b. A fault scarp on the east is formed after 61 | 0603/010031.bin,serve as a warm-up for the next section. 62 | 0349/010036.bin,QUANTUM CHEMICAL STUDIES 63 | 0498/010019.bin,00150 REM - . . . .LINE 2 AS DISTANCES FROM 2 TO 1,2, . . . ,N 64 | 0934/010081.bin,instead of a series over time, to obtain 65 | 1398/010015.bin,immediately curving right). 66 | 0241/010015.bin,design activities, including those for supportive R&D to be conducted, have been developed. 67 | 0012/010108.bin,gy, and magnetic and quadrupole mo- 68 | 0518/010044.bin,infection from percutaneous leads, and hepatic blood flows are 69 | 0033/010060.bin,duction heating time. Using the as-rolled steel, induc- 70 | 1139/010077.bin,tures for each frame. These features are dense, stable, 71 | 0629/010020.bin,Three WM102 dual-frequency GPS receivers were used to track the GPS 72 | 1241/010055.bin,[Joh89] D.S. Johnson, C.R. Aragon, L.A. McGeoch 73 | 0041/010039.bin,IV. Examples 74 | 0415/010033.bin,x drawn by the cell must be the local maximum among the 75 | 1387/010035.bin,based planner providing route information obtained from 76 | 0043/010006.bin,responding reflectance for an image is 0% up to 100%. Thus, each of the mem- 77 | 1127/010102.bin,rectified (unwarped) to present a simulated vertical 78 | 0712/010013.bin,and fit well into the regional picture as well. The choice among 79 | 0313/010021.bin,growth factor is so far unknown, but it is apparently 80 | 1502/010044.bin,variable for the alignment task and does not 81 | 1116/010089.bin,to send the prepared poll to the selected advisers or 82 | 0607/010008.bin,mentations we consider each edge at most 83 | 0222/010017.bin,plane of the horizontal construction joint of light weight concrete under combined 84 | 1074/010040.bin,by the local planner to achieve current goals. 85 | 0417/010033.bin,two adjacent pixels is bounded below only by the size of the 86 | 0528/010015.bin,spect to M) is a simple path, such that 87 | 1362/010055.bin,1977, pp. 80-83. 88 | 0941/010039.bin,If this contact is a point or a line, it is classified 89 | 1449/010075.bin,scale-space for the region decomposition. The result of 90 | 0661/010061.bin,from an equal number of measurements, or deter- 91 | 0855/010082.bin,Pearson (1992); these warnings also apply 92 | 1097/010025.bin,only. 93 | 1026/010010.bin,The irregular sampling hierarchy is thus built recursively from 94 | 1244/010003.bin,is traced and a binary tree is constructed. 95 | 1453/010018.bin,sual modules from examples: a framework for under- 96 | 1355/010089.bin,type is used in a particular computer. So, data generation 97 | 1180/010028.bin,run (currently between $50,000 and $80,000) MO- 98 | 0315/010029.bin,seek access to aggregated patient 99 | 1116/010030.bin,uation results are shown using faces, with obvious 100 | 1590/010011.bin,izontal, some East Asian writing systems 101 | -------------------------------------------------------------------------------- /default_trainer_params.json: -------------------------------------------------------------------------------- 1 | { 2 | "random_seed": 123456, 3 | "batch_size" : 32, 4 | "num_workers" : 0, 5 | "layer_size" : 128, 6 | "neuron_type" : "QLSTM", 7 | "target_height" : 32, 8 | "epochs" : 4000, 9 | "lr" : 1e-4, 10 | "lr_schedule" : "FIXED", 11 | "lr_step" : 40, 12 | "lr_gamma" : 0.5, 13 | "max_norm" : 400, 14 | "seq_to_random_threshold": 20, 15 | "bidirectional" : true, 16 | "reduce_bidirectional": "CONCAT", 17 | "recurrent_bias_enabled": true, 18 | "checkpoint_interval": 10, 19 | "recurrent_weight_bit_width": 32, 20 | "recurrent_weight_quantization": "FP", 21 | "recurrent_bias_bit_width": 32, 22 | "recurrent_bias_quantization": "FP", 23 | "recurrent_activation_bit_width": 32, 24 | "recurrent_activation_quantization": "FP", 25 | "internal_activation_bit_width": 32, 26 | "fc_weight_bit_width": 32, 27 | "fc_weight_quantization": "FP", 28 | "fc_bias_bit_width": 32, 29 | "fc_bias_quantization": "FP", 30 | "quantize_input": true, 31 | "mask_padded": true, 32 | "prefused_bn_fc": false 33 | } 34 | -------------------------------------------------------------------------------- /main.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) 2018, Xilinx, Inc. 2 | # All rights reserved. 3 | # 4 | # Redistribution and use in source and binary forms, with or without 5 | # modification, are permitted provided that the following conditions are met: 6 | # 7 | # 1. Redistributions of source code must retain the above copyright notice, 8 | # this list of conditions and the following disclaimer. 9 | # 10 | # 2. Redistributions in binary form must reproduce the above copyright 11 | # notice, this list of conditions and the following disclaimer in the 12 | # documentation and/or other materials provided with the distribution. 13 | # 14 | # 3. Neither the name of the copyright holder nor the names of its 15 | # contributors may be used to endorse or promote products derived from 16 | # this software without specific prior written permission. 