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import streamlit as st |
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import numpy as np |
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import PIL.Image |
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from st_clickable_images import clickable_images |
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import os |
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from monai.transforms import CenterSpatialCrop, ScaleIntensityRange, Orientation |
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import base64 |
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from io import BytesIO |
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import torch |
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from glob import glob |
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from model import VerseFxClassifier |
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from netdissect import nethook, imgviz |
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import tempfile |
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import nibabel as nib |
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import pathlib |
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import warnings |
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warnings.filterwarnings("ignore") |
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unit_levels = torch.tensor([1.8715330362319946, 1.5618106126785278, 1.2870054244995117, 2.801919937133789, 1.1172661781311035, 2.2070984840393066, 2.3209457397460938, 2.022796392440796, 2.0127036571502686, 2.782788038253784, 1.013718843460083, 2.491750955581665, 1.5298184156417847, 1.7949274778366089, 2.1840720176696777, 2.73867130279541, 1.9927071332931519, 1.4070216417312622, 1.8516860008239746, 1.4621922969818115, 1.7988444566726685, 2.0956199169158936, 2.890246629714966, 0.9635668992996216, 1.8309086561203003, 1.8866947889328003, 1.8208155632019043, 1.3282618522644043, 2.787090301513672, 1.6975336074829102, 2.388171434402466, 3.1032965183258057, 1.996658444404602, 1.8226428031921387, 2.557448148727417, 1.8223134279251099, 1.2595659494400024, 1.8109630346298218, 2.6617250442504883, 1.9107582569122314, 2.254500389099121, 1.218552827835083, 3.087602376937866, 3.3800148963928223, 3.153672218322754, 2.919377326965332, 2.0350027084350586, 3.0219407081604004, 2.4654042720794678, 1.4958505630493164, 2.2895171642303467, 1.3284631967544556, 3.229510545730591, 1.9460035562515259, 1.855022668838501, 3.15183424949646, 2.582113742828369, 1.8321630954742432, 2.7707386016845703, 2.824443817138672, 2.662318468093872, 2.466081380844116, 1.0707639455795288, 1.856846570968628, 1.9820237159729004, 2.5840156078338623, 1.603718638420105, 2.741654396057129, 1.7408792972564697, 1.5616865158081055, 2.621121406555176, 2.187910318374634, 2.029402494430542, 2.3087165355682373, 2.3417551517486572, 2.4370405673980713, 2.363990545272827, 1.7908833026885986, 2.29636287689209, 2.5254483222961426, 3.2696034908294678, 1.4013628959655762, 1.645676851272583, 2.7126476764678955, 2.717543125152588, 1.0994248390197754, 1.9232852458953857, 1.985698938369751, 2.004666328430176, 2.385585069656372, 2.5118658542633057, 3.444154977798462, 2.0752625465393066, 2.9441027641296387, 1.6907892227172852, 2.695660352706909, 3.08571457862854, 1.8869487047195435, 1.5935581922531128, 2.224071502685547, 2.877380609512329, 3.0157597064971924, 2.1446480751037598, 2.4394376277923584, 3.298722267150879, 2.208728313446045, 1.9590588808059692, 1.789717197418213, 