17 | # 18 | # THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" 19 | # AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, 20 | # THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR 21 | # PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR 22 | # CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, 23 | # EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, 24 | # PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; 25 | # OR BUSINESS INTERRUPTION). HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, 26 | # WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR 27 | # OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF 28 | # ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. 29 | 30 | 31 | import argparse 32 | import json 33 | import os 34 | 35 | import torch 36 | import numpy as np 37 | from ocr import PytorchOCRTrainer 38 | 39 | torch.backends.cudnn.enabled = False 40 | torch.set_printoptions(precision=10) 41 | 42 | class objdict(dict): 43 | def __getattr__(self, name): 44 | if name in self: 45 | return self[name] 46 | else: 47 | raise AttributeError("No such attribute: " + name) 48 | 49 | def __setattr__(self, name, value): 50 | self[name] = value 51 | 52 | def __delattr__(self, name): 53 | if name in self: 54 | del self[name] 55 | else: 56 | raise AttributeError("No such attribute: " + name) 57 | 58 | def ascii_encode_dict(data): 59 | ascii_encode = lambda x: x.encode('ascii') 60 | return dict(map(ascii_encode, pair) if isinstance(pair[1], unicode) else pair for pair in data.items()) 61 | 62 | if __name__ == '__main__': 63 | parser = argparse.ArgumentParser(description='OCR training') 64 | parser.add_argument('--params', '-p', default="default_trainer_params.json", help='Path to params JSON file. Default ignored when resuming.') 65 | parser.add_argument('--experiments', '-e', default="experiments", help='Path for experiments. Ignored when resuming.') 66 | parser.add_argument('--input', '-i', help='Path to input checkpoint.') 67 | parser.add_argument('--pretrained_policy', default="RESUME", help='RESUME/RETRAIN.') 68 | parser.add_argument('--init_bn_fc_fusion', default=False, action='store_true', help='Init BN FC fusion.') 69 | parser.add_argument('--eval', default=False, action='store_true', help='Perform only evaluation on val dataset.') 70 | parser.add_argument('--export', default=False, action='store_true', help='Perform only export of quantized weights.') 71 | parser.add_argument('--no_cuda', default=False, action='store_true', help='Run on CPU.') 72 | parser.add_argument('--export_test_image', default=False, action='store_true', help='Export pre-quantized and reshaped test image.') 73 | parser.add_argument('--valid', default="db_files_uw3-500/valid.txt", help='Input path for val file.') 74 | parser.add_argument('--sortedtrain', default="db_files_uw3-500/sortedTrain.txt", help='Input path for train file.') 75 | parser.add_argument('--imgs', default="db_files_uw3-500/imgs", help='Input path for images dir.') 76 | parser.add_argument('--dry_run', default=False, action='store_true', help='Do not write any output file.') 77 | parser.add_argument('--simd_factor', default=1, type=int, help='SIMD factor for export.') 78 | parser.add_argument('--pe', default=1, type=int, help='Number of PEs for export.') 79 | 80 | #Overrides 81 | parser.add_argument('--random_seed', type=int) 82 | parser.add_argument('--batch_size', type=int) 83 | parser.add_argument('--num_workers', type=int) 84 | parser.add_argument('--layer_size', type=int) 85 | parser.add_argument('--neuron_type', type=str) 86 | parser.add_argument('--target_height', type=int) 87 | parser.add_argument('--epochs', type=int) 88 | parser.add_argument('--lr', type=float) 89 | parser.add_argument('--lr_schedule', type=str) 90 | parser.add_argument('--lr_step', type=int) 91 | parser.add_argument('--lr_gamma', type=float) 92 | parser.add_argument('--max_norm', type=float) 93 | parser.add_argument('--seq_to_random_threshold', type=int) 94 | parser.add_argument('--bidirectional', type=bool) 95 | parser.add_argument('--reduce_bidirectional', type=str) 96 | parser.add_argument('--recurrent_bias_enabled', type=bool) 97 | parser.add_argument('--checkpoint_interval', type=int) 98 | parser.add_argument('--recurrent_weight_bit_width', type=int) 99 | parser.add_argument('--recurrent_weight_quantization', type=str) 100 | parser.add_argument('--recurrent_bias_bit_width', type=int) 101 | parser.add_argument('--recurrent_bias_quantization', type=str) 102 | parser.add_argument('--recurrent_activation_bit_width', type=int) 103 | parser.add_argument('--recurrent_activation_quantization', type=str) 104 | parser.add_argument('--internal_activation_bit_width', type=int) 105 | parser.add_argument('--fc_weight_bit_width', type=int) 106 | parser.add_argument('--fc_weight_quantization', type=str) 107 | parser.add_argument('--fc_bias_bit_width', type=int) 108 | parser.add_argument('--fc_bias_quantization', type=str) 109 | parser.add_argument('--quantize_input', type=bool) 110 | parser.add_argument('--mask_padded', type=bool) 111 | 112 | args = parser.parse_args() 113 | 114 | #Set paths relative to main.py 115 | path_args = ['params', 'experiments', 'input', 'valid', 'sortedtrain', 'imgs'] 116 | for path_arg in path_args: 117 | path = getattr(args, path_arg) 118 | if path is not None and not os.path.isabs(path): 119 | abs_path = os.path.abspath(os.path.join(os.path.dirname(__file__), path)) 120 | setattr(args, path_arg, abs_path) 121 | 122 | #Avoid creating new folders etc. 123 | if args.eval or args.export or args.export_test_image: 124 | args.dry_run = True 125 | 126 | #force cpu when exporting weights 127 | if args.export or args.export_test_image: 128 | args.no_cuda = True 129 | 130 | if args.input and args.pretrained_policy == "RESUME" and args.params == "default_trainer_params.json": 131 | package = torch.load(args.input, map_location=lambda storage, loc: storage) 132 | trainer_params = package['trainer_params'] 133 | else: 134 | with open(args.params) as d: 135 | trainer_params = json.load(d, object_hook=ascii_encode_dict) 136 | trainer_params = objdict(trainer_params) 137 | 138 | #Overrides 139 | if args.epochs is not None: 140 | trainer_params.epochs = args.epochs 141 | if args.internal_activation_bit_width is not None: 142 | trainer_params.internal_activation_bit_width = args.internal_activation_bit_width 143 | 144 | trainer = PytorchOCRTrainer(trainer_params, args) 145 | 146 | if args.export_test_image: 147 | trainer.export_test_image(trainer_params.target_height) 148 | exit(0) 149 | 150 | if args.export: 151 | trainer.export_model(args.simd_factor, args.pe) 152 | exit(0) 153 | 154 | if args.eval: 155 | trainer.eval_model() 156 | else: 157 | trainer.train_model() 158 | 159 | 160 | 161 | 162 | 163 | 164 | -------------------------------------------------------------------------------- /ocr/__init__.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) 2018, Xilinx, Inc. 2 | # All rights reserved. 3 | # 4 | # Redistribution and use in source and binary forms, with or without 5 | # modification, are permitted provided that the following conditions are met: 6 | # 7 | # 1. Redistributions of source code must retain the above copyright notice, 8 | # this list of conditions and the following disclaimer. 9 | # 10 | # 2. Redistributions in binary form must reproduce the above copyright 11 | # notice, this list of conditions and the following disclaimer in the 12 | # documentation and/or other materials provided with the distribution. 13 | # 14 | # 3. Neither the name of the copyright holder nor the names of its 15 | # contributors may be used to endorse or promote products derived from 16 | # this software without specific prior written permission. 17 | # 18 | # THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" 19 | # AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, 20 | # THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR 21 | # PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR 22 | # CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, 23 | # EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, 24 | # PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; 25 | # OR BUSINESS INTERRUPTION). HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, 26 | # WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR 27 | # OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF 28 | # ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. 29 | 30 | 31 | from ocr.loss import OCRCTCLoss 32 | from ocr.model import OCRModule 33 | from ocr.image_preprocessor import ImagePreprocessor 34 | from ocr.gt_preprocessor import GtPreprocessor 35 | from ocr.utils import to_tensor 36 | from ocr.data import OCRTrainDataset, OCRValDataset 37 | from ocr.preprocessing_runner import PreprocessingRunner 38 | from ocr.batch_generator import BatchGenerator 39 | from ocr.decoder import GreedyDecoder 40 | from ocr.logger import Logger 41 | from ocr.trainer import PytorchOCRTrainer 42 | 43 | -------------------------------------------------------------------------------- /ocr/__init__.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Xilinx/pytorch-ocr/480ec662149ba4ab1d4358dc6623d8b250a6c56a/ocr/__init__.pyc -------------------------------------------------------------------------------- /ocr/batch_generator.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) 2018, Xilinx, Inc. 2 | # All rights reserved. 3 | # 4 | # Redistribution and use in source and binary forms, with or without 5 | # modification, are permitted provided that the following conditions are met: 6 | # 7 | # 1. Redistributions of source code must retain the above copyright notice, 8 | # this list of conditions and the following disclaimer. 9 | # 10 | # 2. Redistributions in binary form must reproduce the above copyright 11 | # notice, this list of conditions and the following disclaimer in the 12 | # documentation and/or other materials provided with the distribution. 13 | # 14 | # 3. Neither the name of the copyright holder nor the names of its 15 | # contributors may be used to endorse or promote products derived from 16 | # this software without specific prior written permission. 17 | # 18 | # THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" 19 | # AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, 20 | # THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR 21 | # PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR 22 | # CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, 23 | # EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, 24 | # PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; 25 | # OR BUSINESS INTERRUPTION). HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, 26 | # WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR 27 | # OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF 28 | # ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. 