2.6814987659454346, 2.2261674404144287, 3.002722978591919, 3.0650651454925537, 1.9212583303451538, 1.6315948963165283, 1.6328997611999512, 2.4739739894866943, 0.9252153635025024, 3.089088201522827, 2.7511496543884277, 1.997342586517334, 2.5561487674713135, 1.6858017444610596, 2.7134108543395996, 2.513460159301758, 1.8604570627212524, 2.7962076663970947, 1.111690878868103, 2.1877119541168213, 2.1126585006713867, 2.9239501953125, 1.4319941997528076, 3.041599988937378, 2.2168679237365723, 1.792368769645691, 2.1387674808502197, 1.3679250478744507, 1.347702145576477, 3.0506792068481445, 1.5423274040222168, 1.8090440034866333, 1.869529366493225, 2.8993425369262695, 1.5416679382324219, 3.003296375274658, 3.1893210411071777, 2.3816075325012207, 2.281187057495117, 2.7733864784240723, 1.3033744096755981, 1.4627212285995483, 1.942519187927246, 1.4943166971206665, 2.48635196685791, 1.9112900495529175, 2.908750534057617, 1.9310427904129028, 1.8946770429611206, 1.2220033407211304, 2.0171048641204834, 1.197824478149414, 2.093484878540039, 2.240743398666382, 1.4367271661758423, 1.5200153589248657, 2.6623482704162598, 2.34277606010437, 2.378328323364258, 3.4981000423431396, 1.8303442001342773, 2.1322264671325684, 1.8304965496063232, 2.0963211059570312, 1.932998776435852, 0.9879118800163269, 1.989233136177063, 2.0391933917999268, 3.078193187713623, 2.9010426998138428, 1.451486587524414, 1.4458937644958496, 3.3362858295440674, 0.8172016143798828, 2.8464856147766113, 2.3619463443756104, 2.0269312858581543, 1.87027108669281, 2.5867714881896973, 1.0947588682174683, 2.485373020172119, 1.4596120119094849, 2.9054574966430664, 2.267271041870117, 1.9901957511901855, 1.708791971206665, 1.5335347652435303, 3.0039384365081787, 1.581254482269287, 1.6688708066940308, 2.138035535812378, 1.8489503860473633, 1.463232398033142, 2.745103597640991, 1.7890992164611816, 3.209639310836792, 2.186699628829956, 1.384399175643921, 2.347090482711792, 2.911564350128174, 2.7910614013671875, 3.0139355659484863, 2.8508076667785645, 3.1651434898376465, 2.020735263824463, 1.3879002332687378, 1.347353458404541, 1.3600330352783203, 1.563052773475647, 2.427166223526001, 2.3583383560180664, 2.0502967834472656, 1.0467418432235718, 1.5168964862823486, 2.550285816192627, 2.2569706439971924, 1.280961275100708, 2.153566360473633, 0.8621286749839783, 1.5903816223144531, 1.6175390481948853, 1.2808561325073242, 2.129512310028076, 1.923080563545227, 2.4000368118286133, 2.7758114337921143, 2.756497859954834, 2.8936665058135986, 1.9632121324539185, 1.4698351621627808, 2.9193220138549805, 2.2707347869873047, 2.1808905601501465, 2.915626049041748, 2.199504852294922, 2.225417375564575, 1.8788528442382812, 1.6902912855148315, 2.703303098678589, 1.6111797094345093, 1.4749184846878052, 2.7335896492004395, 1.1770113706588745, 1.5911366939544678, 2.5799360275268555, 2.450134515762329, 1.584707498550415, 2.0303263664245605, 1.5416966676712036, 1.6474940776824951, 3.166107654571533, 1.8914194107055664, 2.731400489807129, 3.456698179244995, 3.1407928466796875, 2.657524585723877, 1.8312366008758545, 1.3835384845733643, 1.3457938432693481, 1.1902421712875366, 1.739147663116455, 2.8404054641723633, 1.5782982110977173, 1.4647060632705688, 1.3077998161315918, 1.8057410717010498, 1.1732816696166992, 1.4494800567626953, 2.1183741092681885, 3.5306854248046875, 2.348907470703125, 1.5650557279586792, 1.6930912733078003, 2.298933267593384, 1.1758023500442505, 1.6107817888259888, 1.3251513242721558, 2.080108404159546, 1.862548589706421, 3.099520206451416, 2.8438494205474854, 1.6832661628723145, 2.074307680130005, 2.0457262992858887, 2.8403425216674805, 3.117814540863037, 2.058823823928833, 2.234037160873413, 1.2487999200820923, 1.7322322130203247, 2.6813132762908936, 2.924269199371338, 1.7503197193145752, 3.2688212394714355, 1.8045146465301514, 3.1042702198028564, 2.327272891998291, 2.7761642932891846, 2.3101589679718018, 2.8489952087402344, 2.132847547531128, 1.554833173751831, 1.3879495859146118, 1.8847209215164185, 1.728200912475586, 1.6019946336746216, 3.04852294921875, 3.0847041606903076, 2.528338670730591, 2.277801275253296, 3.1020517349243164, 2.7520859241485596, 3.03950834274292, 1.8526620864868164, 2.6675875186920166, 2.201525926589966, 1.3852479457855225, 1.744421362876892, 2.172621488571167, 2.681896924972534, 2.4530863761901855, 2.0969560146331787, 1.3115235567092896, 2.049104928970337, 1.7683310508728027, 1.7026116847991943, 2.3060457706451416, 3.208275318145752, 2.6523375511169434, 1.7658361196517944, 1.9047954082489014, 2.9763565063476562, 1.834631323814392, 3.142353057861328, 1.9534238576889038, 1.7625831365585327, 2.1041769981384277, 1.945776104927063, 2.970412015914917, 1.8245426416397095, 1.4031907320022583, 1.3985518217086792, 2.8565142154693604, 1.8306998014450073, 2.6509435176849365, 1.452415108680725, 2.7498743534088135, 2.0770175457000732, 1.8407188653945923, 1.5940998792648315, 2.4943857192993164, 3.0113513469696045, 3.450936794281006, 1.2603273391723633, 1.5098024606704712, 1.647451400756836, 2.344951868057251, 2.499359369277954, 1.9027211666107178, 1.6656138896942139, 1.5507005453109741, 2.177579641342163, 1.4274533987045288, 2.7495903968811035, 1.4635711908340454, 2.0104260444641113, 2.4939937591552734, 2.069014072418213, 1.3013184070587158, 3.4216034412384033, 1.9525243043899536, 2.196475028991699, 2.7452564239501953, 2.1965861320495605, 2.8216114044189453, 2.2089548110961914, 2.936760902404785, 1.3354514837265015, 1.3799076080322266, 2.2054338455200195, 1.3158196210861206, 1.084631085395813, 2.4761247634887695, 1.4672796726226807, 1.7008095979690552, 1.5144485235214233, 1.7634273767471313, 2.5879948139190674, 2.024614095687866, 1.7365692853927612, 1.5214873552322388, 1.1093666553497314, 1.7518495321273804, 2.188833713531494, 3.439579963684082, 2.6817214488983154, 1.636168122291565, 2.1104257106781006, 3.0666251182556152, 3.1396965980529785, 1.7993018627166748, 1.897646427154541, 1.2042944431304932, 2.8433687686920166, 2.068439483642578, 2.4039862155914307, 1.3701140880584717, 1.262689471244812, 1.827138066291809, 2.1528568267822266, 3.259542465209961, 1.7049492597579956, 1.9919352531433105, 2.1563854217529297, 2.035381317138672, 3.0388429164886475, 1.8345075845718384, 2.22445011138916, 1.5946440696716309, 2.3479206562042236, 1.281639575958252, 1.4048471450805664, 1.0306495428085327, 1.05494225025177, 1.9470269680023193, 1.6934491395950317, 2.1934640407562256, 2.6225905418395996, 1.974666714668274, 3.4361391067504883, 1.148988127708435, 2.7689907550811768, 2.478999614715576, 2.292860984802246, 1.380311131477356, 1.8914124965667725, 1.251215934753418, 1.3892083168029785, 3.1640305519104004, 2.3226025104522705, 2.3283731937408447, 3.2135708332061768, 1.2665305137634277, 2.8611419200897217, 2.735239267349243, 1.348517894744873, 1.2256826162338257, 2.5687448978424072, 1.9984424114227295, 2.913726568222046, 1.79617440700531, 3.3642163276672363, 1.405514121055603, 1.7745602130889893, 2.080112934112549, 2.5899147987365723, 1.9730525016784668, 1.6167746782302856, 1.2985221147537231, 1.6463950872421265, 1.2983338832855225, 3.4439616203308105, 1.8814938068389893, 