29 | 30 | import torch 31 | 32 | class BatchGenerator(object): 33 | def __init__(self, no_cuda): 34 | self.no_cuda = no_cuda 35 | 36 | #Assume tensor is Seq/Width x Height 37 | def pad_tensor(self, tensor, max_seq_len): 38 | original_seq_len, height = tensor.size() 39 | padded_seq_len = max_seq_len - original_seq_len 40 | 41 | if padded_seq_len: 42 | padding = torch.zeros(padded_seq_len, height) 43 | padded_tensor = torch.cat((tensor, padding), 0).contiguous() 44 | return padded_tensor 45 | 46 | else: 47 | return tensor 48 | 49 | #Assume image tensors are Seq/Width x Height 50 | def get_max_seq_len(self, batch_list): 51 | max_seq_len = 0 52 | 53 | for image, gt in batch_list: 54 | seq_len, height = image.size() 55 | 56 | if seq_len > max_seq_len: 57 | max_seq_len = seq_len 58 | 59 | return max_seq_len 60 | 61 | def get_tensor_lists(self, batch_list, max_seq_len): 62 | image_tensor_list = [] 63 | seq_len_list = [] 64 | gt_list = [] 65 | gt_len_list = [] 66 | 67 | for image, gt in batch_list: 68 | #Non padded sequence length 69 | seq_len_list.append(image.size()[0]) 70 | 71 | #gt is already a list of tokens and CTC wants a SINGLE tensor for the whole batch 72 | gt_list.extend(gt) 73 | 74 | #In order to distinguish the tokens belonging to different samples, we are also passing 75 | #to CTC a tensor of the length of the gt for each sample in the batch 76 | gt_len_list.append(len(gt)) 77 | image_tensor_list.append(self.pad_tensor(image, max_seq_len)) 78 | 79 | return (image_tensor_list, seq_len_list, gt_list, gt_len_list) 80 | 81 | def generate_data_tensors(self, lists_tuple): 82 | image_tensor_list, seq_len_list, gt_list, gt_len_list = lists_tuple 83 | 84 | # CTC wants in input a 1D tensor containing the non-padded size of each output sequence of the model 85 | seq_len_tensor = torch.IntTensor(seq_len_list).contiguous() 86 | 87 | # stack on dim 1, i.e. batch, so that we have SEQUENCE_LEN x BATCH x FEATURES (target_height) 88 | image_tensor = torch.stack(image_tensor_list, dim=1).contiguous() 89 | gt_tensor = torch.IntTensor(gt_list).contiguous() 90 | gt_len_tensor = torch.IntTensor(gt_len_list).contiguous() 91 | 92 | if not self.no_cuda: 93 | image_tensor = image_tensor.cuda() 94 | 95 | return (image_tensor, seq_len_list, seq_len_tensor, gt_tensor, gt_len_tensor) 96 | 97 | def collate_batch(self, batch_list): 98 | batch_list.sort(key=lambda (image, gt): image.size()[0], reverse=True) 99 | max_seq_len = self.get_max_seq_len(batch_list) 100 | lists_tuple = self.get_tensor_lists(batch_list, max_seq_len) 101 | return self.generate_data_tensors(lists_tuple) 102 | 103 | 104 | -------------------------------------------------------------------------------- /ocr/batch_generator.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Xilinx/pytorch-ocr/480ec662149ba4ab1d4358dc6623d8b250a6c56a/ocr/batch_generator.pyc -------------------------------------------------------------------------------- /ocr/data.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) 2018, Xilinx, Inc. 2 | # All rights reserved. 3 | # 4 | # Redistribution and use in source and binary forms, with or without 5 | # modification, are permitted provided that the following conditions are met: 6 | # 7 | # 1. Redistributions of source code must retain the above copyright notice, 8 | # this list of conditions and the following disclaimer. 9 | # 10 | # 2. Redistributions in binary form must reproduce the above copyright 11 | # notice, this list of conditions and the following disclaimer in the 12 | # documentation and/or other materials provided with the distribution. 13 | # 14 | # 3. Neither the name of the copyright holder nor the names of its 15 | # contributors may be used to endorse or promote products derived from 16 | # this software without specific prior written permission. 17 | # 18 | # THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" 19 | # AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, 20 | # THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR 21 | # PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR 22 | # CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, 23 | # EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, 24 | # PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; 25 | # OR BUSINESS INTERRUPTION). HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, 26 | # WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR 27 | # OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF 28 | # ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. 