1.1827762126922607, 3.0138072967529297, 2.0302090644836426, 3.2060086727142334, 1.7749220132827759, 1.6361336708068848, 2.207552194595337, 3.1703994274139404, 2.6205763816833496, 2.2056334018707275, 1.3571845293045044, 2.4915218353271484, 1.3841928243637085, 1.9503673315048218, 1.6178065538406372, 3.2435460090637207, 1.1473424434661865, 2.2226922512054443, 1.9872846603393555, 2.009683132171631, 3.1938722133636475, 3.248166799545288, 2.4461867809295654, 1.8230010271072388, 2.1673691272735596, 2.776118278503418, 2.054086685180664, 1.6877385377883911, 2.3526558876037598, 2.648297071456909, 1.3525688648223877, 2.819364309310913, 2.9533910751342773, 1.636002540588379, 1.5173200368881226, 2.315584421157837, 1.5832545757293701, 3121535301208496, 1.679909348487854, 2.9136874675750732, 2.4349215030670166]) |
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corr_rank = {299: 98, 194: 401, 281: 441, 1: 419, 289: 227, 65: 268, 23: 101, 453: 418, 321: 362, 259: 431, 257: 446, 477: 17, 92: 497, 17: 234, 314: 60, 331: 354, 315: 123, 318: 192, 445: 233, 238: 240, 311: 489, 265: 7, 126: 22, 431: 254, 223: 10, 179: 14, 362: 230, 448: 78, 478: 199, 418: 197, 139: 249, 403: 111, 262: 206, 316: 282, 150: 34, 142: 12, 444: 288, 261: 147, 180: 205, 413: 455, 322: 11, 5: 400, 474: 198, 167: 204, 226: 257, 230: 304, 225: 406, 482: 222, 373: 77, 352: 164, 2: 299, 329: 136, 152: 74, 271: 420, 386: 369, 377: 196, 165: 13, 229: 106, 457: 170, 192: 466, 99: 440, 214: 316, 461: 511, 19: 430, 82: 337, 505: 344, 199: 457, 416: 329, 484: 390, 104: 83, 496: 210, 465: 301, 462: 484, 361: 91, 231: 317, 100: 32, 467: 477, 63: 310, 421: 320, 273: 387, 368: 307, 449: 220, 385: 328, 8: 336, 432: 168, 217: 182, 255: 436, 366: 461, 151: 487, 67: 159, 14: 503, 36: 27, 131: 131, 112: 300, 37: 470, 29: 391, 163: 449, 510: 212, 21: 303, 202: 402, 409: 277, 158: 315, 16: 464, 44: 173, 197: 479, 410: 35, 495: 366, 341: 163, 425: 124, 77: 296, 38: 67, 175: 498, 181: 252, 248: 396, 15: 65, 301: 149, 18: 172, 81: 372, 154: 237, 306: 504, 123: 176, 105: 408, 185: 456, 145: 338, 398: 99, 40: 331, 451: 184, 108: 57, 94: 425, 213: 6, 286: 30, 203: 120, 39: 404, 64: 463, 10: 126, 348: 241, 28: 295, 278: 207, 216: 216, 224: 263, 330: 421, 303: 52, 206: 395, 417: 166, 354: 291, 228: 264, 446: 154, 509: 346, 440: 385, 85: 201, 363: 313, 121: 393, 423: 232, 277: 162, 86: 188, 483: 411, 364: 505, 374: 287, 176: 251, 78: 415, 351: 374, 227: 414, 51: 39, 250: 368, 488: 363, 288: 253, 434: 501, 68: 323, 276: 469, 469: 115, 130: 248, 168: 333, 397: 428, 365: 25, 128: 214, 3: 185, 26: 447, 307: 183, 419: 417, 222: 133, 493: 157, 382: 4, 319: 193, 60: 40, 327: 416, 433: 424, 430: 62, 345: 460, 486: 155, 35: 18, 45: 267, 407: 112, 507: 375, 141: 23, 260: 96, 338: 492, 387: 454, 189: 85, 182: 58, 282: 191, 350: 153, 323: 427, 143: 179, 472: 26, 302: 361, 0: 378, 244: 379, 426: 355, 215: 334, 140: 281, 253: 318, 489: 494, 267: 305, 111: 33, 346: 152, 390: 139, 210: 105, 212: 273, 188: 413, 239: 258, 439: 208, 391: 308, 378: 422, 312: 382, 97: 224, 491: 63, 162: 359, 389: 265, 272: 97, 443: 386, 75: 118, 245: 297, 263: 148, 284: 107, 137: 178, 415: 399, 209: 161, 201: 42, 335: 90, 135: 73, 310: 432, 122: 29, 172: 405, 328: 478, 173: 215, 308: 459, 480: 491, 412: 388, 494: 36, 476: 246, 479: 9, 9: 64, 344: 383, 119: 332, 55: 174, 103: 202, 395: 506, 56: 28, 73: 512, 353: 326, 120: 94, 339: 218, 12: 266, 193: 16, 295: 458, 106: 495, 287: 217, 304: 228, 124: 499, 148: 250, 422: 483, 264: 46, 113: 3, 166: 103, 369: 327, 156: 2, 498: 321, 334: 151, 169: 442, 506: 48, 4: 352, 43: 158, 249: 135, 31: 127, 233: 134, 427: 356, 375: 510, 107: 409, 183: 144, 320: 89, 138: 465, 211: 189, 41: 358, 292: 51, 79: 235, 456: 389, 116: 130, 280: 306, 343: 342, 473: 438, 357: 351, 511: 93, 450: 209, 279: 451, 144: 236, 293: 108, 187: 256, 11: 171, 127: 5, 471: 302, 313: 319, 13: 330, 468: 380, 291: 340, 243: 480, 475: 261, 487: 294, 160: 41, 254: 481, 429: 8, 59: 213, 326: 150, 57: 142, 125: 247, 359: 298, 87: 70, 258: 95, 70: 69, 383: 486, 347: 325, 497: 493, 69: 223, 129: 271, 492: 156, 384: 219, 91: 160, 360: 137, 74: 117, 285: 398, 340: 473, 240: 121, 424: 467, 266: 397, 508: 140, 178: 50, 400: 474, 388: 259, 235: 59, 420: 433, 376: 243, 294: 452, 232: 353, 402: 231, 49: 371, 317: 341, 408: 423, 372: 276, 48: 102, 102: 269, 256: 472, 428: 167, 171: 439, 275: 349, 435: 245, 84: 175, 134: 203, 71: 116, 161: 410, 324: 194, 342: 496, 207: 43, 242: 360, 25: 284, 218: 239, 499: 37, 195: 145, 83: 187, 186: 238, 283: 286, 247: 75, 490: 407, 464: 345, 436: 384, 164: 488, 219: 365, 118: 49, 251: 445, 503: 211, 394: 448, 190: 226, 153: 272, 62: 79, 170: 290, 96: 82, 80: 143, 305: 53, 157: 412, 191: 364, 332: 350, 7: 507, 101: 72, 399: 335, 437: 169, 333: 502, 447: 475, 401: 462, 290: 482, 252: 24, 370: 109, 438: 55, 452: 125, 241: 312, 297: 429, 184: 15, 93: 81, 337: 476, 463: 221, 349: 229, 208: 44, 61: 289, 136: 177, 355: 242, 20: 500, 356: 76, 381: 47, 33: 370, 296: 275, 274: 426, 117: 84, 442: 490, 458: 260, 34: 186, 236: 86, 481: 468, 309: 373, 269: 88, 455: 113, 500: 255, 66: 471, 58: 434, 174: 119, 396: 110, 268: 87, 504: 322, 155: 435, 298: 122, 200: 181, 6: 485, 72: 129, 76: 508, 109: 392, 204: 339, 46: 347, 237: 274, 196: 367, 454: 293, 110: 21, 90: 19, 502: 38, 325: 453, 52: 128, 95: 71, 159: 225, 441: 278, 459: 92, 42: 56, 405: 403, 485: 200, 54: 444, 205: 285, 32: 66, 30: 357, 336: 61, 234: 190, 379: 244, 393: 309, 146: 394, 404: 292, 221: 348, 147: 180, 115: 270, 53: 314, 24: 443, 371: 165, 246: 146, 411: 31, 88: 1, 133: 324, 380: 138, 22: 280, 98: 20, 460: 114, 114: 262, 27: 141, 466: 343, 501: 279, 358: 195, 470: 381, 50: 376, 132: 311, 270: 437, 220: 45, 47: 68, 89: 80, 149: 132, 367: 283, 406: 100, 392: 450, 177: 104, 198: 509, 414: 54, 300: 377} |
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class ActivationsAndGradients: |
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""" Class for extracting activations and |
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registering gradients from targetted intermediate layers """ |
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def __init__(self, model, target_layer, reshape_transform): |
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self.model = model |
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self.gradients = [] |
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self.activations = [] |
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self.reshape_transform = reshape_transform |
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target_layer.register_forward_hook(self.save_activation) |
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if hasattr(target_layer, 'register_full_backward_hook'): |
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target_layer.register_full_backward_hook(self.save_gradient) |
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else: |
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target_layer.register_backward_hook(self.save_gradient) |
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|
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def save_activation(self, module, input, output): |