29 | 30 | import os 31 | 32 | import torch 33 | from torch.utils.data import Dataset 34 | from PIL import Image 35 | 36 | class OCRDataset(Dataset): 37 | def __init__(self, images_dir_abs_path, image_ground_truth_file_abs_path): 38 | self.images_dir_abs_path = images_dir_abs_path 39 | self.image_ground_truth_file_abs_path = image_ground_truth_file_abs_path 40 | self.image_gt_list = [] 41 | self.setup_dataset_list() 42 | 43 | def setup_dataset_list(self): 44 | with open(self.image_ground_truth_file_abs_path) as train_file: 45 | for line in train_file: 46 | (rel_image_path, ground_truth_string) = line.split(',', 1) #1 is maxsplit 47 | abs_image_path = os.path.join(self.images_dir_abs_path, rel_image_path + '.png') 48 | image = Image.open(abs_image_path).convert('L') 49 | self.image_gt_list.append((image, ground_truth_string)) 50 | self.order_by_width() 51 | 52 | def order_by_width(self): 53 | self.image_gt_list = sorted(self.image_gt_list, key=lambda (image, gt): image.size[0]) 54 | 55 | def update_images(self, function): 56 | self.image_gt_list = [(function(image), gt) for (image, gt) in self.image_gt_list] 57 | 58 | def update_gts(self, function): 59 | self.image_gt_list = [(image, function(gt)) for (image, gt) in self.image_gt_list] 60 | 61 | def call_on_each_image(self, function): 62 | for image, gt in self.image_gt_list: 63 | function(image) 64 | 65 | def call_on_each_gt(self, function): 66 | for image, gt in self.image_gt_list: 67 | function(gt) 68 | 69 | def __len__(self): 70 | return len(self.image_gt_list) 71 | 72 | def __getitem__(self, index): 73 | return self.image_gt_list[index] 74 | 75 | 76 | class OCRTrainDataset(OCRDataset): 77 | def __init__(self, images_dir_abs_path, train_ground_truth_file_abs_path): 78 | super(OCRTrainDataset, self).__init__(images_dir_abs_path, train_ground_truth_file_abs_path) 79 | 80 | 81 | 82 | class OCRValDataset(OCRDataset): 83 | def __init__(self, images_dir_abs_path, val_ground_truth_file_abs_path): 84 | super(OCRValDataset, self).__init__(images_dir_abs_path, val_ground_truth_file_abs_path) 85 | 86 | 87 | -------------------------------------------------------------------------------- /ocr/data.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Xilinx/pytorch-ocr/480ec662149ba4ab1d4358dc6623d8b250a6c56a/ocr/data.pyc -------------------------------------------------------------------------------- /ocr/decoder.py: -------------------------------------------------------------------------------- 1 | # ---------------------------------------------------------------------------- 2 | # Copyright 2015-2016 Nervana Systems Inc. 3 | # Licensed under the Apache License, Version 2.0 (the "License"); 4 | # you may not use this file except in compliance with the License. 5 | # You may obtain a copy of the License at 6 | # 7 | # http://www.apache.org/licenses/LICENSE-2.0 8 | # 9 | # Unless required by applicable law or agreed to in writing, software 10 | # distributed under the License is distributed on an "AS IS" BASIS, 11 | # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. 12 | # See the License for the specific language governing permissions and 13 | # limitations under the License. 14 | # ---------------------------------------------------------------------------- 15 | # Modified to support pytorch Tensors 16 | # ---------------------------------------------------------------------------- 17 | # Taken and adapted from: 18 | # https://github.com/SeanNaren/deepspeech.pytorch/blob/master/decoder.py 19 | 20 | import Levenshtein as Lev 21 | import torch 22 | from six.moves import xrange 23 | import torch 24 | 25 | class Decoder(object): 26 | """ 27 | Basic decoder class from which all other decoders inherit. Implements several 28 | helper functions. Subclasses should implement the decode() method. 29 | """ 30 | 31 | def __init__(self, gt_preprocessor): 32 | # e.g. labels = "_'ABCDEFGHIJKLMNOPQRSTUVWXYZ#" 33 | self.labels = ''.join(map(str, gt_preprocessor.chars)) 34 | self.int_to_char = gt_preprocessor.token2char 35 | self.blank_token = gt_preprocessor.blank_token 36 | 37 | def wer(self, s1, s2): 38 | """ 39 | Computes the Word Error Rate, defined as the edit distance between the 40 | two provided sentences after tokenizing to words. 41 | Arguments: 42 | s1 (string): space-separated sentence 43 | s2 (string): space-separated sentence 44 | """ 45 | 46 | # build mapping of words to integers 47 | b = set(s1.split() + s2.split()) 48 | word2char = dict(zip(b, range(len(b)))) 49 | 50 | # map the words to a char array (Levenshtein packages only accepts 51 | # strings) 52 | w1 = [chr(word2char[w]) for w in s1.split()] 53 | w2 = [chr(word2char[w]) for w in s2.split()] 54 | 55 | return Lev.distance(''.join(w1), ''.join(w2)) 56 | 57 | def cer(self, s1, s2, no_spaces=False): 58 | """ 59 | Computes the Character Error Rate, defined as the edit distance. 60 | Arguments: 61 | s1 (string): space-separated sentence 62 | s2 (string): space-separated sentence 63 | """ 64 | if no_spaces: 65 | s1, s2, = s1.replace(' ', ''), s2.replace(' ', '') 66 | return Lev.distance(s1, s2) 67 | 68 | def decode(self, probs, remove_repetitions): 69 | """ 70 | Given a matrix of character probabilities, returns the decoder's 71 | best guess of the transcription 72 | """ 73 | raise NotImplementedError 74 | 75 | 76 | class GreedyDecoder(Decoder): 77 | def __init__(self, gt_preprocessor): 78 | super(GreedyDecoder, self).__init__(gt_preprocessor) 79 | 80 | def convert_to_strings(self, sequences, remove_repetitions): 81 | """Given a list of numeric sequences, returns the corresponding strings""" 82 | string_list = [] 83 | for i in xrange(len(sequences)): 84 | string, tokens = self.process_string(sequences[i], remove_repetitions) 85 | string_list.append((tokens, string)) #encoded, decoded 86 | return string_list 87 | 88 | def process_string(self, sequence, remove_repetitions): 89 | char_string = '' 90 | tokens = [] 91 | for i in range(len(sequence)): 92 | token = int(sequence[i]) 93 | tokens.append(token) 94 | if token != self.blank_token: 95 | char = self.int_to_char[token] 96 | if remove_repetitions and i != 0 and (token == sequence[i-1]): 97 | pass 98 | else: 99 | char_string = char_string + char 100 | return char_string, tokens 101 | 102 | def decode(self, probs, remove_repetitions): 103 | """ 104 | Returns the argmax decoding given the probability matrix. Removes 105 | repeated elements in the sequence, as well as blanks. 