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activation = output[0] |
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if self.reshape_transform is not None: |
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activation = self.reshape_transform(activation) |
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self.activations.append(activation.cpu().detach()) |
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|
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def save_gradient(self, module, grad_input, grad_output): |
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grad = grad_output[0] |
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if self.reshape_transform is not None: |
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grad = self.reshape_transform(grad) |
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self.gradients = [grad.cpu().detach()] + self.gradients |
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|
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def __call__(self, x): |
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self.gradients = [] |
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self.activations = [] |
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return self.model(x) |
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class DetectorGradCAM: |
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def __init__(self, model, target_layer, use_cuda=False, reshape_transform=None): |
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self.model = model.eval() |
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self.target_layer = target_layer |
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self.cuda = use_cuda |
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if self.cuda: |
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self.model = model.cuda() |
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self.reshape_transform = reshape_transform |
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self.activations_and_grads = ActivationsAndGradients(self.model, |
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target_layer, reshape_transform) |
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def forward(self, input_img): |
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return self.model(input_img) |
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def get_cam_weights(self, input_tensor, target_category, activations, grads, k=5): |
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a = torch.tensor(activations) |
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return torch.topk((a * (a > unit_levels.view(unit_levels.shape[0], 1, 1, 1).repeat(1, 8, 8, 8)))[0].sum(dim=(1,2,3)), k=k).indices |
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|
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def get_loss(self, output, target_category): |
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loss = 0 |
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for i in range(len(target_category)): |
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loss = loss + output[i, target_category[i]] |
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return loss |
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|
|
def get_cam_image(self, input_tensor, target_category, activations, grads, eigen_smooth=False): |
|
weights = self.get_cam_weights(input_tensor, target_category, activations, grads) |
|
weighted_activations = weights[:, :, None, None] * activations |
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cam = weighted_activations.sum(axis=1) |
|
return cam |
|
|
|
def forward(self, input_tensor, target_category=None, k=5): |
|
|
|
if self.cuda: |
|
input_tensor = input_tensor.cuda() |
|
|
|
output = self.activations_and_grads(input_tensor) |
|
|
|
if type(target_category) is int: |
|
target_category = [target_category] * input_tensor.size(0) |
|
|
|
if target_category is None: |
|
target_category = np.argmax(output.cpu().data.numpy(), axis=-1) |
|
else: |
|
assert(len(target_category) == input_tensor.size(0)) |
|
|
|
self.model.zero_grad() |
|
loss = self.get_loss(output, target_category) |
|
loss.backward(retain_graph=True) |
|
|
|
activations = self.activations_and_grads.activations[-1].cpu().data.numpy() |
|
grads = self.activations_and_grads.gradients[-1].cpu().data.numpy() |
|
|
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return self.get_cam_weights(input_tensor, target_category, activations, grads, k=k).tolist() |
|
|
|
class AttrDict(dict): |
|
def __init__(self, *args, **kwargs): |
|
super(AttrDict, self).__init__(*args, **kwargs) |
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self.__dict__ = self |
|
|
|
|
|
hide_streamlit_style = """ |
|
<style> |
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header {visibility:hidden;} |
|
</style> |
|
""" |
|
|
|
st.markdown(hide_streamlit_style, unsafe_allow_html=True) |
|
|
|
received_input = None |
|
|
|
scale = ScaleIntensityRange(a_min=-1000, a_max=1000, b_min=0, b_max=1, clip=True) |
|
crop = CenterSpatialCrop(roi_size=(64,64,64)) |
|
|
|
preprocess = lambda arr: scale(crop(arr[None, ...].clip(-1000, 1000))) |
|
to_image = lambda v: PIL.Image.fromarray((255*v[0,:,:,v.shape[-1]//2]).astype('uint8')).convert('RGB') |
|
|
|
def to_base64(image: PIL.Image): |
|
buffered = BytesIO() |
|
image.save(buffered, format="PNG") |
|
return "data:image/png;base64," + base64.b64encode(buffered.getvalue()).decode("utf-8") |
|
|
|
def base64_slice(path: str): |
|
return to_base64(to_image(preprocess(np.load(path)))) |
|
|
|
def bundle_builder(path: str, local=True, desc=""): |
|
if local: path = os.path.join(".", path) |
|
|
|
vertebra = np.float32(preprocess(np.load(path))) |
|
slice = to_base64(to_image(vertebra)) |
|
|
|
return (slice, desc, vertebra) |
|
|
|
examples = [ |
|
bundle_builder("examples/l5.npy", desc="L5 - no fracture"), |
|
] |
|
|
|
with st.empty(): |
|
with st.container(): |
|
upload = st.file_uploader("Upload vertebra to classify (nii, nii.gz, npy)") |
|
|
|
if upload is not None: |
|
suffix = ''.join(pathlib.Path(upload.name).suffixes) |
|
with tempfile.NamedTemporaryFile(suffix=suffix) as fp: |
|
fp.write(upload.getvalue()) |
|
fp.seek(0) |
|
|
|
if 'nii' in suffix: |
|
try: |
|
nii = nib.load(fp.name) |
|
except: |
|
raise Exception("Unable to load uploaded NIfTI file. Please ensure that it has the correct file extensions.") |
|
|
|
nifti_data = nii.get_fdata() |
|
data = Orientation(axcodes='IPL')(nifti_data[None, ...], affine=nii.affine)[0][0] |
|
elif 'npy' in suffix: |
|
try: |
|
data = np.load(fp) |
|
except: |
|
raise Exception("Unable to load provided NumPy file.") |
|
else: |
|
raise Exception("Invalid input data format. Please provide a NIfTI or NumPy array file.") |
|
|
|
assert len(data.shape) == 3, "Invalid number of dimensions. Expects three-dimensional input." |
|
assert all([a >= 64 for a in data.shape]), "Invalid shape. Shape must not be smaller than 64x64x64." |
|
|
|
fp.close() |
|
|
|
vertebra = np.float32(preprocess(data)) |