106 | 107 | Arguments: 108 | probs: Tensor of character probabilities from the network. Expected shape of seq_length x batch x output_dim 109 | sizes(optional): Size of each sequence in the mini-batch 110 | Returns: 111 | strings: sequences of the model's best guess for the transcription on inputs 112 | offsets: time step per character predicted 113 | """ 114 | _, max_probs = torch.max(probs.transpose(0, 1), 2) 115 | string_list = self.convert_to_strings(max_probs.view(max_probs.size(0), max_probs.size(1)), remove_repetitions) 116 | return string_list -------------------------------------------------------------------------------- /ocr/decoder.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Xilinx/pytorch-ocr/480ec662149ba4ab1d4358dc6623d8b250a6c56a/ocr/decoder.pyc -------------------------------------------------------------------------------- /ocr/gt_preprocessor.py: -------------------------------------------------------------------------------- 1 | import ocr.utils as utils 2 | import torch 3 | 4 | class GtPreprocessor(object): 5 | def __init__(self): 6 | self.token2char = {} 7 | self.char2token = {} 8 | self.current_token = 0 9 | 10 | @property 11 | def chars_to_remove(self): 12 | return map(str, ['\n', '\t', '$']) #$ is in the val dataset but not the train one, remove 13 | 14 | @property 15 | def chars(self): 16 | return self.char2token.keys() 17 | 18 | @property 19 | def number_of_tokens(self): 20 | return self.current_token + 1 #accounts for the blank token 21 | 22 | @property 23 | def blank_token(self): 24 | return 0 #Warp-CTC defines 0 as the blank token 25 | 26 | def register_unencoded_gt(self, unencoded_gt): 27 | for c in unencoded_gt: 28 | if c not in self.char2token and c not in self.chars_to_remove: 29 | self.current_token += 1 30 | self.token2char[self.current_token] = c 31 | self.char2token[c] = self.current_token 32 | 33 | def encode_unencoded_gt(self, unencoded_gt): 34 | result = [] 35 | for c in unencoded_gt: 36 | if c not in self.chars_to_remove: 37 | result.append(self.char2token[c]) 38 | return result 39 | 40 | 41 | -------------------------------------------------------------------------------- /ocr/gt_preprocessor.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Xilinx/pytorch-ocr/480ec662149ba4ab1d4358dc6623d8b250a6c56a/ocr/gt_preprocessor.pyc -------------------------------------------------------------------------------- /ocr/image_preprocessor.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) 2018, Xilinx, Inc. 2 | # All rights reserved. 3 | # 4 | # Redistribution and use in source and binary forms, with or without 5 | # modification, are permitted provided that the following conditions are met: 6 | # 7 | # 1. Redistributions of source code must retain the above copyright notice, 8 | # this list of conditions and the following disclaimer. 9 | # 10 | # 2. Redistributions in binary form must reproduce the above copyright 11 | # notice, this list of conditions and the following disclaimer in the 12 | # documentation and/or other materials provided with the distribution. 13 | # 14 | # 3. Neither the name of the copyright holder nor the names of its 15 | # contributors may be used to endorse or promote products derived from 16 | # this software without specific prior written permission. 17 | # 18 | # THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" 19 | # AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, 20 | # THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR 21 | # PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR 22 | # CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, 23 | # EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, 24 | # PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; 25 | # OR BUSINESS INTERRUPTION). HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, 26 | # WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR 27 | # OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF 28 | # ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. 29 | 30 | from __future__ import division # / is always float division in both Python 2 and 3 31 | 32 | import sys 33 | 34 | from PIL import Image 35 | import PIL.ImageOps 36 | from torchvision.transforms.functional import resize, to_tensor 37 | import math 38 | import torch 39 | import numpy as np 40 | from quantization.function.quantization_scheme import ActivationQuantizationScheme 41 | 42 | DELTA = 0.1 43 | 44 | class ImagePreprocessor(object): 45 | def __init__(self, trainer_params): 46 | self.target_height = trainer_params.target_height 47 | self.input_quantization_scheme = ActivationQuantizationScheme(trainer_params.recurrent_activation_bit_width, trainer_params.recurrent_activation_quantization) 48 | 49 | def get_scaling_ratio(self, original_height): 50 | return (self.target_height + DELTA) / original_height 51 | 52 | def invert_image(self, image): 53 | return PIL.ImageOps.invert(image) 54 | 55 | def resize_and_normalize_image(self, image): 56 | original_width, original_height = image.size 57 | scaling_ratio = self.get_scaling_ratio(original_height) 58 | target_width = int(math.ceil(original_width * scaling_ratio)) 59 | size = (int(target_width), int(self.target_height)) 60 | resized_image = image.resize(size, Image.BILINEAR) #This call also normalizes the input image 61 | return resized_image 62 | 63 | def image_to_squeezed_transposed_tensor(self, image): 64 | #C x H x W 65 | tensor_image = to_tensor(image) 66 | #Swap dimensions 1 (H) and 2 (W) to get C x W x H 67 | transposed_tensor = torch.transpose(tensor_image, 1, 2) 68 | #C = 1 so we can remove it 69 | squeezed_tensor = torch.squeeze(transposed_tensor) 70 | return squeezed_tensor 71 | 72 | def shift_scale_tensor_image(self, tensor_image): 73 | return (tensor_image - 0.5) * 2 #put it between -1 and 1 74 | 75 | def quantize_tensor_image(self, tensor_image): 76 | out = self.input_quantization_scheme.q_forward(tensor_image) 77 | return out.data 78 | -------------------------------------------------------------------------------- /ocr/image_preprocessor.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Xilinx/pytorch-ocr/480ec662149ba4ab1d4358dc6623d8b250a6c56a/ocr/image_preprocessor.pyc -------------------------------------------------------------------------------- /ocr/loss.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) 2018, Xilinx, Inc. 2 | # All rights reserved. 3 | # 4 | # Redistribution and use in source and binary forms, with or without 5 | # modification, are permitted provided that the following conditions are met: 6 | # 7 | # 1. Redistributions of source code must retain the above copyright notice, 8 | # this list of conditions and the following disclaimer. 9 | # 10 | # 2. Redistributions in binary form must reproduce the above copyright 11 | # notice, this list of conditions and the following disclaimer in the 12 | # documentation and/or other materials provided with the distribution. 13 | # 14 | # 3. Neither the name of the copyright holder nor the names of its 15 | # contributors may be used to endorse or promote products derived from 16 | # this software without specific prior written permission. 17 | # 18 | # THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" 19 | # AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, 20 | # THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR 21 | # PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR 22 | # CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, 23 | # EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, 24 | # PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; 25 | # OR BUSINESS INTERRUPTION). HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, 26 | # WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR 27 | # OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF 28 | # ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. 29 | 30 | from torch.autograd import Variable 31 | from warpctc_pytorch import CTCLoss 32 | import torch.nn as nn 33 | import torch 34 | 35 | class OCRCTCLoss(nn.Module): 36 | def __init__(self, trainer_params): 37 | super(OCRCTCLoss, self).__init__() 38 | self.trainer_params = trainer_params 39 | self.ctc_layer = CTCLoss() 40 | 41 | def forward(self, x, model_output_size_tensor_var, gt_tensor_var, gt_len_tensor_var): 42 | # x is expected to be of shape of seqLength x batchSize x alphabet_size 43 | loss = self.ctc_layer(x, gt_tensor_var, model_output_size_tensor_var, gt_len_tensor_var) 44 | return loss 45 | 46 | 47 | -------------------------------------------------------------------------------- /ocr/loss.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Xilinx/pytorch-ocr/480ec662149ba4ab1d4358dc6623d8b250a6c56a/ocr/loss.pyc -------------------------------------------------------------------------------- /ocr/model.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Xilinx/pytorch-ocr/480ec662149ba4ab1d4358dc6623d8b250a6c56a/ocr/model.pyc -------------------------------------------------------------------------------- /ocr/preprocessing_runner.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) 2018, Xilinx, Inc. 2 | # All rights reserved. 3 | # 4 | # Redistribution and use in source and binary forms, with or without 5 | # modification, are permitted provided that the following conditions are met: 6 | # 7 | # 1. Redistributions of source code must retain the above copyright notice, 8 | # this list of conditions and the following disclaimer. 9 | # 10 | # 2. Redistributions in binary form must reproduce the above copyright 11 | # notice, this list of conditions and the following disclaimer in the 12 | # documentation and/or other materials provided with the distribution. 13 | # 14 | # 3. Neither the name of the copyright holder nor the names of its 15 | # contributors may be used to endorse or promote products derived from 16 | # this software without specific prior written permission. 