|
slice = to_base64(to_image(vertebra)) |
|
|
|
received_input = (slice, upload.name, vertebra) |
|
|
|
with st.container(): |
|
st.caption("Or pick one of these examples:") |
|
|
|
clicked = clickable_images( |
|
[ex[0] for ex in examples], |
|
titles=[ex[1] for ex in examples], |
|
div_style={"display": "flex", "justify-content": "left", "flex-wrap": "wrap"}, |
|
img_style={"margin": "0 5px 5px 0", "height": "135px"}, |
|
) |
|
|
|
if clicked > -1: |
|
received_input = examples[clicked] |
|
|
|
if received_input is not None: |
|
with st.container(): |
|
col1, col2 = st.columns([1,3]) |
|
with col1: |
|
st.image(received_input[0], width=140) |
|
with col2: |
|
top_container = st.container() |
|
top_container.write("**Concept Visualization**") |
|
top_container.write(f"Input: {received_input[1]}") |
|
with st.spinner('Running inference'): |
|
saved_checkpoint = "moonlit-flower-278.ckpt" |
|
|
|
|
|
checkpoint = torch.load(saved_checkpoint, map_location="cpu") |
|
|
|
checkpoint['hyper_parameters']['dataset_path'] = '.' |
|
checkpoint['hyper_parameters']['batch_size'] = 1 |
|
|
|
module = VerseFxClassifier.load_from_checkpoint(saved_checkpoint, hparams=checkpoint['hyper_parameters'], map_location="cpu") |
|
model = module.backbone |
|
|
|
model.eval() |
|
|
|
sample = torch.tensor(received_input[2][None, ...]) |
|
|
|
cam = DetectorGradCAM(model, model.down_tr512, use_cuda=False) |
|
|
|
detectors = cam.forward(input_tensor=sample, target_category=0, k=5) |
|
ranks = [corr_rank[unit] for unit in detectors] |
|
|
|
model = nethook.InstrumentedModel(model) |
|
model.retain_layer("down_tr512") |
|
|
|
pred = (torch.sigmoid(model(sample)) > 0.5).long().item() |
|
|
|
acts = model.retained_layer("down_tr512")[0] |
|
|
|
ld_res = acts.shape[-1] |
|
img_slices = torch.linspace(int(64/ld_res/2), 64-int(64/ld_res/2), ld_res, dtype=torch.long) |
|
|
|
iv = imgviz.ImageVisualizer(224, image_size=64, source="zc", percent_level=0.99) |
|
|
|
top_container.write(f"Prediction: {'fracture' if pred==1 else 'no fracture'}") |
|
|
|
image_margin = """ |
|
<style> |
|
img{margin-right:5px}*/ |
|
</style> |
|
""" |
|
st.markdown(image_margin, unsafe_allow_html=True) |
|
|
|
for i, detector in enumerate(detectors): |
|
def paper_typo_fix(d): |
|
|
|
|
|
if d != 424 and d!= 22: return str(d) |
|
if d == 424: return "22" |
|
else: return "424" |
|
|
|
st.markdown(f"Detector unit #{paper_typo_fix(detector)} (relevance rank {i+1}, positive correlation rank {ranks[i]})") |
|
concepts = glob(f"concepts/{detector}_*.png") |
|
if len(concepts) == 0: |
|
st.caption("No statistically significant activations, unable to show general concept") |
|
else: |
|
st.caption("General concept") |
|
sorted_concepts = sorted(concepts, key=lambda x: int(x.replace('.png', '').split('/')[-1].split('_')[1])) |
|
st.image([to_base64(PIL.Image.open(c)) for c in sorted_concepts], width=75) |
|
activations = [to_base64(PIL.Image.fromarray(iv.pytorch_masked_image( |
|
(sample[0, ..., img_slices[slice]]).repeat(3, 1, 1), |
|
acts[..., slice], |
|
detector, |
|
level=unit_levels[detector]).permute(1,2,0).cpu().numpy())) for slice in range(0, ld_res)] |
|
st.caption("Image-specific activation") |
|
st.image(activations, width=75) |
|
|
|
st.markdown('<div style="margin-top:20px;border-top: 1px solid rgba(49, 51, 63, 0.2);margin-bottom:40px"></div>', unsafe_allow_html=True) |
|
|
|
def on_click(*args, **kwargs): |
|
|
|
st.markdown('<meta http-equiv="refresh" content="0">', unsafe_allow_html=True) |
|
|
|
st.button("Reset", on_click=on_click) |