17 | # 18 | # THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" 19 | # AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, 20 | # THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR 21 | # PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR 22 | # CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, 23 | # EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, 24 | # PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; 25 | # OR BUSINESS INTERRUPTION). HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, 26 | # WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR 27 | # OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF 28 | # ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. 29 | 30 | class PreprocessingRunner(object): 31 | 32 | def __init__(self, trainer_params, dataset, image_preprocessor, gt_preprocessor): 33 | self.trainer_params = trainer_params 34 | self.dataset = dataset 35 | self.image_preprocessor = image_preprocessor 36 | self.gt_preprocessor = gt_preprocessor 37 | 38 | def run_image_preprocessor(self): 39 | self.dataset.update_images(self.image_preprocessor.invert_image) 40 | self.dataset.update_images(self.image_preprocessor.resize_and_normalize_image) 41 | self.dataset.update_images(self.image_preprocessor.image_to_squeezed_transposed_tensor) 42 | self.dataset.update_images(self.image_preprocessor.shift_scale_tensor_image) 43 | if self.trainer_params.quantize_input: 44 | self.dataset.update_images(self.image_preprocessor.quantize_tensor_image) 45 | 46 | def run_gt_preprocessor(self): 47 | self.dataset.call_on_each_gt(self.gt_preprocessor.register_unencoded_gt) 48 | self.dataset.update_gts(self.gt_preprocessor.encode_unencoded_gt) 49 | 50 | def run(self): 51 | self.run_image_preprocessor() 52 | self.run_gt_preprocessor() -------------------------------------------------------------------------------- /ocr/preprocessing_runner.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Xilinx/pytorch-ocr/480ec662149ba4ab1d4358dc6623d8b250a6c56a/ocr/preprocessing_runner.pyc -------------------------------------------------------------------------------- /ocr/trainer.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Xilinx/pytorch-ocr/480ec662149ba4ab1d4358dc6623d8b250a6c56a/ocr/trainer.pyc -------------------------------------------------------------------------------- /ocr/utils.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) 2018, Xilinx, Inc. 2 | # All rights reserved. 3 | # 4 | # Redistribution and use in source and binary forms, with or without 5 | # modification, are permitted provided that the following conditions are met: 6 | # 7 | # 1. Redistributions of source code must retain the above copyright notice, 8 | # this list of conditions and the following disclaimer. 9 | # 10 | # 2. Redistributions in binary form must reproduce the above copyright 11 | # notice, this list of conditions and the following disclaimer in the 12 | # documentation and/or other materials provided with the distribution. 13 | # 14 | # 3. Neither the name of the copyright holder nor the names of its 15 | # contributors may be used to endorse or promote products derived from 16 | # this software without specific prior written permission. 17 | # 18 | # THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" 19 | # AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, 20 | # THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR 21 | # PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR 22 | # CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, 23 | # EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, 24 | # PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; 25 | # OR BUSINESS INTERRUPTION). HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, 26 | # WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR 27 | # OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF 28 | # ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. 29 | 30 | 31 | import torch 32 | 33 | def to_tensor(x): 34 | return torch.FloatTensor(x) -------------------------------------------------------------------------------- /ocr/utils.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Xilinx/pytorch-ocr/480ec662149ba4ab1d4358dc6623d8b250a6c56a/ocr/utils.pyc -------------------------------------------------------------------------------- /quantized_params/QLSTM128_W2B8A4I32_FC_W32B32.json: -------------------------------------------------------------------------------- 1 | { 2 | "random_seed": 123456, 3 | "batch_size" : 32, 4 | "num_workers" : 0, 5 | "layer_size" : 128, 6 | "neuron_type" : "QLSTM", 7 | "target_height" : 32, 8 | "epochs" : 4000, 9 | "lr" : 1e-4, 10 | "lr_schedule" : "FIXED", 11 | "lr_step" : 40, 12 | "lr_gamma" : 0.5, 13 | "max_norm" : 400, 14 | "seq_to_random_threshold": 20, 15 | "bidirectional" : true, 16 | "reduce_bidirectional": "CONCAT", 17 | "recurrent_bias_enabled": true, 18 | "checkpoint_interval": 10, 19 | "recurrent_weight_bit_width": 2, 20 | "recurrent_weight_quantization": "SIGNED_FIXED_UNIT", 21 | "recurrent_bias_bit_width": 8, 22 | "recurrent_bias_quantization": "SIGNED_FIXED_UNIT", 23 | "recurrent_activation_bit_width": 4, 24 | "recurrent_activation_quantization": "SIGNED_FIXED_UNIT", 25 | "internal_activation_bit_width": 32, 26 | "fc_weight_bit_width": 32, 27 | "fc_weight_quantization": "FP", 28 | "fc_bias_bit_width": 32, 29 | "fc_bias_quantization": "FP", 30 | "quantize_input": true, 31 | "mask_padded": true, 32 | "prefused_bn_fc": false 33 | } 34 | --------------------------------------------------------------------------------